Heal-me PiONEer (personalized online nutrition and exercise): An RCT assessing 2 levels of app-based programming in individuals with chronic disease.

Background App-based strategies are a promising solution to deliver nutrition and exercise interventions during social distancing. With limited RCT data in individuals with chronic disease, further information is required both to determine impact, and to guide delivery. The Heal-Me app is an evidence-based, theoretically informed nutrition and exercise solution that can be tailored for use across a range of individuals with chronic disease. As compared to controls receiving educational material, the aim of this study is to assess the acceptability, effectiveness, and cost of Heal-Me app programming delivered alongside two levels of dietitian and exercise-specialist support. Methods Heal-Me PiONEer is a 12-week, 3-arm RCT with randomization to one of three study groups (n=72 per group, 216 total). Group 1 (control: educational material), Group 2 (Heal-Me app + virtual group dietitian/exercise-specialist sessions), Group 3 (Heal-Me app + virtual group and 1-to-1 dietitian/exercise-specialist sessions). Inclusion criteria: adults with cancer, chronic lung disease or status post-transplantation from liver or lung transplant; previous completion of an exercise rehabilitation program; access to an internet-connected device. Study outcomes measured at study weeks 0 and 12 include: Primary - Lower Extremity Functional Scale; Secondary - virtual physical function tests, loneliness, resilience, anxiety, well-being and health-related quality of life; Exploratory outcomes - protein intake, behavioral beliefs around exercise and nutrition, adherence, adverse events, acceptability, and cost-utility. Conclusions The Heal-Me PiONEer RCT holds promise to provide a comprehensive understanding of the delivery and impact of app-based nutrition and exercise programming in a diverse group of participants with chronic disease.

A Net Benefit Approach for the Optimal Allocation of a COVID-19 Vaccine.

OBJECTIVE: The objective of this study was to implement a model-based approach to identify the optimal allocation of a coronavirus disease 2019 (COVID-19) vaccine in the province of Alberta, Canada. METHODS: We developed an epidemiologic model to evaluate allocation strategies defined by age and risk target groups, coverage, effectiveness and cost of vaccine. The model simulated hypothetical immunisation scenarios within a dynamic context, capturing concurrent public health strategies and population behavioural changes. RESULTS: In a scenario with 80% vaccine effectiveness, 40% population coverage and prioritisation of those over the age of 60 years at high risk of poor outcomes, active cases are reduced by 17% and net monetary benefit increased by $263 million dollars, relative to no vaccine. Concurrent implementation of policies such as school closure and senior contact reductions have similar impacts on incremental net monetary benefit ($352 vs $292 million, respectively) when there is no prioritisation given to any age or risk group. When older age groups are given priority, the relative benefit of school closures is much larger ($214 vs $118 million). Results demonstrate that the rank ordering of different prioritisation options varies by prioritisation criteria, vaccine effectiveness and coverage, and concurrently implemented policies. CONCLUSIONS: Our results have three implications: (i) optimal vaccine allocation will depend on the public health policies in place at the time of allocation and the impact of those policies on population behaviour; (ii) outcomes of vaccine allocation policies can be greatly supported with interventions targeting contact reduction in critical sub-populations; and (iii) identification of the optimal strategy depends on which outcomes are prioritised.