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Latest Submissions Open for Peer Review

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JMIR Submissions under Open Peer Review

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Titles/Abstracts of Articles Currently Open for Review:

  • Background: Compared to conventional outpatient or telephone follow-up, the introduction and use of eHealth technologies provide novel opportunities for enhancing medication adherence in transplant recipients. Nonetheless, the efficacy of eHealth treatments regarding medication adherence in kidney transplant recipients remains ambiguous. Objective: To assess the impact of eHealth interventions on medication adherence in kidney transplant recipients and to understand the underlying factors. Methods: Seven databases (PubMed, Web of Science, Cochrane, Embase, CINAHL, SCOPUS,and OVID) were thoroughly checked from the beginning till November 2024. Each study was assessed for bias using the Cochrane Risk of Bias tool (RoB 2), and the certainty of the evidence for each outcome of interest was rated using the GRADE criteria.The study outcomes were evaluated using a narrative synthesis and a meta-analysis. Results: A total of 12 studies involving 897 kidney transplant recipients were included. The eHealth intervention improved medication adherence monitored by electronic devices compared with the control group [RR=1.46, 95% CI (1.11, 1.90)]. However, the differences in adherence to medication as assessed by the Basel Immunosuppressive Medication Adherence Scale [RR=0.98, 95% CI (0.85, 1.13)], tacrolimus blood concentration [MD=0.15, 95% CI (-0.21, 0.51)], and intra-patient variability of tacrolimus [MD=-0.02, 95% CI (-0.07, 0.03)] were not statistically significant. The overall risk of bias was very high or some concerns, and the evidence for all outcomes was of low quality. Conclusions: The true effectiveness of eHealth interventions is affected by a variety of confounding factors, and more high-quality future studies are still needed to optimise eHealth intervention strategies and clarify their effectiveness in improving medication adherence. Clinical Trial: PROSPERO CRD42025640638; https://www.crd.york.ac.uk/PROSPERO/myprospero

  • Background: With the widespread application of machine learning (ML) in the diagnosis and treatment of colorectal cancer (CRC), some studies have investigated the utilization of ML techniques for the detection of KRAS mutation. Nevertheless, there is scarce evidence from evidence-based medicine to substantiate its efficacy. Objective: Our study was carried out to systematically review the performance of ML models developed using different modeling approaches, in detecting KRAS mutations in CRC. Our findings aim to offer evidence-based foundations for the development and enhancement of future intelligent diagnostic tools. Methods: PubMed, Cochrane Library, Embase, and Web of Science were systematically retrieved, with the search cutoff date set to December 22, 2024. The risk of bias in the encompassed models was evaluated via the Prediction Model Risk of Bias Assessment Tool (PROBAST). In our data analysis, subgroup analyses were undertaken based on the type of modeling variables incorporated into the models, including clinical characteristics, imaging features, and pathological features. Results: 43 studies involving 10,888 patients, were included, of which 12 studies (comprising 3,013 patients) focused specifically on KRAS mutations in rectal cancer (RC). The modeling variables were derived from clinical characteristics (n=6), CT (n=15), MRI (n=15), PET/CT (n=4), and pathological histology (n=7). In the validation cohorts, meta-analysis results demonstrated that the ML models developed based on clinical characteristics, CT, MRI, PET/CT, and pathological histology exhibited c-index values of 0.65 (95% CI: 0.60–0.70), 0.87 (95% CI: 0.84–0.90), 0.77 (95% CI: 0.71–0.83), 0.84 (95% CI: 0.77–0.90), and 0.94 (95% CI: 0.92–0.96). Additionally, deep learning (DL) techniques were found to demonstrate superior predictive performance. Particularly, DL models based on pathological images and MRI achieved pooled c-index values of 0.96 (95% CI: 0.94–0.98) and 0.93 (95% CI: 0.90–0.96). Conclusions: ML is highly accurate in detecting KRAS mutations in CRC, and DL models based on MRI and pathological images exhibit particularly strong predictive performance. Future research should focus on increasing sample sizes, improving model architectures, and developing more advanced DL models to facilitate the creation of highly efficient intelligent diagnostic tools for KRAS mutation detection in CRC. Clinical Trial: N/A

  • Background: Acute decompensated heart failure (ADHF) decompensation is closely associated with pulmonary congestion (PC), which triggers abnormal breathing patterns. Early detection of PC-driven respiratory changes via wearable devices could enable timely intervention and reduce hospitalizations. However, specific respiratory features linked to PC remain unclear. Objective: This study employed a wearable device to analyze nocturnal respiratory signals in hospitalized ADHF patients, comparing those with and without PC. Methods: This prospective trial investigated breathing pattern characteristics in ADHF patients hospitalized. PC was assessed via lung ultrasound (LUS) in 28 standardized zones at admission, with patients stratified by LUS-defined severityusing >5 B-lines as the threshold for significant PC. Concurrently, wearable devices were deployed to continuously capture chest and abdominal movement signals for respiratory waveform analysis. Breathing patterns were quantitatively characterized through three dimensions: respiratory cycle, respiratory amplitude and multiscale entropy (MSE). Logistic regression analysis and the receiver operating characteristic (ROC) curve were used to identify risk factors associated with PC and evaluate the ability of respiratory pattern parameters to identify HF combined with PC. Results: A total of 62 patients with ADHF were included in the study, 44 of whom had more than 5 B-lines. PC patients exhibited longer mean expiratory time (TE_mean), smaller mean ratio of expiratory time (TE_ratio_mean), and greater MSE values in respiratory amplitude (RA). RA_area_1_5 and RA_area_6_20 were identified as risk factors for PC after adjusting for clinical variables. The established logistic regression model could accurately distinguish whether HF patients complicated with PC, the AUC of the multivariable model constructed using respiratory complexity parameters was 0.910(95% CI: 0.837～0.984, P<.001). Conclusions: The study highlights the potential of wearable devices combined with MSE algorithms for monitoring of ADHF patients' respiratory complexity. The identified respiratory complexity parameters, particularly RA_area_1_5 and RA_area_6_20, could serve as an early warning tool for PC exacerbation.

