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Digital Health Reviews (1289) mHealth for Data Collection and Research (955) Wearable Devices and Sensors (888) Fitness Trackers and Smart Pedometers/Accelerometers (561) Novel Sensors and Data Acquisition Methods in Cardiology (196)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/45599, first published .
Accelerometer-Measured Physical Activity Data Sets (Global Physical Activity Data Set Catalogue) That Include Markers of Cardiometabolic Health: Systematic Scoping Review

Accelerometer-Measured Physical Activity Data Sets (Global Physical Activity Data Set Catalogue) That Include Markers of Cardiometabolic Health: Systematic Scoping Review

Accelerometer-Measured Physical Activity Data Sets (Global Physical Activity Data Set Catalogue) That Include Markers of Cardiometabolic Health: Systematic Scoping Review

Authors of this article:

Jonah J C Thomas1, 2, 3 Author Orcid Image ;   Amanda J Daley1, 2, 3 Author Orcid Image ;   Dale W Esliger1, 2, 3, 4 Author Orcid Image ;   Victoria E Kettle1, 2, 3 Author Orcid Image ;   April Coombe5 Author Orcid Image ;   Emmanuel Stamatakis6 Author Orcid Image ;   James P Sanders1, 2, 3 Author Orcid Image
Article Authors Cited by Tweetations (12) Metrics

    Review

    1School of Sport, Exercise and Health Science, Loughborough University, Loughborough, United Kingdom

    2National Centre for Sport and Exercise Medicine, Loughborough University, Loughborough, United Kingdom

    3Centre for Lifestyle Medicine and Behaviour, Loughborough University, Loughborough, United Kingdom

    4Lifestyle, National Institute of Health Research Leicester Biomedical Research Centre, Leicester, United Kingdom

    5Public Health, Epidemiology and Biostatistics, Institute of Applied Health Research, University of Birmingham, Birmingham, United Kingdom

    6Charles Perkin Centre, Faculty of Medicine and Health Science, University of Sydney, Sydney, Australia

    Corresponding Author:

    Jonah J C Thomas, MSc

    School of Sport, Exercise and Health Science

    Loughborough University

    Epinal Way

    Loughborough, LE113TU

    United Kingdom

    Phone: 44 01509222222

    Email: j.j.c.thomas@lboro.ac.uk


    Background: Cardiovascular disease accounts for 17.9 million deaths globally each year. Many research study data sets have been collected to answer questions regarding the relationship between cardiometabolic health and accelerometer-measured physical activity. This scoping review aimed to map the available data sets that have collected accelerometer-measured physical activity and cardiometabolic health markers. These data were then used to inform the development of a publicly available resource, the Global Physical Activity Data set (GPAD) catalogue.

    Objective: This review aimed to systematically identify data sets that have measured physical activity using accelerometers and cardiometabolic health markers using either an observational or interventional study design.

    Methods: Databases, trial registries, and gray literature (inception until February 2021; updated search from February 2021 to September 2022) were systematically searched to identify studies that analyzed data sets of physical activity and cardiometabolic health outcomes. To be eligible for inclusion, data sets must have measured physical activity using an accelerometric device in adults aged ≥18 years; a sample size >400 participants (unless recruited participants in a low- and middle-income country where a sample size threshold was reduced to 100); used an observational, longitudinal, or trial-based study design; and collected at least 1 cardiometabolic health marker (unless only body mass was measured). Two reviewers screened the search results to identify eligible studies, and from these, the unique names of each data set were recorded, and characteristics about each data set were extracted from several sources.

    Results: A total of 17,391 study reports were identified, and after screening, 319 were eligible, with 122 unique data sets in these study reports meeting the review inclusion criteria. Data sets were found in 49 countries across 5 continents, with the most developed in Europe (n=53) and the least in Africa and Oceania (n=4 and n=3, respectively). The most common accelerometric brand and device wear location was Actigraph and the waist, respectively. Height and body mass were the most frequently measured cardiometabolic health markers in the data sets (119/122, 97.5% data sets), followed by blood pressure (82/122, 67.2% data sets). The number of participants in the included data sets ranged from 103,712 to 120. Once the review processes had been completed, the GPAD catalogue was developed to house all the identified data sets.

    Conclusions: This review identified and mapped the contents of data sets from around the world that have collected potentially harmonizable accelerometer-measured physical activity and cardiometabolic health markers. The GPAD catalogue is a web-based open-source resource developed from the results of this review, which aims to facilitate the harmonization of data sets to produce evidence that will reduce the burden of disease from physical inactivity.

    J Med Internet Res 2023;25:e45599

    doi:10.2196/45599

    Keywords

    sedentary behavior; device measured; data harmonization; open science; big data 


    Background

    Regular moderate to vigorous intensity physical activity reduces the risk of cardiovascular disease and improves individuals’ cardiometabolic profile including markers such as waist circumference, high-density lipoprotein (HDL) cholesterol, and triglycerides [1,2]. Although self-reported measures of physical activity have been used extensively in previous research, technological advances have led to accelerometers becoming widely available, making them feasible to be used at scale within health research. Accelerometers are small devices that measure acceleration, and from this measurement, estimates of the intensity of physical activity can be derived. This has led to an increase in the number of cohort or health surveillance studies that have collected device-measured physical activity data alongside cardiometabolic health markers. One such data set is the National Health and Nutrition Examination Survey (NHANES), which collected accelerometer-measured physical activity as well as blood pressure, blood lipids, and blood glucose data from approximately 5000 adults [3]. Furthermore, NHANES provides the opportunity to answer new health research questions without the need for additional and potentially expensive data collection. Other notable large health surveillance data sets that measured physical activity using an accelerometer and collected cardiometabolic health markers include the Canadian Health Measures Survey 2007-2009 and 2009-2011 and the Health Survey for England 2008 [4]. Several large cohort studies have also introduced accelerometry measures, including the UK Biobank [5,6] and the 1970 British Cohort [7].

    In recent years, efforts have been made to pool accelerometer-measured physical activity data sets alongside health-related markers. An example of a large-scale harmonization initiative is the International Children’s Accelerometer Database [8], which has pooled data from 20 studies that collected physical activity and health marker data in children. The International Children’s Accelerometer Database has advanced the field by enhancing our understanding of the correlation between children’s physical activity levels and health markers, enabling the examination of geographical and interstudy variances. There are several other notable studies that have used harmonization methodologies in adults [9,10]. Harmonized data sets can increase statistical power by generating larger sample sizes as well as increase the heterogeneity (eg, ethnicity, body mass, and body fat percentage) of the data, potentially enhancing the representation of the overall study sample.

    A necessary first step to current harmonization efforts, after defining a research question, is the need to perform an initial review to identify all the data sets that may be available for inclusion. Furthermore, the need for this review process is resource intensive, requiring considerable time and effort to complete in a comprehensive manner, limiting the feasibility of such endeavors. Therefore, providing a shared resource to reduce this burden will provide benefits to the wider research community. In addition, to harmonize data sets effectively, a large amount of information about each variable collected must be retrieved, including the methodologies used [11]. The systematic methodological process of accelerometer harmonization is becoming increasingly important as the device used and the data analytic decisions taken impact the derived estimates of physical activity that are available [12,13]. This review aimed to identify previously collected data sets to ease the harmonization process but did not aim to perform or instruct on the data harmonization process.

    Objectives

    Therefore, the aim of this scoping review was to identify and map the contents of the available data sets that have collected data on accelerometer-measured physical activity and cardiometabolic health markers.


    Overview

    To ensure that the methodology used was consistent with that used in previous scoping reviews, the framework constructed by Arksey and O’Malley [14] and later developed by Levac et al [15] was followed. The review was registered on the Open Science Framework [16] and was written in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist [17]. The methodology for this review followed 5 stages.

    Stage 1: Identify the Question

    Three research aims were derived to focus on the review and achieve the overall aim:

    1. To systematically identify data sets that have measured physical activity using accelerometers and cardiometabolic health markers using either an observational or interventional study design
    2. To identify the key characteristics of eligible data sets (eg, study location, population of interest, and the device used to collect the physical activity data and cardiometabolic health markers that were simultaneously collected)
    3. To determine the access status (open, upon request, or restricted) of eligible data sets to assess the feasibility of conducting future harmonized data analysis

    Stage 2: Search for Literature

    The search strategy was developed by an information specialist (AC) who also completed the study searches between February 2, 2021, and February 10, 2021. The search keywords were determined in consultation with the research team. The search terms stemmed from 3 main categories: physical activity (physical*, activ*, and exercise*), accelerometer (acceleromet* and activity monitor*), and study design (cross-sectional and randomized). Searches were devised and tested using MEDLINE. The search strategy was then adapted for other databases, including Embase, CINAHL, CENTRAL, SportDiscus, OpenGrey, WHO ICTRP, ClinicalTrials.gov, and Conference Proceedings Citation Index. The full search strategy is provided in Multimedia Appendix 1. The searches were limited to human adults aged ≥18 years. No date restriction was applied to the searches. No language restriction was applied, with papers not written in English being translated using web-based software. A brief update search was performed in PubMed, covering the period from the initial search to the final analysis (September 1, 2022).

    Stage 3: Study Selection

    The reports identified by the search were uploaded into Covidence (Veritus Health Initiative), and automatic deduplication was performed. Title and abstract screening of each report was independently performed by 2 researchers (JJCT and 1 of AJD, VEK, or JPS). Disagreements were resolved through discussion between the 2 researchers. All the study titles and abstracts were screened based on the inclusion and exclusion criteria outlined in Textbox 1. Although this review aimed to systematically identify eligible data sets in adults, if a data set had collected data on adults but also included participants aged <18 years, the data set would still be deemed eligible. Data sets collected from clinical populations (individuals living with hypertension or type 2 diabetes) were included, provided that the participants were free living.

    Textbox 1. Inclusion and exclusion criteria.

    Inclusion criteria

    • Observational, longitudinal, or trial-based studies of any design
    • Adult participants (aged ≥18 years)
    • Measures physical activity using an accelerometric device (for this study, an accelerometric device excludes mobile phones or commercially available activity trackers that contain an accelerometer) for at least 24 hours
    • Published in any language (this is a criterion of the journal article or report, rather than the data set)
    • A sample size of >400 participants (unless collected in low- and middle-income countries, as defined by the World Bank [18])
    • Collected at least 1 cardiometabolic marker

    Exclusion criteria

    • Nonhuman populations
    • Only reported sleep exposures

    After the title and abstract screening, the number of reports for full-text screening was deemed too large to be feasible (n=2195; Figure 1). Therefore, a second title and abstract screening step was performed with more specific inclusion and exclusion criteria (Textbox 1). A revised sample size inclusion criterion of at least 400 participants was applied, which was chosen to be consistent with previous studies [8,19]. To ensure that data sets from as many countries as possible were included, a reduced sample size criterion (>100 participants) for low- and middle-income countries (LMICs) as defined by the World Bank [18] was applied. This reduced participant sample size criterion was also applied because it was expected that data sets in LMICs would have smaller sample sizes than high-income countries, as typically there is less research funding available for the development of such data sets in LMICs. The desire to include data sets from LMICs (eg, African nations) is also important because these countries tend to have a greater ethnic diversity of citizens, and it is critical to ensure that there is data representation in this catalog from across the ethnicity spectrum. After the second screening process, a full-text screening was conducted by a single researcher (JJCT) for the remaining studies, and the reasons for exclusion were recorded.

