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

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

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Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study

npj Digital Medicine, Journal Year: 2023, Volume and Issue: 6(1)

Published: Feb. 17, 2023

Abstract Recent growth in digital technologies has enabled the recruitment and monitoring of large diverse populations remote health studies. However, generalizability inference drawn from remotely collected data could be severely impacted by uneven participant engagement attrition over course study. We report findings on long-term retention patterns a multinational observational study for depression containing active (surveys) passive sensor via Android smartphones, Fitbit devices 614 participants up to 2 years. Majority (67.6%) continued remain engaged after 43 weeks. Unsupervised clustering participants’ apps usage showed 3 distinct subgroups each stream. found: (i) least group had highest severity (4 PHQ8 points higher) across all streams; (ii) (completed 4 bi-weekly surveys) took significantly longer respond survey notifications (3.8 h more) were 5 years younger compared most 20 surveys); (iii) considerable proportion (44.6%) who stopped completing surveys 8 weeks share (average 42 weeks). Additionally, multivariate survival models age, ownership brand sites associated with Together these inform design future studies enable equitable balanced collection populations.

Language: Английский

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Large-scale digital phenotyping: Identifying depression and anxiety indicators in a general UK population with over 10,000 participants

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

Published: Jan. 1, 2025

Language: Английский

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Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

Journal of Medical Internet Research, Journal Year: 2023, Volume and Issue: 25, P. e45233 - e45233

Published: Aug. 14, 2023

A number of challenges exist for the analysis mHealth data: maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold missing data; distinguishing between cross-sectional longitudinal relationships different features to determine their utility in tracking within-individual variation or screening individuals at high risk; heterogeneity with which depression manifests itself behavioral patterns quantified by passive features. From 479 participants MDD, we extracted 21 capturing mobility, sleep, smartphone use. We investigated impact days available data on feature quality using intraclass correlation coefficient Bland-Altman analysis. then examined nature 8-item Patient Health Questionnaire (PHQ-8) scale (measured every 14 days) individual-mean correlation, repeated measures linear mixed effects model. Furthermore, stratified based difference, features, (depression) low (no depression) PHQ-8 scores Gaussian mixture demonstrated that least 8 (range 2-12) were needed reliable calculation most 14-day window. observed such as sleep onset correlated better cross-sectionally than longitudinally, whereas wakefulness after well longitudinally but worse cross-sectionally. Finally, found could be separated into 3 distinct clusters according difference no depression.

Language: Английский

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Daily life affective dynamics as transdiagnostic predictors of mental health symptoms: An ecological momentary assessment study

Journal of Affective Disorders, Journal Year: 2024, Volume and Issue: 351, P. 808 - 817

Published: Feb. 4, 2024

Affective dynamics have been identified as a correlate of broad span mental health issues, making them key candidate transdiagnostic factors. However, there remains lack knowledge about which aspects affective - especially they manifest in the course daily life relate to general risk for issues versus specific symptoms.

Language: Английский

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Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis

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

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Digital Phenotyping for Mental Health: Reviewing the Challenges of Using Data to Monitor and Predict Mental Health Problems

Current Psychiatry Reports, Journal Year: 2022, Volume and Issue: 24(10), P. 523 - 528

Published: Aug. 24, 2022

Language: Английский

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A Systematic Review of Location Data for Depression Prediction

International Journal of Environmental Research and Public Health, Journal Year: 2023, Volume and Issue: 20(11), P. 5984 - 5984

Published: May 29, 2023

Depression contributes to a wide range of maladjustment problems. With the development technology, objective measurement for behavior and functional indicators depression has become possible through passive sensing technology digital devices. Focusing on location data, we systematically reviewed relationship between data. We searched Scopus, PubMed, Web Science databases by combining terms related data with depression. Thirty-one studies were included in this review. Location demonstrated promising predictive power Studies examining individual variables depression, homestay, entropy, normalized entropy variable dimension showed most consistent significant correlations. Furthermore, distance, irregularity, associations some studies. However, semantic inconsistent results. This suggests that process geographical movement is more mood changes than location. Future research must converge across location-data methods.

Language: Английский

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Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment

Deleted Journal, Journal Year: 2024, Volume and Issue: 2(2), P. 778 - 810

Published: Feb. 22, 2024

Mental health disorders—including depression, anxiety, trauma-related, and psychotic conditions—are pervasive impairing, representing considerable challenges for both individual well-being public health. Often the first to treatment include financial, geographic, stigmatic barriers, which limit accessibility of traditional assessment measures. Further, compounded by frequent misdiagnosis or delayed detection, there is a need effective, accessible, scalable approaches identification management. Considering advances in computing ubiquitous nature personal mobile wearable technology, this narrative review examines utilization passive sensor data as screening diagnostic tool mental disorders. As an alternative measures, sensing offers overcome barriers that prevent many from seeking services. We critically assess literature up September 2023, exploring use data—such heart rate variability, movement patterns, geolocation—to predict outcomes across spectrum From translational perspective, our explores state science, with special emphasis on capacity science be implemented real world clinical general populations, novelty specific best knowledge. Toward aim, we consider multiple study factors, including participant demographics, collection methods, modalities, outcome analytic modeling approaches. find features, such GPS, rate, actigraphy offer promise enhancing early detection improving process Despite promise, however, findings highlight important limitations research (1) trend toward smaller, specialized samples, (2) predominance apps built Android operating system, (3) reliance self-reported measures proxies outcomes. These ultimately stymie efforts implement scale larger more heterogeneous populations. With future mind, emphasize importance validating larger, diverse samples ensuring tools can deployed device types systems. where possible, robust, objectively validated clinician assessment. conclude careful consideration factors design will aid impact studies, broad scale.

Language: Английский

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Navigating the Future of Psychiatry: A Review of Research on Opportunities, Applications, and Challenges of Artificial Intelligence

Current Treatment Options in Psychiatry, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 17, 2025

Language: Английский

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