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  • 1
    Online Resource
    Online Resource
    Predicting Acceptance of e–Mental Health Interventions in Patients With Obesity by Using an Extended Unified Theory of Acceptance Model: Cross-sectional Study
    JMIR Publications Inc. ; 2022
    In:  JMIR Formative Research Vol. 6, No. 3 ( 2022-3-17), p. e31229-
    Details
    In: JMIR Formative Research, JMIR Publications Inc., Vol. 6, No. 3 ( 2022-3-17), p. e31229-
    Abstract: The rapid increase in the number of people who are overweight and obese is a worldwide health problem. Obesity is often associated with physiological and mental health burdens. Owing to several barriers to face-to-face psychotherapy, a promising approach is to exploit recent developments and implement innovative e–mental health interventions that offer various benefits to patients with obesity and to the health care system. Objective This study aims to assess the acceptance of e–mental health interventions in patients with obesity and explore its influencing predictors. In addition, the well-established Unified Theory of Acceptance and Use of Technology (UTAUT) model is compared with an extended UTAUT model in terms of variance explanation of acceptance. Methods A cross-sectional web-based survey study was conducted from July 2020 to January 2021 in Germany. Eligibility requirements were adult age (≥18 years), internet access, good command of the German language, and BMI 〉 30 kg/m2 (obesity). A total of 448 patients with obesity (grades I, II, and III) were recruited via specialized social media platforms. The impact of various sociodemographic, medical, and mental health characteristics was assessed. eHealth-related data and acceptance of e–mental health interventions were examined using a modified questionnaire based on the UTAUT. Results Overall, the acceptance of e–mental health interventions in patients with obesity was moderate (mean 3.18, SD 1.11). Significant differences in the acceptance of e–mental health interventions among patients with obesity exist, depending on the grade of obesity, age, sex, occupational status, and mental health status. In an extended UTAUT regression model, acceptance was significantly predicted by the depression score (Patient Health Questionnaire-8; β=.07; P=.03), stress owing to constant availability via mobile phone or email (β=.06; P=.02), and confidence in using digital media (β=−0.058; P=.04) and by the UTAUT core predictors performance expectancy (β=.45; P 〈 .001), effort expectancy (β=.22; P 〈 .001), and social influence (β=.27; P 〈 .001). The comparison between an extended UTAUT model (16 predictors) and the restrictive UTAUT model (performance expectancy, effort expectancy, and social influence) revealed a significant difference in explained variance (F13,431=2.366; P=.005). Conclusions The UTAUT model has proven to be a valuable instrument to predict the acceptance of e–mental health interventions in patients with obesity. The extended UTAUT model explained a significantly high percentage of variance in acceptance (in total 73.6%). On the basis of the strong association between acceptance and future use, new interventions should focus on these UTAUT predictors to promote the establishment of effective e–mental health interventions for patients with obesity who experience mental health burdens.
    Type of Medium: Online Resource
    ISSN: 2561-326X
    URL: Article
    DOI: 10.2196/31229
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2022
    detail.hit.zdb_id: 2941716-8
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  • 2
    Online Resource
    Online Resource
    Web-based mindfulness and skills-based distress reduction for patients with cancer: study protocol of the multicentre, randomised, controlled confirmatory intervention trial Reduct
    BMJ ; 2022
    In:  BMJ Open Vol. 12, No. 6 ( 2022-06), p. e056973-
    Details
    In: BMJ Open, BMJ, Vol. 12, No. 6 ( 2022-06), p. e056973-
    Abstract: Many patients with cancer experience severe psychological distress, but as a result of various barriers, few of them receive psycho-oncological support. E-mental health interventions try to overcome some of these barriers and the limitation of healthcare offers, enabling patients with cancer to better cope with psychological distress. In the proposed trial, we aim to assess the efficacy and cost-effectiveness of the manualised e-mental health intervention Make It Training- Mindfulness-Based and Skills-Based Distress Reduction in Oncology. Make It Training is a self-guided and web-based psycho-oncological intervention, which includes elements of cognitive behavioural therapy, mindfulness-based stress reduction and acceptance and commitment therapy. The training supports the patients over a period of 4 months. We expect the Make It Training to be superior to treatment as usual optimised (TAU-O) in terms of reducing distress after completing the intervention (T1, primary endpoint). Methods and analysis The study comprises a multicentre, prospective, randomised controlled confirmatory interventional trial with two parallel arms. The proposed trial incorporates four distinct measurement time points: the baseline assessment before randomisation, a post-treatment assessment and 3 and 6 month follow-up assessments. We will include patients who have received a cancer diagnosis in the past 12 months, are in a curative treatment setting, are 18–65 years old, have given informed consent and experience high perceived psychological distress (Hospital Anxiety and Depression Scale ≥13) for at least 1 week. Patients will be randomised into two groups (Make It vs TAU-O). The aim is to allocate 600 patients with cancer and include 556 into the intention to treat analysis. The primary endpoint, distress, will be analysed using a baseline-adjusted ANCOVA for distress measurement once the intervention (T1) has been completed, with study arm as a binary factor, baseline as continuous measurement and study centre as an additional categorical covariate. Ethics and dissemination The Ethics Committee of the Medical Faculty Essen has approved the study (21-10076-BO). Results will be published in peer-reviewed journals, conference presentations, the project website, and among self-help organisations. Trial registration number German Clinical Trial Register (DRKS); DRKS-ID: DRKS00025213.
