This paper is in the following e-collection/theme issue:

Theme Issue 2024: 25 Years of Digital Health Excellence (20) Digital Health Reviews (1249) Fitness Trackers and Smart Pedometers/Accelerometers (555) Wearable Devices and Sensors (867) Evaluation and Research Methodology for mHealth (204) Instruments and Questionnaires for Physical Activity and Lifestyle (71)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59497, first published .
Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review

Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review

Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review

Authors of this article:

Ya-Ting Liang1, 2 Author Orcid Image ;   Charlotte Wang2, 3 Author Orcid Image ;   Chuhsing Kate Hsiao2, 3 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    Review

    1Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan

    2Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei, Taiwan

    3Master of Public Health Program, College of Public Health, National Taiwan University, Taipei, Taiwan

    Corresponding Author:

    Chuhsing Kate Hsiao, PhD

    Institute of Health Data Analytics and Statistics

    College of Public Health

    National Taiwan University

    No.17, Xu-Zhou Rd

    Taipei, 10055

    Taiwan

    Phone: 886 2 33668032

    Email: ckhsiao@ntu.edu.tw


    Background: Monitoring free-living physical activity (PA) through wearable devices enables the real-time assessment of activity features associated with health outcomes and provision of treatment recommendations and adjustments. The conclusions of studies on PA and health depend crucially on reliable statistical analyses of digital data. Data analytics, however, are challenging due to the various metrics adopted for measuring PA, different aims of studies, and complex temporal variations within variables. The application, interpretation, and appropriateness of these analytical tools have yet to be summarized.

    Objective: This research aimed to review studies that used analytical methods for analyzing PA monitored by accelerometers. Specifically, this review addressed three questions: (1) What metrics are used to describe an individual’s free-living daily PA? (2) What are the current analytical tools for analyzing PA data, particularly under the aims of classification, association with health outcomes, and prediction of health events? and (3) What challenges exist in the analyses, and what recommendations for future research are suggested regarding the use of statistical methods in various research tasks?

    Methods: This scoping review was conducted following an existing framework to map research studies by exploring the information about PA. Three databases, PubMed, IEEE Xplore, and the ACM Digital Library, were searched in February 2024 to identify related publications. Eligible articles were classification, association, or prediction studies involving human PA monitored through wearable accelerometers.

    Results: After screening 1312 articles, 428 (32.62%) eligible studies were identified and categorized into at least 1 of the following 3 thematic categories: classification (75/428, 17.5%), association (342/428, 79.9%), and prediction (32/428, 7.5%). Most articles (414/428, 96.7%) derived PA variables from 3D acceleration, rather than 1D acceleration. All eligible articles (428/428, 100%) considered PA metrics represented in the time domain, while a small fraction (16/428, 3.7%) also considered PA metrics in the frequency domain. The number of studies evaluating the influence of PA on health conditions has increased greatly. Among the studies in our review, regression-type models were the most prevalent (373/428, 87.1%). The machine learning approach for classification research is also gaining popularity (32/75, 43%). In addition to summary statistics of PA, several recent studies used tools to incorporate PA trajectories and account for temporal patterns, including longitudinal data analysis with repeated PA measurements and functional data analysis with PA as a continuum for time-varying association (68/428, 15.9%).

    Conclusions: Summary metrics can quickly provide descriptions of the strength, frequency, and duration of individuals’ overall PA. When the distribution and profile of PA need to be evaluated or detected, considering PA metrics as longitudinal or functional data can provide detailed information and improve the understanding of the role PA plays in health. Depending on the research goal, appropriate analytical tools can ensure the reliability of the scientific findings.

    J Med Internet Res 2024;26:e59497

    doi:10.2196/59497

    Keywords

    accelerometer; association; behavioral study; classification; digital biomarkers; digital health; physical activity; prediction; statistical method; wearable 


    Physical Activity

    Physical activity (PA), defined as any body movement that results in energy expenditure [1], has been acknowledged as a crucial factor in health management and disease prevention [1-3]. The energy expenditure due to moderate and vigorous PAs, such as brisk walking and running, is greater than that due to sedentary behaviors such as rest. These moderate and vigorous PAs are beneficial for health [1]. Many studies, including clinical trials, have focused on their effectiveness in treating and preventing chronic diseases, as well as promoting general health, well-being, and longevity [4-8]. Information about PA can be collected through questionnaires and digital devices. While questionnaires may suffer from recall bias, digital devices offer a more objective evaluation. The quantified summary statistics of the values recorded by these devices are often called digital biomarkers [9]. In a scoping review of 31 systematic reviews of clinical evidence involving digital biomarkers, wearables were the most commonly applied measurement tool (22/31, 71%), and PA was the most prevalent intervention (16/31, 52%) [4].

    Wearable Devices

    Wearable devices can continuously monitor an individual’s free-living PA patterns. With the growing emphasis on health consciousness, there has been a notable surge in studies focused on PA, driven by the popularity of activity-monitoring wearables [3]. Earlier studies commonly used uniaxial accelerometer devices that measure only vertical axis acceleration in g-units, corresponding to the acceleration due to gravity (9.81 m/s2) [10-13]. However, in recent decades, triaxial accelerometers operating in 3 orthogonal dimensions (ax, ay, and az) have gained preference due to their ability to offer a broad coverage, thus providing a more comprehensive understanding of overall human activity [14]. Among triaxial accelerometers, wrist-worn wearables are predominant, as devices worn on other body parts such as waists, hips, or thighs may cause inconvenience during movement [3]. Table 1 lists only triaxial wearable accelerometers used in >1 article reviewed here, as well as the placement positions of each device. More than half of them are research-grade wearables (eg, ActiGraph [ActiGraph, LLC], GENEActiv [ActivInsights], Axivity [Axivity Ltd], and RT3 [Stayhealthy, Inc]), while some are commercial wearables (eg, Fitbit Watch [Fitbit Inc]). This review will not discuss the comparison of different devices. Readers interested in the practical use, reliability, and validity of accelerometers are referred to published reviews [15-20].

    Table 1. Triaxial wearable accelerometers that appeared in >1 reviewed article among the 428 articles.
    Brand and modelFrequency (Hz)SoftwareManufacturerReferences, n (%)
    ActiGraph GT3X, GT9X, wGT3X-BT, wGT3X+, GT3X+, and ActiTrainer30-100ActiLife researchActiGraph, LLC180 (42.1)
    GENEActiv original10-100GENEActiv PCActivInsight24 (5.6)
    Axivity AX312.5-3200OMGUIAxivity Ltd37 (8.6)
    RT30.017-1RT3 ASSISTStayhealthy, Inc2 (0.5)
    activPAL activPAL3 and activePAL420-80activPAL softwarePAL Technologies Ltd7 (1.6)
    Actical32Actical softwareMini-Mitter, Respironics30 (7)
    Actiwatch0.25-32ActiwarePhilips Respironics5 (1.2)
    Xiaomi Mi Band 2—aMaster for Mi Band appXiaomi Corp2 (0.5)
    TracmorD0.3-20DirectLife Activity Monitor softwarePhilips DirectLife5 (1.2)
    Hookie AM20100—Traxmeet Ltd6 (1.4)
    UKK AM30 and RM42100—UKK Institute3 (0.7)
    Kenz Lifecorder EX32Lifecorder transfer softwareSuzuken Co, Ltd3 (0.7)
    Shimmer31-512ShimmerCaptureShimmer Research Ltd2 (0.7)
    Fitbit1-256Fitabase serverFitbit Inc6 (1.4)

    aNot available.

    Such individual-centered PA information is usually collected by wearables at a frequency of 30 to 100 observations per second (Hz). Most devices do not have built-in software to output the collected data. However, several external programs such as ActiLife and GGIR [21] are available for analysis. When each person is monitored for multiple days in a free-living environment, the data volume becomes enormous, leading to difficulties in analysis. Various digital variables that summarize daily activities have been proposed. Some are good for representing the total intensity, such as the summation or average of activity intensity (AI), and some are appropriate for the overall heterogeneity, such as variation and range, in either the time domain or the frequency domain [22]. These summary variables, however, do not catch the temporal patterns inherent in individual PAs. Other variables containing time information have been proposed and implemented in analyses. Part of this review will focus on these digital features.

    Previous reviews have often emphasized the applications of wearables in health studies, focusing on the validity and reliability of these devices in evaluating PA and their use in clinical research [23-26]. While these reviews are comprehensive in terms of the practical use of PA metrics as digital data, they do not address the evolving trends in the analytical methods used to analyze such data. With the prevalence of wearables and the exponential increase in PA data volume, scientists now have the capacity to describe daily PA in a more sophisticated manner and examine whether PA trajectories vary across conditions or groups. Some advanced analytical tools have been proposed to address these tasks and will be discussed here.

    In this review, we first describe the metrics currently used to summarize daily activities, particularly those transformed based on 3 acceleration axes. Common statistics derived from single-axis data, either in the time or frequency domain, are presented in Textbox 1 and will not be further discussed here. Second, we review the application of innovative PA metrics in statistical models aimed at different research goals, including the classification of activities, examination of the association of PA with health behavior or outcomes, and prediction of future events. Third, we discuss the limitations of current tools and propose directions for future analysis.

    Textbox 1. Summary metrics and features calculated in time or frequency domain.

    Domains, metrics, and features

    • Time, uniaxial: mean, variance, SD, percentile, minimum, maximum, range
    • Time, between 2 uniaxials: correlation between 2 axials, covariance between 2 axials
    • Frequency: dominant frequency, peak power, spectral energy, entropy, and spectrum centroid

    Protocol and Registration

    The methods used in this review align with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist shown in Multimedia Appendix 1 [27]. The Arksey and O’Malley [28] framework was used after making methodological adaptations for this review. Protocols used in this scoping review are detailed in the following sections.

    Eligibility Criteria

    The eligibility criteria for article reviews have been established (Multimedia Appendix 1). The inclusion criteria were as follows: (1) the PA data reported on were obtained from human participants (adults or children, with disease or healthy) through a wearable accelerometer and (2) the purpose of the study containing the analysis of accelerometer data was to capture human activity patterns and influence on health.

    The exclusion criteria included (1) articles not written in English; (2) articles incorporating other physiological activities detected by electrical signals (eg, electrocardiography); (3) articles conducting only statistical testing or containing no statistical model; (4) articles not focusing on human activity patterns; (5) articles not of original research, including systematic reviews, scoping reviews, books, and book chapters; (6) articles focusing on the reliability of wearable devices; and (7) articles addressing the upstream preprocessing of accelerometer data or measurement design (eg, the choice of epoch length of acceleration data or the placement of devices).

    Information Sources

    To collect the potentially relevant articles, the search was conducted in each of the following 3 publication databases: PubMed, IEEE Xplore, and ACM Digital Library. The final search was performed on February 29, 2024. As the main objective of this scoping review was to provide an overview of analytical methods used for the analysis of PA data in recent years, the search was constrained to articles published between 2000 and 2024. The full search strategies used in the 3 databases can be found in Multimedia Appendix 2.


    Selection of Evidence Sources and Data Extraction

    After removing duplicate articles from the initial 1312 articles obtained by searching the 3 databases, screening for study goals based on title and abstract, and assessing eligibility (Figure 1), a final set of 428 articles was included in this review (Multimedia Appendix 3 [5,13,29-454]). Of them, 407 (95.1%) articles were from PubMed, 20 (4.7%) articles were from IEEE Xplore, and 1 (0.2%) article was from the ACM Digital Library. A total of 2 independent reviewers examined the content of each paper to determine which of the 3 categories of studies (activity classification, association, and prediction) it fit into and recorded the analytical tools used. Any inconsistencies in judgment were discussed and resolved through consensus. Following this, other study variables such as data domain, PA metrics, and summary statistics were examined.

    The extracted data included brands and other details of the accelerometers used, research focus, context, analytic methods, and outcome variables. The extraction process was conducted using a Microsoft Excel (Microsoft Corp) sheet designed by the reviewer YTL. This sheet consolidated the context of each article, capturing information on the brand of the device, PA variables, research issues addressed, activity features examined, statistical methodologies applied, and study outcomes.

    ‎
    Figure 1. Flowchart of the selection strategy.

    Measurements From Accelerometers

    Most wearable accelerometers measure acceleration in 3 dimensions (ax, ay, az), with some also equipped with gyroscopes to assess body angular rates in degrees per second across the 3 axes. The reliability and accuracy of various brands, including ActiGraph, GENEActive, and Axtivity, have been compared and reviewed [15-20]. Because acceleration measurements are more common than gyroscope measurements in most devices, they have become the primary variable for evaluating body movement.

    These accelerometer devices provide either raw accelerations in 3 dimensions or a combined metric as activity count (AC) per epoch produced by the manufacturer’s proprietary software (Table 1) or by other free programs such as GGIR [21]. Raw accelerations have been favored over AC for 3 reasons. First, it is uncertain whether the definition of AC across brands is the same. Second, the development of free software such as GGIR [21] has relieved the computational difficulty and burden on researchers of analyzing acceleration measurements. Third, providing raw acceleration data makes them available for reuse in other types of studies.

    Accelerations in 3 dimensions can be analyzed either separately or after being transformed into a single variable, as shown in Figure 2. Both uniaxial acceleration and transformed triaxial metric data can be used in analysis to classify activity types, explore association, and predict health outcomes. Figure 2 outlines the procedures from the collection of raw data to analysis of uniaxial or triaxial acceleration data. These analyses can be categorized further, as displayed in Figure 3. In both the time and frequency domains, 29 (6.8%; 29 uniaxial in time + 13 uniaxial in frequency – 13 duplicates) of 428 studies examined the uniaxial acceleration separately for its simplicity and straightforwardness. A total of 340 (79.4%; 74 transform triaxial in time + 4 transform triaxial in frequency + 275 summary of transform triaxial in time – 13 duplicates) of 428 studies used a triaxial acceleration metric combining the acceleration data from 3 dimensions in the time domain or frequency domain, or used a summary-type metric based on these transformations. These include the length of the acceleration vector (ax, ay, az), functions of the vector, amplitudes of Fourier-transformed acceleration waves, and summary statistics of these metrics. Analyses based on transformed variables have become prevalent in recent years because they combine information from all 3 acceleration axes. We refer to the variables transformed from the 3 raw accelerations as transformed metrics, which can be represented in the time domain or frequency domain. Some articles (80/428, 18.7%) used indirect acceleration metrics such as step count, AC, and walking time.

    ‎
    Figure 2. Outline of procedures from the collection of raw data to the application of analytical methods. PA: physical activity; ROC: receiver operating characteristic; VM: vector magnitude.
    ‎
    Figure 3. The left column lists the number of articles that used the corresponding physical activity (PA) metrics in analysis, the middle lists the number of articles focusing on each topic, and the right column lists the analytical tools adopted in studies. Note that an article may be counted more than once if it qualified >1 criterion. ROC: receiver operating characteristic.

    Metrics in Analysis: Transformed Metrics in the Time Domain

    Several transformed metrics have been developed as functions of the 3 accelerations (ax, ay, az), including the Euclidean norm minus one (ENMO), signal magnitude area, angle-z, and PA index (Table 2). These metrics either focus on the vector magnitude (VM) of the acceleration (magnitude type) or the variability in the acceleration. For instance, the first developed metric was the straightforward VM based on the 3 uniaxial accelerations at time t:

    VM = [ ax(t)2 + ay(t)2 + az(t)2 ]1/2  (1)

    Table 2. Transformed metrics in time domain and their summary quantities calculated from 3 accelerations (ax, ay, az).
    Metric, feature, and summaryDefinitionReference
    ENMOa
    • max{[ ax(t)2 + ay(t)2 + az(t)2]1/2 – 1, 0}
    		[455]
    Angle-z
    • tan-1{ az(t) / [ ax(t)2 + ay(t)2]1/2} × 180 / π
    		[8]
    PAI or AIb
    • AI(t,H) = [max{ (var(ax(t,H)) + var(ay(t,H)) + var(az(t,H)) – sum.var) / 3, 0}]1/2
    • sum.var = var(ax) + var(ay) + var(az)
    		[456]
    SMAc
    • sum of integration of |ax(t)|, |ay(t)| and |az(t)| from t = T to T + 1 seconds
    		[29]
    VM or SVMd
    • [ ax(t)2 + ay(t)2 + az(t)2 ]1/2
    		[30,31]
    MVPAe
    • number of times when the transformed metrics > threshold
    		[34-36]
    L5f
    • The means of the 5 least active hours during the day
    		[457]
    M10g
    • The means of the 10 most active hours during the day
    		[457]
    IVh
    • {mean of (xh – mean(xh))2 from h = 1 to p} / {mean of (xi – mean(xi))2 from i = 1 to N}
    • N: the total number of data points
    • p: the number of data points per day
    • xh: values of each hour from the mean 24-hour profile
    • xi: each given hour of raw data
    		[457]
    ISi
    • {mean of (xi – xi-1)2 from i = 1 to N} / {mean of (xi – mean(xi))2 from i = 1 to N}
    • N: the total number of data points
    • xi: the value of a given hour
    		[457]
    Acrophase
    • The time at which the peak of a rhythm occurs
    		[458]

    aENMO: Euclidean norm minus one.

    bPAI or AI: physical activity index or activity intensity.

    cSMA: signal magnitude area.

    dSVM: vector magnitude or signal vector magnitude.

    eMVPA: moderate-to-vigorous physical activity.

    fL5: least active 5-hour period.

    gM10: most active 10-hour period.

    hIV: intradaily variability.

    iIS: interdaily stability.

    The second developed metric, ENMO, is intuitive because it combines the strength of acceleration in gravity in the 3 axes, where the effect of gravity is removed from the formula by subtracting 1 [455]:

    ENMO = max{[ ax(t)2 + ay(t)2 + az(t)2 ]1/2 – 1, 0} (2)

    Its calculation can be performed with the R (R Foundation) package GGIR [21]. ENMO does not depend on the sampling frequency and is noted for its higher sensitivity to vertical and horizontal accelerations [455]. It has been widely used in studies for classifying specific movements and sleep stages [459,460]. Studies have showed that ENMO performs better at indicating moderate to heavy activities but is less robust at indicating sedentary and light activities compared to other measurements [456].

    Other metrics similar to ENMO in calculating the “average” or “degree” of accelerations are signal magnitude area [29] and signal VM [30,31]. These metrics are all representations of the average type. These metrics can be further transformed to imply certain levels of activity. For instance, the moderate-to-vigorous PA (MVPA) measure contains cutoff values for ENMO to determine whether the activity is light, moderate, or vigorous [32,33]. In studies where the classification of an individual’s activity is of interest, the cutoff values need to be determined because the values may be person specific.

    Another magnitude-type transformed metric is the heuristic algorithm looking at the distribution of change in z-angle proposed by van Hees et al [8]:

    Angle-z = tan-1{ az(t) / [ ax(t)2 + ay(t)2]1/2} × 180 / π (3)

    As the name suggests, this transformed metric emphasizes the movement in the direction vertical to the x-y plane, and the use of az increases the sensitivity for the detection of activities along this direction [460]. The heuristic algorithm looking at the distribution of change in z-angle was originally used to detect the sleep period time window. Its reliability has been demonstrated through comparisons with polysomnography [8], and it has been commonly used to investigate sleep conditions. For example, it was used by Jones et al [461,462] to differentiate sleep status, and it was combined with genomic data to explore significant sleep factors.

    In contrast, PA index (PAI), also known as AI, examines the variability of accelerations, rather than their magnitude. The PAI metric combines the individual-specific variation in each dimension in a time interval of length H starting at time t as a summary metric [456]:

    PAI(t,H) = [max{ (var(ax(t,H)) + var(ay(t,H)) + var(az(t,H)) − sum.var) / 3, 0}]1/2  (4)

    Here, the normalization quantity sum.var = var(ax) + var(ay) + var(az), summation of 3 variances, is the systematic noise variance that can be computed from raw accelerations when the device is not moving. This PAI can be further modified by dividing the normalization quantity, becoming the relative AI, as opposed to the original version in the absolute scale. This AI is advantageous because of its additive and rotational invariance properties, indicating that PAI is a constant when an individual performs the same activity during the period. PAI can be applied in, among other areas, the surgical recovery process of women who have undergone mastectomies and evaluation of physical intensity during powered wheelchair mobility in people with severe dyskinetic cerebral palsy [463,464].

    Metrics in Analysis: Metrics Summarizing PA in the Time Domain

    Metrics such as those discussed earlier are all computed in the time domain. In other words, they preserve time information and can be used further to decipher longitudinal patterns. These repeated time-series measurements can be easily included in the generalized linear mixed-effects models (GLMMs) for additional analysis, or they can be summarized or categorized to simplify the time effect. One such summary statistic is the threshold that differentiates light activity from MVPA, with the time spent in MVPA also considered as a summary PA feature.

    Examples of MVPA include dancing, gardening, walking briskly, jogging, and running. To be categorized in this group, an activity usually needs to reach a certain intensity evaluated by metabolic equivalent of task (MET), such as the maximum or peak oxygen uptake or heart rate [34-36]. For example, one unit of MET is the amount of oxygen consumed or energy used per kg per minute while sitting at rest. Activities corresponding to MET values falling within a specified interval are categorized as MVPAs. The boundaries or thresholds of this interval need to be determined first. The concept of MVPA is easy to understand and often referenced when exercise is prescribed to people for health benefits; the definition of its thresholds, however, can be individual specific because the thresholds are related to individualized homeostasis disturbance [465].

    Other summary metrics compare PA data within the same person to indicate a person’s rest-activity rhythm, including nonparametric circadian rhythm measures such as interdaily stability (IS), intradaily variability (IV), least active 5-hour period, most active 10-hour period, and the acrophase in cosinor analysis [458]. Both IS and IV are useful in describing rest and activity rhythms: IS provides information on how stable the rest-activity pattern is between different days, and IV quantifies the stability of the fragmentation in the activity pattern between time intervals [457].

    These summary metrics effectively capture and quantify the characteristics of AI and circadian rhythms, enhancing the understanding of their relation to health and behavior [37,462]. However, defining thresholds for PA intensity can be both population and person specific [465]. In addition, these summary statistics may suffer from information loss because daily PA is a continuum [38]. They focus on activities in fragments of the day rather than the overall PA profile throughout the day.

    Metrics in Analysis: Metrics Summarizing Temporal Patterns in the Frequency Domain

    One way to explore daily continuous temporal trends and trajectories is via the Fourier transformation, allowing metrics to be decomposed into periodic curves expressed by both frequency and amplitude. These longitudinal waves in the frequency domain are threshold free. For instance, ENMO, angle-z, and AI can be represented in the frequency domain to examine their patterns over time. Among the 428 reviewed articles, this frequency representation was considered in 4 (0.9%) articles for triaxial accelerations, while 13 (3%) adopted frequency domain representation in uniaxial acceleration.

    We use x(t) to denote the transformed metric in the time domain, where t indexes the time. For example, x(0), x(1),..., x(t),..., and x(T1) represent the ENMO value observed throughout the day. Then, these x(t)s can be converted to XF(0), XF(1), XF(2),..., and XF(T1), where each one is a combination of sinusoid functions. In other words, these XF(k)s are all periodic curves with frequency k and amplitude ||XF(k)||. These new curves are in the same unit as the original x(t), and in the case of ENMO, the unit is g:

    XF(k) = sum{x(t) × exp(−2 πikt / T)} over t from 0 to T−1 (5)

    For example, XF(2) implies that this individual performed an activity of amplitude ||XF(2)|| with frequency 2. Usually, the waves with large strength indicate dominant activities in a day, while the waves with low frequencies can imply regular rhythms such as commuting to and from work or napping habits. The computation of this transformation can be made easy with the fast Fourier transformation (FFT) in R with the package stats.

    The advantages of the FFT are 3-fold. First, it simplifies the interpretation of periodic activities using frequency and strength. Second, as the PA curve is dominated by a few low-frequency waves, each curve can be represented with a small number of waves, significantly reducing data dimension. Third, these dominant waves can be considered as digital features of an individual’s PA and incorporated into further analyses, such as classification [39,40].

    The FFT-based variables are useful for periodic activities, especially when activity metrics from multiple days are collected to establish an individual’s daily cycles. However, the FFT approach can be inappropriate if the original PA lacks periodic patterns. An alternative way would be to relax this constraint when summarizing the PA trajectory.

    Metrics in Analysis: Metrics of Temporal Patterns in the Time Domain

    An alternative threshold free approach to capture the temporal dynamics inherent in accelerometer-based measures involves considering these measures as curves within infinite-dimensional spaces, treating these collected measures as functional data. Functional data analysis (FDA) has emerged as a powerful tool for analyzing PA data, enabling the continuous variation of PA patterns throughout the monitoring period, without the need to assume cycles. Furthermore, considering PA as a function of time facilitates the assessment of time-varying effects of covariates such as BMI, expanded disability status scale, and lung function [38,41,42,466-469]. Among the 428 screened articles, 4 (0.9%) used such features in PA analysis.

    In FDA, the observed transformed metrics in a day from the same individual, x(0), x(1),..., x(t),..., and x(T1) are considered the discretized observations from an unknown continuous curve, Y(t). The patterns inherent among the multiple curves, Y(t)s, from multiple individuals can be explored via functional principal component analysis (FPCA), an analogy to principal component analysis for multivariate data [470,471]. These extracted principal component functions are called eigenfunctions and can indicate the decomposed daily temporal patterns. Each of the original curves is now associated with these eigenfunctions through a score. For instance, the i-th score of a daily curve now corresponds to its representation along with the i-th function. A large positive score indicates greater similarity in pattern and magnitude with this function. In other words, these scores become indications of the temporal trajectories.

