Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis (Preprint) DOI
Yuezhou Zhang, Amos Folarin, Shaoxiong Sun

et al.

Published: Dec. 8, 2023

BACKGROUND Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations real-world settings. OBJECTIVE This study aims to explore associations severity wearable-measured rhythms while accounting for impacts. METHODS Data were sourced from a large longitudinal mHealth study, wherein participants’ assessed biweekly using 8-item Patient Health Questionnaire (PHQ-8), behaviors, including sleep, step count, heart rate (HR), tracked Fitbit devices up 2 years. We extracted 12 14-day data preceding each PHQ-8 assessment, cosinor variables, such as HR peak timing (HR acrophase), nonparametric features, onset most active continuous 10-hour period (M10 onset). To investigate association also assessing impacts, we used three nested linear mixed-effects models feature: (1) incorporating score an independent variable, (2) adding seasonality, (3) interaction term season score. RESULTS Analyzing 10,018 records alongside 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), found that after adjusting effects, higher scores associated with reduced daily steps (β=–93.61, <i>P</i>&lt;.001), increased sleep variability (β=0.96, delayed (ie, onset: β=0.55, <i>P</i>=.001; offset: β=1.12, <i>P</i>&lt;.001; M10 β=0.73, <i>P</i>=.003; acrophase: β=0.71, <i>P</i>=.001). Notably, negative more pronounced spring (β × = –31.51, <i>P</i>=.002) summer –42.61, <i>P</i>&lt;.001) compared winter. Additionally, correlation observed solely 1.06, <i>P=</i>.008). Moreover, winter, experienced shorter duration by 16.6 minutes, increase 394.5, delay 20.5 time 67.9 minutes during summer. CONCLUSIONS Our findings highlight influences on human their depression, underscoring importance considering research applications. indicates potential digital biomarkers depression.

Language: Английский

From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression DOI Creative Commons

Imogen E. Leaning,

Nessa Ikani, Hannah S. Savage

et al.

Neuroscience & Biobehavioral Reviews, Journal Year: 2024, Volume and Issue: 158, P. 105541 - 105541

Published: Jan. 11, 2024

Smartphone-based digital phenotyping enables potentially clinically relevant information to be collected as individuals go about their day. This could improve monitoring and interventions for people with Major Depressive Disorder (MDD). The aim of this systematic review was investigate current features methods used in MDD. We searched PubMed, PsycINFO, Embase, Scopus Web Science (10/11/2023) articles including: (1) MDD population, (2) smartphone-based features, (3) validated ratings. Risk bias assessed using several sources. Studies were compared within analysis goals (correlating depression, predicting symptom severity, diagnosis, mood state/episode, other). Twenty-four studies (9801 participants) included. achieved moderate performance. Common themes included challenges from complex missing data (leading a risk bias), lack external validation. made progress towards relating phenotypes clinical variables, often focusing on time-averaged features. Methods investigating temporal dynamics more directly may beneficial patient monitoring. European Research Council consolidator grant: 101001118, Prospero: CRD42022346264, Open Framework: https://osf.io/s7ay4

Language: Английский

Citations

18
Integration of Artificial Intelligence and Wearable Internet of Things for Mental Health Detection DOI Creative Commons
Wei Wang, Jian Chen, Yuzhu Hu

et al.

International Journal of Cognitive Computing in Engineering, Journal Year: 2024, Volume and Issue: 5, P. 307 - 315

Published: Jan. 1, 2024

The integration of Artificial Intelligence (AI) and Wearable Internet Things (WIoT) for mental health detection is a promising area research with the potential to revolutionize monitoring diagnosis. Since early diseases, i.e., depression, great importance diagnosis treatment, fast convenient way urgently needed. Traditional diagnostic methods are time-consuming, laborious, over-subjective, easily lead misdiagnosis. advance in information techniques wearable devices brings innovation disease detection. Therefore, this article first compares intelligent depression traditional illustrate significance then analyzes opportunities device. Then we provide specific psychophysiological data measured by introduce relevant datasets An illustrative example sleep presented discussed our proposed ensemble method has improved nearly 10% baselines. Analytical results demonstrate using device-measured detect intelligently.

Language: Английский

Citations

11
Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis DOI Creative Commons
Yuezhou Zhang, Amos Folarin, Shaoxiong Sun

et al.