  • Background: The heart and kidneys have vital functions in the human body that reciprocally influence each physiologically and pathological changes in one organ can damage the other. Epidemiologic studies show that greater than 50% of patients with heart failure (HF) have preserved ejection fraction (HFpEF). Additionally, one in six patients identified as having chronic kidney disease (CKD) also has HF. Thus, it is important to be able to predict and identify the cardiorenal relationship between HFpEF and CKD. Objective: Creating an ECG-enabled model that stratifies HFpEF suspected patients would help identify CKD enriched HFpEF clusters and phenogroups. Simultaneously, a minimal set of significant ECG features derived from the stratification model may aid precision medicine and practical diagnoses due to being more accessible and widely readable than a large set of clinical inputs. Methods: Using unsupervised clustering on all extractable ECG features from FinnGen, patients with an indication of HFpEF (filtered by LVEF ≥ 50% and NT-proBNP > 450 pg/mL) were categorized into different phenogroups and analyzed for CKD risk. After isolating significant predictive ECG features, unsupervised clustering and risk analysis were performed again to demonstrate the efficacy of using a minimal set of features for phenogrouping. These clusters were then compared to clusters formed using Dynamic Time Warping (DTW) on raw ECG time series electrical signals. Afterwards, these clusters were analyzed for CKD enrichment. Results: Several HFpEF clusters exhibited a deviation of CKD risk from baseline which may allow for further trajectory analysis. The DTW generated clusters were more stable than either sets of clusters formed on the minimal set of extracted ECG features or all extracted ECG features. PR interval and QRS duration stood out as significant features. Conclusions: This project validates both the known cardiorenal relationship between HFpEF and CKD and the importance of the PR interval and QRS duration. DTW clustering may be capable of phenogrouping and patient stratification for CKD enrichment in HFpEF patients.

  • Background: With the essential role of information technology in human life, the use of electronic devices creates a digital divide, particularly among elderly individuals. However, the long-term impact on cognitive aging and brain structural changes remains unclear. Objective: This study employs large-scale neuroimaging data to examine how the digital divide affects long-term brain structure and cognitive aging in older adults. It specifically investigates: (1) structural and cognitive differences between older adults with and without digital divide engagement; (2) predictive relationships between group-distinctive brain regions and cognitive outcomes; (3) longitudinal impacts of digital divide exposure on accelerated aging trajectories of neural substrates and cognitive functions. Methods: We examined the role of digital divide in protecting cognition and brain structure in a sample of 1280 elderly participants. The longitudinal data involved 689 individuals. Propensity Score Matching (PSM) was used to match individuals from the Overcoming Digital Divide (ODD) and Digital Divide (DD) groups. A computational framework employing the searchlight technique and cross-validation classification model investigated group differences in structural features and cognitive representation. The aging rate of each voxel's structural feature was calculated to explore the long-term influence of the digital divide. Results: Following PSM analysis, each group comprised 640 participants. Executive function and processing speed were most affected by DD. Group differences in structural substrates were observed in the fusiform gyrus, hippocampus, parahippocampal gyrus, and superior temporal sulcus. The computational framework identified the key structural substrates related to executive functions and processing speed, excluding the ventro-orbitofrontal lobe. Longitudinal findings highlighted the long-term impact of the digital divide, particularly on the aging rate of the middle frontal gyrus, and its correlation with changes in episodic memory. Conclusions: The study demonstrated that individuals overcome digital divide exhibit gray matter preservation for intact cognitive performance. Cognitive decline prevention approach though mobile digital devices could be explored further.

  • A randomized controlled trial: Effects of outdoor exercise on blood glucose and sleep in T2DM with wearable devices in a mHealth model

    Date Submitted: Mar 3, 2025
    Open Peer Review Period: Mar 4, 2025 - Apr 29, 2025

    Background: Exercise is considered an important component of lifestyle management for T2DM. Outdoor exercise has been shown to enhance individuals' perception of their overall health status. Objective: This study aimed to explore the role of outdoor exercise on blood glucose management and sleep quality in patients with T2DM. Methods: The study was a randomized controlled trial in which participants were randomized (1:1) to indoor or outdoor training group for 12 weeks. The outdoor Tai Chi group classes are conducted in an open and flat park surrounded mostly by trees. As for the indoor environment, it is a dance studio with a constant temperature maintained at 22.2°C. Visual contact with the outdoors is possible through windows facing the road. The enrolled population performed aerobic 24-style Tai Chi exercise three times a week, lasting 60–90 minutes with 15 minutes of warm-up and cool-down included. During the 12-week long program study, all participants had sensors from the Guardian Sensors 3 continuous glucose monitoring (CGM) system implanted subcutaneously in their upper arms and paired with the Medtronic Guardian Connect CGM device. The sleep quality and quantity were assessed before and after the training program using the Pittsburgh Sleep Quality Index (PSQI) scale and sleep monitoring bracelet.Each patient also simultaneously wore a sunshine duration monitoring bracelet, through which daily sunshine duration was counted. The primary outcome was the absolute change in HbA1c levels within and between the two groups at 12 weeks. The secondary outcomes were changes in BMI, waist circumference, the time in range（TIR） measured by CGM， plasma glucose levels, daily sunshine duration， total sleep time (minutes slept between bedtime and wake time), sleep efficiency (percentage of time asleep while in bed), and wake after sleep onset (minutes awake between sleep onset and wake time). Results: When comparing the primary outcomes at 12 weeks, we found a significant difference in the outdoor and indoor Tai Chi groups in HbA1c. The interaction effect was significant (p = 0.012). The outdoor Tai Chi group showed significant improvement in PSQI, Total sleep time, and Sleep efficiency, and the indoor Tai Chi group showed insignificant changes. Still, the time effect and group effect were significant. Conclusions: In summary, this study demonstrated that outdoor Tai Chi significantly improves waist circumference, BMI, blood glucose levels, sleep quality and sun exposure. In contrast, indoor Tai Chi was relatively weak but also provided some health benefits.

