Multi Agent Architecture for Automated Health Coaching.

For software applications in health coaching domains to be effective, it is vital that they address issues of privacy, modularity, scalability, individualization, data integration, transferability, coordination and flexibility. In this paper, we propose a novel generic multi-agent architecture which serves as a template for health coaching applications involving wearable sensors. Analyzer and communication modules allow different functionalities like goal formation, planning, scheduling, event detection, learning, inter-agent + human communication and long-term data collection, based on the capabilities of the underlying sensor platforms. To show the flexibility of our proposed architecture, we have successfully built two different health coaching systems with the proposed architecture: (1) a static system based on the Fitbit platform where the coaching is done at specific preset times to encourage increased physical activity, and (2) a dynamic system based on the Apple Watch platform where the smart coach adapts and learns when to intervene to encourage physical activity and reduce sedentary behavior.

Prevalence and Determinants of Long-Term Post-COVID Conditions in the United States: 2022 Behavioral Risk Factor Surveillance System.

BACKGROUND: A significant proportion of COVID survivors experience lingering and debilitating symptoms following acute COVID-19 infection. According to the national research plan on long COVID, it is a national priority to identify the prevalence of post-COVID conditions and their associated factors. METHOD: We performed a cross-sectional analysis of the Prevention Behavioral Risk Factor Surveillance System (BRFSS) 2022, the largest continuously gathered health survey dataset worldwide by the Centers for Disease Control. After identifying individuals with a positive history of COVID-19, we grouped COVID-19 survivors based on whether they experienced long-term post-COVID conditions. Using survey-specific R packages, we compared the two groups' socio-demographics, comorbidities, and lifestyle-related factors. A logistic regression model was used to identify factors associated with post-COVID conditions. RESULTS: The overall estimated prevalence of long-term post-COVID conditions among COVID survivors was 21.7%. Fatigue (5.7%), dyspnea (4.2%), and anosmia/ageusia (3.8%) were the most frequent symptoms. Based on multivariate logistic regression analysis, female sex, body mass index (BMI)≥25, lack of insurance, history of pulmonary disease, depression, and arthritis, being a former smoker, and sleep duration <7 h/d were associated with higher odds of post-COVID conditions. On the other hand, age >64 y/o, Black race, and annual household income ≥$100k were associated with lower odds of post-COVID conditions. CONCLUSION: Our findings indicate a notable prevalence of post-COVID conditions, particularly among middle-aged women and individuals with comorbidities or adverse lifestyles. This high-risk demographic may require long-term follow-up and support. Further investigations are essential to facilitate the development of specified healthcare and therapeutic strategies for those suffering from post-COVID conditions.

Self-Management among Stroke Survivors in the United States, 2016 to 2021.

Background: Self-management among stroke survivors is effective in mitigating the risk of a recurrent stroke. This study aims to determine the prevalence of self-management and its associated factors among stroke survivors in the United States. Methods: We analyzed the Behavioral Risk Factor Surveillance System (BRFSS) data from 2016 to 2021, a nationally representative health survey. A new outcome variable, stroke self-management (SSM = low or SSM = high), was defined based on five AHA guideline-recommended self-management practices, including regular physical activity, maintaining body mass index, regular doctor checkups, smoking cessation, and limiting alcohol consumption. A low level of self-management was defined as adherence to three or fewer practices. Results: Among 95,645 American stroke survivors, 46.7% have low self-management. Stroke survivors aged less than 65 are less likely to self-manage (low SSM: 56.8% vs. 42.3%; p < 0.0001). Blacks are less likely to self-manage than non-Hispanic Whites (low SSM: 52.0% vs. 48.6%; p < 0.0001); however, when adjusted for demographic and clinical factors, the difference was dissipated. Higher education and income levels are associated with better self-management (OR: 2.49, [95%CI: 2.16-2.88] and OR: 1.45, [95%CI: 1.26-1.67], respectively). Further sub-analysis revealed that women are less likely to be physically active (OR: 0.88, [95%CI: 0.81-0.95]) but more likely to manage their alcohol consumption (OR: 1.57, [95%CI: 1.29-1.92]). Stroke survivors residing in the Stroke Belt did not self-manage as well as their counterparts (low-SSM: 53.1% vs. 48.0%; p < 0.001). Conclusions: The substantial diversity in self-management practices emphasizes the need for tailored interventions. Particularly, multi-modal interventions should be targeted toward specific populations, including younger stroke survivors with lower education and income.