The microbial biosynthesis of noncanonical terpenoids.

Terpenoids are a class of structurally complex, naturally occurring compounds found predominantly in plant, animal, and microorganism secondary metabolites. Classical terpenoids typically have carbon atoms in multiples of five and follow well-defined carbon skeletons, whereas noncanonical terpenoids deviate from these patterns. These noncanonical terpenoids often result from the methyltransferase-catalyzed methylation modification of substrate units, leading to irregular carbon skeletons. In this comprehensive review, various activities and applications of these noncanonical terpenes have been summarized. Importantly, the review delves into the biosynthetic pathways of noncanonical terpenes, including those with C6, C7, C11, C12, and C16 carbon skeletons, in bacteria and fungi host. It also covers noncanonical triterpenes synthesized from non-squalene substrates and nortriterpenes in Ganoderma lucidum, providing detailed examples to elucidate the intricate biosynthetic processes involved. Finally, the review outlines the potential future applications of noncanonical terpenoids. In conclusion, the insights gathered from this review provide a reference for understanding the biosynthesis of these noncanonical terpenes and pave the way for the discovery of additional unique and novel noncanonical terpenes. KEY POINTS: •The activities and applications of noncanonical terpenoids are introduced. •The noncanonical terpenoids with irregular carbon skeletons are presented. •The microbial biosynthesis of noncanonical terpenoids is summarized.

Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy.

Determining causes of deaths (CODs) occurred outside of civil registration and vital statistics systems is challenging. A technique called verbal autopsy (VA) is widely adopted to gather information on deaths in practice. A VA consists of interviewing relatives of a deceased person about symptoms of the deceased in the period leading to the death, often resulting in multivariate binary responses. While statistical methods have been devised for estimating the cause-specific mortality fractions (CSMFs) for a study population, continued expansion of VA to new populations (or "domains") necessitates approaches that recognize between-domain differences while capitalizing on potential similarities. In this article, we propose such a domain-adaptive method that integrates external between-domain similarity information encoded by a prespecified rooted weighted tree. Given a cause, we use latent class models to characterize the conditional distributions of the responses that may vary by domain. We specify a logistic stick-breaking Gaussian diffusion process prior along the tree for class mixing weights with node-specific spike-and-slab priors to pool information between the domains in a data-driven way. The posterior inference is conducted via a scalable variational Bayes algorithm. Simulation studies show that the domain adaptation enabled by the proposed method improves CSMF estimation and individual COD assignment. We also illustrate and evaluate the method using a validation dataset. The article concludes with a discussion of limitations and future directions.

Etiology of Acute Lower Respiratory Illness Hospitalizations Among Infants in 4 Countries.

Background: Recent studies explored which pathogens drive the global burden of pneumonia hospitalizations among young children. However, the etiology of broader acute lower respiratory tract infections (ALRIs) remains unclear. Methods: Using a multicountry study (Albania, Jordan, Nicaragua, and the Philippines) of hospitalized infants and non-ill community controls between 2015 and 2017, we assessed the prevalence and severity of viral infections and coinfections. We also estimated the proportion of ALRI hospitalizations caused by 21 respiratory pathogens identified via multiplex real-time reverse transcription polymerase chain reaction with bayesian nested partially latent class models. Results: An overall 3632 hospitalized infants and 1068 non-ill community controls participated in the study and had specimens tested. Among hospitalized infants, 1743 (48.0%) met the ALRI case definition for the etiology analysis. After accounting for the prevalence in non-ill controls, respiratory syncytial virus (RSV) was responsible for the largest proportion of ALRI hospitalizations, although the magnitude varied across sites-ranging from 65.2% (95% credible interval, 46.3%-79.6%) in Albania to 34.9% (95% credible interval, 20.0%-49.0%) in the Philippines. While the fraction of ALRI hospitalizations caused by RSV decreased as age increased, it remained the greatest driver. After RSV, rhinovirus/enterovirus (range, 13.4%-27.1%) and human metapneumovirus (range, 6.3%-12.0%) were the next-highest contributors to ALRI hospitalizations. Conclusions: We observed substantial numbers of ALRI hospitalizations, with RSV as the largest source, particularly in infants aged <3 months. This underscores the potential for vaccines and long-lasting monoclonal antibodies on the horizon to reduce the burden of ALRI in infants worldwide.

