Cargando…
Passive Way of Measuring QOL/Well-Being Levels Using Smartphone Log
Research on mental health states involves paying increasing attention to changes in daily life. Researchers have attempted to understand such daily changes by relying on self-reporting through frequent assessment using devices (smartphones); however, they are mostly focused on a single aspect of men...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8960057/ https://www.ncbi.nlm.nih.gov/pubmed/35355683 http://dx.doi.org/10.3389/fdgth.2022.780566 |
_version_ | 1784677303889952768 |
---|---|
author | Yao, Wenhao Kaminishi, Kohei Yamamoto, Naoki Hamatani, Takashi Yamada, Yuki Kawada, Takahiro Hiyama, Satoshi Okimura, Tsukasa Terasawa, Yuri Maeda, Takaki Mimura, Masaru Ota, Jun |
author_facet | Yao, Wenhao Kaminishi, Kohei Yamamoto, Naoki Hamatani, Takashi Yamada, Yuki Kawada, Takahiro Hiyama, Satoshi Okimura, Tsukasa Terasawa, Yuri Maeda, Takaki Mimura, Masaru Ota, Jun |
author_sort | Yao, Wenhao |
collection | PubMed |
description | Research on mental health states involves paying increasing attention to changes in daily life. Researchers have attempted to understand such daily changes by relying on self-reporting through frequent assessment using devices (smartphones); however, they are mostly focused on a single aspect of mental health. Assessing the mental health of a person from various perspectives may help in the primary prevention of mental illness and the comprehensive measurement of mental health. In this study, we used users' smartphone logs to build a model to estimate whether the scores on three types of questionnaires related to quality of life and well-being would increase compared to the previous week (fluctuation model) and whether they would be higher compared to the average for that user (interval model). Sixteen participants completed three questionnaires once per week, and their smartphone logs were recorded over the same period. Based on the results, estimation models were built, and the F-score ranged from 0.739 to 0.818. We also analyzed the features that the estimation model emphasized. Information related to “physical activity,” such as acceleration and tilt of the smartphone, and “environment,” such as atmospheric pressure and illumination, were given more weight in the estimation than information related to “cyber activity,” such as usage of smartphone applications. In particular, in the Positive and Negative Affect Schedule (PANAS), 9 out of 10 top features in the fluctuation model and 7 out of 10 top features in the interval model were related to activities in the physical world, suggesting that short-term mood may be particularly heavily influenced by subjective activities in the human physical world. |
format | Online Article Text |
id | pubmed-8960057 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89600572022-03-29 Passive Way of Measuring QOL/Well-Being Levels Using Smartphone Log Yao, Wenhao Kaminishi, Kohei Yamamoto, Naoki Hamatani, Takashi Yamada, Yuki Kawada, Takahiro Hiyama, Satoshi Okimura, Tsukasa Terasawa, Yuri Maeda, Takaki Mimura, Masaru Ota, Jun Front Digit Health Digital Health Research on mental health states involves paying increasing attention to changes in daily life. Researchers have attempted to understand such daily changes by relying on self-reporting through frequent assessment using devices (smartphones); however, they are mostly focused on a single aspect of mental health. Assessing the mental health of a person from various perspectives may help in the primary prevention of mental illness and the comprehensive measurement of mental health. In this study, we used users' smartphone logs to build a model to estimate whether the scores on three types of questionnaires related to quality of life and well-being would increase compared to the previous week (fluctuation model) and whether they would be higher compared to the average for that user (interval model). Sixteen participants completed three questionnaires once per week, and their smartphone logs were recorded over the same period. Based on the results, estimation models were built, and the F-score ranged from 0.739 to 0.818. We also analyzed the features that the estimation model emphasized. Information related to “physical activity,” such as acceleration and tilt of the smartphone, and “environment,” such as atmospheric pressure and illumination, were given more weight in the estimation than information related to “cyber activity,” such as usage of smartphone applications. In particular, in the Positive and Negative Affect Schedule (PANAS), 9 out of 10 top features in the fluctuation model and 7 out of 10 top features in the interval model were related to activities in the physical world, suggesting that short-term mood may be particularly heavily influenced by subjective activities in the human physical world. Frontiers Media S.A. 2022-03-10 /pmc/articles/PMC8960057/ /pubmed/35355683 http://dx.doi.org/10.3389/fdgth.2022.780566 Text en Copyright © 2022 Yao, Kaminishi, Yamamoto, Hamatani, Yamada, Kawada, Hiyama, Okimura, Terasawa, Maeda, Mimura and Ota. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Digital Health Yao, Wenhao Kaminishi, Kohei Yamamoto, Naoki Hamatani, Takashi Yamada, Yuki Kawada, Takahiro Hiyama, Satoshi Okimura, Tsukasa Terasawa, Yuri Maeda, Takaki Mimura, Masaru Ota, Jun Passive Way of Measuring QOL/Well-Being Levels Using Smartphone Log |
title | Passive Way of Measuring QOL/Well-Being Levels Using Smartphone Log |
title_full | Passive Way of Measuring QOL/Well-Being Levels Using Smartphone Log |
title_fullStr | Passive Way of Measuring QOL/Well-Being Levels Using Smartphone Log |
title_full_unstemmed | Passive Way of Measuring QOL/Well-Being Levels Using Smartphone Log |
title_short | Passive Way of Measuring QOL/Well-Being Levels Using Smartphone Log |
title_sort | passive way of measuring qol/well-being levels using smartphone log |
topic | Digital Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8960057/ https://www.ncbi.nlm.nih.gov/pubmed/35355683 http://dx.doi.org/10.3389/fdgth.2022.780566 |
work_keys_str_mv | AT yaowenhao passivewayofmeasuringqolwellbeinglevelsusingsmartphonelog AT kaminishikohei passivewayofmeasuringqolwellbeinglevelsusingsmartphonelog AT yamamotonaoki passivewayofmeasuringqolwellbeinglevelsusingsmartphonelog AT hamatanitakashi passivewayofmeasuringqolwellbeinglevelsusingsmartphonelog AT yamadayuki passivewayofmeasuringqolwellbeinglevelsusingsmartphonelog AT kawadatakahiro passivewayofmeasuringqolwellbeinglevelsusingsmartphonelog AT hiyamasatoshi passivewayofmeasuringqolwellbeinglevelsusingsmartphonelog AT okimuratsukasa passivewayofmeasuringqolwellbeinglevelsusingsmartphonelog AT terasawayuri passivewayofmeasuringqolwellbeinglevelsusingsmartphonelog AT maedatakaki passivewayofmeasuringqolwellbeinglevelsusingsmartphonelog AT mimuramasaru passivewayofmeasuringqolwellbeinglevelsusingsmartphonelog AT otajun passivewayofmeasuringqolwellbeinglevelsusingsmartphonelog |