  • Background: Recruiting and retaining young adult participants in digitally delivered healthy eating interventions can be challenging. Understanding how to support researchers to overcome methodological challenges with recruitment and retention, particularly within rural settings, will inform strategies for future digital interventions. Objective: This manuscript describes the lessons learned from recruitment and retention of rural-dwelling young adults (18–35 years) into a randomized controlled trial that aimed to assess the feasibility of a digital healthy eating intervention (Veg4Me). Methods: Digital registration was initially set up as a one-step process without researcher involvement. Recruitment materials referenced a AUD75 compensation upon completion of the 12-week study. Participant registrations and recruitment rates were monitored daily using predetermined online preventative measures to identify fraudulent responses and to amend the digital registration process where necessary. Retention rates were monitored daily to identify any necessary amendments to the follow-up protocol. Results: During data collection, n=279 fraudulent responses were identified from n=536 total responses (52%). One month into recruitment (September 2023), amendments were made to the registration process to reduce fraudulent responses. To address the first two bot attacks, Qualtrics passwords were added; in response to the third bot attack, the final solution was a two-factor authentication process added to the Veg4Me landing page. Four months into recruitment (December 2023), minor changes were made to the baseline survey to collect information on the most effective recruitment strategy. Targeted recruitment strategies, such as unpaid social media posts by the research team in local community groups and media releases corresponded to peaks in the recruitment rate. In the final month of recruitment (February 2024), a question was embedded within follow-up correspondence to participants to encourage completion of the post-intervention survey and to understand reasons for attrition. This resulted in an additional n=8 (7%) participants completing the intervention. Conclusions: Studies recruiting young adults that employ digital recruitment protocols without direct researcher involvement should consider multiple in-built strategies for identifying and preventing fraudulent responses, including a two-factor authentication process and minimizing the over-promotion of financial incentives in recruitment strategies. In rural settings, recruitment strategies should consider the use of social media posts in local community groups, while the use of reminders and notifications could support retention. Clinical Trial: Australia New Zealand Clinical Trials Registry, ACTRN12623000179639, prospectively registered on 21/02/2023, according to the World Health Organizational Trial Registration Data Set. Universal Trial Number U1111-1284-9027.

  • Effects of Haodf Mobile App Based Intervention on Relieving Patients With Arrhythmia Depression and Anxiety: a Cross-sectional Study

    Date Submitted: Mar 3, 2025
    Open Peer Review Period: Mar 2, 2025 - Apr 27, 2025

    Background: The proportion of patients with anxiety and depression in the cardiology outpatient clinic is relatively high, and patients who consult cardiology through an internet medical platform also have a higher risk of mental illness, but there are fewer related studies. Objective: To explore the risk factors related to anxiety and depression in patients consulting a cardiologist through an internet medical platform, and to evaluate the online patient education. Methods: The basic information were collected. The self-rating scales were used. The risk factors were analyzed by multivariate regression analysis. Those online patients were invited to read science popularization articles and to watch video live broadcasts about cardiovascular diseases. The differences in anxiety and depression scores were compared using Mann-Whitney U test between the patients. Results: A total of 10,817 patients were sent PHQ-9 and GAD-7 self-assessment scales and 160 cases were finally included in the analysis. 613 offline patients were included in the analysis. Online consultation, female and being younger were independent risk factors for anxiety and depression (P<0.001), and arrhythmia was the only cardiovascular disease independently associated with depression (P<0.001). Online consultation and being female were independent risk factors for depression in those with arrhythmia (P<0.001). There was no significant difference in the scores of anxiety and depression between those who received popular science education and those who did not (P>0.05). Conclusions: Cardiology patients who consult through Internet medical platforms are more likely to have anxiety and depression. People with arrhythmia are more likely to have depression. The utilization efficiency of network science popularization is low, and it has not played a role in improving anxiety and depression.

  • Digital innovations in Assessment of Acquired Brain Injury: A Scoping Review.

    Date Submitted: Mar 2, 2025
    Open Peer Review Period: Mar 2, 2025 - Apr 27, 2025

    Background: Acquired Brain Injury (ABI) is a leading global cause of morbidity; affecting millions who often suffer from a diverse range of complications and limited access to appropriate care. Advances in digital technology offer promising opportunities for more effective and accessible assessments; however, there is limited comprehensive research on the scope and utilization of these innovations. Objective: This scoping review aimed to identify and synthesize contemporary research on digital technologies to aid screening or assessment of ABI complications, in order to uncover trends, themes and priorities for future research. Methods: Using the Arksey and O’Malley framework, a systematic search was conducted across Embase, MEDLINE, and Scopus, with additional searches in four trial registries to capture grey literature. A search string incorporating terms related to “ABI,” “clinical assessment,” and “digital tools” was developed a priory. Studies from 2013 to 2024 leveraging digital technologies for ABI complication assessment were included. Exclusion criteria comprised studies involving bespoke hardware, non-human subjects or review articles. Data synthesis and domain mapping were performed. Results: From 5,293 studies extracted, 88 met inclusion criteria: 2 retrospective studies, 4 qualitative studies, 35 cohort studies, 42 cross-sectional studies, and 5 randomized controlled trials. The median sample included 26 participants with ABI, 51 studies also involved non-ABI participants (median of 10 participants included). Most studies (n=70) focused solely on TBI cases, with 36 exclusively on mild TBI or concussion; 16 included mixed ABI etiologies. Digital platforms varied, with 45 studies using smartphone or tablet technologies, 23 PC or web-based platforms, 11 telemedicine solutions, and 9 virtual reality (VR) platforms. The predominant research themes included: the use of digital technology to aid in screening for TBI, identifying symptoms or functional outcomes; the assessment of cognition and communication; as well as comprehensive consultation. Most tools were well-tolerated, with accuracy often described as comparable to standard assessments. However, the majority of studies had smaller sample sizes, lacked long term outcomes, were limited in the diversity of patients included, and there were few studies assessing digital tools for comprehensive evaluation. Conclusions: This investigation provides clinicians and researchers with an extensive overview of current research trends, and highlights the need for larger, more rigorous studies to optimize the use of digital technologies in ABI assessment. Current studies are often small-scale, designed as pilot or feasibility trials, and show variability in their focus, leaving gaps in the assessment of common complications such as pain, seizures, or participation restrictions. Expanding research into underexplored ABI complications, broadening the scope of assessments and including diverse populations will be critical for advancing the field and improving outcomes for individuals with ABI. Clinical Trial: NA

  • Digital mental health interventions for young people aged 16-25: scoping review

    Date Submitted: Feb 20, 2025
    Open Peer Review Period: Feb 28, 2025 - Apr 28, 2025

    Background: Digital mental health interventions for young people offer a promising avenue for promoting mental wellbeing and addressing mental health issues in this population. Objective: This study aims to explore the range of digital mental health supports available for young people aged 16 - 25 years, with a particular focus on the types of digital tools, modalities, delivery format, target population, and study retention rates. Methods: The scoping review was conducted in six databases (PubMed, Web of Science, Scopus, Medline, Cochrane Library and PsychInfo), and a total of 145 articles were included. The findings reveal a diverse landscape of studies globally, equally focusing on prevention and promotion of mental health as well as treatment of mental ill health, most commonly using cognitive behavioural therapy, with apps, web-based resources and websites being the most common digital tools. Results: The results highlight the over emphasis on convenience sampling with participants mainly recruited from universities or colleges, and the lack of representation from marginalised groups, including LGBTQ+ youth, those from socioeconomically deprived backgrounds, and neurodivergent individuals. Moreover, the focus on anxiety and depression leaves other mental health conditions underrepresented. Retention rates were moderate, indicating room for improvement. There is a need for more research on preventative measures for those aged under 25 years when young people are at increased risk of mental health issues. This includes exploring different intervention approaches and modalities beyond cognitive behaviour therapy and ensuring inclusivity in study populations. Standardising intervention lengths and incorporating long-term follow-up data could provide valuable insights into the efficacy and effectiveness of digital interventions. Conclusions: Future studies should aim for greater inclusivity, ensuring representation from marginalised groups to address the diverse mental health needs of young people effectively. By adopting these approaches, digital mental health interventions can become more accessible, engaging, and impactful for young people worldwide.

