Using the Unified Theory of Acceptance and Use of Technology (UTAUT) to Investigate the Intention to Use Physical Activity Apps: Cross-Sectional Survey

Liu, Di; Maimaitijiang, Remina; Gu, Jing; Zhong, Shijie; Zhou, Mengping; Wu, Ziyue; Luo, Ao; Liu, Cong; Hao, Yuantao

doi:10.2196/13127

Cited by 66 publications

(72 citation statements)

References 31 publications

(24 reference statements)

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“…Although some studies show that only perceived usefulness predicts intention to use mHealth technology [ 19 ], most studies show that both perceived usefulness and perceived ease-of-use have a significant impact on intention to use [ 20 , 21 ]. In the context of smartphone fitness apps, most of the studies focused on initial app adoption [ 15 ]. One study that used the TAM framework to predict users’ continuance usage [ 22 ] found that perceived usefulness and perceived ease of use, along with social norms, predicted intentions to continue using a particular fitness app among users.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The theory presents four constructs that determine users’ acceptance and usage behavior: performance expectancy, effort expectancy, social influence, and facilitating conditions. The constructs of the UTAUT have been positively associated with the intention to adopt a fitness app [ 15 ]. UTAUT overlaps with a general theory that explains individual behavior—the Theory of Reasoned Action (TRA) [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

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The Use of Smartphone Fitness Applications: The Role of Self-Efficacy and Self-Regulation

Vinnikova

Wei

et al. 2020

IJERPH

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With the popularity of the health and wellness trend in recent years, smartphone fitness applications have become more and more popular. Thus, this study explored factors affecting the behavioral intention to use and the actual usage behavior of smartphone fitness apps from technical, health, and social perspectives by integrating the Social Cognitive Theory (SCT) and Unified Theory of Acceptance and Use of Technology (UTAUT). We examined whether perceived usefulness, perceived ease-of-use, social influence, self-efficacy, goal-setting, and self-monitoring predict usage behavior. Based on the survey responses of 1066 smartphone fitness apps users, we revealed that all of the variables, except for self-monitoring, significantly influence usage behavior, while behavioral intention acts as a total mediator between perceived usefulness, perceived ease-of-use and usage behavior. Drawing on the research findings, we suggest that influencing behavioral intention to use a fitness app can be an effective method to increase its adoption. Therefore, app developers need to pay attention to interventions that seek to enhance the usefulness of the app, provide professional counseling, as well as an opportunity for effortless goal setting features.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Use of Smartphone Fitness Applications: The Role of Self-Efficacy and Self-Regulation

Vinnikova

Wei

et al. 2020

IJERPH

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“…Previous studies investigated factors influencing mHealth use in specific target groups (e.g. [54,55]), for specific diseases or health topics [56][57][58][59][60], and for sustained mHealth engagement [61]. This study aims to explicate the effect of social support.…”

Section: Social Support and Technology-supported Diabetes Selfmanagementmentioning

confidence: 99%

Differential influences of social support on app use for diabetes self-management – a mixed methods approach

