Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone
Open accessPublished version of a preprint
Abstract
Background: Although geriatric depression is prevalent, diagnosis using self-reporting instruments has limitations when measuring the depressed mood of older adults in a community setting. Ecological momentary assessment (EMA) by using wearable devices could be used to collect data to classify older adults into depression groups. Objective: The objective of this study was to develop a machine learning algorithm to predict the classification of depression groups among older adults living alone. We focused on utilizing diverse data collected through a survey, an Actiwatch, and an EMA report related to depression. Methods: The prediction model using machine learning was developed in 4 steps: (1) data collection, (2) data processing and representation, (3) data modeling (feature engineering and selection), and (4) training and validation to test the prediction model. Older adults (N=47), living alone in community settings, completed an EMA to report depressed moods 4 times a day for 2 weeks between May 2017 and January 2018. Participants wore an Actiwatch that measured their activity and ambient light exposure every 30 seconds for 2 weeks. At baseline and the end of the 2-week observation, depressive symptoms were assessed using the Korean versions of the Short Geriatric Depression Scale (SGDS-K) and the Hamilton Depression Rating Scale (K-HDRS). Conventional classification based on binary logistic regression was built and compared with 4 machine learning models (the logit, decision tree, boosted trees, and random forest models). Results: On the basis of the SGDS-K and K-HDRS, 38% (18/47) of the participants were classified into the probable depression group. They reported significantly lower scores of normal mood and physical activity and higher levels of white and red, green, and blue (RGB) light exposures at different degrees of various 4-hour time frames (all P<.05). Sleep efficiency was chosen for modeling through feature selection. Comparing diverse combinations of the selected variables, daily mean EMA score, daily mean activity level, white and RGB light at 4:00 pm to 8:00 pm exposure, and daily sleep efficiency were selected for modeling. Conventional classification based on binary logistic regression had a good model fit (accuracy: 0.705; precision: 0.770; specificity: 0.859; and area under receiver operating characteristic curve or AUC: 0.754). Among the 4 machine learning models, the logit model had the best fit compared with the others (accuracy: 0.910; precision: 0.929; specificity: 0.940; and AUC: 0.960). Conclusions: This study provides preliminary evidence for developing a machine learning program to predict the classification of depression groups in older adults living alone. Clinicians should consider using this method to identify underdiagnosed subgroups and monitor daily progression regarding treatment or therapeutic intervention in the community setting. Furthermore, more efforts are needed for researchers and clinicians to diversify data collection methods by using a survey, EMA, and a sensor.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Actigraphy-based biomarkers that quantify time sequences of rest and active behaviors with accelerometer sensors are known to be useful in predicting mood disorders such as depression and bipolar disorder.
“Actigraphy-based biomarkers that quantify time sequences of rest and active behaviors with accelerometer sensors are known to be useful in predicting mood disorders such as depression and bipolar disorder [34,35].”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Development and validation of a geriatric depression screening scale: a preliminary report
Yesavage, J. A., Brink, T. L., Rose, T. L., Lum, O., Huang, V., Adey, M., & Leirer, V. O. (1982) · Journal of Psychiatric Research · doi:10.1016/0022-3956(82)90033-4 · Cited by 15,025 · see citing works
Vos, T., Allen, C., Arora, M., Barber, R. M., Bhutta, Z. A., Brown, A., Carter, A., Casey, D. C., Charlson, F. J., Chen, A. Z., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)31678-6 · Cited by 7,253 · see citing works
Ecological momentary assessment
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008) · Annual Review of Clinical Psychology · doi:10.1146/annurev.clinpsy.3.022806.091415 · Cited by 6,454 · see citing works
Cognitive Stimulation in Patients with Dementia: Randomized Controlled Trial
Mapelli, D., Rosa, E. D., Nocita, R., & Sava, D. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353457 · Cited by 3,468 · see citing works
The role and validity of actigraphy in sleep medicine: an update
Sadeh, A. (2011) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2010.10.001 · Cited by 1,415 · see citing works
Severity classification on the Hamilton depression rating scale
Zimmerman, M., Martinez, J. H., Young, D., Chelminski, I., & Dalrymple, K. (2013) · Journal of Affective Disorders · doi:10.1016/j.jad.2013.04.028 · Cited by 1,199 · see citing works
Data Mining Concepts and Techniques Third Edition
Han, J., Kamber, M., & Pei, J. (2021) · Thammasat University Digital Collections · Cited by 1,189 · see citing works