Identifying Design Opportunities for Adaptive mHealth Interventions That Target General Well-Being: Interview Study With Informal Care Partners.

BACKGROUND: Mobile health (mHealth) interventions can deliver personalized behavioral support to users in daily contexts. These interventions have been increasingly adopted to support individuals who require low-cost and low-burden support. Prior research has demonstrated the feasibility and acceptability of an mHealth intervention app (CareQOL) designed for use with informal care partners. To further optimize the intervention delivery, we need to investigate how care partners, many of whom lack the time for self-care, react and act in response to different behavioral messages. OBJECTIVE: The goal of this study was to understand the factors that impact care partners' decision-making and actions in response to different behavioral messages. Insights from this study will help optimize future tailored and personalized behavioral interventions. METHODS: We conducted semistructured interviews with participants who had recently completed a 3-month randomized controlled feasibility trial of the CareQOL mHealth intervention app. Of the 36 participants from the treatment group of the randomized controlled trial, 23 (64%) participated in these interviews. To prepare for each interview, the team first selected representative behavioral messages (eg, targeting different health dimensions) and presented them to participants during the interview to probe their influence on participants' thoughts and actions. The time of delivery, self-reported perceptions of the day, and user ratings of a message were presented to the participants during the interviews to assist with recall. RESULTS: The interview data showed that after receiving a message, participants took various actions in response to different messages. Participants performed suggested behaviors or adjusted them either immediately or in a delayed manner (eg, sometimes up to a month later). We identified 4 factors that shape the variations in user actions in response to different behavioral messages: uncertainties about the workload required to perform suggested behaviors, concerns about one's ability to routinize suggested behaviors, in-the-moment willingness and ability to plan for suggested behaviors, and overall capability to engage with the intervention. CONCLUSIONS: Our study showed that care partners use mHealth behavioral messages differently regarding the immediacy of actions and the adaptation to suggested behaviors. Multiple factors influence people's perceptions and decisions regarding when and how to take actions. Future systems should consider these factors to tailor behavioral support for individuals and design system features to support the delay or adaptation of the suggested behaviors. The findings also suggest extending the assessment of user adherence by considering the variations in user actions on behavioral support (ie, performing suggested or adjusted behaviors immediately or in a delayed manner). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/32842.

Use of mobile technology to identify behavioral mechanisms linked to mental health outcomes in Kenya: protocol for development and validation of a predictive model.

OBJECTIVE: This study proposes to identify and validate weighted sensor stream signatures that predict near-term risk of a major depressive episode and future mood among healthcare workers in Kenya. APPROACH: The study will deploy a mobile application (app) platform and use novel data science analytic approaches (Artificial Intelligence and Machine Learning) to identifying predictors of mental health disorders among 500 randomly sampled healthcare workers from five healthcare facilities in Nairobi, Kenya. EXPECTATION: This study will lay the basis for creating agile and scalable systems for rapid diagnostics that could inform precise interventions for mitigating depression and ensure a healthy, resilient healthcare workforce to develop sustainable economic growth in Kenya, East Africa, and ultimately neighboring countries in sub-Saharan Africa. This protocol paper provides an opportunity to share the planned study implementation methods and approaches. CONCLUSION: A mobile technology platform that is scalable and can be used to understand and improve mental health outcomes is of critical importance.

PROBABILISTIC LEARNING OF TREATMENT TREES IN CANCER.

Accurate identification of synergistic treatment combinations and their underlying biological mechanisms is critical across many disease domains, especially cancer. In translational oncology research, preclinical systems such as patient-derived xenografts (PDX) have emerged as a unique study design evaluating multiple treatments administered to samples from the same human tumor implanted into genetically identical mice. In this paper, we propose a novel Bayesian probabilistic tree-based framework for PDX data to investigate the hierarchical relationships between treatments by inferring treatment cluster trees, referred to as treatment trees (Rx-tree). The framework motivates a new metric of mechanistic similarity between two or more treatments accounting for inherent uncertainty in tree estimation; treatments with a high estimated similarity have potentially high mechanistic synergy. Building upon Dirichlet Diffusion Trees, we derive a closed-form marginal likelihood encoding the tree structure, which facilitates computationally efficient posterior inference via a new two-stage algorithm. Simulation studies demonstrate superior performance of the proposed method in recovering the tree structure and treatment similarities. Our analyses of a recently collated PDX dataset produce treatment similarity estimates that show a high degree of concordance with known biological mechanisms across treatments in five different cancers. More importantly, we uncover new and potentially effective combination therapies that confer synergistic regulation of specific downstream biological pathways for future clinical investigations. Our accompanying code, data, and shiny application for visualization of results are available at: https://github.com/bayesrx/RxTree.

