Towards Personalised Depression Modelling and Explanation from Wearable Data DOI Creative Commons
Sobhan Chatterjee, Jyoti Mishra, Frederick Sundram

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: June 9, 2023

Abstract Depression and anxiety are the leading causes of health loss globally, Covid-19 pandemic has significantly exacerbated effect these disorders. There is a widening gap between available resources mental needs globally. Digital applications using artificial Intelligence (AI) promising opportunity to address this gap. Increasingly, passively acquired data from wearables augmented with carefully selected active participants develop machine learning (ML) models depression. However, ML black-box in nature, hence outputs not explainable. also multi-modal, reasons for depression may vary individuals. Explainable personalised will thus be beneficial clinicians determining main features that lead decline mood state patient, enabling suitable therapy. This currently lacking. Therefore, study presents first methodology developing accurate deep (DL)-based depression, along novel methods identifying key facets exacerbation depressive symptoms. We illustrate our approach an existing multi-modal dataset containing longitudinal ecological momentary assessments lifestyle wearables, neurocognitive 14 mild moderately depressed over one month. train classification- regression-based DL predict participants’ scores - discrete score given participant based on severity their The trained inside eight different evolutionaryalgorithm-based optimisation schemes optimise model parameters maximum predictive performance. A 5-fold cross-validation scheme used verify performance, error as low 6% some participants. use best process extract indicators, SHAP, ALE Anchors AI literature explain why certain predictions made how they affect mood. These feature insights can assist professionals incorporating interventions into patient’s treatment regimen.

Language: Английский

Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression DOI Creative Commons
Alaa Abd‐Alrazaq, Rawan AlSaad, Farag Shuweihdi

et al.

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

Published: May 5, 2023

Abstract Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one technologies that have been exploited to detect or predict depression. The current review aimed at examining performance AI in detecting and predicting search sources this systematic were 8 electronic databases. Study selection, data extraction, risk bias assessment carried out by two reviewers independently. extracted results synthesized narratively statistically. Of 1314 citations retrieved from databases, 54 studies included review. pooled mean highest accuracy, sensitivity, specificity, root square error (RMSE) was 0.89, 0.87, 0.93, 4.55, respectively. lowest RMSE 0.70, 0.61, 0.73, 3.76, Subgroup analyses revealed there a statistically significant difference specificity between algorithms, sensitivity devices. Wearable promising tool for depression detection prediction although it its infancy not ready use clinical practice. Until further research improve performance, should be used conjunction with other methods diagnosing Further are needed examine based on combination device neuroimaging distinguish patients those diseases.

Language: Английский

Citations

47
Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches DOI Creative Commons
Lin Sze Khoo, Mei Kuan Lim, Chun Yong Chong

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(2), P. 348 - 348

Published: Jan. 6, 2024

As mental health (MH) disorders become increasingly prevalent, their multifaceted symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing a risk of underdiagnosis. While machine learning (ML) has been explored mitigate these challenges, we hypothesized that multiple data modalities support more comprehensive detection non-intrusive collection approaches better capture natural behaviors. To understand the current trends, systematically reviewed 184 studies assess feature extraction, fusion, ML methodologies applied detect MH from passively sensed multimodal data, including audio video recordings, social media, smartphones, wearable devices. Our findings revealed varying correlations modality-specific features in individualized contexts, potentially influenced by demographics personalities. We also observed growing adoption neural network architectures for model-level fusion as algorithms, which have demonstrated promising efficacy handling high-dimensional while modeling within cross-modality relationships. This work provides future researchers clear taxonomy methodological inspire advancements. The analysis guides supports making informed decisions select an optimal source aligns specific use cases based on disorder interest.

Language: Английский

Citations

22
Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review DOI Creative Commons
Alaa Abd‐Alrazaq, Rawan AlSaad, Sarah Aziz

et al.

Journal of Medical Internet Research, Journal Year: 2022, Volume and Issue: 25, P. e42672 - e42672

Published: Dec. 11, 2022

Anxiety and depression are the most common mental disorders worldwide. Owing to lack of psychiatrists around world, incorporation artificial intelligence (AI) into wearable devices (wearable AI) has been exploited provide health services.This review aimed explore features AI used for anxiety identify application areas open research issues.We searched 8 electronic databases (MEDLINE, PsycINFO, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, Google Scholar) included studies that met inclusion criteria. Then, we checked cited screened were by studies. The study selection data extraction carried out 2 reviewers independently. extracted aggregated summarized using narrative synthesis.Of 1203 identified, 69 (5.74%) in this review. Approximately, two-thirds depression, whereas remaining it anxiety. frequent was diagnosing depression; however, none treatment purposes. Most targeted individuals aged between 18 65 years. device Actiwatch AW4 (Cambridge Neurotechnology Ltd). Wrist-worn type commonly category model development physical activity data, followed sleep heart rate data. frequently set from sources Depresjon. algorithm random forest, support vector machine.Wearable can offer great promise providing services related depression. Wearable be prescreening assessment Further reviews needed statistically synthesize studies' results performance effectiveness AI. Given its potential, technology companies should invest more