    ‎
    Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) diagram of the screening and extraction process. The black dotted line represents the separation between the first and second stage of screening. CPCI-S: Conference Proceedings Citation Index–Science; WHO ICTRP: World Health Organization International Clinical Trials Registry Platform.

    Stage 4: Describing and Charting the Data

    Data extraction was performed by a single researcher (JJCT). Descriptive information about each data set was extracted from the eligible reports. To extract as much outcome information as possible about the data sets, several additional sources were used, including websites, methodology or protocol reports or publications that describe the data set cohort, and individual articles stemming from the data set. If the information was irretrievable at this stage, another researcher verified that it could not be retrieved (JPS), and the variable was marked as unknown.

    Several variables were extracted regarding each data set: the number of participants, mean age, the proportion of male and female participants, country and continent of data collection, and the data access status. Information was extracted regarding the physical activity measurement method, including accelerometer brand and model, and deployment protocol (eg, the number of days the accelerometer was worn for and the raw sampling frequency [Hz] of the accelerometer data). Furthermore, the cardiometabolic health markers measured were recorded. In this review, cardiometabolic health markers were defined as follows: height, weight, waist circumference, hip circumference, fat mass, visceral fat, systolic blood pressure, diastolic blood pressure, resting heart rate, HDL cholesterol, low-density lipoprotein cholesterol, total cholesterol, triglycerides, very low–density lipoprotein, glycated hemoglobin, blood glucose, blood insulin, and oral glucose tolerance test.

    Owing to the large number of health markers extracted, outcomes were combined into 4 categories: anthropometry (ie, height, body mass, and waist circumference), blood pressure, blood lipids (ie, HDL cholesterol), and blood glucose control (ie, blood glucose and glycated hemoglobin; outlined in Multimedia Appendix 2).

    Stage 5: Collating, Summarizing, and Reporting the Extracted Data

    The retrieved information is presented in 2 ways. Following the example of the Maelstrom Catalogue, a web-based catalog was created to summarize the identified data sets. This catalog is available as a web-based data visualization tool [20] and will hereby be referred to as the Global Physical Activity Data set (GPAD) catalogue. The GPAD catalogue provides an overview of the data sets identified in this review and the markers assessed in each data set. Second, a narrative summary of the key findings was produced to highlight the patterns found within the identified data sets.


    Overview

    The database searches yielded 19,586 references. After the duplicates were removed (n=2195), title and abstract screening was performed on 17,391 articles, and 2958 reports were identified as eligible for full-text review. The second-stage title and abstract screening with stricter exclusion criteria returned 556 papers for full-text screening. The updated search returned 671 reports (with no duplicates), from which an additional 109 papers were included for full-text screening, making the number of total papers that went through full-text screening to be 655. From these, 362 papers were excluded, resulting in the identification of 319 reports (Multimedia Appendix 3). From these, 122 data sets were identified (Multimedia Appendix 4). The full screening process is detailed in Figure 1, and all the identified data sets are included in Table 1.

    Table 1. Characteristics of the data sets identified as part of this review.
    Data set or study nameStudy typeCountry of data collectionMean age (years)Sample size, nDevice brand and modelPlacementNumber of days of wearing the device
    Framingham Heart Study Generation 3 [21]LongitudinalUnited States404094Phillips ActicalHip8
    Framingham Heart Study Omni 2 [22]LongitudinalUnited States—a410Phillips ActicalHip8
    Malaysian Government Employees with MetSb [23]ObservationalMalaysia40490KenzWaist7
    EVIDENTc [24]ObservationalSpain54.92636Actigraph GT3xRight waist7
    Generation 100 [25]InterventionalNorway721567Sensewear Armband and Actigraph GT3xArm and waist7
    National Health and Nutrition Examination Survey 2003-2004d [26]ObservationalUnited States486830Actigraph AM-7164Waist7
    National Health and Nutrition Examination Survey 2005-2006d [27]ObservationalUnited States—3081Actigraph AM-7164Waist7
    UK Biobank [28]ObservationalUnited Kingdom57103,712Axivity AX3Wrist7
    Tasmanian Older Adults Cohort [29]ObservationalAustralia66636Actigraph GT1M—7
    Malaysian Government Employees [30]ObservationalMalaysia32233KenzWaist—
    OPACHe [31]ObservationalUnited States—7058Actigraph GT3xWaist7
    CARDIAf year 20d [32]LongitudinalUnited States—2332Actigraph 7164Waist—
    CARDIA year 30d [32]LongitudinalUnited States—2332Actigraph wGT3X-BTWaist—
    Walking Away From Diabetes [33]InterventionalUnited Kingdom63725Actigraph GT3xWaist7
    PROPELg [34]InterventionalUnited Kingdom59.41308Actigraph GT3XWaist7
    HCHSh or SOLi 2008-2011 [35]LongitudinalUnited States—8049Actical B1Waist7
    HCHS/SOL 2014-2017 [35]LongitudinalUnited States—8049Actical B1Waist7
    Dallas Heart Study 2 [36]LongitudinalUnited States—3401Actical——
    EpiFloripa Aging Cohort [37]ObservationalBrazil73.9604Actigraph GT3x and GT3x+Right hip7
    Japanese metabolic syndrome [38]ObservationalJapan47483Omron HJA-350ITRight hip7
    Association of metabolic syndrome and blood pressure nondipping profile in untreated hypertension [39]ObservationalSpain48.71770Ambulatory Monitoring Mini-motion loggerWrist—
    LIFE [40]InterventionalUnited States—1635Actigraph GT3XHip7
    Health 2011 Study [41]ObservationalFinland531398Hookie AM20Hip7
    PATHj [42]InterventionalUnited States51434Mini-Mitter Actical—7
    Health Survey for England [5]ObservationalUnited Kingdom512131Actigraph GT1MWaist7
    ADDITION-PRO [43]ObservationalDenmark58.52082ActiheartChest7
    Healthy Aging Initiative [44]ObservationalSweden70.53343Actigraph GT3x+Hip7
    Feedback, Awareness and Behavior study [45]ObservationalUnited Kingdom47453Actiheart—6
    Being physically active modifies the detrimental effect of sedentary behavior on obesity and cardiometabolic markers in adults [46]ObservationalChile—314Actigraph GT1MHip7
    Body composition among elderly and its relationship with physical activity pattern [47]ObservationalIran—368Actigraph——
    Maastricht Study [48]ObservationalNetherlands59.710,000ActivPAL 3Thigh7
    Copenhagen City Heart Study [49]LongitudinalDenmark—1053Actigraph GT3xThigh and waist7
    ACTION! Worksite Wellness Program [50]ObservationalUnited States—850Actigraph 7164—7
    Nutrition and Exercise Intervention Study [51]InterventionalJapan—1085ActiMarkerThigh14
    ALSPACk Mother Cohort [52]LongitudinalUnited Kingdom—4834Actigraph 7164——
    Combined effects of obesity and objectively measured daily physical activity on the risk of hypertension in middle-aged Japanese men: a 4-year prospective cohort study [53]ObservationalJapan49426Kenz Lifecorder—7
    PREVIEWl [54]InterventionalDenmark, Finland, Netherlands, United Kingdom, Spain, Bulgaria, Australia, and New Zealand—2500Actigraph ActiSleep+Waist7
    CHMSm 2007-2009 [55]ObservationalCanada—2832ActicalWaist7
    CHMS 2009-2011 [55]ObservationalCanada—2103ActicalWaist7
    North Finland Birth Cohort 1966 [56]LongitudinalFinland46.63443Polar ActiveNondominant wrist14
    Food4Me [57]InterventionalIreland, Netherlands, Spain, Greece, United Kingdom, Poland, and Germany40.11441Phillips DirectLifePocket, belt, necklace, or bra180 days
    DPHACTOn [58]ObservationalDenmark45.1669Actigraph GT3xThigh7
    Abstract P022: determinants of energy balance: differences related to body weight and body composition [59]Abstract only——430SenseWear——
    Device-based measures of sedentary time and physical activity are associated with physical fitness and body fat content [60]ObservationalFinland26415Hookie AM20Waist7
    MAPECo [61]InterventionalSpain55.62156Mini-motion loggerWrist—
    Al-Andulus [62]ObservationalSpain49.9653Actigraph GT3X+Lower back9
    British Regional Heart Study [63]LongitudinalUnited Kingdom78.41566Actigraph GT3xRight hip7
    Pelotas [64]LongitudinalBrazil454426GeneActivWrist7
    EPIMOVp [65]ObservationalBrazil421040Actigraph GT3XWaist—
    PACE-UPq [66]InterventionalUnited Kingdom591023Actigraph GT3XHip7
    INFORMr [67]InterventionalUnited Kingdom—956Axivity AX3Wrist7
    Effectiveness of physical activity intervention among government employees with metabolic syndrome [68]InterventionalMalaysia—165Lifecorder e-step—3
    Effects of substituting sedentary behavior with light and moderate to vigorous physical activity on obesity indices in adults [69]Abstract only——780Actigraph GT3XWaist4
    Whitehall [70]LongitudinalUnited Kingdom66445Actigraph GT3X—7
    RISCs [71]Observational13 European countries58801Actigraph AM7164Waist8
    SCAPISt (pilot study) [72]ObservationalSweden—661Actigraph GT3x-BTHip7
    Identifying associations between sedentary time and cardiometabolic risk factors in working adults using objective and subjective measures: a cross-sectional analysis [73]ObservationalJapan—671Omron Active Style Pro HJA 350-IT——
    Heredity and Phenotype Intervention Heart Study [74]InterventionalUnited States42.9671ActicalHip7
    Canadian Nurse [75]ObservationalCanada—472Actigraph GT3XRight hip9
    Insulin resistance in Chileans of European and Indigenous descent: evidence for an ethnicity × environment interaction [76]ObservationalChile80.1873Actigraph ActiTrainerLeft hip7
    Rush Memory and Aging Project [77]LongitudinalUnited States69.81556Phillips ActicalWrist10
    REGARDSu [78]ObservationalUnited States657873ActicalWaist7
    PREDIMEDv-plus [79]InterventionalSpain—6000GeneActiv——
    British Birth Cohort [80]ObservationalUnited Kingdom61.14756ActivPAL 3Thigh—
    PUREw [81]LongitudinalSouth Africa50.6189ActiheartChest7
    DonorInsight [82]LongitudinalHolland—807Actigraph GT3X or GT3X-BTWaist—
    Light-intensity physical activity is associated with insulin resistance in elderly Japanese women independent of moderate to vigorous intensity physical activity [83]ObservationalJapan27.6807Actimarker EW4800—28
    Low levels of physical activity are associated with dysregulation of energy intake and fat mass gain over 1 year [84]LongitudinalUnited States51.1421SenseWear Mini ArmbandArm10
    ERMAx [85]ObservationalFinland—1393Actigraph GT3X+ or wGT3X+Waist7
    National Health Program [86]ObservationalPoland47.91471Actical——
    Objectively measured light-intensity lifestyle activity and sedentary time are independently associated with metabolic syndrome: a cross-sectional study of Japanese adults [87]ObservationalJapan—483Omron HJA-350ITRight hip7
    Objectively measured physical activity of Vietnamese adults with type 2 diabetes: opportunities to intervene [88]ObservationalVietnam76120Actigraph GT3XHip5
    Longitudinal assessment of bariatric surgery [89]LongitudinalUnited States68.5927StepWatch 3Ankle7
    Hisayama [90]LongitudinalJapan29.91758Omron Active Style Pro——
    MESAy Sleep Studyd [91]ObservationalUnited States43.92000Phillips ActiwatchNondominant wrist7
    Stork Groruddalen Study [92]ObservationalNorway—759SenseWear Armband Pro3—4
    Inuit Health in Transition study [93]ObservationalGreenland71.91497ActiHeartChest5
    Jackson Heart Study [94]ObservationalUnited States72.7423Actigraph 7164Waist1
    Walking and leg circulation study (subsample) [95]ObservationalUnited States48.9460Caltrac—7
    VIBEz [96]ObservationalUnited Kingdom68.91182GCDC X15-1cWaist7
    AusDiab [97]ObservationalAustralia53.73352Actigraph 7164 and ActivPAL 3Waist and thigh
    European Prospective Investigation into Cancer and Nutrition–Norfolk [98]LongitudinalUnited Kingdom52.72012Actigraph GT1M or GT3XRight hip7
    Relationship between metabolic syndrome, circadian treatment time, and blood pressure nondipping profile in essential hypertension [99]ObservationalSpain531006Mini-motion logger Ambulatory MonitoringWrist2
    Risk of new-onset diabetes: influence of class and treatment-time regimen of hypertension medications [100]Abstract onlySpain—2012———
    Seasonal variation of fibrinogen in dipper and nondipper hypertensive patients [101]ObservationalSpain48508Mini-motion logger Ambulatory MonitoringWrist—
    Osteoartheritus Initiative [102]Abstract onlyUnited States—969———
    Ryobi Health Survey [103]ObservationalJapan59.8691Omron Active Style Pro—7
    Rotterdam Study [104]ObservationalNetherlands441116GeneActivWrist—
    Early activity in diabetes [105]InterventionalUnited Kingdom46528Actigraph GT1MWaist—
    Child Health Checkpoint [106]ObservationalAustralia—877GeneActivWrist8
    European Fans in Training [107]InterventionalUnited Kingdom, Netherlands, Norway, and Portugal481113ActivPAL 3Thigh7
    Examining Neighborhood Activity in Built Living Environments London [108]ObservationalUnited Kingdom56.1877Actigraph GT3X+Hip7
    The morning surge in blood pressure and heart rate is dependent on levels of physical activity after waking [109]ObservationalIreland61420Gaehwiler ElectronicsWrist—
    Prospective Rural Urban Epidemiology (subsample) [110]ObservationalSouth Africa—341Actigraph GT3X+—4
    Innovative Medicines Initiative Diabetes Research on Patient Stratification cohorts [111]ObservationalDenmark, Sweden, Netherlands, United Kingdom, and Finland721355Actigraph GT3X+Nondominant wrist10
    Tromsø [112]ObservationalNorway69.53653Actigraph wGT3X-BTRight hip8
    Baltimore Longitudinal Aging Study [113]LongitudinalUnited States—546ActiHeartChest7
    Migration and Ethnicity on Diabetes In Malmö (MEDIM) [114]ObservationalSweden47.7962Actigraph GT1MWaist—
    Oulu 45 [115]Abstract onlyFinland59.6570Polar ElectroWrist—
    Toyota Motor Corporation Physical Activity and Fitness [116]Abstract only—71.8756———
    Mitchelstown cohort [117]ObservationalIreland52.63344ActivInsight GeneActivWrist7
    Heijo-Kyo [118]ObservationalJapan40528Respironics ActiWatch 2Wrist2
    Gender differences in ambulatory blood pressure thresholds for defining hypertension based on cardiovascular outcome [119]Abstract onlySpain—3344—Wrist—
    Associations between self-reported and objectively measured physical activity and overweight or obesity among adults in Kota Bharu and Penang, Malaysia [120]ObservationalMalaysia—490Actigraph GT3X+Waist7
    Interactive Diet and Activity Tracking in American Association of Retired People study [121]ObservationalUnited States63.2584Actigraph GT3XWaist7
    Genes Environment Diabetes and Obesity [122]ObservationalChile—409Actigraph GT1MRight hip7
    Latin American Study of Nutrition and Health [123]ObservationalArgentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Peru, and Venezuela38.32524Actigraph GT3XRight hip7
    Echocardiographic Study of Latinos [124]ObservationalUnited States56.41206Actical B1Waist—
    Community of Mine [125]ObservationalUnited States59598Actigraph GT3X+Hip14
    Korean National Health and Nutrition Evaluation Surveyd [126]ObservationalSouth Korea48.92197Actigraph GT3XWaist7
    Dynamics of Lifestyle and Neighborhood Community on Health Studyd [127]ObservationalJapan58.3440Omron Active Style ProLeft hip14
    Alberta Moving Beyond Breast Cancer [128]ObservationalCanada561528Actigraph GT3x and ActivPal 3Waist and thigh7
    Lifestyle Biomarkers and atherosclerosis Study [129]ObservationalSweden—658Actigraph GT3X+Waist7
    Brazilian Longitudinal Study of Adult Health [130]ObservationalBrazil—0Actiwatch 2Nondominant wrist7
    Effect of Reducing Sedentary behavior on Blood Pressure [131]InterventionalUnited States—0Actigraph GT3x and AvticPAL 3Waist and thigh9
    National Social, Health, and Aging project [132]ObservationalUnited States—1023———
    Vitamin and Lifestyle Intervention for gestational diabetes mellitus prevention [133]InterventionalAustria, Belgium, Denmark, Ireland, Italy, Netherlands, Poland, Spain, and United Kingdom32436Actigraph GT1M, GT3x+, and ActitrainerRight hip3
    Middle-aged Soweto Cohort [134]ObservationalSouth Africa—727Actigraph GT3x and ActivPal 3Right hip and thigh7
    Stand More at Work and Life [135]InterventionalUnited Kingdom44.7756ActivPal 3, Axivity AX3Thigh, wrist8
    Chronotype of Patients with Type 2 Diabetes and Effect on Glycaemic Control [136]ObservationalUnited Kingdom—998GeneactivWrist8
    Physical activity and health in older women [137]ObservationalChina651105Actigraph wGT3x-BTLeft waist7
    Objectively measured step cadence and walking patterns in a rural African setting: a cross-sectional analysis [138]ObservationalSouth Africa35.1236Actigraph AM-7164-2.2Waist7

