Published on in Vol 7 , No 10 (2019) :October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14149, first published .
Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

Journals

  1. Kim H, Kim S, Kong S, Jeong Y, Kim H, Kim N. Possible Application of Ecological Momentary Assessment to Older Adults’ Daily Depressive Mood: Integrative Literature Review. JMIR Mental Health 2020;7(6):e13247 View
  2. Lewczuk K, Gorowska M, Li Y, Gola M. Mobile Internet Technologies, Ecological Momentary Assessment, and Intervention—Poison and Remedy for New Online Problematic Behaviors in ICD-11. Frontiers in Psychiatry 2020;11 View
  3. Niculescu I, Arora T, Iaboni A. Screening for depression in older adults with cognitive impairment in the homecare setting: a systematic review. Aging & Mental Health 2021;25(9):1585 View
  4. Grossman J, Frumkin M, Rodebaugh T, Lenze E. mHealth Assessment and Intervention of Depression and Anxiety in Older Adults. Harvard Review of Psychiatry 2020;28(3):203 View
  5. Bincy K, Logaraj M, Ramraj B. Depression and its associated factors among the older adults in rural, Tamilnadu, India. Clinical Epidemiology and Global Health 2021;10:100677 View
  6. He-Yueya J, Buck B, Campbell A, Choudhury T, Kane J, Ben-Zeev D, Althoff T. Assessing the relationship between routine and schizophrenia symptoms with passively sensed measures of behavioral stability. npj Schizophrenia 2020;6(1) View
  7. Minaeva O, Riese H, Lamers F, Antypa N, Wichers M, Booij S. Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method. Journal of Medical Internet Research 2020;22(12):e22634 View
  8. Koo J, Son N, Yoo K. Relationship between the living-alone period and depressive symptoms among the elderly. Archives of Gerontology and Geriatrics 2021;94:104341 View
  9. Anýž J, Bakštein E, Dally A, Kolenič M, Hlinka J, Hartmannová T, Urbanová K, Correll C, Novák D, Španiel F. Validity of the Aktibipo Self-rating Questionnaire for the Digital Self-assessment of Mood and Relapse Detection in Patients With Bipolar Disorder: Instrument Validation Study. JMIR Mental Health 2021;8(8):e26348 View
  10. Opoku Asare K, Terhorst Y, Vega J, Peltonen E, Lagerspetz E, Ferreira D. Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study. JMIR mHealth and uHealth 2021;9(7):e26540 View
  11. Massar S, Chua X, Soon C, Ng A, Ong J, Chee N, Lee T, Ghosh A, Chee M. Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data. npj Digital Medicine 2021;4(1) View
  12. Niculescu I, Quirt H, Arora T, Borsook T, Green R, Ford B, Iaboni A. Ecological Momentary Assessment of Depression in People With Advanced Dementia: Longitudinal Pilot Study. JMIR Aging 2021;4(3):e29021 View
  13. De Calheiros Velozo J, Habets J, George S, Niemeijer K, Minaeva O, Hagemann N, Herff C, Kuppens P, Rintala A, Vaessen T, Riese H, Delespaul P. Designing daily-life research combining experience sampling method with parallel data. Psychological Medicine 2022:1 View
  14. Kamath J, Barriera R, Jain N, Keisari E, Wang B. Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives. World Journal of Psychiatry 2022;12(3):393 View
  15. Barber R, Ortiz F, Garrido S, Calatrava-Nicolás F, Mora A, Prados A, Vera-Repullo J, Roca-González J, Méndez I, Mozos Ó. A Multirobot System in an Assisted Home Environment to Support the Elderly in Their Daily Lives. Sensors 2022;22(20):7983 View
  16. Liu Y, Kang K, Doe M. HADD: High-Accuracy Detection of Depressed Mood. Technologies 2022;10(6):123 View
  17. Opoku Asare K, Moshe I, Terhorst Y, Vega J, Hosio S, Baumeister H, Pulkki-Råback L, Ferreira D. Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: A longitudinal data analysis. Pervasive and Mobile Computing 2022;83:101621 View