    In addition, similar to FFT, the top few principal component functions in FPCA can explain most of the variability in the data. This greatly reduces the original data size to a few dominant functions, which can describe the major activity patterns in the population. In other words, the original data set can be approximated with only a few functions and scores with this FPCA approach. FPCA has previously been used in modeling yearly temperature curves and constructing growth curves of children; however, few studies have considered the application of FPCA and FDA in PA studies [43,469]. Another group of articles incorporated within-person and between-person heterogeneity into their analysis using 2-level FPCA or multilevel FPCA [470,472]. One level of scores represents the person-to-person variation, while the second level represents the day-to-day variation [470,472]. FDA involves smoothing techniques, FPCA, functional linear regression, and clustering or classification methods for analyzing data observed over continuous domains. It has been adopted mostly in classification research, which we will discuss in the next sections.

    Activity Classification Studies With Threshold-Based Methods

    Most early studies involving PA measured by accelerometers were classification studies that classified activity types or intensities, such as sleep-wake or moderate or vigorous activities. Of the 428 reviewed studies, 75 (17.5%) fall into this category (middle panel in Figure 3 and Multimedia Appendix 3). The main 2 topics in these classification studies are the classification of specific activities and sleep-wake stages. Specific activities for classification include daily activities (sitting, walking, and lying) [13,44,45], housework (washing dishes and dust mopping) [46,456], fall detection [473,474], and rehabilitation activities [475,476], while sleep stages for classification include wake, light sleep, and deep sleep stages [8,460,477]. Each activity type corresponds to a range of AI that must be determined first. In other words, the threshold values of these intensity intervals are necessary to classify activities. Once the cutoff values of the PA metrics are selected, several models are available for classification. Among the 75 classification studies reviewed, researchers opted for the threshold-based method (32/75, 43%), machine learning (ML; 32/75, 43%), regression (11/75, 15%), and the hidden Markov model (HMM; 6/75, 8%). These numbers are indicated above the edges connecting the corresponding nodes in Figure 4.

    Threshold-based methods are commonly used to identify activities with different intensities after specific cutoff points of PA metrics are determined. These cutoff values are often established by the receiver operating characteristic (ROC) curves based on metrics transformed from the triaxial accelerations and aggregated over a specific period, such as a week. The AI values are then categorized into multiple levels or ranges. For instance, this approach was applied in studies monitoring the daily behaviors of children who were instructed to engage in specific activities [47-49]. The intensity level of each activity is determined based on its characteristics, followed by the use of an ROC curve to evaluate the differentiation between target behaviors.

    Threshold-based classification offers the advantage of easy calculation and interpretation. However, this method may lack robustness because AI levels can be affected by various factors, such as the placement of the device on the body and differences among populations, and are highly dependent on the individual, limiting their generalizability [50,51,465]. If a large amount of data is collected from each individual during specific activities, individual-specific thresholds can be established and used to classify this individual’s behavior. This generalization can be particularly useful when considering PA as a form of treatment.

    ‎
    Figure 4. Network association plot of the articles. The number inside the node indicates the number of articles, and the number by the edge indicates the number of articles containing terms in both connecting nodes. ROC: receiver operating characteristic.

    Activity Classification Studies With Regression Models

    Rather than determining thresholds using ROC curves, several studies opt for linear regression models [44,50,51]. Such models allow for individualized threshold values, selected to correspond to a predetermined energy expenditure through a linear relationship established in the regression model. These values are then used in classification tasks, particularly beneficial for those considering PA for older adults or individuals with injuries or diseases [44,50,51]. The rationale behind this design is that the range of physical intensity may differ in populations with specific conditions, necessitating the determination of thresholds tailored to their characteristics. Therefore, these studies first apply linear regression to establish the threshold values between light activity and MVPA in settings such as wheelchair use or treadmill exercise and subsequently evaluate classification using these derived threshold values.

    The use of the study-specific thresholds for PA metrics helps mitigate the influence of sampling variation and allows for the incorporation of confounding factors such as age, gender, and health status when establishing the cutoff values. Consequently, the conclusions drawn from such studies are more reliable and applicable to research involving similar populations. However, conducting this type of study requires a larger sample size and repeated measurements of PA to establish, calibrate, and validate the threshold values.

    Activity Classification Studies With ML Models

    To avoid the determination of thresholds a priori, other studies have formulated activity classification as a task of activity recognition and resolved this task with supervised ML approaches [13,44,52,53,478-480] or unsupervised methods such as those using k-means and Gaussian mixture models [54]. Almost half (32/75, 43%) of the classification studies reviewed here adopted the ML approach (Figure 4). ML tools, including decision trees, random forest, logistic regression, naive Bayes, and multilayer neural networks, have been used to perform recognition among walking, jogging, going upstairs, going downstairs, sitting, standing, and lying at rest [13,52]. Except for 2 (2/32, 6%) studies based on the National Health and Nutrition Examination Survey [54,317], 2 (6%) on children or adults [53,480], 2 (6%) on older adults (aged >60 and >70 years, respectively [55,56]), and 2 (6%) on participants with diseases (spinal cord injury [51] and cognitive impairment [57]), the remaining articles (24/32, 75%) either used public data sets, such as the experimental data collected during the DARPA’98 Intrusion Detection Evaluation program [478], or recruited convenient samples, such as community residents aged between 20 and 89 years [52], to investigate whether ML algorithms can differentiate between activity types and intensities across groups. In addition, in 7 (22%) out of the 32 articles, participants were asked to wear the device for 1 to 14 days to record their free-living activities, and the data were used to cluster activity types, intensities, or sleep-wake stages. In another 23 (72%) articles, participants were asked to perform specific movements for 30 to 60 minutes, and the data were used to differentiate specific activities.

    The performance of these tools was compared using criteria such as F1-scores, confusion matrices, and accuracy rates. No consistent conclusion of the best tool could be drawn. A logistic regression model with or without cross-validation offered the best performance in some studies [478,480], while multilayer neural networks performed the best in others [479].

    Several factors may have affected the inconsistency in performance. First, the sample sizes in these studies were not large, potentially resulting in greater sampling variation and susceptibility to individual variability. Moreover, the ML approaches typically assumed independence among activity data for each epoch, overlooking potential correlations that may have existed. Such interdependencies and associations within the data can be essential when tackling classification problems [481]. In addition, it is worth noting that when identifying specific activities, using individual’s directional accelerations may yield better results compared to using the transformed metric of the triaxial accelerations [482].

    Activity Classification Studies With HMMs

    Another classification approach that does not rely on predetermined thresholds is the HMM. Widely used in analyzing time series or correlated data, the HMM originated in the field of speech recognition, where it demonstrated effectiveness in handling sequences of feature vectors. On the basis of augmenting the Markov chain, the fundamental assumption of the HMM is that the observed events depend on hidden states that are not directly observable. A typical HMM consists of essential components, including distinct observations, hidden states, a state transition model, emission probabilities, and an initial state distribution. These components collectively contribute to the modeling and analysis of underlying processes captured by the HMM [29,58-60,483,484].

    The application steps for the HMM can be summarized as follows. The first step involves conducting a study to collect labeled data consisting of accelerometer counts and their corresponding activities. These labeled data are then used to estimate the parameters in the HMM classification model. Next, the trained model is applied to unlabeled activity data to estimate the activity type with a probability [29,58-60,483,484]. In the context of activity recognition, the HMM is constructed in 2 different ways. The first one involves a single HMM model for all types of activities to be classified [29,58-60,483,484]. The second one builds an HMM for each specific activity, taking each activity as a distinct entity [483]. These 2 approaches represent different strategies in activity recognition.

    The use of the HMM comes with both advantages and limitations. One of the advantages is its ability to leverage the inherent autocorrelation in temporally close activities, which enhances estimation strength in classification tasks [59]. Consequently, the HMM tends to achieve a higher accuracy in classifying types of activities compared to threshold-based methods [484]. Second, HMM provides an estimated probability for each activity label, offering a quantification of uncertainty in estimation. However, parametric HMMs can be computationally expensive due to the large number of parameters involved. In addition, a substantial amount of training data is typically required to ensure stability in parameter estimation. Furthermore, determining the optimal hyperparameter settings before training the model can pose challenges [485].

    Studies on the Association Between PA and Health Conditions With Regression Models

    In addition to classifying activities, there has been a growing emphasis on researching the relationship between PA and health outcomes. Figure 5 illustrates the distribution of articles over the years, with association research consistently comprising the majority (Figure 5). Specifically, association research accounts for 79.9% (342/428) of the reviewed literature (Figure 3 and Multimedia Appendix 3). The studied health outcomes encompass a wide range, including cardiovascular health [61,62], metabolic markers [486], mental well-being [63,64], chronic disease risk [5], and overall mortality [65,66]. By examining the relationship between PA and health outcomes, scientists can gain insights into the potential benefits and mechanisms through which PA impacts overall well-being and disease prevention. Such studies highlight the broader implications and significance of PA in promoting optimal health while controlling confounding factors.

    When investigating and interpreting the association between PA and health conditions, regression models are commonly used as powerful tools. The results can provide evidence to identify protective or risk factors of health conditions. In addition, the regression approach can be directly generalized to health outcomes with either discrete or continuous values. Depending on whether the PA metrics are summary statistics over a period such a week or are included as time series in analysis, these regression models can be grouped into the ones using summary-type PA metrics as longitudinal PA and ones emphasizing PA temporal trajectories in FDA, as discussed in subsequent sections.

    ‎
    Figure 5. The value on the top of each bar is the sum of the number of articles per topic per year. The solid black line indicates the number of articles considering longitudinal patterns in physical activity (PA).

    Regression Models With Summary PA Metrics

    In regression models of this type, the primary aim is to evaluate the impact of overall PA on specific health outcomes. Therefore, summary PA metrics are frequently adopted as explanatory variables. One commonly used summary statistic is the time spent in MVPA per day or week. This metric represents the duration of activities with higher intensity. With this time of MVPA included as a predictor in regression models, studies have revealed beneficial effects of PA on various health conditions, such as healthy aging [487] and reduced frailty [67]. Similar predictors include the times spent in each of the different specified levels of activity (such as low PA, vigorous PA, and sedentary time). Because these variables are correlated with each other, multivariate pattern analysis to handle the multicollinearity was used to examine their association with metabolic health measures such as the cardiometabolic variables [68,69]. Another study used a quantile regression model with the Pittsburgh Fatigability Scale score as the outcome variable and considered the 24-hour rest-activity rhythm as a summary of PA to examine its association with physical fatigability in older adults [70].

    Depending on the outcome variable, either a linear regression model or a generalized linear model can be included for analysis, with or without random effects for individual influence. When the health outcome is a time-to-event measurement, a Cox regression model would be used to assess the association. Cox regression models are used for survival analysis focusing on the estimated hazard ratios of predictor variables (eg, PA levels) and the time-to-event outcomes (eg, disease occurrence or mortality). Examples include the incidence of cardiovascular disease [71,72] and all-cause mortality [65,73]. By adjusting for covariates in the model, such as age, sex, smoking status, and comorbidities, researchers can better evaluate the impact of PA on the survival time of the outcome.

    The regression models described earlier provide a direct interpretation of the relationship between overall PA patterns and health conditions. However, they do not account for temporal variations, such as longitudinal trends, as the variable can indicate PA changes across time. In other words, these models overlook the variability in time inherent in the original PA metrics such as ENMO, angle-z, and AI. To address this limitation, alternative regression models designed for longitudinal or functional data would offer a more suitable approach.

    Regression Models With PA Metrics as Repeated Measurements

    When the study objective is to examine the impact of PA temporal patterns on health outcomes, the identification of trends across time and the use of PA metrics indexed by time are essential. Extracting information from repeatedly measured PA is crucial for achieving this goal. Therefore, the modeling approach must account for correlation in the longitudinally measured metrics. Various statistical models for longitudinal data analysis have been developed to incorporate within-person variation, including repeated-measures ANOVA and mixed-effects models. These models were used in 65 (19%) out of the 342 association studies reviewed, as indicated by the line connecting these 2 nodes in Figure 4.

    Repeated-measures ANOVA is a statistical method primarily used to analyze differences among ≥3 dependent samples, with the assumption that different individuals are independent from each other, while different visits or time points for the same individual exhibit correlation. For instance, in a school-based behavior intervention study, 2-way ANOVA was applied to analyze the effect of treatment, time, and their interaction on various PA variables, such as the intensity, frequency, and duration of PA [488]. These PA variables were considered repeated measurements, as they were collected before and after the intervention. This study concluded that the intervention positively impacted the psychological constructs and the PA levels of the adolescents involved. Similarly, in a study on children’s motor skills, repeated-measures multivariate ANOVA (MANOVA) was used to explore skill competence and PA [489].

    The calculation of the covariance matrix in repeated-measures MANOVA assumes compound symmetry, implying that variances are equal across time points, and the correlations between any 2 time points are constant. However, this assumption may not hold true if the study spans a long period. Other requirements for repeated-measures MANOVA include complete cases, where no data are missing, and covariates are time-independent. Both conditions may be compromised due to factors such as loss to follow-up, device malfunction or misplacement, or variations in human PA rhythms.

    The linear mixed-effects model and GLMM can accommodate continuous or discrete longitudinal response variables. The response variable can be the time spent in various levels of PA, in MVPAs across seasons, or in categories of sleep [74,75]. In a bidirectional association study on adolescents, the linear mixed-effects model was used to assess the effect of night sleep on the PA performed the following day, and the GLMM investigated the effect of daytime PA on night sleep [74]. Both models used the random intercept to account for the day-to-day association at the individual level while adjusting for demographic variables as the fixed effects. When modeling the within-person correlation, some studies chose to model via the correlation matrix, rather than the random effect. For instance, a generalized linear model was used in a cohort study of aging workers to assess the effect of neighborhood socioeconomic disadvantages and greenness on leisure-time PA [76]. The time spent was monitored during working and nonworking days. The within-person variation in the repeatedly measured PA time was included in the covariance structure, in contrast to the person-specific or cluster-specific random effect in mixed-effects models. The quantified association can then be estimated by generalized estimating equations, as demonstrated in studies of frailty and daily PA [77,490].

    Regression Models With PA Metrics as Functions of Time

    An alternative approach to modeling PA recognizes it as a continuum and considers the collected PA measurements as discretized observations from a continuous curve. This involves applying FDA in regression models to establish association. Unlike the amplitudes from the FFTs or the scores from FPCA discussed earlier, FDA-based regression models can capture time-varying relationships between 2 variables, both of which can be functions of time. The use of this approach has gained much attention (3, 0.9% out of 342 association studies in Figure 4). Functional regression models can be categorized into three groups, depending on whether the response or covariates are functional or vector data: (1) functional responses with functional covariates, (2) functional responses with scalar covariates, and (3) scalar responses with functional covariates [491].

    Function-on-function regression is the model for a functional response involving ≥1 functional covariates [38]. The formula for this model can be expressed as follows:

    yi(t) = β0(t) +∫β(t)xi(t)dt + εi(t) (6)

    Here, the response, covariate, and coefficient are all time-varying functions. In a study investigating the association between PA and lung function, both the flow-volume curve (FVC) and PA of recruited smokers and nonsmokers were monitored through time [38]. The FVC, measured in mL, indicates the volume of air one can blow out after full inspiration. The FVC was denoted as yi(t), and the PA of the i-th person monitored through time was denoted as xi(t). This model allows researchers to investigate how the functional response changes in relation to ≥1 functional covariates, providing valuable insights into the complex associations between functional data.

    Function-on-scalar regression (FoSR) allows for the analysis of the relationship between a functional response and scalar covariate. It enables researchers to examine how functional predictors, such as activity patterns, relate to scalar covariates, such as health indicators or disease risk [78,466,468,492]. The model equation of FoSR can be expressed as follows:

    yi(t) = β0(t) + β1(t)xi + εi(t) (7)

    Here, the only scalar is the covariate xi. FoSR was applied in studies investigating the relationships between 24-hour rest or activity rhythms (the response function y) and scalar covariates (x) such as clinical characteristics and demographics [42]. In addition, FoSR was used in a study of children’s behavior [468] and a study of activity patterns in individuals with different mental statuses [466]. By leveraging FoSR, these studies evaluated the dynamic relationships between functional responses and scalar covariates over time, providing important findings for understanding various health and behavioral outcomes.

    Scalar-on-function regression studies the relationship between a scalar response and functional covariates [493]:

    yi = β0 +∫β(t)xi(t)dt + εi  (8)

    Different from the above-mentioned FoSR, the association of interest is now between the scalar response yi and the functional covariate xi(t). For instance, the percentage of time spent at or above a certain intensity was used as a functional predictor xi(t) to investigate the influence of this PA profile on the same person’s biological aging yi, a scalar response [41].

    The concept of scalar-on-function regression has been extended for types of accelerometer data analysis. For example, the scalar-on-time-by-distribution regression model was proposed to study the association between the scalar response and temporally local distributional information in wearable data [467]. In another study, researchers introduced the scalar-on-quantile-function regression model, which used the user-specific quantile function as a predictor to analyze the distributional nature of accelerometer data [494].

    Prediction of Health Conditions With PA Variables

    Of the 428 studies reviewed, 32 (7.5%) were prediction studies (Multimedia Appendix 3), involving the prediction of health-related outcomes or types of PAs [79-82]. Prediction can be derived based on statistical regression models or ML approaches. Once the association between covariates and response is established with the regression models discussed in earlier sections, new responses can be predicted based on newly observed values of predictors [495]. To predict all-cause mortality, a study applied sparse partial least square regression as the ML tool to determine the importance of 21 PA and sedentary behavior features and to construct a composite score based on their relevance [83]. Similarly, PA measures such as the total AC and active-to-sedentary transition probability were identified in logistic regression models as strong stand-alone predictors of 5-year all-cause mortality based on the National Health and Nutrition Examination Survey study, and these PA features can improve the prediction performance of traditional risk factors [43]. The scores derived from FPCA on PA counts were also considered in predicting mortality, sleep efficiency, and decreases in cognition [43,471].

    Prediction studies forecasting activity-related energy expenditure are also prevalent [496-499]. Many studies of this category have relied on the explained variance R2 from linear Pearson correlation analysis or the nonlinear Spearman rank correlation to determine the interpretability of PA variables such as step counts per day or VM counts per day [496]. It is important to note that such predictions are often population specific [84,497-500], and caution is warranted when generalizing these results to children, adults, and patients.


    Principal Findings

    There has been a notable increase in the use of accelerometer-based wearable devices to study the association between PA and health. These devices, worn in a free-living environment, offer objective and dynamic information about an individual’s activity patterns, allowing researchers to delve into various health issues. Selecting appropriate activity metrics and analytical approaches is crucial once study aims are established. Our review indicates that these aims typically involve classification, association, and prediction, while commonly used PA metrics often comprise VM values. These metrics can be computed as summary statistics to indicate the overall information regarding PA frequency, intensity, and duration. Alternatively, these metrics can be constructed as longitudinal or functional data, offering insights into PA profiles and time-varying effects. This perspective has attracted attention in recent literature, where the emphasis has shifted toward predominant PA trajectories and temporal variation. We anticipate a continued surge in the use of accelerometer-based wearable devices for PA monitoring, alongside the development of more advanced analytical tools to tackle increasingly complex scientific inquiries.

    Limitations

    This review did not include comparison and validation studies between different brands of devices and various PA metrics. Such studies may be crucial in determining whether data collected from different sources can be merged and provide information on data calibration methods. The findings from these studies may be essential when integrating various databases is needed. However, this task extends beyond the current scope of this review. Future research could initiate by gathering studies that compare different brands and subsequently apply network meta-analysis methodologies if the number of studies is not small.

    Studies about the validation of PA metrics are few because this requires a gold standard that may not be easy to derive or set up. Studies that relied on self-report questionnaires may draw criticisms of recall bias and lack of reliability. The comparison between different PA metrics is not straightforward either. One solution would be to wear multiple devices that record different metrics and examine their consistency. Nevertheless, as long as the same device is worn by all participants in the study and the number of participants is large, the analysis of PA metrics can still offer insights into the effect of PA on outcomes of interest.

    Other limitations of this review pertain to the choice of keywords used to screen articles. Because “physical activity” was a required phrase, articles containing only the word “activity” were excluded. For instance, studies focusing on sleep quality variation or sleep disturbances monitored by wearables may not be included if they did not emphasize the pattern or intensity of PA. However, research using accelerometers to detect sleep quality and efficiency, particularly for evaluating the association between sleep and cognition functions or chronic diseases, has garnered significant interest in recent years [471,492,501,502]. In addition, this review did not categorize articles based on study designs. Further categorizing them into cross-sectional, observational, or clinical studies could provide insights into the necessity and application of the analytical tools adopted in these studies. Similarly, this review did not focus on the use of wearables in clinical settings. However, a quick comparison of the selected articles from 2005 to 2009 with those from 2022 to 2023 revealed that only 2 (15%) out of 13 studies in the former period used accelerometers in clinical settings, while 23 (19.7%) out of 117 studies in the latter period did so. This difference demonstrates the increasing acceptance and adoption of accelerometers within the clinical community.

    Conclusions

    PA has been shown to benefit human health and prevent diseases, and wearable accelerometers offer a cost-efficient means to monitor an individual’s daily PA. Data generated by these wearable devices can be used as either summary-type PA metrics or continuous PA functions in ML algorithms or statistical models to address scientific questions regarding the effects of various PAs in digital health studies. Such studies can focus on increasing or maintaining cognitive functions in participants with neurodegenerative conditions, decreasing the risk of chronic diseases such as cardiovascular diseases, and maintaining physical fitness in general populations. Moreover, research on the use of PA for personalized health is expected to increase. For instance, studies leveraging biobank databases that contain both PA and genetic information have been conducted to investigate their impacts on health outcomes such as psychiatric disorders and circadian rhythms [30,85,458] As analytical tools continue to evolve alongside technological advancements, this review delineates the dimensions, developments, and transformations within methodological frameworks providing foundational guidelines for future research on PA and health.

    Acknowledgments

    This work was partially supported by Ministry of Science and Technology, Taiwan (110-2314-B-002-078-MY3).

    Authors' Contributions

    YTL drafted the manuscript and designed the Microsoft Excel file for managing the reviewed articles. CW and CKH revised the manuscript. All authors participated in developing the review’s strategy and reviewed the selected articles.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

    DOCX File , 109 KB
    Multimedia Appendix 2

    Key literature search terms and search results.

    DOCX File , 19 KB
    Multimedia Appendix 3

    References of articles included in the review.

