Keith Decker

To improve nudge outcome classification accuracy in a context-aware personalized nudging framework using wearable sensor data targeted to reduce sedentary behavior using Just- in-Time Adaptive Interventions (JITAIs). Data were collected using a custom smartwatch application in a free-living observational study conducted at the University of Delaware (Newark, Delaware, USA) between Spring 2021 and Fall 2022. A total of 18 participants were enrolled. The system continuously recorded motion, physiological, and contextual data and delivered adaptive behavioral prompts. A decision- tree model was trained using sitting and walking bouts enriched with contextual features such as time, location, physiological state, and prior intervention outcomes. Behavioral responses were automatically evaluated using sensor-derived outcomes. The proposed model improved classification accuracy for nudge outcomes from 0.42 to 0.78 across 787 sitting bouts. A walking-nudge model achieved an accuracy of 0.70 on 207 walking bouts. Nudged walking bouts were longer in duration, covered greater distances, and exhibited higher average speeds than non-nudged bouts. Context-aware adaptive nudging can improve both the timing and behavioral effectiveness of wearable-based interventions. Incorporating contextual and historical features enables personalized and behaviorally meaningful intervention delivery. Wearable-based adaptive interventions offer a scalable and cost-effective strategy to reduce sedentary behavior and support population-level health promotion.

Background: A Just-in-Time Adaptive Intervention (JITAI) recognizes the dynamic nature of individuals’ states and contexts, predicts support needs, and sends tailored support at more opportune, actionable times. Objective: This paper outlines the application architecture and protocol for the pilot “Walking With Just-in-Time Adaptive Interventions” (WWJ) study, which uses a JITAI approach to improve walking behavior—duration, speed, and distance—and reduce stationary time, defined as idle sitting or standing. Methods: This study targets 20 adults who are physically inactive and leverages the Apple Watch to deliver fully automated tailored intervention notifications to “walk faster,” “walk longer,” or “stand up and move around” based on real-time data and contextual factors, including time-of-day activity patterns, geographic locations (eg, home, work, park, and gymnasium), weather conditions (eg, precipitation, wind speed, and humidity), and receptiveness. The protocol involves a preintervention assessment of demographics, behavior change constructs, anthropometrics, and resting vital signs; a 2-week observation period to establish walking behavior and stationary time baselines; a 2-week just-in-time learning period to evaluate receptiveness to untailored prompts at all applicable times; the 2-week JITAI intervention phase; and a postintervention assessment. Feasibility will be evaluated through protocol fidelity, participant adherence, Apple Watch wear-time compliance, user burden, acceptability ratings, and perceptions of benefits and preferences. Results: The WWJ architecture development began in spring 2021 and concluded in fall 2022. Participant recruitment and enrollment began in fall 2022. A total of 18 participants were recruited. Upon completion of the analyses, the results of this study are expected to be submitted for publication. Conclusions: Distinctively, the WWJ just-in-time learning period aims to train the learner based on user receptiveness within contexts by sending interventions whenever a participant meets the predetermined thresholds regardless of the likelihood that the user will be receptive to the notification to prune out nonopportune or “nonactionable” times. This approach may allow for greater customization during the JITAI period. International Registered Report Identifier (IRRID): DERR1-10.2196/79022

BACKGROUND A Just-in-Time Adaptive Intervention (JITAI) recognizes the dynamic nature of individuals’ states and contexts, predicts support needs, and sends tailored support at more opportune, actionable times. OBJECTIVE This paper outlines the application architecture and protocol for the pilot “Walking With Just-in-Time Adaptive Interventions” (WWJ) study, which uses a JITAI approach to improve walking behavior—duration, speed, and distance—and reduce stationary time, defined as idle sitting or standing. METHODS This study targets 20 adults who are physically inactive and leverages the Apple Watch to deliver fully automated tailored intervention notifications to “walk faster,” “walk longer,” or “stand up and move around” based on real-time data and contextual factors, including time-of-day activity patterns, geographic locations (eg, home, work, park, and gymnasium), weather conditions (eg, precipitation, wind speed, and humidity), and receptiveness. The protocol involves a preintervention assessment of demographics, behavior change constructs, anthropometrics, and resting vital signs; a 2-week observation period to establish walking behavior and stationary time baselines; a 2-week just-in-time learning period to evaluate receptiveness to untailored prompts at all applicable times; the 2-week JITAI intervention phase; and a postintervention assessment. Feasibility will be evaluated through protocol fidelity, participant adherence, Apple Watch wear-time compliance, user burden, acceptability ratings, and perceptions of benefits and preferences. RESULTS The WWJ architecture development began in spring 2021 and concluded in fall 2022. Participant recruitment and enrollment began in fall 2022. A total of 18 participants were recruited. Upon completion of the analyses, the results of this study are expected to be submitted for publication. CONCLUSIONS Distinctively, the WWJ just-in-time learning period aims to train the learner based on user receptiveness within contexts by sending interventions whenever a participant meets the predetermined thresholds regardless of the likelihood that the user will be receptive to the notification to prune out nonopportune or “nonactionable” times. This approach may allow for greater customization during the JITAI period. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/79022