Luo, W., Phung, D., Tran, T., Gupta, S., Rana, S., Karmakar, C., Shilton, A., Yearwood, J., Dimitrova, N., Ho, T. B., et al. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5870 · Cited by 988 · see citing works
Digital Phenotyping: Technology for a New Science of Behavior
Insel, T. R. (2017) · JAMA · doi:10.1001/jama.2017.11295 · Cited by 833 · see citing works
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning
Mohr, D. C., Zhang, M., & Schueller, S. M. (2017) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-044949 · Cited by 802 · see citing works
Schuch, F., Vancampfort, D., Firth, J., Rosenbaum, S., Ward, P., Reichert, T., Bagatini, N. C., Bgeginski, R., & Stubbs, B. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.10.050 · Cited by 663 · see citing works
Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors
Patel, S., Lorincz, K., Hughes, R., Huggins, N., Growdon, J., Standaert, D., Akay, M., Dy, J., Welsh, M., & Bonato, P. (2009) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2033471 · Cited by 623 · see citing works
Bae, J. N., & Cho, M. J. (2004) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2004.01.004 · Cited by 619 · see citing works
MoodScope: building a mood sensor from smartphone usage patterns
LiKamWa, R., Liu, Y., Lane, N. D., & Zhong, L. (2013) · Proceeding of the 11th annual international conference on Mobile systems, applications, and services · doi:10.1145/2462456.2464449 · Cited by 444 · see citing works
Ben-Zeev, D., Scherer, E. A., Wang, R., Xie, H., & Campbell, A. T. (2015) · Psychiatric Rehabilitation Journal · doi:10.1037/prj0000130 · Cited by 429 · see citing works
Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild
Wahle, F., Kowatsch, T., Fleisch, E., Rufer, M., & Weidt, S. (2016) · JMIR mHealth and uHealth · doi:10.2196/mhealth.5960 · Cited by 333 · see citing works
Activity monitoring in patients with depression: A systematic review
Burton, C., McKinstry, B., Szentagotai Tătar, A., Serrano-Blanco, A., Pagliari, C., & Wolters, M. (2013) · Journal of Affective Disorders · doi:10.1016/j.jad.2012.07.001 · Cited by 249 · see citing works
Sleep Quality Prediction From Wearable Data Using Deep Learning
Sathyanarayana, A., Joty, S., Fernández-Luque, L., Ofli, F., Srivastava, J., Elmagarmid, A. K., Arora, T., & Taheri, S. (2016) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6562 · Cited by 242 · see citing works
Cheng, Q., Li, T. M., Kwok, C.-L., Zhu, T., & Yip, P. S. F. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7276 · Cited by 236 · see citing works
Merikangas, K. R., Swendsen, J., Hickie, I. B., Cui, L., Shou, H., Merikangas, A. K., Zhang, J., Lamers, F., Crainiceanu, C., Volkow, N. D., et al. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.3546 · Cited by 231 · see citing works
Use of ecological momentary assessment in mood disorders research
Wenze, S. J., & Miller, I. W. (2010) · Clinical Psychology Review · doi:10.1016/j.cpr.2010.06.007 · Cited by 205 · see citing works
Düking, P., Andreas Hotho, A., Holmberg, H., Fuss, F. K., & Sperlich, B. (2016) · Frontiers in Physiology · doi:10.3389/fphys.2016.00071 · Cited by 184 · see citing works
Cho, C.-H., Lee, T., Kim, M.-G., In, H. P., Kim, L., & Lee, H.-J. (2019) · Journal of Medical Internet Research · doi:10.2196/11029 · Cited by 183 · see citing works
Sleep correlates of depression and anxiety in an elderly A sian population
Yu, J., Rawtaer, I., Fam, J., Jiang, M., Feng, L., Kua, E. H., & Mahendran, R. (2015) · Psychogeriatrics · doi:10.1111/psyg.12138 · Cited by 178 · see citing works
Ecological momentary assessment in aging research: A critical review
Cain, A. E., Depp, C. A., & Jeste, D. V. (2009) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2009.01.014 · Cited by 164 · see citing works
Retrospective recall of affect in clinically depressed individuals and controls
Ben-Zeev, D., Young, M. A., & Madsen, J. W. (2009) · Cognition & Emotion · doi:10.1080/02699930802607937 · Cited by 158 · see citing works
Daily and seasonal variation in the spectral composition of light exposure in humans
Thorne, H. C., Jones, K. H., Peters, S. P., Archer, S. N., & Dijk, D.-J. (2009) · Chronobiology International · doi:10.1080/07420520903044315 · Cited by 153 · see citing works
Relationship Between Sleep Quality and Mood: Ecological Momentary Assessment Study
Triantafillou, S., Saeb, S., Lattie, E. G., Mohr, D. C., & Kording, K. P. (2018) · JMIR Mental Health · doi:10.2196/12613 · Cited by 142 · see citing works
Attitudes toward mental health services: Age-group differences in Korean American adults
Jang, Y., Chiriboga, D. A., & Okazaki, S. (2009) · Aging & Mental Health · doi:10.1080/13607860802591070 · Cited by 138 · see citing works
Assessment of late-life depression via self-report measures: a review
Balsamo, M., Cataldi, F., Carlucci, L., Padulo, C., & Fairfield, B. (2018) · Clinical Interventions in Aging · doi:10.2147/cia.s178943 · Cited by 121 · see citing works
DeLight: biofeedback through ambient light for stress intervention and relaxation assistance
Yu, B., Hu, J., Funk, M., & Feijs, L. M. G. (2018) · Personal and Ubiquitous Computing · doi:10.1007/s00779-018-1141-6 · Cited by 117 · see citing works
Glenn, T., & Monteith, S. (2014) · Current Psychiatry Reports · doi:10.1007/s11920-014-0523-3 · Cited by 104 · see citing works
Depression in the elderly: clinical features and risk factors.
