Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: JMIR Formative Research

Date Submitted: Aug 3, 2023
Open Peer Review Period: Aug 3, 2023 - Sep 28, 2023
Date Accepted: Oct 23, 2023
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

mHealth Intervention to Promote Physical Activity Among Employees Using a Deep Learning Model for Passive Monitoring of Depression and Anxiety: Single-Arm Feasibility Trial

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 Form Res 2023;7:e51334

DOI: 10.2196/51334

PMID: 37976094

PMCID: 10692887

An mHealth intervention to promote physical activity among employees using a deep learning model for passive monitoring of depression and anxiety: A single-arm feasibility trial

  • Kazuhiro Watanabe; 
  • Shoichi Okusa; 
  • Mitsuhiro Sato; 
  • Hideki Miura; 
  • Masahiro Morimoto; 
  • Akizumi Tsutsumi

ABSTRACT

Background:

Physical activity effectively prevents depression and anxiety. Although mobile health (mHealth) technologies offer promising results in promoting physical activity and improving mental health, conflicting evidence exists on their effectiveness, and workers face barriers to using mHealth services.

Objective:

This study aimed to preliminarily investigate the effectiveness and implementation of a newly developed smartphone app, ASHARE, by employing a deep learning model based on physical activity to monitor depression and anxiety.

Methods:

We conducted a single-arm interventional study. From March to April 2023, workers aged 18 years and older who were not absent were recruited. The participants were asked to install and use the app for one month. Self-reported physical activity and psychological distress were measured at baseline and after one month. The duration of physical activity was also recorded digitally. Implementation Outcome Scales for Digital Mental Health were used to measure acceptability, appropriateness, feasibility, satisfaction, and harm. Paired t-tests and χ2-tests were done to evaluate changes in scores.

Results:

The study included 24 workers. On average, the app was used for 12.54 days (44.8% of the study period). After using the app, no significant change was observed in physical activity (-12.59 MET-hours/week, P=.309) or psychological distress (-0.43, P=.926). The number of participants with severe psychological distress decreased significantly (P=.011). The digitally recorded duration of physical activity increased during the intervention period (+0.60 min/day, P=.078). The scores for acceptability, appropriateness, and satisfaction were lower than those in previous mHealth studies, whereas those for feasibility and harm were better.

Conclusions:

The ASHARE app was insufficient in promoting physical activity or improving psychological distress. Improving acceptability, appropriateness, and satisfaction are key issues in the implementation of this app. Clinical Trial: UMIN000050430


 Citation

Please cite as:

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 Form Res 2023;7:e51334

DOI: 10.2196/51334

PMID: 37976094

PMCID: 10692887

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.

Advertisement