This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2828) Mobile Health (mhealth) (3162) Occupational Health and Ergonomics/Prevention at the Workplace (264) mHealth for Data Collection and Research (1083) Wearable Devices and Sensors (998) Anxiety and Stress Disorders (1185) Digital Biomarkers and Digital Phenotyping (229)

Published on in Vol 6, No 11 (2022): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40339, first published .
The Passive Monitoring of Depression and Anxiety Among Workers Using Digital Biomarkers Based on Their Physical Activity and Working Conditions: 2-Week Longitudinal Study

The Passive Monitoring of Depression and Anxiety Among Workers Using Digital Biomarkers Based on Their Physical Activity and Working Conditions: 2-Week Longitudinal Study

The Passive Monitoring of Depression and Anxiety Among Workers Using Digital Biomarkers Based on Their Physical Activity and Working Conditions: 2-Week Longitudinal Study

Authors of this article:

Kazuhiro Watanabe1 Author Orcid Image ;   Akizumi Tsutsumi1 Author Orcid Image
Article Authors Cited by (9) Tweetations (4) Metrics

Journals

  1. Watanabe K, Tsutsumi A. Workers’ perceptions of mHealth services for physical activity and mental health: A qualitative study using a text-mining method. Environmental and Occupational Health Practice 2023;5(1):n/a View
  2. Watanabe K, Okusa S, Sato M, Miura H, Morimoto M, Tsutsumi A. mHealth Intervention to Promote Physical Activity Among Employees Using a Deep Learning Model for Passive Monitoring of Depression and Anxiety: Single-Arm Feasibility Trial. JMIR Formative Research 2023;7:e51334 View
  3. Arji G, Erfannia L, alirezaei S, Hemmat M. A systematic literature review and analysis of deep learning algorithms in mental disorders. Informatics in Medicine Unlocked 2023;40:101284 View
  4. Watanabe K, Hikichi H, Imamura K, Sakuraya A, Yoshikawa T, Izawa S, Eguchi H, Inoue A, Yoshida K, Orihashi Y, Tsutsumi A. Multifaceted ORganizational InterventiONs (M-ORION) project for prevention of depression and anxiety among workers: study protocol for a five-arm cluster randomized controlled trial. BMC Public Health 2024;24(1) View
  5. WATANABE K. Promoting Physical Activity Among Workers for Better Mental Health: An mHealth Intervention With Deep Learning. Journal of UOEH 2024;46(1):119 View
  6. dos Santos M, Heckler W, Bavaresco R, Barbosa J. Machine learning applied to digital phenotyping: A systematic literature review and taxonomy. Computers in Human Behavior 2024;161:108422 View
  7. Watanabe K, Sato M, Okusa S, Tsutsumi A. Effectiveness and Implementation Outcomes of an mHealth App Aimed at Promoting Physical Activity and Improving Psychological Distress in the Workplace Setting: Cluster-Level Nonrandomized Controlled Trial. JMIR mHealth and uHealth 2025;13:e70473 View

Conference Proceedings

  1. Lai S, Li Z. 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Detection of potential anxiety in social media based on multimodal fusion with deep learning methods View
  2. Grozdani N, Muñoz A, Pietrick A, Flores R, Shrestha A, Guo X, Liu S, Rundensteiner E. 2023 IEEE MIT Undergraduate Research Technology Conference (URTC). Wearable Wellness: Depression Screening via Fitbit Data Collected During COVID-19 Pandemic View