mHealth Intervention to Promote Physical Activity Among Employees Using a Deep Learning Model for Passive Monitoring of Depression and Anxiety: Single-Arm Feasibility Trial
BackgroundPhysical 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 employees face ba...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2023-11-01
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| Series: | JMIR Formative Research |
| Online Access: | https://formative.jmir.org/2023/1/e51334 |
| _version_ | 1827747753589997568 |
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| author | Kazuhiro Watanabe Shoichi Okusa Mitsuhiro Sato Hideki Miura Masahiro Morimoto Akizumi Tsutsumi |
| author_facet | Kazuhiro Watanabe Shoichi Okusa Mitsuhiro Sato Hideki Miura Masahiro Morimoto Akizumi Tsutsumi |
| author_sort | Kazuhiro Watanabe |
| collection | DOAJ |
| description |
BackgroundPhysical 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 employees face barriers to using mHealth services. To address these problems, we recently developed a smartphone app named ASHARE to prevent depression and anxiety in the working population; it uses a deep learning model for passive monitoring of depression and anxiety from information about physical activity.
ObjectiveThis study aimed to preliminarily investigate (1) the effectiveness of the developed app in improving physical activity and reducing depression and anxiety and (2) the app’s implementation outcomes (ie, its acceptability, appropriateness, feasibility, satisfaction, and potential harm).
MethodsWe conducted a single-arm interventional study. From March to April 2023, employees aged ≥18 years who were not absent were recruited. The participants were asked to install and use the app for 1 month. The ideal usage of the app was for the participants to take about 5 minutes every day to open the app, check the physical activity patterns and results of an estimated score of psychological distress, and increase their physical activity. Self-reported physical activity (using the Global Physical Activity Questionnaire, version 2) and psychological distress (using the 6-item Kessler Psychological Distress Scale) were measured at baseline and after 1 month. The duration of physical activity was also recorded digitally. Paired t tests (two-tailed) and chi-square tests were performed to evaluate changes in these variables. Implementation Outcome Scales for Digital Mental Health were also measured for acceptability, appropriateness, feasibility, satisfaction, and harm. These average scores were assessed by comparing them with those reported in previous studies.
ResultsThis study included 24 employees. On average, the app was used for 12.54 days (44.8% of this study’s period). After using the app, no significant change was observed in physical activity (–12.59 metabolic equivalent hours per week, P=.31) or psychological distress (–0.43 metabolic equivalent hours per week, P=.93). However, the number of participants with severe psychological distress decreased significantly (P=.01). The digitally recorded duration of physical activity increased during the intervention period (+0.60 minutes per day, P=.08). The scores for acceptability, appropriateness, and satisfaction were lower than those in previous mHealth studies, whereas those for feasibility and harm were better.
ConclusionsThe ASHARE app was insufficient in promoting physical activity or improving psychological distress. At this stage, the app has many issues that are to be addressed in terms of both implementation and effectiveness. The main reason for this low effectiveness might be the poor evaluation of the implementation outcomes by app users. Improving acceptability, appropriateness, and satisfaction are identified as key issues to be addressed in future implementation.
