Persuasive e-Health Design for Behavior Change

Baumeister, Harald; Baumel, Amit; Pryss, Rüdiger; Meßner, Eva-Maria

doi:10.1007/978-3-030-98546-2_20

Cited by 8 publications

(6 citation statements)

References 78 publications

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“…Hence, approaches fostering the maintenance of adherence and the prevention of disengagement over time need to be explored. For instance, therapeutic persuasiveness, user engagement, and usability may be important factors based on the findings in eHealth interventions ( 68 , 70 ). Overall, the promising findings of smart sensing ( 30 – 35 ) will only translate into healthcare improvements if the requirement of acceptance is met and the underlying processes for the initial and long-term usage are understood.…”

Section: Discussionmentioning

confidence: 99%

Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial

Terhorst,

Weilbacher,

Suda

et al. 2023

Front. Digit. Health

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BackgroundAccurate and timely diagnostics are essential for effective mental healthcare. Given a resource- and time-limited mental healthcare system, novel digital and scalable diagnostic approaches such as smart sensing, which utilizes digital markers collected via sensors from digital devices, are explored. While the predictive accuracy of smart sensing is promising, its acceptance remains unclear. Based on the unified theory of acceptance and use of technology, the present study investigated (1) the effectiveness of an acceptance facilitating intervention (AFI), (2) the determinants of acceptance, and (3) the acceptance of adults toward smart sensing.MethodsThe participants (N = 202) were randomly assigned to a control group (CG) or intervention group (IG). The IG received a video AFI on smart sensing, and the CG a video on mindfulness. A reliable online questionnaire was used to assess acceptance, performance expectancy, effort expectancy, facilitating conditions, social influence, and trust. The self-reported interest in using and the installation of a smart sensing app were assessed as behavioral outcomes. The intervention effects were investigated in acceptance using t-tests for observed data and latent structural equation modeling (SEM) with full information maximum likelihood to handle missing data. The behavioral outcomes were analyzed with logistic regression. The determinants of acceptance were analyzed with SEM. The root mean square error of approximation (RMSEA) and standardized root mean square residual (SRMR) were used to evaluate the model fit.ResultsThe intervention did not affect the acceptance (p = 0.357), interest (OR = 0.75, 95% CI: 0.42–1.32, p = 0.314), or installation rate (OR = 0.29, 95% CI: 0.01–2.35, p = 0.294). The performance expectancy (γ = 0.45, p < 0.001), trust (γ = 0.24, p = 0.002), and social influence (γ = 0.32, p = 0.008) were identified as the core determinants of acceptance explaining 68% of its variance. The SEM model fit was excellent (RMSEA = 0.06, SRMR = 0.05). The overall acceptance was M = 10.9 (SD = 3.73), with 35.41% of the participants showing a low, 47.92% a moderate, and 10.41% a high acceptance.DiscussionThe present AFI was not effective. The low to moderate acceptance of smart sensing poses a major barrier to its implementation. The performance expectancy, social influence, and trust should be targeted as the core factors of acceptance. Further studies are needed to identify effective ways to foster the acceptance of smart sensing and to develop successful implementation strategies.Clinical Trial Registrationidentifier 10.17605/OSF.IO/GJTPH.

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Section: Discussionmentioning

confidence: 99%

Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial

Terhorst,

Weilbacher,

Suda

et al. 2023

Front. Digit. Health

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“…This could include the development of technological mechanisms that interrupt a user when they demonstrate online signs of anger or sadness, in turn preventing their engagement in TAR. Technology developers could also explore the potential of “persuasive design” mechanisms that seek to promote prosocial or healthful behaviors, for example recognizing escalation of emotions and suggesting healthy behaviors such as stepping away from the digital device or speaking with friends ( Baumeister et al, 2023 ; Purpura et al, 2011 ; Toscos et al, 2006 ). Thus, for policymakers, practitioners, and industry alike, the findings from the current study offer important insights for use to inform, create, and reform work in this sphere.…”

