Obesity
Conditions
Keywords
Weight Prejudice, Sleep, Eating, Exercise, Ecological Momentary Assessment, Actigraphy, Diet Records
Brief summary
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.
Interventions
BEHAVIORALWeight stigma intervention
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.
Sponsors
University of California, Los Angeles
University of California, San Francisco
Miami University
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
BASIC_SCIENCE
Masking
SINGLE (Subject)
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Hyperpalatable Food Intake | Hyperpalatable food intake will be measured directly after the intervention, on average 10 minutes later. | 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. |
| Change in Self-reported Dietary Intake | 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. | 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 Physical Activity | 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. | Physical activity, quantified as Metabolic Equivalent of Task (MET) units, will be assessed using ActivPAL4 actigraphs. |
| Change in Sleep Duration | 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 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 Self-reported Sleep Quality | 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. | 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. |
| Change in Sleep Onset Latency | 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 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 Efficiency | 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. | 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. |
Countries
United States
Contacts
PRINCIPAL_INVESTIGATORA. Janet Tomiyama, Ph.D.
University of California, Los Angeles
Participant flow
Recruitment details
Recruitment strategies included online postings (e.g., Facebook), physical flyers around the surrounding community, listings on clinicaltrials.gov, clinicaltrials.ucla.edu, and other study recruitment portals such as researchmatch.com, and the psychology subject pools for our university, as well churches and beauty salons/barber shops, and recruitment at community centers and events. Recruitment took place from April 2022 to July 2024.
Pre-assignment details
We excluded participants who did not meet all of our inclusion criteria: a BMI of greater \>= 28; English-speaking; no diagnosis of major mental disorders including any eating disorder, mood disorder, schizophrenia, or PTSD within past year; no diagnosis of major physical conditions that limit physical movement within past; no recent diagnosis of a sleep disorder within past year; and no allergy to any of the foods in the food buffet. Four participants dropped out prior to manipulation.
Baseline characteristics
| Characteristic | — |
|---|---|
| Age, Continuous | 36.74 years STANDARD_DEVIATION 15.58 |
| Race/Ethnicity, Customized Asian / Asian American | 22 Participants |
| Race/Ethnicity, Customized Biracial / Multiracial | 16 Participants |
| Race/Ethnicity, Customized Black / African American | 10 Participants |
| Race/Ethnicity, Customized Latinx / Hispanic / Latin American | 100 Participants |
| Race/Ethnicity, Customized Middle Eastern / Middle Eastern American | 3 Participants |
| Race/Ethnicity, Customized Native Hawaiian / Pacific Islander | 1 Participants |
| Race/Ethnicity, Customized Other Race | 2 Participants |
| Race/Ethnicity, Customized White | 49 Participants |
| Sex/Gender, Customized Men | 61 Participants |
| Sex/Gender, Customized Non-binary | 0 Participants |
| Sex/Gender, Customized Women | 67 Participants |
Adverse events
| Event type | EG000 affected / at risk | EG001 affected / at risk |
|---|---|---|
| deaths Total, all-cause mortality | 0 / 125 | 0 / 123 |
| other Total, other adverse events | 0 / 125 | 0 / 123 |
| serious Total, serious adverse events | 0 / 125 | 0 / 123 |
Outcome results
None listed