Ontrack: Development and feasibility of a smartphone app designed to predict and prevent dietary lapses. Forman, E., M.; Goldstein, S., P.; Zhang, F.; Evans, B., C.; Manasse, S., M.; Butryn, M., L.; Juarascio, A., S.; Abichandani, P.; Martin, G., J.; and Foster, G., D. Translational Behavioral Medicine, 9(2):236-245, Oxford University Press, 2019.
Ontrack: Development and feasibility of a smartphone app designed to predict and prevent dietary lapses [link]Website  abstract   bibtex   
Given that the overarching goal of weight loss programs is to remain adherent to a dietary prescription, specific moments of nonadherence known as “dietary lapses” can threaten weight control via the excess energy intake they represent and by provoking future lapses. Just-in-time adaptive interventions could be particularly useful in preventing dietary lapses because they use real-time data to generate interventions that are tailored and delivered at a moment computed to be of high risk for a lapse. To this end, we developed a smartphone application (app) called OnTrack that utilizes machine learning to predict dietary lapses and deliver a targeted intervention designed to prevent the lapse from occurring. This study evaluated the feasibility, acceptability, and preliminary effectiveness of OnTrack among weight loss program participants. An open trial was conducted to investigate subjective satisfaction, objective usage, algorithm performance, and changes in lapse frequency and weight loss among individuals (N = 43; 86% female; body mass index = 35.6 kg/m2) attempting to follow a structured online weight management plan for 8 weeks. Participants were adherent with app prompts to submit data, engaged with interventions, and reported high levels of satisfaction. Over the course of the study, participants averaged a 3.13% weight loss and experienced a reduction in unplanned lapses. OnTrack, the first Just-in-time adaptive intervention for dietary lapses was shown to be feasible and acceptable, and OnTrack users experienced weight loss and lapse reduction over the study period. These data provide the basis for further development and evaluation.
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 title = {Ontrack: Development and feasibility of a smartphone app designed to predict and prevent dietary lapses},
 type = {article},
 year = {2019},
 identifiers = {[object Object]},
 keywords = {Diet,Digital,Lapses,Smartphone app,Weight},
 pages = {236-245},
 volume = {9},
 websites = {/pmc/articles/PMC6610167/?report=abstract,https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6610167/},
 publisher = {Oxford University Press},
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 abstract = {Given that the overarching goal of weight loss programs is to remain adherent to a dietary prescription, specific moments of nonadherence known as “dietary lapses” can threaten weight control via the excess energy intake they represent and by provoking future lapses. Just-in-time adaptive interventions could be particularly useful in preventing dietary lapses because they use real-time data to generate interventions that are tailored and delivered at a moment computed to be of high risk for a lapse. To this end, we developed a smartphone application (app) called OnTrack that utilizes machine learning to predict dietary lapses and deliver a targeted intervention designed to prevent the lapse from occurring. This study evaluated the feasibility, acceptability, and preliminary effectiveness of OnTrack among weight loss program participants. An open trial was conducted to investigate subjective satisfaction, objective usage, algorithm performance, and changes in lapse frequency and weight loss among individuals (N = 43; 86% female; body mass index = 35.6 kg/m2) attempting to follow a structured online weight management plan for 8 weeks. Participants were adherent with app prompts to submit data, engaged with interventions, and reported high levels of satisfaction. Over the course of the study, participants averaged a 3.13% weight loss and experienced a reduction in unplanned lapses. OnTrack, the first Just-in-time adaptive intervention for dietary lapses was shown to be feasible and acceptable, and OnTrack users experienced weight loss and lapse reduction over the study period. These data provide the basis for further development and evaluation.},
 bibtype = {article},
 author = {Forman, Evan M. and Goldstein, Stephanie P. and Zhang, Fengqing and Evans, Brittney C. and Manasse, Stephanie M. and Butryn, Meghan L. and Juarascio, Adrienne S. and Abichandani, Pramod and Martin, Gerald J. and Foster, Gary D.},
 journal = {Translational Behavioral Medicine},
 number = {2}
}
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