Day-to-day regularity and diurnal switching of physical activity reduce depression-related behaviors: a time-series analysis of wearable device data
Abstract Background Wearable devices have been widely used in research to understand the relationship between habitual physical activity and mental health in the real world. However, little attention has been paid to the temporal variability in continuous physical activity patterns measured by these...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-01-01
|
| Series: | BMC Public Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12889-023-14984-6 |
| _version_ | 1797958465409253376 |
|---|---|
| author | Satoshi Yokoyama Fumi Kagawa Masahiro Takamura Koki Takagaki Kohei Kambara Yuki Mitsuyama Ayaka Shimizu Go Okada Yasumasa Okamoto |
| author_facet | Satoshi Yokoyama Fumi Kagawa Masahiro Takamura Koki Takagaki Kohei Kambara Yuki Mitsuyama Ayaka Shimizu Go Okada Yasumasa Okamoto |
| author_sort | Satoshi Yokoyama |
| collection | DOAJ |
| description | Abstract Background Wearable devices have been widely used in research to understand the relationship between habitual physical activity and mental health in the real world. However, little attention has been paid to the temporal variability in continuous physical activity patterns measured by these devices. Therefore, we analyzed time-series patterns of physical activity intensity measured by a wearable device and investigated the relationship between its model parameters and depression-related behaviors. Methods Sixty-six individuals used the wearable device for one week and then answered a questionnaire on depression-related behaviors. A seasonal autoregressive integral moving average (SARIMA) model was fitted to the individual-level device data and the best individual model parameters were estimated via a grid search. Results Out of 64 hyper-parameter combinations, 21 models were selected as optimal, and the models with a larger number of affiliations were found to have no seasonal autoregressive parameter. Conversely, about half of the optimal models indicated that physical activity on any given day fluctuated due to the previous day’s activity. In addition, both irregular rhythms in day-to-day activity and low-level of diurnal variability could lead to avoidant behavior patterns. Conclusion Automatic and objective physical activity data from wearable devices showed that diurnal switching of physical activity, as well as day-to-day regularity rhythms, reduced depression-related behaviors. These time-series parameters may be useful for detecting behavioral issues that lie outside individuals’ subjective awareness. |
| first_indexed | 2024-04-11T00:19:25Z |
| format | Article |
| id | doaj.art-2a1066cdbeb74183b236fd4e8d03f240 |
| institution | Directory Open Access Journal |
| issn | 1471-2458 |
| language | English |
| last_indexed | 2024-04-11T00:19:25Z |
| publishDate | 2023-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj.art-2a1066cdbeb74183b236fd4e8d03f2402023-01-08T12:22:40ZengBMCBMC Public Health1471-24582023-01-012311910.1186/s12889-023-14984-6Day-to-day regularity and diurnal switching of physical activity reduce depression-related behaviors: a time-series analysis of wearable device dataSatoshi Yokoyama0Fumi Kagawa1Masahiro Takamura2Koki Takagaki3Kohei Kambara4Yuki Mitsuyama5Ayaka Shimizu6Go Okada7Yasumasa Okamoto8Department of Psychiatry and Neurosciences, Hiroshima UniversityHiroshima Prefectural Mental Health CenterDepartment of Neurology, Shimane UniversityHealth Service Center, Hiroshima UniversityFaculty of Psychology, Doshisha UniversityDepartment of Psychiatry and Neurosciences, Hiroshima UniversityDepartment of Psychiatry and Neurosciences, Hiroshima UniversityDepartment of Psychiatry and Neurosciences, Hiroshima UniversityDepartment of Psychiatry and Neurosciences, Hiroshima UniversityAbstract Background Wearable devices have been widely used in research to understand the relationship between habitual physical activity and mental health in the real world. However, little attention has been paid to the temporal variability in continuous physical activity patterns measured by these devices. Therefore, we analyzed time-series patterns of physical