Sözeri-Varma, G. (2012) · Europe PMC (PubMed Central) · Cited by 102 · see citing works
From eHealth to iHealth: Transition to Participatory and Personalized Medicine in Mental Health
Berrouiguet, S., Perez-Rodriguez, M. M., Larsen, M., Baca-García, E., Courtet, P., & Oquendo, M. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.7412 · Cited by 102 · see citing works
Ramsey, A. T., Wetherell, J. L., Depp, C. A., Dixon, D. J., & Lenze, E. J. (2016) · Journal of Technology in Human Services · doi:10.1080/15228835.2016.1170649 · Cited by 91 · see citing works
Daily minority stress and affect among gay and bisexual men: A 30-day diary study
Eldahan, A. I., Pachankis, J. E., Rendina, H. J., Ventuneac, A., Grov, C., & Parsons, J. T. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2015.10.066 · Cited by 86 · see citing works
Physical (in)activity and depression in older people
Wassink‐Vossen, S., Collard, R. M., Voshaar, R. C. O., Comijs, H. C., de Vocht, H. M., & Naarding, P. (2014) · Journal of Affective Disorders · doi:10.1016/j.jad.2014.03.001 · Cited by 84 · see citing works
Depression and sleep duration: findings from middle-aged and elderly people in China
Ouyang, P., & Sun, W. (2018) · Public Health · doi:10.1016/j.puhe.2018.10.007 · Cited by 82 · see citing works
Baune, B. T., Caliskan, S., & Todder, D. (2006) · Human Psychopharmacology Clinical and Experimental · doi:10.1002/hup.817 · Cited by 80 · see citing works
Smartphone-centred wearable sensors network for monitoring patients with bipolar disorder
Puiatti, A., Mudda, S., Giordano, S., & Mayora, O. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6090613 · Cited by 75 · see citing works
Impact of Physical Activity on Sleep:A Deep Learning Based ExplorationPreprint
Sathyanarayana, A., Joty, S., Fernández-Luque, L., Ofli, F., Srivastava, J., Elmagarmid, A. K., Taheri, S., & Arora, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.07034 · Cited by 73 · see citing works
Teipel, S. J., König, A., Hoey, J., Kaye, J. A., Krüger, F., Robillard, J. M., Kirste, T., & Babiloni, C. (2018) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2018.05.003 · Cited by 66 · see citing works
The intra-day dynamics of affect, self-esteem, tiredness, and suicidality in Major Depression
Crowe, E., Daly, M., Delaney, L. D., Carroll, S., & Malone, K. M. (2018) · Psychiatry Research · doi:10.1016/j.psychres.2018.02.032 · Cited by 64 · see citing works
Goodwin, M. S., Haghighi, M., Tang, Q., Akçakaya, M., Deniz Erdogmus, B., & Intille, S. S. (2014) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2632048.2632096 · Cited by 57 · see citing works
Acebo, C., & LeBourgeois, M. K. (2006) · PubMed · doi:10.1016/j.rcc.2005.11.010 · Cited by 55 · see citing works
Depression and Associated Factors in the Elderly Cadres in Fuzhou, China: A Community-based Study
Cong, L., Dou, P., Chen, D., & Cai, L. (2015) · International journal of gerontology · doi:10.1016/j.ijge.2015.02.001 · Cited by 51 · see citing works
Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study
Hung, G. C., Yang, P.-C., Chang, C.-C., Chiang, J.-H., & Chen, Y. (2016) · JMIR Research Protocols · doi:10.2196/resprot.5551 · Cited by 49 · see citing works
Kim, H., Kwon, S., Hong, S., & Lee, S. (2018) · BMC Geriatrics · doi:10.1186/s12877-018-0882-4 · Cited by 44 · see citing works
Self-citationDiminished perception of ambient light: a symptom of clinical depression?
Friberg, T. R., & Borrero, G. (2000) · Journal of Affective Disorders · doi:10.1016/s0165-0327(99)00194-9 · Cited by 38 · see citing works
Daily Temporal Associations Between Physical Activity and Symptoms in Multiple Sclerosis
Kratz, A. L., Fritz, N. E., Braley, T. J., Scott, E. L., Foxen-Craft, E. C., & Murphy, S. L. (2018) · Annals of Behavioral Medicine · doi:10.1093/abm/kay018 · Cited by 38 · see citing works
A gold nanocluster chemical tongue sensor array for Alzheimer’s disease diagnosis
Han, X., Man, Z., Xu, S., Cong, L., Wang, Y., Wang, X., Du, Y., Zhang, Q., Tang, S., Liu, Z., et al. (2018) · Colloids and Surfaces B Biointerfaces · doi:10.1016/j.colsurfb.2018.10.020 · Cited by 36 · see citing works
Seo, J. Y., & Chao, Y. (2017) · Journal of Gerontological Nursing · doi:10.3928/00989134-20171024-01 · Cited by 34 · see citing works
Rullier, L., Atzeni, T., Husky, M. M., Bouisson, J., Dartigues, J., Swendsen, J. D., & Bergua, V. (2013) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.1425 · Cited by 31 · see citing works
Mapping Movement: Applying Motion Measurement Technologies to the Psychiatric Care of Older Adults
Collier, S. A., Monette, P., Hobbs, K., Tabasky, E., Forester, B. P., & Vahia, I. V. (2018) · Current Psychiatry Reports · doi:10.1007/s11920-018-0921-z · Cited by 30 · see citing works
