Predicting Acceptance of e-Mental Health Interventions in Patients with Obesity by using an extended Unified Theory of Acceptance Model: Cross-sectional study (Preprint)

Assessing Electronic Health Literacy in Individuals With the Post-COVID-19 Condition Using the German Revised eHealth Literacy Scale: Validation Study

BACKGROUND

The eHealth Literacy Scale (eHEALS) is a widely used instrument for measuring eHealth literacy (eHL). However, little is known so far about whether the instrument is valid for the assessment of eHL in persons who are affected by the post-COVID-19 condition. This is particularly important as people with the post-COVID-19 condition are frequently affected by false information from the internet.

OBJECTIVE

The objective of our study was to evaluate the validity and reliability of the German Revised eHealth Literacy Scale (GR-eHEALS) in individuals with the post-COVID-19 condition.

METHODS

A cross-sectional study was conducted from January to May 2022. The self-assessment survey consisted of the GR-eHEALS, health status- and internet use-related variables, sociodemographic data, and (post)-COVID-19-related medical data. Confirmatory factor analysis (CFA), correlational analyses, and tests of measurement invariance were deployed.

RESULTS

In total, 330 participants were included in the statistical analyses. CFA revealed that the 2-factor model reached an excellent model fit (comparative fit index=1.00, Tucker-Lewis index=0.99, root mean square error of approximation=0.036, standardized root mean square residual=0.038). Convergent validity was confirmed by significant positive correlations between eHL and knowledge of internet-based health promotion programs, experience in using these programs, and the duration of private internet use. In addition, a significantly negative relationship of eHL with internet anxiety supported convergent validity. Further, significant relationships of eHL with mental health status and internal health locus of control confirmed the criterion validity of the instrument. However, relationships of eHL with physical health status and quality of life could not be confirmed. The 2-factor model was fully measurement invariant regarding gender. Regarding age and educational level, partial measurement invariance was confirmed. The subscales as well as the overall GR-eHEALS reached good-to-excellent reliability (Cronbach α≥.86).

CONCLUSIONS

The GR-eHEALS is a reliable and largely valid instrument for assessing eHL in individuals with the post-COVID-19 condition. Measurement invariance regarding gender was fully confirmed and allows the interpretation of group differences. Regarding age and educational level, group differences should be interpreted with caution. Given the high likelihood that individuals with the post-COVID-19 condition will be confronted with misinformation on the Internet, eHL is a core competency that is highly relevant in this context, in both research and clinical practice. Therefore, future research should also explore alternative instruments to capture eHL to overcome shortcomings in the validity of the GR-eHEALS.

Efficacy of eHealth interventions to reduce depression symptoms in individuals with obesity: a systematic review of randomized controlled trials

Introduction

Obesity and depression are inter-related health concerns, demanding a high level of treatment and costs in the health care system. The development of eHealth interventions that simultaneously address obesity and mental health can be supportive in this regard. However, evidence of the efficacy of eHealth interventions in the treatment of depression symptoms in individuals with obesity is lacking. The aim of this systematic literature review is to evaluate the efficacy of existing eHealth interventions for individuals with obesity that target depression symptoms.

Methods

We systematically searched electronic databases (Cochrane Library, PubMed, Scopus) to identify studies published in English between January 2016 and January 2023, that focused on eHealth interventions, targeting depression symptoms in individuals with obesity people. Exclusion criteria were study objectives that (1) focused specifically on one or more metabolic comorbidities of individuals with obesity, e.g., hypertension, hyperlipidemia, diabetes; (2) focused specifically on eating disorders comorbidities e.g., binge eating disorder, and (3) focused specifically on patients before or after bariatric surgery.

Results

The database search identified 214 records. Six articles were included in this review. Sample sizes ranged from 70 to 1267 participants of ages 18-60 years. All included studies were randomized controlled trials. Two of the six included studies were web-based interventions guided either by medical doctors or psychologists. All interventions included video, printed materials, and interactive parts of which two studies integrated elements of Cognitive Behavioural Therapy and Social Cognitive Therapy. The findings showed that eHealth treatment services, supported and guided throughout the intervention had high acceptance and efficacy in the reduction of depression symptoms among individuals with obesity.

Conclusion

EHealth interventions that address and target both mental and physical health with interactive strategies calls for better efficacy in the reduction of depression symptoms. Future eHealth interventions that target depression symptoms in individuals with obesity should integrate digital strategies that address both mental and physical health through interactive modules.