Section: Discussionmentioning

confidence: 99%

Perpetrator Perceptions on the Emotions and Motivations Driving Technology-Facilitated Abuse in Relationships: A Story Completion Study

Fiolet,

Brown,

McKay

et al. 2023

J Interpers Violence

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Technology-facilitated abuse in relationships (TAR) is a widespread social problem that has a significant impact on victim-survivors. Most contemporary evidence on TAR focuses on victim-survivor and practitioner perspectives rather than those of perpetrators who choose to enact this form of harm. Addressing this deficit, this study explored perpetrators’ discourses on emotions and motivations associated with engaging in TAR. Using story completion method, 35 self-identified perpetrators of TAR completed story stems describing scenarios that may precede the use of abusive online behaviors. Reflexive thematic analysis generated three themes. Abusive behaviors and negative emotions speaks to maladaptive experiences of anger and/or sadness that can precede a decision to use TAR. A loss of trust, a desire for control describes potential motives for using TAR. Finally, inhibitors of abusive behavior investigates rationales perpetrators use for avoidance of TAR behaviors, suggesting avenues for working with perpetrators to refrain from using TAR. We conclude by discussing policy, practice, and research recommendations including strategies for technology designers and suggestions for primary prevention and response to TAR.

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“…Future research should take into account that internet use starts at a very early age ( Byrne & Burton, 2017 ), thus it would be important to evaluate digital health interventions for children and adolescents to prevent IUDs at an early stage. In this context it might be necessary to adapt the intervention to the specific needs of children and adolescents by taking user experience (UX-design) and persuasive design principles into account ( Baumeister, Kraft, Baumel, Pryss, & Messner, 2023 ). Moreover, the sample showed an above-average level of education, which is common in guided self-help internet-based interventions and limits the generalizability of the results.…”

Section: Discussionmentioning

confidence: 99%

Treating internet use disorders via the internet? Results of a two-armed randomized controlled trial

Bernstein,

Schaub,

Baumeister

et al. 2023

JBA

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Background and aimsInternet Use Disorders (IUDs) are emerging as a societal challenge. Evidence-based treatment options are scarce. Digital health interventions may be promising to deliver psychological treatment to individuals with IUDs directly in their online setting. The aim of this study was to evaluate the efficacy of a digital health intervention for IUDs compared to a waitlist control group (WCG).MethodsIn a two-armed randomized controlled trial, N = 130 individuals showing IUDs (Internet Addiction Test; IAT ≥49) were randomly allocated to the intervention group (IG; n = 65) or WCG (n = 65). The intervention consisted of 7 sessions based on cognitive behavioral therapy. The primary outcome was IUD symptom severity measured via the IAT at post treatment 7 weeks after randomization. Secondary outcomes included IUD symptoms (Compulsive Internet Use Scale; CIUS), quality of life, depressive and anxiety symptoms, and other psychosocial variables associated with IUDs.ResultsParticipants were on average 28.45 years old (SD = 10.59) and 50% identified as women, 49% as men, and 1% as non-binary. The IG (n = 65) showed significantly less IUD symptom severity (IAT) (d = 0.54, 95% CI 0.19–0.89) and symptoms (d = 0.57, 95% CI 0.22–0.92) than the WCG (n = 65) at post-treatment. Study attrition was 20%. Effects on all other secondary outcomes were not significant. On average, participants completed 67.5% of the intervention.Discussion and ConclusionsA digital health intervention could be a promising first step to reduce IUD symptom severity.

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Persuasive e-Health Design for Behavior Change

Cited by 8 publications

References 78 publications

Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial

Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial

Perpetrator Perceptions on the Emotions and Motivations Driving Technology-Facilitated Abuse in Relationships: A Story Completion Study

Treating internet use disorders via the internet? Results of a two-armed randomized controlled trial

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