    Type of Medium: Online Resource
    ISSN: 2044-6055 , 2044-6055
    URL: Article
    DOI: 10.1136/bmjopen-2021-056973
    Language: English
    Publisher: BMJ
    Publication Date: 2022
    detail.hit.zdb_id: 2599832-8
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  • 3
    Online Resource
    Online Resource
    Determining Acceptance of e-Mental Health Interventions in Digital Psychodiabetology Using a Quantitative Web-Based Survey: Cross-sectional Study
    JMIR Publications Inc. ; 2021
    In:  JMIR Formative Research Vol. 5, No. 7 ( 2021-7-30), p. e27436-
    Details
    In: JMIR Formative Research, JMIR Publications Inc., Vol. 5, No. 7 ( 2021-7-30), p. e27436-
    Abstract: Diabetes is a very common chronic disease that exerts massive physiological and psychological burdens on patients. The digitalization of mental health care has generated effective e-mental health approaches, which offer an indubitable practical value for patient treatment. However, before implementing and optimizing e-mental health tools, their acceptance and underlying barriers and resources should be first determined for developing and establishing effective patient-oriented interventions. Objective This study aims to assess the acceptance of e-mental health interventions among patients with diabetes and explore its underlying barriers and resources. Methods A cross-sectional study was conducted in Germany from April 9, 2020, to June 15, 2020, through a web-based survey for which patients were recruited via web-based diabetes channels. The eligibility requirements were adult age (18 years or older), a good command of the German language, internet access, and a diagnosis of diabetes. Acceptance was measured using a modified questionnaire, which was based on the well-established Unified Theory of Acceptance and Use of Technology (UTAUT) and assessed health-related internet use, acceptance of e-mental health interventions, and its barriers and resources. Mental health was measured using validated and established instruments, namely the Generalized Anxiety Disorder Scale-7, Patient Health Questionnaire-2, and Distress Thermometer. In addition, sociodemographic and medical data regarding diabetes were collected. Results Of the 340 participants who started the survey, 261 (76.8%) completed it and the final sample comprised 258 participants with complete data sets. The acceptance of e-mental health interventions in patients with diabetes was overall moderate (mean 3.02, SD 1.14). Gender and having a mental disorder had a significant influence on acceptance (P 〈 .001). In an extended UTAUT regression model (UTAUT predictors plus sociodemographics and mental health variables), distress (β=.11; P=.03) as well as the UTAUT predictors performance expectancy (β=.50; P 〈 .001), effort expectancy (β=.15; P=.001), and social influence (β=.28; P 〈 .001) significantly predicted acceptance. The comparison between an extended UTAUT regression model (13 predictors) and the UTAUT-only regression model (performance expectancy, effort expectancy, social influence) revealed no significant difference in explained variance (F10,244=1.567; P=.12). Conclusions This study supports the viability of the UTAUT model and its predictors in assessing the acceptance of e-mental health interventions among patients with diabetes. Three UTAUT predictors reached a notable amount of explained variance of 75% in the acceptance, indicating that it is a very useful and efficient method for measuring e-mental health intervention acceptance in patients with diabetes. Owing to the close link between acceptance and use, acceptance-facilitating interventions focusing on these three UTAUT predictors should be fostered to bring forward the highly needed establishment of effective e-mental health interventions in psychodiabetology.
    Type of Medium: Online Resource
    ISSN: 2561-326X
    URL: Article
    DOI: 10.2196/27436
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2021
    detail.hit.zdb_id: 2941716-8
    Location Call Number Limitation Availability
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