BACKGROUND: Physical activity is an effective modifiable behavior for preventing recurrent strokes. This study aims to determine the adherence to physical activity recommendations among stroke survivors in the United States. We further compared our findings with the adherence observed among myocardial infarction (MI) survivors and healthy adults, each assessed against distinct physical activity guidelines specific to their respective populations. METHODS: We utilized data from the 2011 to 2019 Behavioral Risk Factor Surveillance System, a nationally representative survey. To establish benchmarks for stroke survivors, we referenced 3 different physical activity recommendations outlined in 2011, 2014, and 2021 American Heart Association stroke prevention guidelines. Similarly, for MI survivors and healthy adults, we used the 2011 guidelines for secondary prevention of coronary vascular diseases and the 2020 World Health Organization physical activity guidelines, respectively. Adherence to recommendations was determined by the respondents’ self-reported intensity, duration, and frequency of physical activity. Multivariate logistic regression compared adherence in stroke survivors, MI survivors, and healthy adults. RESULTS: Among 48 222 stroke survivors in the United States, the overall adherence rates to 2011, 2014, and 2021 physical activity guidelines were 75.4%, 40.2%, and 69.2%. For independently mobile stroke survivors, the adherence rates increased to 78.1%, 42.1%, and 69.9%. Among MI survivors and healthy adults, the adherence rates were 42.7% and 72%. When 2021 recommendations were used as a benchmark, older (aged ≥65 years) stroke survivors were more likely to adhere to recommendations than younger survivors (71.9% versus 62.3%; P P P <0.0001]). Stroke and MI survivors were less likely to adhere to the latest recommendations than healthy adults (adjusted odds ratio, 0.74 [95% CI, 0.69–0.8]; (adjusted odds ratio, 0.24 [95% CI, 0.22–0.26]). CONCLUSIONS: A substantial number of stroke survivors do not meet physical activity recommendations. Tailored interventions should be designed for at-risk populations, for example, non-Hispanic Black survivors and lower educated stroke survivors.

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.

We build a profitable electronic trading agent with Reinforcement Learning that places buy and sell orders in the stock market. An environment model is built only with historical observational data, and the RL agent learns the trading policy by interacting with the environment model instead of with the real-market to minimize the risk and potential monetary loss. Trained in unsupervised and self-supervised fashion, our environment model learned a temporal and causal representation of the market in latent space through deep neural networks. We demonstrate that the trading policy trained entirely within the environment model can be transferred back into the real market and maintain its profitability. We believe that this environment model can serve as a robust simulator that predicts market movement as well as trade impact for further studies.

To serve large user populations, autonomous intervention systems (i.e. intelligent agents) are being developed to play more active roles such as fitness coaches and clinical disease prevention aids. Although generic user models have been developed, users may require extensive individualization to meet their personal needs. Machine learning techniques may be applied to learn tailored intervention policies for users. However, traditional machine learning requires significant amounts of data to learn an optimal policy. For wearable technology, this may mean probing the user to perform some activity and gauging user response. This paper presents a feasible intervention system model and discusses learners for tailoring user intervention policies. We examine how similar the general user model has to be with respect to the tailored model in order for our learner to perform well.

Pathway models for organisms beyond the most popular model organisms are often notoriously incomplete, even for commercially important species such as gallus gallus. This can make experimental expression data hard to interpret. The paper describes ESCAPE [Experimental System for Curation Assistance of Pathways via Espression data], under development to use available expression data, along with existing orthology mappings and curated machine-interpretable pathways, to assist in rapidly curating new species-specific pathways. Some of these techniques can also be extended to help in the analysis of the expression data in a curated pathway context as well.

Planning and scheduling have been a key topic in both Operations Research and Multi-Agent Systems. Most approaches are concentrated at an abstract system level on developing interaction protocols to be imposed on agents. There has been less concern about how the internal task structures of individual agents affect these higher-level coordination behaviors. Collaborative multi-agent planning addresses problems like uncertainty in plan outcomes, anticipating likely contingencies, and evaluating how agent actions achieve worth-oriented goals. This article presents extensions and restrictions, called extended hierarchical task networks (EHTN), to the traditional plan and schedule representations that allow the formal definition of an integrated multi-agent coordination problem. This chapter discusses open issues in multi-agent coordination (e.g. what to coordinate among agents, how much information to be exchanged, how to evaluate a planning approach) and proposes a general solution towards successful distributed goal achievement by analyzing the task structures of participating agents.

We present a software system that integrates information from multiple resources and hypothesizes pathways for one organism (e.g. Gallus gallus) using the curated pathways of another organism (e.g. Homo sapiens). The hypothetical pathways are also automatically visualized into graphs and posted online with related detailed information.