Why stay home? Temporal association of pain, fatigue and depression with being at home
Ravesloot, C., Ward, B. A., Hargrove, T., Wong, J., Livingston, N., Torma, L. M., & Ipsen, C. (2015) · Disability and health journal · doi:10.1016/j.dhjo.2015.10.010 · Cited by 27 · see citing works
Vachon, H., Fortes-Bourbousson, M., Deschamps, T., Doron, J., Bulteau, S., Sauvaget, A., & Thomas-Ollivier, V. (2016) · Psychiatry Research · doi:10.1016/j.psychres.2016.08.034 · Cited by 27 · see citing works
Parsey, C. M., & Schmitter‐Edgecombe, M. (2018) · JMIR Aging · doi:10.2196/11331 · Cited by 19 · see citing works
Tanaka, T., Kumiko, K., Iwasa, K., Sawa, K., Yamada, N., & Komori, M. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.02314 · Cited by 15 · see citing works
Jeon, S. W., Yoon, S. Y., Ko, Y., Joe, S.-H., Kim, Y., Han, C., Yoon, H.-K., & Liu, C. (2016) · Journal of Korean Medical Science · doi:10.3346/jkms.2016.31.12.2002 · Cited by 14 · see citing works
Collins, S., Šabanović, S., Fraune, M. R., Randall, N., Eldridge, L. A., Piatt, J. A., Bennett, C. C., & Nagata, S. (2018) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3173386.3177047 · Cited by 14 · see citing works
강은나, & Lee, M. (2018) · Health and Social Welfare Review · doi:10.15709/hswr.2018.38.4.196 · Cited by 13 · see citing works
Design and Evaluation of an Ambient Lighting Interface of HRV Biofeedback System in Home Setting
Yu, B., Hu, J., & Feijs, L. M. G. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13102-3_17 · Cited by 11 · see citing works
Gordon, I., Ling, J., Robinson, L. A., Hayes, C., & Crosland, A. (2018) · BMC Family Practice · doi:10.1186/s12875-018-0857-8 · Cited by 8 · see citing works
Kim, H., Hong, S., Kwon, S., & Kim, N. (2018) · Journal of Korean Gerontological Nursing · doi:10.17079/jkgn.2018.20.s1.s119 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- [5]Mobile Health Insurance Review & Assessment Service 2017 2019-02-28 Disease Information by Statistics. http://www.hira.or.kr/dummy.do?pgmid=HIRAA030502030000&ilnsCtgyNm=sixtyAge&sortSno=46
- [38]Alpaydin E Introduction To Machine Learning. Second edition 2004 Cambridge, MA The MIT Press
Cited by
Sorted by citations
Cho, C.-H., Lee, T., Kim, M.-G., In, H. P., Kim, L., & Lee, H.-J. (2019) · Journal of Medical Internet Research · doi:10.2196/11029 · Cited by 183 · see citing works
Opoku Asare, K., Terhorst, Y., Vega, J., Peltonen, E., Lagerspetz, E., & Ferreira, D. (2021) · JMIR mHealth and uHealth · doi:10.2196/26540 · Cited by 145 · see citing works
Abd-Alrazaq, A., AlSaad, R., Shuweihdi, F., Ahmed, A., Aziz, S., & Sheikh, J. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00828-5 · Cited by 138 · see citing works
Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review
Abd-Alrazaq, A. A., AlSaad, R., Aziz, S., Ahmed, A., Denecke, K., Househ, M. S., Farooq, F., & Sheikh, J. I. (2022) · Journal of Medical Internet Research · doi:10.2196/42672 · Cited by 113 · see citing works
Asare, K. O., Moshe, I., Terhorst, Y., Vega, J., Hosio, S. J., Baumeister, H., Pulkki-Råbäck, L., & Ferreira, D. (2022) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2022.101621 · Cited by 82 · see citing works
Zarate, D., Stavropoulos, V., Ball, M., Collier, G. D. S., & Jacobson, N. C. (2022) · BMC Psychiatry · doi:10.1186/s12888-022-04013-y · Cited by 79 · see citing works
Kargarandehkordi, A., Li, S., Lin, K., Phillips, K. T., Benzo, R. M., & Washington, P. Y. (2025) · Biosensors · doi:10.3390/bios15040202 · Cited by 72 · see citing works
Stress research during the COVID-19 pandemic and beyond
Pfeifer, L. S., Heyers, K., Ocklenburg, S., & Wolf, O. T. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.09.045 · Cited by 67 · see citing works
mHealth Assessment and Intervention of Depression and Anxiety in Older Adults
Grossman, J. T., Frumkin, M., Rodebaugh, T., & Lenze, E. (2020) · Harvard Review of Psychiatry · doi:10.1097/hrp.0000000000000255 · Cited by 45 · see citing works
Prediction models for depression risk among older adults: systematic review and critical appraisal
Tan, J., Ma, C., Zhu, C., Wang, Y., Zou, X., Li, H., Li, J., He, Y., & Wu, C. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101803 · Cited by 62 · see citing works
Personalized Prediction of Behaviors and Experiences: An Idiographic Person–Situation Test
Beck, E. D., & Jackson, J. J. (2022) · Psychological Science · doi:10.1177/09567976221093307 · Cited by 56 · see citing works
Heyat, M. B. B., Akhtar, F., Munir, F., Sultana, A., Muaad, A. Y. M., Gul, I., Sawan, M., Asghar, W., Iqbal, S. M. A., Baig, A. A., et al. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01346-x · Cited by 56 · see citing works