  • Large Language Model-Assisted Surgical Consent Forms in a Non-English Language: A Multicenter Study on Readability and Content Integrity

    Date Submitted: Feb 27, 2025
    Open Peer Review Period: Feb 28, 2025 - Apr 25, 2025

    Background: Surgical consent forms must convey critical information, yet their complex language can limit patient comprehension. Large language models (LLMs) may improve readability, but evidence of their impact on content preservation is lacking in non-English contexts. Objective: This study evaluates the impact of LLM-assisted editing on the readability and content quality of surgical consent forms in Korean, focusing on standardized liver resection consent documents across multiple institutions. Methods: Standardized liver resection consent forms were collected from seven South Korean medical institutions and simplified using ChatGPT-4o. Readability was assessed using KReaD and Natmal indices, while text structure was evaluated based on character count, word count, sentence count, words per sentence, and difficult word ratio. Content quality was analyzed across four domains—Risk, Benefit, Alternative, and Overall Impression—using evaluations from seven liver resection specialists. Statistical comparisons were conducted using paired t-tests, and a linear mixed-effects model (LME) was applied to account for institutional and evaluator variability. Results: AI-assisted editing significantly improved readability, reducing the KReaD score from 1777 ± 28.47 to 1335.6 ± 59.95 (p<0.001) and the Natmal score from 1452.3 ± 88.67 to 1245.3 ± 96.96 (p=0.007). Sentence length and difficult word ratio decreased significantly, contributing to increased accessibility. However, content quality analysis showed a decline in risk description scores (2.29 ± 0.47 before vs. 1.92 ± 0.32 after, p=0.0549) and overall impression scores (2.21 ± 0.49 before vs. 1.71 ± 0.64 after, p=0.134). The LME confirmed significant reductions in risk descriptions (β₁ = -0.371, p=0.012) and overall impression (β₁ = -0.500, p=0.025), suggesting potential omissions in critical safety information. Despite this, qualitative analysis indicated that evaluators did not find explicit omissions but perceived the text as overly simplified and less professional. Conclusions: While LLM-assisted surgical consent forms significantly enhance readability, they may compromise certain aspects of content completeness, particularly in risk disclosure. These findings highlight the need for a balanced approach that maintains accessibility while ensuring medical and legal accuracy. Future research should include patient-centered evaluations to assess comprehension and informed decision-making, as well as broader multilingual validation to determine LLM applicability across diverse healthcare settings. Clinical Trial: N/A

  • Background: Identifying Human Phenotype Ontology (HPO) terms is crucial for diagnosing and managing rare diseases. However, clinicians, especially junior physicians, often face challenges due to the complexity of describing patient phenotypes accurately. Traditional manual search methods using HPO databases are time-consuming and prone to errors. Objective: To investigate whether the use of multimodal large language models (MLLMs) can improve the accuracy of junior physicians in identifying HPO terms from patient images related to rare diseases. Methods: Twenty junior physicians from 10 specialties participated. Each physician evaluated 27 patient images sourced from publicly available literature, with phenotypes relevant to rare diseases listed in the Chinese Rare Disease Catalogue. The study was divided into two groups: the manual search group relied on the Chinese Human Phenotype Ontology (CHPO) website, while the MLLM-assisted group used an electronic questionnaire that included HPO terms pre-identified by ChatGPT-4o as prompts, followed by a search using the CHPO. The primary outcome was the accuracy of HPO identification, defined as the proportion of correctly identified HPO terms compared to a standard set determined by an expert panel. Results: A total of 270 descriptions were evaluated per group. The MLLM-assisted group achieved a significantly higher accuracy rate of 67.41% compared to 20.37% in the manual group (RR = 3.31, 95% CI: 2.58–4.25, P < .001). The MLLM-assisted group demonstrated consistent performance across departments, whereas the manual group exhibited greater variability. Among standalone MLLMs, ChatGPT-4o achieved an accuracy of 84.15%, while the open-source models Llama3.2:11b and Llama3.2:90b achieved 14.81% and 18.52%, respectively. However, MLLMs exhibited a high hallucination rate, frequently generating HPO terms with incorrect IDs or entirely fabricated content. Conclusions: The integration of MLLMs into clinical workflows significantly enhances the accuracy of HPO identification by junior physicians, offering promising potential to improve the diagnosis of rare diseases and standardize phenotype descriptions in medical research. However, the notable hallucination rate observed in MLLMs underscores the necessity for further refinement and rigorous validation before widespread adoption in clinical practice.

  • Advancing Signaling Theory in Online Health Communities: Navigating Medical Asymmetry with a Holistic Approach

    Date Submitted: Feb 27, 2025
    Open Peer Review Period: Feb 27, 2025 - Apr 24, 2025

    Background: In online health communities, signaling theory has been widely applied to address information asymmetry and reduce uncertainty. Specifically, various signals are evaluated to convey the quality of healthcare services and influence patients' decision-making. However, the literature on signals in online health communities faces challenges, including arbitrary and fragmented classifications of signals and the lack of a common framework. Objective: To establish a common foundation for understanding the role of signals in online health communities, this study aims to provide a comprehensive framework for the signals conveyed in these communities and their influence on managing information asymmetry between physicians and patients. Methods: A systematic literature review using Narrative Analysis was conducted, summarizing 80 articles on signals in online health communities. The review aimed to classify, clarify, and explore the nature of these signals, their relationships, and the underlying mechanisms in the context of OHCs. Results: Among the 80 studies analyzed, 96.3% focused on the effects of one or more signals. However, only 2.5% examined the characteristics of signalers or their moderating effects, such as age, gender, and competence. Additionally, 31.3% explored signal interactions, including comparisons between online and offline signals and bundled services, while 30% investigated how environmental factors, such as uncertainty and consistency, affect signal transmission. Most studies (75%) concentrated on informative signals, with a notable increase in research on affective signals. Lastly, research on the interaction between affective signals and the environment remains limited. Conclusions: This framework provides a more comprehensive understanding of how signals in online health communities manage information asymmetry. It clarifies the construct of signals, explores their relationships, and outlines their mechanisms. Additionally, the study identifies gaps in the existing literature and offers recommendations for future research directions to enhance the role of online health communities in addressing medical asymmetry.

