Background: Individually designed interventions delivered through mobile health applications (mHealth apps)may be able to effectively support diabetes self-care. Our aim was to review and synthesize available evidence in theliterature regarding perception of adults with type 1 diabetes on the features of mHealth apps that help promote dia-betes self-care, as well as facilitators and barriers to their use. An additional aim was to review literature on changes inpatient reported outcome measures (PROMs) in the same population while using mHealth apps for diabetes self-care.Methods: Quantitative and qualitative studies focusing on adults aged 18 years and over with type 1 diabetes in anycontext were included. A systematic literature search using selected databases was conducted. Data was synthesisedusing narrative synthesis.Results: We found that features of mHealth apps designed to help promote and maintain diabetes self-care could becategorized into self-care data monitoring, app display, feedback & reminders, data entry, data sharing, and additionalfeatures. Factors affecting the use of mHealth apps reported in the literature were personal factors, app design orusability factors, privacy and safety factors, or socioeconomic factors. Quality of life and diabetes distress were themost commonly reported PROMs in the included studies.Conclusion: We are unable to reach a conclusive result due to the heterogeneity of the included studies as well asthe limited number of studies reporting on these areas among adults with type 1 diabetes. We therefore recommendfurther large-scale studies looking into these areas that can ultimately improve mHealth app use in type 1 diabetesself-care.

Introduction The management of type 1 diabetes, a non-preventable chronic disease, leads to a high physical and psychological burden on the individual. Digital health technology can improve a person's psychosocial self-efficacy and thereby contribute to improved diabetes self-care. The aim of this study was to explore associations between psychosocial self-efficacy and demographic-, disease specific-, well-being as well as digital health technology (DHT) related factors among adults with type 1 diabetes.Methods A primarily web-based cross sectional survey was conducted among adults with type 1 diabetes in Sweden (n = 301). Psychosocial self-efficacy was assessed using the Swedish version of the Diabetes Empowerment Scale, Swe-DES-23. The survey also contained questions related to demographic-, disease specific-, well-being as well as digital health technology related variables.Results Higher well-being scores and lower HbA1c levels were associated with higher psychosocial self-efficacy in multiple linear regression analysis. In multivariate analysis, gender, body mass index, well-being scores, and HbA1c levels showed association with psychosocial self-efficacy. None of the DHT factors were found associated with psychosocial self-efficacy.Conclusions In this study, higher well-being score and lower self-reported HbA1c levels were associated with higher psychosocial self-efficacy in both univariate- and multivariate analysis and accounted for 30% of the variation in psychosocial self-efficacy in the regression model. Thus, measures to improve psychosocial self-efficacy in adults with type 1 diabetes may help maintain their psychological well-being and blood glucose control.

Background: The use of digital health technology in diabetes self-care is increasing, making eHealth literacy an importantfactor to consider among people with type 1 diabetes. There are very few studies investigating eHealth literacy among adultswith type 1 diabetes, highlighting the need to explore this area further.Objective: The aim of this study was to explore associations between eHealth literacy and demographic factors, disease-specific factors, and well-being among adults with type 1 diabetes.Methods: The study used data from a larger cross-sectional survey conducted among adults with type 1 diabetes in Sweden(N=301). Participants were recruited using a convenience sampling method primarily through advertisements on social media.Data were collected between September and November 2022 primarily through a web-based survey, although participantscould opt to answer a paper-based survey. Screening questions at the beginning of the survey determined eligibility toparticipate. In this study, eHealth literacy was assessed using the Swedish version of the eHealth Literacy Scale (Sw-eHEALS).The predictor variables, well-being was assessed using the World Health Organization-5 Well-Being Index and psychosocialself-efficacy using the Swedish version of the Diabetes Empowerment Scale. The survey also included research group–developed questions on demographic and disease-specific variables as well as digital health technology use. Data wereanalyzed using multiple linear regression presented as nested models. A sample size of 270 participants was required in orderto detect an association between the dependent and predictor variables using a regression model based on an F test. The finalsample size included in the nested regression model was 285.Results: The mean Sw-eHEALS score was 33.42 (SD 5.32; range 8‐40). The model involving both demographic anddisease-specific variables explained 31.5% of the total variation in eHealth literacy and was deemed the best-fitting model.Younger age (P=.01; B=–0.07, SE=0.03;95% CI –0.12 to –0.02), lower self-reported glycated hemoglobin levels (P=.04; B=–0.06, SE=0.03; 95% CI –0.12 to 0.00), and higher psychosocial self-efficacy (P<.001; B=3.72, SE=0.53; 95% CI 2.68-4.75)were found associated with higher Sw-eHEALS scores when adjusted for demographic and disease-specific variables in thismodel. Well-being was not associated with eHealth literacy in this study.Conclusions: The demographic and disease-specific factors explained the variation in eHealth literacy in this sample. Furtherstudies in this area using newer eHealth literacy tools are important to validate our findings. The study highlights theimportance of development and testing of interventions to improve eHealth literacy in this population for better glucosecontrol. These eHealth literacy interventions should be tailored to meet the needs of people in varying age groups and withdiffering levels of psychosocial self-efficacy.