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dc.contributor.authorMcGowan, A.
dc.contributor.authorSittig, S.
dc.contributor.authorBourrie, D.
dc.contributor.authorBenton, R.
dc.contributor.authorIyengar, S.
dc.date.accessioned2022-11-18T22:12:29Z
dc.date.available2022-11-18T22:12:29Z
dc.date.issued2022
dc.identifier.citationMcGowan, A., Sittig, S., Bourrie, D., Benton, R., & Iyengar, S. (2022). The Intersection of Persuasive System Design and Personalization in Mobile Health: Statistical Evaluation. JMIR MHealth and UHealth, 10(9), e40576.
dc.identifier.issn2291-5222
dc.identifier.pmid36103226
dc.identifier.doi10.2196/40576
dc.identifier.urihttp://hdl.handle.net/10150/666866
dc.description.abstractBACKGROUND: Persuasive technology is an umbrella term that encompasses software (eg, mobile apps) or hardware (eg, smartwatches) designed to influence users to perform preferable behavior once or on a long-term basis. Considering the ubiquitous nature of mobile devices across all socioeconomic groups, user behavior modification thrives under the personalized care that persuasive technology can offer. However, there is no guidance for developing personalized persuasive technologies based on the psychological characteristics of users. OBJECTIVE: This study examined the role that psychological characteristics play in interpreted mobile health (mHealth) screen perceived persuasiveness. In addition, this study aims to explore how users' psychological characteristics drive the perceived persuasiveness of digital health technologies in an effort to assist developers and researchers of digital health technologies by creating more engaging solutions. METHODS: An experiment was designed to evaluate how psychological characteristics (self-efficacy, health consciousness, health motivation, and the Big Five personality traits) affect the perceived persuasiveness of digital health technologies, using the persuasive system design framework. Participants (n=262) were recruited by Qualtrics International, Inc, using the web-based survey system of the XM Research Service. This experiment involved a survey-based design with a series of 25 mHealth app screens that featured the use of persuasive principles, with a focus on physical activity. Exploratory factor analysis and linear regression were used to evaluate the multifaceted needs of digital health users based on their psychological characteristics. RESULTS: The results imply that an individual user's psychological characteristics (self-efficacy, health consciousness, health motivation, and extraversion) affect interpreted mHealth screen perceived persuasiveness, and combinations of persuasive principles and psychological characteristics lead to greater perceived persuasiveness. The F test (ie, ANOVA) for model 1 was significant (F9,6540=191.806; P<.001), with an adjusted R2 of 0.208, indicating that the demographic variables explained 20.8% of the variance in perceived persuasiveness. Gender was a significant predictor, with women having higher perceived persuasiveness (P=.008) relative to men. Age was a significant predictor of perceived persuasiveness with individuals aged 40 to 59 years (P<.001) and ≥60 years (P<.001). Model 2 was significant (F13,6536=341.035; P<.001), with an adjusted R2 of 0.403, indicating that the demographic variables self-efficacy, health consciousness, health motivation, and extraversion together explained 40.3% of the variance in perceived persuasiveness. CONCLUSIONS: This study evaluates the role that psychological characteristics play in interpreted mHealth screen perceived persuasiveness. Findings indicate that self-efficacy, health consciousness, health motivation, extraversion, gender, age, and education significantly influence the perceived persuasiveness of digital health technologies. Moreover, this study showed that varying combinations of psychological characteristics and demographic variables affected the perceived persuasiveness of the primary persuasive technology category. ©Aleise McGowan, Scott Sittig, David Bourrie, Ryan Benton, Sriram Iyengar. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 14.09.2022.
dc.language.isoen
dc.publisherJMIR Publications
dc.rightsCopyright © Aleise McGowan, Scott Sittig, David Bourrie, Ryan Benton, Sriram Iyengar. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 14.09.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjecthealth consciousness
dc.subjecthealth motivation
dc.subjectmHealth
dc.subjectmobile health
dc.subjectmobile phone
dc.subjectpersonality traits
dc.subjectpersonalization
dc.subjectpersuasive technology
dc.subjectpsychological characteristics
dc.subjectself-efficacy
dc.titleThe Intersection of Persuasive System Design and Personalization in Mobile Health: Statistical Evaluation
dc.typeArticle
dc.typetext
dc.contributor.departmentUniversity of Arizona
dc.identifier.journalJMIR mHealth and uHealth
dc.description.noteOpen access journal
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal published version
dc.source.journaltitleJMIR mHealth and uHealth
refterms.dateFOA2022-11-18T22:12:29Z


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Copyright © Aleise McGowan, Scott Sittig, David Bourrie, Ryan Benton, Sriram Iyengar. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 14.09.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as Copyright © Aleise McGowan, Scott Sittig, David Bourrie, Ryan Benton, Sriram Iyengar. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 14.09.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).