  • Background: Social media has become a vital source of cancer-related health information, offering patients, caregivers, and the public a platform for sharing knowledge and experiences. However, concerns regarding the quality, accuracy, and potential misinformation of cancer information on social media persist. Objective: This study systematically reviewed literature published between 2014 and 2023 evaluating the quality of cancer-related information on social media. It aimed to identify common characteristics of these studies, assess patterns in information quality across platforms and cancer types, and explore factors associated with study outcomes. Methods: This systematic review searched PubMed, Web of Science, Scopus, and Medline. Studies were included if they analyzed cancer-related social media content and assessed information quality using standardized tools (e.g., the DISCERN tool). Extracted data included study characteristics, social media platform, cancer types, and quality assessment methods. Meta-analysis and ordinal logistic regression analysis were performed to pool findings from multiple studies. Results: A total of 75 studies were included, covering various a range of social media platforms, such as YouTube, TikTok, Facebook, Twitter, and Reddit. Findings indicated that video-based platforms, particularly YouTube and TikTok, were the most studied but also contained misinformation. Overall, 27% of social media cancer-related content included misinformation, with common false claims regarding alternative treatments and unproven therapies. Studies assessing rare cancers reported lower information quality compared to those focusing on common cancers. Additionally, content from medical professionals was of higher quality but less engaging than user-generated content. Conclusions: While social media serves as an essential platform for cancer-related health information, concerns remain about misinformation, completeness, and actionability. Future research should prioritize improving information accuracy, leveraging AI for content verification, and promoting authoritative sources to enhance public health outcomes.

  • Background: Patients with prediabetes can easily progress to diabetes. Objective: We aimed to develop a 5-year risk prediction model of progression from prediabetes to diabetes for the Chinese population. Methods: A retrospective cohort study was conducted on 2 prediabetes cohorts, who were tracked from 2019 to 2024. Patients were split into the training (70%) and test (30%) sets randomly in the primary cohort. Significant predictors were selected on the training set, followed by the application of 7 machine learning algorithms, namely logistic regression, random forest, support vector machine, multilayer perceptron, XGBoost, LightGBM, and CatBoost, to develop prediction models. Model performance was assessed using ROC, the precision-recall curves as well as multiple other metrics on both of the test set and the external test set. Results: The average annual conversion rate from prediabetes to diabetes was 8.34% and 7.04% in the primary cohort and the external cohort, respectively. Utilizing 14 features, the CatBoost model excelled in the test set and the external test set with an AUC of 0.819 and 0.807, respectively. It also had the highest discrimination performance across several other metrics, and presented outstanding calibration performances. Conclusions: We developed a 5-year risk prediction model of progression from prediabetes to diabetes for the Chinese population, with the CatBoost model showing the best predictive performance, could effectively identify individuals at high risk of diabetes.

  • Exploring the value of continuous plantar temperature monitoring for diabetic foot health management

    Date Submitted: Feb 27, 2025
    Open Peer Review Period: Feb 26, 2025 - Apr 23, 2025

    Background: Diabetic foot ulcers (DFUs) are a life-changing complication of diabetes. There is increasing evidence that plantar temperature monitoring can reduce the incidence and recurrence of DFUs. Once daily foot temperature monitoring is the current guideline for identifying early signs of foot inflammation and DFUs. However, single readings of physiological signals are known to increase the risk of misdiagnosis when there are fluctuations of the signal throughout the day. Objective: The purpose of this study was to evaluate whether intra-day temperature asymmetry signals were stable or varied as a function of time in individuals at risk of DFU. Methods: Sixty-four participants with diabetes (mean age = 68 ± 13.8 years) were provided with multi-modal sensory insoles (Orpyx® Sensory Insoles) to monitor continuous temperature data at five plantar locations with a frequency of once/minute during a 90-day study window. 1,080 data days, 5400 contralateral temperature asymmetry signals, were included. The Augmented Dickey-Fuller test was used to determine whether the temperature asymmetry signals were stationary (stable) or non-stationary (time-varying). Results: The majority (82%) of temperature asymmetry signals were time-varying, with intra-day fluctuations potentially driven by physiological and environmental factors. Nearly half (44%) of time-varying signals included a mix of concerning (>2.2°C) and non-concerning (≤2.2°C) measurements, indicating the limitations of single measurements in reliably identifying DFU risk. Statistical analysis revealed significant variability in stable versus time-varying patterns both within and across participants. Notably, days with time-varying signals and concerning asymmetry measurements showed dispersion of concerning periods across participants, time windows and days, rather than consistent daily patterns. Conclusions: Continuous monitoring could provide deeper insights into plantar temperature dynamics, uncovering associations with individual-specific factors such as vascular status, historical ulcer locations, activity, gait, and foot anatomy. These findings support the need for personalized monitoring protocols and leveraging continuous data to better inform clinical decision-making.

  • Background: Stroke inevitably results in a range of disabilities. Both virtual reality (VR) and mirror therapy (MT) have shown efficacy in stroke rehabilitation. In recent years, the combination of these two approaches has emerged as a potential treatment for stroke patients. Objective: This systematic review and meta-analysis aim to assess the efficacy of combination treatment of VR and MT in stroke rehabilitation. Methods: Five electronic databases were systematically searched for relevant articles published up to Jan. 2025. Randomized controlled trials (RCTs) that investigated combination treatment of VR and MT for participants with stroke were included. The risk of bias and the certainty of the evidence were assessed using the Cochrane collaboration’s tool and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) guideline, respectively. Results: A total of 293 participants across 10 RCTs were included, with 6 RCTs contributing to the meta-analysis. The statistical analysis indicated significant improvements in the Fugl-Meyer Assessment of Upper Extremity (FMA-UE) (MD 3.49, 95% CI 1.43 to 5.55; P=0.0009) and manual function test (MD 2.64, 95% CI 1.78 to 3.49; P<0.00001), and box and block test (MD 1.02, 95% CI 0.16 to 1.88; P=0.02). Subgroup differences were observed in FMA-UE, manual function test and box and block test. Conclusions: Moderate-quality evidence supports the combination treatment of VR and MT as a beneficial nonpharmacological approach to improve upper extremity motor function and hand dexterity in patients with stroke. However, the limited number of studies and small sample sizes restrict the generalizability of these findings, highlighting the need for further research. Clinical Trial: PROSPERO CRD42024572150

