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...

Descripción completa

Detalles Bibliográficos
Autores principales: Yao, Wenhao, Kaminishi, Kohei, Yamamoto, Naoki, Hamatani, Takashi, Yamada, Yuki, Kawada, Takahiro, Hiyama, Satoshi, Okimura, Tsukasa, Terasawa, Yuri, Maeda, Takaki, Mimura, Masaru, Ota, Jun
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