Two of the projects within the ILHBN Network include research surrounding physical activity. These were discussed at the ILHBN Annual Meeting, held in Los Angeles, California in October, 2022.
Temporal Influences of Movement and Exercise (TIME) Weight gain increases dramatically in early adulthood, increasing risk of cancer and chronic disease later in life. The Temporal Influences of Movement and Exercise (TIME) Study led jointly by Drs. Genevieve Dunton and Stephen Intille at the University of Southern California and Northeastern University, respectively, uses mobile technologies to collect ILD from young adults (18 to 24 years old) to elucidate the micro-temporal mechanisms underlying the adoption and maintenance of physical activity, limited sedentary time, and sufficient sleep duration in emerging adults. Results emerging from this study can help build more predictive health behavior theories and inform personalized behavior interventions to reduce obesity and improve public health. Publications include “Early effects of the COVID-19 pandemic on physical activity and sedentary behavior in children living in the U.S.”[1] and “Ambulatory Assessment for Physical Activity Research: State of the Science, Best Practices and Future Directions”[2].
Dynamic Models of Behavior (DMB) The Operationalizing Behavioral Theory for mHealth: Dynamics, Context and Personalization (denoted herein as the Model of Behavior - MOB) Study led jointly by Drs. Donna Spruijt-Metz, Benjamin Marlin, and Pedja Klasnja at the University of Southern California, University of Massachusetts, Amherst, and University of Michigan, respectively, is a micro randomized trial (MRT). An MRT is an experimental study design aimed at optimizing just-in-time adaptive interventions. The study seeks to produce convenient, economical, scalable, and effective JITAI solutions to sustain healthy physical activity and limit sedentary time for adults aged above 18 years old. Through the use of a computational modeling framework that integrates dynamic behavior theories and continuous sensing of individuals' activities and context, the project designs and evaluate ways to adapt to individuals’ ongoing dynamical changes to intervene “just-in-time.” To optimize a mobile intervention that is meant to be woven into the participant’s daily lives in order to continue to be helpful in developing and maintaining healthy physical activity habits, a myriad of considerations must be taken into account that fluctuate between as well as within participants over time. These include environmental considerations such as weather, but also time of day, mood, social environment. To examine these juxtapositions as they fluctuate in real time, ILD are essential. Publications include “Advancing Behavioral Intervention and Theory Development for Mobile Health: The Heart Steps II Protocol”[3] and “BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data”[4].
[1] Dunton, Genevieve F; Do, Bridgette; Wang, Shirlene D; https://www.ncbi.nlm.nih.gov/pubmed?otool=nihlib&term=32887592
[2] Reichert, Markus; Giurgiu, Marco; Koch, Elena; et al., https://www.ncbi.nlm.nih.gov/pubmed?otool=nihlib&term=32831643
[3] Donna Spruijt-Metz, Benjamin M. Marlin, Misha Pavel, Daniel E. Rivera, Eric Hekler, Steven De La Torre; et al.; https://doi.org/10.3390/ijerph19042267
[4] Karine Tung, Steven De La Torre, Mohamed El Mistiri, et. al. https://dblp.uni-trier.de/db/journals/corr/corr2209.html#abs-2209-05581