Journal of Medical Internet Research, Journal Year: 2024, Volume and Issue: 26, P. e55302 - e55302

Published: March 29, 2024

Background Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations real-world settings. Objective This study aims to explore associations severity wearable-measured rhythms while accounting for impacts. Methods Data were sourced from a large longitudinal mHealth study, wherein participants’ assessed biweekly using 8-item Patient Health Questionnaire (PHQ-8), behaviors, including sleep, step count, heart rate (HR), tracked Fitbit devices up 2 years. We extracted 12 14-day data preceding each PHQ-8 assessment, cosinor variables, such as HR peak timing (HR acrophase), nonparametric features, onset most active continuous 10-hour period (M10 onset). To investigate association also assessing impacts, we used three nested linear mixed-effects models feature: (1) incorporating score an independent variable, (2) adding seasonality, (3) interaction term season score. Results Analyzing 10,018 records alongside 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), found that after adjusting effects, higher scores associated with reduced daily steps (β=–93.61, P<.001), increased sleep variability (β=0.96, delayed (ie, onset: β=0.55, P=.001; offset: β=1.12, P<.001; M10 β=0.73, P=.003; acrophase: β=0.71, P=.001). Notably, negative more pronounced spring (β × = –31.51, P=.002) summer –42.61, P<.001) compared winter. Additionally, correlation observed solely 1.06, P=.008). Moreover, winter, experienced shorter duration by 16.6 minutes, increase 394.5, delay 20.5 time 67.9 minutes during summer. Conclusions Our findings highlight influences on human their depression, underscoring importance considering research applications. indicates potential digital biomarkers depression.

Language: Английский

Citations

6
Comparing self reported and physiological sleep quality from consumer devices to depression and neurocognitive performance DOI Creative Commons
Samir Akre, Zachary D. Cohen,

Amelia Welborn

et al.

npj Digital Medicine, Journal Year: 2025, Volume and Issue: 8(1)

Published: Feb. 9, 2025

Abstract This study examines the relationship between self-reported and physiologically measured sleep quality their impact on neurocognitive performance in individuals with depression. Using data from 249 participants medium to severe depression monitored over 13 weeks, was assessed via retrospective self-report physiological measures consumer smartphones smartwatches. Correlations were generally weak. Machine learning models revealed that could detect all symptoms using Patient Health Questionnaire-14, whereas detected “sleeping too much” low libido. Notably, only disturbances correlated significantly performance, specifically processing speed. Physiological able changes sleep, medication use, latency. These findings emphasize are not measuring same construct, both important monitor when studying relation

Language: Английский

Citations

0
Uncovering Social States in Healthy and Clinical Populations Using Digital Phenotyping and Hidden Markov Models: Observational Study DOI Creative Commons
Imogen E Leaning, Andrea Costanzo, Raj R. Jagesar

et al.

Journal of Medical Internet Research, Journal Year: 2025, Volume and Issue: 27, P. e64007 - e64007

Published: April 28, 2025

Background Brain-related disorders are characterized by observable behavioral symptoms, for example, social withdrawal. Smartphones can passively collect data reflecting digital activities such as communication app usage and calls. These collected objectively in real time, avoiding recall bias, may, therefore, be a useful tool measuring behaviors related to functioning. Despite promising clinical utility, analyzing smartphone is challenging datasets often include range of temporal features prone missingness. Objective Hidden Markov models (HMMs) provide interpretable, lower-dimensional representations data, allowing This study aimed investigate the HMM method modeling time series data. Methods We applied an aggregate dataset measures designed assess phone-related functioning healthy controls (HCs) participants with schizophrenia, Alzheimer disease (AD), memory complaints. trained on subset HCs (91/348, 26.1%) selected model socially active inactive states. Then, we generated hidden state sequences per participant calculated their “total dwell time,” that is, percentage spent state. Linear regression were used compare total available measures, logistic was times between diagnostic groups HCs. primarily reported results from 2-state but also verified HMMs more states whole dataset. Results identified lower AD (26/257, 10.1%) versus withheld (156/257, 60.7%; odds ratio 0.95, 95% CI 0.92-0.97; false discovery rate [FDR]–corrected P<.001), well complaints (57/257, 22.2%; 0.97, 0.96-0.99; FDR-corrected P=.004). The result group very robust across variations, whereas less robust. observed interaction when predicting (FDR-corrected P=.02). No significant relationships regarding schizophrenia (18/257, 7%; P>.99). Conclusions found practical, interpretable phenotyping analysis, providing objective phenotype possible indicator

Language: Английский

Citations

0
Predicting Relapse of Depressive Episodes During Maintenance Treatment: The Canadian Biomarker Integration Network in Depression (CAN-BIND) Wellness Monitoring in Major Depressive Disorder Study: Prédire la rechute d’épisodes dépressifs pendant le traitement d’entretien : Une étude de suivi du bien-être dans les troubles dépressifs majeurs du Réseau canadien d’intégration des biomarqueurs pour la dépression (CAN-BIND) DOI
Raymond W. Lam, Katerina Rnic, John-Jose Nuñez

et al.