    DOCX File , 248 KB
    1. Caspersen CJ, Powell KE, Christenson GM. Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public Health Rep. Apr 1985;100(2):126-131. [FREE Full text] [Medline]
    2. Physical activity and health; a report of the Surgeon General : executive summary. Centers for Disease Control and Prevention. 1996. URL: https://stacks.cdc.gov/view/cdc/13242 [accessed 2024-03-31]
    3. Troiano RP, McClain JJ, Brychta RJ, Chen KY. Evolution of accelerometer methods for physical activity research. Br J Sports Med. Jul 2014;48(13):1019-1023. [FREE Full text] [CrossRef] [Medline]
    4. Motahari-Nezhad H, Fgaier M, Mahdi Abid M, Péntek M, Gulácsi L, Zrubka Z. Digital biomarker-based studies: scoping review of systematic reviews. JMIR Mhealth Uhealth. Oct 24, 2022;10(10):e35722. [FREE Full text] [CrossRef] [Medline]
    5. Barker J, Smith Byrne K, Doherty A, Foster C, Rahimi K, Ramakrishnan R, et al. Physical activity of UK adults with chronic disease: cross-sectional analysis of accelerometer-measured physical activity in 96 706 UK Biobank participants. Int J Epidemiol. Aug 01, 2019;48(4):1167-1174. [FREE Full text] [CrossRef] [Medline]
    6. Ramakrishnan R, Doherty A, Smith-Byrne K, Rahimi K, Bennett D, Woodward M, et al. Accelerometer measured physical activity and the incidence of cardiovascular disease: Evidence from the UK Biobank cohort study. PLoS Med. Jan 12, 2021;18(1):e1003487. [CrossRef]
    7. van Hees VT, Sabia S, Anderson KN, Denton SJ, Oliver J, Catt M, et al. A novel, open access method to assess sleep duration using a wrist-worn accelerometer. PLoS One. 2015;10(11):e0142533. [FREE Full text] [CrossRef] [Medline]
    8. van Hees VT, Sabia S, Jones SE, Wood AR, Anderson KN, Kivimäki M, et al. Estimating sleep parameters using an accelerometer without sleep diary. Sci Rep. Aug 28, 2018;8(1):12975. [FREE Full text] [CrossRef] [Medline]
    9. Vasudevan S, Saha A, Tarver ME, Patel B. Digital biomarkers: convergence of digital health technologies and biomarkers. NPJ Digit Med. Mar 25, 2022;5(1):36. [FREE Full text] [CrossRef] [Medline]
    10. Lee M, Khan AM, Kim J, Cho Y, Kim T. A single tri-axial accelerometer-based real-time personal life log system capable of activity classification and exercise information generation. Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:1390-1393. [CrossRef] [Medline]
    11. Vallance JK, Winkler EA, Gardiner PA, Healy GN, Lynch BM, Owen N. Associations of objectively-assessed physical activity and sedentary time with depression: NHANES (2005-2006). Prev Med. Oct 2011;53(4-5):284-288. [CrossRef] [Medline]
    12. Moy ML, Garshick E, Matthess KR, Lew R, Reilly JJ. Accuracy of uniaxial accelerometer in chronic obstructive pulmonary disease. J Rehabil Res Dev. 2008;45(4):611-617. [FREE Full text] [CrossRef] [Medline]
    13. Long X, Yin B, Aarts RM. Single-accelerometer-based daily physical activity classification. Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6107-6110. [CrossRef] [Medline]
    14. Chen KY, Bassett DR. The technology of accelerometry-based activity monitors: current and future. Med Sci Sports Exerc. Nov 2005;37(11 Suppl):S490-S500. [Medline]
    15. Liu F, Wanigatunga AA, Schrack JA. Assessment of physical activity in adults using wrist accelerometers. Epidemiol Rev. Jan 14, 2022;43(1):65-93. [FREE Full text] [CrossRef] [Medline]
    16. Esliger DW, Rowlands AV, Hurst TL, Catt M, Murray P, Eston RG. Validation of the GENEA accelerometer. Med Sci Sports Exerc. Jun 2011;43(6):1085-1093. [CrossRef] [Medline]
    17. Murphy SL. Review of physical activity measurement using accelerometers in older adults: considerations for research design and conduct. Prev Med. Feb 2009;48(2):108-114. [CrossRef] [Medline]
    18. Godinho C, Domingos J, Cunha G, Santos AT, Fernandes RM, Abreu D, et al. A systematic review of the characteristics and validity of monitoring technologies to assess Parkinson's disease. J Neuroeng Rehabil. Mar 12, 2016;13:24. [FREE Full text] [CrossRef] [Medline]
    19. Kelly LA, McMillan DG, Anderson A, Fippinger M, Fillerup G, Rider J. Validity of actigraphs uniaxial and triaxial accelerometers for assessment of physical activity in adults in laboratory conditions. BMC Med Phys. Nov 26, 2013;13(1):5. [FREE Full text] [CrossRef] [Medline]
    20. Montoye AH, Conger SA, Connolly CP, Imboden MT, Nelson MB, Bock JM, et al. Validation of accelerometer-based energy expenditure prediction models in structured and simulated free-living settings. Meas Phys Educ Exerc Sci. Jun 22, 2017;21(4):223-234. [CrossRef]
    21. Migueles J, Rowlands A, Huber F, Sabia S, van HV. GGIR: a research community driven open source R package for generating physical activity and sleep outcomes from multi-day raw accelerometer data. J Meas Phys Behav Sep. 2019;2(3):188-196. [CrossRef]
    22. Avci A, Bosch S, Marin-Perianu M, Marin-Perianu R, Havinga P. Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: a survey. In: Proceedings of the 23th International Conference on Architecture of Computing Systems. 2010. Presented at: ACS '10; February 22-23, 2010:1-10; Hannover, Germany. URL: https://ieeexplore.ieee.org/document/5759000 [CrossRef]
    23. Plasqui G, Bonomi AG, Westerterp KR. Daily physical activity assessment with accelerometers: new insights and validation studies. Obes Rev. Jun 2013;14(6):451-462. [CrossRef] [Medline]
    24. Garatachea N, Torres LG, González GJ. Physical activity and energy expenditure measurements using accelerometers in older adults. Nutr Hosp. 2010;25(2):224-230. [FREE Full text] [Medline]
    25. Rao PJ, Phan K, Maharaj MM, Pelletier MH, Walsh WR, Mobbs RJ. Accelerometers for objective evaluation of physical activity following spine surgery. J Clin Neurosci. Apr 2016;26:14-18. [CrossRef] [Medline]
    26. Broderick JM, Ryan J, O'Donnell DM, Hussey J. A guide to assessing physical activity using accelerometry in cancer patients. Support Care Cancer. Apr 2014;22(4):1121-1130. [CrossRef] [Medline]
    27. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. Oct 02, 2018;169(7):467-473. [CrossRef] [Medline]
    28. Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. Feb 2005;8(1):19-32. [CrossRef]
    29. Li A, Ji L, Wang S, Wu J. Physical activity classification using a single triaxial accelerometer based on HMM. In: Proceedings of the 2010 IET International Conference on Wireless Sensor Network. 2010. Presented at: IET-WSN '10; November 15-17, 2010:155-160; Beijing, China. URL: https://ieeexplore.ieee.org/document/5741087 [CrossRef]
    30. Dennison CA, Legge SE, Bracher-Smith M, Menzies G, Escott-Price V, Smith DJ, et al. Association of genetic liability for psychiatric disorders with accelerometer-assessed physical activity in the UK Biobank. PLoS One. 2021;16(3):e0249189. [FREE Full text] [CrossRef] [Medline]
    31. Nawab KA, Storey BC, Staplin N, Walmsley R, Haynes R, Sutherland S, et al. Accelerometer-measured physical activity and functional behaviours among people on dialysis. Clin Kidney J. Mar 2021;14(3):950-958. [FREE Full text] [CrossRef] [Medline]
    32. McDevitt B, Connolly J, Duddy D, Doherty R, Condell J. Preliminary investigations of the validity and interinstrument reliability for classification of accelerometer physical activity cut-points against indirect caliometry in healthy adults. In: Proceedings of the 33rd Irish Signals and Systems Conference. 2022. Presented at: ISSC '22; June 9-10, 2022:1-9; Cork, Ireland. URL: https://ieeexplore.ieee.org/document/9826199 [CrossRef]
    33. Migueles JH, Cadenas-Sanchez C, Alcantara JM, Leal-Martín J, Mañas A, Ara I, et al. Calibration and cross-validation of accelerometer cut-points to classify sedentary time and physical activity from hip and non-dominant and dominant wrists in older adults. Sensors (Basel). May 11, 2021;21(10):3326. [FREE Full text] [CrossRef] [Medline]
    34. Agiovlasitis S, Ballenger BK, Schultz EE, Du Q, Motl RW. Calibration of hip accelerometers for measuring physical activity and sedentary behaviours in adults with Down syndrome. J Intellect Disabil Res. Feb 2023;67(2):172-181. [FREE Full text] [CrossRef] [Medline]
    35. Pulsford RM, Cortina-Borja M, Rich C, Kinnafick F, Dezateux C, Griffiths LJ. Actigraph accelerometer-defined boundaries for sedentary behaviour and physical activity intensities in 7 year old children. PLoS One. 2011;6(8):e21822. [FREE Full text] [CrossRef] [Medline]
    36. Roscoe CM, James RS, Duncan MJ. Calibration of GENEActiv accelerometer wrist cut-points for the assessment of physical activity intensity of preschool aged children. Eur J Pediatr. Aug 3, 2017;176(8):1093-1098. [CrossRef] [Medline]
    37. Qi G, Dutta D, Leroux A, Ray D, Muschelli J, Crainiceanu C, et al. Genome-wide association studies of 27 accelerometry-derived physical activity measurements identified novel loci and genetic mechanisms. Genet Epidemiol. Mar 2022;46(2):122-138. [FREE Full text] [CrossRef] [Medline]
    38. Benadjaoud MA, Menai M, van Hees VT, Zipunnikov V, Regnaux J, Kivimäki M, et al. The association between accelerometer-assessed physical activity and respiratory function in older adults differs between smokers and non-smokers. Sci Rep. Jul 16, 2019;9(1):10270. [FREE Full text] [CrossRef] [Medline]
    39. Chuang FC, Wang JS, Yang YT, Kao TP. A wearable activity sensor system and its physical activity classification scheme. In: Proceedings of the 2012 International Joint Conference on Neural Networks. 2012. Presented at: IJCNN '12; June 10-15, 2012:1-6; Brisbane, Australia. URL: https://ieeexplore.ieee.org/document/6252581 [CrossRef]
    40. Zhang S, Rowlands AV, Murray P, Hurst TL. Physical activity classification using the GENEA wrist-worn accelerometer. Med Sci Sports Exerc. Apr 2012;44(4):742-748. [CrossRef] [Medline]
    41. Banker M, Song PX. Supervised learning of physical activity features from functional accelerometer data. IEEE J Biomed Health Inform. Dec 2023;27(12):5710-5721. [CrossRef] [Medline]
    42. Huang W, Hao L, Wu X, Yu X, Cui E, Leroux A. Gender difference in "second-shift" physical activity: new insights from analyzing accelerometry data in a nationally representative sample. SSM Popul Health. Dec 2023;24:101536. [FREE Full text] [CrossRef] [Medline]
    43. Tabacu L, Ledbetter M, Leroux A, Crainiceanu C, Smirnova E. Quantifying the varying predictive value of physical activity measures obtained from wearable accelerometers on all-cause mortality over short to medium time horizons in NHANES 2003-2006. Sensors (Basel). Dec 22, 2020;21(1):4. [FREE Full text] [CrossRef] [Medline]
    44. Bonomi AG, Goris AH, Yin B, Westerterp KR. Detection of type, duration, and intensity of physical activity using an accelerometer. Med Sci Sports Exerc. Sep 2009;41(9):1770-1777. [CrossRef] [Medline]
    45. Stewart T, Narayanan A, Hedayatrad L, Neville J, Mackay L, Duncan S. A dual-accelerometer system for classifying physical activity in children and adults. Med Sci Sports Exerc. Dec 2018;50(12):2595-2602. [CrossRef] [Medline]
    46. García-Massó X, Serra-Añó P, Gonzalez LM, Ye-Lin Y, Prats-Boluda G, Garcia-Casado J. Identifying physical activity type in manual wheelchair users with spinal cord injury by means of accelerometers. Spinal Cord. Oct 2015;53(10):772-777. [CrossRef] [Medline]
    47. Chandler JL, Brazendale K, Beets MW, Mealing BA. Classification of physical activity intensities using a wrist-worn accelerometer in 8-12-year-old children. Pediatr Obes. Apr 20, 2016;11(2):120-127. [CrossRef] [Medline]
    48. Duncan MJ, Roscoe CM, Faghy M, Tallis J, Eyre EL. Estimating physical activity in children aged 8-11 years using accelerometry: contributions from fundamental movement skills and different accelerometer placements. Front Physiol. 2019;10:242. [FREE Full text] [CrossRef] [Medline]
    49. Gába A, Dygrýn J, Mitáš J, Jakubec L, Frömel K. Effect of accelerometer cut-off points on the recommended level of physical activity for obesity prevention in children. PLoS One. 2016;11(10):e0164282. [FREE Full text] [CrossRef] [Medline]
    50. Bammann K, Thomson NK, Albrecht BM, Buchan DS, Easton C. Generation and validation of ActiGraph GT3X+ accelerometer cut-points for assessing physical activity intensity in older adults. The OUTDOOR ACTIVE validation study. PLoS One. 2021;16(6):e0252615. [FREE Full text] [CrossRef] [Medline]
    51. McCracken LA, Ma JK, Voss C, Chan FH, Martin Ginis KA, West CR. Wrist accelerometry for physical activity measurement in individuals with spinal cord injury-a need for individually calibrated cut-points. Arch Phys Med Rehabil. Apr 2018;99(4):684-689. [CrossRef] [Medline]
    52. Mardini MT, Bai C, Wanigatunga AA, Saldana S, Casanova R, Manini TM. Age differences in estimating physical activity by wrist accelerometry using machine learning. Sensors (Basel). May 12, 2021;21(10):3352. [FREE Full text] [CrossRef] [Medline]
    53. Xie C, Bi S, Dong M, Li L, Chi S. Walking recognition method for physical activity analysis system of child based on wearable accelerometer. In: Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics. 2017. Presented at: ROBIO '17; December 5-8, 2017:2439-2443; Macau, Macao. URL: https://ieeexplore.ieee.org/document/8324785 [CrossRef]
    54. Shim J, Fleisch E, Barata F. precise segmentation of U.S. adults from 24-hour wearable-based physical activity profiles using machine learning clustering. In: Proceedings of the 2023 IEEE 11th International Conference on Healthcare Informatics. 2023. Presented at: ICHI '23; June 26-29, 2023:498-500; New York, NY. URL: https://www.computer.org/csdl/proceedings-article/ichi/2023/026300a498/1SN7x53nsVG [CrossRef]
    55. Kos M, Bogdan M, Glynn NW, Harezlak J. Classification of human physical activity based on raw accelerometry data via spherical coordinate transformation. Stat Med. Sep 30, 2020;39(22):2901-2920. [FREE Full text] [CrossRef] [Medline]
    56. Wullems JA, Verschueren SM, Degens H, Morse CI, Onambélé GL. Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults. PLoS One. Nov 20, 2017;12(11):e0188215. [FREE Full text] [CrossRef] [Medline]
    57. Qi J, Liang H, Chen J, Peng X, Newcombe L, Yang P. A hybrid hierarchical model for accessing physical activity recognition towards free-living environments. In: Proceedings of the 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing. 2020. Presented at: ISPA-BD '20; December 17-19, 2020:1342-1347; Exeter, UK. URL: https://ieeexplore.ieee.org/document/9443709 [CrossRef]
    58. Mannini A, Sabatini AM. Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors (Basel). 2010;10(2):1154-1175. [FREE Full text] [CrossRef] [Medline]
    59. Pober DM, Staudenmayer J, Raphael C, Freedson PS. Development of novel techniques to classify physical activity mode using accelerometers. Med Sci Sports Exerc. Sep 2006;38(9):1626-1634. [CrossRef] [Medline]
    60. Thornton CB, Kolehmainen N, Nazarpour K. Using unsupervised machine learning to quantify physical activity from accelerometry in a diverse and rapidly changing population. PLOS Digit Health. Apr 2023;2(4):e0000220. [FREE Full text] [CrossRef] [Medline]
    61. Cochrane SK, Chen S, Fitzgerald JD, Dodson JA, Fielding RA, King AC, et al. LIFE Study Research Group. Association of accelerometry-measured physical activity and cardiovascular events in mobility-limited older adults: the LIFE (Lifestyle Interventions and Independence for Elders) study. J Am Heart Assoc. Dec 02, 2017;6(12):e007215. [FREE Full text] [CrossRef] [Medline]
    62. Vásquez PM, Durazo-Arvizu RA, Marquez DX, Argos M, Lamar M, Odoms-Young A, et al. Association of accelerometer-measured physical activity and cardiovascular health in the Hispanic community health study/study of Latinos (HCHS/SOL). Hisp Health Care Int. Mar 2022;20(1):15-24. [CrossRef] [Medline]
    63. Appelqvist-Schmidlechner K, Raitanen J, Vasankari T, Kyröläinen H, Häkkinen A, Honkanen T, et al. Relationship between accelerometer-based physical activity, sedentary behavior, and mental health in young finnish men. Front Public Health. 2022;10:820852. [FREE Full text] [CrossRef] [Medline]
    64. Ghorbani S, Afshari M, Eckelt M, Dana A, Bund A. Associations between physical activity and mental health in iranian adolescents during the COVID-19 pandemic: an accelerometer-based study. Children (Basel). Nov 07, 2021;8(11):1022. [FREE Full text] [CrossRef] [Medline]
    65. Dohrn I, Sjöström M, Kwak L, Oja P, Hagströmer M. Accelerometer-measured sedentary time and physical activity—a 15 year follow-up of mortality in a Swedish population-based cohort. J Sci Med Sport. Jul 2018;21(7):702-707. [CrossRef]
    66. Strain T, Wijndaele K, Dempsey PC, Sharp SJ, Pearce M, Jeon J, et al. Wearable-device-measured physical activity and future health risk. Nat Med. Sep 2020;26(9):1385-1391. [CrossRef] [Medline]
    67. Mañas A, Del Pozo-Cruz B, Guadalupe-Grau A, Marín-Puyalto J, Alfaro-Acha A, Rodríguez-Mañas L, et al. Reallocating accelerometer-assessed sedentary time to light or moderate- to vigorous-intensity physical activity reduces frailty levels in older adults: an isotemporal substitution approach in the TSHA study. J Am Med Dir Assoc. Feb 2018;19(2):185.e1-185.e6. [CrossRef] [Medline]
    68. Aadland E, Andersen LB, Resaland GK, Kvalheim OM. Interpretation of multivariate association patterns between multicollinear physical activity accelerometry data and cardiometabolic health in children-a tutorial. Metabolites. Jul 02, 2019;9(7):129. [FREE Full text] [CrossRef] [Medline]
    69. Aadland E, Kvalheim OM, Hansen BH, Kriemler S, Ried-Larsen M, Wedderkopp N, et al. International Children's Accelerometry Database (ICAD) Collaborators. The multivariate physical activity signature associated with metabolic health in children and youth: an International Children's Accelerometry Database (ICAD) analysis. Prev Med. Dec 2020;141:106266. [FREE Full text] [CrossRef] [Medline]
    70. Graves JL, Qiao YS, Moored KD, Boudreau RM, Venditti EM, Krafty RT, et al. Profiles of accelerometry-derived physical activity are related to perceived physical fatigability in older adults. Sensors (Basel). Mar 02, 2021;21(5):1718. [FREE Full text] [CrossRef] [Medline]
    71. Del Pozo Cruz B, Ahmadi M, Inan-Eroglu E, Huang B, Stamatakis E. Prospective associations of accelerometer-assessed physical activity with mortality and incidence of cardiovascular disease among adults with hypertension: the UK Biobank study. J Am Heart Assoc. Mar 15, 2022;11(6):e023290. [FREE Full text] [CrossRef] [Medline]
    72. Dohrn I, Welmer A, Hagströmer M. Accelerometry-assessed physical activity and sedentary time and associations with chronic disease and hospital visits - a prospective cohort study with 15 years follow-up. Int J Behav Nutr Phys Act. Dec 09, 2019;16(1):125. [FREE Full text] [CrossRef] [Medline]
    73. LaMonte MJ, Buchner DM, Rillamas-Sun E, Di C, Evenson KR, Bellettiere J, et al. Accelerometer-measured physical activity and mortality in women aged 63 to 99. J Am Geriatr Soc. May 2018;66(5):886-894. [FREE Full text] [CrossRef] [Medline]
    74. Berninger N, Knell G, Gabriel KP, Plasqui G, Crutzen R, Hoor GT. Bidirectional day-to-day associations of reported sleep duration with accelerometer measured physical activity and sedentary time among Dutch adolescents: an observational study. J Meas Phys Behav. Dec 2020;3(4):304-314. [FREE Full text] [CrossRef] [Medline]
    75. Sit CH, Huang WY, Yu JJ, McKenzie TL. Accelerometer-assessed physical activity and sedentary time at school for children with disabilities: seasonal variation. Int J Environ Res Public Health. Aug 30, 2019;16(17):3163. [FREE Full text] [CrossRef] [Medline]
    76. Halonen JI, Pulakka A, Pentti J, Kallio M, Koskela S, Kivimäki M, et al. Cross-sectional associations of neighbourhood socioeconomic disadvantage and greenness with accelerometer-measured leisure-time physical activity in a cohort of ageing workers. BMJ Open. Aug 16, 2020;10(8):e038673. [FREE Full text] [CrossRef] [Medline]
    77. Oliver M, Schluter PJ, Schofield GM, Paterson J. Factors related to accelerometer-derived physical activity in Pacific children aged 6 years. Asia Pac J Public Health. Jan 2011;23(1):44-56. [CrossRef] [Medline]
    78. Keller JL, Tian F, Fitzgerald KC, Mische L, Ritter J, Costello MG, et al. Using real-world accelerometry-derived diurnal patterns of physical activity to evaluate disability in multiple sclerosis. J Rehabil Assist Technol Eng. Jan 12, 2022;9:20556683211067362. [FREE Full text] [CrossRef] [Medline]
    79. Assah FK, Ekelund U, Brage S, Corder K, Wright A, Mbanya JC, et al. Predicting physical activity energy expenditure using accelerometry in adults from sub-Sahara Africa. Obesity (Silver Spring). Aug 2009;17(8):1588-1595. [FREE Full text] [CrossRef] [Medline]
    80. Ellis K, Kerr J, Godbole S, Lanckriet G, Wing D, Marshall S. A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers. Physiol Meas. Nov 2014;35(11):2191-2203. [FREE Full text] [CrossRef] [Medline]
    81. Ladlow P, Nightingale TE, McGuigan MP, Bennett AN, Phillip R, Bilzon JL. Impact of anatomical placement of an accelerometer on prediction of physical activity energy expenditure in lower-limb amputees. PLoS One. 2017;12(10):e0185731. [FREE Full text] [CrossRef] [Medline]
    82. Smirnova E, Leroux A, Cao Q, Tabacu L, Zipunnikov V, Crainiceanu C, et al. The predictive performance of objective measures of physical activity derived from accelerometry data for 5-year all-cause mortality in older adults: National Health and Nutritional Examination Survey 2003-2006. J Gerontol A Biol Sci Med Sci. Sep 16, 2020;75(9):1779-1785. [FREE Full text] [CrossRef] [Medline]
    83. Chen M, Landré B, Marques-Vidal P, van Hees VT, van Gennip AC, Bloomberg M, et al. Identification of physical activity and sedentary behaviour dimensions that predict mortality risk in older adults: development of a machine learning model in the Whitehall II accelerometer sub-study and external validation in the CoLaus study. EClinicalMedicine. Jan 2023;55:101773. [FREE Full text] [CrossRef] [Medline]
    84. White T, Westgate K, Wareham NJ, Brage S. Estimation of physical activity energy expenditure during free-living from wrist accelerometry in UK adults. PLoS One. 2016;11(12):e0167472. [FREE Full text] [CrossRef] [Medline]
    85. Luo M, Yu C, Del Pozo Cruz B, Chen L, Ding D. Accelerometer-measured intensity-specific physical activity, genetic risk and incident type 2 diabetes: a prospective cohort study. Br J Sports Med. Oct 2023;57(19):1257-1264. [FREE Full text] [CrossRef] [Medline]
    86. Aadland E, Kvalheim OM, Anderssen SA, Resaland GK, Andersen LB. Multicollinear physical activity accelerometry data and associations to cardiometabolic health: challenges, pitfalls, and potential solutions. Int J Behav Nutr Phys Act. Aug 27, 2019;16(1):74. [FREE Full text] [CrossRef] [Medline]
    87. Aadland E, Steene-Johannessen J. The use of individual cut points from treadmill walking to assess free-living moderate to vigorous physical activity in obese subjects by accelerometry: is it useful? BMC Med Res Methodol. Dec 15, 2012;12:172. [FREE Full text] [CrossRef] [Medline]
    88. Agarwal V, Smuck M, Shah NH. Quantifying the relative change in physical activity after total knee arthroplasty using accelerometer based measurements. AMIA Jt Summits Transl Sci Proc. 2017;2017:463-472. [FREE Full text] [Medline]
