Identification of Core Outcome Domains and Design of a Survey Questionnaire to Evaluate Impacts of Digital Health Solutions That Matter to People With Diabetes.

BACKGROUND: Digital health solutions (DHS) are increasingly used to support people with diabetes (PwD) to help manage their diabetes and to gather and manage health and treatment data. There is a need for scientifically reliable and valid methods to measure the value and impact of DHS on outcomes that matter to PwD. Here, we describe the development of a survey questionnaire designed to assess the perceptions of PwD toward DHS and their prioritized outcomes for DHS evaluation. METHOD: We applied a structured process for engagement of a total of nine PwD and representatives of diabetes advocacy organizations. Questionnaire development consisted of a scoping literature review, individual interviews, workshops, asynchronous virtual collaboration, and cognitive debriefing interviews. RESULTS: We identified three overarching categories of DHS, which were meaningful to PwD and crucial for the identification of relevant outcomes: (1) online/digital tools for information, education, support, motivation; (2) personal health monitoring to support self-management; (3) digital and telehealth solutions for engaging with health professionals. Overall outcome domains identified to be important were diabetes-related quality of life, distress, treatment burden, and confidence in self-management. Additional positive and negative outcomes specific to DHS were identified and corresponding questions were incorporated into the survey questionnaire. CONCLUSION: We identified the need for self-reporting of quality of life, diabetes distress, treatment burden, and confidence in self-management, as well as specific positive and negative impacts of DHS. We designed a survey questionnaire to further assess the perceptions and perspectives of people with type 1 and 2 diabetes on outcomes relevant for DHS evaluations.

Perceived built environment, health-related quality of life and health care utilization.

Previous research has shown that the built environment plays a crucial role for health-related quality of life (HRQoL) and health care utilization. But, there is limited evidence on the independence of this association from lifestyle and social environment. The objective of this cross-sectional study was to investigate these associations, independent of the social environment, physical activity and body mass index (BMI). We used data from the third follow-up of the Swiss study on Air Pollution and Lung and Heart diseases In Adults (SAPALDIA), a population based cohort with associated biobank. Covariate adjusted multiple quantile and polytomous logistic regressions were performed to test associations of variables describing the perceived built environment with HRQoL and health care utilization. Higher HRQoL and less health care utilization were associated with less reported transportation noise annoyance. Higher HRQoL was also associated with greater satisfaction with the living environment and more perceived access to greenspaces. These results were independent of the social environment (living alone and social engagement) and lifestyle (physical activity level and BMI). This study provides further evidence that the built environment should be designed to integrate living and green spaces but separate living and traffic spaces in order to improve health and wellbeing and potentially save health care costs.

The modifying role of physical activity in the cross-sectional and longitudinal association of health-related quality of life with physiological functioning-based latent classes and metabolic syndrome.

BACKGROUND: Single cardio-metabolic risk factors are each known modifiable risk factors for adverse health and quality of life outcomes. Yet, evidence on the clustered effect of these parameters and the metabolic syndrome (MetS) on health-related quality of life (HRQoL) is still limited and mostly cross-sectional. The objectives of this study were to identify clusters of cardio-metabolic physiological functioning, to assess their associations with HRQoL in comparison with the MetS, to elucidate the modifying role of physical activity, and to assess differences in health service utilization. METHODS: This study is based on longitudinal data from two time points (2010/11 & 2017/18) of the Swiss Study on Air Pollution and Lung and Heart Diseases (SAPALDIA). Latent class analysis (LCA) grouped participants based on a priori selected cardio-metabolic and MetS related physiological functioning variables (Body mass index, body fat, glycated hemoglobin, blood triglycerides, blood pressure). The 36-item Short-Form Health Survey (SF-36) was used to assess HRQoL. Quantile regressions were performed with and without adjustment for physical activity, to detect independent associations of the latent classes, MetS and physical activity with HRQoL. To assess the modifying role of physical activity, we additionally grouped participants based on the combination of physical activity and latent classes or MetS, respectively. Logistic regressions were used to investigate health service utilization as outcome. RESULTS: The LCA resulted in three classes labeled "Healthy" (30% of participants in 2017/18), "At risk" and "Unhealthy" (29%). The Unhealthy class scored lowest in all physical component scores of HRQoL. Compared to healthy and active participants, inactive participants in the "Unhealthy" class showed lower scores in the physical functioning domain both cross-sectionally (- 9.10 (- 12.02; - 6.18)) and longitudinally. This group had an odds ratio of 2.69 (1.52; 4.74) for being hospitalized in the previous 12 months. CONCLUSIONS: These results point to subjects with adverse cardio-metabolic physiological functioning and low activity levels as an important target group for health promotion and maintenance of well-being. The promotion of physical activity at the early stages of aging seems pivotal to mitigate the impact of the MetS on HRQoL at higher age.

The independent association of source-specific transportation noise exposure, noise annoyance and noise sensitivity with health-related quality of life.

Noise exposure is affecting health-related quality of life (HRQoL). There are many modelling approaches linking specific noise sources with single health-related outcomes. However, an integrated approach is missing taking into account measured levels as well as noise annoyance and sensitivity and assessing their independent association with HRQoL domains. Therefore, we investigated the predictive association of most common transportation noise sources (aircraft, railway and road traffic) as well as transportation noise annoyance and noise sensitivity with HRQoL using data from SAPALDIA (Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults). We assessed 2035 subjects, who participated in the second and third wave of SAPALDIA (3&4) and had complete information on exposure, outcome and covariates. At SAPALDIA3, we calculated annual means (Lden) of source-specific transportation noise exposure at the most exposed facade of participant's dwelling floor height. Participants reported noise annoyance on the widely used 11-point ICBEN scale and answered to 10 questions assessing individual noise sensitivity. To assess the potentially predictive effect of these noise exposures, HRQoL was assessed about 8 years later (SAPALDIA4) using the SF-36. We performed predictive multiple quantile regression models to elucidate associations of noise parameters measured at SAPALDIA3 with median SF-36 scores at SAPALDIA4. Source-specific transportation noise exposures showed few yet not consistent associations with HRQoL scores. We observed statistically significant negative associations of transportation noise annoyance with HRQoL scores covering mental health components (adjusted difference in SF-36 mental health score between highest vs. lowest annoyance tertile: -2.54 (95%CI: -3.89; -1.20). Noise sensitivity showed strongest and most consistent associations with HRQoL scores covering both general and mental health components (adjusted difference in SF-36 scores between highest vs. lowest sensitivity tertile: Mental health -5.96 (-7.57; -4.36); general health -5.16 (-7.08; -3.24)). Within all noise parameters, we predominantly observed negative associations of noise sensitivity with HRQoL attaining a magnitude of potential clinical relevance. This implies that factors other than transportation noise exposure may be relevant for this exposure-outcome relation. Nonetheless, transportation noise annoyance showed relevant associations with mental health components, indicating a negative association of transportation noise with HRQoL.