Richter, T., Fishbain, B., Richter-Levin, G., & Okon-Singer, H. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11100957 · Cited by 52 · see citing works
Wearable devices for anxiety & depression: A scoping review
Ahmed, A., Aziz, S., Alzubaidi, M., Schneider, J., Irshaidat, S., Abu Serhan, H., Abd-alrazaq, A. A., Solaiman, B., & Househ, M. (2023) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2023.100095 · Cited by 50 · see citing works
Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives
Kamath, J., León-Barriera, R., Jain, N., Keisari, E. J., & Wang, B. (2022) · World Journal of Psychiatry · doi:10.5498/wjp.v12.i3.393 · Cited by 54 · see citing works
Identifying Predictors of Opioid Overdose Death at a Neighborhood Level With Machine Learning
Schell, R. C., Allen, B., Goedel, W. C., Hallowell, B. D., Scagos, R. P., Li, Y., Krieger, M. S., Neill, D. B., Marshall, B. D. L., Cerdá, M., et al. (2021) · American Journal of Epidemiology · doi:10.1093/aje/kwab279 · Cited by 50 · see citing works
A Multirobot System in an Assisted Home Environment to Support the Elderly in Their Daily Lives
Barber, R., Ortiz, F. J., Garrido, S., Calatrava-Nicolás, F. M., Mora, A., Prados, A., Vera-Repullo, J. A., González, J. R., Méndez, I., & Mozos, Ó. M. (2022) · Sensors · doi:10.3390/s22207983 · Cited by 47 · see citing works
Depressed Mood Prediction of Elderly People with a Wearable Band
Choi, J., Lee, S., Kim, S., Kim, D., & Kim, H. (2022) · Sensors · doi:10.3390/s22114174 · Cited by 43 · see citing works
Self-citationHow machine learning is used to study addiction in digital healthcare: A systematic review
Chhetri, B., Goyal, L. M., & Mittal, M. (2023) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2023.100175 · Cited by 42 · see citing works
Vergaray, A. D., Herrera, J. C., Cornelio, J. B., Carranza, A. R. L., & Sánchez, C. F. P. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101295 · Cited by 38 · see citing works
Relationship between the living-alone period and depressive symptoms among the elderly
Koo, J. H., Son, N., & Yoo, K.-B. (2021) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2021.104341 · Cited by 37 · see citing works
Machine Learning on Early Diagnosis of Depression
Lee, K.-S., & Ham, B. (2022) · Psychiatry Investigation · doi:10.30773/pi.2022.0075 · Cited by 35 · see citing works
Depression and its associated factors among the older adults in rural, Tamilnadu, India
Bincy, K., Logaraj, M., & Ramraj, B. (2020) · Clinical Epidemiology and Global Health · doi:10.1016/j.cegh.2020.100677 · Cited by 37 · see citing works
Zhao, Y., Wu, X., Tang, M., Shi, L., Gong, S., Mei, X., Zhao, Z., He, J., Huang, L., & Cui, W. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1017203 · Cited by 37 · see citing works
IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review
Bo, F., Yerebakan, M., Dai, Y., Wang, W., Li, J., Hu, B., & Gao, S. (2022) · Healthcare · doi:10.3390/healthcare10071210 · Cited by 35 · see citing works
Sun, Y., Chen, J., Ji, M., & Li, X. (2025) · Journal of Medical Internet Research · doi:10.2196/69077 · Cited by 38 · see citing works
He-Yueya, J., Buck, B., Campbell, A. T., Choudhury, T., Kane, J. M., Ben‐Zeev, D., & Althoff, T. (2020) · Schizophrenia · doi:10.1038/s41537-020-00123-2 · Cited by 33 · see citing works
Intelligent Internet of Medical Things for Depression: Current Advancements, Challenges, and Trends
Heyat, M. B. B., Adhikari, D., Akhtar, F., Parveen, S., Zeeshan, H. M., Ullah, H., Chen, Y.-H., Wang, L., & Sawan, M. (2025) · International Journal of Intelligent Systems · doi:10.1155/int/6801530 · Cited by 32 · see citing works
Idiographic artificial intelligence to explain students' self-regulation: Toward precision education
Saqr, M., Cheng, R., López‐Pernas, S., & Beck, E. D. (2024) · Learning and Individual Differences · doi:10.1016/j.lindif.2024.102499 · Cited by 34 · see citing works
Minaeva, O., Riese, H., Lamers, F., Antypa, N., Wichers, M., & Booij, S. H. (2020) · Journal of Medical Internet Research · doi:10.2196/22634 · Cited by 28 · see citing works
Robotic-Based Well-Being Monitoring and Coaching System for the Elderly in Their Daily Activities
Calatrava-Nicolás, F. M., Maestro, E. G., Bautista‐Salinas, D., Ortiz, F. J., González, J. R., Vera-Repullo, J. A., Buendía, M. J., Méndez, I., Esteban, C. R., & Mozos, Ó. M. (2021) · Sensors · doi:10.3390/s21206865 · Cited by 32 · see citing works
Song, S., Seo, Y., Hwang, S., Kim, H., & Kim, J. (2024) · JMIR mhealth and uhealth · doi:10.2196/55842 · Cited by 31 · see citing works
Self-citationMassar, S. A. A., Chua, X. Y., Soon, C. S., Ng, A., Ong, J. L., Chee, N. I. Y. N., Lee, T.-S. W., Ghosh, A., & Chee, M. W. L. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00466-9 · Cited by 29 · see citing works
