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

Philippi, Paula; Baumeister, Harald; Apolinário-Hagen, Jennifer; Ebert, David Daniel; Hennemann, Severin; Kott, Leonie; Lin, Jiaxi; Meßner, Eva-Maria; Terhorst, Yannik

doi:10.1016/j.invent.2021.100459

Cited by 84 publications

(92 citation statements)

References 50 publications

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“…In summary, EMH acceptance of PiT may be explained according to the UTAUT model when coupled with their perceptions of barriers and drivers as well as their practical experience as healthcare providers with EMH, knowledge about EMH and their perception of the scientific evidence base of EMH services. Even though the UTAUT model has recently been successfully validated and adapted to digital health care (9), our results show that it is necessary to extend this model and adapt it to the context of PiT given the complex nature of EMH acceptance and its determinants. In short, we did not assess all factors that could potentially influence EMH acceptance and focused on those that we perceived as being most important for PiT, knowing that there might still be missing factors that could be relevant.…”

Section: Determinants Of Emh Service Acceptancementioning

confidence: 96%

“…To offer quick, safe and location-independent help, the World Health Organization ( 8 ) has recommended to ensure access to psychosocial support services through digital systems. Consequently, the need for easily accessible, effective and flexible services as alternatives or additions to traditional mental health treatment to support vulnerable populations became even more evident during the COVID-19 pandemic ( 9 ).…”

Section: Introductionmentioning

confidence: 99%

“…Efficient implementation of EMH services depends on the utilization and acceptance by potential users and health experts. According to the Unified Theory of Acceptance and Use of Technology (UTAUT), acceptance can be operationalized as the intention to use technology and serves as a direct predictor of the actual usage ( 9 , 28 ). Thus, low uptake rates can be explained by EMH acceptance being low to moderate among patients ( 25 , 29 – 31 ) and health professionals ( 32 , 33 ).…”

Section: Introductionmentioning

confidence: 99%

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Acceptance of E-Mental Health Services for Different Application Purposes Among Psychotherapists in Clinical Training in Germany and Switzerland: Secondary Analysis of a Cross-Sectional Survey

Braun

Drüge

Hennemann³

et al. 2022

Front. Digit. Health

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BackgroundDespite solid evidence supporting the efficacy of electronic mental health (EMH) services, their acceptance among psychotherapists is limited and uptake rates remain low. However, the acceptance of different EMH services has yet barely been examined in future generations of psychotherapists in a differentiated manner. The aims of this study were (1) to elaborate the intention to use various EMH services for different application purposes and (2) to determine predictors of EMH service acceptance among psychotherapists in clinical training (PiT).Materials and MethodsOur paper is based on a secondary data analysis of a cross-sectional survey. Respondents were recruited via recognized educational institutions for psychotherapy within Germany and the German-speaking part of Switzerland between June and July of 2020. The survey contained items on the intention to use different EMH services (i.e., guided and unguided programs, virtual reality, psychotherapy by telephone and videoconference) for various application purposes (i.e., prevention, treatment addition, treatment substitute, aftercare). Potential predictors of EMH service acceptance (e.g., barriers and advantages) were examined based on an extension of the Unified Theory of Acceptance and Use of Technology (UTAUT).ResultsMost of the n = 216 respondents were female (88.4%) and located in Germany (72.2%). General acceptance of EMH was moderate (M = 3.4, SD = 1.12, range 1–5), while acceptance of psychotherapy via videoconference was highest (M = 3.7, SD = 1.15) and acceptance of unguided programs was lowest (M = 2.55, SD = 1.14). There was an interaction effect of EMH service and application purpose (η2 = 0.21). Barriers and advantages both had a uniform influence on EMH service acceptance (Pr > 0.999), while impersonality, legal concerns, concerns about therapeutic alliance, simplified information provision, simplified contact maintenance, time flexibility, and geographic flexibility were significant predictors (all p < 0.05). Results showed that the extended UTAUT model was the best fitting model to predict EMH service acceptance (Pr > 0.999).ConclusionsThe intention to use different EMH services varied between application purposes among PiT. To increase acceptance of EMH services and reduce misconceptions, we identified predictors that should be addressed in future acceptance-facilitating interventions when educating PiT.

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Section: Determinants Of Emh Service Acceptancementioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Acceptance of E-Mental Health Services for Different Application Purposes Among Psychotherapists in Clinical Training in Germany and Switzerland: Secondary Analysis of a Cross-Sectional Survey

Braun

Drüge

Hennemann³

et al. 2022

Front. Digit. Health

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“…Concerning acceptability, almost three-fourths of our participants (35/49, 71%) preferred face-to-face diagnostics by a health professional over the symptom checker, which is comparable with preference ratings from the German general population [ 18 ]. This could be critical regarding the reshaping of diagnostic practice as acceptance represents a crucial premise for the implementation of health resources [ 69 ]. As symptom checkers are more likely to complement rather than substitute diagnostic processes, it would be interesting to also investigate patients’ and health professionals’ views on the combination of traditional and digital diagnostic procedures, for example, whether symptom checkers would be preferred as a first or second opinion in differential diagnoses or as assistance in clinical decision-making.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Performance of an App-Based Symptom Checker in Mental Disorders: Comparative Study in Psychotherapy Outpatients

