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Sleep apps and behavioral constructs: A content analysis

Although sleep apps are among the most popular commercially available health apps, little is known about how well these apps are grounded in behavioral theory. Three-hundred and sixty-nine apps were initially identified using the term “sleep” from the Google play store and Apple iTunes in September...

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Autores principales: Grigsby-Toussaint, Diana S., Shin, Jong Cheol, Reeves, Dayanna M., Beattie, Ariana, Auguste, Evan, Jean-Louis, Girardin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5350571/
https://www.ncbi.nlm.nih.gov/pubmed/28316907
http://dx.doi.org/10.1016/j.pmedr.2017.02.018
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author Grigsby-Toussaint, Diana S.
Shin, Jong Cheol
Reeves, Dayanna M.
Beattie, Ariana
Auguste, Evan
Jean-Louis, Girardin
author_facet Grigsby-Toussaint, Diana S.
Shin, Jong Cheol
Reeves, Dayanna M.
Beattie, Ariana
Auguste, Evan
Jean-Louis, Girardin
author_sort Grigsby-Toussaint, Diana S.
collection PubMed
description Although sleep apps are among the most popular commercially available health apps, little is known about how well these apps are grounded in behavioral theory. Three-hundred and sixty-nine apps were initially identified using the term “sleep” from the Google play store and Apple iTunes in September 2015. The final sample consisted of 35 apps that met the following inclusion criteria: 1) Stand-alone functionality; 2) Sleep tracker or monitor apps ranked by 100 + users; 3) Sleep Alarm apps ranked by 1000 + users; and 4) English language. A coding instrument was developed to assess the presence of 19 theoretical constructs. All 35 apps were downloaded and coded. The inter-rater reliability between coders was 0.996. A “1” was assigned if a construct was present in the app and “0” if it was not. Mean scores were calculated across all apps, and comparisons were made between total scores and app ratings using R. The mean behavior construct scores (BCS) across all apps was 34% (5% - 84%). Behavioral constructs for realistic goal setting (86%), time management (77%), and self-monitoring (66%) were most common. Although a positive association was observed between BCS and user ratings, this was not found to be statistically significant (p > 0.05). The mean persuasive technology score was 42% (20% to 80%), with higher scores for paid compared to free apps (p < 0.05). While the overall behavior construct scores were low, an opportunity exists to develop or modify existing apps to support sustainable sleep hygiene practices.
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spelling pubmed-53505712017-03-17 Sleep apps and behavioral constructs: A content analysis Grigsby-Toussaint, Diana S. Shin, Jong Cheol Reeves, Dayanna M. Beattie, Ariana Auguste, Evan Jean-Louis, Girardin Prev Med Rep Short Communication Although sleep apps are among the most popular commercially available health apps, little is known about how well these apps are grounded in behavioral theory. Three-hundred and sixty-nine apps were initially identified using the term “sleep” from the Google play store and Apple iTunes in September 2015. The final sample consisted of 35 apps that met the following inclusion criteria: 1) Stand-alone functionality; 2) Sleep tracker or monitor apps ranked by 100 + users; 3) Sleep Alarm apps ranked by 1000 + users; and 4) English language. A coding instrument was developed to assess the presence of 19 theoretical constructs. All 35 apps were downloaded and coded. The inter-rater reliability between coders was 0.996. A “1” was assigned if a construct was present in the app and “0” if it was not. Mean scores were calculated across all apps, and comparisons were made between total scores and app ratings using R. The mean behavior construct scores (BCS) across all apps was 34% (5% - 84%). Behavioral constructs for realistic goal setting (86%), time management (77%), and self-monitoring (66%) were most common. Although a positive association was observed between BCS and user ratings, this was not found to be statistically significant (p > 0.05). The mean persuasive technology score was 42% (20% to 80%), with higher scores for paid compared to free apps (p < 0.05). While the overall behavior construct scores were low, an opportunity exists to develop or modify existing apps to support sustainable sleep hygiene practices. Elsevier 2017-02-21 /pmc/articles/PMC5350571/ /pubmed/28316907 http://dx.doi.org/10.1016/j.pmedr.2017.02.018 Text en © 2017 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Short Communication
Grigsby-Toussaint, Diana S.
Shin, Jong Cheol
Reeves, Dayanna M.
Beattie, Ariana
Auguste, Evan
Jean-Louis, Girardin
Sleep apps and behavioral constructs: A content analysis
title Sleep apps and behavioral constructs: A content analysis
title_full Sleep apps and behavioral constructs: A content analysis
title_fullStr Sleep apps and behavioral constructs: A content analysis
title_full_unstemmed Sleep apps and behavioral constructs: A content analysis
title_short Sleep apps and behavioral constructs: A content analysis
title_sort sleep apps and behavioral constructs: a content analysis
topic Short Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5350571/
https://www.ncbi.nlm.nih.gov/pubmed/28316907
http://dx.doi.org/10.1016/j.pmedr.2017.02.018
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