    aDenotes that a variable was not retrieved for that data set.

    bMetS: Metabolic Syndrome.

    cEVIDENT: Effectiveness of Internet-based Depression Treatment.

    dDenotes data sets that were identified as open access.

    eOPACH: The Objective Physical Activity and Cardiovascular Health Study.

    fCARDIA: The Coronary Artery Risk Development in Young Adults Study.

    gPROPEL: The Promotion of Physical Activity through structured education with differing levels of ongoing support for those with prediabetes.

    hHCHS: Hispanic Community Health Study.

    iSOL: Study of Latinos.

    jPATH: Population Assessment of Tobacco and Health.

    kALSPAC: Avon Longitudinal Study of Parents and Children.

    lPREVIEW: Prevention of diabetes through lifestyle intervention and population studies in Europe and around the World.

    mCHMS: Canadian Health Measures Survey.

    nDPHACTO: Danish Physical Activity cohort with Objective Measures.

    oMAPEC: Monitorización Ambulatoria para Predicción de Eventos Cardiovascular.

    pEPIMOV: Epidemiology of Human Movement Study.

    qPACE-UP: Pedometer and consultation evaluation–UP.

    rINFORM: Information and Risk Modification Trial.

    sRISC: Relationship between Insulin Sensitivity and cardiovascular disease.

    tSCAPIS: Swedish Cardiopulmonary BioImage Study.

    uREGARDS: Reasons for Geographic and Racial Differences in Strokes.

    vPREDIMED: Primary Prevention of Cardiovascular disease with a Mediterranean diet.

    wPURE: Prospective Urban Rural Epidemiology.

    xERMA: Estrogenic Regulation of Muscle Apoptosis.

    yMESA: multiethnic study of atherosclerosis.

    zVIBE: Vertical Impacts on Bone in the Elderly.

    The GPAD catalogue, as shown in Figures 2 and 3, is a web-based tool developed to highlight the findings of this review. Several filters can be applied, including the continent or country of data collection, age, sex, ethnicity, and accelerometric device used. The complete catalog of data sets is also available on a separate page where a single data set can be selected, and a summary can be provided. Alternatively, 2 data sets can be selected and compared using a side-by-side summary of each. In addition, the users can select either a single health marker or a group of health markers, and a visual summary of all data sets that have collected the given health marker or markers is produced. The GPAD catalogue [20] is available on the web, and the source code can be found on GitHub [139].

    ‎
    Figure 2. A screenshot of the home page of the Global Physical Activity Data set (GPAD) cards’ web-based data visualization tool.
    ‎
    Figure 3. More detailed screenshots of the Global Physical Activity Data set (GPAD) catalogue tool. (A) The health markers screen that allows data sets shown to be filtered by the health markers they collect, (B) a visualization of a single data set within the tool, (C) a comparison of 2 data sets within the GPAD, and (D) the bottom of the home page showing grouped health markers and accelerometer information.

    Narrative Summary

    From the 122 unique data sets collected, 301,075 individual participants’ data were identified. Of the data sets where sex could be retrieved (111/122, 90.9%), 131,700 participants were female and 101,848 were male. The mean age of the participants was 54.1 (SD 12.35) years, with a range of 15 to 98 years, where mean age could be retrieved from the data set (110/122, 90.2%). Data sets were identified in 5 continents, with the majority being retrieved from Europe and North America (n=85 data sets) [5,21,22,24,25,28,31-36,39-45,48-50,52,55-58,60-63,66,67,70-72,​74,75,77-80,82,84-86,89,91-96,98-102,104,105,107-109,111-115,117,​119,121,124,125,128,129,131,132,140]. A wide range of health markers have been collected, with most studies measuring height (119/122, 97.5%), body mass (119/122, 97.5%), and blood pressure (82/122, 67.2%).

    Study Design and Data Access Status

    Of the identified data sets, the majority (n=75) had an observational study design, 21 were longitudinal, and 19 were interventional. For 7 data sets, only an abstract could be found, and the study design could not be retrieved. The access status of the 36 data sets could be ascertained. Of these, 37 data sets were publicly available, 22 mentioned explicitly being available on request from the authors, and 5 reported that the data could not be shared.

    Countries of Data Collection

    Seven studies were collected across multiple countries. Of these cross-country studies, 5 were collected solely in Europe; 1 was collected in European countries, Australia, and New Zealand; and 1 was collected across 8 South American countries. Most data sets were collected in the United States (n=28) and the United Kingdom (n=18). The United Kingdom had the largest volume of participant data (n=135,544), followed by the United States (n=67,040). Africa and South America had a smaller number of data sets collected (n=12 data sets combined). A total of 18 data sets were located in Asia; however, all had a relatively small mean number of participants per data set (mean 605; range 120-1758).

