Browsing by Author "Kaczynski, Andrew T"
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- ItemComparison of traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss program.(2013-04-12) Turner-McGrievy, Gabrielle M; Beets, Michael W; Moore, Justin B; Kaczynski, Andrew T; Barr-Anderson, Daheia J; Tate, Deborah FOBJECTIVE Self monitoring of physical activity PA and diet are key components of behavioral weight loss programs The purpose of this study was to assess the relationship between diet mobile app website or paper journal and PA mobile app vs no mobile app self monitoring and dietary and PA behaviors MATERIALS AND METHODS This study is a post hoc analysis of a 6 month randomized weight loss trial among 96 overweight men and women body mass index BMI 25 45 kg m 2 conducted from 2010 to 2011 Participants in both randomized groups were collapsed and categorized by their chosen self monitoring method for diet and PA All participants received a behavioral weight loss intervention delivered via podcast and were encouraged to self monitor dietary intake and PA RESULTS Adjusting for randomized group and demographics PA app users self monitored exercise more frequently over the 6 month study 2 6 0 5 days week and reported greater intentional PA 196 4 45 9 kcal day than non app users 1 2 0 5 days week PA self monitoring pUnder0 01 100 9 45 1 kcal day intentional PA p 0 02 PA app users also had a significantly lower BMI at 6 months 31 5 0 5 kg m 2 than non users 32 5 0 5 kg m 2 p 0 02 Frequency of self monitoring did not differ by diet self monitoring method p 0 63 however app users consumed less energy 1437 188 kcal day than paper journal users 2049 175 kcal day p 0 01 at 6 months BMI did not differ among the three diet monitoring methods p 0 20 CONCLUSIONS These findings point to potential benefits of mobile monitoring methods during behavioral weight loss trials Future studies should examine ways to predict which self monitoring method works best for an individual to increase adherence
- ItemCrowdsourcing for self-monitoring: Using the Traffic Light Diet and crowdsourcing to provide dietary feedback.(0000-00-00) Turner-McGrievy, Gabrielle M; Wilcox, Sara; Kaczynski, Andrew T; Spruijt-Metz, Donna; Hutto, Brent E; Muth, Eric R; Hoover, AdamBackground Smartphone photography and crowdsourcing feedback could reduce participant burden for dietary self monitoring Objectives To assess if untrained individuals can accurately crowdsource diet quality ratings of food photos using the Traffic Light Diet TLD approach Methods Participants were recruited via Amazon Mechanical Turk and read a one page description on the TLD The study examined the participant accuracy score total number of correctly categorized foods as red yellow or green per person the food accuracy score accuracy by which each food was categorized and if the accuracy of ratings increased when more users were included in the crowdsourcing For each of a range of possible crowd sizes n 15 n 30 etc 10 000 bootstrap samples were drawn and a 95 confidence interval CI for accuracy constructed using the 2 5th and 97 5th percentiles Results Participants n 75 body mass index 28 0 7 5 age 36 11 59 attempting weight loss rated 10 foods as red yellow or green Raters demonstrated high red yellow green accuracy 75 examining all foods Mean accuracy score per participant was 77 6 14 0 Individual photos were rated accurately the majority of the time range 50 100 There was little variation in the 95 CI for each of the five different crowd sizes indicating that large numbers of individuals may not be needed to accurately crowdsource foods Conclusions Nutrition novice users can be trained easily to rate foods using the TLD Since feedback from crowdsourcing relies on the agreement of the majority this method holds promise as a low burden approach to providing diet quality feedback