Background: Cardiac diseases are a major global health issue with an increasing prevalence of affected people. Rehabilitation following cardiac events is underutilized, despite its proven effectiveness. Digital interventions might present a useful addition to traditional cardiac rehabilitation. Aims: This study aims to assess the acceptance of mobile health (mHealth) cardiac rehabilitation and to investigate the underlying factors of acceptance in patients with ischemic heart disease and congestive heart failure. Methods: A cross-sectional study was conducted from November 2021 to September 2022 with N = 290 patients. Sociodemographic, medical, and eHealth-related data were assessed. The Unified Theory of Acceptance and Use of Technology (UTAUT) was applied. Group differences in acceptance were examined and a multiple hierarchical regression analysis was conducted. Results: The overall acceptance of mHealth cardiac rehabilitation was high (M = 4.05, SD = 0.93). Individuals with mental illness reported significantly higher acceptance (t(288) = 3.15, p_adj = 0.007, d = 0.43). Depressive symptoms (β = 0.34, p < 0.001); digital confidence (β = 0.19, p = 0.003); and the UTAUT predictors of performance expectancy (β = 0.34, p < 0.001), effort expectancy (β = 0.34, p < 0.001), and social influence (β = 0.26, p < 0.001) significantly predicted acceptance. The extended UTAUT model explained 69.5% of the variance in acceptance. Conclusions: As acceptance is associated with the actual use of mHealth, the high level of acceptance found in this study is a promising basis for the future implementation of innovative mHealth offers in cardiac rehabilitation.

Background: Chronic pain is a complex disease with high prevalence rates, and many individuals who are affected do not receive adequate treatment. As a complement to conventional therapies, eHealth interventions could provide many benefits to a multimodal treatment approach for patients with chronic pain, whereby future use is associated with the acceptance of these interventions. Objective: This study aims to assess the acceptance of eHealth pain management interventions among patients with chronic pain and identify the influencing factors on acceptance. A further objective of the study is to evaluate the viability of the Unified Theory of Acceptance and Use of Technology (UTAUT) model and compare it with its extended version in terms of explained variance of acceptance. Methods: We performed a cross-sectional web-based study. In total, 307 participants with chronic pain, as defined according to the International Association for the Study of Pain criteria, were recruited through flyers, posters, and web-based inquiries between December 2020 and July 2021. In addition to sociodemographic and medical data, the assessment included validated psychometric instruments and an extended version of the well-established UTAUT model. For statistical analyses, group comparisons and multiple hierarchical regression analyses were performed. Results: The acceptance of eHealth pain management interventions among patients with chronic pain was overall moderate to high (mean 3.67, SD 0.89). There was significant difference in acceptance among age groups (W=9674.0; r=0.156; P=.04). Effort expectancy (β=.37; P<.001), performance expectancy (β=.33; P<.001), and social influence (β=.34; P<.001) proved to be the most important predictors of acceptance. The extended UTAUT (including the original UTAUT factors as well as sociodemographic, medical, and eHealth-related factors) model explained 66.4% of the variance in acceptance, thus supporting the viability of the model. Compared with the original UTAUT model (performance expectancy, effort expectancy, and social influence), the extended model explained significantly more variance (F25,278=1.74; P=.02). Conclusions: Given the association between acceptance and future use, the knowledge of the influencing factors on acceptance should be used in the development and promotion of eHealth pain management interventions. Overall, the acceptance of eHealth pain management interventions was moderate to high. In total, 8 predictors proved to be significant predictors of acceptance. The UTAUT model is a valuable instrument for determining acceptance as well as the factors that influence acceptance of eHealth pain management interventions among patients with chronic pain. The extended UTAUT model provided the greatest predictive value for acceptance.

Introduction: Several studies indicate an association between mental disorders and overweight or obesity. E-Mental health interventions offer an effective way to overcome barriers to health care access for individuals with overweight and obesity. The objective of this study was to examine the needs and demands for e-mental health interventions among individuals with overweight and obesity. Methods: A cross-sectional study was conducted from 2020 to 2021 in Germany. A total of 643 participants were recruited through specialized social media platforms and the Alfried-Krupp hospital in Essen, Germany. Sociodemographic and medical data were analysed, as well as data on depressive symptoms and on the needs and demands for e-mental health interventions. Results: Contact with and recommendation by experts appear to be key aspects in the acceptance and use of e-mental health interventions. In summary, most participants preferred a 20–30-min weekly session via smartphone over a 4-month period. The highest preference in terms of features included practicing coping skills and being provided with information; in regard to desired topics, nutrition consultation, quality of life, and adapting to new life situations were considered most important. Discussion: e-Mental health interventions can be highly beneficial for individuals, especially when developed through a user-centred design approach. The results of the study indicate which content and design are preferred and, thereby, provide valuable information for consideration when developing a tailored e-mental health intervention.