  • Background: Although the integration of self-monitored patient data into mental health care offers potential for advancing personalised approaches, its application in clinical practice remains largely underexplored. Capturing individuals' mental health outside the therapy room using Experience Sampling Methods (ESM) may bridge this gap by supporting shared decision-making and personalised interventions. Objective: This qualitative study investigated the perspectives of German mental health professionals regarding prototypes of ESM data visualisations designed for integration into a digital mental health tool. Methods: Semi-structured interviews were conducted with clinicians on their perceptions of such visualisations in routine care. Results: Using reflexive thematic analysis, three key findings were: (1) visualisations were seen as valuable tools for enhancing patient motivation and engagement; (2) simplicity and clarity of visual formats were crucial for usability; and (3) practical concerns, such as integration into clinical workflows, influenced perceived utility. Challenges, including the risk of cognitive overload, were also highlighted. Conclusions: These findings underline the importance of designing digital tools that align with clinical needs while addressing potential barriers to implementation by exploring the opportunities and challenges associated with ESM visualisations.

  • Background: Cancer survivors often experience declining engagement in digital health interventions (DHIs). However, predictors of engagement with provider-guided DHIs remain unclear. Nurse WRITE, an effective 8-week nurse-directed symptom management DHI, offers an opportunity to identify factors influencing engagement and enhance intervention efficacy evaluation. Objective: This study aims to (1) understand engagement phenomena (dimensions, influencing factors, and challenges) and (2) assess the relationship between engagement and patient symptom control in Nurse WRITE. Methods: The study included 68 women with recurrent ovarian cancer randomized to the Nurse WRITE arm of a 3-arm symptom management trial. We analyzed socio-affective and cognitive engagement through message board data and behavioral engagement using website usage data. Regression analyses examined patient characteristics, engagement, and symptom control perceptions. Through content analysis, we explored participant challenges and activities before disengagement. Results: Regarding influencing factors, higher education was associated with a 22% increased likelihood of engagement (p = 0.04). Education positively influenced cognitive and socio-affective engagement, including total count of cognitive classes (p = 0.01) and total word count (p = 0.03), with marginal associations for socio-affective classes (p = 0.07). Comorbidities tended to reduce both socio-affective (p = 0.06) and cognitive class counts (p = 0.07). Regarding behavioral engagement, education increased the odds of completing an extra symptom care plan by 23% (p = 0.02) and a plan review by 29% (p = 0.02). We observed a trend that higher symptom severity increased the odds of completing an additional plan review by 21% (p = 0.09). The most common engagement challenges included worsening health and treatment, busy family life, and website difficulties. Moderate- and low-engagers also experienced confusion about the intervention timeline and process. Among low engagers, 63% discontinued communication at specific intervention phases: introduction (33%), symptom representational assessment (21%), and goal setting and planning (21%). At the end of the intervention, improved symptom control was associated with higher overall engagement (p = 0.02), a higher frequency of cognitive engagement classes (p = 0.02), average question completion percentage (p = 0.01), a higher frequency of socio-affective classes (p = 0.01) and total word count (p = 0.01), as well as more completed symptom care plans (p = 0.04). Conclusions: Participant education level significantly influenced Nurse WRITE engagement across socio-affective, cognitive, and behavioral dimensions. Comorbidity and symptom severity warrant further investigation. Future provider-guided DHIs should employ additional strategies to engage less well-educated participants, address challenges like health issues and family activities, and re-engage participants during critical phases. Our findings underscore the importance of meaningful engagement through socio-affective, cognitive, and behavioral dimensions in Nurse WRITE, informing future DHIs to aim for a balance of protocol adherence and flexibility to enhance engagement and improve outcomes.

  • Virtual Reality for analgesia during intrauterine device insertion: A randomised controlled trial

    Date Submitted: Feb 23, 2025
    Open Peer Review Period: Feb 24, 2025 - Apr 21, 2025

    Background: Intrauterine devices (IUD) are safe and effective contraceptive therapies which are also used for treatment for heavy menstrual bleeding, endometrial hyperplasia and early-stage endometrial cancer. Barriers to insertion of IUDs in the outpatient setting are predominantly due to patient discomfort and there is little consensus on effective analgesic strategies to address this. Virtual reality (VR) has demonstrated moderate benefits in acute pain management and has been explored for similar gynaecological procedures including outpatient hysteroscopy with some promising results. Objective: To explore the effectiveness of VR at improving patient pain and anxiety during outpatient IUD insertion. Methods: This randomised control trial compared the use of a VR headset to standard care during IUD insertion in the outpatient clinic setting. Outcomes measured were patient reported pain and anxiety. Secondary outcomes included clinician reported ease of insertion and time required to complete the procedure. Results: A total of 70 patients were recruited with 34 randomised to the control and 36 randomised to VR headset use. Patients with VR headsets reported a pain score of 5.5 +/- 3.2 during IUD insertion, which was not significantly different to 4.3 +/- 3.2 for the control group. Anxiety scores during the procedure were 4.0 +/- 3.0 in the VR group, compared to 4.8 +/- 3.5 in the control group, which was also not significantly different. Anxiety was the most significant predictor of pain and this in turn significantly increased insertion time (p <0.001). Of the patients who did respond and benefit from VR use, their baseline anxiety was significantly less than in those who did not (p <0.05). Conclusions: The use of VR headsets did not significantly alter the pain or anxiety experienced by patients during IUD insertion, however satisfaction and recommendation that others use VR was high which may suggest other benefits to their use. Additionally, pre-procedural anxiety appears to have a significant adverse impact on pain scores and the ability of patients to benefit from the VR headsets. This importantly contributes to the previously ambiguous data regarding VR use for gynaecological procedures and highlights a new important avenue for further research into alleviating anxiety prior to procedures to improve pain and patient experience. Clinical Trial: Australia and New Zealand Clinical Trial registration: ACTRN12622000088741p. https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=383191

  • The Concerning New Trend of Nicotine Pouches on TikTok: Content Analysis Study

    Date Submitted: Feb 23, 2025
    Open Peer Review Period: Feb 24, 2025 - Apr 21, 2025

    Background: This study highlights TikTok’s influence in shaping youth perceptions of nicotine pouches as trendy and relatively harmless. This underlines the need for more restrictions to be applied on the content that is so readily available for youth. Objective: This study employs a qualitative descriptive design to explore how nicotine pouches (specifically Zyns) are represented on TikTok. Methods: A total of 200 TikTok posts were screened under the #zyn, #zyns, and #nicotinepouch hashtags, with 132 being analyzed. Posts were analyzed using Braun and Clarke’s thematic analysis approach. Collaborative coding ensured reliability and identified key themes in the content. Results: : Five themes emerged: (1) The Zyn Movement; (2) “Boy Heaven”; (3) “Unintended Negative Consequences”; (4) Product Design: “Life doesn’t need to stop”; (5) Physical Benefits: “It’s like IcyHot for your mouth.” Overall, the content heavily condoned nicotine pouch use and normalized it, with males disproportionately represented as the primary users of nicotine pouches. Conclusions: This study highlights TikTok’s influence in shaping youth perceptions of nicotine pouches as trendy and relatively harmless. This underlines the need for more restrictions on the content that is so readily available to youth.