ASSESSING TIME-VARYING CAUSAL EFFECT MODERATION IN THE PRESENCE OF CLUSTER-LEVEL TREATMENT EFFECT HETEROGENEITY AND INTERFERENCE.

The micro-randomized trial (MRT) is a sequential randomized experimental design to empirically evaluate the effectiveness of mobile health (mHealth) intervention components that may be delivered at hundreds or thousands of decision points. MRTs have motivated a new class of causal estimands, termed "causal excursion effects", for which semiparametric inference can be conducted via a weighted, centered least squares criterion (Boruvka et al., 2018). Existing methods assume between-subject independence and non-interference. Deviations from these assumptions often occur. In this paper, causal excursion effects are revisited under potential cluster-level treatment effect heterogeneity and interference, where the treatment effect of interest may depend on cluster-level moderators. Utility of the proposed methods is shown by analyzing data from a multi-institution cohort of first year medical residents in the United States.

The Dosing of Mobile-Based Just-in-Time Adaptive Self-Management Prompts for Caregivers: Preliminary Findings From a Pilot Microrandomized Study.

BACKGROUND: Caregivers of people with chronic illnesses often face negative stress-related health outcomes and are unavailable for traditional face-to-face interventions due to the intensity and constraints of their caregiver role. Just-in-time adaptive interventions (JITAIs) have emerged as a design framework that is particularly suited for interventional mobile health studies that deliver in-the-moment prompts that aim to promote healthy behavioral and psychological changes while minimizing user burden and expense. While JITAIs have the potential to improve caregivers' health-related quality of life (HRQOL), their effectiveness for caregivers remains poorly understood. OBJECTIVE: The primary objective of this study is to evaluate the dose-response relationship of a fully automated JITAI-based self-management intervention involving personalized mobile app notifications targeted at decreasing the level of caregiver strain, anxiety, and depression. The secondary objective is to investigate whether the effectiveness of this mobile health intervention was moderated by the caregiver group. We also explored whether the effectiveness of this intervention was moderated by (1) previous HRQOL measures, (2) the number of weeks in the study, (3) step count, and (4) minutes of sleep. METHODS: We examined 36 caregivers from 3 disease groups (10 from spinal cord injury, 11 from Huntington disease, and 25 from allogeneic hematopoietic cell transplantation) in the intervention arm of a larger randomized controlled trial (subjects in the other arm received no prompts from the mobile app) designed to examine the acceptability and feasibility of this intensive type of trial design. A series of multivariate linear models implementing a weighted and centered least squares estimator were used to assess the JITAI efficacy and effect. RESULTS: We found preliminary support for a positive dose-response relationship between the number of administered JITAI messages and JITAI efficacy in improving caregiver strain, anxiety, and depression; while most of these associations did not meet conventional levels of significance, there was a significant association between high-frequency JITAI and caregiver strain. Specifically, administering 5-6 messages per week as opposed to no messages resulted in a significant decrease in the HRQOL score of caregiver strain with an estimate of -6.31 (95% CI -11.76 to -0.12; P=.046). In addition, we found that the caregiver groups and the participants' levels of depression in the previous week moderated JITAI efficacy. CONCLUSIONS: This study provides preliminary evidence to support the effectiveness of the self-management JITAI and offers practical guidance for designing future personalized JITAI strategies for diverse caregiver groups. TRIAL REGISTRATION: ClinicalTrials.gov NCT04556591; https://clinicaltrials.gov/ct2/show/NCT04556591.

An Automatically Adaptive Digital Health Intervention to Decrease Opioid-Related Risk While Conserving Counselor Time: Quantitative Analysis of Treatment Decisions Based on Artificial Intelligence and Patient-Reported Risk Measures.

