Literature DB >> 30927357

A self-report measure of engagement with digital behavior change interventions (DBCIs): development and psychometric evaluation of the "DBCI Engagement Scale".

Olga Perski1, Ann Blandford2, Claire Garnett3, David Crane1, Robert West3, Susan Michie1.   

Abstract

Engagement with digital behavior change interventions (DBCIs) is a potentially important mediator of effectiveness; however, we lack validated measures of engagement. This study describes (a) the development of a self-report scale that captures the purported behavioral and experiential facets of engagement and (b) the evaluation of its validity in a real-world setting. A deductive approach to item generation was taken. The sample consisted of adults in the UK who drink excessively, downloaded the freely available Drink Less app with the intention to reduce alcohol consumption, and completed the scale immediately after their first login. Five types of validity (i.e., construct, criterion, predictive, incremental, divergent) were examined using exploratory factor analysis, correlational analyses, and through regressing the number of subsequent logins in the next 14 days onto total scale scores. Cronbach's α was calculated to assess internal reliability. A 10-item scale assessing amount and depth of use, interest, enjoyment, and attention was generated. Of 5,460 eligible users, only 203 (3.7%) users completed the scale. Seven items were retained, and the scale was found to be unifactorial and internally reliable (α = 0.77). Divergent and criterion validity were not established. Total scale scores were not significantly associated with the number of subsequent logins (B = 0.02; 95% CI = -0.01 to 0.05; p = .14). Behavioral and experiential indicators of engagement with DBCIs may constitute a single dimension, but low response rates to engagement surveys embedded in DBCIs may make their use impracticable in real-world settings.
© The Author(s) 2019. Published by Oxford University Press on behalf of the Society of Behavioral Medicine.

Entities:  

Keywords:  Alcohol reduction; Digital behavior change interventions; Engagement; Psychometric evaluation; Self-report scale; eHealth; mHealth

Year:  2020        PMID: 30927357     DOI: 10.1093/tbm/ibz039

Source DB:  PubMed          Journal:  Transl Behav Med        ISSN: 1613-9860            Impact factor:   3.046


  16 in total

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Journal:  Wellcome Open Res       Date:  2020-11-11

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Journal:  J Med Internet Res       Date:  2022-05-03       Impact factor: 7.076

4.  Assessing the Psychometric Properties of the Digital Behavior Change Intervention Engagement Scale in Users of an App for Reducing Alcohol Consumption: Evaluation Study.

Authors:  Olga Perski; Jim Lumsden; Claire Garnett; Ann Blandford; Robert West; Susan Michie
Journal:  J Med Internet Res       Date:  2019-11-20       Impact factor: 5.428

5.  Examining the Usage, User Experience, and Perceived Impact of an Internet-Based Cognitive Behavioral Therapy Program for Adolescents With Anxiety: Randomized Controlled Trial.

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6.  Participatory Development and Pilot Testing of an Adolescent Health Promotion Chatbot.

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7.  Exclusively Digital Health Interventions Targeting Diet, Physical Activity, and Weight Gain in Pregnant Women: Systematic Review and Meta-Analysis.

Authors:  Helen Croker; Clare H Llewellyn; Alexandra Rhodes; Andrea D Smith; Paul Chadwick
Journal:  JMIR Mhealth Uhealth       Date:  2020-07-10       Impact factor: 4.773

8.  Digital Micro Interventions for Behavioral and Mental Health Gains: Core Components and Conceptualization of Digital Micro Intervention Care.

Authors:  Amit Baumel; Theresa Fleming; Stephen M Schueller
Journal:  J Med Internet Res       Date:  2020-10-29       Impact factor: 5.428

9.  Psychometric Evaluation of the TWente Engagement with Ehealth Technologies Scale (TWEETS): Evaluation Study.

Authors:  Saskia Marion Kelders; Hanneke Kip; Japie Greeff
Journal:  J Med Internet Res       Date:  2020-10-09       Impact factor: 5.428

10.  Exploring and Characterizing Patient Multibehavior Engagement Trails and Patient Behavior Preference Patterns in Pathway-Based mHealth Hypertension Self-Management: Analysis of Use Data.

Authors:  Dan Wu; Xiaoyuan Huyan; Yutong She; Junbin Hu; Huilong Duan; Ning Deng
Journal:  JMIR Mhealth Uhealth       Date:  2022-02-03       Impact factor: 4.773

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