Lin, S., Wu, Y., & Fang, Y. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.764806 · Cited by 28 · see citing works
Wang, L., & Miller, L. C. (2022) · JMIR Formative Research · doi:10.2196/37270 · Cited by 28 · see citing works
Kim, S., & Lee, K. (2021) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s339412 · Cited by 25 · see citing works
Kim, H., Kim, S., Kong, S. S., Jeong, Y.-R., Kim, H., & Kim, N. (2020) · JMIR Mental Health · doi:10.2196/13247 · Cited by 20 · see citing works
Self-citationWang, Y., Wang, X., Zhao, L., & Jones, K. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.09.147 · Cited by 22 · see citing works
Designing daily-life research combining experience sampling method with parallel data
Velozo, J. D. C., Habets, J., George, S. V., Niemeijer, K., Minaeva, O., Hagemann, N., Herff, C., Kuppens, P., Rintala, A., Vaessen, T., et al. (2022) · Psychological Medicine · doi:10.1017/s0033291722002367 · Cited by 20 · see citing works
Zhang, P., Fonnesbeck, C., Schmidt, D. C., White, J., Kleinberg, S., & Mulvaney, S. A. (2021) · JMIR mhealth and uhealth · doi:10.2196/21959 · Cited by 20 · see citing works
Verhagen, S. J. W., van Os, J., & Delespaul, P. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822201-0.00017-4 · Cited by 20 · see citing works
Seong, S., Park, S., Ahn, Y., & Kim, H. (2022) · BMC Public Health · doi:10.1186/s12889-022-13973-5 · Cited by 19 · see citing works
Self-citationHong, S., Lee, S., Song, K., Kim, M.-J., Kim, Y., Kim, H., & Kim, H. (2022) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2022.104431 · Cited by 22 · see citing works
Self-citationČihařová, M., Amarti, K., van Breda, W., Peng, X., Lorente-Català, R., Funk, B., Hoogendoorn, M., Koutsouleris, N., Fusar‐Poli, P., Karyotaki, E., et al. (2024) · Biological Psychiatry · doi:10.1016/j.biopsych.2024.06.002 · Cited by 19 · see citing works
Wang, Q., Zheng, W., Wu, F., Xu, A., Zhu, H., & Liu, Z. (2021) · Frontiers in Earth Science · doi:10.3389/feart.2021.730565 · Cited by 18 · see citing works
Recent evidence and progress for developing precision nursing in symptomatology: A scoping review
Liu, Q., Wang, F., Wang, G., Liu, L., & Hu, X. (2023) · International Nursing Review · doi:10.1111/inr.12816 · Cited by 16 · see citing works
Woll, S., Birkenmaier, D., Biri, G. I., Nissen, R., Lutz, L., Schroth, M., Ebner‐Priemer, U., & Giurgiu, M. (2025) · JMIR mhealth and uhealth · doi:10.2196/59660 · Cited by 19 · see citing works
A Machine Learning Based Study to Predict Depression with Monitoring Actigraph Watch Data
Raihan, M., Bairagi, A. K., & Rahman, S. (2021) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt51525.2021.9579614 · Cited by 16 · see citing works
Polysomnographic identification of anxiety and depression using deep learning
Thakre, T. P., Kulkarni, H. R., Adams, K. S., Mischel, R., Hayes, R., & Pandurangi, A. (2022) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2022.03.027 · Cited by 15 · see citing works
Niculescu, I., Quirt, H., Arora, T., Borsook, T. K., Green, R., Ford, B. Q., & Iaboni, A. (2021) · JMIR Aging · doi:10.2196/29021 · Cited by 15 · see citing works
Schulz, P. J., Andersson, E. M., Bizzotto, N., & Norberg, M. (2021) · Journal of Medical Internet Research · doi:10.2196/26743 · Cited by 13 · see citing works
Krohn, H., Guintivano, J., Frische, R. M., Steed, J., Rackers, H., & Meltzer-Brody, S. E. (2022) · JMIR Formative Research · doi:10.2196/28081 · Cited by 12 · see citing works
Susanty, S., Sufriyana, H., Su, E. C., & Chuang, Y. (2023) · PLoS ONE · doi:10.1371/journal.pone.0280330 · Cited by 11 · see citing works
Walschots, Q., Zarchev, M. G., Unkel, M., & Kamperman, A. M. (2024) · Algorithms · doi:10.3390/a17090408 · Cited by 10 · see citing works
Tran, T., Tan, Y. Z., Lin, S., Zhao, F., Ng, Y. S., Ma, D., Ko, J., & Balan, R. K. (2024) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2024.105647 · Cited by 12 · see citing works
Ahn, J. S., Jeong, I., Park, S., Lee, J., Jeon, M.-J., Lee, S., Do, G., Jung, D., & Park, J. Y. (2025) · Journal of Medical Internet Research · doi:10.2196/69320 · Cited by 15 · see citing works
Self-citationAnýž, J., Bakštein, E., Dally, A., Kolenič, M., Hlinka, J., Hartmannová, T., Urbanová, K., Correll, C. U., Novák, D., & Španiel, F. (2021) · JMIR Mental Health · doi:10.2196/26348 · Cited by 10 · see citing works
Sakal, C., Li, J., Xiang, Y., & Li, X. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.09.034 · Cited by 10 · see citing works
Pieters, L. E., Deenik, J., de Vet, S., Delespaul, P., & van Harten, P. N. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1107812 · Cited by 10 · see citing works
Depression in Older Adults: Do Current DSM Diagnostic Criteria Really Fit?