    89. Agbaje AO. Associations of accelerometer-based sedentary time, light physical activity and moderate-to-vigorous physical activity with resting cardiac structure and function in adolescents according to sex, fat mass, lean mass, BMI, and hypertensive status. Scand J Med Sci Sports. Aug 2023;33(8):1399-1411. [FREE Full text] [CrossRef] [Medline]
    90. Agbaje AO. Mediating effect of fat mass, lean mass, blood pressure and insulin resistance on the associations of accelerometer-based sedentary time and physical activity with arterial stiffness, carotid IMT and carotid elasticity in 1574 adolescents. J Hum Hypertens. May 2024;38(5):393-403. [FREE Full text] [CrossRef] [Medline]
    91. Agbaje AO, Perng W, Tuomainen T. Effects of accelerometer-based sedentary time and physical activity on DEXA-measured fat mass in 6059 children. Nat Commun. Dec 12, 2023;14(1):8232. [FREE Full text] [CrossRef] [Medline]
    92. Aguiar B, Silva J, Rocha T, Carneiro S, Sousa I. Monitoring physical activity and energy expenditure with smartphones. In: Proceedings of the 2014 International Conference on Biomedical and Health Informatics. 2014. Presented at: BHI '14; June 1-4, 2014:664-667; Valencia, Spain. URL: https://ieeexplore.ieee.org/document/6864451 [CrossRef]
    93. Ahn H, Choi E, Rhee T, Choi J, Lee K, Kwon S, et al. Accelerometer-derived physical activity and the risk of death, heart failure, and stroke in patients with atrial fibrillation: a prospective study from UK Biobank. Br J Sports Med. May 02, 2024;58(8):427-434. [CrossRef] [Medline]
    94. Aittasalo M, Vähä-Ypyä H, Vasankari T, Husu P, Jussila A, Sievänen H. Mean amplitude deviation calculated from raw acceleration data: a novel method for classifying the intensity of adolescents' physical activity irrespective of accelerometer brand. BMC Sports Sci Med Rehabil. 2015;7:18. [FREE Full text] [CrossRef] [Medline]
    95. Ajja R, Wikkeling-Scott LF, Brazendale K, Hijazi R, Abdulle A. Accelerometer measured physical activity patterns of children during segmented school day in Abu Dhabi. BMC Pediatr. May 17, 2021;21(1):182. [FREE Full text] [CrossRef] [Medline]
    96. Albrecht BM, Stalling I, Recke C, Bammann K. Accelerometer-assessed outdoor physical activity is associated with meteorological conditions among older adults: cross-sectional results from the OUTDOOR ACTIVE study. PLoS One. 2020;15(1):e0228053. [FREE Full text] [CrossRef] [Medline]
    97. Ali H, Messina E, Bisiani R. Subject-dependent physical activity recognition model framework with a semi-supervised clustering approach. In: Proceedings of the 2013 European Modelling Symposium. 2013. Presented at: EMS '13; November 20-22, 2013:42-47; Manchester, UK. URL: https://ieeexplore.ieee.org/document/6779819 [CrossRef]
    98. Allen CL, Montes E, Hoang T, Romo T, Peña J, Navarro J. Can stereotype threat and lift visual messages affect subsequent physical activity? Evidence from a controlled experiment using accelerometers. Health Commun. Dec 08, 2023:1-12. [CrossRef] [Medline]
    99. Alsamman RA, Shousha TM, Faris ME, Abdelrahim DN, Arumugam A. Association of sociodemographic, anthropometric, and sleep quality factors with accelerometer-measured sitting and physical activity times among Emirati working women during the COVID-19 pandemic: a cross-sectional study. Womens Health (Lond). 2024;20:17455057231225539. [FREE Full text] [CrossRef] [Medline]
    100. Anastasopoulou P, Tansella M, Stumpp J, Shammas L, Hey S. Classification of human physical activity and energy expenditure estimation by accelerometry and barometry. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:6451-6454. [CrossRef] [Medline]
    101. Andersen MB, Ostenfeld EB, Fuglsang J, Møller M, Daugaard M, Ovesen PG. Maternal prepregnancy body mass index and physical activity during pregnancy assessed by accelerometer. Am J Obstet Gynecol MFM. Dec 2020;2(4):100182. [CrossRef] [Medline]
    102. Andersson C, Lyass A, Larson MG, Spartano NL, Vita JA, Benjamin EJ, et al. Physical activity measured by accelerometry and its associations with cardiac structure and vascular function in young and middle-aged adults. J Am Heart Assoc. Mar 19, 2015;4(3):e001528. [FREE Full text] [CrossRef] [Medline]
    103. Armstrong B, Covington LB, Hager ER, Black MM. Objective sleep and physical activity using 24-hour ankle-worn accelerometry among toddlers from low-income families. Sleep Health. Oct 2019;5(5):459-465. [FREE Full text] [CrossRef] [Medline]
    104. Arteaga D, Donnelly T, Crum K, Markham L, Killian M, Burnette WB, et al. Assessing physical activity using accelerometers in youth with Duchenne muscular dystrophy. J Neuromuscul Dis. 2020;7(3):331-342. [FREE Full text] [CrossRef] [Medline]
    105. Augustin NH, Mattocks C, Cooper AR, Ness AR, Faraway JJ. Modelling fat mass as a function of weekly physical activity profiles measured by actigraph accelerometers. Physiol Meas. Dec 2012;33(11):1831-1839. [CrossRef] [Medline]
    106. Ayabe M, Kumahara H. Effect of handling breaks on estimation of heart rate responses to bouts of physical activity among young women: an accelerometer research issue. Gait Posture. Oct 2020;81:1-6. [CrossRef] [Medline]
    107. Baldanzi G, Sayols-Baixeras S, Ekblom-Bak E, Ekblom Ö, Dekkers KF, Hammar U, et al. Accelerometer-based physical activity is associated with the gut microbiota in 8416 individuals in SCAPIS. EBioMedicine. Feb 2024;100:104989. [FREE Full text] [CrossRef] [Medline]
    108. Ballin M, Antonsson O, Rosenqvist V, Nordström P, Nordström A. Association of dog ownership with accelerometer-measured physical activity and daily steps in 70-year-old individuals: a population-based cross-sectional study. BMC Public Health. Dec 21, 2021;21(1):2313. [FREE Full text] [CrossRef] [Medline]
    109. Bangen KJ, Calcetas AT, Thomas KR, Wierenga C, Smith CN, Bordyug M, et al. Greater accelerometer-measured physical activity is associated with better cognition and cerebrovascular health in older adults. J Int Neuropsychol Soc. Feb 15, 2023;29(9):859-869. [CrossRef]
    110. Baque E, Sakzewski L, Trost SG, Boyd RN, Barber L. Validity of accelerometry to measure physical activity intensity in children with an acquired brain injury. Pediatr Phys Ther. Oct 2017;29(4):322-329. [CrossRef] [Medline]
    111. Barrett S, Begg S, O Halloran P, Kingsley M. The effect of a physical activity coaching intervention on accelerometer-measured sedentary behaviours in insufficiently physically active ambulatory hospital patients. Int J Environ Res Public Health. May 22, 2021;18(11):5543. [FREE Full text] [CrossRef] [Medline]
    112. Baumann S, Groß S, Voigt L, Ullrich A, Weymar F, Schwaneberg T, et al. Pitfalls in accelerometer-based measurement of physical activity: the presence of reactivity in an adult population. Scand J Med Sci Sports. Oct 12, 2017;28(3):1056-1063. [CrossRef]
    113. Baumann S, Guertler D, Weymar F, Bahls M, Dörr M, van den Berg N, et al. Do accelerometer-based physical activity patterns differentially affect cardiorespiratory fitness? A growth mixture modeling approach. J Behav Med. Mar 2020;43(1):99-107. [CrossRef] [Medline]
    114. Bayartai M, Määttä J, Karppinen J, Oura P, Takatalo J, Auvinen J, et al. Association of accelerometer-measured physical activity, back static muscular endurance and abdominal obesity with radicular pain and non-specific low back pain. Sci Rep. May 12, 2023;13(1):7736. [FREE Full text] [CrossRef] [Medline]
    115. Bayartai M, Taulaniemi A, Tokola K, Vähä-Ypyä H, Parkkari J, Husu P, et al. Role of the interaction between lumbar kinematics and accelerometer-measured physical activity in bodily pain, physical functioning and work ability among health care workers with low back pain. J Electromyogr Kinesiol. May 2023;69:102744. [CrossRef] [Medline]
    116. Bergh IH, van Stralen MM, Grydeland M, Bjelland M, Lien N, Andersen LF, et al. Exploring mediators of accelerometer assessed physical activity in young adolescents in the health in adolescents study - a group randomized controlled trial. BMC Public Health. Oct 21, 2012;12:814. [FREE Full text] [CrossRef] [Medline]
    117. Biswas A, Chen C, Prince SA, Smith PM, Mustard CA. Daily accelerometer-measured physical activity patterns and associations with cardiometabolic health among Canadian working adults. Health Rep. Mar 15, 2023;34(3):15-29. [FREE Full text] [CrossRef] [Medline]
    118. Bonomi AG, Plasqui G, Goris AH, Westerterp KR. Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer. J Appl Physiol (1985). Sep 2009;107(3):655-661. [FREE Full text] [CrossRef] [Medline]
    119. Browne RA, Macêdo GA, Cabral LL, Oliveira GT, Vivas A, Fontes EB, et al. Initial impact of the COVID-19 pandemic on physical activity and sedentary behavior in hypertensive older adults: an accelerometer-based analysis. Exp Gerontol. Dec 2020;142:111121. [FREE Full text] [CrossRef] [Medline]
    120. Carlson RH, Huebner DR, Hoarty CA, Whittington J, Haynatzki G, Balas MC, et al. Treadmill gait speeds correlate with physical activity counts measured by cell phone accelerometers. Gait Posture. Jun 2012;36(2):241-248. [FREE Full text] [CrossRef] [Medline]
    121. Carvalho EV, Reboredo MM, Gomes EP, Teixeira DR, Roberti NC, Mendes JO, et al. Physical activity in daily life assessed by an accelerometer in kidney transplant recipients and hemodialysis patients. Transplant Proc. 2014;46(6):1713-1717. [FREE Full text] [CrossRef] [Medline]
    122. Cassidy S, Fuller H, Chau J, Catt M, Bauman A, Trenell MI. Accelerometer-derived physical activity in those with cardio-metabolic disease compared to healthy adults: a UK Biobank study of 52,556 participants. Acta Diabetol. Sep 2018;55(9):975-979. [FREE Full text] [CrossRef] [Medline]
    123. Chan JA, Bosma H, Drosinou C, Timmermans EJ, Savelberg H, Schaper N, et al. Association of perceived and objective neighborhood walkability with accelerometer-measured physical activity and sedentary time in the Maastricht study. Scand J Med Sci Sports. Dec 2023;33(11):2313-2322. [CrossRef] [Medline]
    124. Chen G, Qi Q, Hua S, Moon J, Spartano NL, Vasan RS, et al. Accelerometer-assessed physical activity and incident diabetes in a population covering the adult life span: the Hispanic community health study/study of Latinos. Am J Clin Nutr. Dec 11, 2020;112(5):1318-1327. [FREE Full text] [CrossRef] [Medline]
    125. Chen T, Honda T, Chen S, Narazaki K, Kumagai S. Dose-response association between accelerometer-assessed physical activity and incidence of functional disability in older Japanese adults: a 6-year prospective study. J Gerontol A Biol Sci Med Sci. Oct 16, 2020;75(9):1763-1770. [FREE Full text] [CrossRef] [Medline]
    126. Cheng TS, Brage S, van Sluijs EM, Ong KK. Pre-pubertal accelerometer-assessed physical activity and timing of puberty in British boys and girls: the millennium cohort study. Int J Epidemiol. Oct 05, 2023;52(5):1316-1327. [FREE Full text] [CrossRef] [Medline]
    127. Choe EY, He D, Sun G. Trading-off transit and non-transit physical activity among older people: evidence from longitudinal accelerometer data of a natural experiment study. J Urban Health. May 2023;100(2):408-417. [FREE Full text] [CrossRef] [Medline]
    128. Cohn-Schwartz E, Khalaila R. Accelerometer-assessed physical activity and cognitive performance among European adults aged 50+: the mediating effects of social contacts and depressive symptoms. Healthcare (Basel). Dec 14, 2022;10(11):2279. [FREE Full text] [CrossRef] [Medline]
    129. Cooper AR, Goodman A, Page AS, Sherar LB, Esliger DW, van Sluijs EM, et al. Objectively measured physical activity and sedentary time in youth: the International children's accelerometry database (ICAD). Int J Behav Nutr Phys Act. Sep 17, 2015;12:113. [FREE Full text] [CrossRef] [Medline]
    130. Corcoran MP, Chui KK, White DK, Reid KF, Kirn D, Nelson ME, et al. Accelerometer assessment of physical activity and its association with physical function in older adults residing at assisted care facilities. J Nutr Health Aging. 2016;20(7):752-758. [CrossRef] [Medline]
    131. Corder K, Sharp SJ, Atkin AJ, Andersen LB, Cardon G, Page A, et al. International Children's Accelerometry Database (ICAD) Collaborators. Age-related patterns of vigorous-intensity physical activity in youth: the International Children's Accelerometry Database. Prev Med Rep. Dec 2016;4:17-22. [FREE Full text] [CrossRef] [Medline]
    132. Crouter SE, Flynn JI, Bassett DR. Estimating physical activity in youth using a wrist accelerometer. Med Sci Sports Exerc. May 2015;47(5):944-951. [FREE Full text] [CrossRef] [Medline]
    133. Crouter SE, Oody JF, Bassett DR. Estimating physical activity in youth using an ankle accelerometer. J Sports Sci. Oct 08, 2018;36(19):2265-2271. [FREE Full text] [CrossRef] [Medline]
    134. D'Silva A, Bebb G, Boyle T, Johnson ST, Vallance JK. Demographic and clinical correlates of accelerometer assessed physical activity and sedentary time in lung cancer survivors. Psychooncology. Mar 2018;27(3):1042-1049. [CrossRef] [Medline]
    135. da SI, van HV, Ramires VV, Knuth AG, Bielemann RM, Ekelund U, et al. Physical activity levels in three Brazilian birth cohorts as assessed with raw triaxial wrist accelerometry. Int J Epidemiol. Dec 2014;43(6):1959-1968. [FREE Full text] [CrossRef] [Medline]
    136. da Silva SG, Evenson KR, da Silva ICM, Mendes MA, Domingues MR, da Silveira MF, et al. Correlates of accelerometer-assessed physical activity in pregnancy-the 2015 Pelotas (Brazil) birth cohort study. Scand J Med Sci Sports. Aug 2018;28(8):1934-1945. [FREE Full text] [CrossRef] [Medline]
    137. Dankel SJ, Loenneke JP, Loprinzi PD. Combined associations of muscle-strengthening activities and accelerometer-assessed physical activity on multimorbidity: findings from NHANES. Am J Health Promot. Jul 2017;31(4):274-277. [CrossRef] [Medline]
    138. DasMahapatra P, Chiauzzi E, Bhalerao R, Rhodes J. Free-living physical activity monitoring in adult US patients with multiple sclerosis using a consumer wearable device. Digit Biomark. 2018;2(1):47-63. [FREE Full text] [CrossRef] [Medline]
    139. Davoudi A, Ozrazgat-Baslanti T, Tighe P, Bihorac A, Rashidi P. Pain and physical activity association in critically Ill patients. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:5696-5699. [FREE Full text] [CrossRef] [Medline]
    140. de Vries EA, Heijenbrok-Kal MH, van Kooten F, Giurgiu M, Ribbers GM, van den Berg-Emons RJ, et al. Unraveling the interplay between daily life fatigue and physical activity after subarachnoid hemorrhage: an ecological momentary assessment and accelerometry study. J Neuroeng Rehabil. Oct 26, 2023;20(1):127. [FREE Full text] [CrossRef] [Medline]
    141. Delgado-Gonzalo R, Celka P, Renevey P, Dasen S, Sola J, Bertschi M. Physical activity profiling: activity-specific step counting and energy expenditure models using 3D wrist acceleration. In: Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2015. Presented at: EMBC '15; August 25-29, 2015:25-29; Milan, Italy. URL: https://ieeexplore.ieee.org/document/7320271 [CrossRef]
    142. Diaz KM, Krupka DJ, Chang MJ, Kronish IM, Moise N, Goldsmith J, et al. Wrist-based cut-points for moderate- and vigorous-intensity physical activity for the actical accelerometer in adults. J Sports Sci. Jan 2018;36(2):206-212. [FREE Full text] [CrossRef] [Medline]
    143. Dibben GO, Gandhi MM, Taylor RS, Dalal HM, Metcalf B, Doherty P, et al. Physical activity assessment by accelerometry in people with heart failure. BMC Sports Sci Med Rehabil. 2020;12:47. [FREE Full text] [CrossRef] [Medline]
    144. Dijk-Huisman HC, Senden R, Smeets MH, Marcellis RG, Magdelijns FJ, Lenssen AF. The effect of a smartphone app with an accelerometer on the physical activity behavior of hospitalized patients: a randomized controlled trial. Sensors (Basel). Oct 25, 2023;23(21):8704. [FREE Full text] [CrossRef] [Medline]
    145. Diniz-Sousa F, Veras L, Ribeiro JC, Boppre G, Devezas V, Santos-Sousa H, et al. Accelerometry calibration in people with class II-III obesity: energy expenditure prediction and physical activity intensity identification. Gait Posture. Mar 2020;76:104-109. [CrossRef] [Medline]
    146. Do B, Zink J, Mason TB, Belcher BR, Dunton GF. Physical activity and sedentary time among mothers of school-aged children: differences in accelerometer-derived pattern metrics by demographic, employment, and household factors. Womens Health Issues. 2022;32(5):490-498. [FREE Full text] [CrossRef] [Medline]
    147. Doherty A, Jackson D, Hammerla N, Plötz T, Olivier P, Granat MH, et al. Large scale population assessment of physical activity using wrist worn accelerometers: the UK Biobank study. PLoS One. 2017;12(2):e0169649. [FREE Full text] [CrossRef] [Medline]
    148. Domazet SL, Tarp J, Thomsen RW, Højlund K, Stidsen JV, Brønd JC, et al. Accelerometer-derived physical activity and sedentary behaviors in individuals with newly diagnosed type 2 diabetes: a cross-sectional study from the Danish nationwide DD2 cohort. Front Sports Act Living. 2022;4:1089579. [FREE Full text] [CrossRef] [Medline]
    149. Domingos C, Picó-Pérez M, Magalhães R, Moreira M, Sousa N, Pêgo JM, et al. Free-living physical activity measured with a wearable device is associated with larger hippocampus volume and greater functional connectivity in healthy older adults: an observational, cross-sectional study in Northern Portugal. Front Aging Neurosci. 2021;13:729060. [FREE Full text] [CrossRef] [Medline]
    150. Dougherty EN, Randall I, Haedt-Matt AA, Pila E, Smith K, Wang S, et al. Accelerometer-based physical activity and shape and weight concerns among youth with overweight and obesity: a pilot exploratory ecological momentary assessment study. Child Obes. May 2024;20(4):236-242. [CrossRef] [Medline]
    151. Douma JAJ, Verdonck-de Leeuw IM, Leemans CR, Jansen F, Langendijk JA, Baatenburg de Jong RJ, et al. Demographic, clinical and lifestyle-related correlates of accelerometer assessed physical activity and fitness in newly diagnosed patients with head and neck cancer. Acta Oncol. Mar 2020;59(3):342-350. [CrossRef] [Medline]
    152. Dugas LR, Kliethermes S, Plange-Rhule J, Tong L, Bovet P, Forrester TE, et al. Accelerometer-measured physical activity is not associated with two-year weight change in African-origin adults from five diverse populations. PeerJ. 2017;5:e2902. [FREE Full text] [CrossRef] [Medline]
    153. Edwards MK, Loprinzi PD. Associations between accelerometer-assessed sedentary behavior, physical activity and objectively-measured cardiorespiratory fitness with red blood cell distribution width. Int J Cardiol. Oct 15, 2016;221:755-758. [CrossRef] [Medline]
    154. Ehakeem A, Gregson CL, Tobias JH, Lawlor DA. Age at puberty and accelerometer-measured physical activity: findings from two independent UK cohorts. Ann Hum Biol. Jul 2020;47(4):391-399. [CrossRef] [Medline]
    155. Ekblom-Bak E, Börjesson M, Ekblom Ö, Angerås O, Bergman F, Berntsson C, et al. Accelerometer derived physical activity and subclinical coronary and carotid atherosclerosis: cross-sectional analyses in 22 703 middle-aged men and women in the SCAPIS study. BMJ Open. Dec 23, 2023;13(11):e073380. [FREE Full text] [CrossRef] [Medline]
    156. Ekblom-Bak E, Börjesson M, Bergman F, Bergström G, Dahlin-Almevall A, Drake I, et al. Ekblom. Accelerometer derived physical activity patterns in 27.890 middle-aged adults: the SCAPIS cohort study. Scand J Med Sci Sports. May 2022;32(5):866-880. [FREE Full text] [CrossRef] [Medline]
    157. Ekstedt M, Nyberg G, Ingre M, Ekblom Ö, Marcus C. Sleep, physical activity and BMI in six to ten-year-old children measured by accelerometry: a cross-sectional study. Int J Behav Nutr Phys Act. Jul 22, 2013;10:82. [FREE Full text] [CrossRef] [Medline]
    158. Elhakeem A, Hannam K, Deere KC, Hartley A, Clark EM, Moss C, et al. Associations of lifetime walking and weight bearing exercise with accelerometer-measured high impact physical activity in later life. Prev Med Rep. Dec 2017;8:183-189. [FREE Full text] [CrossRef] [Medline]
    159. Evenson KR, Herring AH, Wen F. Accelerometry-assessed latent class patterns of physical activity and sedentary behavior with mortality. Am J Prev Med. Feb 2017;52(2):135-143. [FREE Full text] [CrossRef] [Medline]
    160. Evenson KR, Wen F, Herring AH, Di C, LaMonte MJ, Tinker LF, et al. Calibrating physical activity intensity for hip-worn accelerometry in women age 60 to 91 years: the women's health initiative OPACH calibration study. Prev Med Rep. 2015;2:750-756. [FREE Full text] [CrossRef] [Medline]
    161. Ezeugwu V, Klaren RE, A Hubbard E, Manns P, Motl RW. Mobility disability and the pattern of accelerometer-derived sedentary and physical activity behaviors in people with multiple sclerosis. Prev Med Rep. 2015;2:241-246. [FREE Full text] [CrossRef] [Medline]
    162. Fairclough SJ, Rowlands AV, Del Pozo Cruz B, Crotti M, Foweather L, Graves LE, et al. Reference values for wrist-worn accelerometer physical activity metrics in England children and adolescents. Int J Behav Nutr Phys Act. Mar 25, 2023;20(1):35. [FREE Full text] [CrossRef] [Medline]
    163. Farooq M, Sazonov E. A novel wearable device for food intake and physical activity recognition. Sensors. Jul 11, 2016;16(7):1067. [CrossRef]
    164. Feinglass J, Lee J, Semanik P, Song J, Dunlop D, Chang R. The effects of daily weather on accelerometer-measured physical activity. J Phys Act Health. Oct 2011;8(7):934-943. [FREE Full text] [CrossRef] [Medline]
    165. Fernández-Verdejo R, Alcantara JM, Galgani JE, Acosta FM, Migueles JH, Amaro-Gahete FJ, et al. Deciphering the constrained total energy expenditure model in humans by associating accelerometer-measured physical activity from wrist and hip. Sci Rep. Jul 10, 2021;11(1):12302. [FREE Full text] [CrossRef] [Medline]
    166. Foldager F, Jørgensen PB, Tønning LU, Petersen ET, Jakobsen SS, Vainorius D, et al. The relationship between muscle power, functional performance, accelerometer-based measurement of physical activity and patient-reported outcomes in patients with hip osteoarthritis: a cross-sectional study. Musculoskelet Sci Pract. Dec 2022;62:102678. [FREE Full text] [CrossRef] [Medline]
    167. Foong YC, Chherawala N, Aitken D, Scott D, Winzenberg T, Jones G. Accelerometer-determined physical activity, muscle mass, and leg strength in community-dwelling older adults. J Cachexia Sarcopenia Muscle. Jun 2016;7(3):275-283. [FREE Full text] [CrossRef] [Medline]
    168. Fox FA, Diers K, Lee H, Mayr A, Reuter M, Breteler MM, et al. Association between accelerometer-derived physical activity measurements and brain structure: a population-based cohort study. Neurology. Sep 13, 2022;99(11):e1202-e1215. [FREE Full text] [CrossRef] [Medline]
    169. Francis SL, Simmering JE, Polgreen LA, Evans NJ, Hosteng KR, Carr LJ, et al. Gamifying accelerometer use increases physical activity levels of individuals pre-disposed to type II diabetes. Prev Med Rep. Oct 2021;23:101426. [FREE Full text] [CrossRef] [Medline]
    170. Frehlich L, Turin TC, Doyle-Baker PK, Lang JJ, McCormack GR. Mediation analysis of the associations between neighbourhood walkability and greenness, accelerometer-measured physical activity, and health-related fitness in urban dwelling Canadians. Prev Med. Jan 2024;178:107792. [CrossRef] [Medline]
    171. Fridolfsson J, Arvidsson D, Ekblom-Bak E, Ekblom Ö, Bergström G, Börjesson M. Accelerometer-measured absolute versus relative physical activity intensity: cross-sectional associations with cardiometabolic health in midlife. BMC Public Health. Dec 24, 2023;23(1):2322. [FREE Full text] [CrossRef] [Medline]
