ABSTRACT
Background: Increasing evidence suggests that targeting self-regulatory processes may improve obesity treatment outcomes. Incorporating gamification principles in inhibitory control training may promote sustained training adherence and resulting benefits. This pilot study evaluated the preliminary efficacy of supplementing an evidence-based weight management program (WW) with sustained gamified inhibitory control training (PolyRules!) on change in Body Mass Index (BMI) among adults with overweight/obesity. Methods: 30 adults with overweight/obesity (M age 49.9 ± 12.4, 86.7% female; 23.3% Hispanic, mean BMI 35.3 ± 6.3) were randomly assigned to receive WW with or without PolyRules! for 12 weeks. The primary outcome was change in BMI from baseline to post-intervention across study arms. Implementation and process indicators were captured to inform larger trials. Results: Average change in BMI was -0.9 in the WW arm and -1.2 in the WW + PolyRules! arm (Cohen's d = 0.26). In the WW + PolyRules! arm, increased training was associated with greater decreases in BMI (r = -0.506, p = 0.0454). WW + PolyRules! participants completed an average of 60.4% sessions and reported positive experiences. There was no difference in frequency of food (d = -0.02) and weight tracking (d = -0.19) between arms. Conclusions: Studies in larger samples should evaluate training-related effects on weight. Supplementing WW with gamified inhibitory training appears feasible, with no detrimental effect on engagement.
ABSTRACT
OBJECTIVES: Obtaining body temperature is a quick and easy method to screen for acute infection such as COVID-19. Currently, the predictive value of body temperature for acute infection is inhibited by failure to account for other readily available variables that affect temperature values. In this proof-of-concept study, we sought to improve COVID-19 pretest probability estimation by incorporating covariates known to be associated with body temperature, including patient age, sex, comorbidities, month, and time of day. METHODS: For patients discharged from an academic hospital emergency department after testing for COVID-19 in March and April of 2020, we abstracted clinical data. We reviewed physician documentation to retrospectively generate estimates of pretest probability for COVID-19. Using patients' COVID-19 PCR test results as a gold standard, we compared AUCs of logistic regression models predicting COVID-19 positivity that used: (1) body temperature alone; (2) body temperature and pretest probability; (3) body temperature, pretest probability, and body temperature-relevant covariates. Calibration plots and bootstrap validation were used to assess predictive performance for model #3. RESULTS: Data from 117 patients were included. The models' AUCs were: (1) 0.69 (2) 0.72, and (3) 0.76, respectively. The absolute difference in AUC was 0.029 (95% CI -0.057 to 0.114, p=0.25) between model 2 and 1 and 0.038 (95% CI -0.021 to 0.097, p=0.10) between model 3 and 2. CONCLUSIONS: By incorporating covariates known to affect body temperature, we demonstrated improved pretest probability estimates of acute COVID-19 infection. Future work should be undertaken to further develop and validate our model in a larger, multi-institutional sample.