Needs and Demands for e-Health Interventions in Patients Attending a Psychosomatic Outpatient Clinic: A Baseline Assessment for a User-Centered Design Approach

Objective: Increased utilization of e-health services can help to meet shortages of psychotherapeutic treatment. e-Health interventions can be effective if tailored according to the individual needs and demands of the target group. To gather comprehensive data for the development of a user-centered e-health intervention, a cross-sectional study was conducted among a heterogeneous cohort of 309 patients seeking treatment or consultation at psychosomatic university hospital in a densely populated region of Germany. Methods: Sociodemographic data, psychometric dimensions of mental burden, as well as needs and demands regarding an e-health intervention were assessed. A descriptive statistical analysis and a cluster analysis were performed to examine distribution of preferences and differences based on level of burden regarding needs and demands for e-health interventions. Results: Two hundred thirty-nine (N = 239) participants were included in the final data analysis. Among this primarily urban target group smartphone availability was favored by 77.8% of the participants. The cluster analysis revealed significant differences dependent on mental burden. 75.2% of participants with a high mental burden preferred longer interventions of 1-4 months compared with 49% in the low burden group, which also considered short interventions of up to 1 month (46%). Differences were also identified for content preferences and daily-life integration and were consistent irrespective of the initial reason for consultation. Conclusion: The findings of this study can provide a foundational framework for developing user-centered psychosomatic interventions. The potential relationship between individual burden and individual needs and demands highlights the crucial role of preliminary research to tailor interventions to effectively address diverse needs and preferences.

An e-mental health intervention to reduce depression symptoms in individuals with obesity: study protocol for the randomized, controlled, two-armed, confirmatory LightMood trial

BACKGROUND

Patients with obesity often experience psychological distress, specifically depression symptoms. Due to various barriers, such as limitations of healthcare offers, digital interventions, for example medical apps, can provide a suitable approach to support affected people. In the envisaged prospective randomized controlled trial, we aim to examine the efficacy of the LightMood intervention. The LightMood intervention is a manualized and user-centered, digital intervention for patients with obesity, with a duration of 4 months, which contains elements of cognitive behavioral therapy and mindfulness-based and skills-based exercises. We expect the LightMood intervention to be superior to treatment as usual (TAU) in terms of reducing depression symptoms.

METHODS

The trial incorporates four distinct measurement time points: the baseline assessment, the post-treatment assessment, and 1- and 3-month follow-up assessments. Furthermore, we implemented in-treatment assessments for both groups. Participants will be randomized into two groups (LightMood intervention vs TAU). The aim is to include 128 participants (64 per group) in the study. Inclusion criteria are patients who are obese, at least 18 years old, with a private Internet access, and with adequate digital literacy and show depression symptoms (PHQ ≥ 10). Exclusion criteria are weekly outpatient individual psychotherapy, obesity surgery within the last year or planned within the next 7 months, no private Internet access, and the prescription of a new psychotropic drug within the last 2 weeks. The primary outcome is the post-assessment reduction in depression symptoms. Secondary outcomes will include the improvement in self-efficacy, quality of life, mindfulness, reduction in eating disorder symptoms, and body mass index (BMI). Furthermore, we expect a positive development of depression symptoms throughout the different time points (T1, T2, and T3) in patients with obesity.

DISCUSSION

LightMood is an evidence-based, efficient, low-threshold online intervention that aims to reduce depression symptoms in people with obesity. Online interventions could offer a promising alternative to conventional face-to-face therapy. The primary objective of the current study is to add essential insight into the feasibility, efficacy, effectiveness, and acceptance of e-mental health interventions for people with obesity and depression symptoms.

TRIAL REGISTRATION

German Clinical Trial Register (DRKS), DRKS00029219. Registered on May 19, 2023.

Drivers and barriers of patients' acceptance of video consultation in cancer care

Background

Due to digitization in the medical sector, many healthcare interactions are switched to online services. This study assessed the acceptance of video consultations (VCs) in cancer care, and determined drivers and barriers of acceptance.

Methods

A cross-sectional online-based survey study was conducted in Germany from February 2022 to February 2023. Recruitment took place at oncology outpatient clinics, general practitioners, oncology practices and via cancer-related social media channels. Inclusion criteria were a cancer diagnosis, cancer treatment and internet access. Sociodemographic, medical data, eHealth-related data were acquired via an online assessment. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was used to determine the acceptance of VC and its predictors.

Results

Of N = 350 cancer patients, 56.0% (n = 196) reported high acceptance of VC, 28.0% (n = 98) stated moderate acceptance and 16.0% (n = 56) indicated low acceptance. Factors influencing acceptance were younger age (β = -.28, p < .001), female gender (β = .35, p = .005), stage of disease (β = .11, p = .032), high digital confidence (β = .14, p = .010), low internet anxiety (β = -.21, p = .001), high digital overload (β = -.12, p = .022), high eHealth literacy (β = .14, p = .028), personal trust (β = -.25, p < .001), internet use (β = .17, p = .002), and the UTAUT predictors: performance expectancy (β = .24, p < .001), effort expectancy (β = .26, p < .001), and social influence (β = .34, p < .001).

Conclusions

Patients' acceptance of VC in cancer care is high. Drivers and barriers to acceptance identified should be considered for personalized applications. Considering the growing demand for cancer care establishing digital healthcare solutions is justified.

Needs and demands for mHealth cardiac health promotion among individuals with cardiac diseases: a patient-centred design approach

Aims

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.