Language: Английский

Citations

57
Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study DOI Creative Commons
Gerard Anmella, Filippo Corponi, Bryan M. Li

et al.

JMIR mhealth and uhealth, Journal Year: 2023, Volume and Issue: 11, P. e45405 - e45405

Published: March 20, 2023

Depressive and manic episodes within bipolar disorder (BD) major depressive (MDD) involve altered mood, sleep, activity, alongside physiological alterations wearables can capture. Firstly, we explored whether wearable data could predict (aim 1) the severity of an acute affective episode at intra-individual level 2) polarity euthymia among different individuals. Secondarily, which were related to prior predictions, generalization across patients, associations between symptoms data. We conducted a prospective exploratory observational study including patients with BD MDD on (manic, depressed, mixed) whose recorded using research-grade (Empatica E4) 3 consecutive time points (acute, response, remission episode). Euthymic healthy controls during single session (approximately 48 h). Manic assessed standardized psychometric scales. Physiological included following channels: acceleration (ACC), skin temperature, blood volume pulse, heart rate (HR), electrodermal activity (EDA). Invalid removed rule-based filter, channels aligned 1-second units segmented window lengths 32 seconds, as best-performing parameters. developed deep learning predictive models, channels' individual contribution permutation feature importance analysis, computed scales' items normalized mutual information (NMI). present novel, fully automated method for preprocessing analysis from device, viable supervised pipeline time-series analyses. Overall, 35 sessions (1512 hours) 12 mixed, euthymic) 7 (mean age 39.7, SD 12.6 years; 6/19, 32% female) analyzed. The mood was predicted moderate (62%-85%) accuracies 1), their (70%) accuracy 2). most relevant features former tasks ACC, EDA, HR. There fair agreement in classification (Kendall W=0.383). Generalization models unseen overall low accuracy, except models. ACC associated "increased motor activity" (NMI>0.55), "insomnia" (NMI=0.6), "motor inhibition" (NMI=0.75). EDA "aggressive behavior" (NMI=1.0) "psychic anxiety" (NMI=0.52). show potential identify specific mania depression quantitatively, both MDD. Motor stress-related (EDA HR) stand out digital biomarkers predicting depression, respectively. These findings represent promising pathway toward personalized psychiatry, allow early identification intervention episodes.

Language: Английский

Citations

19
Digital biomarkers for non-motor symptoms in Parkinson’s disease: the state of the art DOI Creative Commons
Jules M. Janssen Daalen,

Robin van den Bergh,

Eva M. Prins

et al.

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

Published: July 11, 2024

Digital biomarkers that remotely monitor symptoms have the potential to revolutionize outcome assessments in future disease-modifying trials Parkinson's disease (PD), by allowing objective and recurrent measurement of signs collected participant's own living environment. This biomarker field is developing rapidly for assessing motor features PD, but non-motor domain lags behind. Here, we systematically review assess digital under development measuring PD. We also consider relevant developments outside PD field. focus on technological readiness level evaluate whether identified progression, covering spectrum from prodromal advanced stages. Furthermore, provide perspectives deployment these trials. found various wearables show high promise autonomic function, constipation sleep characteristics, including REM behavior disorder. Biomarkers neuropsychiatric are less well-developed, increasing accuracy non-PD populations. Most not been validated specific use their sensitivity capture progression remains untested where need greatest. External validation real-world environments large longitudinal cohorts necessary integrating into research, ultimately daily clinical practice.