activity intensity measured by a wearable device and investigated the relationship between its model parameters and depression-related behaviors. Methods Sixty-six individuals used the wearable device for one week and then answered a questionnaire on depression-related behaviors. A seasonal autoregressive integral moving average (SARIMA) model was fitted to the individual-level device data and the best individual model parameters were estimated via a grid search. Results Out of 64 hyper-parameter combinations, 21 models were selected as optimal, and the models with a larger number of affiliations were found to have no seasonal autoregressive parameter. Conversely, about half of the optimal models indicated that physical activity on any given day fluctuated due to the previous day’s activity. In addition, both irregular rhythms in day-to-day activity and low-level of diurnal variability could lead to avoidant behavior patterns. Conclusion Automatic and objective physical activity data from wearable devices showed that diurnal switching of physical activity, as well as day-to-day regularity rhythms, reduced depression-related behaviors. These time-series parameters may be useful for detecting behavioral issues that lie outside individuals’ subjective awareness.https://doi.org/10.1186/s12889-023-14984-6Time-series analysisPhysical activityWearable deviceDepressive behavior |
| spellingShingle | Satoshi Yokoyama Fumi Kagawa Masahiro Takamura Koki Takagaki Kohei Kambara Yuki Mitsuyama Ayaka Shimizu Go Okada Yasumasa Okamoto Day-to-day regularity and diurnal switching of physical activity reduce depression-related behaviors: a time-series analysis of wearable device data BMC Public Health Time-series analysis Physical activity Wearable device Depressive behavior |
| title | Day-to-day regularity and diurnal switching of physical activity reduce depression-related behaviors: a time-series analysis of wearable device data |
| title_full | Day-to-day regularity and diurnal switching of physical activity reduce depression-related behaviors: a time-series analysis of wearable device data |
| title_fullStr | Day-to-day regularity and diurnal switching of physical activity reduce depression-related behaviors: a time-series analysis of wearable device data |
| title_full_unstemmed | Day-to-day regularity and diurnal switching of physical activity reduce depression-related behaviors: a time-series analysis of wearable device data |
| title_short | Day-to-day regularity and diurnal switching of physical activity reduce depression-related behaviors: a time-series analysis of wearable device data |
| title_sort | day to day regularity and diurnal switching of physical activity reduce depression related behaviors a time series analysis of wearable device data |
| topic | Time-series analysis Physical activity Wearable device Depressive behavior |
| url | https://doi.org/10.1186/s12889-023-14984-6 |
| work_keys_str_mv | AT satoshiyokoyama daytodayregularityanddiurnalswitchingofphysicalactivityreducedepressionrelatedbehaviorsatimeseriesanalysisofwearabledevicedata AT fumikagawa daytodayregularityanddiurnalswitchingofphysicalactivityreducedepressionrelatedbehaviorsatimeseriesanalysisofwearabledevicedata AT masahirotakamura daytodayregularityanddiurnalswitchingofphysicalactivityreducedepressionrelatedbehaviorsatimeseriesanalysisofwearabledevicedata AT kokitakagaki daytodayregularityanddiurnalswitchingofphysicalactivityreducedepressionrelatedbehaviorsatimeseriesanalysisofwearabledevicedata AT koheikambara daytodayregularityanddiurnalswitchingofphysicalactivityreducedepressionrelatedbehaviorsatimeseriesanalysisofwearabledevicedata AT yukimitsuyama daytodayregularityanddiurnalswitchingofphysicalactivityreducedepressionrelatedbehaviorsatimeseriesanalysisofwearabledevicedata AT ayakashimizu daytodayregularityanddiurnalswitchingofphysicalactivityreducedepressionrelatedbehaviorsatimeseriesanalysisofwearabledevicedata AT gookada daytodayregularityanddiurnalswitchingofphysicalactivityreducedepressionrelatedbehaviorsatimeseriesanalysisofwearabledevicedata AT yasumasaokamoto daytodayregularityanddiurnalswitchingofphysicalactivityreducedepressionrelatedbehaviorsatimeseriesanalysisofwearabledevicedata |