{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:06:12Z","timestamp":1782846372728,"version":"3.54.5"},"reference-count":50,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Mhealth Uhealth"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Continuous follow-up for patients with major depressive disorder (MDD) is essential for treatment decisions and a better prognosis. There remains limited evidence regarding the critical issue of depression variation trajectory prediction using mobile health (mHealth) measures. Moreover, the temporal dynamics of mHealth measures have not been fully modeled in previous studies, and the poor patient adherence to mHealth records poses great challenges to the dynamic feature modeling.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to examine the contribution of mHealth measures in predicting depression variation trajectory for patients with MDD, with full consideration of the temporal dynamics of mHealth measures.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A total of 229 patients with MDD from a multiple-center, prospective cohort were included. A 12-week follow-up was conducted involving the collection of the Hamilton Depression Rating Scale (HAMD-17), along with patient-reported outcomes (Immediate Mood Scaler and Altman Self-Rating Mania Scale) via mobile devices and sleep duration through wearable wristbands. We used functional data analysis to extract dynamic features from the sparse mHealth records, rather than aggregating the data to a single scalar summary measure through collapsing over time. Subsequently, 3 machine learning models were applied to predict the depression variation trajectory classes based on the baseline characteristics and these extracted dynamic features.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Based on the variation of HAMD-17 scores within 12 weeks, the participants were labeled into 4 classes through the\n k<\/jats:italic>\n -means algorithm. The classes included stable decline (n=93), fluctuate decline (n=44), fast decline (n=60), and delayed and fluctuate (n=32), in light of the shape of depression trajectories. With both baseline features and dynamic features of the mHealth measures, accuracy rates for the overall data were 54.35%, 60.87%, and 56.52%, for the stable decline patients were 78.95%, 84.21%, and 73.68%, for the nonstable decline patients were 59.26%, 62.96%, and 70.37% based on the 3 machine learning models, respectively. The results were significantly superior to the prediction obtained without mHealth measures (with an overall accuracy below 50%) and only showed a marginal reduction in accuracy relative to the ideal prediction with assessment obtained from clinical visits. Moreover, in the construction of the most accurate prediction model, dynamic features of the Immediate Mood Scaler, the Altman Self-Rating Mania Scale, and sleep duration emerged as the most influential predictors, ranking first, third, and fourth, respectively, in terms of their relative importance.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Longitudinal mHealth measures show potential in depression variation trajectory monitoring for patients with MDD even under poor patient adherence. Our work provides practical help in alleviating the follow-up burden for patients with MDD and validates the effectiveness of mHealth measures in clinical applications.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/81397","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T20:44:45Z","timestamp":1778532285000},"page":"e81397-e81397","source":"Crossref","is-referenced-by-count":0,"title":["Contribution of Longitudinal Mobile Health Measures in the Dynamic Track of Patients With Major Depressive Disorder: Multiple Centers, Prospective Cohort Study Using Functional Data Analysis and Machine Learning"],"prefix":"10.2196","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5747-5822","authenticated-orcid":false,"given":"Rou","family":"Zhong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4830-7891","authenticated-orcid":false,"given":"Nanxi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4323-036X","authenticated-orcid":false,"given":"Le","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7810-5070","authenticated-orcid":false,"given":"Lei","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7849-5592","authenticated-orcid":false,"given":"Yuan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3949-1188","authenticated-orcid":false,"given":"Gang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5551-2810","authenticated-orcid":false,"given":"Xuequan","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"issue":"23","key":"R1","doi-asserted-by":"publisher","first-page":"3095","DOI":"10.1001\/jama.289.23.3095","article-title":"The