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BACKGROUND: Knowledge about user experiences may lead to insights about how to improve treatment activity in Internet-based cognitive behavioral therapy (iCBT) to reduce symptoms of depression and anxiety among people with a somatic disease. There is a need for studies conducted alongside randomized trials, to explore treatment activity and user experiences related to such interventions, especially among people with older age who are recruited in routine care. OBJECTIVE: The aim of the study was to explore treatment activity, user satisfaction, and usability experiences among patients allocated to treatment in the U-CARE Heart study, a randomized clinical trial of an iCBT intervention for treatment of depression and anxiety following a recent myocardial infarction. METHODS: This was a mixed methods study where quantitative and qualitative approaches were used. Patients were recruited consecutively from 25 cardiac clinics in Sweden. The study included 117 patients allocated to 14 weeks of an iCBT intervention in the U-CARE Heart study. Quantitative data about treatment activity and therapist communication were collected through logged user patterns, which were analyzed with descriptive statistics. Qualitative data with regard to positive and negative experiences, and suggestions for improvements concerning the intervention, were collected through semistructured interviews with 21 patients in the treatment arm after follow-up. The interviews were analyzed with qualitative manifest content analysis. RESULTS: Treatment activity was low with regard to number of completed modules (mean 0.76, SD 0.93, range 0-5) and completed assignments (mean 3.09, SD 4.05, range 0-29). Most of the participants initiated the introduction module (113/117, 96.6%), and about half (63/117, 53.9%) of all participants completed the introductory module, but only 18 (15.4%, 18/117) continued to work with any of the remaining 10 modules, and each of the remaining modules was completed by 7 or less of the participants. On average, patients sent less than 2 internal messages to their therapist during the intervention (mean 1.42, SD 2.56, range 0-16). Interviews revealed different preferences with regard to the internet-based portal, the content of the treatment program, and the therapist communication. Aspects related to the personal situation and required skills included unpleasant emotions evoked by the intervention, lack of time, and technical difficulties. CONCLUSIONS: Patients with a recent myocardial infarction and symptoms of depression and anxiety showed low treatment activity in this guided iCBT intervention with regard to completed modules, completed assignments, and internal messages sent to their therapist. The findings call attention to the need for researchers to carefully consider the preferences, personal situation, and technical skills of the end users during the development of these interventions. The study indicates several challenges that need to be addressed to improve treatment activity, user satisfaction, and usability in internet-based interventions in this population.

Background Low adherence to recommended treatments is a multifactorial problem for patients in rehabilitation after myocardial infarction (MI). In a nationwide trial of internet-delivered cognitive behavior therapy (iCBT) for the high-risk subgroup of patients with MI also reporting symptoms of anxiety, depression, or both (MI-ANXDEP), adherence was low. Since low adherence to psychotherapy leads to a waste of therapeutic resources and risky treatment abortion in MI-ANXDEP patients, identifying early predictors for adherence is potentially valuable for effective targeted care. Objectives The goal of the research was to use supervised machine learning to investigate both established and novel predictors for iCBT adherence in MI-ANXDEP patients. Methods Data were from 90 MI-ANXDEP patients recruited from 25 hospitals in Sweden and randomized to treatment in the iCBT trial Uppsala University Psychosocial Care Programme (U-CARE) Heart study. Time point of prediction was at completion of the first homework assignment. Adherence was defined as having completed more than 2 homework assignments within the 14-week treatment period. A supervised machine learning procedure was applied to identify the most potent predictors for adherence available at the first treatment session from a range of demographic, clinical, psychometric, and linguistic predictors. The internal binary classifier was a random forest model within a 3×10–fold cross-validated recursive feature elimination (RFE) resampling which selected the final predictor subset that best differentiated adherers versus nonadherers. Results Patient mean age was 58.4 years (SD 9.4), 62% (56/90) were men, and 48% (43/90) were adherent. Out of the 34 potential predictors for adherence, RFE selected an optimal subset of 56% (19/34; Accuracy 0.64, 95% CI 0.61-0.68, P

BACKGROUND: U-CARE is a multidisciplinary eHealth research program that involves the disciplines of caring science, clinical psychology, health economics, information systems, and medical science. It was set up from scratch in a university setting in 2010, funded by a governmental initiative. While establishing the research program, many challenges were faced. Systematic documentation of experiences from establishing new research environments is scarce. OBJECTIVE: The aim of this paper was to describe the challenges of establishing a publicly funded multidisciplinary eHealth research environment. METHODS: Researchers involved in developing the research program U-CARE identified challenges in the formal documentation and by reflecting on their experience of developing the program. The authors discussed the content and organization of challenges into themes until consensus was reached. RESULTS: The authors identified 15 major challenges, some general to establishing a new research environment and some specific for multidisciplinary eHealth programs. The challenges were organized into 6 themes: Organization, Communication, Implementation, Legislation, Software development, and Multidisciplinarity. CONCLUSIONS: Several challenges were faced during the development of the program and several accomplishments were made. By sharing our experience, we hope to help other research groups embarking on a similar journey to be prepared for some of the challenges they are likely to face on their way. ispartof: Journal of Medical Internet Research vol:19 issue:5 ispartof: location:Canada status: published

Background: Social support plays an important role for the perceived health in people with health problems and chronic diseases. Provision of different kinds of support during the disease trajectory is crucial for many people. Online support is ubiquitous and represents a promising modality for people with chronic diseases. There are no existing instruments that measure various aspects of online support. Objective: The objective of this study was to create a generic questionnaire regarding health-related support online that can be applied to people with various health problems and illnesses. Additionally, we wanted to test the questionnaire in a cancer population to assess its adequacy in the context of severe disease. Methods: Initial items for the Health Online Support Questionnaire (HOSQ) were inspired by sociologist James House regarding social support. An exploratory factor analysis was conducted in healthy persons or with minor health problems (n=243) on 31 initial items. The scale was reduced to 18 items and the internal consistency and reliability of the scale was examined along with content validity. Further validation was conducted by a confirmatory analysis on the 18-item scale in a cancer population (n=215). In addition, data on demographics, health problems experienced, and Internet use were collected. Results: The exploratory factor analysis on the final 18-item scale resulted in 2 factors. After scrutinizing the content, these factors were labeled “reading” and “interacting” and they demonstrated good internal consistency (Cronbach alphas .88 and .77, respectively). The factors were confirmed in the cancer population. The response pattern revealed expected differences both between the interaction and reading scales and according to age, gender, education, and health problems thereby supporting the validity of the HOSQ. Conclusions: The HOSQ may be a reliable and valid instrument for measuring the use of online support for people with health problems, but the results ought to be replicated in more studies to confirm the results for different diagnoses. If the results of this study are corroborated by future studies, the HOSQ may be used as a basis for the development of different forms of support on the Internet. [J Med Internet Res 2015;17(11):e266]