Brew‐Sam

Chib

Rossmann

2020

BMC Med Inform Decis Mak

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Background: Recent studies increasingly examine social support for diabetes self-management delivered via mHealth. In contrast to previous studies examining social support as an outcome of technology use, or technology as a means for delivering social support, this paper argues that social support has an impact on the use of diabetes mHealth apps. Specifically, we postulate differences between the impact of healthcare professional versus nonprofessional (family/friends) support on mobile app use for diabetes self-management. Methods: This research employed a triangulation of methods including exploratory semi-structured face-to-face interviews (N = 21, Study 1) and an online survey (N = 65, Study 2) with adult type 1 and type 2 diabetes patients. Thematic analysis (Study 1) was used to explore the relevance of social support (by professionals versus nonprofessionals) for diabetes app use. Binary logistic regression (Study 2) was applied to compare healthcare decisionmaking, healthcare-patient communication, and the support by the personal patient network as predictors of diabetes app use, complemented by other predictors from self-management and technology adoption theory. Results: The interviews (Study 1) demonstrated that (technology-supported) shared decision-making and supportive communication by healthcare professionals depended on their medical specialty. The personal patient network was perceived as either facilitating or hindering the use of mHealth for self-management. Binary logistic regression (Study 2) showed that the physician specialty significantly predicted the use of diabetes apps, with supervision by diabetes specialists increasing the likelihood of app use (as opposed to general practitioners). Additionally, specialist care positively related to a higher chance of shared decision-making and better physicianpatient communication. The support by the personal patient network predicted diabetes app use in the opposite direction, with less family/friend support increasing the likelihood of app use. Conclusion: The results emphasize the relevance of support by healthcare professionals and by the patient network for diabetes app use and disclose differences from the existing literature. In particular, the use of diabetes apps may increase in the absence of social support by family or friends (e.g., compensation for lack of support), and may decrease when such support is high (e.g., no perceived need to use technology).

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“…While theoretical models of technology adoption (51) are one step towards understanding factors that in uence (mobile) technology adoption and use, there is a gap in proving both signi cant and substantive usage effects for mHealth (52,53). Previous studies investigated factors in uencing mHealth use in speci c target groups (e.g., 54,55), for speci c diseases or health topics (56)(57)(58)(59)(60), and for sustained mHealth engagement (61). This study aims to explicate the effect of social support.…”

Section: Social Support and Technology-supported Diabetes Self-managementioning

confidence: 99%

Differential Influences of Social Support on App Use for Diabetes Self-Management – a Mixed Methods Approach

Brew‐Sam

Chib²,

Rossmann

2020

Preprint

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Background Recent studies increasingly examine social support for diabetes self-management delivered via mHealth. In contrast to previous studies examining social support as an outcome of technology use, or technology as a means for delivering social support, this paper argues that social support has an impact on the use of diabetes mHealth apps. Specifically, we postulate differences between the impact of healthcare professional versus non-professional (family/friends) support on mobile app use for diabetes self-management.Methods This research employed a triangulation of methods including exploratory semi-structured face-to-face interviews (N= 21, Study 1) and an online survey (N= 65, Study 2) with adult type 1 and type 2 diabetes patients. Thematic analysis (Study 1) was used to explore the relevance of social support (by professionals versus non-professionals) for diabetes app use. Binary logistic regression (Study 2) was applied to compare healthcare decision-making, healthcare-patient communication, and the support by the personal patient network as predictors of diabetes app use, complemented by other predictors from self-management and technology adoption theory.Results The interviews (Study 1) demonstrated that (technology-supported) shared decision-making and supportive communication by healthcare professionals depended on their medical specialty. The personal patient network was perceived as either facilitating or hindering the use of mHealth for self-management. Binary logistic regression (Study 2) showed that the physician specialty significantly predicted the use of diabetes apps, with supervision by diabetes specialists increasing the likelihood of app use (as opposed to general practitioners). Additionally, specialist care positively related to a higher chance of shared decision-making and better physician-patient communication. The support by the personal patient network predicted diabetes app use in the opposite direction, with less family/friend support increasing the likelihood of app use.Conclusion The results emphasize the relevance of support by healthcare professionals and by the patient network for diabetes app use and disclose differences from the existing literatureThe use of diabetes apps may increase in the absence of social support by family or friends (e.g., compensation for lack of support), and that use of diabetes apps may decrease when such support is high (e.g., no perceived need to use technology).

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Using the Unified Theory of Acceptance and Use of Technology (UTAUT) to Investigate the Intention to Use Physical Activity Apps: Cross-Sectional Survey

Cited by 66 publications

References 31 publications

The Use of Smartphone Fitness Applications: The Role of Self-Efficacy and Self-Regulation

The Use of Smartphone Fitness Applications: The Role of Self-Efficacy and Self-Regulation

Differential influences of social support on app use for diabetes self-management – a mixed methods approach

Differential Influences of Social Support on App Use for Diabetes Self-Management – a Mixed Methods Approach

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