    172. Fridolfsson J, Börjesson M, Ekblom-Bak E, Ekblom Ö, Arvidsson D. Stronger association between high intensity physical activity and cardiometabolic health with improved assessment of the full intensity range using accelerometry. Sensors (Basel). Mar 18, 2020;20(4):1118. [FREE Full text] [CrossRef] [Medline]
    173. Frith E, Loprinzi PD. Accelerometer-assessed light-intensity physical activity and mortality among those with mobility limitations. Disabil Health J. May 2018;11(2):298-300. [CrossRef] [Medline]
    174. Full KM, Moran K, Carlson J, Godbole S, Natarajan L, Hipp A, et al. Latent profile analysis of accelerometer-measured sleep, physical activity, and sedentary time and differences in health characteristics in adult women. PLoS One. 2019;14(6):e0218595. [FREE Full text] [CrossRef] [Medline]
    175. Full KM, Whitaker KM, Pettee Gabriel K, Lewis CE, Sternfeld B, Sidney S, et al. Cardiovascular risk and functional burden at midlife: prospective associations of isotemporal reallocations of accelerometer-measured physical activity and sedentary time in the CARDIA study. Prev Med. Oct 2021;150:106626. [FREE Full text] [CrossRef] [Medline]
    176. Gába A, Mitáš J, Jakubec L. Associations between accelerometer-measured physical activity and body fatness in school-aged children. Environ Health Prev Med. May 28, 2017;22(1):43. [FREE Full text] [CrossRef] [Medline]
    177. Gao N, Shao W, Salim FD. Predicting personality traits from physical activity intensity. Computer. Jul 2019;52(7):47-56. [CrossRef]
    178. Gao Y, Hua S, Mok Y, Salameh M, Qi Q, Chen G, et al. Joint associations of peripheral artery disease and accelerometry-based physical activity with mortality: the Hispanic community health study/study of Latinos (HCHS/SOL). Atherosclerosis. May 2022;347:55-62. [FREE Full text] [CrossRef] [Medline]
    179. Garay JL, Barreira TV, Wang Q, Brutsaert TD. Size at birth and accelerometer-measured physical activity or sedentary behavior in healthy term-born adults. Am J Hum Biol. Jul 2022;34(6):e23717. [FREE Full text] [CrossRef] [Medline]
    180. Garnotel M, Bastian T, Romero-Ugalde HM, Maire A, Dugas J, Zahariev A, et al. Prior automatic posture and activity identification improves physical activity energy expenditure prediction from hip-worn triaxial accelerometry. J Appl Physiol (1985). Mar 01, 2018;124(3):780-790. [FREE Full text] [CrossRef] [Medline]
    181. Glass NL, Bellettiere J, Jain P, LaMonte MJ, LaCroix AZ, Women’s Health Initiative. Evaluation of light physical activity measured by accelerometry and mobility disability during a 6-year follow-up in older women. JAMA Netw Open. Mar 01, 2021;4(2):e210005. [FREE Full text] [CrossRef] [Medline]
    182. Gråstén A, Huhtiniemi M, Hakonen H, Jaakkola T. Development of accelerometer-based light to vigorous physical activity in fitness profiles of school-aged children. Scand J Med Sci Sports. Dec 2021;31(12):2343-2355. [CrossRef] [Medline]
    183. Gråstén A, Huhtiniemi M, Kolunsarka I, Jaakkola T. Developmental associations of accelerometer measured moderate-to-vigorous physical activity and sedentary time with cardiorespiratory fitness in schoolchildren. J Sci Med Sport. Dec 2022;25(11):884-889. [FREE Full text] [CrossRef] [Medline]
    184. Gupta N, Dencker-Larsen S, Lund Rasmussen C, McGregor D, Rasmussen CD, Thorsen SV, et al. The physical activity paradox revisited: a prospective study on compositional accelerometer data and long-term sickness absence. Int J Behav Nutr Phys Act. Jul 20, 2020;17(1):93. [FREE Full text] [CrossRef] [Medline]
    185. Gupta N, Heiden M, Aadahl M, Korshøj M, Jørgensen MB, Holtermann A. What is the effect on obesity indicators from replacing prolonged sedentary time with brief sedentary bouts, standing and different types of physical activity during working days? A cross-sectional accelerometer-based study among blue-collar workers. PLoS One. 2016;11(5):e0154935. [FREE Full text] [CrossRef] [Medline]
    186. Guthrie N, Bradlyn A, Thompson SK, Yen S, Haritatos J, Dillon F, et al. Development of an accelerometer-linked online intervention system to promote physical activity in adolescents. PLoS One. 2015;10(5):e0128639. [FREE Full text] [CrossRef] [Medline]
    187. Gutiérrez-Hervás A, Cortés-Castell E, Juste-Ruíz M, Palazón-Bru A, Gil-Guillén V, Rizo-Baeza MM. Physical activity values in two- to seven-year-old children measured by accelerometer over five consecutive 24-hour days. Nutr Hosp. Jul 27, 2018;35(3):527-532. [CrossRef] [Medline]
    188. Ha L, Wakefield CE, Diaz C, Mizrahi D, Signorelli C, Yacef K, et al. Patterns of physical activity and sedentary behavior in child and adolescent cancer survivors assessed using wrist accelerometry: a cluster analysis approach. Health Informatics J. 2023;29(4):14604582231212525. [FREE Full text] [CrossRef] [Medline]
    189. Haapala EA, Rantalainen T, Hesketh KD, Rodda CP, Duckham RL. Accelerometer-based osteogenic indices, moderate-to-vigorous and vigorous physical activity, and bone traits in adolescents. J Musculoskelet Neuronal Interact. Dec 01, 2022;22(4):514-523. [FREE Full text] [Medline]
    190. Hachenberger J, Teuber Z, Li Y, Abkai L, Wild E, Lemola S. Investigating associations between physical activity, stress experience, and affective wellbeing during an examination period using experience sampling and accelerometry. Sci Rep. May 31, 2023;13(1):8808. [FREE Full text] [CrossRef] [Medline]
    191. Hager ER, Gormley CE, Latta LW, Treuth MS, Caulfield LE, Black MM. Toddler physical activity study: laboratory and community studies to evaluate accelerometer validity and correlates. BMC Public Health. Oct 06, 2016;16(1):936. [FREE Full text] [CrossRef] [Medline]
    192. Hagströmer M, Kwak L, Oja P, Sjöström M. A 6 year longitudinal study of accelerometer-measured physical activity and sedentary time in Swedish adults. J Sci Med Sport. Oct 2015;18(5):553-557. [CrossRef] [Medline]
    193. Hajna S, Brage S, Dalton A, Griffin SJ, Jones AP, Khaw K, et al. Cross-sectional and prospective associations between active living environments and accelerometer-assessed physical activity in the EPIC-Norfolk cohort. Health Place. Jan 2021;67:102490. [FREE Full text] [CrossRef] [Medline]
    194. Hale JL, Knell G, Swartz MD, Shiroma EJ, Ellis T, Lee I, et al. Association of Parkinson's disease status with accelerometer-derived physical activity and sedentary behavior in older women: the Women's Health Study (WHS). Prev Med Rep. Oct 2023;35:102361. [FREE Full text] [CrossRef] [Medline]
    195. Halloway S, Arfanakis K, Wilbur J, Schoeny ME, Pressler SJ. Accelerometer physical activity is associated with greater Gray matter volumes in older adults without dementia or mild cognitive impairment. J Gerontol B Psychol Sci Soc Sci. Oct 15, 2019;74(7):1142-1151. [FREE Full text] [CrossRef] [Medline]
    196. Hamasaki H, Noda M, Moriyama S, Yoshikawa R, Katsuyama H, Sako A, et al. Daily physical activity assessed by a triaxial accelerometer is beneficially associated with waist circumference, serum triglycerides, and insulin resistance in Japanese patients with prediabetes or untreated early type 2 diabetes. J Diabetes Res. 2015;2015:526201. [FREE Full text] [CrossRef] [Medline]
    197. Hansen BH, Kolle E, Dyrstad SM, Holme I, Anderssen SA. Accelerometer-determined physical activity in adults and older people. Med Sci Sports Exerc. Feb 2012;44(2):266-272. [CrossRef] [Medline]
    198. Hazizi AS, Aina MB, Mohd NM, Zaitun Y, Hamid JJ, Tabata I. Accelerometer-determined physical activity level among government employees in Penang, Malaysia. Malays J Nutr. Apr 2012;18(1):57-66. [Medline]
    199. Healy GN, Winkler EA, Brakenridge CL, Reeves MM, Eakin EG. Accelerometer-derived sedentary and physical activity time in overweight/obese adults with type 2 diabetes: cross-sectional associations with cardiometabolic biomarkers. PLoS One. 2015;10(3):e0119140. [FREE Full text] [CrossRef] [Medline]
    200. Heitmann KA, Løchen M, Hopstock LA, Stylidis M, Welde B, Schirmer H, et al. Cross-sectional associations between accelerometry-measured physical activity, left atrial size, and indices of left ventricular diastolic dysfunction: the Tromsø study. Prev Med Rep. Mar 2021;21:101290. [FREE Full text] [CrossRef] [Medline]
    201. Helgadóttir B, Forsell Y, Ekblom Ö. Physical activity patterns of people affected by depressive and anxiety disorders as measured by accelerometers: a cross-sectional study. PLoS One. 2015;10(1):e0115894. [FREE Full text] [CrossRef] [Medline]
    202. Hernando C, Hernando C, Collado EJ, Panizo N, Martinez-Navarro I, Hernando B. Establishing cut-points for physical activity classification using triaxial accelerometer in middle-aged recreational marathoners. PLoS One. 2018;13(8):e0202815. [FREE Full text] [CrossRef] [Medline]
    203. Hesketh KR, Evenson KR, Stroo M, Clancy SM, Østbye T, Benjamin-Neelon SE. Physical activity and sedentary behavior during pregnancy and postpartum, measured using hip and wrist-worn accelerometers. Prev Med Rep. Jul 2018;10:337-345. [FREE Full text] [CrossRef] [Medline]
    204. Hikihara Y, Tanaka C, Oshima Y, Ohkawara K, Ishikawa-Takata K, Tanaka S. Prediction models discriminating between nonlocomotive and locomotive activities in children using a triaxial accelerometer with a gravity-removal physical activity classification algorithm. PLoS One. 2014;9(4):e94940. [FREE Full text] [CrossRef] [Medline]
    205. Ho EC, Hawkley L, Dale W, Waite L, Huisingh-Scheetz M. Social capital predicts accelerometry-measured physical activity among older adults in the U.S.: a cross-sectional study in the National Social Life, Health, and Aging project. BMC Public Health. Jul 27, 2018;18(1):804. [FREE Full text] [CrossRef] [Medline]
    206. Hooker SA, Masters KS. Purpose in life is associated with physical activity measured by accelerometer. J Health Psychol. Jun 07, 2016;21(6):962-971. [CrossRef] [Medline]
    207. Hooker SP, Diaz KM, Blair SN, Colabianchi N, Hutto B, McDonnell MN, et al. Association of accelerometer-measured sedentary time and physical activity with risk of stroke among US adults. JAMA Netw Open. Jun 01, 2022;5(6):e2215385. [FREE Full text] [CrossRef] [Medline]
    208. Hossain SS, Lazar DM, Begum M. Ordinal statistical models of physical activity levels from accelerometer data. Int J Exerc Sci. 2021;14(7):338-357. [FREE Full text] [Medline]
    209. Hsiao C, Wen C, Yen H, Hsueh M, Liao Y. Association between accelerometer-measured light-intensity physical activity and cognitive function in older adults. J Nutr Health Aging. 2022;26(3):230-235. [FREE Full text] [CrossRef] [Medline]
    210. Hughes RL, Pindus DM, Khan NA, Burd NA, Holscher HD. Associations between accelerometer-measured physical activity and fecal microbiota in adults with overweight and obesity. Med Sci Sports Exerc. May 01, 2023;55(4):680-689. [FREE Full text] [CrossRef] [Medline]
    211. Huisingh-Scheetz M, Wroblewski K, Kocherginsky M, Huang E, Dale W, Waite L, et al. The relationship between physical activity and frailty among U.S. older adults based on hourly accelerometry data. J Gerontol A Biol Sci Med Sci. Apr 17, 2018;73(5):622-629. [FREE Full text] [CrossRef] [Medline]
    212. Hyde ET, LaCroix AZ, Evenson KR, Howard AG, Anuskiewicz B, Di C, et al. Accelerometer-measured physical activity and postmenopausal breast cancer incidence in the women's health accelerometry collaboration. Cancer. May 15, 2023;129(10):1579-1590. [FREE Full text] [CrossRef] [Medline]
    213. Hylkema TH, Brouwer S, Kooijman CM, De Vries AJ, Breukelman F, Dekker H, et al. Accelerometer measured sedentary and physical activity behaviors of working patients after total knee arthroplasty, and their compensation between occupational and leisure time. J Occup Rehabil. Jul 2021;31(2):350-359. [FREE Full text] [CrossRef] [Medline]
    214. Ikeda E, Guagliano JM, Atkin AJ, Sherar LB, Ekelund U, Hansen B, et al. International Children’s Accelerometry Database (ICAD) Collaborators. Cross-sectional and longitudinal associations of active travel, organised sport and physical education with accelerometer-assessed moderate-to-vigorous physical activity in young people: the international children's accelerometry database. Int J Behav Nutr Phys Act. May 02, 2022;19(1):41. [FREE Full text] [CrossRef] [Medline]
    215. Ishikawa-Takata K, Kaneko K, Koizumi K, Ito C. Comparison of physical activity energy expenditure in Japanese adolescents assessed by EW4800P triaxial accelerometry and the doubly labelled water method. Br J Nutr. Oct 2013;110(7):1347-1355. [CrossRef] [Medline]
    216. Izawa KP, Watanabe S, Hiraki K, Morio Y, Kasahara Y, Takeichi N, et al. Determination of the effectiveness of accelerometer use in the promotion of physical activity in cardiac patients: a randomized controlled trial. Arch Phys Med Rehabil. Nov 2012;93(11):1896-1902. [CrossRef] [Medline]
    217. Jaeschke L, Steinbrecher A, Boeing H, Gastell S, Ahrens W, Berger K, et al. Factors associated with habitual time spent in different physical activity intensities using multiday accelerometry. Sci Rep. Jan 21, 2020;10(1):774. [FREE Full text] [CrossRef] [Medline]
    218. Jago R, Salway R, House D, Walker R, Emm-Collison L, Sansum K, et al. Short and medium-term effects of the COVID-19 lockdowns on child and parent accelerometer-measured physical activity and sedentary time: a natural experiment. Int J Behav Nutr Phys Act. May 27, 2023;20(1):42. [FREE Full text] [CrossRef] [Medline]
    219. Jain RK, Vokes T. Physical activity as measured by accelerometer in NHANES 2005-2006 is associated with better bone density and trabecular bone score in older adults. Arch Osteoporos. Mar 02, 2019;14(1):29. [CrossRef] [Medline]
    220. Janney CA, Fagiolini A, Swartz HA, Jakicic JM, Holleman RG, Richardson CR. Are adults with bipolar disorder active? Objectively measured physical activity and sedentary behavior using accelerometry. J Affect Disord. Jan 2014;152-154:498-504. [FREE Full text] [CrossRef] [Medline]
    221. Jeng B, Cederberg KL, Lai B, Sasaki JE, Bamman MM, Motl RW. Wrist-based accelerometer cut-points for quantifying moderate-to-vigorous intensity physical activity in Parkinson's disease. Gait Posture. Jan 2022;91:235-239. [FREE Full text] [CrossRef] [Medline]
    222. Jeran S, Steinbrecher A, Haas V, Mähler A, Boschmann M, Westerterp KR, et al. Prediction of activity-related energy expenditure under free-living conditions using accelerometer-derived physical activity. Sci Rep. Oct 04, 2022;12(1):16578. [FREE Full text] [CrossRef] [Medline]
    223. Jin X, Chen Y, Feng H, Zhou M, Chan JW, Liu Y, et al. Association of accelerometer-measured sleep duration and different intensities of physical activity with incident type 2 diabetes in a population-based cohort study. J Sport Health Sci. Mar 2024;13(2):222-232. [FREE Full text] [CrossRef] [Medline]
    224. Kim J, Nakamura T, Kikuchi H, Yoshiuchi K, Yamamoto Y. Co-variation of depressive mood and spontaneous physical activity evaluated by ecological momentary assessment in major depressive disorder. Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:6635-6638. [CrossRef] [Medline]
    225. Júdice PB, Hetherington-Rauth M, Northstone K, Andersen LB, Wedderkopp N, Ekelund U, et al. International Children's Accelerometry Database (ICAD) Collaborators. Changes in physical activity and sedentary patterns on cardiometabolic outcomes in the transition to adolescence: international children's accelerometry database 2.0. J Pediatr. Oct 2020;225:166-73.e1. [CrossRef] [Medline]
    226. Jung ME, Locke SR, Bourne JE, Beauchamp MR, Lee T, Singer J, et al. Cardiorespiratory fitness and accelerometer-determined physical activity following one year of free-living high-intensity interval training and moderate-intensity continuous training: a randomized trial. Int J Behav Nutr Phys Act. Feb 26, 2020;17(1):25. [FREE Full text] [CrossRef] [Medline]
    227. Junno J, Keisu A, Niemelä M, Modarress Julin M, Korpelainen R, Jämsä T, et al. Accelerometer-measured physical activity is associated with knee breadth in middle-aged Finns - a population-based study. BMC Musculoskelet Disord. May 31, 2022;23(1):517. [FREE Full text] [CrossRef] [Medline]
    228. Junttila HE, Vaaramo MM, Huikari SM, Kari JT, Leinonen A, Farrahi V, et al. Association of accelerometer-measured physical activity and midlife income: a Northern Finland Birth Cohort 1966 study. Scand J Med Sci Sports. Oct 2023;33(9):1765-1778. [CrossRef] [Medline]
    229. Jussila A, Husu P, Vähä-Ypyä H, Tokola K, Kokko S, Sievänen H, et al. Accelerometer-measured physical activity levels and patterns vary in an age- and sex-dependent fashion among Finnish children and adolescents. Int J Environ Res Public Health. Jul 06, 2022;19(11):6950. [FREE Full text] [CrossRef] [Medline]
    230. Kanai M, Izawa KP, Kobayashi M, Onishi A, Kubo H, Nozoe M, et al. Effect of accelerometer-based feedback on physical activity in hospitalized patients with ischemic stroke: a randomized controlled trial. Clin Rehabil. Aug 2018;32(8):1047-1056. [CrossRef] [Medline]
    231. Kantomaa MT, Tikanmäki M, Kankaanpää A, Vääräsmäki M, Sipola-Leppänen M, Ekelund U, et al. Accelerometer-measured physical activity and sedentary time differ according to education level in young adults. PLoS One. 2016;11(7):e0158902. [FREE Full text] [CrossRef] [Medline]
    232. Ketels M, Rasmussen CL, Korshøj M, Gupta N, De Bacquer D, Holtermann A, et al. The relation between domain-specific physical behaviour and cardiorespiratory fitness: a cross-sectional compositional data analysis on the physical activity health paradox using accelerometer-assessed data. Int J Environ Res Public Health. Oct 29, 2020;17(21):7929. [FREE Full text] [CrossRef] [Medline]
    233. Khurshid S, Al-Alusi MA, Churchill TW, Guseh JS, Ellinor PT. Accelerometer-derived "weekend warrior" physical activity and incident cardiovascular disease. JAMA. Jul 18, 2023;330(3):247-252. [CrossRef] [Medline]
    234. Khurshid S, Weng L, Al-Alusi MA, Halford JL, Haimovich JS, Benjamin EJ, et al. Accelerometer-derived physical activity and risk of atrial fibrillation. Eur Heart J. Jul 01, 2021;42(25):2472-2483. [FREE Full text] [CrossRef] [Medline]
    235. Khurshid S, Weng L, Nauffal V, Pirruccello JP, Venn RA, Al-Alusi MA, et al. Wearable accelerometer-derived physical activity and incident disease. NPJ Digit Med. Sep 02, 2022;5(1):131. [FREE Full text] [CrossRef] [Medline]
    236. Kijima T, Akai K, Amagasa S, Inoue S, Yamagata S, Ishibashi Y, et al. Accelerometer-measured physical activity and posture among older adults in assisted-living residences. SAGE Open Med. 2024;12:20503121231220798. [FREE Full text] [CrossRef] [Medline]
    237. Kim Y, Wijndaele K, Sharp SJ, Strain T, Pearce M, White T, et al. Specific physical activities, sedentary behaviours and sleep as long-term predictors of accelerometer-measured physical activity in 91,648 adults: a prospective cohort study. Int J Behav Nutr Phys Act. May 07, 2019;16(1):41. [FREE Full text] [CrossRef] [Medline]
    238. Kinnunen TI, Tennant PW, McParlin C, Poston L, Robson SC, Bell R. Agreement between pedometer and accelerometer in measuring physical activity in overweight and obese pregnant women. BMC Public Health. Jul 27, 2011;11:501. [FREE Full text] [CrossRef] [Medline]
    239. Kong S, Park HY, Kang D, Lee JK, Lee G, Kwon OJ, et al. Seasonal variation in physical activity among preoperative patients with lung cancer determined using a wearable device. J Clin Med. Jan 27, 2020;9(2):349. [FREE Full text] [CrossRef] [Medline]
    240. Kretzschmar M, Lin W, Nardo L, Joseph GB, Dunlop DD, Heilmeier U, et al. Association of physical activity measured by accelerometer, knee joint abnormalities, and cartilage T2 measurements obtained from 3T magnetic resonance imaging: data from the osteoarthritis initiative. Arthritis Care Res (Hoboken). Oct 2015;67(9):1272-1280. [FREE Full text] [CrossRef] [Medline]
    241. Kruisdijk F, Deenik J, Tenback D, Tak E, Beekman A, van Harten P, et al. Accelerometer-measured sedentary behaviour and physical activity of inpatients with severe mental illness. Psychiatry Res. Aug 2017;254:67-74. [CrossRef] [Medline]
    242. Kulinski JP, Khera A, Ayers CR, Das SR, de Lemos JA, Blair SN, et al. Association between cardiorespiratory fitness and accelerometer-derived physical activity and sedentary time in the general population. Mayo Clin Proc. Aug 2014;89(8):1063-1071. [FREE Full text] [CrossRef] [Medline]
    243. Kumar D, Thanikkal A, Krishnamurthy P, Chen X, Zhang P. Accelerometer-based alcohol consumption detection from physical activity. In: Proceedings of the 2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications. 2021. Presented at: WiMob '21; October 11-13, 2021:415-418; Bologna, Italy. URL: https://ieeexplore.ieee.org/document/9606257 [CrossRef]
    244. Kuritz A, Mall C, Schnitzius M, Mess F. Physical activity and sedentary behavior of children in afterschool programs: an accelerometer-based analysis in full-day and half-day elementary schools in Germany. Front Public Health. 2020;8:463. [FREE Full text] [CrossRef] [Medline]
    245. Kwon S, Andersen LB, Grøntved A, Kolle E, Cardon G, Davey R, et al. ICAD Collaborators. A closer look at the relationship among accelerometer-based physical activity metrics: ICAD pooled data. Int J Behav Nutr Phys Act. May 29, 2019;16(1):40. [FREE Full text] [CrossRef] [Medline]
    246. Kwon S, Janz KF, International Children’s Accelerometry Database (ICAD) Collaborators. Tracking of accelerometry-measured physical activity during childhood: ICAD pooled analysis. Int J Behav Nutr Phys Act. Jun 07, 2012;9:68. [FREE Full text] [CrossRef] [Medline]
    247. LaCroix AZ, Bellettiere J, Rillamas-Sun E, Di C, Evenson KR, Lewis CE, et al. Women’s Health Initiative (WHI). Association of light physical activity measured by accelerometry and incidence of coronary heart disease and cardiovascular disease in older women. JAMA Netw Open. Mar 01, 2019;2(3):e190419. [FREE Full text] [CrossRef] [Medline]
    248. LaMonte MJ, LaCroix AZ, Nguyen S, Evenson KR, Di C, Stefanick ML, et al. Accelerometer-measured physical activity, sedentary time, and heart failure risk in women aged 63 to 99 years. JAMA Cardiol. May 01, 2024;9(4):336-345. [CrossRef] [Medline]
    249. LaMonte MJ, Lewis CE, Buchner DM, Evenson KR, Rillamas-Sun E, Di C, et al. Both light intensity and moderate-to-vigorous physical activity measured by accelerometry are favorably associated with cardiometabolic risk factors in older women: the objective physical activity and cardiovascular health (OPACH) study. J Am Heart Assoc. Oct 17, 2017;6(10):e007064. [FREE Full text] [CrossRef] [Medline]
    250. Länsitie M, Kangas M, Jokelainen J, Venojärvi M, Vaaramo E, Härkönen P, et al. Association between accelerometer-measured physical activity, glucose metabolism, and waist circumference in older adults. Diabetes Res Clin Pract. Aug 2021;178:108937. [FREE Full text] [CrossRef] [Medline]
    251. Länsitie M, Kangas M, Jokelainen J, Venojärvi M, Timonen M, Keinänen-Kiukaanniemi S, et al. Cardiovascular disease risk and all-cause mortality associated with accelerometer-measured physical activity and sedentary time ‒ a prospective population-based study in older adults. BMC Geriatr. Oct 05, 2022;22(1):729. [FREE Full text] [CrossRef] [Medline]