  18. Wang L, Miller L. Assessment and Disruption of Ruminative Episodes to Enhance Mobile Cognitive Behavioral Therapy Just-in-Time Adaptive Interventions in Clinical Depression: Pilot Randomized Controlled Trial. JMIR Formative Research 2023;7:e37270 View
  19. Ahmed A, Aziz S, Alzubaidi M, Schneider J, Irshaidat S, Abu Serhan H, Abd-alrazaq A, Solaiman B, Househ M. Wearable devices for anxiety & depression: A scoping review. Computer Methods and Programs in Biomedicine Update 2023;3:100095 View
  20. Lee K, Ham B. Machine Learning on Early Diagnosis of Depression. Psychiatry Investigation 2022;19(8):597 View
  21. Susanty S, Sufriyana H, Su E, Chuang Y, Rashid T. Questionnaire-free machine-learning method to predict depressive symptoms among community-dwelling older adults. PLOS ONE 2023;18(1):e0280330 View
  22. Beck E, Jackson J. Personalized Prediction of Behaviors and Experiences: An Idiographic Person–Situation Test. Psychological Science 2022;33(10):1767 View
  23. Thakre T, Kulkarni H, Adams K, Mischel R, Hayes R, Pandurangi A. Polysomnographic identification of anxiety and depression using deep learning. Journal of Psychiatric Research 2022;150:54 View
  24. Seong S, Park S, Ahn Y, Kim H. Development of an integrated fatigue measurement system for construction workers: a feasibility study. BMC Public Health 2022;22(1) View
  25. Schell R, Allen B, Goedel W, Hallowell B, Scagos R, Li Y, Krieger M, Neill D, Marshall B, Cerda M, Ahern J. Identifying Predictors of Opioid Overdose Death at a Neighborhood Level With Machine Learning. American Journal of Epidemiology 2022;191(3):526 View
  26. Wang Q, Zheng W, Wu F, Xu A, Zhu H, Liu Z. A New GNSS-R Altimetry Algorithm Based on Machine Learning Fusion Model and Feature Optimization to Improve the Precision of Sea Surface Height Retrieval. Frontiers in Earth Science 2021;9 View
  27. Zhang P, Fonnesbeck C, Schmidt D, White J, Kleinberg S, Mulvaney S. Using Momentary Assessment and Machine Learning to Identify Barriers to Self-management in Type 1 Diabetes: Observational Study. JMIR mHealth and uHealth 2022;10(3):e21959 View
  28. Zarate D, Stavropoulos V, Ball M, de Sena Collier G, Jacobson N. Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence. BMC Psychiatry 2022;22(1) View
  29. Pfeifer L, Heyers K, Ocklenburg S, Wolf O. Stress research during the COVID-19 pandemic and beyond. Neuroscience & Biobehavioral Reviews 2021;131:581 View
  30. Calatrava-Nicolás F, Gutiérrez-Maestro E, Bautista-Salinas D, Ortiz F, González J, Vera-Repullo J, Jiménez-Buendía M, Méndez I, Ruiz-Esteban C, Mozos O. Robotic-Based Well-Being Monitoring and Coaching System for the Elderly in Their Daily Activities. Sensors 2021;21(20):6865 View
  31. Richter T, Fishbain B, Richter-Levin G, Okon-Singer H. Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions. Journal of Personalized Medicine 2021;11(10):957 View
  32. Kim S, Lee K. Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods. Neuropsychiatric Disease and Treatment 2021;Volume 17:3415 View
  33. Cho Y, Lim K, Lee S, Kim Y, Kim M, Kim C, Kim Y, Kim H. Developing a Multimodal Monitoring System for Geriatric Depression. CIN: Computers, Informatics, Nursing 2023;41(1):46 View
  34. Bo F, Yerebakan M, Dai Y, Wang W, Li J, Hu B, Gao S. IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review. Healthcare 2022;10(7):1210 View
  35. Schulz P, Andersson E, Bizzotto N, Norberg M. Using Ecological Momentary Assessment to Study the Development of COVID-19 Worries in Sweden: Longitudinal Study. Journal of Medical Internet Research 2021;23(11):e26743 View