BACKGROUND: Some patients prescribed opioid analgesic (OA) medications for pain experience serious side effects, including dependence, sedation, and overdose. As most patients are at low risk for OA-related harms, risk reduction interventions requiring multiple counseling sessions are impractical on a large scale. OBJECTIVE: This study evaluates whether an intervention based on reinforcement learning (RL), a field of artificial intelligence, learned through experience to personalize interactions with patients with pain discharged from the emergency department (ED) and decreased self-reported OA misuse behaviors while conserving counselors' time. METHODS: We used data representing 2439 weekly interactions between a digital health intervention ("Prescription Opioid Wellness and Engagement Research in the ED" [PowerED]) and 228 patients with pain discharged from 2 EDs who reported recent opioid misuse. During each patient's 12 weeks of intervention, PowerED used RL to select from 3 treatment options: a brief motivational message delivered via an interactive voice response (IVR) call, a longer motivational IVR call, or a live call from a counselor. The algorithm selected session types for each patient each week, with the goal of minimizing OA risk, defined in terms of a dynamic score reflecting patient reports during IVR monitoring calls. When a live counseling call was predicted to have a similar impact on future risk as an IVR message, the algorithm favored IVR to conserve counselor time. We used logit models to estimate changes in the relative frequency of each session type as PowerED gained experience. Poisson regression was used to examine the changes in self-reported OA risk scores over calendar time, controlling for the ordinal session number (1st to 12th). RESULTS: Participants on average were 40 (SD 12.7) years of age; 66.7% (152/228) were women and 51.3% (117/228) were unemployed. Most participants (175/228, 76.8%) reported chronic pain, and 46.2% (104/225) had moderate to severe depressive symptoms. As PowerED gained experience through interactions over a period of 142 weeks, it delivered fewer live counseling sessions than brief IVR sessions (P=.006) and extended IVR sessions (P<.001). Live counseling sessions were selected 33.5% of the time in the first 5 weeks of interactions (95% CI 27.4%-39.7%) but only for 16.4% of sessions (95% CI 12.7%-20%) after 125 weeks. Controlling for each patient's changes during the course of treatment, this adaptation of treatment-type allocation led to progressively greater improvements in self-reported OA risk scores (P<.001) over calendar time, as measured by the number of weeks since enrollment began. Improvement in risk behaviors over time was especially pronounced among patients with the highest risk at baseline (P=.02). CONCLUSIONS: The RL-supported program learned which treatment modalities worked best to improve self-reported OA risk behaviors while conserving counselors' time. RL-supported interventions represent a scalable solution for patients with pain receiving OA prescriptions. TRIAL REGISTRATION: Clinicaltrials.gov NCT02990377; https://classic.clinicaltrials.gov/ct2/show/NCT02990377.

Using source-associated mobile genetic elements to identify zoonotic extraintestinal E. coli infections.

A one-health perspective may provide new and actionable information about Escherichia coli transmission. E. coli colonizes a broad range of vertebrates, including humans and food-production animals, and is a leading cause of bladder, kidney, and bloodstream infections in humans. Substantial evidence supports foodborne transmission of pathogenic E. coli strains from food animals to humans. However, the relative contribution of foodborne zoonotic E. coli (FZEC) to the human extraintestinal disease burden and the distinguishing characteristics of such strains remain undefined. Using a comparative genomic analysis of a large collection of contemporaneous, geographically-matched clinical and meat-source E. coli isolates (n = 3111), we identified 17 source-associated mobile genetic elements - predominantly plasmids and bacteriophages - and integrated them into a novel Bayesian latent class model to predict the origins of clinical E. coli isolates. We estimated that approximately 8 % of human extraintestinal E. coli infections (mostly urinary tract infections) in our study population were caused by FZEC. FZEC strains were equally likely to cause symptomatic disease as non-FZEC strains. Two FZEC lineages, ST131-H22 and ST58, appeared to have particularly high virulence potential. Our findings imply that FZEC strains collectively cause more urinary tract infections than does any single non-E. coli uropathogenic species (e.g., Klebsiella pneumoniae). Our novel approach can be applied in other settings to identify the highest-risk FZEC strains, determine their sources, and inform new one-health strategies to decrease the heavy public health burden imposed by extraintestinal E. coli infections.

The reliability and validity of the TBI-CareQOL system in four diverse caregiver groups.