Language: Английский

Citations

8
Clinical research on neurological and psychiatric diagnosis and monitoring using wearable devices: A literature review DOI Creative Commons
Jielin Huang, Huidi Wang,

Qiheng Wu

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: 2(4)

Published: May 11, 2024

Abstract Wearable devices have opened up exciting possibilities for monitoring and managing home health, particularly in the realm of neurological psychiatric diseases. These capture signals related to physiological behavioral changes, including heart rate, sleep patterns, motor functions. Their emergence has resulted significant advancements management such conditions. Traditional clinical diagnosis assessment methods heavily rely on patient reports evaluations conducted by healthcare professionals, often leading a detachment patients from their environment creating additional burdens both providers. The increasing popularity wearable offers potential solution these challenges. This review focuses utility diagnosing Through research findings practical examples, we highlight role conditions as autism spectrum disorder, depression, epilepsy, stroke prognosis, Parkinson's disease, dementia, other Additionally, discusses benefits limitations applications, while highlighting challenges they face. Finally, it provides prospects enhancing value

Language: Английский

Citations

5
Predicting the severity of mood and neuropsychiatric symptoms from digital biomarkers using wearable physiological data and deep learning DOI Creative Commons
Yuri Rykov, Kok Pin Ng, Michael D. Patterson

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 180, P. 108959 - 108959

Published: July 31, 2024

Neuropsychiatric symptoms (NPS) and mood disorders are common in individuals with mild cognitive impairment (MCI) increase the risk of progression to dementia. Wearable devices collecting physiological behavioral data can help remote, passive, continuous monitoring moods NPS, overcoming limitations inconveniences current assessment methods. In this longitudinal study, we examined predictive ability digital biomarkers based on sensor from a wrist-worn wearable determine severity NPS daily basis older adults predominant MCI. addition conventional biomarkers, such as heart rate variability skin conductance levels, leveraged deep-learning features derived using self-supervised convolutional autoencoder. Models combining deep predicted depression scores correlation r = 0.73 average, total disorder 0.67, 0.69 study population. Our findings demonstrated potential collected wearables learning methods be used for unobtrusive assessments mental health adults, including those TRIAL REGISTRATION: This trial was registered ClinicalTrials.gov (NCT05059353) September 28, 2021, titled "Effectiveness Safety Digitally Based Multidomain Intervention Mild Cognitive Impairment".

Language: Английский

Citations

4
Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study DOI Creative Commons
Shayan Nejadshamsi, Vania Karami, Negar Ghourchian

et al.

JMIR Aging, Journal Year: 2025, Volume and Issue: 8, P. e67715 - e67715

Published: March 3, 2025

Depression, characterized by persistent sadness and loss of interest in daily activities, greatly reduces quality life. Early detection is vital for effective treatment intervention. While many studies use wearable devices to classify depression based on physical activity, these often rely intrusive methods. Additionally, most classification involve large participant groups single-stage classifiers without explainability. This study aims assess the feasibility classifying using nonintrusive Wi-Fi-based motion sensor data a novel machine learning model limited number participants. We also conduct an explainability analysis interpret model's predictions identify key features associated with classification. In this study, we recruited adults aged 65 years older through web-based in-person methods, supported McGill University health care facility directory. Participants provided consent, collected 6 months activity sleep via sensors, along Edmonton Frailty Scale Geriatric Depression data. For classification, proposed HOPE (Home-Based Older Adults' Prediction) feature selection, dimensionality reduction, stages, evaluating various combinations accuracy, sensitivity, precision, F1-score. Shapely addictive explanations local interpretable model-agnostic were used explain predictions. A total participants enrolled study; however, 2 withdrew later due internet connectivity issues. Among 4 remaining participants, 3 classified as not having depression, while 1 was identified depression. The accurate model, which combined sequential forward selection principal component decision tree achieved accuracy 87.5%, sensitivity 90%, precision 88.3%, effectively distinguishing individuals those revealed that influential order importance, "average duration," "total interruptions," "percentage nights duration "Edmonton Scale." findings from preliminary demonstrate sensors highlight effectiveness our even small sample size. These results suggest potential further research larger cohort more comprehensive validation. collection method architecture offer promising applications remote monitoring, particularly who may face challenges devices. Furthermore, importance patterns aligns previous research, emphasizing need in-depth role mental health, suggested explainable study.

Language: Английский

Citations

0
The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: A Systematic Review DOI

H. Lee,

Seung‐Gul Kang, Seon Heui Lee

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Predicting daily cognition and lifestyle behaviors for older adults using smart home data and ecological momentary assessment DOI
Maureen Schmitter‐Edgecombe, Catherine Luna, Shenghai Dai

et al.

The Clinical Neuropsychologist, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 25

Published: March 19, 2024

Extraction of digital markers from passive sensors placed in homes is a promising method for understanding real-world behaviors. In this study, machine learning (ML) and multilevel modeling (MLM) are used to examine types whether smart home can predict cognitive functioning, lifestyle behaviors, contextual factors measured through ecological momentary assessment (EMA).

Language: Английский

Citations

3