Daily Habits & Consumer Preferences Study

Primary outcomes

1. Hyperpalatable Food Intake

   Time frame: 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.

2. Change in Self-reported Dietary Intake

   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.

   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.

3. Change in Physical Activity

   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.

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

4. Change in Sleep Duration

   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 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.

5. Change in Self-reported Sleep Quality

   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.

   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.

6. Change in Sleep Onset Latency

   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 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).

7. Change in 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.

   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.