Daily Habits & Consumer Preferences Study

University of California, Los Angeles330 enrolled

Overview

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.

Study Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

BASIC_SCIENCE

Masking

SINGLE

Enrollment

330

Conditions

Obesity

Interventions

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.

Eligibility

Sex: ALLMin age: 18 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: 1. Age 18+ 2. English-speaking 3. BMI greater than or equal to 28 Exclusion Criteria: 1. Major mental disorder including eating disorder, mood disorder, schizophrenia, PTSD 2. Recent (\<1 year) diagnosis of major physical conditions that limit physical movement 3. Recent (\<1 year) diagnosis of sleep disorder 4. Allergy to any of the foods in the food buffet

Outcomes

Primary Outcomes

Hyperpalatable Food Intake

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.

Time frame: Hyperpalatable food intake will be measured directly after the intervention, on average 10 minutes later.

Change in Self-reported Dietary Intake

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.

Time frame: 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.

Change in Physical Activity

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

Time frame: 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

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.

Time frame: 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.

Change in Self-reported Sleep Quality

Participants will respond to a single item assessing past night's sleep quality, with response options ranging from 1 (very bad) to 4 (very good). Change in subjective sleep quality will be calculated by taking the difference of the item score pre- and post-intervention. The possible minimum for change in self-reported sleep quality is -3 and the possible maximum is 3. In this difference score, higher scores indicate improvements in sleep quality from baseline to post.

Time frame: 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

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).

Time frame: 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

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. The possible minimum value is -100 and the possible maximum value is 100. Higher scores indicate better sleep efficiency.

Time frame: 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.