    A limited number of data sets (n=13) were identified within LMICs. These data sets were collected in Malaysia, Chile, Iran, South Africa, and Vietnam. They contained data on 4955 participants, accounting for 1.64% (4955/301,075) of the total number of identified participants. The average number of participants in data sets within these countries was 381. Figure 4 illustrates the countries in which data were collected, with a darker color representing fewer participants.

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    Figure 4. Choropleth world map showing countries where data sets have been collected and how many participants’ data have been collected in each country (log transformed).

    Accelerometric Devices Used

    Actigraphs and Actical devices were the most commonly used accelerometric devices (n=63). The most commonly used Actigraph model was GT3X (ActiGraph; n=38). Six wear locations were identified in the retrieved data sets. The 3 most common wear locations were the waist (n=63), followed by the wrist (n=22) and the thigh (n=11).

    Accelerometer Initialization and Processing

    Only limited information could be retrieved on the methods surrounding device deployment and processing (raw data sampling frequency, choice of epoch, number of axes measured, and number of days of wear). For 29 data sets, it was not possible to determine the number of days an accelerometer was worn; for 73 data sets, it was not possible to determine the epoch used to analyze physical activity; for 69 data sets, the number of axes over which the device was initialized could not be retrieved; and for 94 data sets, the sampling rate used could not be retrieved. From what could be extracted, the most common number of days of wear was 7 (63/122, 51.6% of total data sets identified), whereas the most used epoch and raw data sampling frequency were 60 seconds (30/122, 24.6% of total data sets identified) and 30 Hz (11/122, 9%), respectively.

    Cardiometabolic Health Markers

    Height and body mass were the most reported health markers, with the data collected in 119 data sets (n=303,144 participants). Blood pressure (n=82 data sets and n=270,961 participants), waist circumference (n=75 data sets and n=253,683 participants), HDL cholesterol (n=68 data sets and n=239,113 participants), and blood glucose (n=65 data sets and n=225,185 participants) were the most collected cardiometabolic health markers. When health markers were broken down by the continent they were collected in, North America and Europe collected the widest range of health markers. Africa only reported studies that have measured more basic health markers (height, body mass, and blood pressure). Figure 5 shows the data sets which collected each health outcome split by the continent of data collection. Larger images of Figures 3-5 are provided in Multimedia Appendix 5.

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    Figure 5. Number of data sets that collected each health outcome split by the continent of data collection. DBP: diastolic blood pressure; HDL: high-density lipoprotein; LDL: low-density lipoprotein; SBP: systolic blood pressure; VLDL: very low-density lipoprotein.

    Grouped Health Markers

    When health markers were grouped (based on the criteria defined in Multimedia Appendix 2), a total of 92 data sets measured anthropometry, 82 measured blood pressure, 75 measured blood glucose, and 70 measured blood lipids. When the collected grouped health markers were examined across continents, Africa only had data collected on anthropometry and blood pressure, whereas North America, Europe, Asia, South America, and Oceania had data sets that collected health markers across all 4 categories.


    Principal Findings

    Accelerometer-measured physical activity has been collected alongside cardiometabolic health outcomes in many published data sets, which can be pooled to produce more heterogeneous data, providing greater statistical power. However, comprehensively identifying data sets to be included in pooled analysis is a time-consuming endeavor. The findings from this review informed the creation of the GPAD catalogue, a web-based resource to reduce the burden of identifying relevant data sets and allow researchers and other interested parties to explore and address important research questions regarding physical activity and cardiometabolic health in an efficient manner. The GPAD catalogue also includes 7 data sets from LMICs.

    Previous Research

    A review by Wijndaele et al [19] of accelerometer-measured data sets identified Actigraphs as the most frequently used accelerometric device (39/76, 51% of identified data sets). This is similar to our review, with 41.8% (51/122) of the data sets using Actigraphs. Compared with the previous review by Wijndaele et al [19], this review benefits from the inclusion of an additional 29 data sets. Both reviews adopted a threshold of 400 participants as an eligibility criterion for the inclusion of data sets, but our review reduced this limit for LMICs (100 participants) to ensure there was data representation from across the world and from different ethnic and cultural groups. Our review also extends the work of Wijndaele et al [19] by extracting more variables from the data sets, which will facilitate future harmonization efforts. A more recent review and expert statement published in 2020 that collected thigh-worn accelerometer-measured physical activity included only data sets using observational study designs [141]. In contrast, all intervention or observational study designs were eligible for this review, resulting in more data sets being available for inclusion.

    The GPAD Catalogue

    Although other reviews have aimed to identify data sets that have measured physical activity using an accelerometer [19,141], to our knowledge, this is the first study to create an interactive web-based catalog that can inform future harmonization and data pooling processes. The GPAD catalogue serves a similar purpose to the Maelstrom Catalogue, with a greater emphasis on accelerometer-measured physical activity. The Maelstrom Catalogue is a data discovery tool that allows users to identify data sets that may help to answer novel research questions as well as provide information on the data sets, which may ease the data harmonization process [142]. The GPAD catalogue also implemented elements of the Global Observatory of Physical Activity country cards by using visualizations to communicate information in an understandable and comparable manner to interested parties and organizations.

    The concept of a repository for data sets with similar variables is not new and exists in several other research fields. For example, the database of Genotypes and Phenotypes is a database that archives and distributes the data and results from studies that have investigated the genotype and phenotype in humans, allowing users to identify data sets that may be of interest to them [143]. By providing this information in a central resource, the database of Genotypes and Phenotypes has the same aim as the GPAD catalogue, which is to increase collaboration, facilitate the processes of research, and reduce researcher burden. However, the GPAD catalogue does not aim to hold or provide access to individual study data; rather, it provides a summary of what is available to researchers if they were to collaborate with the appropriate data set owners. It is hoped that by taking a continual and collaborative approach to the development of the GPAD catalogue, frequent updates and adaptations can be made so that the resource remains relevant to the inclusion of new data sets over time.

    Accelerometer Device Use, Wear Location, and Reporting

    Actigraph, followed by Actical, were the most used devices both regarding the number of data sets they were used in as well as the number of participants who wore them. The waist was the most used wear location (n=63), followed by the wrist (n=21) and the thigh (n=11). The dominance of the waist as a wear location is likely because of its extensive use in the early years of measuring physical activity using accelerometric devices. However, evidence from previous waist-worn data sets highlights that only approximately 25% of participants provided the requested 7 days of accelerometer wear data [144]. Wrist-worn devices have been shown to be more acceptable and lead to greater participant compliance [6,144]. In addition, advances in analytic techniques allowing posture to be assessed from the devices worn at the thigh means that both these wear locations are likely to increase in popularity over the coming years [145]. These findings highlight that the insights offered by a tool such as the GPAD catalogue can be used by researchers both for secondary data analysis and informing planned primary data collection.

    A notable finding of this review was the lack of reporting of accelerometer deployment and analysis variables, consistent with a review by Montoye et al [146]. They found that, overall, the reporting of accelerometer variables was poor, with only marginal increases in reporting over time. Our findings corroborate this and highlight that future research should aim to report the key and detailed characteristics of the locations where accelerometers were worn alongside the analytic decisions taken to ease future pooling and harmonization efforts. Montoye et al [146] provided several key items that should be reported when an accelerometer is included in a study.

    Health Markers

    Height and body mass were the most frequently collected health markers, followed by blood pressure. As evidence supports the role of both BMI and blood pressure as potential risk factors for cardiovascular disease [147,148], this is not unexpected. Another trend from this review is that health markers that are easier to assess (require less expensive equipment or are less time-consuming) were more commonly collected than health markers that require more specialists or expensive equipment. When health markers were grouped into larger categories (defined in Multimedia Appendix 2), anthropometry was the most frequently collected data. LMICs collected anthropometry (11/11, 100% data sets) and blood pressure (6/11, 55% data sets) data frequently; however, blood lipids and blood glucose were measured less frequently (5/11, 45% and 6/11, 55% data sets, respectively).

    Geographic Distribution of Data

    There was a notable lack of data sets collected in LMICs that met the inclusion criteria for this study, highlighting the need for more relevant data sets. The lack of data sets in these countries could also be explained by the extensive knowledge, skills, and funding required to conduct cardiometabolic health assessments, on a large number of participants, as well as how to effectively initialize, deploy, and analyze accelerometers and their data. Furthermore, the financial burden of purchasing the necessary number of accelerometric devices to collect a large data set poses additional challenges for LMICs. Therefore, the GPAD catalogue resource may prove particularly useful to researchers in such regions to answer questions using the existing data.

    Access Status

    The data access status could not be determined for 69.7% (85/122) of data sets. This highlights the need for better reporting of the access status of the data used within publications, which would allow researchers aiming to conduct secondary analysis or data harmonization a greater level of understanding of how to seek access to the data sets. Notably, over time, the number of data sets reporting their access status has increased. Between 2000 and 2010, only 11 data sets reported their access status, and between 2011 and 2021, a total of 17 data sets reported their access status. This improved reporting shows that journals requiring data set access statements may ease the harmonization process for future researchers. However, a recent review found that only 14% of papers that included a data access statement responded to a request to share data, and of these, only 6.8% of the authors provided the requested data, indicating that a data access statement may not be sufficient to ensure data sharing [149].

    Strengths and Limitations

    A key strength of this review was the assistance from an information specialist in devising the search strategy. This ensured that the systematic search for eligible data sets was as robust and complete as possible, increasing the probability that all available papers and data sets were identified. The large number of studies returned from the initial search required the eligibility criteria to be refined during the review process, ultimately resulting in a more focused review. By making the results available interactively on the web in the GPAD catalogue, this review allows the findings to be disseminated to more potential users. The extraction of variables from each data set is more comprehensive than that in previous reviews [19], allowing those seeking to harmonize findings from across data sets to have access to more information. By adopting a lower participant number inclusion criteria for LMICs, 7 data sets from countries such as Vietnam, Chile, and Iran were included, which otherwise would not have been the case. The inclusion of these data sets into a resource such as the GPAD catalogue is important for the integrity and representativeness of the resource. This is also important because it means that data from a wide range of ethnic and cultural groups can be included in future studies resulting from the GPAD and because physical activity patterns will differ across geographic regions of the world. Moreover, this highlights how important it is for future reviews to include methodological decisions from the outset that will allow such data sets to be part of data catalogs.

    This review has some limitations that should be considered. Data sets were only identified through published reports identified by our systematic searches; it is possible that some data sets may have been collected but not yet published. This was mitigated by searching for a wide range of sources, including gray literature and trial registries. Furthermore, it is possible that data sets may have collected health markers that are yet to be published and therefore may not be included within the GPAD catalogue. It was also hoped that the methodology related to how each health marker was collected could be recorded. However, these data were poorly reported in most data sets and therefore were not discussed in this review. What data could be extracted has been made available in the GPAD catalogue data set. Although we aimed to extract key variables from each data set related to markers of cardiometabolic health and accelerometer-measured physical activity, we appreciate that certain variables that researchers may find useful are omitted from the review. We welcome collaborators to help add these variables to future iterations of the resource.

    Future Developments and Implications

    We plan to update the GPAD catalogue resource periodically (at least once per year) to include new data sets as they become publicly available. Furthermore, on the release of the resource, an email will be sent to the primary investigators on each data set to make them aware of the resource and to encourage them to inform us if their data set contains variables currently omitted from the resource.