Bergua, V., Blanchard, C., & Amieva, H. (2023) · Clinical Gerontologist · doi:10.1080/07317115.2023.2274053 · Cited by 9 · see citing works
Digital Literacy and Associated Factors in Older Adults Living in Urban South Korea
Baek, J., Kim, H., Choi, S., Hong, S., Kim, Y., Kim, E., Lee, T. W., Chu, S. H., & Choi, J. (2024) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000001109 · Cited by 9 · see citing works
Self-citationLei, D., Sun, J., & Xia, J. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e18497 · Cited by 9 · see citing works
HADD: High-Accuracy Detection of Depressed Mood
Liu, Y., Kang, K., & Doe, M. J. (2022) · Technologies · doi:10.3390/technologies10060123 · Cited by 8 · see citing works
IMU-Based Monitoring for Assistive Disease Diagnosis and Management of IoHT: A ReviewPreprint
Bo, F., Yerebakan, M. O., Dai, Y., Wang, W., Li, J., Gao, S., & Boyi, H. (2022) · doi:10.36227/techrxiv.19200482 · Cited by 10 · see citing works
Kang, B., Park, M. K., Kim, J. I., Yoon, S., Heo, S., Kang, C., Lee, S., Choi, Y.-S., & Hong, D. (2025) · Journal of Medical Internet Research · doi:10.2196/69379 · Cited by 7 · see citing works
Self-citationLewczuk, K., Górowska, M. P., Li, Y., & Gola, M. K. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00807 · Cited by 7 · see citing works
Lee, W., Schwartz, N. R. M., Bansal, A., Khor, S., Hammarlund, N. E., Basu, A., & Devine, B. (2022) · Value in Health · doi:10.1016/j.jval.2022.08.005 · Cited by 7 · see citing works
Kang, B., Hong, D., Yoon, S., Kang, C., & Kim, J. I. (2025) · JMIR mhealth and uhealth · doi:10.2196/64853 · Cited by 5 · see citing works
Adhibai, R., Kosiyaporn, H., Markchang, K., Nasueb, S., Waleewong, O., & Suphanchaimat, R. (2024) · PLoS ONE · doi:10.1371/journal.pone.0304845 · Cited by 5 · see citing works
Xu, J., Zhang, W., & Liu, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-10645-3 · Cited by 5 · see citing works
Using Mobile Daily Mood and Anxiety Self-ratings to Predict Depression Symptom Improvement
Sahoo, S., Shende, C., Hossain, M. Z., Patel, P. A., Wang, X., Mahmud, M. I., Bi, J., Kamath, J., Russell, A. C., Song, D., et al. (2024) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase60773.2024.00012 · Cited by 5 · see citing works
Consumption of Licit and Illicit Substances leading to Mental Illness: A Prevalence Study
Chhetri, B., Goyal, L. M., Mittal, M., & Gurung, S. (2020) · EAI Endorsed Transactions on Pervasive Health and Technology · doi:10.4108/eai.11-5-2020.164415 · Cited by 5 · see citing works
Use of the Time-Based Experience Sampling Method in Older Adults
Vanhollebeke, J., Appel, J. E., Dezutter, J., & Dewitte, L. (2024) · European Journal of Psychological Assessment · doi:10.1027/1015-5759/a000859 · Cited by 4 · see citing works
Dewitte, L., Dejonckheere, E., Dezutter, J., Vandenbulcke, M., Ahmed, R. M., & Irish, M. (2025) · npj Dementia · doi:10.1038/s44400-025-00023-1 · Cited by 4 · see citing works
Ecological Momentary Assessment in Acquired Brain Injury: A Scoping Review
Burnett, J., Cobden, A. L., Firman-Sadler, L., Fuller‐Tyszkiewicz, M., Akhlaghi, H., D, J. F. D., & Caeyenberghs, K. (2025) · Journal of Neurotrauma · doi:10.1177/08977151251359986 · Cited by 4 · see citing works
Błachnio, A., Przepiórka, A., Gorbaniuk, O., McNeill, M., Bendayan, R., Durak, M., Şenol-Durak, E., Ben‐Ezra, M., Benvenuti, M., Angeluci, A. C. B., et al. (2022) · Frontiers research topics · doi:10.3389/978-2-88974-249-3 · Cited by 4 · see citing works
Advances in the assessment and study of suicide in late-life depression
Xu, T., Mao, Y., Wang, Y., Zhang, W., Cao, H., & Yu, E. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1610730 · Cited by 4 · see citing works
Jin, H., Nath, S. S., Schneider, S., Junghaenel, D. U., Wu, S., & Kaplan, C. D. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103913 · Cited by 4 · see citing works
Eickhoff, S. B., & Heinrichs, B. (2021) · Der Nervenarzt · doi:10.1007/s00115-021-01197-8 · Cited by 5 · see citing works
Personalized Behavior Prediction: An Idiographic Person-Situation TestPreprint
Beck, E. D., & Jackson, J. J. (2021) · doi:10.31234/osf.io/syhw5 · Cited by 4 · see citing works
Zorro, C. A. C., Fonzo, G. A., & Moscoso-Barrera, W. D. (2026) · Digital Health · doi:10.1177/20552076251404514 · Cited by 4 · see citing works
Niculescu, I., Arora, T., & Iaboni, A. (2020) · Aging & Mental Health · doi:10.1080/13607863.2020.1793899 · Cited by 3 · see citing works
Cao, Y., Zhao, X., Yang, Y., Zhu, S., Zheng, L.-D., Ying, T., Sha, Z., Zhu, R., & Wu, T. (2023) · Geriatric Nursing · doi:10.1016/j.gerinurse.2023.02.007 · Cited by 3 · see citing works