  • Background: Hearing loss is a global health issue affecting millions and creating significant communication barriers, particularly in accessing healthcare services. These barriers can lead to complications and iatrogenic events, emphasizing the need for assistive technologies that enhance communication efficiency. Objective: To develop a corpus of medical terms for the "Captar-Libras" project, designed to improve communication between healthcare professionals and deaf patients through a bidirectional sign language system. Methods: This study used the Delphi method to obtain consensus on key terms for a sign language translation system in healthcare emergency consultations. Initially, a questionnaire with common emergency questions was developed and distributed to healthcare professionals. The collected data were analyzed by a team of experts and adapted to Brazilian Sign Language (Libras). Simulated clinical scenarios were then created to validate the system and ensure the vocabulary's accuracy in the medical context. Results: Among the 16 participants, most were physicians (87.5%) with experience in emergency care, and half had previously treated patients with hearing loss in emergency settings. The questions evaluated received high average importance scores, particularly those related to initial symptoms and pain intensity. Some suggestions for adjustments were made, with two wording modifications significantly improving clarity regarding smoking and alcohol use. Additional suggestions to enhance the medical interview were also proposed. This study aimed to identify essential questions for emergency consultations with deaf patients, focusing on developing a corpus for a Brazilian Sign Language (Libras) recognition system. The findings emphasize the importance of effective communication and highlight the challenges of translating medical terms into Libras. To address these complexities, a multidisciplinary team used the Delphi method to ensure linguistic and cultural accuracy. Additionally, the study reinforces the need for clear, structured medical queries to improve accessibility in emergency care. As a next step, system validation through simulated scenarios will be conducted. Despite certain limitations, this research lays a solid foundation for advancing sign language recognition in medical settings. Conclusions: This study represents a step forward in improving communication between healthcare professionals and deaf individuals in emergencies, where accuracy and overcoming translation challenges are essential. The development of a structured corpus of medical terms in Brazilian Sign Language (Libras) enhances accessibility and inclusivity. Future validation will focus on assessing the recognition system’s performance and reliability in real-world scenarios.

  • Background: Background: Health portraits powered by big data integrate diverse health data into actionable insights, supporting precise risk prediction and personalized management of non-communicable diseases (NCDs). Despite their promise, the adoption and application of health portraits remain fragmented, lacking a standardized framework to harness their potential fully. Objective: Objective: This scoping review aims to categorize existing health portrait research in non-communicable diseases management, evaluate the use of big data based on the 3V framework, external validation, and comprehensiveness, and identify challenges, opportunities, and future research directions in this field. Methods: Methods: This study conducted a scoping review following the PRISMA-ScR guidelines and Levac et al.'s 6-step framework. A comprehensive search was performed in PubMed, Embase, EBSCO, Ovid, Scopus, Web of Science, and Springer Link, focusing on observational and interventional studies using big data, public databases, EHR systems, wearables, and sensors for NCD management from January 2014 to July 2024. Data extraction included study characteristics, modeling approaches, and external validation, with synthesis through keyword analysis, the 3V framework, and visual tools such as word clouds, heat maps, and spider diagrams. Results: Results: A total of 8,707 records were identified, and 90 studies were included for full-text review. The studies were categorized into four types of health portraits: diagnostic, prognostic, monitoring, and recommender. Data utilization based on the 3V framework showed that only 17.78% of studies met all 3V criteria. In terms of Volume, structured data was widely used (64.29%–100% depending on portrait type), while unstructured data showed substantial variation (19.05%–93.33%). Regarding Velocity, monitoring and recommender portraits showed high reliance on online interactive data (85%+). For Variety, only 31.11% of portraits utilized all three attributes. Comprehensive capability assessment revealed that only 30% of studies had external validation, and only 10% met both external validation and 3V criteria, with recommender portraits performing better in both areas. Conclusions: Conclusions: The results highlight significant disparities in data utilization across portrait types and underscore critical challenges, including data continuity and reliability, limited cross-functional integration, and privacy risks, constraining their multidimensional utilization and external validation. Future research should focus on cost-effective cohort datasets from wearable and contactless devices and improve intervention and follow-up evidence to ensure reliability and effectiveness in real-world applications.

  • Background: Ovarian follicles and endometrial thickness are monitored repeatedly for assisted reproduction, imposing a significant burden on patients and clinics. Self-scans with a home ultrasound device can relieve this burden. Objective: We aimed to evaluate the real-life reliability and accuracy of patient self-scans using the smartphone-based Pulsenmore FC vaginal self-scan device (FC) in comparison to standard in-clinic (IC) sonographies, in patients undergoing ovarian stimulation and oocyte pick-up for assisted reproduction or fertility preservation. Methods: A single-center, interventional, controlled, prospective study including 44 patients without pelvic pathologies undergoing stimulation for in-vitro fertilization (IVF) (2022-2024). Patients were trained to use a vaginal home ultrasound device and scan their uterus and ovaries with remote guidance in each cycle check-point. Clinical decisions were based on standard IC sonographies. Image quality, endometrial thickness, number and size of follicles obtained from home scans were compared to those obtained IC. Aspirated oocytes numbers were compared to the follicles recorded at the last visit by home and IC scans. Differences in follicular count and endometrial thickness between IC and FC scans were compared with absolute differences using means, standard deviations and 95% confidence intervals. Relations between IC and FC outcomes were analyzed by the Spearman correlation. The time difference between the first and last self-scans was calculated using the T-test for dependent samples. All tests applied were two-tailed, and a p-value of 5% or less was considered statistically significant. Results: The image quality scores of all home scans were at minimum suitable, and most of them were of better quality. The endometrial measurements and follicular counts/measurements obtained from home scans were in correlation with IC scans in all the clinically significant parameters; antral follicle count, number of stimulated follicles, identification of the leading follicle>14mm, and follicular number/size pre-triggering. The aspirated oocyte/last visit stimulated follicles and mature oocytes/follicles>13mm ratios were well-correlated between the home and standard scans. Conclusions: The home ultrasound device, its method of operation and result interpretation were found to be comparable to conventional sonographies, laying the basis for remote home-base monitoring of follicular development during ovarian stimulation. We believe this also applies to monitoring milder stimulations and even natural cycles, helping those seeking to achieve or avoid natural fertility. Clinical Trial: The study was registered at ClinicalTrials.gov (NCT05485623).