    For example, as the Prospective Physical Activity, Sitting, and Sleep consortium grows [150], a greater number of thigh worn accelerometry data sets with cardiometabolic health information will become available in the future. Additional health markers, such as mental health and mortality outcomes, will also be added to the resource over time. We hope that this will further increase the usefulness of the resource and the ability of the GPAD catalogue to assist in the development of harmonized analyses on a broader range of research questions related to physical activity and health.

    Conclusions

    This review represents the most comprehensive analysis of its kind conducted to date, with 122 data sets identified that have quantified physical activity using an accelerometer and assessed cardiometabolic health markers. We have shown that data sets exist in all 5 inhabited continents of the world that have used a wide range of devices to measure physical activity. Future efforts to collect larger data sets with more comprehensive health markers are required, particularly in LMICs. The GPAD catalogue was created to allow important questions about physical activity and cardiometabolic health to be answered in an efficient manner and to ultimately produce evidence that will reduce the likelihood that adults die from diseases related to physical inactivity.

    Acknowledgments

    AJD was supported by the National Institute for Health and Care Research (NIHR) research professorship award. This research was supported by the NIHR Leicester Biomedical Research Center. The views expressed are those of the authors and not necessarily those of the National Health Service, the NIHR, or the Department of Health and Social Care.

    Data Availability

    The data sets generated or analyzed during this study are available in Multimedia Appendices 3 and 4 as well as on GitHub [139] and on the Global Physical Activity Data set catalogue Shiny app [20].

    Authors' Contributions

    JJCT, JPS, AJD, DWE, and ES generated the original idea for the study. AC created the search strategy in collaboration with JJCT, JPS, and DWE. AC ran the searches. JJCT, JPS, AJD, and VEK screened the studies and data sets, and all data extraction was performed by JJCT and verified by JPS and DWE. Data analysis and writing of the manuscript were performed by JJCT. All the authors commented on and approved the manuscript before its submission.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Full search strategy.

    DOCX File , 33 KB
    Multimedia Appendix 2

    Logical statements used to generate grouped health outcomes from extracted health outcomes.

    DOCX File , 22 KB
    Multimedia Appendix 3

    Paper extraction table.

    XLSX File (Microsoft Excel File), 98 KB
    Multimedia Appendix 4

    Data set extraction table.

    XLSX File (Microsoft Excel File), 77 KB
    Multimedia Appendix 5

    Additional images of the Global Physical Activity Data set catalog.