PURPOSE: Establishing the psychometric reliability and validity of new measures is an ongoing process. More work is needed in to confirm the clinical utility of the TBI-CareQOL measurement development system in both an independent cohort of caregivers of traumatic brain injury (TBI), as well as in additional caregiver groups. METHODS: An independent cohort of caregivers of people with TBI (n = 139), as well as three new diverse caregiver cohorts (n = 19 caregivers of persons with spinal cord injury, n = 21 caregivers for persons with Huntington disease, and n = 30 caregivers for persons with cancer), completed 11 TBI-CareQOL measures (caregiver strain; caregiver-specific anxiety; anxiety; depression; anger; self-efficacy; positive affect and well-being; perceived stress; satisfaction with social roles and activities; fatigue; sleep-related impairment), as well as two additional measures to examine convergent and discriminant validity (PROMIS Global Health; the Caregiver Appraisal Scale). RESULTS: Findings support the internal consistency reliability (all alphas > 0.70 with the vast majority being > 0.80 across the different cohorts) of the TBI-CareQOL measures. All measures were free of ceiling effects, and the vast majority were also free of floor effects. Convergent validity was supported by moderate to high correlations between the TBI-CareQOL and related measures, while discriminant validity was supported by low correlations between the TBI-CareQOL measures and unrelated constructs. CONCLUSION: Findings indicate that the TBI-CareQOL measures have clinical utility in caregivers of people with TBI, as well as in other caregiver groups. As such, these measures should be considered as important outcome measures for clinical trials aiming to improve caregiver outcomes.

Advances in the biosynthesis and metabolic engineering of rare ginsenosides.

Rare ginsenosides are the deglycosylated secondary metabolic derivatives of major ginsenosides, and they are more readily absorbed into the bloodstream and function as active substances. The traditional preparation methods hindered the potential application of these effective components. The continuous elucidation of ginsenoside biosynthesis pathways has rendered the production of rare ginsenosides using synthetic biology techniques effective for their large-scale production. Previously, only the progress in the biosynthesis and biotechnological production of major ginsenosides was highlighted. In this review, we summarized the recent advances in the identification of key enzymes involved in the biosynthetic pathways of rare ginsenosides, especially the glycosyltransferases (GTs). Then the construction of microbial chassis for the production of rare ginsenosides, mainly in Saccharomyces cerevisiae, was presented. In the future, discovery of more GTs and improving their catalytic efficiencies are essential for the metabolic engineering of rare ginsenosides. This review will give more clues and be helpful for the characterization of the biosynthesis and metabolic engineering of rare ginsenosides. KEY POINTS: • The key enzymes involved in the biosynthetic pathways of rare ginsenosides are summarized. • The recent progress in metabolic engineering of rare ginsenosides is presented. • The discovery of glycosyltransferases is essential for the microbial production of rare ginsenosides in the future.

Feasibility pilot trial for the Trajectories of Recovery after Intravenous propofol versus inhaled VolatilE anesthesia (THRIVE) pragmatic randomised controlled trial.

INTRODUCTION: Millions of patients receive general anaesthesia for surgery annually. Crucial gaps in evidence exist regarding which technique, propofol total intravenous anaesthesia (TIVA) or inhaled volatile anaesthesia (INVA), yields superior patient experience, safety and outcomes. The aim of this pilot study is to assess the feasibility of conducting a large comparative effectiveness trial assessing patient experiences and outcomes after receiving propofol TIVA or INVA. METHODS AND ANALYSIS: This protocol was cocreated by a diverse team, including patient partners with personal experience of TIVA or INVA. The design is a 300-patient, two-centre, randomised, feasibility pilot trial. Patients 18 years of age or older, undergoing elective non-cardiac surgery requiring general anaesthesia with a tracheal tube or laryngeal mask airway will be eligible. Patients will be randomised 1:1 to propofol TIVA or INVA, stratified by centre and procedural complexity. The feasibility endpoints include: (1) proportion of patients approached who agree to participate; (2) proportion of patients who receive their assigned randomised treatment; (3) completeness of outcomes data collection and (4) feasibility of data management procedures. Proportions and 95% CIs will be calculated to assess whether prespecified thresholds are met for the feasibility parameters. If the lower bounds of the 95% CI are above the thresholds of 10% for the proportion of patients agreeing to participate among those approached and 80% for compliance with treatment allocation for each randomised treatment group, this will suggest that our planned pragmatic 12 500-patient comparative effectiveness trial can likely be conducted successfully. Other feasibility outcomes and adverse events will be described. ETHICS AND DISSEMINATION: This study is approved by the ethics board at Washington University (IRB# 202205053), serving as the single Institutional Review Board for both participating sites. Recruitment began in September 2022. Dissemination plans include presentations at scientific conferences, scientific publications, internet-based educational materials and mass media. TRIAL REGISTRATION NUMBER: NCT05346588.