Jung, D., Jin, G., Choi, J., Park, S., Park, S., Seo, D. G., & Choi, K. (2024) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2024.03.008 · Cited by 3 · see citing works
Kim, B., Chae, M., Kim, Y., Kong, S., Kim, Y., Jung, T., Jeong, J., Park, S., Cho, C., Yeom, J. W., et al. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822654 · Cited by 3 · see citing works
IMU-Based Monitoring for Assistive Disease Diagnosis and Management of IoHT: A ReviewPreprint
Bo, F., Yerebakan, M. O., Dai, Y., Wang, W., Li, J., Gao, S., & Boyi, H. (2022) · doi:10.36227/techrxiv.19200482.v1 · Cited by 3 · see citing works
Developing a Multimodal Monitoring System for Geriatric Depression
Cho, Y., Lim, K., Lee, S., Kim, Y., Kim, M.-J., Kim, C. O., Kim, Y., & Kim, H. (2022) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000000925 · Cited by 2 · see citing works
Self-citationMao, N., Wang, Y., Zhou, A., Dai, X., Han, Z. R., He, L., & Zhang, Z. (2025) · The Journals of Gerontology Series B · doi:10.1093/geronb/gbaf158 · Cited by 2 · see citing works
Song, S., Hwang, S., Kim, H.-Y., & Kim, J. (2026) · JMIR Aging · doi:10.2196/85611 · Cited by 2 · see citing works
Self-citationDepression and Mood Monitoring for Pregnant Women Using IoT-Based Wearables
Devi, D. H., Naresh, R., Kumar, C. N. S. V., Senthilkumar, S., & Jovin, A. A. (2023) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-1718-1.ch009 · Cited by 1 · see citing works
Ranjan, R., Yadav, J., Ranjan, V., Venkateswaran, C., & John, D. (2024) · Indian Journal of Psychological Medicine · doi:10.1177/02537176241253338 · Cited by 1 · see citing works
Measuring outcomes of communication intervention in adults with neurodegenerative disorders
Roberts, A., Fegter, O., Orange, J. B., & Rogalski, E. J. (2025) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198888482.013.0022 · Cited by 1 · see citing works
Cheng, S., Chen, W., Chang, C., Chang, J. P., Chang, C., Hsu, J.-M., Lin, S., Mischoulon, D., Kishimoto, T., & Su, K.-P. (2026) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.70034 · Cited by 1 · see citing works
Han, Q., & Ren, Y. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.110183 · Cited by 1 · see citing works
Kim, A. Y., Kim, S., Lee, J., Han, Y., Lee, H.-J., & Cho, C. (2026) · Internet Interventions · doi:10.1016/j.invent.2026.100934 · Cited by 2 · see citing works
Enkhbayar, D., Oh, S., Lee, J., Kim, M., Urtnasan, E., & Key, J. (2026) · JMIR mhealth and uhealth · doi:10.2196/91479 · Cited by 1 · see citing works
Wearable Technology for Depression Assessment: A Scoping Review of Datasets
Tlachac, M., Elsbree, H. K., & Heinz, M. V. (2026) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2026.3704930
Peerenboom, N., Aryal, S., Blankenship, J. M., Swibas, T., Zhai, Y., Clay, I., & Lyden, K. A. (2023) · Digital Biomarkers · doi:10.1159/000533523 · Cited by 1 · see citing works
Lee, J.-J., Bennett, C. C., Stanojević, Č., Kim, S., Henkel, Z., Baugus, K., Piatt, J. A., Bethel, C. L., & Šabanović, S. (2023) · Advanced Robotics · doi:10.1080/01691864.2023.2277159 · Cited by 2 · see citing works
FENAP: Foundation Models for EMA-Derived Narrative Analysis and Prediction
Zhao, F., Shang, Y., & Trull, T. J. (2024) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-024-00170-9 · Cited by 1 · see citing works
- Authors
- Kim, H., Lee, S., Lee, S., Hong, S., Kang, H., & Kim, N.
- Affiliations
- Yonsei University (KR)
- Corresponding author
- Kim, Heejung
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2019-10-16
- Volume
- 7
- Issue
- 10
- Pages
- e14149
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC6913579
- OpenAlex
- W2981182836
- Cited by
- 131
- References
- 64
- Citations by year
- 2026: 16 · 2025: 18 · 2024: 24 · 2023: 16 · 2022: 27 · 2021: 21 · 2020: 8 · 2019: 1
- FWCI
- 16.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- YUHSpace (Yonsei University Medical Library) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- geriatric depression, depression prediction, elderly living alone, ecological momentary assessment, Actiwatch, wearable devices, physical activity, ambient light exposure, sleep efficiency, Hamilton Depression Rating Scale, random forest, decision tree, boosted trees, feature selection, community-dwelling older adults, white light exposure
- MeSH terms
- Machine Learning, Ecological Momentary Assessment, Wearable Electronic Devices, Aged, Aged, 80 and over, Depression, Female, Humans, Male, Surveys and Questionnaires, Residence Characteristics, Logistic Models, Republic of Korea
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