epidemiology of major depressive disorder","volume":"289","author":"Kessler","journal-title":"JAMA"},{"issue":"10161","key":"R2","doi-asserted-by":"publisher","first-page":"2299","DOI":"10.1016\/S0140-6736(18)31948-2","article-title":"Depression","volume":"392","author":"Malhi","journal-title":"Lancet"},{"issue":"11","key":"R3","doi-asserted-by":"publisher","first-page":"1905","DOI":"10.1176\/ajp.2006.163.11.1905","article-title":"Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report","volume":"163","author":"Rush","journal-title":"Am J Psychiatry"},{"key":"R4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpsychires.2017.07.003","article-title":"Early improvement as a resilience signal predicting later remission to antidepressant treatment in patients with major depressive disorder: systematic review and meta-analysis","volume":"94","author":"Wagner","journal-title":"J Psychiatr Res"},{"issue":"1","key":"R5","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1192\/bjp.2018.122","article-title":"Predicting antidepressant response by monitoring early improvement of individual symptoms of depression: individual patient data meta-analysis","volume":"214","author":"de Vries","journal-title":"Br J Psychiatry"},{"key":"R6","doi-asserted-by":"publisher","DOI":"10.2196\/45233","article-title":"Challenges in using mHealth data from smartphones and wearable devices to predict depression symptom severity: retrospective analysis","volume":"25","author":"Sun","journal-title":"J Med Internet Res"},{"issue":"1","key":"R7","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-46075-2","article-title":"A smartphone- and wearable-based biomarker for the estimation of unipolar depression severity","volume":"13","author":"Zhuparris","journal-title":"Sci Rep"},{"issue":"1","key":"R8","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01669-0","article-title":"Digital measures of activity and motivation impact depression and anxiety in the real world","volume":"8","author":"Beltr\u00e1n","journal-title":"NPJ Digit Med"},{"issue":"1","key":"R9","doi-asserted-by":"publisher","DOI":"10.2196\/24333","article-title":"Smartphone-based self-reports of depressive symptoms using the remote monitoring application in psychiatry (ReMAP): interformat validation study","volume":"8","author":"Goltermann","journal-title":"JMIR Ment Health"},{"issue":"12","key":"R10","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1109\/JPROC.2025.3542324","article-title":"Digital phenotyping and feature extraction on smartphone data for depression detection","volume":"112","author":"Yang","journal-title":"Proc IEEE"},{"issue":"1","key":"R11","doi-asserted-by":"publisher","DOI":"10.2196\/mental.3889","article-title":"Utilizing a personal smartphone custom app to assess the Patient Health Questionnaire-9 (PHQ-9) depressive symptoms in patients with major depressive disorder","volume":"2","author":"Torous","journal-title":"JMIR Ment Health"},{"issue":"7","key":"R12","doi-asserted-by":"publisher","first-page":"1691","DOI":"10.1038\/npp.2016.7","article-title":"Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health","volume":"41","author":"Onnela","journal-title":"Neuropsychopharmacology"},{"key":"R13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2020.05.116","article-title":"The utility of smartphone-based, ecological momentary assessment for depressive symptoms","volume":"274","author":"Yim","journal-title":"J Affect Disord"},{"key":"R14","doi-asserted-by":"publisher","DOI":"10.1016\/j.psychres.2020.113239","article-title":"Clarifying the role of sleep in depression: a narrative review","volume":"291","author":"Pandi-Perumal","journal-title":"Psychiatry Res"},{"issue":"7","key":"R15","doi-asserted-by":"publisher","DOI":"10.1007\/s11920-019-1043-y","article-title":"Smartphone-based tracking of sleep in depression, anxiety, and psychotic disorders","volume":"21","author":"Aledavood","journal-title":"Curr Psychiatry Rep"},{"key":"R16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2024.07.136","article-title":"The relationship between wearable-derived sleep features and relapse in major depressive disorder","volume":"363","author":"Matcham","journal-title":"J Affect Disord"},{"issue":"1","key":"R17","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01333-z","article-title":"Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features","volume":"7","author":"Lim","journal-title":"NPJ Digit Med"},{"issue":"5","key":"R18","doi-asserted-by":"publisher","first-page":"2602","DOI":"10.1109\/JBHI.2023.3258601","article-title":"An ensemble classification model for depression based on wearable device sleep data","volume":"28","author":"Hu","journal-title":"IEEE