The Behavioral Intervention with Technology for E-Weight Loss Study (BITES): Incorporating Energy Balance Models and the Bite Counter into an Online Behavioral Weight Loss Program

Abstract

This study evaluated feasibility and acceptability of adding energy balance modeling displayed on weight graphs combined with a wrist-worn bite counting sensor against a traditional online behavioral weight loss program. Adults with a BMI of 27–45 kg/m2 (83.3% women) were randomized to receive a 12-week online behavioral weight loss program with 12 weeks of continued contact (n = 9; base program), the base program plus a graph of their actual and predicted weight change based on individualized physiological parameters (n = 7), or the base program, graph, and a Bite Counter device for monitoring and limiting eating (n = 8). Participants attended weekly clinic weigh-ins plus baseline, midway (12 weeks), and study culmination (24 weeks) assessments of feasibility, acceptability, weight, and behavioral outcomes. In terms of feasibility, participants completed online lessons (M = 7.04 of 12 possible lessons, SD = 4.02) and attended weigh-ins (M = 16.81 visits, SD = 7.24). Six-month retention appears highest among nomogram participants, and weigh-in attendance and lesson completion appear highest in Bite Counter participants. Acceptability was sufficient across groups. Bite Counter use (days with ≥ 2 eating episodes) was moderate (47.8%) and comparable to other studies. Participants lost 4.6% ± 4.5 of their initial body weight at 12 weeks and 4.5% ± 5.8 at 24 weeks. All conditions increased their total physical activity minutes and use of weight control strategies (behavioral outcomes). Although all groups lost weight and the study procedures were feasible, acceptability can be improved with advances in the technology. Participants were satisfied with the online program and nomograms, and future research on engagement, adherence, and integration with other owned devices is needed. ClinicalTrials.gov Identifier: NCT02857595

Machine learning modeling practices to support the principles of AI and ethics in nutrition research

“…On the other hand, measurements that are obtained from accelerometers are also objective, but can be extremely noisy and are not able to estimate physical activity expenditure well in comparison to gold standard methods [27]. However, the largest source of measurement in nutrition research, self-reported energy intake, is not objective and sometimes not reliable without triangulating with other methods [28] for deriving scientific conclusions [29][30][31].…”

“…This does not mean that self-reported dietary intake data is not valuable during interventions. There are examples of self-reported dietary intake data being used in tandem with other tools such as energy intake wearables [44,45] and mathematical models that predict weight loss to guide intake [46] improving dietary adherence even more than any of the dietary assessment methods used alone [28]. The danger of using data like selfreported dietary intake as true intake to train AI/ML models is that the models will identify patterns that are artifacts of error from the input data which will then be used to make erroneous predictions that inform decision-making.…”

Using Personalized Avatars as an Adjunct to an Adult Weight Loss Management Program: Randomized Controlled Feasibility Study

“…Technology-based interventions for weight loss have shown promise in the short term [ 15 , 16 ]; however, the potential of computer engineering has not been fully realized when designing weight loss interventions [ 17 ]. It has been argued that new multicomponent (integrated) electronic platforms integrating education with individually tailored weight loss programs, including the promotion of autonomous motivation, self-efficacy, self-regulation, and positive body image, may present a way forward for wide-scale weight loss solutions and obesity management [ 18 - 20 ].…”