    252. Lawman HG, Horn ML, Wilson DK, Pate RR. A multilevel approach to examining time-specific effects in accelerometer-assessed physical activity. J Sci Med Sport. Dec 2015;18(6):667-672. [FREE Full text] [CrossRef] [Medline]
    253. Le Cornu Q, Chen M, van Hees V, Léger D, Fayosse A, Yerramalla MS, et al. Association of physical activity, sedentary behaviour, and daylight exposure with sleep in an ageing population: findings from the Whitehall accelerometer sub-study. Int J Behav Nutr Phys Act. Dec 09, 2022;19(1):144. [FREE Full text] [CrossRef] [Medline]
    254. Lee I, Shiroma EJ, Evenson KR, Kamada M, LaCroix AZ, Buring JE. Accelerometer-measured physical activity and sedentary behavior in relation to all-cause mortality: the women's health study. Circulation. Dec 09, 2018;137(2):203-205. [FREE Full text] [CrossRef] [Medline]
    255. Lee J, Walker ME, Gabriel KP, Vasan RS, Xanthakis V. Associations of accelerometer-measured physical activity and sedentary time with chronic kidney disease: The Framingham Heart Study. PLoS One. 2020;15(6):e0234825. [FREE Full text] [CrossRef] [Medline]
    256. Lee PH. Examining non-linear associations between accelerometer-measured physical activity, sedentary behavior, and all-cause mortality using segmented cox regression. Front Physiol. 2016;7:272. [FREE Full text] [CrossRef] [Medline]
    257. Lee S, Kim SC, Gim J, Park SJ, Seo SH, Kim SJ, et al. Accelerometer-derived physical activity analysis of elderly osteoarthritis patients. Musculoskelet Sci Pract. Aug 2023;66:102808. [CrossRef] [Medline]
    258. Leininger B, Schulz C, Gao Z, Bronfort G, Evans R, Pope Z, et al. Accelerometer-determined physical activity and clinical low back pain measures in adolescents with chronic or subacute recurrent low back pain. J Orthop Sports Phys Ther. Oct 2017;47(10):769-774. [FREE Full text] [CrossRef] [Medline]
    259. Lemberg GM, Riso E, Fjørtoft I, Kjønniksen L, Kull M, Mäestu E. School children's physical activity and preferred activities during outdoor recess in Estonia: using accelerometers, recess observation, and schoolyard mapping. Children (Basel). May 10, 2023;10(4):702. [FREE Full text] [CrossRef] [Medline]
    260. Li L, Nakamura T. An epidemiological sleep study based on a large-scale physical activity database. In: Proceedings of the 2019 IEEE 1st Global Conference on Life Sciences and Technologies. 2019. Presented at: LifeTech '19; March 12-14, 2019:292-293; Osaka, Japan. URL: https://ieeexplore.ieee.org/document/8883989 [CrossRef]
    261. Li S, Howard JT, Sosa ET, Cordova A, Parra-Medina D, Yin Z. Calibrating wrist-worn accelerometers for physical activity assessment in preschoolers: machine learning approaches. JMIR Form Res. Aug 31, 2020;4(8):e16727. [FREE Full text] [CrossRef] [Medline]
    262. Li T, Zong G, Peng P, Wang S, Cheng B. Accelerometer-measured physical activity and sample-based frailty in older women: does pattern really matter? Front Public Health. 2023;11:1304279. [FREE Full text] [CrossRef] [Medline]
    263. Li X, Kearney PM, Keane E, Harrington JM, Fitzgerald AP. levels and sociodemographic correlates of accelerometer-based physical activity in Irish children: a cross-sectional study. J Epidemiol Community Health. Jul 2017;71(6):521-527. [CrossRef] [Medline]
    264. Liang YY, Feng H, Chen Y, Jin X, Xue H, Zhou M, et al. Joint association of physical activity and sleep duration with risk of all-cause and cause-specific mortality: a population-based cohort study using accelerometry. Eur J Prev Cardiol. Jul 12, 2023;30(9):832-843. [CrossRef] [Medline]
    265. Lin C, Lin K, Hsueh M, Liao Y. Associations of accelerometer-measured sedentary behavior and physical activity with sleep in older adults. J Formos Med Assoc (Forthcoming). Aug 14, 2023. [FREE Full text] [CrossRef] [Medline]
    266. Lin H, Hartley P, Forsyth F, Pilling M, Hobbs FD, Taylor CJ, et al. Clinical and demographic correlates of accelerometer-measured physical activity in participants enrolled in the OPTIMISE HFpEF study. Eur J Cardiovasc Nurs. Jan 11, 2022;21(1):67-75. [CrossRef] [Medline]
    267. Lin H, Lynk N, Moore LL, Cabral HJ, Heffernan KS, Dumas AK, et al. A pragmatic approach to the comparison of wrist-based cutpoints of physical activity intensity for the MotionWatch8 accelerometer in children. PLoS One. 2020;15(6):e0234725. [FREE Full text] [CrossRef] [Medline]
    268. Liu M, Gan X, Ye Z, Zhang Y, He P, Zhou C, et al. Association of accelerometer-measured physical activity intensity, sedentary time, and exercise time with incident Parkinson's disease. NPJ Digit Med. Nov 28, 2023;6(1):224. [FREE Full text] [CrossRef] [Medline]
    269. Liu M, Zhang Y, Zhang Y, He P, Zhou C, Ye Z, et al. Association of accelerometer-measured physical activity and its change with progression to chronic kidney disease in adults with type 2 diabetes and overweight/obesity. Br J Sports Med. Mar 13, 2024;58(6):313-319. [CrossRef] [Medline]
    270. Long MT, Pedley A, Massaro JM, Hoffmann U, Esliger DW, Vasan RS, et al. Hepatic steatosis is associated with lower levels of physical activity measured via accelerometry. Obesity (Silver Spring). Jul 2015;23(6):1259-1266. [FREE Full text] [CrossRef] [Medline]
    271. Loprinzi PD. Accelerometer-determined physical activity and mortality in a national prospective cohort study of adults at high risk of a first atherosclerotic cardiovascular disease event. Int J Cardiol. Jan 01, 2016;202:417-418. [CrossRef] [Medline]
    272. Loprinzi PD. Accelerometer-determined physical activity and all-cause mortality in a national prospective cohort study of hypertensive adults. J Hypertens. May 2016;34(5):848-852. [CrossRef] [Medline]
    273. Loprinzi PD, Addoh O. Accelerometer-determined physical activity and all-cause mortality in a national prospective cohort study of adults post-acute stroke. Am J Health Promot. Jan 2018;32(1):24-27. [CrossRef] [Medline]
    274. Loprinzi PD, Frith E. Accelerometer-assessed physical activity and school absenteeism due to illness or injury among children and adolescents: NHANES 2003 to 2006. Am J Health Promot. Mar 2018;32(3):571-577. [CrossRef] [Medline]
    275. Loprinzi PD, Gilham B, Cardinal BJ. Association between accelerometer-assessed physical activity and objectively measured hearing sensitivity among U.S. adults with diabetes. Res Q Exerc Sport. Oct 2014;85(3):390-397. [CrossRef] [Medline]
    276. Loprinzi PD, Joyner C. Accelerometer-determined physical activity and mortality in a national prospective cohort study: considerations by visual acuity. Prev Med. Jul 2016;87:18-21. [CrossRef] [Medline]
    277. Loprinzi PD, Lee H, Gilham B, Cardinal BJ. Association between accelerometer-assessed physical activity and tinnitus, NHANES 2005-2006. Res Q Exerc Sport. Jun 2013;84(2):177-185. [CrossRef] [Medline]
    278. Loprinzi PD, Sheffield J, Tyo BM, Fittipaldi-Wert J. Accelerometer-determined physical activity, mobility disability, and health. Disabil Health J. Oct 2014;7(4):419-425. [CrossRef] [Medline]
    279. Loprinzi PD, Smit E, Lin FR, Gilham B, Ramulu PY. Accelerometer-assessed physical activity and objectively determined dual sensory impairment in US adults. Mayo Clin Proc. Jul 2013;88(7):690-696. [CrossRef] [Medline]
    280. Loprinzi P, Smit E, Lee H, Crespo C, Andersen R, Blair SN. The "fit but fat" paradigm addressed using accelerometer-determined physical activity data. N Am J Med Sci. Jul 2014;6(7):295-301. [FREE Full text] [CrossRef] [Medline]
    281. Loyen A, Clarke-Cornwell AM, Anderssen SA, Hagströmer M, Sardinha LB, Sundquist K, et al. Sedentary time and physical activity surveillance through accelerometer pooling in four European countries. Sports Med. Jul 10, 2017;47(7):1421-1435. [FREE Full text] [CrossRef] [Medline]
    282. Lund K, Larsen MD, Knudsen T, Kjeldsen J, Nielsen RG, Brage S, et al. Physical activity measured by accelerometry in paediatric and young adult patients with inflammatory bowel disease. BMC Gastroenterol. Jul 07, 2022;22(1):290. [FREE Full text] [CrossRef] [Medline]
    283. Lund Rasmussen C, Palarea-Albaladejo J, Korshøj M, Gupta N, Nabe-Nielsen K, Holtermann A, et al. Is high aerobic workload at work associated with leisure time physical activity and sedentary behaviour among blue-collar workers? A compositional data analysis based on accelerometer data. PLoS One. 2019;14(6):e0217024. [FREE Full text] [CrossRef] [Medline]
    284. Ma J, Kim H, Kim J. Isotemporal substitution analysis of accelerometer-derived sedentary behavior and physical activity on cardiometabolic health in Korean adults: a population-based cross-sectional study. Int J Environ Res Public Health. Oct 22, 2021;18(21):11102. [FREE Full text] [CrossRef] [Medline]
    285. Machado-Rodrigues AM, Rodrigues D, Gama A, Nogueira H, Silva MG, Mascarenhas LP, et al. Tri-axial accelerometer-assessed physical activity and its association with weight status in a sample of elementary-school children. Obes Res Clin Pract. 2023;17(3):192-197. [CrossRef] [Medline]
    286. Madimenos FC, Snodgrass JJ, Blackwell AD, Liebert MA, Sugiyama LS. Physical activity in an indigenous Ecuadorian forager-horticulturalist population as measured using accelerometry. Am J Hum Biol. 2011;23(4):488-497. [FREE Full text] [CrossRef] [Medline]
    287. Maes I, Van Dyck D, Van Cauwenberg J, Mertens L. Age-related differences in the associations of physical environmental factors and psychosocial factors with accelerometer-assessed physical activity. Health Place. Jan 2021;67:102492. [CrossRef] [Medline]
    288. Malek ME, Norman Å, Elinder LS, Patterson E, Nyberg G. Relationships between physical activity parenting practices and children's activity measured by accelerometry with children's activity style as a moderator-a cross sectional study. Children (Basel). Mar 12, 2022;9(2):248. [FREE Full text] [CrossRef] [Medline]
    289. Mañas A, Pozo-Cruz BD, Rodríguez-Gómez I, Losa-Reyna J, Rodríguez-Mañas L, García-García FJ, et al. Can physical activity offset the detrimental consequences of sedentary time on frailty? A moderation analysis in 749 older adults measured with accelerometers. J Am Med Dir Assoc. May 2019;20(5):634-8.e1. [CrossRef] [Medline]
    290. Manglani HR, Phansikar M, Duraney EJ, McKenna MR, Canter R, Nicholas JA, et al. Accelerometry measures of physical activity and sedentary behavior: associations with cognitive functioning in MS. Mult Scler Relat Disord. Dec 2023;79:104963. [CrossRef] [Medline]
    291. Manns P, Ezeugwu V, Armijo-Olivo S, Vallance J, Healy GN. Accelerometer-derived pattern of sedentary and physical activity time in persons with mobility disability: national health and nutrition examination survey 2003 to 2006. J Am Geriatr Soc. Jul 2015;63(7):1314-1323. [CrossRef] [Medline]
    292. Maslow AL, Colabianchi N. Youth physical activity resource use and activity measured by accelerometry. Am J Health Behav. 2011;35(2):219-227. [FREE Full text] [CrossRef] [Medline]
    293. Matabuena M, Félix P, Hammouri ZA, Mota J, Del Pozo Cruz B. Physical activity phenotypes and mortality in older adults: a novel distributional data analysis of accelerometry in the NHANES. Aging Clin Exp Res. Dec 2022;34(12):3107-3114. [FREE Full text] [CrossRef] [Medline]
    294. Matsuzawa R, Matsunaga A, Kutsuna T, Ishii A, Abe Y, Yoneki K, et al. Association of habitual physical activity measured by an accelerometer with high-density lipoprotein cholesterol levels in maintenance hemodialysis patients. ScientificWorldJournal. 2013;2013:780783. [FREE Full text] [CrossRef] [Medline]
    295. Matthews CE, Keadle SK, Troiano RP, Kahle L, Koster A, Brychta R, et al. Accelerometer-measured dose-response for physical activity, sedentary time, and mortality in US adults. Am J Clin Nutr. Nov 2016;104(5):1424-1432. [FREE Full text] [CrossRef] [Medline]
    296. Maylor BD, Edwardson CL, Clarke-Cornwell AM, Davies MJ, Dawkins NP, Dunstan DW, et al. Physical activity assessed by wrist and thigh worn accelerometry and associations with cardiometabolic health. Sensors (Basel). Aug 23, 2023;23(17):7353. [FREE Full text] [CrossRef] [Medline]
    297. McDonald L, Oguz M, Carroll R, Thakkar P, Yang F, Dhalwani N, et al. Comparison of accelerometer-derived physical activity levels between individuals with and without cancer: a UK Biobank study. Future Oncol. Dec 2019;15(33):3763-3774. [CrossRef] [Medline]
    298. McGarty AM, Penpraze V, Melville CA. Calibration and cross-validation of the ActiGraph wGT3X+ accelerometer for the estimation of physical activity intensity in children with intellectual disabilities. PLoS One. 2016;11(10):e0164928. [FREE Full text] [CrossRef] [Medline]
    299. McLellan G, Arthur R, Donnelly S, Buchan DS. Segmented sedentary time and physical activity patterns throughout the week from wrist-worn ActiGraph GT3X+ accelerometers among children 7-12 years old. J Sport Health Sci. Mar 2020;9(2):179-188. [FREE Full text] [CrossRef] [Medline]
    300. Melin M, Hagerman I, Gonon A, Gustafsson T, Rullman E. Variability in physical activity assessed with accelerometer is an independent predictor of mortality in CHF patients. PLoS One. 2016;11(4):e0153036. [FREE Full text] [CrossRef] [Medline]
    301. Memari AH, Ghaheri B, Ziaee V, Kordi R, Hafizi S, Moshayedi P. Physical activity in children and adolescents with autism assessed by triaxial accelerometry. Pediatr Obes. May 2013;8(2):150-158. [CrossRef] [Medline]
    302. Micklesfield LK, Westgate K, Smith A, Kufe CN, Mendham AE, Lindsay T, et al. Physical activity behaviors of a middle-age South African cohort as determined by integrated hip and thigh accelerometry. Med Sci Sports Exerc. Oct 01, 2022;54(9):1493-1505. [FREE Full text] [CrossRef] [Medline]
    303. Midorikawa T, Tanaka S, Kaneko K, Koizumi K, Ishikawa-Takata K, Futami J, et al. Evaluation of low-intensity physical activity by triaxial accelerometry. Obesity (Silver Spring). Dec 2007;15(12):3031-3038. [FREE Full text] [CrossRef] [Medline]
    304. Miller NE, Strath SJ, Swartz AM, Cashin SE. Estimating absolute and relative physical activity intensity across age via accelerometry in adults. J Aging Phys Act. Apr 2010;18(2):158-170. [FREE Full text] [CrossRef] [Medline]
    305. Mossavar-Rahmani Y, Lin J, Pan S, Song RJ, Xue X, Spartano NL, et al. Characterizing longitudinal change in accelerometry-based moderate-to-vigorous physical activity in the Hispanic community health study/study of Latinos and the Framingham heart study. BMC Public Health. Aug 24, 2023;23(1):1614. [FREE Full text] [CrossRef] [Medline]
    306. Moy ML, Matthess K, Stolzmann K, Reilly J, Garshick E. Free-living physical activity in COPD: assessment with accelerometer and activity checklist. J Rehabil Res Dev. 2009;46(2):277-286. [FREE Full text] [CrossRef] [Medline]
    307. Murabito JM, Pedley A, Massaro JM, Vasan RS, Esliger D, Blease SJ, et al. Moderate-to-vigorous physical activity with accelerometry is associated with visceral adipose tissue in adults. J Am Heart Assoc. Mar 03, 2015;4(3):e001379. [FREE Full text] [CrossRef] [Medline]
    308. Nagayoshi S, Oshima Y, Ando T, Aoyama T, Nakae S, Usui C, et al. Validity of estimating physical activity intensity using a triaxial accelerometer in healthy adults and older adults. BMJ Open Sport Exerc Med. 2019;5(1):e000592. [FREE Full text] [CrossRef] [Medline]
    309. Nakabazzi B, Wachira LM, Oyeyemi AL, Ssenyonga R, Onywera VO. Parental-perceived home and neighborhood environmental correlates of accelerometer-measured physical activity among school-going children in Uganda. PLOS Glob Public Health. 2021;1(12):e0000089. [FREE Full text] [CrossRef] [Medline]
    310. Nam S, Jeon S, Ash G, Whittemore R, Vlahov D. Racial discrimination, sedentary time, and physical activity in African Americans: quantitative study combining ecological momentary assessment and accelerometers. JMIR Form Res. Jul 07, 2021;5(6):e25687. [FREE Full text] [CrossRef] [Medline]
    311. Neil-Sztramko SE, Rafn BS, Gotay CC, Campbell KL. Determining activity count cut-points for measurement of physical activity using the Actiwatch2 accelerometer. Physiol Behav. May 01, 2017;173:95-100. [CrossRef] [Medline]
    312. Nero H, Benka Wallén M, Franzén E, Ståhle A, Hagströmer M. Accelerometer cut points for physical activity assessment of older adults with Parkinson’s disease. PLoS One. 2015;10(9):e0135899. [FREE Full text] [CrossRef] [Medline]
    313. Nguyen S, LaCroix AZ, Hayden KM, Di C, Palta P, Stefanick ML, et al. Accelerometer-measured physical activity and sitting with incident mild cognitive impairment or probable dementia among older women. Alzheimers Dement. Jul 2023;19(7):3041-3054. [FREE Full text] [CrossRef] [Medline]
    314. Noor MH, Salcic Z, Wang KI. Dynamic sliding window method for physical activity recognition using a single tri-axial accelerometer. In: Proceedings of the 2015 IEEE 10th Conference on Industrial Electronics and Applications. 2015. Presented at: ICIEA '15; June 15-17, 2015:102-105; Auckland, New Zealand. URL: https://ieeexplore.ieee.org/document/7334092 [CrossRef]
    315. Nordstrøm M, Hansen BH, Paus B, Kolset SO. Accelerometer-determined physical activity and walking capacity in persons with Down syndrome, Williams syndrome and Prader-Willi syndrome. Res Dev Disabil. Dec 2013;34(12):4395-4403. [FREE Full text] [CrossRef] [Medline]
    316. Nørgaard M, Lomholt JJ, Thastum M, Herlin M, Twilt M, Herlin T. Accelerometer-assessed daily physical activity in relation to pain cognition in juvenile idiopathic arthritis. Scand J Rheumatol. Jan 2017;46(1):22-26. [CrossRef] [Medline]
    317. Nu U, Touati T, Buddhadev S, Sun R, Smuck M, Song I. Who is physically active? Classification and analysis of physical activity using NHANES data. In: Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence. 2020. Presented at: SSCI '20; December 1-4, 2020:527-533; Canberra. Australia. URL: https://ieeexplore.ieee.org/document/9308353 [CrossRef]
    318. Ohkawara K, Oshima Y, Hikihara Y, Ishikawa-Takata K, Tabata I, Tanaka S. Real-time estimation of daily physical activity intensity by a triaxial accelerometer and a gravity-removal classification algorithm. Br J Nutr. Jun 2011;105(11):1681-1691. [CrossRef] [Medline]
    319. Orme MW, Steiner MC, Morgan MD, Kingsnorth AP, Esliger DW, Singh SJ, et al. 24-hour accelerometry in COPD: exploring physical activity, sedentary behavior, sleep and clinical characteristics. Int J Chron Obstruct Pulmon Dis. 2019;14:419-430. [FREE Full text] [CrossRef] [Medline]
    320. Ortlieb S, Dias A, Gorzelniak L, Nowak D, Karrasch S, Peters A, et al. KORA Study Group. Exploring patterns of accelerometry-assessed physical activity in elderly people. Int J Behav Nutr Phys Act. Mar 28, 2014;11(1):28. [FREE Full text] [CrossRef] [Medline]
    321. Ortlieb S, Gorzelniak L, Nowak D, Strobl R, Grill E, Thorand B, et al. Associations between multiple accelerometry-assessed physical activity parameters and selected health outcomes in elderly people--results from the KORA-age study. PLoS One. 2014;9(11):e111206. [FREE Full text] [CrossRef] [Medline]
    322. Parada H, McDonald E, Bellettiere J, Evenson KR, LaMonte MJ, LaCroix AZ. Associations of accelerometer-measured physical activity and physical activity-related cancer incidence in older women: results from the WHI OPACH Study. Br J Cancer. May 2020;122(9):1409-1416. [FREE Full text] [CrossRef] [Medline]
    323. Park J, Ishikawa-Takata K, Tanaka S, Mekata Y, Tabata I. Effects of walking speed and step frequency on estimation of physical activity using accelerometers. J Physiol Anthropol. 2011;30(3):119-127. [FREE Full text] [CrossRef] [Medline]
    324. Pärkkä J, Ermes M, Antila K, van Gils M, Mänttäri A, Nieminen H. Estimating intensity of physical activity: a comparison of wearable accelerometer and gyro sensors and 3 sensor locations. Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:1511-1514. [CrossRef] [Medline]
    325. Paschali AA, Goodrick GK, Kalantzi-Azizi A, Papadatou D, Balasubramanyam A. Accelerometer feedback to promote physical activity in adults with type 2 diabetes: a pilot study. Percept Mot Skills. Mar 2005;100(1):61-68. [CrossRef] [Medline]
    326. Peter-Marske KM, Evenson KR, Moore CC, Cuthbertson CC, Howard AG, Shiroma EJ, et al. Association of accelerometer-measured physical activity and sedentary behavior with incident cardiovascular disease, myocardial infarction, and ischemic stroke: the women's health study. J Am Heart Assoc. Apr 04, 2023;12(7):e028180. [FREE Full text] [CrossRef] [Medline]
    327. Peters TM, Moore SC, Xiang YB, Yang G, Shu XO, Ekelund U, et al. Accelerometer-measured physical activity in Chinese adults. Am J Prev Med. Jun 2010;38(6):583-591. [FREE Full text] [CrossRef] [Medline]
    328. Petersen TL, Brønd JC, Kristensen PL, Aadland E, Grøntved A, Jepsen R. Resemblance in accelerometer-assessed physical activity in families with children: the Lolland-Falster Health Study. Int J Behav Nutr Phys Act. Dec 04, 2020;17(1):161. [FREE Full text] [CrossRef] [Medline]
    329. Pettee Gabriel K, Karvonen-Gutierrez CA, Colvin AB, Ylitalo KR, Whitaker KM, Lange-Maia BS, et al. Associations of accelerometer-determined sedentary behavior and physical activity with physical performance outcomes by race/ethnicity in older women. Prev Med Rep. Oct 2021;23:101408. [FREE Full text] [CrossRef] [Medline]
    330. Pettee Gabriel K, Sternfeld B, Shiroma EJ, Pérez A, Cheung J, Lee I. Bidirectional associations of accelerometer-determined sedentary behavior and physical activity with reported time in bed: women's health study. Sleep Health. Mar 2017;3(1):49-55. [FREE Full text] [CrossRef] [Medline]
    331. Phan A, Askim T, Lydersen S, Indredavik B, Wethal T. Accelerometer-measured physical activity at 3 months as a predictor of symptoms of depression and anxiety 1 year after stroke: a multicentre prospective cohort study in central Norway. J Rehabil Med. Dec 16, 2023;55:jrm12309. [FREE Full text] [CrossRef] [Medline]
    332. Plasqui G, Joosen AM, Kester AD, Goris AH, Westerterp KR. Measuring free-living energy expenditure and physical activity with triaxial accelerometry. Obes Res. Aug 2005;13(8):1363-1369. [FREE Full text] [CrossRef] [Medline]
    333. Posis AIB, Bellettiere J, Salem RM, LaMonte MJ, Manson JE, Casanova R, et al. Associations of accelerometer-measured physical activity and sedentary time with all-cause mortality by genetic predisposition for longevity. J Aging Phys Act. May 01, 2023;31(2):265-275. [FREE Full text] [CrossRef] [Medline]
    334. Prioreschi A, Hodkinson B, Tikly M, McVeigh JA. Changes in physical activity measured by accelerometry following initiation of DMARD therapy in rheumatoid arthritis. Rheumatology (Oxford). May 2014;53(5):923-926. [CrossRef] [Medline]
    335. Pugh ME, Buchowski MS, Robbins IM, Newman JH, Hemnes AR. Physical activity limitation as measured by accelerometry in pulmonary arterial hypertension. Chest. Dec 2012;142(6):1391-1398. [FREE Full text] [CrossRef] [Medline]
    336. Qiu S, Xing Z. Association between accelerometer-derived physical activity and incident cardiac arrest. Europace. Dec 06, 2023;25(12):euad353. [FREE Full text] [CrossRef] [Medline]
    337. Rahman H, Carrault G, Ge D, Amoud H, Prioux J, Le FA. Ambulatory physical activity representation and classification using spectral distances approach. In: Proceedings of the 2015 International Conference on Advances in Biomedical Engineering. 2015. Presented at: ICABME '15; September 16-18, 2015:69-72; Beirut, Lebanon. URL: https://ieeexplore.ieee.org/document/7323253 [CrossRef]
