Daily Habits & Consumer Preferences Study

The study will use a between-subjects design in a sample of individuals with BMI greater than or equal to 28 from the Los Angeles community (N=330). Participants will be randomly assigned to a weight stigma vs. control manipulation. Changes to the following health behaviors will be subsequently measured in their everyday lives: 3-day diet as captured by ecological momentary assessment (EMA) food diaries, objectively measured eating of obesogenic foods, objectively measured physical activity captured by 24-hour actigraphy, and sleep, captured objectively by overnight actigraphy and subjectively self-reported sleep measures. The investigators hypothesize that weight stigma causes decrements in health behaviors (e.g., sleep, eating, and physical activity) in everyday life.

Participants undergoing the experimental (Weight Stigma) arm are exposed to an interaction partner (a trained confederate) who endorses anti-fat attitudes. Prior to their interaction, participants exchange a "Getting to Know You Questionnaire" with the confederate, where the confederate endorses anti-fat attitudes. During their interaction with the confederate, participants are told they will be completing consumer rating tasks with another participant in the study. During the ostensible consumer rating task, participants in the experimental condition rate items such as a diet magazine, body sunscreen, running shoes, and a size small T-shirt labeled as size large to bolster the weight stigma manipulation.

Participants undergoing the control arm are also exposed to an interaction partner (a trained confederate) who does not endorse anti-fat attitudes. Prior to their interaction, participants exchange a "Getting to Know You Questionnaire" with the confederate; however, the confederate does not endorse anti-fat attitudes. During their interaction with the confederate, participants are told they will be completing consumer rating tasks with another participant in the study. During the ostensible consumer rating task, participants in the control condition rate items such as an interior design magazine, face sunscreen, regular (non-running) shoes, and a size large T-shirt correctly labeled as size large to avoid weight stigma.

Those undergoing the weight stigma manipulation will be exposed to an interaction partner (a trained confederate) who will endorse anti-fat attitudes. The purpose of this interaction is to examine the causal effects of weight stigma on eating behaviors, physical activity, and sleep.

Hyperpalatable food intake will initially be measured in grams and then converted into kilocalories. The food will consist of the following items: chocolate chip cookies, M&Ms, potato chips, and Sprite. These foods were chosen because processed foods, added sugars, refined grains, starchy vegetables, and sugar sweetened beverages are foods to avoid according to the 2019 American Diabetes Association Nutrition Consensus Report and are high in carbohydrates and glycemic index.

Dietary intake data for food recalls will be collected and analyzed using the Automated Self-Administered 24-hour (ASA24) Dietary Assessment Tool developed by the National Cancer Institute, Bethesda, MD. The primary eating outcome for the food diaries will be kilocalories.

Change in self-reported dietary intake will be assessed by measuring self-reported dietary intake 72 hours before the intervention as part of the baseline, and 72 hours after the intervention.

Physical activity, quantified as Metabolic Equivalent of Task (MET) units, will be assessed using ActivPAL4 actigraphs.

Change in physical activity will be assessed by measuring physical activity for 72 hours before the intervention as part of the baseline, and 72 hours after the intervention.

Change in sleep duration will be assessed using an Actiwatch-2 (Philips Respironics). Data will be captured in 30-second epochs and validated. Actiware 6.0.9 software algorithms will be used to estimate sleep parameters with the following sleep/wake algorithm: D = A-2*(1/25) + A1*(1/5) + A*(1) + A + 1*(1/5) + A + 2*(1/25), where AX = accelerometer activity for that minute.

Change in sleep duration will be assessed by measuring sleep duration for three days before the intervention as part of the baseline, and three days after the intervention.

Participants will complete a questionnaire assessing self-reported measures of the past night's sleep quality, bedtime, number of minutes it took to fall asleep, number of minutes awake during the night, and the present morning's wake time using the consensus sleep diary (Carney et al., 2012). Data from the sleep diary will not be scored numerically, but instead will be used to supplement scoring of the behavioral actigraphy data. Sleep and wake times will be used to set rest intervals, which are needed to capture accurate sleep intervals. Subjective sleep quality will be used as a fourth dependent sleep variable.

Change in self-reported sleep quality will be assessed by measuring self-reported sleep quality during the mornings of the first 72 hour baseline period before the intervention, and in the mornings of the 72 hour period after the intervention.

Change in sleep onset latency will be assessed using an Actiwatch-2 (Philips Respironics). Data will be captured in 30-second epochs and validated. Actiware 6.0.9 software algorithms will be used to estimate sleep parameters with the following sleep/wake algorithm: D = A-2*(1/25) + A1*(1/5) + A*(1) + A + 1*(1/5) + A + 2*(1/25), where AX = accelerometer activity for that minute. Sleep onset is operationalized as after 10 consecutive minutes of D ≤ 40 (as D > 40 indicates participants are awake).

Change in sleep onset latency will be assessed by measuring sleep onset latency for three days before the intervention as part of the baseline, and three days after the intervention.

Change in sleep efficiency will be assessed using an Actiwatch-2 (Philips Respironics). Data will be captured in 30-second epochs and validated. Actiware 6.0.9 software algorithms will be used to estimate sleep parameters with the following sleep/wake algorithm: D = A-2*(1/25) + A1*(1/5) + A*(1) + A + 1*(1/5) + A + 2*(1/25), where AX = accelerometer activity for that minute.

Change in sleep efficiency will be assessed by measuring sleep efficiency for three days before the intervention as part of the baseline, and three days after the intervention.

Inclusion Criteria: Age 18+ English-speaking BMI greater than or equal to 28 Exclusion Criteria: Major mental disorder including eating disorder, mood disorder, schizophrenia, PTSD Recent (<1 year) diagnosis of major physical conditions that limit physical movement Recent (<1 year) diagnosis of sleep disorder Allergy to any of the foods in the food buffet