    DOCX File , 926 KB
    1. Chastin SF, Palarea-Albaladejo J, Dontje ML, Skelton DA. Combined effects of time spent in physical activity, sedentary behaviors and sleep on obesity and cardio-metabolic health markers: a novel compositional data analysis approach. PLoS One. Oct 13, 2015;10(10):e0139984. [FREE Full text] [CrossRef] [Medline]
    2. 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]
    3. Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. Jan 2008;40(1):181-188. [CrossRef] [Medline]
    4. Scholes S, Coombs N, Pedisic Z, Mindell JS, Bauman A, Rowlands AV, et al. Age- and sex-specific criterion validity of the health survey for England Physical Activity and Sedentary Behavior Assessment Questionnaire as compared with accelerometry. Am J Epidemiol. Jun 15, 2014;179(12):1493-1502. [FREE Full text] [CrossRef] [Medline]
    5. Health survey for England - 2008: physical activity and fitness. National Health Service Digital. Dec 17, 2019. URL: https:/​/digital.​nhs.uk/​data-and-information/​publications/​statistical/​health-survey-for-england/​health-survey-for-england-2008-​physical-activity-and-fitness [accessed 2021-09-15]
    6. 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. Feb 01, 2017;12(2):e0169649. [FREE Full text] [CrossRef] [Medline]
    7. Hamer M, Stamatakis E, Chastin S, Pearson N, Brown M, Gilbert E, et al. Feasibility of measuring sedentary time using data from a thigh-worn accelerometer. Am J Epidemiol. Sep 01, 2020;189(9):963-971. [FREE Full text] [CrossRef] [Medline]
    8. Sherar LB, Griew P, Esliger DW, Cooper AR, Ekelund U, Judge K, et al. International children's accelerometry database (ICAD): design and methods. BMC Public Health. Jun 21, 2011;11(1):485. [FREE Full text] [CrossRef] [Medline]
    9. Ekelund U, Steene-Johannessen J, Brown WJ, Fagerland MW, Owen N, Powell KE, Lancet Physical Activity Series 2 Executive Committe; et al. Lancet Sedentary Behaviour Working Group. Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. Lancet. Sep 24, 2016;388(10051):1302-1310. [CrossRef] [Medline]
    10. Chastin S, McGregor D, Palarea-Albaladejo J, Diaz KM, Hagströmer M, Hallal PC, et al. Joint association between accelerometry-measured daily combination of time spent in physical activity, sedentary behaviour and sleep and all-cause mortality: a pooled analysis of six prospective cohorts using compositional analysis. Br J Sports Med. Nov 2021;55(22):1277-1285. [FREE Full text] [CrossRef] [Medline]
    11. Atkin AJ, Biddle SJ, Broyles ST, Chinapaw M, Ekelund U, Esliger DW, et al. International Children’s Accelerometry Database (ICAD) Collaborators. Harmonising data on the correlates of physical activity and sedentary behaviour in young people: methods and lessons learnt from the international Children's Accelerometry Database (ICAD). Int J Behav Nutr Phys Act. Dec 20, 2017;14(1):174. [FREE Full text] [CrossRef] [Medline]
    12. Loprinzi PD, Smith B. Comparison between wrist-worn and waist-worn accelerometry. J Phys Act Health. Jul 2017;14(7):539-545. [CrossRef] [Medline]
    13. Migueles JH, Aadland E, Andersen LB, Brønd JC, Chastin SF, Hansen BH, et al. External review group. GRANADA consensus on analytical approaches to assess associations with accelerometer-determined physical behaviours (physical activity, sedentary behaviour and sleep) in epidemiological studies. Br J Sports Med. Apr 2022;56(7):376-384. [FREE Full text] [CrossRef] [Medline]
    14. Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Social Res Methodol. Feb 2005;8(1):19-32. [FREE Full text] [CrossRef]
    15. Levac D, Colquhoun H, O'Brien KK. Scoping studies: advancing the methodology. Implement Sci. Sep 20, 2010;5:69. [FREE Full text] [CrossRef] [Medline]
    16. Thomas J, Daley A, Sanders J, Esliger D. Accelerometer-measured physical activity datasets: a cross-country scoping review. OSF Home. Feb 1, 2021. URL: https://osf.io/6uzqg/ [accessed 2022-12-12]
    17. 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. [FREE Full text] [CrossRef] [Medline]
    18. Low and middle income data. The World Bank. 2016. URL: https://data.worldbank.org/country/XO [accessed 2021-09-03]
    19. Wijndaele K, Westgate K, Stephens SK, Blair SN, Bull FC, Chastin SF, et al. Utilization and harmonization of adult accelerometry data: review and expert consensus. Med Sci Sports Exerc. Oct 2015;47(10):2129-2139. [FREE Full text] [CrossRef] [Medline]
    20. Jonah T. Global physical activity catalogue. The Global Physical Activity Dataset. 2022. URL: https://lboro-climb.shinyapps.io/gpad/ [accessed 2023-03-13]
    21. Tsao CW, Vasan RS. Cohort profile: the Framingham Heart Study (FHS): overview of milestones in cardiovascular epidemiology. Int J Epidemiol. Dec 2015;44(6):1800-1813. [FREE Full text] [CrossRef] [Medline]
    22. Framingham heart study (FHS) third generation (Gen III), OMNI 2, and new offspring (NOS) cohorts. NIH National Heart, Lung, and Blood institute. URL: https://biolincc.nhlbi.nih.gov/studies/gen3/ [accessed 2021-09-15]
    23. YiYi L, Samat N, Wan Manan WM. Accelerometer-measured physical activity and its relationship with body mass index (BMI) and waist circumference (WC) measurements: a cross-sectional study on Malaysian adults. Mal J Nutr. 2017;23(3):397-408. [FREE Full text]
    24. Recio-Rodríguez JI, Sanchez-Aguadero N, Rodríguez-Sánchez E, Martinez-Vizcaino V, Martin-Cantera C, Patino-Alonso MC, et al. EVIDENT Group. Physical activity and adiposity among older adults of the EVIDENT Study. J Aging Phys Act. Apr 2017;25(2):254-260. [CrossRef] [Medline]
    25. Stensvold D, Viken H, Rognmo Ø, Skogvoll E, Steinshamn S, Vatten LJ, et al. A randomised controlled study of the long-term effects of exercise training on mortality in elderly people: study protocol for the generation 100 study. BMJ Open. Feb 12, 2015;5(2):e007519. [FREE Full text] [CrossRef] [Medline]
    26. National health and nutrition examination survey NHANES 2003-2004. Center for Disease Prevention and Control. 2003. URL: https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2003 [accessed 2022-11-24]
    27. NHANES questionnaires, datasets and related documentation NHANES 2005-2006. Center for Disease Control and Prevention. 2005. URL: https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2005 [accessed 2022-11-24]
    28. UK Biobank homepage. UK Biobank. URL: https://www.ukbiobank.ac.uk/ [accessed 2021-09-15]
    29. 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]
    30. 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]
    31. LaCroix AZ, Rillamas-Sun E, Buchner D, Evenson KR, Di C, Lee IM, et al. The objective physical activity and cardiovascular disease health in older women (OPACH) Study. BMC Public Health. Feb 14, 2017;17(1):192. [FREE Full text] [CrossRef] [Medline]
    32. Coronary artery risk development in young adults (CARDIA). NIH National Heart, Lung, and Blood institute. 2013. URL: https://biolincc.nhlbi.nih.gov/studies/cardia/ [accessed 2021-09-15]
    33. Yates T, Edwardson CL, Henson J, Gray LJ, Ashra NB, Troughton J, et al. Walking away from type 2 diabetes: a cluster randomized controlled trial. Diabet Med. May 2017;34(5):698-707. [CrossRef] [Medline]
    34. Yates T, Griffin S, Bodicoat DH, Brierly G, Dallosso H, Davies MJ, et al. PRomotion of physical activity through structured education with differing levels of ongoing support for people at high risk of type 2 diabetes (PROPELS): study protocol for a randomized controlled trial. Trials. Jul 02, 2015;16:289. [FREE Full text] [CrossRef] [Medline]
    35. Hispanic community health study / study of Latinos homepage. Hispanic Community Health Study / Study of Latinos. 2006. URL: https://sites.cscc.unc.edu/hchs/ [accessed 2021-09-15]
    36. Dallas heart study 2: return clinic visit for the Dallas heart study cohort. NIH U.S. National Library of Medicine. URL: https://clinicaltrials.gov/ct2/show/NCT00344903 [accessed 2021-09-15]
    37. Dos Santos CE, Manta SW, Maximiano GP, Confortin SC, Benedetti TR, d'Orsi E, et al. Accelerometer-measured physical activity and sedentary behavior: a cross-sectional study of Brazilian older adults. J Phys Act Health. Nov 01, 2018;15(11):811-818. [CrossRef] [Medline]
    38. Kim J, Tanabe K, Yokoyama N, Zempo H, Kuno S. Association between physical activity and metabolic syndrome in middle-aged Japanese: a cross-sectional study. BMC Public Health. Aug 05, 2011;11:624. [FREE Full text] [CrossRef] [Medline]
    39. Hermida RC, Chayán L, Ayala DE, Mojón A, Domínguez MJ, Fontao MJ, et al. Association of metabolic syndrome and blood pressure nondipping profile in untreated hypertension. Am J Hypertens. Mar 2009;22(3):307-313. [CrossRef] [Medline]
    40. Life Study. 2017. URL: http://www.lifestudy.ac.uk/index.html
    41. Vasankari V, Husu P, Vähä-Ypyä H, Suni J, Tokola K, Halonen J, et al. Association of objectively measured sedentary behaviour and physical activity with cardiovascular disease risk. Eur J Prev Cardiol. Aug 22, 2017;24(12):1311-1318. [CrossRef] [Medline]
    42. Coulon SM, Wilson DK, Egan BM. Associations among environmental supports, physical activity, and blood pressure in African-American adults in the PATH trial. Soc Sci Med. Jun 2013;87:108-115. [FREE Full text] [CrossRef] [Medline]
    43. Johansen NB, Hansen AS, Jensen TM, Philipsen A, Rasmussen SS, Jørgensen ME, et al. Protocol for ADDITION-PRO: a longitudinal cohort study of the cardiovascular experience of individuals at high risk for diabetes recruited from Danish primary care. BMC Public Health. Dec 14, 2012;12:1078. [FREE Full text] [CrossRef] [Medline]
    44. Ballin M, Nordström P, Niklasson J, Nordström A. Associations of objectively measured physical activity and sedentary time with the risk of stroke, myocardial infarction or all-cause mortality in 70-year-old men and women: a prospective cohort study. Sports Med. Feb 2021;51(2):339-349. [FREE Full text] [CrossRef] [Medline]
    45. Godino JG, Watkinson C, Corder K, Sutton S, Griffin SJ, van Sluijs EM. Awareness of physical activity in healthy middle-aged adults: a cross-sectional study of associations with sociodemographic, biological, behavioural, and psychological factors. BMC Public Health. May 02, 2014;14:421. [FREE Full text] [CrossRef] [Medline]
    46. Salas C, Cristi-Montero C, Fan Y, Durán E, Labraña AM, Martínez MA, et al. [Being physically active modifies the detrimental effect of sedentary behavior on obesity and cardiometabolic markers in adults]. Rev Med Chil. Nov 2016;144(11):1400-1409. [FREE Full text] [CrossRef] [Medline]
    47. Zeinali F, Samadi M, Azam K, Djafarian K. Body composition among elderly and its relationship with physical activity pattern. J Mazandaran Univ Med Sci. 2016;26(135):62-74. [FREE Full text]
    48. Schram MT, Sep SJ, van der Kallen CJ, Dagnelie PC, Koster A, Schaper N, et al. The Maastricht study: an extensive phenotyping study on determinants of type 2 diabetes, its complications and its comorbidities. Eur J Epidemiol. Jun 2014;29(6):439-451. [CrossRef] [Medline]
    49. The Copenhagen City Heart Study (CCHS). NIH U.S. National Library of Medicine. URL: https://clinicaltrials.gov/ct2/show/NCT02993172 [accessed 2021-09-15]
    50. Webber LS, Rice JC, Johnson CC, Rose D, Srinivasan SR, Berenson GS. Cardiovascular risk factors and physical activity behavior among elementary school personnel: baseline results from the ACTION! worksite wellness program. J Sch Health. Sep 2012;82(9):410-416. [CrossRef] [Medline]
    51. Nutrition and exercise intervention study (NEXIS). NIH U.S. National Library of Medicine. URL: https://clinicaltrials.gov/ct2/show/NCT00926744 [accessed 2021-09-15]
    52. Fraser A, Macdonald-Wallis C, Tilling K, Boyd A, Golding J, Davey Smith G, et al. Cohort profile: the Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort. Int J Epidemiol. Feb 2013;42(1):97-110. [FREE Full text] [CrossRef] [Medline]
    53. Adachi T, Kamiya K, Takagi D, Ashikawa H, Hori M, Kondo T, et al. Combined effects of obesity and objectively-measured daily physical activity on the risk of hypertension in middle-aged Japanese men: a 4-year prospective cohort study. Obes Res Clin Pract. Jul 2019;13(4):365-370. [CrossRef] [Medline]
    54. Swindell N, Rees P, Fogelholm M, Drummen M, MacDonald I, Martinez JA, et al. Compositional analysis of the associations between 24-h movement behaviours and cardio-metabolic risk factors in overweight and obese adults with pre-diabetes from the PREVIEW study: cross-sectional baseline analysis. Int J Behav Nutr Phys Act. Mar 04, 2020;17(1):29. [FREE Full text] [CrossRef] [Medline]
    55. Bryan S, St-Denis M, Wojtas D. Canadian Health Measures Survey: clinic operations and logistics. Health Rep. 2007;18 Suppl:53-70. [FREE Full text] [Medline]