    338. Ramulu PY, Maul E, Hochberg C, Chan ES, Ferrucci L, Friedman DS. Real-world assessment of physical activity in glaucoma using an accelerometer. Ophthalmology. Jul 2012;119(6):1159-1166. [FREE Full text] [CrossRef] [Medline]
    339. Ricketts HC, Buchan DS, Steffensen F, Chaudhuri R, Baker JS, Cowan DC. Physical activity levels in asthma: relationship with disease severity, body mass index and novel accelerometer-derived metrics. J Asthma. May 2023;60(4):824-834. [FREE Full text] [CrossRef] [Medline]
    340. Robertson MC, Green CE, Liao Y, Durand CP, Basen-Engquist KM. Self-efficacy and physical activity in overweight and obese adults participating in a worksite weight loss intervention: multistate modeling of wearable device data. Cancer Epidemiol Biomarkers Prev. Apr 2020;29(4):769-776. [FREE Full text] [CrossRef] [Medline]
    341. Roscoe CM, James RS, Duncan MJ. Accelerometer-based physical activity levels differ between week and weekend days in British preschool children. J Funct Morphol Kinesiol. Oct 12, 2019;4(3):65. [FREE Full text] [CrossRef] [Medline]
    342. Rowlands AV, Dempsey PC, Gillies C, Kloecker DE, Razieh C, Chudasama Y, et al. Association between accelerometer-assessed physical activity and severity of COVID-19 in UK biobank. Mayo Clin Proc Innov Qual Outcomes. Dec 2021;5(6):997-1007. [FREE Full text] [CrossRef] [Medline]
    343. Rowlands AV, Henson JJ, Coull NA, Edwardson CL, Brady E, Hall A, et al. The impact of COVID-19 restrictions on accelerometer-assessed physical activity and sleep in individuals with type 2 diabetes. Diabet Med. Oct 2021;38(10):e14549. [FREE Full text] [CrossRef] [Medline]
    344. Rowlands AV, van Hees VT, Dawkins NP, Maylor BD, Plekhanova T, Henson J, et al. Accelerometer-assessed physical activity in people with type 2 diabetes: accounting for sleep when determining associations with markers of health. Sensors (Basel). Jul 07, 2023;23(12):5382. [FREE Full text] [CrossRef] [Medline]
    345. Rubin DS, Huisingh-Scheetz M, Hung A, Ward RP, Nagele P, Arena R, et al. Accuracy of physical function questions to predict moderate-vigorous physical activity as measured by Hip Accelerometry. Anesthesiology. Dec 2019;131(5):992-1003. [FREE Full text] [CrossRef] [Medline]
    346. Salerno EA, Saint-Maurice PF, Wan F, Peterson LL, Park Y, Cao Y, et al. Prospective associations between accelerometry-derived physical activity and sedentary behaviors and mortality among cancer survivors. JNCI Cancer Spectr. Mar 01, 2023;7(2):pkad007. [FREE Full text] [CrossRef] [Medline]
    347. Salvo D, Torres C, Villa U, Rivera JA, Sarmiento OL, Reis RS, et al. Accelerometer-based physical activity levels among Mexican adults and their relation with sociodemographic characteristics and BMI: a cross-sectional study. Int J Behav Nutr Phys Act. Jul 20, 2015;12:79. [FREE Full text] [CrossRef] [Medline]
    348. Salway R, Foster C, de Vocht F, Tibbitts B, Emm-Collison L, House D, et al. Accelerometer-measured physical activity and sedentary time among children and their parents in the UK before and after COVID-19 lockdowns: a natural experiment. Int J Behav Nutr Phys Act. May 16, 2022;19(1):51. [FREE Full text] [CrossRef] [Medline]
    349. Sandroff BM, Motl RW, Kam JP, Pula JH. Accelerometer measured physical activity and the integrity of the anterior visual pathway in multiple sclerosis. Mult Scler Relat Disord. Jan 2014;3(1):117-122. [CrossRef] [Medline]
    350. Sasaki S, Nakamura K, Ukawa S, Okada E, Amagasa S, Inoue S, et al. Association of accelerometer-measured physical activity with kidney function in a Japanese population: the DOSANCO Health Study. BMC Nephrol. Jan 03, 2022;23(1):7. [FREE Full text] [CrossRef] [Medline]
    351. Savikangas T, Suominen TH, Alén M, Rantalainen T, Sipilä S. Changes in femoral neck bone mineral density and structural strength during a 12-month multicomponent exercise intervention among older adults - does accelerometer-measured physical activity matter? Bone. Jan 2024;178:116951. [FREE Full text] [CrossRef] [Medline]
    352. Schumacher BT, Bellettiere J, LaMonte MJ, Evenson KR, Di C, Lee I, et al. Accelerometer-measured daily steps, physical function, and subsequent fall risk in older women: the objective physical activity and cardiovascular disease in older women study. J Aging Phys Act. Aug 01, 2022;30(4):635-645. [FREE Full text] [CrossRef] [Medline]
    353. Schwendinger F, Wagner J, Infanger D, Schmidt-Trucksäss A, Knaier R. Methodological aspects for accelerometer-based assessment of physical activity in heart failure and health. BMC Med Res Methodol. Dec 14, 2021;21(1):251. [FREE Full text] [CrossRef] [Medline]
    354. Scott D, Johansson J, Gandham A, Ebeling PR, Nordstrom P, Nordstrom A. Associations of accelerometer-determined physical activity and sedentary behavior with sarcopenia and incident falls over 12 months in community-dwelling Swedish older adults. J Sport Health Sci. Oct 2021;10(5):577-584. [FREE Full text] [CrossRef] [Medline]
    355. Seguin-Fowler RA, LaCroix AZ, LaMonte MJ, Liu J, Maddock JE, Rethorst CD, et al. Association of neighborhood walk score with accelerometer-measured physical activity varies by neighborhood socioeconomic status in older women. Prev Med Rep. Oct 2022;29:101931. [FREE Full text] [CrossRef] [Medline]
    356. Serra MC, Balraj E, DiSanzo BL, Ivey FM, Hafer-Macko CE, Treuth MS, et al. Validating accelerometry as a measure of physical activity and energy expenditure in chronic stroke. Top Stroke Rehabil. Jan 2017;24(1):18-23. [FREE Full text] [CrossRef] [Medline]
    357. Sevil M, Rashid M, Maloney Z, Hajizadeh I, Samadi S, Askari MR, et al. Determining physical activity characteristics from wristband data for use in automated insulin delivery systems. IEEE Sens J. Nov 1, 2020;20(21):12859-12870. [FREE Full text] [CrossRef] [Medline]
    358. Shadyab AH, LaMonte MJ, Kooperberg C, Reiner AP, Carty CL, Manini TM, et al. Association of accelerometer-measured physical activity with leukocyte telomere length among older women. J Gerontol A Biol Sci Med Sci. Oct 12, 2017;72(11):1532-1537. [FREE Full text] [CrossRef] [Medline]
    359. Sharma B, Obeid J, DeMatteo C, Noseworthy MD, Timmons BW. New insights into accelerometer-measured habitual physical activity and sedentary time during early recovery in pediatric concussion. Pediatr Exerc Sci. May 01, 2024;36(2):58-65. [FREE Full text] [CrossRef] [Medline]
    360. Shin SW, Kim H, Kang YH, Kim J. Accelerometer-derived physical activity and sedentary behavior patterns among Korean adults. Phys Act Nutr. Jul 2023;27(2):25-33. [FREE Full text] [CrossRef] [Medline]
    361. Shin YE, Choi WH, Shin TM. Physical activity recognition based on rotated acceleration data using quaternion in sedentary behavior : a preliminary study. In: Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2014. Presented at: EMBC '14; August 26-30, 2014:26-30; Chicago, IL. URL: https://ieeexplore.ieee.org/document/6944741 [CrossRef]
    362. Shwetar Y, Huang Z, Veerubhotla A, Knezevic S, Hong E, Spungen AM, et al. Predicting physical activity intensity using raw accelerometer signals in manual wheelchair users with spinal cord injury. Spinal Cord. Mar 2022;60(2):149-156. [CrossRef] [Medline]
    363. Si K, Cao Z, Liu Q, Yang Y, Dai Q, Yao Y, et al. Accelerometer-measured physical activity, sedentary behavior, and risk of incident pelvic organ prolapse: a prospective cohort study in the UK Biobank. Int J Behav Nutr Phys Act. Mar 02, 2024;21(1):12. [FREE Full text] [CrossRef] [Medline]
    364. Siddique J, Aaby D, Montag SE, Sidney S, Sternfeld B, Welch WA, et al. Individualized relative-intensity physical activity accelerometer cut points. Med Sci Sports Exerc. Mar 2020;52(2):398-407. [FREE Full text] [CrossRef] [Medline]
    365. Silva RD, Cabral LL, Browne RA, Lemos TM, Alves CP, Crochemore-Silva I, et al. Joint associations of accelerometer-measured physical activity and sedentary time with cardiometabolic risk in older adults: a cross-sectional study. Exp Gerontol. Aug 2022;165:111839. [CrossRef] [Medline]
    366. Simón-Vicente L, Rivadeneyra-Posadas J, Soto-Célix M, Raya-González J, Castillo D, Calvo S, García-Bustillo, et al. Accelerometer cut-points for physical activity assessment in adults with mild to moderate Huntington’s disease: a cross-sectional multicentre study. Int J Environ Res Public Health. Dec 11, 2022;19(22):14834. [FREE Full text] [CrossRef] [Medline]
    367. Sjöros T, Vähä-Ypyä H, Laine S, Garthwaite T, Lahesmaa M, Laurila SM, et al. Both sedentary time and physical activity are associated with cardiometabolic health in overweight adults in a 1 month accelerometer measurement. Sci Rep. Nov 25, 2020;10(1):20578. [FREE Full text] [CrossRef] [Medline]
    368. Slootmaker SM, Chinapaw MJ, Seidell JC, van MW, Schuit AJ. Accelerometers and Internet for physical activity promotion in youth? Feasibility and effectiveness of a minimal intervention [ISRCTN93896459]. Prev Med. Jul 2010;51(1):31-36. [CrossRef] [Medline]
    369. Smith DM, DeCaro JA, Murphy SL, Parmelee PA. Momentary reports of fatigue predict physical activity level: wrist, waist, and combined accelerometry. J Aging Health. Oct 2020;32(9):921-925. [CrossRef] [Medline]
    370. Smith HA, Storti KL, Arena VC, Kriska AM, Gabriel KK, Sutton-Tyrrell K, et al. Associations between accelerometer-derived physical activity and regional adiposity in young men and women. Obesity (Silver Spring). Jul 2013;21(6):1299-1305. [FREE Full text] [CrossRef] [Medline]
    371. Smith ID, Ross LM, Gabaldon JR, Holdgate N, Pieper CF, Ning TC, et al. The relation of accelerometer-measured physical activity and serum uric acid using the national health and nutrition survey (NHANES) 2003-2004. Front Sports Act Living. 2021;3:775398. [FREE Full text] [CrossRef] [Medline]
    372. Smith KE, O'Connor SM, Mason TB, Wang S, Dzubur E, Crosby RD, et al. Associations between objective physical activity and emotional eating among adiposity-discordant siblings using ecological momentary assessment and accelerometers. Pediatr Obes. Mar 2021;16(3):e12720. [FREE Full text] [CrossRef] [Medline]
    373. Spartano NL, Davis-Plourde KL, Himali JJ, Andersson C, Pase MP, Maillard P, et al. Association of accelerometer-measured light-intensity physical activity with brain volume: the Framingham heart study. JAMA Netw Open. May 05, 2019;2(4):e192745. [FREE Full text] [CrossRef] [Medline]
    374. Spartano NL, Demissie S, Himali JJ, Dukes KA, Murabito JM, Vasan RS, et al. Accelerometer-determined physical activity and cognitive function in middle-aged and older adults from two generations of the Framingham Heart Study. Alzheimers Dement (N Y). 2019;5:618-626. [FREE Full text] [CrossRef] [Medline]
    375. Spartano NL, Heffernan KS, Dumas AK, Gump BB. Accelerometer-determined physical activity and the cardiovascular response to mental stress in children. J Sci Med Sport. Jan 2017;20(1):60-65. [FREE Full text] [CrossRef] [Medline]
    376. Spartano NL, Wang R, Yang Q, Chernofsky A, Murabito JM, Vasan RS, et al. Association of accelerometer-measured physical activity and sedentary time with epigenetic markers of aging. Med Sci Sports Exerc. Mar 01, 2023;55(2):264-272. [FREE Full text] [CrossRef] [Medline]
    377. Stamatakis E, Ahmadi MN, Friedenreich CM, Blodgett JM, Koster A, Holtermann A, et al. Vigorous intermittent lifestyle physical activity and cancer incidence among nonexercising adults: the UK biobank accelerometry study. JAMA Oncol. Oct 01, 2023;9(9):1255-1259. [FREE Full text] [CrossRef] [Medline]
    378. Stamatakis E, Ahmadi MN, Gill JMR, Thøgersen-Ntoumani C, Gibala MJ, Doherty A, et al. Association of wearable device-measured vigorous intermittent lifestyle physical activity with mortality. Nat Med. Dec 2022;28(12):2521-2529. [FREE Full text] [CrossRef] [Medline]
    379. Stevens ML, Crowley P, Rasmussen CL, Hallman DM, Mortensen OS, Nygård C, et al. Accelerometer-measured physical activity at work and need for recovery: a compositional analysis of cross-sectional data. Ann Work Expo Health. Mar 20, 2020;64(2):138-151. [FREE Full text] [CrossRef] [Medline]
    380. Straight CR, Ward-Ritacco CL, Evans EM. Association between accelerometer-measured physical activity and muscle capacity in middle-aged postmenopausal women. Menopause. Dec 2015;22(11):1204-1211. [CrossRef] [Medline]
    381. Stubbs B, Chen L, Chung M, Ku P. Physical activity ameliorates the association between sedentary behavior and cardiometabolic risk among inpatients with schizophrenia: a comparison versus controls using accelerometry. Compr Psychiatry. May 2017;74:144-150. [CrossRef] [Medline]
    382. Sufrinko AM, Howie EK, Elbin RJ, Collins MW, Kontos AP. A preliminary investigation of accelerometer-derived sleep and physical activity following sport-related concussion. J Head Trauma Rehabil. 2018;33(5):E64-E74. [CrossRef] [Medline]
    383. Sutin AR, Luchetti M, Stephan Y, Terracciano A. Meaning in life and accelerometer-measured physical activity: association based on 67,038 UK biobank participants. Ment Health Phys Act. Oct 2021;21:100412. [FREE Full text] [CrossRef] [Medline]
    384. Sutin AR, Stephan Y, Kekäläinen T, Luchetti M, Terracciano A. Purpose in life and accelerometer-measured physical activity among older adults. Psychol Health. May 18, 2023:1-15. [CrossRef] [Medline]
    385. Sweegers MG, Buffart LM, Huijsmans RJ, Konings IR, van Zweeden AA, Brug J, et al. From accelerometer output to physical activity intensities in breast cancer patients. J Sci Med Sport. Mar 2020;23(2):176-181. [CrossRef] [Medline]
    386. Takae R, Hatamoto Y, Yasukata J, Kose Y, Komiyama T, Ikenaga M, et al. Association of lower-extremity muscle performance and physical activity level and intensity in middle-aged and older adults: a doubly labeled water and accelerometer study. J Nutr Health Aging. 2020;24(9):1023-1030. [CrossRef] [Medline]
    387. Tanaka C, Hikihara Y, Ando T, Oshima Y, Usui C, Ohgi Y, et al. Prediction of physical activity intensity with accelerometry in young children. Int J Environ Res Public Health. Mar 15, 2019;16(6):931. [FREE Full text] [CrossRef] [Medline]
    388. Tanaka C, Shikano A, Imai N, Chong KH, Howard SJ, Tanabe K, et al. Accelerometer-measured physical activity and sedentary time among children in Japan before and during COVID-19: a cross-sectional and longitudinal analysis. Int J Environ Res Public Health. Jan 09, 2023;20(2):1130. [FREE Full text] [CrossRef] [Medline]
    389. Tanaka C, Tanaka S. Daily physical activity in Japanese preschool children evaluated by triaxial accelerometry: the relationship between period of engagement in moderate-to-vigorous physical activity and daily step counts. J Physiol Anthropol. Dec 2009;28(6):283-288. [FREE Full text] [CrossRef] [Medline]
    390. Tanaka C, Tanaka S, Kawahara J, Midorikawa T. Triaxial accelerometry for assessment of physical activity in young children. Obesity (Silver Spring). May 2007;15(5):1233-1241. [FREE Full text] [CrossRef] [Medline]
    391. Tanaka S, Ishikawa-Takata K, Nakae S, Sasaki S. Prediction of the physical activity level of community-dwelling older Japanese adults with a triaxial accelerometer containing a classification algorithm for ambulatory and non-ambulatory activities. Sensors (Basel). May 22, 2023;23(10):4960. [FREE Full text] [CrossRef] [Medline]
    392. Tao K, Liu W, Xiong S, Ken L, Zeng N, Peng Q, et al. Associations between self-determined motivation, accelerometer-determined physical activity, and quality of life in Chinese college students. Int J Environ Res Public Health. Aug 16, 2019;16(16):2941. [FREE Full text] [CrossRef] [Medline]
    393. Tarp J, Bugge A, Andersen LB, Sardinha LB, Ekelund U, Brage S, et al. Does adiposity mediate the relationship between physical activity and biological risk factors in youth?: a cross-sectional study from the International Children's Accelerometry Database (ICAD). Int J Obes (Lond) (Forthcoming). Oct 03, 2017. [FREE Full text] [CrossRef] [Medline]
    394. Tarp J, Hansen BH, Fagerland MW, Steene-Johannessen J, Anderssen SA, Ekelund U. Accelerometer-measured physical activity and sedentary time in a cohort of US adults followed for up to 13 years: the influence of removing early follow-up on associations with mortality. Int J Behav Nutr Phys Act. Mar 14, 2020;17(1):39. [FREE Full text] [CrossRef] [Medline]
    395. Tasheva P, Kraege V, Vollenweider P, Roulet G, Méan M, Marques-Vidal P. Accelerometry assessed physical activity of older adults hospitalized with acute medical illness - an observational study. BMC Geriatr. Oct 02, 2020;20(1):382. [FREE Full text] [CrossRef] [Medline]
    396. Taylor RW, Williams SM, Farmer VL, Taylor BJ. Changes in physical activity over time in young children: a longitudinal study using accelerometers. PLoS One. 2013;8(11):e81567. [FREE Full text] [CrossRef] [Medline]
    397. Thraen-Borowski KM, Gennuso KP, Cadmus-Bertram L. Accelerometer-derived physical activity and sedentary time by cancer type in the United States. PLoS One. 2017;12(8):e0182554. [FREE Full text] [CrossRef] [Medline]
    398. Thralls KJ, Godbole S, Manini TM, Johnson E, Natarajan L, Kerr J. A comparison of accelerometry analysis methods for physical activity in older adult women and associations with health outcomes over time. J Sports Sci. Oct 2019;37(20):2309-2317. [FREE Full text] [CrossRef] [Medline]
    399. Toivo K, Vähä-Ypyä H, Kannus P, Tokola K, Alanko L, Heinonen OJ, et al. Physical activity measured by accelerometry among adolescents participating in sports clubs and non-participating peers. Eur J Sport Sci. Jul 2023;23(7):1426-1434. [FREE Full text] [CrossRef] [Medline]
    400. Toomey CM, Whittaker JL, Doyle-Baker PK, Emery CA. Does a history of youth sport-related knee injury still impact accelerometer-measured levels of physical activity after 3-12 years? Phys Ther Sport. May 2022;55:90-97. [CrossRef] [Medline]
    401. Trinh L, Motl RW, Roberts SA, Gibbons T, McAuley E. Estimation of physical activity intensity cut-points using accelerometry in breast cancer survivors and age-matched controls. Eur J Cancer Care (Engl). Sep 2019;28(5):e13090. [CrossRef] [Medline]
    402. Trost SG, Brookes DS, Ahmadi MN. Evaluation of wrist accelerometer cut-points for classifying physical activity intensity in youth. Front Digit Health. 2022;4:884307. [FREE Full text] [CrossRef] [Medline]
    403. Trumpeter NN, Lawman HG, Wilson DK, Pate RR, Van Horn ML, Tate AK. Accelerometry cut points for physical activity in underserved African Americans. Int J Behav Nutr Phys Act. Jul 14, 2012;9:73. [FREE Full text] [CrossRef] [Medline]
    404. Tsai L, Boyle E, Buhl SF, Kock G, Brønd JC, Visser M, et al. Associations between appetite, physical activity and sedentary behaviour from hip- and wrist-worn accelerometers in community-dwelling older adults. Geriatr Gerontol Int. Jul 2023;23(6):411-417. [CrossRef] [Medline]
    405. Tsunoda K, Kitano N, Kai Y, Jindo T, Uchida K, Arao T. Dose-response relationships of accelerometer-measured sedentary behaviour and physical activity with non-alcoholic fatty liver disease. Aliment Pharmacol Ther. Dec 2021;54(10):1330-1339. [CrossRef] [Medline]
    406. Vaara JP, Kyröläinen H, Vasankari T, Kainulainen H, Raitanen J, Kujala UM. Accelerometer-based sedentary time, physical activity, and serum metabolome in young men. Metabolites. Jul 27, 2022;12(8):700. [FREE Full text] [CrossRef] [Medline]
    407. Vähä-Ypyä H, Vasankari T, Husu P, Mänttäri A, Vuorimaa T, Suni J, et al. Validation of cut-points for evaluating the intensity of physical activity with accelerometry-based mean amplitude deviation (MAD). PLoS One. 2015;10(8):e0134813. [FREE Full text] [CrossRef] [Medline]
    408. Valenti G, Camps SG, Verhoef SP, Bonomi AG, Westerterp KR. Validating measures of free-living physical activity in overweight and obese subjects using an accelerometer. Int J Obes (Lond). Jul 2014;38(7):1011-1014. [CrossRef] [Medline]
    409. van Bakergem M, Sommer EC, Heerman WJ, Hipp JA, Barkin SL. Objective reports versus subjective perceptions of crime and their relationships to accelerometer-measured physical activity in Hispanic caretaker-child dyads. Prev Med. Feb 2017;95 Suppl:S68-S74. [FREE Full text] [CrossRef] [Medline]
    410. van Ballegooijen AJ, van der Ploeg HP, Visser M. Daily sedentary time and physical activity as assessed by accelerometry and their correlates in older adults. Eur Rev Aging Phys Act. 2019;16:3. [CrossRef] [Medline]
    411. van der Zee-Neuen A, Wirth W, Hösl K, Osterbrink J, Eckstein F. The association of physical activity and depression in patients with, or at risk of, osteoarthritis is captured equally well by patient reported outcomes (PROs) and accelerometer measurements - analyses of data from the Osteoarthritis Initiative. Semin Arthritis Rheum. Dec 2019;49(3):325-330. [CrossRef] [Medline]
    412. Van Dyck D, Barnett A, Cerin E, Conway TL, Esteban-Cornejo I, Hinckson E, et al. Associations of accelerometer measured school- and non-school based physical activity and sedentary time with body mass index: IPEN adolescent study. Int J Behav Nutr Phys Act. Jul 14, 2022;19(1):85. [FREE Full text] [CrossRef] [Medline]
    413. Van Dyck D, Cerin E, De Bourdeaudhuij I, Salvo D, Christiansen LB, Macfarlane D, et al. Moderating effects of age, gender and education on the associations of perceived neighborhood environment attributes with accelerometer-based physical activity: the IPEN adult study. Health Place. Nov 2015;36:65-73. [FREE Full text] [CrossRef] [Medline]
    414. van Eijk RP, Bakers JN, Bunte TM, de Fockert AJ, Eijkemans MJ, van den Berg LH. Accelerometry for remote monitoring of physical activity in amyotrophic lateral sclerosis: a longitudinal cohort study. J Neurol. Oct 2019;266(10):2387-2395. [FREE Full text] [CrossRef] [Medline]
    415. van GS, Boonen A, van DH, Heuft L, Luime J, Spoorenberg A, et al. Accelerometer quantification of physical activity and activity patterns in patients with ankylosing spondylitis and population controls. J Rheumatol. Dec 2015;42(12):2369-2375. [CrossRef] [Medline]
    416. van Landingham SW, Willis JR, Vitale S, Ramulu PY. Visual field loss and accelerometer-measured physical activity in the United States. Ophthalmology. Dec 2012;119(12):2486-2492. [CrossRef] [Medline]
    417. van Langenberg DR, Papandony MC, Gibson PR. Sleep and physical activity measured by accelerometry in Crohn's disease. Aliment Pharmacol Ther. May 2015;41(10):991-1004. [CrossRef] [Medline]
    418. Vandercappellen EJ, Koster A, Savelberg HH, Eussen SJ, Dagnelie PC, Schram MT, et al. Accelerometer-derived physical activity and sedentary time and cardiac biomarkers: the Maastricht Study. Front Cardiovasc Med. 2023;10:1081713. [FREE Full text] [CrossRef] [Medline]
    419. Vanhelst J, Béghin L, Turck D, Gottrand F. New validated thresholds for various intensities of physical activity in adolescents using the Actigraph accelerometer. Int J Rehabil Res. Jun 2011;34(2):175-177. [CrossRef] [Medline]
    420. Vanhelst J, Hurdiel R, Mikulovic J, Bui-Xuân G, Fardy P, Theunynck D, et al. Validation of the Vivago wrist-worn accelerometer in the assessment of physical activity. BMC Public Health. 2012;12:690. [FREE Full text] [CrossRef] [Medline]
