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Can smartphones measure momentary quality of life and participation? A proof of concept using experience sampling surveys with university students
Author(s) -
Liddle Jacki,
Wishink Anna,
Springfield Liz,
Gustafsson Louise,
Ireland David,
Silburn Peter
Publication year - 2017
Publication title -
australian occupational therapy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.595
H-Index - 44
eISSN - 1440-1630
pISSN - 0045-0766
DOI - 10.1111/1440-1630.12360
Subject(s) - experience sampling method , quality of life (healthcare) , logistic regression , context (archaeology) , psychology , applied psychology , sample (material) , quality (philosophy) , sampling (signal processing) , medical education , medicine , gerontology , social psychology , computer science , nursing , geography , philosophy , chemistry , archaeology , epistemology , chromatography , filter (signal processing) , computer vision
Background Understanding quality of life and participation is a key aspect of occupational therapy research. The use of smartphones to deliver experience‐sampling surveys may provide an accessible way to monitor these outcomes. This study used smartphone‐based experience sampling methods ( ESM ) to investigate factors influencing momentary quality of life (m QOL ) of university students. Methods A convenience sample of students at an Australian university participated. Using a custom smartphone application, ESM surveys were sent six to eight times, every second day, over a week. Participants indicated their m QOL , occupational participation, occupational enjoyment, social context and location via surveys and provided demographic and health information in a single self‐report questionnaire. The relationship between m QOL and variables was analysed at the survey level using logistic regression. Results Forty students completed 391 surveys. Higher m QOL was significantly related to participation in productive occupations ( z = 3.48; P = 0.001), moderate ( z = 4.00; P < 0.001) or high occupational enjoyment ( z = 7.06; P < 0.001), being with someone ( z = 2.15, P = 0.031), being at home ( z = 2.49; P = 0.013) and an excellent self‐rated health status ( z = 2.35; P = 0.019). The magnitude of differences in m QOL was small. Conclusion This study suggests that m QOL amongst university students relates to personal, environmental and occupational factors. The use of smartphone‐based ESM appears to be a practical approach for investigating participation and QOL . Further research utilising a more diverse sample, analysing at the individual level, and using ESM in conjunction with other methodologies is recommended.