RESUMO
Objectives: Overeating interventions and research often focus on single determinants and use subjective or nonpersonalized measures. We aim to (1) identify automatically detectable features that predict overeating and (2) build clusters of eating episodes that identify theoretically meaningful and clinically known problematic overeating behaviors (e.g., stress eating), as well as new phenotypes based on social and psychological features. Method: Up to 60 adults with obesity in the Chicagoland area will be recruited for a 14-day free-living observational study. Participants will complete ecological momentary assessments and wear 3 sensors designed to capture features of overeating episodes (e.g., chews) that can be visually confirmed. Participants will also complete daily dietitian-administered 24-hour recalls of all food and beverages consumed. Analysis: Overeating is defined as caloric consumption exceeding 1 standard deviation of an individual's mean consumption per eating episode. To identify features that predict overeating, we will apply 2 complementary machine learning methods: correlation-based feature selection and wrapper-based feature selection. We will then generate clusters of overeating types and assess how they align with clinically meaningful overeating phenotypes. Conclusions: This study will be the first to assess characteristics of eating episodes in situ over a multiweek period with visual confirmation of eating behaviors. An additional strength of this study is the assessment of predictors of problematic eating during periods when individuals are not on a structured diet and/or engaged in a weight loss intervention. Our assessment of overeating episodes in real-world settings is likely to yield new insights regarding determinants of overeating that may translate into novel interventions.
RESUMO
BACKGROUND: Multiple national organizations and leaders have called for increased attention to dementia prevention in those most vulnerable, for example persons with limited formal education. Prevention recommendations have included calls for multicomponent interventions that have the potential to improve both underlying neurobiological health and the ability to function despite neurobiological pathology, or what has been termed cognitive reserve. OBJECTIVES: Test feasibility, treatment modifier, mechanism, and cognitive function effects of a multicomponent intervention consisting of foods high in polyphenols (i.e., MIND foods) to target neurobiological health, and speed of processing training to enhance cognitive reserve. We refer to this multicomponent intervention as MINDSpeed. DESIGN: MINDSpeed is being evaluated in a 2â¯×â¯2 randomized factorial design with 180 participants residing independently in a large Midwestern city. Qualifying participants are 60â¯years of age or older with no evidence of dementia, and who have completed 12â¯years or less of education. All participants receive a study-issued iPad to access the custom study application that enables participants, depending on randomization, to select either control or MIND food, and to play online cognitive games, either speed of processing or control games. METHODS: All participants complete informed consent and baseline assessment, including urine and blood samples. Additionally, up to 90 participants will complete neuroimaging. Assessments are repeated immediately following 12â¯weeks of active intervention, and at 24â¯weeks post-randomization. The primary outcome is an executive cognitive composite score. Secondary outcomes include oxidative stress, pro-inflammatory cytokines, and neuroimaging-captured structural and functional metrics of the hippocampus and cortical brain regions. SUMMARY: MINDSpeed is the first study to evaluate the multicomponent intervention of high polyphenol intake and speed of processing training. It is also one of the first dementia prevention trials to target older adults with low education. The results of the study will guide future dementia prevention efforts and trials in high risk populations.