Acceptance towards digital health interventions-Model validation and further development of the Unified Theory of Acceptance and Use of Technology

被引:75
作者
Philippi, Paula [1 ]
Baumeister, Harald [1 ]
Apolinario-Hagen, Jennifer [2 ]
Ebert, David Daniel [3 ]
Hennemann, Severin [4 ]
Kott, Leonie [1 ]
Lin, Jiaxi [5 ]
Messner, Eva-Maria [1 ]
Terhorst, Yannik [1 ,6 ]
机构
[1] Ulm Univ, Inst Psychol & Educ, Dept Clin Psychol & Psychotherapy, Ulm, Germany
[2] Heinrich Heine Univ Dusseldorf, Fac Med, Inst Occupat Social & Environm Med, Dusseldorf, Germany
[3] Tech Univ Munich, Dept Sport & Hlth Sci, Munich, Germany
[4] Johannes Gutenberg Univ Mainz, Dept Clin Psychol Psychotherapy & Expt Psychopath, Mainz, Germany
[5] Univ Freiburg, Med Ctr, Fac Med, Dept Psychiat & Psychotherapy, Freiburg, Germany
[6] Ulm Univ, Inst Psychol & Educ, Dept Res Methods, Ulm, Germany
来源
INTERNET INTERVENTIONS-THE APPLICATION OF INFORMATION TECHNOLOGY IN MENTAL AND BEHAVIOURAL HEALTH | 2021年 / 26卷
关键词
Unified Theory of Acceptance and Use of; Technology; Internet-and mobile-based interventions; Acceptance; Digital health; Implementation science; eHealth; COVARIANCE STRUCTURE-ANALYSIS; INFORMATION-TECHNOLOGY; MENTAL-HEALTH; FACILITATING INTERVENTION; INTERNET; DEPRESSION; EFFICACY; ANXIETY; ATTITUDES; ADOPTION;
D O I
10.1016/j.invent.2021.100459
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
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 (N = 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 (gamma = 0.68, p < .001). Internet anxiety was identified as an additional determinant of acceptance (gamma = -0.07, p < .05) and moderated the effects of Social Influence (gamma = 0.07, p < .05) and Effort Expectancy (gamma = -0.05, p < .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.
引用
收藏
页数:9
相关论文
共 68 条
  • [1] Ajzen I, 1985, ACTION CONTROL COGNI, V2, P11, DOI [10.1007/978-3-642-69746-32, 10.1007/978-3-642-69746-3_2, DOI 10.1007/978-3-642-69746-3_2]
  • [2] Internet interventions: Past, present and future
    Andersson, Gerhard
    [J]. INTERNET INTERVENTIONS-THE APPLICATION OF INFORMATION TECHNOLOGY IN MENTAL AND BEHAVIOURAL HEALTH, 2018, 12 : 181 - 188
  • [3] Barriers to mental health treatment: results from the WHO World Mental Health surveys
    Andrade, L. H.
    Alonso, J.
    Mneimneh, Z.
    Wells, J. E.
    Al-Hamzawi, A.
    Borges, G.
    Bromet, E.
    Bruffaerts, R.
    de Girolamo, G.
    de Graaf, R.
    Florescu, S.
    Gureje, O.
    Hinkov, H. R.
    Hu, C.
    Huang, Y.
    Hwang, I.
    Jin, R.
    Karam, E. G.
    Kovess-Masfety, V.
    Levinson, D.
    Matschinger, H.
    O'Neill, S.
    Posada-Villa, J.
    Sagar, R.
    Sampson, N. A.
    Sasu, C.
    Stein, D. J.
    Takeshima, T.
    Viana, M. C.
    Xavier, M.
    Kessler, R. C.
    [J]. PSYCHOLOGICAL MEDICINE, 2014, 44 (06) : 1303 - 1317
  • [4] Apolinario-Hagen Jennifer, 2018, JMIR Form Res, V2, pe11977, DOI 10.2196/11977
  • [5] Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study
    Asare, Kennedy Opoku
    Terhorst, Yannik
    Vega, Julio
    Peltonen, Ella
    Lagerspetz, Eemil
    Ferreira, Denzil
    [J]. JMIR MHEALTH AND UHEALTH, 2021, 9 (07):
  • [6] Patients' Expectations Predict Surgery Outcomes: A Meta-Analysis
    Auer, Charlotte J.
    Glombiewski, Julia A.
    Doering, Bettina K.
    Winkler, Alexander
    Laferton, Johannes A. C.
    Broadbent, Elizabeth
    Rief, Winfried
    [J]. INTERNATIONAL JOURNAL OF BEHAVIORAL MEDICINE, 2016, 23 (01) : 49 - 62
  • [7] Bandura A., 1986, SOCIAL FDN THOUGHT A
  • [8] 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
    Batterham, Philip J.
    Calear, Alison L.
    Sunderland, Matthew
    Kay-Lambkin, Frances
    Farrer, Louise M.
    Gulliver, Amelia
    [J]. CONTEMPORARY CLINICAL TRIALS, 2019, 78 : 107 - 115
  • [9] Impact of an acceptance facilitating intervention on diabetes patients' acceptance of Internet-based interventions for depression: A randomized controlled trial
    Baumeister, H.
    Nowoczin, L.
    Lin, J.
    Seifferth, H.
    Seufert, J.
    Laubner, K.
    Ebert, D. D.
    [J]. DIABETES RESEARCH AND CLINICAL PRACTICE, 2014, 105 (01) : 30 - 39
  • [10] Baumeister H., 2020, DTSCH REGIST KLIN ST