J Biomed Health Inform"},{"issue":"2","key":"R19","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1037\/abn0000884","article-title":"Depressive symptoms as a heterogeneous and constantly evolving dynamical system: idiographic depressive symptom networks of rapid symptom changes among persons with major depressive disorder","volume":"133","author":"Nemesure","journal-title":"J Psychopathol Clin Sci"},{"issue":"3","key":"R20","doi-asserted-by":"publisher","DOI":"10.2196\/24365","article-title":"Tracking and monitoring mood stability of patients with major depressive disorder by machine learning models using passive digital data: prospective naturalistic multicenter study","volume":"9","author":"Bai","journal-title":"JMIR Mhealth Uhealth"},{"issue":"1","key":"R21","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-023-02669-y","article-title":"Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data","volume":"13","author":"Price","journal-title":"Transl Psychiatry"},{"issue":"7","key":"R22","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.1017\/S0033291713001979","article-title":"The dynamic nature of depression: a new micro-level perspective of mental disorder that meets current challenges","volume":"44","author":"Wichers","journal-title":"Psychol Med"},{"issue":"3","key":"R23","doi-asserted-by":"publisher","DOI":"10.2196\/34898","article-title":"Longitudinal relationships between depressive symptom severity and phone-measured mobility: dynamic structural equation modeling study","volume":"9","author":"Zhang","journal-title":"JMIR Ment Health"},{"key":"R24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2024.105541","article-title":"From smartphone data to clinically relevant predictions: a systematic review of digital phenotyping methods in depression","volume":"158","author":"Leaning","journal-title":"Neurosci Biobehav Rev"},{"issue":"1","key":"R25","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1038\/s41386-020-0771-3","article-title":"Opportunities and challenges in the collection and analysis of digital phenotyping data","volume":"46","author":"Onnela","journal-title":"Neuropsychopharmacology"},{"key":"R26","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2023.3260301","article-title":"Sequence modeling of passive sensing data for treatment response prediction in major depressive disorder","volume":"31","author":"Zou","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"R27","doi-asserted-by":"publisher","DOI":"10.2196\/63622","article-title":"Multimodal digital phenotyping study in patients with major depressive episodes and healthy controls (mobile monitoring of mood): observational longitudinal study","volume":"12","author":"Aledavood","journal-title":"JMIR Ment Health"},{"issue":"1","key":"R28","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1136\/jnnp.23.1.56","article-title":"A rating scale for depression","volume":"23","author":"Hamilton","journal-title":"J Neurol Neurosurg Psychiatry"},{"issue":"1","key":"R29","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1111\/j.2044-8341.1959.tb00467.x","article-title":"The assessment of anxiety states by rating","volume":"32","author":"Hamilton","journal-title":"Br J Med Psychol"},{"issue":"2","key":"R30","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1111\/appy.12182","article-title":"Validity and reliability of the Chinese version of the Sheehan Disability Scale (SDS-C)","volume":"7","author":"Leu","journal-title":"Asia Pac Psychiatry"},{"issue":"4","key":"R31","doi-asserted-by":"publisher","DOI":"10.2196\/mhealth.6544","article-title":"Immediate Mood Scaler: tracking symptoms of depression and anxiety using a novel mobile mood scale","volume":"5","author":"Nahum","journal-title":"JMIR Mhealth Uhealth"},{"issue":"1","key":"R32","doi-asserted-by":"publisher","DOI":"10.1186\/s12888-024-06418-3","article-title":"The Chinese version of the immediate mood scaler (IMS): a study evaluating its validity, reliability, and responsiveness in patients with MDD in China","volume":"25","author":"Chen","journal-title":"BMC Psychiatry"},{"issue":"10","key":"R33","doi-asserted-by":"publisher","first-page":"948","DOI":"10.1016\/S0006-3223(96)00548-3","article-title":"The Altman Self-Rating Mania Scale","volume":"42","author":"Altman","journal-title":"Biol Psychiatry"},{"key":"R34","doi-asserted-by":"crossref","unstructured":"Ramsay JO Silverman BW . Functional Data Analysis. Springer; 2005. ISBN: 9780387227511","DOI":"10.1007\/b98888"},{"issue":"1","key":"R35","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-024-02956-2","article-title":"Functional changes in sleep-related arousal after ketamine administration in individuals with treatment-resistant