Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

Background: Prediction of the dynamics of new SARS-CoV-2 infections during the current COVID-19 pandemic is critical for public health planning of efficient health care allocation and monitoring the effects of policy interventions. We describe a new approach that forecasts the number of incident cases in the near future given past occurrences using only a small number of assumptions. Methods: Our approach to forecasting future COVID-19 cases involves 1) modeling the observed incidence cases using a Poisson distribution for the daily incidence number, and a gamma distribution for the series interval; 2) estimating the effective reproduction number assuming its value stays constant during a short time interval; and 3) drawing future incidence cases from their posterior distributions, assuming that the current transmission rate will stay the same, or change by a certain degree. Results: We apply our method to predicting the number of new COVID-19 cases in a single state in the U.S. and for a subset of counties within the state to demonstrate the utility of this method at varying scales of prediction. Our method produces reasonably accurate results when the effective reproduction number is distributed similarly in the future as in the past. Large deviations from the predicted results can imply that a change in policy or some other factors have occurred that have dramatically altered the disease transmission over time. Conclusion: We presented a modelling approach that we believe can be easily adopted by others, and immediately useful for local or state planning.

Introduction. The adult population is increasingly experiencing one or more chronic illnesses and living with such conditions longer. The Chronic Disease Self-Management Program (CDSMP) helps participants cope with chronic disease-related symptomatology and improve their health-related quality of life. Nevertheless, the long-term effectiveness of this evidence-based program on older adults as compared to the middle-aged populations has not been examined in a large-scale, national rollout. Method. We identified baseline characteristics of CDSMP participants aged 65 years or older (n = 687, M = 74.8 years) in the National Study of CDSMP from 2010 to 2012. Comparisons were made to middle-aged participants aged 50 to 64 (n = 325, M = 58.3 years). Assessments were conducted at baseline and 12-month follow-up. Linear and generalized linear mixed models were performed to assess changes in primary and secondary outcomes, controlling the key sociodemographics and number of chronic conditions. Results. All primary outcomes (i.e., social/role activities limitation, depression, communication with doctors) significantly improved in both the older and middle-aged cohorts. Although improvements in illness symptomatology (e.g., fatigue, pain, shortness of breath, and sleep problems) were similar across both cohorts, only the middle-aged cohort benefitted significantly in terms of overall quality of life and unhealthy mental health days. Effect sizes were larger among the middle-aged population who were also more likely to enter the program in poorer health and be from minority backgrounds. Conclusions. The current study documented improved health outcomes but more so among the middle-aged population. Findings suggest the importance of examining how age and interacting life circumstances may affect chronic disease self-management.

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Patient-Centered Innovations, Education and Technology for Cancer Care and Cancer Research

Objective: We examined the measurement and mediating role of social support in dietary intake among participants in Texercise Select, an intervention for improving lifestyle behaviours.Design: Quasi-experimental study. Participants reported their dietary intake, level of social support measured by the new Social Support for Healthy Eating scale, sociodemographics and disease profile. We conducted exploratory factor analysis for scale evaluation and structural equation modelling for mediation analysis to test if changes in dietary-specific social support mediate the relationship between the intervention and changes in dietary intake.Setting: Texas.Participants: Community-dwelling middle-aged and older adults completed a self-reported survey at baseline and 3-month follow-up (intervention group n 211, comparison group n 175).Results: The majority of the sample was aged ≥70 years (mean 74·30, sd 8·54), female (82·1 %) and had at least two chronic conditions (63·5 %). The acceptable levels of reliability and validity of the dietary-specific social support scale were confirmed. Compared with the comparison group, the intervention group reported improved intake of fruit/vegetables and water, and improved dietary-specific social support. Improved dietary-specific social support mediated the association between intervention and change in fruit/vegetable intake, controlling for sociodemographics, number of chronic conditions and geographic residence. About 12 % of intervention effect was mediated by social support.Conclusions: The current study confirms positive intervention effects on healthy eating, and highlights social support relating to dietary behaviours that may be helpful for healthy eating. Future research should investigate additional social support for developing healthy eating behavioural skills.