Methods and results

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.

Conclusion

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.

Acceptance, drivers, and barriers to use eHealth interventions in patients with post-COVID-19 syndrome for management of post-COVID-19 symptoms: a cross-sectional study

Background

Post-COVID-19 syndrome is a new and debilitating disease without adequate treatment options. eHealth could be a reasonable approach for symptom management.

Objectives

This study aims to evaluate the acceptance for eHealth interventions for symptom management in individuals with post-COVID-19 syndrome, as well as drivers and barriers influencing acceptance.

Design

Cross-sectional study.

Methods

This study was conducted from January 19 until 24 May 2022. Recruitment took place with a web-based survey. Acceptance and predictors of eHealth interventions were measured by the extended UTAUT model. Included in the model were the core predictor performance expectancy, social influence, and effort expectancy. Previously diagnosed mental illness was estimated and mental health by using the well-established Generalized Anxiety Disorder Scale-7 and the Patient Health Questionnaire Depression Scale. The effect of sociodemographic and medical data was assessed. Multiple hierarchical regression analyses as well as group comparisons were performed.

Results

342 individuals with post-COVID-19 syndrome were examined. The acceptance of eHealth interventions for symptom management was moderate to high (M = 3.60, SD = 0.89). Acceptance was significantly higher in individuals with lower/other education, patients with moderate to severe symptoms during initial COVID-19 infection, still significantly impaired patients, and individuals with a mental illness. Identified predictors of acceptance were age (β = .24, p < .001), current condition including moderate (β = .49, p = .002) and still significantly impaired (β = .67, p < .001), digital confidence (β = .19, p < .001), effort expectancy (β = .26, p < .001), performance expectancy (β = .33, p < .001), and social influence (β = .26, p < .001).

Conclusion

Patients with post-COVID-19 syndrome reported a satisfying level of acceptance and drivers and barriers could be identified. These factors need to be considered for the implementation and future use of eHealth interventions.

Determining the Acceptance of Digital Cardiac Rehabilitation and Its Influencing Factors among Patients Affected by Cardiac Diseases

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, padj = 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.

Psychometric properties of the German revised version of the eHealth literacy scale in individuals with cardiac diseases: Validation and test of measurement invariance

Background

The internet is most people's primary source of (health) information. However, no validated instrument exists to assess eHealth literacy in the group of patient with cardiac diseases.

Objective

The objective of this study was the evaluation of the psychometric properties of the German revised version of the eHealth literacy scale (GR-eHEALS) in individuals with coronary artery disease (CAD) and congestive heart failure (CHF).

Methods

A cross-sectional study was conducted. N = 455 were included in the statistical analyses. The assessment compromised the GR-eHEALS, medical history, sociodemographic data, and technology-related data. Confirmatory factor analyses, correlational analyses, and tests of measurement invariance were performed.

Results

The two-factorial model reached a good model fit. The sub-scales information seeking and information appraisal, as well as the eHealth literacy total score, reached high reliability coefficients. Construct and criterion validity was fully confirmed For the two-factorial model, measurement invariance up to the scalar level could be confirmed regarding the sociodemographic characteristics sex, age, and educational level.

Conclusions

This study confirmed the two-factor structure, construct, and criterion validity as well as measurement invariance at the scalar level for sex, age, and educational level of the GR-eHEALS scale in a sample of individuals with CAD and CHF.

Needs and Demands for e-Mental Health Interventions in Individuals with Overweight and Obesity: User-Centred Design Approach

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.

Determining the Influencing Factors on Acceptance of eHealth Pain Management Interventions Among Patients With Chronic Pain Using the Unified Theory of Acceptance and Use of Technology: Cross-sectional Study

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 (F_25,278=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.

Determinants of Acceptance of Weight Management Applications in Overweight and Obese Individuals: Using an Extended Unified Theory of Acceptance and Use of Technology Model

Overweight and obesity carry a tremendous burden in terms of physiological and psychological comorbidities. There is a great variety of weight management applications to support weight reduction, but a systematical analysis of individuals’ needs and requirements to adopt sustaining lifestyle changes is missing so far. This study aimed to assess the acceptance of such applications and its underlying predictors in individuals with overweight/obesity. A cross-sectional study was conducted, including 439 overweight/obese individuals. Health-related internet use and acceptance of weight management applications were examined using a modified questionnaire based on the Unified Theory of Acceptance and Use of Technology (UTAUT). The general acceptance of weight management applications was high, with significant age differences. Compared to older individuals, younger ones showed a higher acceptance. BMI was not significantly associated with acceptance. Besides psychometric data and eHealth-related data, performance expectancy, effort expectancy, and social influence proved to be significant predictors for acceptance. The total variance explanation provided by the extended UTAUT model was 61.2%. The knowledge of the influencing factors on acceptance might be useful in developing, optimizing, and establishing weight management applications. For determining acceptance and its predictors of weight management applications, the UTAUT model is a valid approach.