Trial RegistrationUniversity Hospital Medical Information Network Clinical Trials Registry UMIN000050430; https://tinyurl.com/mrx5ntcmrecptno=R000057438 |
| first_indexed | 2024-03-11T05:56:43Z |
| format | Article |
| id | doaj.art-e315fd41fa0e4e31baf823aab168c5c2 |
| institution | Directory Open Access Journal |
| issn | 2561-326X |
| language | English |
| last_indexed | 2024-03-11T05:56:43Z |
| publishDate | 2023-11-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Formative Research |
| spelling | doaj.art-e315fd41fa0e4e31baf823aab168c5c22023-11-17T13:45:34ZengJMIR PublicationsJMIR Formative Research2561-326X2023-11-017e5133410.2196/51334mHealth Intervention to Promote Physical Activity Among Employees Using a Deep Learning Model for Passive Monitoring of Depression and Anxiety: Single-Arm Feasibility TrialKazuhiro Watanabehttps://orcid.org/0000-0002-3342-6142Shoichi Okusahttps://orcid.org/0009-0009-8399-3004Mitsuhiro Satohttps://orcid.org/0009-0006-8935-5327Hideki Miurahttps://orcid.org/0009-0006-7682-7495Masahiro Morimotohttps://orcid.org/0009-0009-6808-0936Akizumi Tsutsumihttps://orcid.org/0000-0003-0966-4869 BackgroundPhysical 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 employees face barriers to using mHealth services. To address these problems, we recently developed a smartphone app named ASHARE to prevent depression and anxiety in the working population; it uses a deep learning model for passive monitoring of depression and anxiety from information about physical activity. ObjectiveThis study aimed to preliminarily investigate (1) the effectiveness of the developed app in improving physical activity and reducing depression and anxiety and (2) the app’s implementation outcomes (ie, its acceptability, appropriateness, feasibility, satisfaction, and potential harm). MethodsWe conducted a single-arm interventional study. From March to April 2023, employees aged ≥18 years who were not absent were recruited. The participants were asked to install and use the app for 1 month. The ideal usage of the app was for the participants to take about 5 minutes every day to open the app, check the physical activity patterns and results of an estimated score of psychological distress, and increase their physical activity. Self-reported physical activity (using the Global Physical Activity Questionnaire, version 2) and psychological distress (using the 6-item Kessler Psychological Distress Scale) were measured at baseline and after 1 month. The duration of physical activity was also recorded digitally. Paired t tests (two-tailed) and chi-square tests were performed to evaluate changes in these variables. Implementation Outcome Scales for Digital Mental Health were also measured for acceptability, appropriateness, feasibility, satisfaction, and harm. These average scores were assessed by comparing them with those reported in previous studies. ResultsThis study included 24 employees. On average, the app was used for 12.54 days (44.8% of this study’s period). After using the app, no significant change was observed in physical activity (–12.59 metabolic equivalent hours per week, P=.31) or psychological distress (–0.43 metabolic equivalent hours per week, P=.93). However, the number of participants with severe psychological distress decreased significantly (P=.01). The digitally recorded duration of physical activity increased during the intervention period (+0.60 minutes per day, P=.08). The scores for acceptability, appropriateness, and satisfaction were lower than those in previous mHealth studies, whereas those for feasibility and harm were better. ConclusionsThe ASHARE app was insufficient in promoting physical activity or improving psychological distress. At this stage, the app has many issues that are to be addressed in terms of both implementation and effectiveness. The main reason for this low effectiveness might be the poor evaluation of the implementation outcomes by app users. Improving acceptability, appropriateness, and satisfaction are identified as key issues to be addressed in future implementation. Trial RegistrationUniversity Hospital Medical Information Network Clinical Trials Registry UMIN000050430; https://tinyurl.com/mrx5ntcmrecptno=R000057438https://formative.jmir.org/2023/1/e51334 |
| spellingShingle | Kazuhiro Watanabe Shoichi Okusa Mitsuhiro Sato Hideki Miura Masahiro Morimoto Akizumi Tsutsumi 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 |
| title | mHealth Intervention to Promote Physical Activity Among Employees Using a Deep Learning Model for Passive Monitoring of Depression and Anxiety: Single-Arm Feasibility Trial |
| title_full | mHealth Intervention to Promote Physical Activity Among Employees Using a Deep Learning Model for Passive Monitoring of Depression and Anxiety: Single-Arm Feasibility Trial |
| title_fullStr | mHealth Intervention to Promote Physical Activity Among Employees Using a Deep Learning Model for Passive Monitoring of Depression and Anxiety: Single-Arm Feasibility Trial |
| title_full_unstemmed | mHealth Intervention to Promote Physical Activity Among Employees Using a Deep Learning Model for Passive Monitoring of Depression and Anxiety: Single-Arm Feasibility Trial |
| title_short | mHealth Intervention to Promote Physical Activity Among Employees Using a Deep Learning Model for Passive Monitoring of Depression and Anxiety: Single-Arm Feasibility Trial |
| title_sort | mhealth intervention to promote physical activity among employees using a deep learning model for passive monitoring of depression and anxiety single arm feasibility trial |
| url | https://formative.jmir.org/2023/1/e51334 |
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