The Canadian Journal of Psychiatry, Journal Year: 2025, Volume and Issue: unknown

Published: April 29, 2025

Background Relapse rates in major depressive disorder (MDD) remain high even after treatment to remission. Identifying predictors of relapse is, therefore, crucial for improving maintenance strategies and preventing future episodes. Remote data collection sensing technologies may allow more comprehensive longitudinal assessment potential predictors. Methods The Canadian Biomarker Integration Network Depression Wellness Monitoring MDD (CBN-WELL) study was a prospective, multicentre observational with an aim identify biomarkers associated patients on MDD. Participants had DSM-5-TR diagnosis remission Montgomery–Åsberg Rating Scale (MADRS) score ≤14. remained their baseline medication regimens were followed bimonthly up 2 years. criteria included MADRS > 22 consecutive weeks, suicidality or hospitalization, initiation change worsening symptoms. Data clinical assessments, self-report questionnaires, remote monitoring using wrist-worn actigraphs smartphones. Results A total 96 participants follow-up data. Of these, 28.9% experienced during the period, average time 211 days. Baseline severity, as measured by MADRS, higher who relapsed compared those did not, but few other measures differentiated these groups. Conclusions Individuals continued have despite treatment. paucity factors that predict underscores need biomarkers. CBN-WELL database can be used research integrate multiple predictive objective individuals.

Language: Английский

Citations

0
Large-scale digital phenotyping: Identifying depression and anxiety indicators in a general UK population with over 10,000 participants DOI
Yuezhou Zhang, Callum Stewart, Yatharth Ranjan

et al.

Journal of Affective Disorders, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0
Wearables research for continuous monitoring of patient outcomes: A scoping review DOI Creative Commons

Kalee Lodewyk,

Madeleine Wiebe, Liz Dennett

et al.

PLOS Digital Health, Journal Year: 2025, Volume and Issue: 4(5), P. e0000860 - e0000860

Published: May 9, 2025

Background The use of wearable devices for remote health monitoring is a rapidly expanding field. These might benefit patients and providers; however, they are not yet widely used in healthcare. This scoping review assesses the current state literature on non-hospital settings. Methods CINAHL, Scopus, Embase MEDLINE were searched until August 5, 2024. We performed citation searching Google Scholar. Studies an outpatient setting with clinically relevant, measurable outcome included categorized according to intended data: existing disease vs. diagnosis new disease. Results Eighty studies met eligibility criteria. Most device data monitor chronic (68/80, 85%), most often neurodegenerative (22/68, 32%). Twelve (12/80, 15%) diagnose disease, majority being cardiovascular (9/12, 75%). A range studied watches bracelets common (50/80, 63%). Only six (8%) randomized controlled trials, four which (67%) showed evidence positive clinical impact. Feasibility determinants inconsistently reported, including compliance (51/80, 64%), patient-reported useability (13/80, 16%), participant technology literacy (1/80, 1%). Conclusions Evidence effectiveness remains scant. Heterogeneity across terms devices, targets protocols makes synthesis challenging, especially given rapid pace technical innovation. findings provide direction future research implementation

Language: Английский

Citations

0
Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features DOI Creative Commons
Dongju Lim,

Jaegwon Jeong,

Yun Min Song

et al.

npj Digital Medicine, Journal Year: 2024, Volume and Issue: 7(1)

Published: Nov. 18, 2024

Wearable devices enable passive collection of sleep, heart rate, and step-count data, offering potential for mood episode prediction in disorder patients. However, current models often require various data types, limiting real-world application. Here, we develop that predict future episodes using only sleep-wake easily gathered through smartphones wearables when trained on an individual's history past episodes. Using mathematical modeling to longitudinal from 168 patients (587 days average clinical follow-up, 267 wearable data), derived 36 sleep circadian rhythm features. These features enabled accurate next-day predictions depressive, manic, hypomanic (AUCs: 0.80, 0.98, 0.95). Notably, daily phase shifts were the most significant predictors: delays linked depressive episodes, advances manic This prospective observational cohort study (ClinicalTrials.gov: NCT03088657, 2017-3-23) shows combined with prior history, can effectively enhancing management.

Language: Английский

Citations

3
A Template and Tutorial for Preregistering Studies Using Passive Smartphone Measures DOI Open Access
Anna M. Langener,

Björn S. Siepe,

Mahmoud Medhat Elsherif

et al.

Published: March 19, 2024

Passive smartphone measures hold significant potential and are increasingly employed in psychological biomedical research to capture an individual's behavior. These involve the near-continuous unobtrusive collection of data from smartphones without requiring active input participants. For example, GPS sensors used determine (social) context a person, accelerometers measure movement. However, utilizing passive presents methodological challenges during analysis. Researchers must make multiple decisions when working with such measures, which can result different conclusions. Unfortunately, transparency these decision-making processes is often lacking. The implementation open science practices only beginning emerge digital phenotyping studies varies widely across studies. Well-intentioned researchers may fail report on some due variety choices that be made. To address this issue enhance reproducibility studies, we propose adoption preregistration as way forward. Although there have been attempts preregister template for registering currently missing. This could problematic high level complexity requires well-structured template. Therefore, our objective was develop easy use understandable researchers. Additionally, explain provide resources assist making informed regarding collection, cleaning, Overall, aim researchers' explicit, transparency, elevate standards measures.

Language: Английский

Citations

1