Advances in the optimization of central carbon metabolism in metabolic engineering.

Central carbon metabolism (CCM), including glycolysis, tricarboxylic acid cycle and the pentose phosphate pathway, is the most fundamental metabolic process in the activities of living organisms that maintains normal cellular growth. CCM has been widely used in microbial metabolic engineering in recent years due to its unique regulatory role in cellular metabolism. Using yeast and Escherichia coli as the representative organisms, we summarized the metabolic engineering strategies on the optimization of CCM in eukaryotic and prokaryotic microbial chassis, such as the introduction of heterologous CCM metabolic pathways and the optimization of key enzymes or regulatory factors, to lay the groundwork for the future use of CCM optimization in metabolic engineering. Furthermore, the bottlenecks in the application of CCM optimization in metabolic engineering and future application prospects are summarized.

Layperson-Facilitated Internet-Delivered Cognitive Behavioral Therapy for Homebound Older Adults With Depression: Protocol for a Randomized Controlled Trial.

BACKGROUND: Depression in older adults has serious biological, psychological, and social consequences. Homebound older adults experience a high burden of depression and significant barriers to accessing mental health treatments. Few interventions to address their specific needs have been developed. Existing treatment modalities can be challenging to scale up, are not tailored to unique population concerns, and require significant staffing support. Technology-assisted, layperson-facilitated psychotherapy has the potential to overcome these challenges. OBJECTIVE: The aim of this study is to assess the efficacy of a layperson-facilitated internet-delivered cognitive behavioral therapy program tailored for homebound older adults. The novel intervention, Empower@Home, was developed based on user-centered design principles and partnerships between researchers, social service agencies, care recipients, and other stakeholders serving low-income homebound older adults. METHODS: This 2-arm, 20-week pilot randomized controlled trial (RCT) with a waitlist control crossover design aims to enroll 70 community-dwelling older adults with elevated depressive symptoms. The treatment group will receive the 10-week intervention immediately, whereas the waitlist control group will cross over and receive the intervention after 10 weeks. This pilot is part of a multiphase project involving a single-group feasibility study (completed in December 2022). This project consists of a pilot RCT (described in this protocol) and an implementation feasibility study running in parallel with the pilot RCT. The primary clinical outcome of the pilot is the change in depressive symptoms after the intervention and at the 20-week postrandomization follow-up. Additional outcomes include acceptability, adherence, and changes in anxiety, social isolation, and quality of life. RESULTS: Institutional review board approval was obtained for the proposed trial in April 2022. Recruitment for the pilot RCT began in January 2023 and is anticipated to end in September 2023. On completion of the pilot trial, we will examine the preliminary efficacy of the intervention on depression symptoms and other secondary clinical outcomes in an intention-to-treat analysis. CONCLUSIONS: Although web-based cognitive behavioral therapy programs are available, most programs have low adherence and very few are tailored for older adults. Our intervention addresses this gap. Older adults, particularly those with mobility difficulties and multiple chronic health conditions, could benefit from internet-based psychotherapy. This approach can serve a pressing need in society while being cost-effective, scalable, and convenient. This pilot RCT builds on a completed single-group feasibility study by determining the preliminary effects of the intervention compared with a control condition. The findings will provide a foundation for a future fully-powered randomized controlled efficacy trial. If our intervention is found to be effective, implications extend to other digital mental health interventions and populations with physical disabilities and access restrictions who face persistent disparities in mental health. TRIAL REGISTRATION: ClinicalTrials.gov NCT05593276; https://clinicaltrials.gov/ct2/show/NCT05593276. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/44210.

Effectiveness of gamified team competition as mHealth intervention for medical interns: a cluster micro-randomized trial.