Background: Cardiovascular disease is the major cause of death worldwide, leading to a significant socioeconomic burden. Although secondary prevention is a cornerstone in chronic disease management, adherence to guideline recommendations in this regard often falters, leading to suboptimal outcomes. While eHealth technologies are promising for improving treatment adherence, they also represent a new approach to secondary prevention. However, a common critique is that extensive digitalization may not adequately address the needs of older adults with chronic medical conditions. Objective: This study aims to analyze eHealth literacy, digital use patterns, and general attitudes toward digital technologies in a collective of patients with cardiovascular disease to identify potential obstacles in implementing mobile health technologies in secondary preventive therapy. Methods: This survey-based study was a part of the baseline examination of the PreventiPlaque trial. It involved 240 participants with known coronary artery disease. The assessment evaluated their current understanding of the general use of digital devices. The questionnaire covered aspects such as the duration of daily use, personal attitudes, and the perceived burden associated with digital media. eHealth literacy was assessed within the target population and general demographic data were gathered, focusing on cardiovascular comorbidities and risk factors. Results: The analysis revealed an average age of 61.9 (SD 8.9) years, with 59.9% (n=144) of the participants being male. Overall, 37.3% (n=90) of the participants had previous knowledge of digital health interventions, while only 17.8% (n=41) had used them. Despite the generally low practical application within this study population, there was a high level of confidence in handling digital devices, with 61.9% (n=149) expressing themselves as either rather confident or very confident. Regarding the levels of eHealth literacy among the participants, 71.2% (n=170) claimed to be familiar with locating health information on the internet, and 64% (n=153) of participants felt capable of critically evaluating its quality. These levels of digital confidence were consistent across all age groups. Moreover, internet use rates remained high even among the older participants, with 80% (n=192) of those participants older than 75 years using the internet for 1-3 hours a day. Conclusions: The study unveiled a notable confidence level among participants regarding the use of digital devices, coupled with a favorable attitude toward digital media evident across all age brackets. Remarkably, internet use rates remained high, even among older participants. The actual utilization of digital health interventions was relatively low, potentially stemming from challenges in locating reliable sources. These findings emphasize the prospect of future eHealth interventions customized to the distinct needs and preferences of patients in cardiovascular disease management. Recognizing the incongruity between confidence in device use and the restricted adoption of digital health tools can guide the development of focused interventions to narrow this divide.

Cardiovascular diseases are one of the main contributors to disability and mortality worldwide. Meanwhile, risk factors can be modified by lifestyle changes. mHealth is an innovative and effective way to deliver cardiac health promotion. This study aims to examine the needs and demands regarding the design and contents of an mHealth intervention for cardiac health promotion among individuals with cardiac diseases. Different clusters were determined and analysed in terms of the intention to use an mHealth intervention. A cross-sectional study was conducted via a web-based survey. Three hundred and four individuals with coronary artery diseases (CADs) and/or congestive heart failure (CHF) were included in the data analysis. Descriptive statistics were applied to evaluate needs and demands regarding an mHealth intervention. A k-medoids cluster analysis was performed. Individuals with CAD and CHF favoured an mHealth intervention that supports its users permanently and is easily integrated into everyday life. Handheld devices and content formats that involve active user participation and regular updates were preferred. Three clusters were observed and labelled high, moderate, and low burden, according to their psychometric properties. The high burden cluster indicated higher behavioural intention towards use of an mHealth intervention than the other clusters. The results of the study are a valuable foundation for the development of an mHealth intervention for cardiac health promotion following a user-centred design approach. Individuals with cardiac diseases report positive attitudes in the form of high usage intention regarding mHealth. Highly burdened individuals report a high intention to use such interventions.

Introduction: The Stress Overload Scale (SOS; Amirkhan, 2012, 2018) was introduced as a two-factorial self-report measure of stress to overcome limitations of other scales. Methods: We developed a German translation of the SOS and validated it in addition to a short version and an extra-short version. Furthermore, we tested whether manipulating the time frame introduced as retention interval in the instructions affected its psychometric properties. Results: Using two independent age-heterogeneous convenience samples (N_total = 1,239), we found good psychometric properties for a modified German short version of the SOS-S (SOS-S-G) and a new extra-short version (SOS-XS-G), but not for the German long version of the SOS. Moreover, manipulating the time frame of the SOS did not affect its psychometric quality. Discussion The SOS enriches the repertoire of self-report measures of stress as it captures the nonpathological core facets of stress in line with theoretical stress conceptualizations.