    421. Vásquez E, Strizich G, Isasi CR, Echeverria SE, Sotres-Alvarez D, Evenson KR, et al. Is there a relationship between accelerometer-assessed physical activity and sedentary behavior and cognitive function in US Hispanic/Latino adults? The Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Prev Med. Oct 2017;103:43-48. [CrossRef] [Medline]
    422. Vásquez PM, Tarraf W, Chai A, Doza A, Sotres-Alvarez D, Diaz KM, et al. Accelerometer-measured latent physical activity profiles and neurocognition among middle-aged and older Hispanic/Latino Adults in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). J Gerontol B Psychol Sci Soc Sci. Dec 29, 2022;77(12):e263-e278. [FREE Full text] [CrossRef] [Medline]
    423. Verbestel V, De HS, Bammann K, Barba G, Hadjigeorgiou C, Eiben G, et al. IDEFICS Consortium. Are context-specific measures of parental-reported physical activity and sedentary behaviour associated with accelerometer data in 2-9-year-old European children? Public Health Nutr. Apr 2015;18(5):860-868. [CrossRef] [Medline]
    424. Verma VK, Lin W. Machine learning-based 30-day hospital readmission predictions for COPD patients using physical activity data of daily living with accelerometer-based device. Biosensors (Basel). Aug 05, 2022;12(8):605. [FREE Full text] [CrossRef] [Medline]
    425. Vorwerg Y, Petroff D, Kiess W, Blüher S. Physical activity in 3-6 year old children measured by SenseWear Pro®: direct accelerometry in the course of the week and relation to weight status, media consumption, and socioeconomic factors. PLoS One. 2013;8(4):e60619. [FREE Full text] [CrossRef] [Medline]
    426. Wang Y, Wattelez G, Frayon S, Caillaud C, Galy O, Yacef K. ABIPA: ARIMA-based integration of accelerometer-based physical activity for adolescent weight status prediction. ACM Trans Comput Healthcare. Mar 30, 2023;4(1):1-19. [CrossRef]
    427. Watts EL, Saint-Maurice PF, Doherty A, Fensom GK, Freeman JR, Gorzelitz JS, et al. Association of accelerometer-measured physical activity level with risks of hospitalization for 25 common health conditions in UK adults. JAMA Netw Open. Mar 01, 2023;6(2):e2256186. [FREE Full text] [CrossRef] [Medline]
    428. Weaver RG, Crimarco A, Brusseau TA, Webster CA, Burns RD, Hannon JC. Accelerometry-derived physical activity of first through third grade children during the segmented school day. J Sch Health. Oct 2016;86(10):726-733. [CrossRef] [Medline]
    429. Wendt A, da Silva IC, Gonçalves H, Menezes A, Barros F, Wehrmeister FC. Short-term effect of physical activity on sleep health: a population-based study using accelerometry. J Sport Health Sci. Oct 2022;11(5):630-638. [FREE Full text] [CrossRef] [Medline]
    430. Werneck AO, Jago R, Kriemler S, Andersen LB, Wedderkopp N, Northstone K, et al. International Children’s Accelerometry Database (ICAD) Collaborators. Association of change in the school travel mode with changes in different physical activity intensities and sedentary time: a international children's accelerometry database study. Prev Med. Dec 2021;153:106862. [FREE Full text] [CrossRef] [Medline]
    431. Whitaker KM, Pettee Gabriel K, Buman MP, Pereira MA, Jacobs DR, Reis JP, et al. Associations of accelerometer-measured sedentary time and physical activity with prospectively assessed cardiometabolic risk factors: the CARDIA study. J Am Heart Assoc. Jan 08, 2019;8(1):e010212. [FREE Full text] [CrossRef] [Medline]
    432. Whitaker KM, Pettee Gabriel K, Laddu D, White DK, Sidney S, Sternfeld B, et al. Bidirectional associations of accelerometer measured sedentary behavior and physical activity with knee pain, stiffness, and physical function: the CARDIA study. Prev Med Rep. Jul 2021;22:101348. [FREE Full text] [CrossRef] [Medline]
    433. Whitaker KM, Xiao Q, Pettee Gabriel K, Gordon Larsen P, Jacobs DR, Sidney S, et al. Perceived and objective characteristics of the neighborhood environment are associated with accelerometer-measured sedentary time and physical activity, the CARDIA Study. Prev Med. Jul 2019;123:242-249. [FREE Full text] [CrossRef] [Medline]
    434. Whitaker KM, Zhang D, Pettee Gabriel K, Ahrens M, Sternfeld B, Sidney S, et al. Longitudinal associations of midlife accelerometer determined sedentary behavior and physical activity with cognitive function: the CARDIA study. J Am Heart Assoc. Mar 02, 2021;10(3):e018350. [FREE Full text] [CrossRef] [Medline]
    435. Willis JR, Jefferys JL, Vitale S, Ramulu PY. Visual impairment, uncorrected refractive error, and accelerometer-defined physical activity in the United States. Arch Ophthalmol. Mar 2012;130(3):329-335. [CrossRef] [Medline]
    436. Wu J, Olson JL, Brunke-Reese D, Lagoa CM, Conroy DE. Wearable device adherence among insufficiently-active young adults is independent of identity and motivation for physical activity. J Behav Med. May 2024;47(2):197-206. [CrossRef] [Medline]
    437. Wu Y, Goodrich JM, Dolinoy DC, Sánchez BN, Ruiz-Narváez EA, Banker M, et al. Accelerometer-measured physical activity, reproductive hormones, and DNA methylation. Med Sci Sports Exerc. Mar 2020;52(3):598-607. [FREE Full text] [CrossRef] [Medline]
    438. Wu Z, Wang Z, Hu B, Zhang X, Zhang F, Wang H, et al. Relationships of accelerometer-based measured objective physical activity and sedentary behaviour with cognitive function: a comparative cross-sectional study of China's elderly population. BMC Geriatr. May 22, 2020;20(1):149. [FREE Full text] [CrossRef] [Medline]
    439. Yang X, Dinh A, Chen L. Implementation of a wearerable real-time system for physical activity recognition based on Naive Bayes classifier. In: Proceedings of the 2010 International Conference on Bioinformatics and Biomedical Technology. 2010. Presented at: ICBBT '10; April 16-18, 2010:101-105; Chengdu, China. URL: https://ieeexplore.ieee.org/document/5479000 [CrossRef]
    440. Yamaga Y, Svensson T, Chung U, Svensson AK. Association between metabolic syndrome status and daily physical activity measured by a wearable device in Japanese office workers. Int J Environ Res Public Health. Mar 28, 2023;20(5):4315. [FREE Full text] [CrossRef] [Medline]
    441. Yang C, Wang S, Wang W, Belcher BR, Dunton GF. Day-level associations of physical activity and sedentary time in mother-child dyads across three years: a multi-wave longitudinal study using accelerometers. J Behav Med. Oct 2022;45(5):702-715. [FREE Full text] [CrossRef] [Medline]
    442. Yang W, Li MH, Yu JJ, Wong SH, Sum RK, Sit CH. The associations between accelerometer-measured physical activity levels and mental health in children and adolescents with intellectual disabilities during the COVID-19 pandemic. J Autism Dev Disord. Dec 2023;53(12):4809-4821. [FREE Full text] [CrossRef] [Medline]
    443. Yen H, Liao Y, Huang H. Smart wearable device users' behavior is essential for physical activity improvement. Int J Behav Med. Jun 2022;29(3):278-285. [CrossRef] [Medline]
    444. Yerrakalva D, Hajna S, Wijndaele K, Dempsey PC, Westgate K, Wareham N, et al. Bidirectional associations of accelerometer-assessed physical activity and sedentary time with physical function among older English adults: the EPIC-Norfolk cohort study. Eur J Ageing. Dec 2022;19(4):1507-1517. [FREE Full text] [CrossRef] [Medline]
    445. Yi L, Mason TB, Yang C, Chu D, Dunton GF. Longitudinal associations between neighborhood park and open space access and children's accelerometer-assessed measured physical activity: the evidence from the MATCH study. J Phys Act Health. Oct 01, 2021;18(9):1058-1066. [FREE Full text] [CrossRef] [Medline]
    446. Yoshioka M, Ayabe M, Yahiro T, Higuchi H, Higaki Y, St-Amand J, et al. Long-period accelerometer monitoring shows the role of physical activity in overweight and obesity. Int J Obes (Lond). May 2005;29(5):502-508. [CrossRef] [Medline]
    447. You Y, Chen Y, Wang X, Wei M, Zhang Q, Cao Q. Accelerometer-measured physical activity patterns are associated with phenotypic age: isotemporal substitution effects. Heliyon. Oct 2023;9(9):e19158. [FREE Full text] [CrossRef] [Medline]
    448. Yu X, Hao L, Crainiceanu C, Leroux A. Occupational determinants of physical activity at work: evidence from wearable accelerometer in 2005-2006 NHANES. SSM Popul Health. Mar 2022;17:100989. [FREE Full text] [CrossRef] [Medline]
    449. Zhang X, Yang Z, Du L, Xiong C, Wang Z, Pan Y, et al. Associations of accelerometer-measured physical activity, sedentary behavior with self-reported insomnia in older women: does pattern matter? Sleep Med. May 2023;104:58-63. [CrossRef] [Medline]
    450. Zhong Q, Zhou R, Huang Y, Chen H, Liu H, Huang Z, et al. The independent and joint association of accelerometer-measured physical activity and sedentary time with dementia: a cohort study in the UK Biobank. Int J Behav Nutr Phys Act. May 17, 2023;20(1):59. [FREE Full text] [CrossRef] [Medline]
    451. Zhou W, Veliz PT, Smith EML, Chen W, Reddy RM, Larson JL. Comparison of pre-diagnosis physical activity and its correlates between lung and other cancer patients: accelerometer data from the UK Biobank prospective cohort. Int J Environ Res Public Health. Jan 05, 2023;20(2):1001. [FREE Full text] [CrossRef] [Medline]
    452. Zijlstra WM, Ploegstra M, Vissia-Kazemier T, Roofthooft MT, Sarvaas GD, Bartelds B, et al. Physical activity in pediatric pulmonary arterial hypertension measured by accelerometry. a candidate clinical endpoint. Am J Respir Crit Care Med. Dec 15, 2017;196(2):220-227. [CrossRef] [Medline]
    453. Zisko N, Nauman J, Sandbakk SB, Aspvik NP, Salvesen Ø, Carlsen T, et al. Absolute and relative accelerometer thresholds for determining the association between physical activity and metabolic syndrome in the older adults: the Generation-100 study. BMC Geriatr. May 16, 2017;17(1):109. [FREE Full text] [CrossRef] [Medline]
    454. Zlatar ZZ, Hays CC, Mestre Z, Campbell LM, Meloy MJ, Bangen KJ, et al. Dose-dependent association of accelerometer-measured physical activity and sedentary time with brain perfusion in aging. Exp Gerontol. Oct 01, 2019;125:110679. [FREE Full text] [CrossRef] [Medline]
    455. van Hees VT, Gorzelniak L, Dean LE, Eder M, Pias M, Taherian S, et al. Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PLoS One. 2013;8(4):e61691. [FREE Full text] [CrossRef] [Medline]
    456. Bai J, Di C, Xiao L, Evenson KR, LaCroix AZ, Crainiceanu CM, et al. An activity index for raw accelerometry data and its comparison with other activity metrics. PLoS One. 2016;11(8):e0160644. [FREE Full text] [CrossRef] [Medline]
    457. Danilevicz IM, van Hees VT, van der Heide FC, Jacob L, Landré B, Benadjaoud MA, et al. Measures of fragmentation of rest activity patterns: mathematical properties and interpretability based on accelerometer real life data. BMC Med Res Methodol. Jun 07, 2024;24(1):132. [FREE Full text] [CrossRef] [Medline]
    458. Hare DJ, Jones S, Evershed K. A comparative study of circadian rhythm functioning and sleep in people with Asperger syndrome. Autism. Nov 2006;10(6):565-575. [CrossRef] [Medline]
    459. Hurter L, Fairclough SJ, Knowles ZR, Porcellato LA, Cooper-Ryan AM, Boddy LM. Establishing raw acceleration thresholds to classify sedentary and stationary behaviour in children. Children (Basel). Dec 19, 2018;5(12):172. [FREE Full text] [CrossRef] [Medline]
    460. Sundararajan K, Georgievska S, Te Lindert BH, Gehrman PR, Ramautar J, Mazzotti DR, et al. Sleep classification from wrist-worn accelerometer data using random forests. Sci Rep. Jan 08, 2021;11(1):24. [FREE Full text] [CrossRef] [Medline]
    461. Jones SE, Lane JM, Wood AR, van Hees VT, Tyrrell J, Beaumont RN, et al. Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms. Nat Commun. Jan 29, 2019;10(1):343. [FREE Full text] [CrossRef] [Medline]
    462. Jones SE, van Hees VT, Mazzotti DR, Marques-Vidal P, Sabia S, van der Spek A, et al. Genetic studies of accelerometer-based sleep measures yield new insights into human sleep behaviour. Nat Commun. Apr 05, 2019;10(1):1585. [FREE Full text] [CrossRef] [Medline]
    463. Abraham B, Duong D, George M, Greene M, Kepley J, Rosenburg K. Using patient-generated health data to facilitate preoperative decision making for breast cancer patients. In: Proceedings of the 2017 Conference on Systems and Information Engineering Design Symposium. 2017. Presented at: SIEDS '17; April 28, 2017:271-276; Charlottesville, VA. URL: https://ieeexplore.ieee.org/document/7937730 [CrossRef]
    464. Bekteshi S, Nica IG, Gakopoulos S, Konings M, Maes R, Cuyvers B, et al. Exercise load and physical activity intensity in relation to dystonia and choreoathetosis during powered wheelchair mobility in children and youth with dyskinetic cerebral palsy. Disabil Rehabil. Aug 2022;44(17):4794-4805. [CrossRef] [Medline]
    465. Jamnick NA, Pettitt RW, Granata C, Pyne DB, Bishop DJ. An examination and critique of current methods to determine exercise intensity. Sports Med. Oct 2020;50(10):1729-1756. [CrossRef] [Medline]
    466. Difrancesco S, Riese H, Merikangas KR, Shou H, Zipunnikov V, Antypa N, et al. Sociodemographic, health and lifestyle, sampling, and mental health determinants of 24-hour motor activity patterns: observational study. J Med Internet Res. Feb 17, 2021;23(2):e20700. [FREE Full text] [CrossRef] [Medline]
    467. Ghosal R, Varma VR, Volfson D, Urbanek J, Hausdorff JM, Watts A, et al. Scalar on time-by-distribution regression and its application for modelling associations between daily-living physical activity and cognitive functions in Alzheimer's disease. Sci Rep. Jul 07, 2022;12(1):11558. [FREE Full text] [CrossRef] [Medline]
    468. Goldsmith J, Liu X, Jacobson JS, Rundle A. New insights into activity patterns in children, found using functional data analyses. Med Sci Sports Exerc. Sep 2016;48(9):1723-1729. [FREE Full text] [CrossRef] [Medline]
    469. Zeitzer JM, David R, Friedman L, Mulin E, Garcia R, Wang J, et al. Phenotyping apathy in individuals with Alzheimer disease using functional principal component analysis. Am J Geriatr Psychiatry. Apr 2013;21(4):391-397. [FREE Full text] [CrossRef] [Medline]
    470. Lin W, Zou J, Di C, Sears DD, Rock CL, Natarajan L. Longitudinal associations between timing of physical activity accumulation and health: application of functional data methods. Stat Biosci. Jul 2023;15(2):309-329. [FREE Full text] [CrossRef] [Medline]
    471. Zeitzer JM, Blackwell T, Hoffman AR, Cummings S, Ancoli-Israel S, Stone K, et al. Osteoporotic Fractures in Men (MrOS) Study Research Group. Daily patterns of accelerometer activity predict changes in sleep, cognition, and mortality in older men. J Gerontol A Biol Sci Med Sci. Apr 17, 2018;73(5):682-687. [FREE Full text] [CrossRef] [Medline]
    472. Goldsmith J, Zipunnikov V, Schrack J. Generalized multilevel function-on-scalar regression and principal component analysis. Biometrics. Jun 2015;71(2):344-353. [FREE Full text] [CrossRef] [Medline]
    473. Bagalà F, Becker C, Cappello A, Chiari L, Aminian K, Hausdorff JM, et al. Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS One. 2012;7(5):e37062. [FREE Full text] [CrossRef] [Medline]
    474. Li Q, Stankovic JA, Hanson MA, Barth A, Lach J, Zhou G. Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. In: Proceedings of the 6th International Workshop on Wearable and Implantable Body Sensor Networks. 2009. Presented at: BSN '09; June 3-5, 2009:138-143; Berkeley, CA. URL: https://ieeexplore.ieee.org/document/5226903 [CrossRef]
    475. Bavan L, Surmacz K, Beard D, Mellon S, Rees J. Adherence monitoring of rehabilitation exercise with inertial sensors: a clinical validation study. Gait Posture. May 2019;70:211-217. [CrossRef] [Medline]
    476. Bidargaddi N, Sarela A, Klingbeil L, Karunanithi M. Detecting walking activity in cardiac rehabilitation by using accelerometer. In: Proceedings of the 3rd International Conference on Intelligent Sensors, Sensor Networks and Information. 2007. Presented at: ISSNIP '07; December 3-6, 2007:555-560; Melbourne, Australia. URL: https://ieeexplore.ieee.org/document/4496903 [CrossRef]
    477. Boe AJ, McGee Koch LL, O'Brien MK, Shawen N, Rogers JA, Lieber RL, et al. Automating sleep stage classification using wireless, wearable sensors. NPJ Digit Med. 2019;2:131. [FREE Full text] [CrossRef] [Medline]
    478. Gaur S, Gupta GP. Framework for monitoring and recognition of the activities for elderly people from accelerometer sensor data using apache spark. In: Proceedings of the 1st International Conference on Data Science, Machine Learning and Applications. 2019. Presented at: ICDSMLA '19; March 29-30, 2019:29-30; Singapore, Singapore. URL: https://tinyurl.com/yejz6sce [CrossRef]
    479. Kwapisz JR, Weiss GM, Moore SA. Activity recognition using cell phone accelerometers. SIGKDD Explor Newsl. Mar 31, 2011;12(2):74-82. [CrossRef]
    480. Zdravevski E, Risteska Stojkoska B, Standl M, Schulz H. Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions. PLoS One. 2017;12(9):e0184216. [FREE Full text] [CrossRef] [Medline]
    481. Attal F, Mohammed S, Dedabrishvili M, Chamroukhi F, Oukhellou L, Amirat Y. Physical human activity recognition using wearable sensors. Sensors (Basel). Dec 11, 2015;15(12):31314-31338. [FREE Full text] [CrossRef] [Medline]
    482. Javed AR, Sarwar MU, Khan S, Iwendi C, Mittal M, Kumar N. Analyzing the effectiveness and contribution of each axis of tri-axial accelerometer sensor for accurate activity recognition. Sensors (Basel). Apr 14, 2020;20(8):2216. [FREE Full text] [CrossRef] [Medline]
    483. Abreu M, Barandas M, Leonardo R, Gamboa H. Detailed human activity recognition based on multiple HMM. In: Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies. 2019. Presented at: BIOSTEC '19; February 22-24, 2019:171-178; Prague, Czech Republic. URL: https://www.scitepress.org/Papers/2019/73869/73869.pdf [CrossRef]
    484. Witowski V, Foraita R, Pitsiladis Y, Pigeot I, Wirsik N. Using hidden markov models to improve quantifying physical activity in accelerometer data - a simulation study. PLoS One. 2014;9(12):e114089. [FREE Full text] [CrossRef] [Medline]
    485. Scheffer C, Engelbrecht H, Heyns PS. A comparative evaluation of neural networks and hidden Markov models for monitoring turning tool wear. Neural Comput Appl. Jun 14, 2005;14(4):325-336. [CrossRef]
    486. Metcalf BS, Jeffery AN, Hosking J, Voss LD, Sattar N, Wilkin TJ. Objectively measured physical activity and its association with adiponectin and other novel metabolic markers: a longitudinal study in children (EarlyBird 38). Diabetes Care. Mar 2009;32(3):468-473. [FREE Full text] [CrossRef] [Medline]
    487. Menai M, van Hees VT, Elbaz A, Kivimaki M, Singh-Manoux A, Sabia S. Accelerometer assessed moderate-to-vigorous physical activity and successful ageing: results from the Whitehall II study. Sci Rep. Apr 03, 2017;8:45772. [FREE Full text] [CrossRef] [Medline]
    488. Zhang Y, Yin Y, Liu J, Yang M, Liu Z, Ma X. Impact of combined theory-based intervention on psychological effects and physical activity among Chinese adolescents. Int J Environ Res Public Health. Apr 27, 2020;17(9):3026. [FREE Full text] [CrossRef] [Medline]
    489. Lee J, Zhang T, Chu TL, Gu X. Effects of a need-supportive motor skill intervention on children's motor skill competence and physical activity. Children (Basel). Mar 17, 2020;7(3):21. [FREE Full text] [CrossRef] [Medline]
    490. Yuki A, Otsuka R, Tange C, Nishita Y, Tomida M, Ando F, et al. Daily physical activity predicts frailty development among community-dwelling older Japanese adults. J Am Med Dir Assoc. Aug 2019;20(8):1032-1036. [CrossRef] [Medline]
    491. Ramsay JO, Silverman BW. Functional Data Analysis. New York, NY. Springer; 2005.
    492. Spira AP, Zipunnikov V, Raman R, Choi J, Di J, Bai J, et al. Brain amyloid burden, sleep, and 24-hour rest/activity rhythms: screening findings from the anti-amyloid treatment in asymptomatic Alzheimer’s and longitudinal evaluation of amyloid risk and neurodegeneration studies. Sleep Adv. 2021;2(1):zpab015. [FREE Full text] [CrossRef] [Medline]
    493. Reiss PT, Goldsmith J, Shang HL, Ogden RT. Methods for scalar-on-function regression. Int Stat Rev. Aug 2017;85(2):228-249. [FREE Full text] [CrossRef] [Medline]
    494. Ghosal R, Varma VR, Volfson D, Hillel I, Urbanek J, Hausdorff JM, et al. Distributional data analysis via quantile functions and its application to modeling digital biomarkers of gait in Alzheimer's disease. Biostatistics. Jul 14, 2023;24(3):539-561. [FREE Full text] [CrossRef] [Medline]
    495. Tang L, Halloran S, Shi JQ, Guan Y, Cao C, Eyre J. Evaluating upper limb function after stroke using the free-living accelerometer data. Stat Methods Med Res. Nov 2020;29(11):3249-3264. [CrossRef] [Medline]
    496. Jeran S, Steinbrecher A, Pischon T. Prediction of activity-related energy expenditure using accelerometer-derived physical activity under free-living conditions: a systematic review. Int J Obes (Lond). Aug 2016;40(8):1187-1197. [CrossRef] [Medline]
    497. Nightingale TE, Walhin J, Thompson D, Bilzon JL. Influence of accelerometer type and placement on physical activity energy expenditure prediction in manual wheelchair users. PLoS One. 2015;10(5):e0126086. [FREE Full text] [CrossRef] [Medline]
    498. Nightingale TE, Walhin JP, Thompson D, Bilzon JL. Predicting physical activity energy expenditure in wheelchair users with a multisensor device. BMJ Open Sport Exerc Med. Aug 2015;1(1):bmjsem-2015-000008. [FREE Full text] [CrossRef] [Medline]
    499. Sardinha LB, Júdice PB. Usefulness of motion sensors to estimate energy expenditure in children and adults: a narrative review of studies using DLW. Eur J Clin Nutr. Mar 2017;71(3):331-339. [CrossRef] [Medline]
    500. Dabare P, Wickramasinghe P, Waidyatilaka I, Devi S, Kurpad AV, Samaranayake D, et al. Prediction equation for physical activity energy expenditure in 11-13-year-old Sri Lankan children. Nutrients. Feb 10, 2023;15(4):906. [FREE Full text] [CrossRef] [Medline]
    501. Sakal C, Li T, Li J, Yang C, Li X. Association between sleep efficiency variability and cognition among older adults: cross-sectional accelerometer study. JMIR Aging. Apr 04, 2024;7:e54353. [FREE Full text] [CrossRef] [Medline]
    502. McBeth J, Dixon WG, Moore SM, Hellman B, James B, Kyle SD, et al. Sleep disturbance and quality of life in rheumatoid arthritis: prospective mHealth study. J Med Internet Res. Apr 22, 2022;24(4):e32825. [FREE Full text] [CrossRef] [Medline]

    ‎
    AC: activity count
    AI: activity intensity
    ENMO: Euclidean norm minus one
    FDA: functional data analysis
    FFT: fast Fourier transformation
    FoSR: function-on-scalar regression
    FPCA: functional principal component analysis
    FVC: flow-volume curve
    GLMM: generalized linear mixed-effects model
    HMM: hidden Markov model
    IS: interdaily stability
    IV: intradaily variability
    MANOVA: multivariate ANOVA
    MET: metabolic equivalent of task
    ML: machine learning
    MVPA: moderate-to-vigorous physical activity
    PA: physical activity
    PAI: physical activity index
    PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews
    ROC: receiver operating characteristic
    VM: vector magnitude

    Edited by G Eysenbach; submitted 14.04.24; peer-reviewed by P Bergman, C Sakal, U Sinha; comments to author 14.05.24; revised version received 27.05.24; accepted 16.07.24; published 11.09.24.

    Copyright

    ©Ya-Ting Liang, Charlotte Wang, Chuhsing Kate Hsiao. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.09.2024.

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