    56. Farrahi V, Kangas M, Walmsley R, Niemelä M, Kiviniemi A, Puukka K, et al. Compositional associations of sleep and activities within the 24-h cycle with cardiometabolic health markers in adults. Med Sci Sports Exerc. Feb 01, 2021;53(2):324-332. [FREE Full text] [CrossRef] [Medline]
    57. Celis-Morales C, Livingstone KM, Affleck A, Navas-Carretero S, San-Cristobal R, Martinez JA, et al. Food4Me Study. Correlates of overall and central obesity in adults from seven European countries: findings from the Food4Me Study. Eur J Clin Nutr. Feb 2018;72(2):207-219. [CrossRef] [Medline]
    58. Gupta N, Korshøj M, Dumuid D, Coenen P, Allesøe K, Holtermann A. Daily domain-specific time-use composition of physical behaviors and blood pressure. Int J Behav Nutr Phys Act. Jan 10, 2019;16(1):4. [FREE Full text] [CrossRef] [Medline]
    59. Hand GA, Shook RP, Jaggers JR, Paluch A, Prasad VK, Burgess S, et al. Abstract P022: determinants of energy balance: differences related to body weight and body composition. Circulation. Mar 23, 2013;127(suppl_12):AP022. [CrossRef]
    60. Vaara JP, Vasankari T, Wyss T, Pihlainen K, Ojanen T, Raitanen J, et al. Device-based measures of sedentary time and physical activity are associated with physical fitness and body fat content. Front Sports Act Living. Dec 18, 2020;2:587789. [FREE Full text] [CrossRef] [Medline]
    61. Ambulatory blood pressure monitoring in the prediction of cardiovascular events and effects of chronotherapy (MAPEC). NIH U.S. National Library of Medicine. URL: https://www.clinicaltrials.gov/ct2/show/NCT00295542 [accessed 2021-09-15]
    62. Acosta-Manzano P, Segura-Jiménez V, Estévez-López F, Álvarez-Gallardo IC, Soriano-Maldonado A, Borges-Cosic M, et al. Do women with fibromyalgia present higher cardiovascular disease risk profile than healthy women? The al-Ándalus project. Clin Exp Rheumatol. May 2017;35 Suppl 105(3):61-67. [Medline]
    63. British Regional Heart Study (BRHS). University College London. URL: https:/​/www.​ucl.ac.uk/​epidemiology-health-care/​research/​primary-care-and-population-health/​research/​brhs [accessed 2021-09-16]
    64. Victora CG, Barros FC. Cohort profile: the 1982 Pelotas (Brazil) birth cohort study. Int J Epidemiol. Apr 2006;35(2):237-242. [CrossRef] [Medline]
    65. de Sousa TL, Ostoli TL, Sperandio EF, Arantes RL, Gagliardi AR, Romiti M, et al. Dose-response relationship between very vigorous physical activity and cardiovascular health assessed by heart rate variability in adults: cross-sectional results from the EPIMOV study. PLoS One. Jan 31, 2019;14(1):e0210216. [FREE Full text] [CrossRef] [Medline]
    66. Harris T, Kerry SM, Limb ES, Victor CR, Iliffe S, Ussher M, et al. Effect of a primary care walking intervention with and without nurse support on physical activity levels in 45- to 75-year-olds: the pedometer and consultation evaluation (PACE-UP) cluster randomised clinical trial. PLoS Med. Jan 03, 2017;14(1):e1002210. [FREE Full text] [CrossRef] [Medline]
    67. Silarova B, Sharp S, Usher-Smith JA, Lucas J, Payne RA, Shefer G, et al. Effect of communicating phenotypic and genetic risk of coronary heart disease alongside web-based lifestyle advice: the INFORM randomised controlled Trial. Heart. Jul 2019;105(13):982-989. [FREE Full text] [CrossRef] [Medline]
    68. Huei Phing C, Abu Saad H, Barakatun Nisak MY, Mohd Nasir MT. Effectiveness of physical activity intervention among government employees with metabolic syndrome. J Exerc Sci Fit. Dec 2017;15(2):55-62. [FREE Full text] [CrossRef] [Medline]
    69. Gonze BB, Sperandio EF, Gagliardi AR, Romiti M, Arantes RL, Dourado VZ. Abstract MP026: effects of substituting sedentary behavior with light and moderate-to-vigorous physical activity on obesity indices in adults. Circulation. Mar 07, 2017;135(suppl_1) [FREE Full text] [CrossRef]
    70. Hamer M, Stamatakis E, Steptoe A. Effects of substituting sedentary time with physical activity on metabolic risk. Med Sci Sports Exerc. Oct 2014;46(10):1946-1950. [FREE Full text] [CrossRef] [Medline]
    71. Gastaldelli A, Kozakova M, Højlund K, Flyvbjerg A, Favuzzi A, Mitrakou A, et al. RISC Investigators. Fatty liver is associated with insulin resistance, risk of coronary heart disease, and early atherosclerosis in a large European population. Hepatology. May 2009;49(5):1537-1544. [CrossRef] [Medline]
    72. Lindgren M, Börjesson M, Ekblom Ö, Bergström G, Lappas G, Rosengren A. Physical activity pattern, cardiorespiratory fitness, and socioeconomic status in the SCAPIS pilot trial - a cross-sectional study. Prev Med Rep. Dec 2016;4:44-49. [FREE Full text] [CrossRef] [Medline]
    73. Honda T, Chen S, Kishimoto H, Narazaki K, Kumagai S. Identifying associations between sedentary time and cardio-metabolic risk factors in working adults using objective and subjective measures: a cross-sectional analysis. BMC Public Health. Dec 19, 2014;14:1307. [FREE Full text] [CrossRef] [Medline]
    74. Heredity and Phenotype Intervention (HAPI) Heart Study (HAPI). NIH U.S. National Library of Medicine. 2008. URL: https://clinicaltrials.gov/ct2/show/NCT00664040 [accessed 2021-09-15]
    75. Reed JL, Prince SA, Pipe AL, Attallah S, Adamo KB, Tulloch HE, et al. Influence of the workplace on physical activity and cardiometabolic health: results of the multi-centre cross-sectional Champlain Nurses' study. Int J Nurs Stud. May 2018;81:49-60. [FREE Full text] [CrossRef] [Medline]
    76. Celis-Morales CA, Perez-Bravo F, Ibañes L, Sanzana R, Hormazabal E, Ulloa N, et al. Insulin resistance in Chileans of European and indigenous descent: evidence for an ethnicity x environment interaction. PLoS One. 2011;6(9):e24690. [FREE Full text] [CrossRef] [Medline]
    77. Bennett DA, Wilson RS, Arvanitakis Z, Boyle PA, de Toledo-Morrell L, Schneider JA. Selected findings from the religious orders study and rush memory and aging project. J Alzheimer Dis. Dec 27, 2012;33(s1):S397-S403. [CrossRef]
    78. Zhu W, Cheng Z, Howard VJ, Judd SE, Blair SN, Sun Y, et al. Is adiposity associated with objectively measured physical activity and sedentary behaviors in older adults? BMC Geriatr. Jul 28, 2020;20(1):257. [FREE Full text] [CrossRef] [Medline]
    79. Martínez-González MA, Buil-Cosiales P, Corella D, Bulló M, Fitó M, Vioque J, et al. PREDIMED-Plus Study Investigators. Cohort Profile: design and methods of the PREDIMED-Plus randomized trial. Int J Epidemiol. Apr 01, 2019;48(2):387-38o. [CrossRef] [Medline]
    80. Elliott J, Shepherd P. Cohort profile: 1970 British Birth Cohort (BCS70). Int J Epidemiol. Aug 2006;35(4):836-843. [CrossRef] [Medline]
    81. Sotunde OF, Kruger HS, Wright HH, Havemann-Nel L, Kruger IM, Wentzel-Viljoen E, et al. Lean mass appears to be more strongly associated with bone health than fat mass in urban Black South African women. J Nutr Health Aging. Jun 2015;19(6):628-636. [CrossRef] [Medline]
    82. de Groot R, Lakerveld J, Brug J, Lagerberg JW, de Korte D, Hoekstra T, et al. Lifestyle behaviours are not associated with haemolysis: results from Donor InSight. Blood Transfus. Mar 2020;18(2):86-95. [FREE Full text] [CrossRef] [Medline]
    83. Gando Y, Murakami H, Kawakami R, Tanaka N, Sanada K, Tabata I, et al. Light-intensity physical activity is associated with insulin resistance in elderly Japanese women independent of moderate-to vigorous-intensity physical activity. J Phys Act Health. Feb 2014;11(2):266-271. [CrossRef] [Medline]
    84. Shook RP, Hand GA, Drenowatz C, Hebert JR, Paluch AE, Blundell JE, et al. Low levels of physical activity are associated with dysregulation of energy intake and fat mass gain over 1 year. Am J Clin Nutr. Dec 2015;102(6):1332-1338. [FREE Full text] [CrossRef] [Medline]
    85. Kovanen V, Aukee P, Kokko K, Finni T, Tarkka IM, Tammelin T, et al. Design and protocol of Estrogenic Regulation of Muscle Apoptosis (ERMA) study with 47 to 55-year-old women's cohort: novel results show menopause-related differences in blood count. Menopause. Sep 2018;25(9):1020-1032. [FREE Full text] [CrossRef] [Medline]
    86. Traczyk I, Respondek W, Szostak-Węgierek D, Raciborski F. Nutritional knowledge and practice preliminary results of the nationally representative study on nutrition of Polish population, (National Health Programme 2016–2020). Proc Nutr Soc. Jun 10, 2020;79(OCE2) [CrossRef]
    87. Kim J, Tanabe K, Yokoyama N, Zempo H, Kuno S. Objectively measured light-intensity lifestyle activity and sedentary time are independently associated with metabolic syndrome: a cross-sectional study of Japanese adults. Int J Behav Nutr Phys Act. Mar 04, 2013;10:30. [FREE Full text] [CrossRef] [Medline]
    88. Do VV, Jancey J, Pham NM, Nguyen CT, Hoang MV, Lee AH. Objectively measured physical activity of vietnamese adults with type 2 diabetes: opportunities to intervene. J Prev Med Public Health. Mar 2019;52(2):101-108. [FREE Full text] [CrossRef] [Medline]
    89. Longitudinal Assessment of Bariatric Surgery (LABS). NIH National Institute of Diabetes and Digestive and Kidney Diseases. 2020. URL: https://repository.niddk.nih.gov/studies/labs/ [accessed 2021-09-16]
    90. Ninomiya T. Japanese legacy cohort studies: the Hisayama study. J Epidemiol. Nov 05, 2018;28(11):444-451. [FREE Full text] [CrossRef] [Medline]
    91. Multi-ethnic study of atherosclerosis. National Sleep Research Resource. URL: https://sleepdata.org/datasets/mesa [accessed 2021-09-16]
    92. Mørkrid K, Jenum AK, Berntsen S, Sletner L, Richardsen KR, Vangen S, et al. Objectively recorded physical activity and the association with gestational diabetes. Scand J Med Sci Sports. Oct 2014;24(5):e389-e397. [CrossRef] [Medline]
    93. Bjerregaard P. Inuit Health in Transition Greenland Survey 2005-2010 Population Sample and Survey Methods 2nd Revised Edition. Copenhagen. National Institute of Public Health; 2011.
    94. Dubbert PM, Carithers T, Ainsworth BE, Taylor Jr HA, Wilson G, Wyatt SB. Physical activity assessment methods in the Jackson Heart Study. Ethn Dis. 2005;15(4 Suppl 6):S6-56. [Medline]
    95. Garg PK, Tian L, Criqui MH, Liu K, Ferrucci L, Guralnik JM, et al. Physical activity during daily life and mortality in patients with peripheral arterial disease. Circulation. Jul 18, 2006;114(3):242-248. [FREE Full text] [CrossRef] [Medline]
    96. Elhakeem A, Hannam K, Deere KC, Hartley A, Clark EM, Moss C, et al. Physical activity producing low, but not medium or higher, vertical impacts is inversely related to BMI in older adults: findings from a multicohort study. J Gerontol A Biol Sci Med Sci. Apr 17, 2018;73(5):643-651. [FREE Full text] [CrossRef] [Medline]
    97. Tanamas SK, Magliano DJ, Lynch B, Sethi P, Willenberg L, Polkinghorne KR, et al. AusDiab 2012: The Australian Diabetes, Obesity and Lifestyle Study. Baker IDI Heart and Diabetes Institute. Melbourne, Australia. Baker IDI Heart and Diabetes institute; 2013. URL: https://www.baker.edu.au/-/media/documents/impact/ausdiab/reports/ausdiab-report-2012.pdf?la=en [accessed 2021-09-16]
    98. Dempsey PC, Strain T, Khaw KT, Wareham NJ, Brage S, Wijndaele K. Prospective associations of accelerometer-measured physical activity and sedentary time with incident cardiovascular disease, cancer, and all-cause mortality. Circulation. Mar 31, 2020;141(13):1113-1115. [FREE Full text] [CrossRef] [Medline]
    99. Hermida RC, Chayán L, Ayala DE, Mojón A, Fontao MJ, Fernández JR. Relationship between metabolic syndrome, circadian treatment time, and blood pressure non-dipping profile in essential hypertension. Chronobiol Int. Jul 2011;28(6):509-519. [CrossRef] [Medline]
    100. Hermida R, Ayala D, Mojon A, Fernandez J. Risk of new-onset diabetes: influence of class and treatment-time regimen of hypertension medications. J Am Soc Hypertension. Apr 2014;8(4):e72. [CrossRef]
    101. Hermida RC, Calvo C, Ayala DE, López JE, Fernández JR, Mojón A, et al. Seasonal variation of fibrinogen in dipper and nondipper hypertensive patients. Circulation. Sep 02, 2003;108(9):1101-1106. [CrossRef] [Medline]
    102. Ahn G, Song J, Lee J, Semanik P, Chang R, Sharma L, et al. Relationship of pase to moderate/vigorous physical activity measured by accelerometry in the osteoarthritis initiative. Osteoarthritis Cartilage. Apr 2012;20:S163. [CrossRef]
    103. Honda T, Chen S, Yonemoto K, Kishimoto H, Chen T, Narazaki K, et al. Sedentary bout durations and metabolic syndrome among working adults: a prospective cohort study. BMC Public Health. Aug 26, 2016;16(1):888. [FREE Full text] [CrossRef] [Medline]
    104. Koolhaas CM, Dhana K, van Rooij FJ, Kocevska D, Hofman A, Franco OH, et al. Sedentary time assessed by actigraphy and mortality: the Rotterdam Study. Prev Med. Feb 2017;95:59-65. [CrossRef] [Medline]
