Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions

The impact of EV71 vaccination program on hand, foot and mouth disease in Zhejiang Province, China: A negative control study

Objective

To estimate the potential causal impact of Enterovirus A71 (EV71) vaccination program on the reduction of EV71-infected hand, foot, and mouth disease (HFMD) in Zhejiang Province.

Methods

We utilized the longitudinal surveillance dataset of HFMD and EV71 vaccination in Zhejiang Province during 2010-2019. We estimated vaccine efficacy using a Bayesian structured time series (BSTS) model, and employed a negative control outcome (NCO) model to detect unmeasured confounding and reveal potential causal association.

Results

We estimated that 20,132 EV71 cases (95% CI: 16,733, 23,532) were prevented by vaccination program during 2017-2019, corresponding to a reduction of 29% (95% CI: 24%, 34%). The effectiveness of vaccination increased annually, with reductions of 11% (95% CI: 6%, 16%) in 2017 and 66% (95% CI: 61%, 71%) in 2019. Children under 5 years old obtained greater benefits compared to those over 5 years. Cities with higher vaccination coverage experienced a sharper EV71 reduction compared to those with lower coverage. The NCO model detected no confounding factors in the association between vaccination and EV71 cases reduction.

Conclusions

This study suggested a potential causal effect of the EV71 vaccination, highlighting the importance of achieving higher vaccine coverage to control the HFMD.

Health policy considerations for combining exercise prescription into noncommunicable diseases treatment: a narrative literature review

Objectives

In this review, we aim to highlight the evidence base for the benefits of exercise in relation to the treatment of noncommunicable diseases (NCDs), draw on the Health Triangular Policy Framework to outline the principal facilitators and barriers for implementing exercise in health policy, and make concrete suggestions for action.

Methods

Literature review and framework analysis were conducted to deal with the research questions.

Results

Exercise prescription is a safe solution for noncommunicable diseases prevention and treatment that enables physicians to provide and instruct patients how to apply exercise as an important aspect of disease treatment and management. Combining exercise prescription within routine care, in inpatient and outpatient settings, will improve patients' life quality and fitness levels.

Conclusion

Inserting exercise prescription into the healthcare system would improve population health status and healthy lifestyles. The suggestions outlined in this study need combined efforts from the medical profession, governments, and policymakers to facilitate practice into reality in the healthcare arena.

Applications of artificial intelligence-machine learning for detection of stress: a critical overview

Psychological distress is a major contributor to human physiology and pathophysiology, and it has been linked to several conditions, such as auto-immune diseases, metabolic syndrome, sleep disorders, and suicidal thoughts and inclination. Therefore, early detection and management of chronic stress is crucial for the prevention of several diseases. Artificial intelligence (AI) and Machine Learning (ML) have promoted a paradigm shift in several areas of biomedicine including diagnosis, monitoring, and prognosis of disease. Here, our review aims to present some of the AI and ML applications for solving biomedical issues related to psychological stress. We provide several lines of evidence from previous studies highlighting that AI and ML have been able to predict stress and detect the brain normal states vs. abnormal states (notably, in post-traumatic stress disorder (PTSD)) with accuracy around 90%. Of note, AI/ML-driven technology applied to identify ubiquitously present stress exposure may not reach its full potential, unless future analytics focus on detecting prolonged distress through such technology instead of merely assessing stress exposure. Moving forward, we propose that a new subcategory of AI methods called Swarm Intelligence (SI) can be used towards detecting stress and PTSD. SI involves ensemble learning techniques to efficiently solve a complex problem, such as stress detection, and it offers particular strength in clinical settings, such as privacy preservation. We posit that AI and ML approaches will be beneficial for the medical and patient community when applied to predict and assess stress levels. Last, we encourage additional research to bring AI and ML into the standard clinical practice for diagnostics in the not-too-distant future.

Urges to Move and Other Motivation States for Physical Activity in Clinical and Healthy Populations: A Scoping Review Protocol

Motivation for bodily movement, physical activity and exercise varies from moment to moment. These motivation states may be “affectively-charged,” ranging from instances of lower tension (e.g., desires, wants) to higher tension (e.g., cravings and urges). Currently, it is not known how often these states have been investigated in clinical populations (e.g., eating disorders, exercise dependence/addiction, Restless Legs Syndrome, diabetes, obesity) vs. healthy populations (e.g., in studies of motor control; groove in music psychology). The objective of this scoping review protocol is to quantify the literature on motivation states, to determine what topical areas are represented in investigations of clinical and healthy populations, and to discover pertinent details, such as instrumentation, terminology, theories, and conceptual models, correlates and mechanisms of action. Iterative searches of scholarly databases will take place to determine which combination of search terms (e.g., “motivation states” and “physical activity”; “desire to be physically active,” etc.) captures the greatest number of relevant results. Studies will be included if motivation states for movement (e.g., desires, urges) are specifically measured or addressed. Studies will be excluded if referring to motivation as a trait. A charting data form was developed to scan all relevant documents for later data extraction. The primary outcome is simply the extent of the literature on the topic. Results will be stratified by population/condition. This scoping review will unify a diverse literature, which may result in the creation of unique models or paradigms that can be utilized to better understand motivation for bodily movement and exercise.

Detecting Smartwatch-based Behavior Change in Response to a Multi-domain Brain Health Intervention

In this study, we introduce and validate a computational method to detect lifestyle change that occurs in response to a multi-domain healthy brain aging intervention. To detect behavior change, digital behavior markers (DM) are extracted from smartwatch sensor data and a Permutation-based Change Detection (PCD) algorithm quantifies the change in marker-based behavior from a pre-intervention, one-week baseline. To validate the method, we verify that changes are successfully detected from synthetic data with known pattern differences. Next, we employ this method to detect overall behavior change for n=28 BHI subjects and n=17 age-matched control subjects. For these individuals, we observe a monotonic increase in behavior change from the baseline week with a slope of 0.7460 for the intervention group and a slope of 0.0230 for the control group. Finally, we utilize a random forest algorithm to perform leave-one-subject-out prediction of intervention versus control subjects based on digital marker delta values. The random forest predicts whether the subject is in the intervention or control group with an accuracy of 0.87. This work has implications for capturing objective, continuous data to inform our understanding of intervention adoption and impact.