  • Depathologizing Queer Adults’ Dating App Use: A Convergent Mixed Methods Study

    Date Submitted: Feb 10, 2025
    Open Peer Review Period: Feb 10, 2025 - Apr 7, 2025

    Background: Dating apps have considerably changed how many queer individuals are forming social, sexual, and romantical connections. Despite evidence that social media use is associated with both diminished and improved mental health outcomes, few studies have explored the association between dating apps and the mental health of queer adults. Objective: Using reparative theory and a transformative paradigm, this research sought to critically explore the association between dating apps and mental health outcomes among queer adults in Canada. Methods: We employed a convergent mixed methods design comprising an online survey (N=250) and one-on-one interviews (N=22) among queer adults from across Canada. Participants were recruited using advertisements on Grindr. Participants were selected to represent a diversity of gender identities, sexual orientations, ethno-racial identities, ages, and locations. The survey and interview guide collected information on similar domains including dating app use characteristics and mental health symptoms. A structural equation model assessed the association between the intensity of dating app use and mental health outcomes (life satisfaction, depression, anxiety, self-esteem). The model further assessed how motivations for use were related to mental health outcomes and the role of discrimination and community connectedness as intermediary variables. Hybrid reflexive thematic analysis (data-driven and theoretically informed) interrogated the in-depth accounts of queer adults to elucidate the mechanisms of power relations and how they are resisted/refused through the mundane and everyday. Results: Participants used an average of 3.22 dating apps, most commonly for casual sex (n=208, 83.5%). Dating app use was associated with increased life satisfaction (β=0.31, P<.001) and self-esteem (β=0.21, P=.02) but not depression (β=-0.16, P=.07) or anxiety (β=-0.11, P=.20). Discrimination and seeking social approval were associated with adverse mental health. Although seeking friendship was the least commonly reported motivation (n=98, 39.4%), queer people often made friends through intimacy and unintentionally, and increased community connection was associated with heightened life satisfaction (β=0.18, P=.01) and self-esteem (β=0.13, P=.04). Participants described managing negative impacts of use, including by adjusting expectations, utilizing technological features to avoid unwanted interactions, and welcoming unexpected interactions in addition to their desired connections from use. Conclusions: Queer peoples use dating apps conscientiously, leveraging hope and serendipity to stumble upon novel and welcomed connections. Queer users are employing strategies to promote their wellbeing to navigate the threatening virtual socio-sexual space. This research provides nuance to the relationship between dating app use and wellbeing, underscoring the context-dependent and temporally dynamic association between them.

  • Background: Background: Climate change, driven by greenhouse gas emissions, threatens human health and biodiversity. While the digitalization of healthcare, including telemedicine and artificial intelligence, offers sustainability benefits, it also raises concerns about energy use and electronic waste. Balancing these factors is key to a sustainable healthcare future. Objective: Objectives: The objective of this review was to examine the extent to which digitalization in the healthcare sector influences environmental sustainability. Specifically, it aimed to assess how digitalization can contribute to reducing the healthcare sector’s impact on global climate change. From these findings, conclusions were drawn regarding the extent to which digitalization aligns with the objectives of the Planetary Health movement and how these two movements may mutually reinforce each other. Methods: Method: A scoping review, guided by PRISMA 2020 [1], using databases such as PubMed and Scopus, 58 quantitative studies from 2009 to 2024 were analyzed for environmental, social, and economic outcomes aligned with Planetary Health goals. Results: Results: The review analyzed 58 studies on the environmental impact of digitalization in healthcare, primarily focusing on telemedicine, which was examined in 91.38% of the studies. The majority of studies quantified transport-related emissions avoided through digitalization, with some also assessing emissions from healthcare facilities, medical equipment, and energy consumption. Findings indicated that telemedicine significantly reduces CO₂ emissions, with total avoided emissions amounting to approximately 830 million kgCO₂, and additional benefits observed in social and economic aspects, such as patient satisfaction, time savings, and cost reductions. However, only a few studies evaluated the full life cycle impact of digital technologies, highlighting the need for further research on their long-term environmental sustainability. Conclusions: Conclusion: The review calls for further research beyond telemedicine, advocating for life cycle analyses and actionable strategies for a sustainable digitalization in healthcare systems. The Planetary Health framework is highlighted as a guide for ensuring sustainable digital transformation in healthcare.

  • Background: Mood disorders, including bipolar disorder (BP) and major depressive disorder (MDD), are characterized by significant psychological and behavioral fluctuations, with mobility patterns serving as potential markers of emotional states. Objective: Leveraging GPS data as an objective measure, this study explores the diagnostic and monitoring capabilities of Fourier transform, a frequency-domain analysis method, in mood disorders. Methods: A total of 62 participants (BP: 20; MDD: 27; healthy controls: 15) contributed 5,177 person-days of data over observation periods ranging from 5 days to 6 months. Key GPS indicators—location variance (LV), transition time (TT), and entropy (EN)—were identified as reflective of mood fluctuations and diagnostic differences between BP and MDD. Results: Fourier transform analysis revealed that the maximum power spectra of LV and EN differed significantly between BP and MDD groups, with BP patients exhibiting greater periodicity and intensity in mobility patterns. Notably, BP participants demonstrated consistent periodic waves (e.g., 1-day, 4-day, and 9-day cycles), while such patterns were absent in MDD. Daily GPS data showed stronger correlations with ecological momentary assessment (EMA)-reported mood states compared to weekly or monthly aggregations, emphasizing the importance of day-to-day monitoring. Depressive states were associated with reduced LV and TT on weekdays, and lower EN on weekends, indicating that mobility features vary with social and temporal contexts. Conclusions: This study underscores the potential of GPS-derived mobility data, analyzed through Fourier transform, as a non-invasive and real-time diagnostic and monitoring tool for mood disorders. The findings suggest that the intensity of mobility patterns, rather than their frequency, may better differentiate BP from MDD. Integrating GPS data with EMA could enhance the precision of clinical assessments, provide early warnings for mood episodes, and support personalized interventions, ultimately improving mental health outcomes. This approach represents a promising step toward digital phenotyping and advanced mental health monitoring strategies.