    105. Cooper AR, Sebire S, Montgomery AA, Peters TJ, Sharp DJ, Jackson N, et al. Sedentary time, breaks in sedentary time and metabolic variables in people with newly diagnosed type 2 diabetes. Diabetologia. Mar 2012;55(3):589-599. [CrossRef] [Medline]
    106. Matricciani L, Dumuid D, Paquet C, Fraysse F, Wang Y, Baur LA, et al. Sleep and cardiometabolic health in children and adults: examining sleep as a component of the 24-h day. Sleep Med. Feb 2021;78:63-74. [CrossRef] [Medline]
    107. Wyke S, Bunn C, Andersen E, Silva MN, van Nassau F, McSkimming P, et al. The effect of a programme to improve men's sedentary time and physical activity: the European Fans in Training (EuroFIT) randomised controlled trial. PLoS Med. Feb 2019;16(2):e1002736. [FREE Full text] [CrossRef] [Medline]
    108. Nightingale CM, Limb ES, Ram B, Shankar A, Clary C, Lewis D, et al. The effect of moving to East Village, the former London 2012 Olympic and Paralympic Games Athletes' Village, on physical activity and adiposity (ENABLE London): a cohort study. Lancet Public Health. Aug 2019;4(8):e421-e430. [FREE Full text] [CrossRef] [Medline]
    109. Leary AC, Struthers AD, Donnan PT, MacDonald TM, Murphy MB. The morning surge in blood pressure and heart rate is dependent on levels of physical activity after waking. J Hypertens. May 2002;20(5):865-870. [CrossRef] [Medline]
    110. Malambo P, De Villiers A, Lambert EV, Puoane T, Kengne AP. The relationship between objectively-measured attributes of the built environment and selected cardiovascular risk factors in a South African urban setting. BMC Public Health. Jul 09, 2018;18(1):847. [FREE Full text] [CrossRef] [Medline]
    111. Koivula RW, Atabaki-Pasdar N, Giordano GN, White T, Adamski J, Bell JD, et al. IMI DIRECT Consortium. The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study. Diabetologia. Apr 2020;63(4):744-756. [FREE Full text] [CrossRef] [Medline]
    112. Njølstad I, Mathiesen EB, Schirmer H, Thelle DS. The Tromsø study 1974-2016: 40 years of cardiovascular research. Scand Cardiovasc J. Oct 2016;50(5-6):276-281. [CrossRef] [Medline]
    113. Schrack JA, Leroux A, Fleg JL, Zipunnikov V, Simonsick EM, Studenski SA, et al. Using heart rate and accelerometry to define quantity and intensity of physical activity in older adults. J Gerontol A Biol Sci Med Sci. Apr 17, 2018;73(5):668-675. [FREE Full text] [CrossRef] [Medline]
    114. Arvidsson D, Lindblad U, Sundquist J, Sundquist K, Groop L, Bennet L. Vigorous physical activity may be important for the insulin sensitivity in immigrants from the Middle East and native Swedes. J Phys Act Health. Feb 2015;12(2):273-281. [CrossRef] [Medline]
    115. Metsämarttila E, Rodilla E, Jokelainen J, Herrala S, Leppäluoto J, Keinänen-Kiukaanniemi S, et al. Effect of physical activity on pulse wave velocity in elderly subjects with normal glucose, prediabetes or type 2 diabetes. Sci Rep. May 23, 2018;8(1):8045. [FREE Full text] [CrossRef] [Medline]
    116. Yagi K, Chen T, Chen S, Suwa M, Imoto T, Kida A, et al. Associations of sedentary time, physical activity, and cardiorespiratory fitness with metabolic syndrome in Japanese industrial workers: The Toyota Motor Corporation Physical Activity and Fitness Study. J Physical Fitness Sports Med. 2023;12(1):9-16. [CrossRef]
    117. Phillips CM, Dillon CB, Perry IJ. Does replacing sedentary behaviour with light or moderate to vigorous physical activity modulate inflammatory status in adults? Int J Behav Nutr Phys Act. Oct 11, 2017;14(1):138. [FREE Full text] [CrossRef] [Medline]
    118. Yamagami Y, Saeki K, Tai Y, Maegawa T, Iwamoto J, Kataoka H, et al. Objectively measured physical activity relates to night-time blood pressure in older adults: cross-sectional analysis in the HEIJO-KYO cohort. J Hypertens. Apr 2020;38(4):649-655. [CrossRef] [Medline]
    119. Abstracts of the American Society of Hypertension, Inc. Twenty-Seventh Annual Scientific Meeting and Exposition. May 19-22, 2012. New York, New York, USA. J Clin Hypertens (Greenwich). Apr 2012;14 Suppl 1(Suppl 1):A1-44. [FREE Full text] [CrossRef] [Medline]
    120. Lee YY, Kamarudin KS, Wan Muda WA. Associations between self-reported and objectively measured physical activity and overweight/obesity among adults in Kota Bharu and Penang, Malaysia. BMC Public Health. May 22, 2019;19(1):621. [FREE Full text] [CrossRef] [Medline]
    121. Willis EA, Creasy SA, Saint-Maurice PF, Keadle SK, Pontzer H, Schoeller D, et al. Physical activity and total daily energy expenditure in older US adults: constrained versus additive models. Med Sci Sports Exerc. Jan 01, 2022;54(1):98-105. [FREE Full text] [CrossRef] [Medline]
    122. Martorell M, Mardones L, Petermann-Rocha F, Martinez-Sanguinetti MA, Leiva-Ordoñez AM, Troncoso-Pantoja C, et al. Epidemiology of Lifestyle and Health Outcomes in Chile Consortium. The FTO rs17817449 polymorphism is not associated with sedentary time, physical activity, or cardiorespiratory fitness: findings from the GENADIO cross-sectional study. J Phys Act Health. Sep 27, 2021;18(11):1352-1357. [CrossRef] [Medline]
    123. Ferrari G, Marques A, Barreira TV, Kovalskys I, Gómez G, Rigotti A, et al. On Behalf Of The Elans Study Group. Accelerometer-measured daily step counts and adiposity indicators among Latin American adults: a multi-country study. Int J Environ Res Public Health. Apr 27, 2021;18(9):4641. [FREE Full text] [CrossRef] [Medline]
    124. Berdy AE, Upadhya B, Ponce S, Swett K, Stacey RB, Kaplan R, et al. Associations between physical activity, sedentary behaviour and left ventricular structure and function from the Echocardiographic Study of Latinos (ECHO-SOL). Open Heart. Jul 2021;8(2):1647. [FREE Full text] [CrossRef] [Medline]
    125. Crist K, Benmarhnia T, Zamora S, Yang JA, Sears DD, Natarajan L, et al. Device-measured and self-reported active travel associations with cardiovascular disease risk factors in an ethnically diverse sample of adults. Int J Environ Res Public Health. Apr 08, 2021;18(8):3909. [FREE Full text] [CrossRef] [Medline]
    126. 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]
    127. 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]
    128. Friedenreich CM, Vallance JK, McNeely ML, Culos-Reed SN, Matthews CE, Bell GJ, et al. The Alberta moving beyond breast cancer (AMBER) cohort study: baseline description of the full cohort. Cancer Causes Control. Mar 2022;33(3):441-453. [FREE Full text] [CrossRef] [Medline]
    129. Fernberg U, Fernström M, Hurtig-Wennlöf AH. Higher total physical activity is associated with lower arterial stiffness in Swedish, young adults: the cross-sectional lifestyle, biomarkers, and atherosclerosis study. Vasc Health Risk Manag. Apr 29, 2021;17:175-185. [FREE Full text] [CrossRef] [Medline]
    130. Souza SP, Santos RB, Santos IS, Parise BK, Giatti S, Aielo AN, et al. Obstructive sleep apnea, sleep duration, and associated mediators with carotid intima-media thickness: the ELSA-Brasil Study. Arterioscler Thromb Vasc Biol. Apr 2021;41(4):1549-1557. [FREE Full text] [CrossRef] [Medline]
    131. Barone Gibbs B, Conroy MB, Huber K, Muldoon MF, Perera S, Jakicic JM. Effect of Reducing Sedentary Behavior on Blood Pressure (RESET BP): rationale, design, and methods. Contemp Clin Trials. Jul 2021;106:106428. [FREE Full text] [CrossRef] [Medline]
    132. O'Doherty K, Lawrence D, Wiencrot A, Walsh S, Satorius J, Burgess E, et al. Ongoing refinement and innovation in the data collection protocols of the third round of the national social life, health, and aging project. J Gerontol B Psychol Sci Soc Sci. Dec 17, 2021;76(Suppl 3):S215-S225. [FREE Full text] [CrossRef] [Medline]
    133. Dieberger AM, Desoye G, Stolz E, Hill DJ, Corcoy R, Simmons D, et al. Less sedentary time is associated with a more favourable glucose-insulin axis in obese pregnant women-a secondary analysis of the DALI study. Int J Obes (Lond). Feb 2021;45(2):296-307. [FREE Full text] [CrossRef] [Medline]
    134. Kufe CN, Goedecke JH, Masemola M, Chikowore T, Soboyisi M, Smith A, et al. Physical behaviors and their association with type 2 diabetes mellitus risk markers in urban South African middle-aged adults: an isotemporal substitutionapproach. BMJ Open Diabetes Res Care. Jul 2022;10(4):e002815. [FREE Full text] [CrossRef] [Medline]
    135. Edwardson CL, Biddle SJ, Clemes SA, Davies MJ, Dunstan DW, Eborall H, et al. Effectiveness of an intervention for reducing sitting time and improving health in office workers: three arm cluster randomised controlled trial. BMJ. Aug 17, 2022;378:e069288. [FREE Full text] [CrossRef] [Medline]
    136. Brady EM, Hall AP, Baldry E, Chatterjee S, Daniels LJ, Edwardson C, et al. Rationale and design of a cross-sectional study to investigate and describe the chronotype of patients with type 2 diabetes and the effect on glycaemic control: the CODEC study. BMJ Open. Nov 11, 2019;9(11):e027773. [FREE Full text] [CrossRef] [Medline]
    137. Chen K, He Q, Pan Y, Kumagai S, Chen S, Zhang X. Short video viewing, and not sedentary time, is associated with overweightness/obesity among Chinese women. Nutrients. Mar 21, 2022;14(6):1309. [FREE Full text] [CrossRef] [Medline]
    138. Cook I. Objectively-measured step cadence and walking patterns in a rural African setting: a cross-sectional analysis. BMC Res Notes. May 04, 2022;15(1):155. [FREE Full text] [CrossRef] [Medline]
    139. Jonah T. Global physical activity catalogue. GitHub. 2022. URL: https://github.com/lboro-climb/gpad.cards [accessed 2023-03-13]
    140. National Health and Nutrition Examination Survey NHANES 2005-2006. Centers for Disease Control and Prevention. URL: https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2005 [accessed 2021-09-15]
    141. Stevens ML, Gupta N, Inan Eroglu E, Crowley PJ, Eroglu B, Bauman A, et al. Thigh-worn accelerometry for measuring movement and posture across the 24-hour cycle: a scoping review and expert statement. BMJ Open Sport Exerc Med. Dec 24, 2020;6(1):e000874. [FREE Full text] [CrossRef] [Medline]
    142. Bergeron J, Doiron C, Marcon Y, Ferretti V, Fortier I. Fostering population-based cohort data discovery: the Maelstrom Research cataloguing toolkit. PLoS One. 2018;13(7):e0200926. [FREE Full text] [CrossRef] [Medline]
    143. Mailman MD, Feolo M, Jin Y, Kimura M, Tryka K, Bagoutdinov R, et al. The NCBI dbGaP database of genotypes and phenotypes. Nat Genet. Oct 2007;39(10):1181-1186. [FREE Full text] [CrossRef] [Medline]
    144. 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]
    145. Edwardson CL, Rowlands AV, Bunnewell S, Sanders J, Esliger DW, Gorely T, et al. Accuracy of posture allocation algorithms for thigh- and waist-worn accelerometers. Med Sci Sports Exerc. Jun 2016;48(6):1085-1090. [CrossRef] [Medline]
    146. Montoye AH, Moore RW, Bowles HR, Korycinski R, Pfeiffer KA. Reporting accelerometer methods in physical activity intervention studies: a systematic review and recommendations for authors. Br J Sports Med. Dec 2018;52(23):1507-1516. [CrossRef] [Medline]
    147. Koliaki C, Liatis S, Kokkinos A. Obesity and cardiovascular disease: revisiting an old relationship. Metabolism. Mar 2019;92:98-107. [CrossRef] [Medline]
    148. American Diabetes Association. 9. Cardiovascular disease and risk management. Diabetes Care. Jan 2017;40(Suppl 1):S75-S87. [CrossRef] [Medline]
    149. Gabelica M, Bojčić R, Puljak L. Many researchers were not compliant with their published data sharing statement: a mixed-methods study. J Clin Epidemiol. Oct 2022;150:33-41. [FREE Full text] [CrossRef] [Medline]
    150. Stamatakis E, Koster A, Hamer M, Rangul V, Lee I, Bauman AE, et al. Emerging collaborative research platforms for the next generation of physical activity, sleep and exercise medicine guidelines: the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS). Br J Sports Med. Apr 2020;54(8):435-437. [FREE Full text] [CrossRef] [Medline]

    ‎
    GPAD: Global Physical Activity Data set
    HDL: high-density lipoprotein
    LMICs: low- and middle-income countries
    NHANES: National Health and Nutrition Examination Survey
    PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews

    Edited by A Mavragani; submitted 12.01.23; peer-reviewed by M Walker, A Lee, D Arvidsson; comments to author 25.02.23; revised version received 18.04.23; accepted 08.05.23; published 19.07.23.

    Copyright

    ©Jonah J C Thomas, Amanda J Daley, Dale W Esliger, Victoria E Kettle, April Coombe, Emmanuel Stamatakis, James P Sanders. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.07.2023.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.