  36. Sakal C, Li J, Xiang Y, Li X. Development and validation of the Chinese Geriatric Depression Risk calculator (CGD-risk): A screening tool to identify elderly Chinese with depression. Journal of Affective Disorders 2022;319:428 View
  37. Choi J, Lee S, Kim S, Kim D, Kim H. Depressed Mood Prediction of Elderly People with a Wearable Band. Sensors 2022;22(11):4174 View
  38. Tan J, Ma C, Zhu C, Wang Y, Zou X, Li H, Li J, He Y, Wu C. Prediction models for depression risk among older adults: systematic review and critical appraisal. Ageing Research Reviews 2023;83:101803 View
  39. Abd-alrazaq A, AlSaad R, Aziz S, Ahmed A, Denecke K, Househ M, Farooq F, Sheikh J. Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review. Journal of Medical Internet Research 2023;25:e42672 View
  40. Lee W, Schwartz N, Bansal A, Khor S, Hammarlund N, Basu A, Devine B. A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 1—Data From Wearable Devices. Value in Health 2023;26(2):292 View
  41. Krohn H, Guintivano J, Frische R, Steed J, Rackers H, Meltzer-Brody S. App-Based Ecological Momentary Assessment to Enhance Clinical Care for Postpartum Depression: Pilot Acceptability Study. JMIR Formative Research 2022;6(3):e28081 View
  42. Eickhoff S, Heinrichs B. Der vorhersagbare Mensch. Der Nervenarzt 2021;92(11):1140 View
  43. Liu Q, Wang F, Wang G, Liu L, Hu X. Recent evidence and progress for developing precision nursing in symptomatology: A scoping review. International Nursing Review 2023 View
  44. Lin S, Wu Y, Fang Y. Comparison of Regression and Machine Learning Methods in Depression Forecasting Among Home-Based Elderly Chinese: A Community Based Study. Frontiers in Psychiatry 2022;12 View
  45. Jin H, Nath S, Schneider S, Junghaenel D, Wu S, Kaplan C. An informatics approach to examine decision-making impairments in the daily life of individuals with depression. Journal of Biomedical Informatics 2021;122:103913 View
  46. Hong S, Lee S, Song K, Kim M, Kim Y, Kim H, Kim H. A nurse-led mHealth intervention to alleviate depressive symptoms in older adults living alone in the community: A quasi-experimental study. International Journal of Nursing Studies 2023;138:104431 View
  47. Pieters L, Deenik J, de Vet S, Delespaul P, van Harten P. Combining actigraphy and experience sampling to assess physical activity and sleep in patients with psychosis: A feasibility study. Frontiers in Psychiatry 2023;14 View
  48. Cao Y, Zhao X, Yang Y, Zhu S, Zheng L, Ying T, Sha Z, Zhu R, Wu T. Potential of electronic devices for detection of health problems in older adults at home: A systematic review and meta-analysis. Geriatric Nursing 2023;51:54 View
  49. Zhao Y, Wu X, Tang M, Shi L, Gong S, Mei X, Zhao Z, He J, Huang L, Cui W. Late-life depression: Epidemiology, phenotype, pathogenesis and treatment before and during the COVID-19 pandemic. Frontiers in Psychiatry 2023;14 View
  50. Chhetri B, Goyal L, Mittal M. How machine learning is used to study addiction in digital healthcare: A systematic review. International Journal of Information Management Data Insights 2023;3(2):100175 View
  51. Abd-Alrazaq A, AlSaad R, Shuweihdi F, Ahmed A, Aziz S, Sheikh J. Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression. npj Digital Medicine 2023;6(1) View

Books/Policy Documents

  1. Verhagen S, van Os J, Delespaul P. Mental Health in a Digital World. View
  2. Tumanov K, Spanakis G. Explainable AI Within the Digital Transformation and Cyber Physical Systems. View