Hennemann¹,

Kühn²,

Witthöft³

et al. 2022

JMIR Ment Health

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Background Digital technologies have become a common starting point for health-related information-seeking. Web- or app-based symptom checkers aim to provide rapid and accurate condition suggestions and triage advice but have not yet been investigated for mental disorders in routine health care settings. Objective This study aims to test the diagnostic performance of a widely available symptom checker in the context of formal diagnosis of mental disorders when compared with therapists’ diagnoses based on structured clinical interviews. Methods Adult patients from an outpatient psychotherapy clinic used the app-based symptom checker Ada–check your health (ADA; Ada Health GmbH) at intake. Accuracy was assessed as the agreement of the first and 1 of the first 5 condition suggestions of ADA with at least one of the interview-based therapist diagnoses. In addition, sensitivity, specificity, and interrater reliabilities (Gwet first-order agreement coefficient [AC1]) were calculated for the 3 most prevalent disorder categories. Self-reported usability (assessed using the System Usability Scale) and acceptance of ADA (assessed using an adapted feedback questionnaire) were evaluated. Results A total of 49 patients (30/49, 61% women; mean age 33.41, SD 12.79 years) were included in this study. Across all patients, the interview-based diagnoses matched ADA’s first condition suggestion in 51% (25/49; 95% CI 37.5-64.4) of cases and 1 of the first 5 condition suggestions in 69% (34/49; 95% CI 55.4-80.6) of cases. Within the main disorder categories, the accuracy of ADA’s first condition suggestion was 0.82 for somatoform and associated disorders, 0.65 for affective disorders, and 0.53 for anxiety disorders. Interrater reliabilities ranged from low (AC1=0.15 for anxiety disorders) to good (AC1=0.76 for somatoform and associated disorders). The usability of ADA was rated as high in the System Usability Scale (mean 81.51, SD 11.82, score range 0-100). Approximately 71% (35/49) of participants would have preferred a face-to-face over an app-based diagnostic. Conclusions Overall, our findings suggest that a widely available symptom checker used in the formal diagnosis of mental disorders could provide clinicians with a list of condition suggestions with moderate-to-good accuracy. However, diagnostic performance was heterogeneous between disorder categories and included low interrater reliability. Although symptom checkers have some potential to complement the diagnostic process as a screening tool, the diagnostic performance should be tested in larger samples and in comparison with further diagnostic instruments.

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“…Strategies that increase expectations about new technologies include promoting trust in the new technology (Thatcher et al 2007). For instance, when introducing a new therapy app, therapists should provide a thorough explanation of the app and answer clients' questions to reduce hesitancies surrounding the new app (Philippi et al 2021). Despite these hesitancies, adolescents had positive shifts in belief surrounding adjunctive mobile apps or group therapy.…”

Section: Discussionmentioning

confidence: 99%

Adolescent Perspectives on How an Adjunctive Mobile App for Social Anxiety Treatment Impacts Treatment Engagement in Telehealth Group Therapy

Chu

Madden

et al. 2021

Social Sciences

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Adjunctive mobile mental health apps to supplement mental health treatment have been growing in recent years given their ability to address treatment engagement barriers. However, few studies have explicitly examined how these mobile apps impact treatment engagement, and even fewer have investigated this topic through adolescents’ perspectives. To this end, we conducted semi-structured interviews with five adolescents who used an adjunctive mobile mental health app in combination with telehealth cognitive behavioral group therapy for social anxiety. Using a multidimensional framework of treatment engagement, we elicited their perspectives on how the app impacted their engagement in telehealth group therapy and gathered their suggestions for improving the app. Using a consensual qualitative research approach, we learned that adolescents felt the app increased their comfort with others in therapy and their expectations about the effectiveness of mental health apps. They also indicated that the app prepared them for in-session participation and facilitated out-of-session skills practice. Adolescents had valuable suggestions such as adding app features to facilitate social connectedness between group members and adding appointment reminders in the app. This preliminary study highlights implications for future adjunctive mobile mental health app developers and researchers to increase adolescents’ treatment engagement in mental health services.

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

Cited by 84 publications

References 50 publications

Acceptance of E-Mental Health Services for Different Application Purposes Among Psychotherapists in Clinical Training in Germany and Switzerland: Secondary Analysis of a Cross-Sectional Survey

Acceptance of E-Mental Health Services for Different Application Purposes Among Psychotherapists in Clinical Training in Germany and Switzerland: Secondary Analysis of a Cross-Sectional Survey

Diagnostic Performance of an App-Based Symptom Checker in Mental Disorders: Comparative Study in Psychotherapy Outpatients

Adolescent Perspectives on How an Adjunctive Mobile App for Social Anxiety Treatment Impacts Treatment Engagement in Telehealth Group Therapy

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