depression","volume":"14","author":"Ballard","journal-title":"Transl Psychiatry"},{"issue":"470","key":"R36","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1198\/016214504000001745","article-title":"Functional data analysis for sparse longitudinal data","volume":"100","author":"Yao","journal-title":"J Am Stat Assoc"},{"key":"R37","doi-asserted-by":"crossref","unstructured":"Breiman L Friedman J Olshen RA Stone CJ . Classification and Regression Trees. Chapman and Hall\/CRC; 1984. [doi: 10.1201\/9781315139470]","DOI":"10.1201\/9781315139470"},{"key":"R38","unstructured":"Therneau TM Atkinson EJ . An introduction to recursive partitioning using the RPART routines. Mar 26, 2026. URL: https:\/\/cran.r-project.org\/web\/packages\/rpart\/vignettes\/longintro.pdf [Accessed 20-04-2026]"},{"key":"R39","unstructured":"Breiman L . Manual on setting up, using, and understanding random forests V3.1. University of California, Berkeley; URL: https:\/\/www.stat.berkeley.edu\/~breiman\/Using_random_forests_V3.1.pdf [Accessed 20-04-2026]"},{"key":"R40","doi-asserted-by":"crossref","unstructured":"Chen T Guestrin C . XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery; 2016:785-794. [doi: 10.1145\/2939672.2939785]","DOI":"10.1145\/2939672.2939785"},{"key":"R41","unstructured":"Archer E . rfPermute: estimate permutation p-values for random forest importance metrics. R package version 2.5.5. Jul 23, 2025. URL: https:\/\/cran.r-project.org\/web\/packages\/rfPermute\/rfPermute.pdf [Accessed 20-04-2026]"},{"issue":"1","key":"R42","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1177\/070674370705200108","article-title":"Definition, assessment, and staging of treatment\u2014resistant refractory major depression: a review of current concepts and methods","volume":"52","author":"Berlim","journal-title":"Can J Psychiatry"},{"issue":"8","key":"R43","doi-asserted-by":"publisher","first-page":"1367","DOI":"10.1017\/S0033291709991528","article-title":"Trajectories of change in depression severity during treatment with antidepressants","volume":"40","author":"Uher","journal-title":"Psychol Med"},{"issue":"3","key":"R44","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/j.jaac.2018.07.908","article-title":"Trajectories of symptom change in the treatment for adolescents with depression study","volume":"58","author":"Scott","journal-title":"J Am Acad Child Adolesc Psychiatry"},{"issue":"4","key":"R45","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1007\/s00406-024-01883-z","article-title":"Identifying latent subtypes of symptom trajectories in major depressive disorder patients and their predictors","volume":"275","author":"Meng","journal-title":"Eur Arch Psychiatry Clin Neurosci"},{"issue":"1","key":"R46","doi-asserted-by":"publisher","DOI":"10.1186\/s12966-024-01585-8","article-title":"Using functional principal component analysis (FPCA) to quantify sitting patterns derived from wearable sensors","volume":"21","author":"Zablocki","journal-title":"Int J Behav Nutr Phys Act"},{"issue":"4","key":"R47","doi-asserted-by":"publisher","DOI":"10.2196\/24604","article-title":"Relationship between major depression symptom severity and sleep collected using a wristband wearable device: multicenter longitudinal observational study","volume":"9","author":"Zhang","journal-title":"JMIR Mhealth Uhealth"},{"key":"R48","doi-asserted-by":"publisher","DOI":"10.2196\/56874","article-title":"Predicting and monitoring symptoms in patients diagnosed with depression using smartphone data: observational study","volume":"26","author":"Ik\u00e4heimonen","journal-title":"J Med Internet Res"},{"issue":"3","key":"R49","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2025.1310","article-title":"Generalizability of treatment outcome prediction across antidepressant treatment trials in depression","volume":"8","author":"Zhukovsky","journal-title":"JAMA Netw Open"},{"issue":"1","key":"R50","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-024-02761-x","article-title":"EEG-vigilance regulation is associated with and predicts ketamine response in major depressive disorder","volume":"14","author":"Ip","journal-title":"Transl Psychiatry"}],"container-title":["JMIR mHealth and uHealth"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/mhealth.jmir.org\/2026\/1\/e81397","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:15:10Z","timestamp":1782843310000},"score":1,"resource":{"primary":{"URL":"https:\/\/mhealth.jmir.org\/2026\/1\/e81397"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,11]]},"references-count":50,"URL":"https:\/\/doi.org\/10.2196\/81397","relation":{},"ISSN":["2291-5222"],"issn-type":[{"value":"2291-5222","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,11]]},"article-number":"v14i23e81397"}}