Gamification, the application of gaming elements to increase enjoyment and engagement, has the potential to improve the effectiveness of digital health interventions, while the effectiveness of competition gamification components remains poorly understood on residency. To address this gap, we evaluate the effect of smartphone-based gamified team competition intervention on daily step count and sleep duration via a micro-randomized trial on medical interns. Our aim is to assess potential improvements in the factors (namely step count and sleep) that may help interns cope with stress and improve well-being. In 1779 interns, team competition intervention significantly increases the mean daily step count by 105.8 steps (SE 35.8, p = 0.03) relative to the no competition arm, while does not significantly affect the mean daily sleep minutes (p = 0.76). Moderator analyses indicate that the causal effects of competition on daily step count and sleep minutes decreased by 14.5 steps (SE 10.2, p = 0.16) and 1.9 minutes (SE 0.6, p = 0.003) for each additional week-in-study, respectively. Intra-institutional competition negatively moderates the causal effect of competition upon daily step count by -90.3 steps (SE 86.5, p = 0.30). Our results show that gamified team competition delivered via mobile app significantly increases daily physical activity which suggests that team competition can function as a mobile health intervention tool to increase short-term physical activity levels for medical interns. Future improvements in strategies of forming competition opponents and introducing occasional competition breaks may improve the overall effectiveness.

Use of Mobile Technology to Identify Behavioral Mechanisms Linked to Mental Health Outcomes in Kenya: Protocol for Development and Validation of a Predictive Model.

Objective: This study proposes to identify and validate weighted sensor stream signatures that predict near-term risk of a major depressive episode and future mood among healthcare workers in Kenya. Approach: The study will deploy a mobile app platform and use novel data science analytic approaches (Artificial Intelligence and Machine Learning) to identifying predictors of mental health disorders among 500 randomly sampled healthcare workers from five healthcare facilities in Nairobi, Kenya. Expectation: This study will lay the basis for creating agile and scalable systems for rapid diagnostics that could inform precise interventions for mitigating depression and ensure a healthy, resilient healthcare workforce to develop sustainable economic growth in Kenya, East Africa, and ultimately neighboring countries in sub-Saharan Africa. This protocol paper provides an opportunity to share the planned study implementation methods and approaches. Conclusion : A mobile technology platform that is scalable and can be used to understand and improve mental health outcomes is of critical importance.

Integrating sample similarities into latent class analysis: a tree-structured shrinkage approach.

This paper is concerned with using multivariate binary observations to estimate the probabilities of unobserved classes with scientific meanings. We focus on the setting where additional information about sample similarities is available and represented by a rooted weighted tree. Every leaf in the given tree contains multiple samples. Shorter distances over the tree between the leaves indicate a priori higher similarity in class probability vectors. We propose a novel data integrative extension to classical latent class models with tree-structured shrinkage. The proposed approach enables (1) borrowing of information across leaves, (2) estimating data-driven leaf groups with distinct vectors of class probabilities, and (3) individual-level probabilistic class assignment given the observed multivariate binary measurements. We derive and implement a scalable posterior inference algorithm in a variational Bayes framework. Extensive simulations show more accurate estimation of class probabilities than alternatives that suboptimally use the additional sample similarity information. A zoonotic infectious disease application is used to illustrate the proposed approach. The paper concludes by a brief discussion on model limitations and extensions.

Preferences for Sexually Transmitted Infection and Cancer Vaccines in the United States and in China.

OBJECTIVES: This study assessed preferences for hypothetical vaccines for children in 2 large vaccine markets according to how the vaccine-preventable disease is transmitted via a discrete choice experiment. METHODS: Surveys in China (N = 1350) and the United States (N = 1413) were conducted from April to May 2021. The discrete choice experiment included attributes of cost, age at vaccination, transmission mode of the vaccine-preventable disease, and whether the vaccine prevents cancer. Preference utilities were modeled in a Bayesian, multinomial logistic regression model, and respondents were grouped by vaccine preference classification through a latent class analysis. RESULTS: Individuals favored vaccines against diseases with transmission modes other than sexual transmission (vaccine for sexually transmitted infection [STI] vs airborne disease, in the United States, odds ratio 0.71; 95% credible interval 0.64-0.78; in China, odds ratio 0.76; 95% credible interval 0.69-0.84). The latent class analysis revealed 6 classes: vaccine rejecters (19% in the United States and 8% in China), careful deciders (18% and 17%), preferring cancer vaccination (20% and 19%), preferring vaccinating children at older ages (10% and 11%), preferring vaccinating older ages, but indifferent about cancer vaccines (23% and 25%), and preferring vaccinating children at younger ages (10% and 19%). Vaccine rejection was higher with age in the United States versus more vaccine rejection among those at the age of 18 to 24 and ≥ 64 years in China. CONCLUSION: The public had strong preferences against giving their child an STI vaccine, and the class preferring a cancer vaccine was less accepting of an STI vaccine. Overall, this study points to the need for more education about how some STI vaccines could also prevent cancers.