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Acceptance Towards Digital Health Interventions - Model Validation and Further Development of the Unified Theory of Acceptance and Use of Technology

Overview
Journal Internet Interv
Date 2021 Oct 4
PMID 34603973
Citations 49
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Abstract

Internet- and mobile-based interventions (IMI) offer an effective way to complement health care. Acceptance of IMI, a key facilitator of their implementation in routine care, is often low. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), this study validates and adapts the UTAUT to digital health care. Following a systematic literature search, 10 UTAUT-grounded original studies ( = 1588) assessing patients' and health professionals' acceptance of IMI for different somatic and mental health conditions were included. All included studies assessed Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions and acceptance as well as age, gender, internet experience, and internet anxiety via self-report questionnaires. For the model validation primary data was obtained and analyzed using structural equation modeling. The best fitting model (RMSEA = 0.035, SRMR = 0.029) replicated the basic structure of UTAUT's core predictors of acceptance. Performance Expectancy was the strongest predictor (γ = 0.68,  < .001). Internet anxiety was identified as an additional determinant of acceptance (γ = -0.07,  < .05) and moderated the effects of Social Influence (γ = 0.07,  < .05) and Effort Expectancy (γ = -0.05,  < .05). Age, gender and experience had no moderating effects. Acceptance is a fundamental prerequisite for harnessing the full potential of IMI. The adapted UTAUT provides a powerful model identifying important factors - primarily Performance Expectancy - to increase the acceptance across patient populations and health professionals.

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References
1.
Lin J, Faust B, Ebert D, Kramer L, Baumeister H . A Web-Based Acceptance-Facilitating Intervention for Identifying Patients' Acceptance, Uptake, and Adherence of Internet- and Mobile-Based Pain Interventions: Randomized Controlled Trial. J Med Internet Res. 2018; 20(8):e244. PMC: 6123541. DOI: 10.2196/jmir.9925. View

2.
Baumel A, Muench F, Edan S, Kane J . Objective User Engagement With Mental Health Apps: Systematic Search and Panel-Based Usage Analysis. J Med Internet Res. 2019; 21(9):e14567. PMC: 6785720. DOI: 10.2196/14567. View

3.
Batterham P, Calear A, Sunderland M, Kay-Lambkin F, Farrer L, Gulliver A . A brief intervention to increase uptake and adherence of an online program for depression and anxiety: Protocol for the Enhancing Engagement with Psychosocial Interventions (EEPI) Randomized Controlled Trial. Contemp Clin Trials. 2019; 78:107-115. DOI: 10.1016/j.cct.2019.01.015. View

4.
Auer C, Glombiewski J, Doering B, Winkler A, Laferton J, Broadbent E . Patients' Expectations Predict Surgery Outcomes: A Meta-Analysis. Int J Behav Med. 2015; 23(1):49-62. DOI: 10.1007/s12529-015-9500-4. View

5.
Baumeister H, Seifferth H, Lin J, Nowoczin L, Luking M, Ebert D . Impact of an Acceptance Facilitating Intervention on Patients' Acceptance of Internet-based Pain Interventions: A Randomized Controlled Trial. Clin J Pain. 2014; 31(6):528-35. DOI: 10.1097/AJP.0000000000000118. View