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Weight Loss for Prediabetes Using Episodic Future Thinking

Delay Discounting as a Target for Self-Regulation in Prediabetes

Status
Completed
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT03670602
Acronym
MINDD4
Enrollment
64
Registered
2018-09-13
Start date
2019-01-17
Completion date
2021-06-30
Last updated
2022-12-09

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

PreDiabetes

Keywords

Weight loss, delay discounting

Brief summary

The goals of the UH3 are to assess the effectiveness of adding Episodic Future Thinking (EFT) to the investigators standard behavioral weight control program to improve weight loss, delay discounting (DD), working memory, glycemic control (HbA1c) and behavioral medication adherence over a 6 month period in persons with prediabetes and comorbid hypertension and/or hyperlipidemia. This will be accomplished by a randomized trial (N = 71 randomized) comparing the effects of EFT versus control that matches attention and use of technology.

Detailed description

Participants in both groups will first attend weekly group meetings followed by monthly group meetings for up to 6 months. They will be provided general information on healthy diet, physical activity and medication adherence that the investigators will develop combining strengths of the investigators well validated family-based behavioral treatment for obesity and the Diabetes Prevention Program (DPP) lifestyle intervention for prediabetes. The behavioral treatment (BT) is a rigorously tested, multi-component intervention that targets diet, activity, and behavioral skills. The treatment will include: 1) a modified version of the Traffic Light Diet, which utilizes RED, YELLOW, GREEN labels for food to guide participants toward the goal of consuming low energy dense, low glycemic, high nutrient dense foods; 2) the Traffic Light Activity Program, which also utilizes RED, YELLOW and GREEN labels for different levels of caloric expenditure, and 3) a variety of behavioral techniques, including stimulus control, self-monitoring, goal setting, problem solving, resetting rewarding mechanisms by reducing need for immediate gratification, finding behavioral substitutes for highly reinforcing food, and EFT. The investigators have used a traffic light-based intervention in combination with EFT in a pilot study to demonstrate therapeutic effects of EFT on BMI and dietary intake beyond the effects of BT alone. During treatment meetings, participants will be weighed and have a 30-60 minute group session (up to 20 per group) either preceded or followed by an individualized session with an interventionist. The group sessions review information about weight loss and maintenance and engage in group problem solving for participants who are struggling with behavior change. During the individual meeting with their interventionist, participants are taught behavior change techniques and review and address diet and activity self-monitoring and any barriers to adherence with the weight-loss behaviors. A study website will be developed that will be used to provide information about the intervention, downloadable manuals for the Traffic Light Diet and Activity Program, manage the EFT component of the intervention, and provide tools for cooking, and getting more physical activity. Quizzes to assess mastery of educational materials will be implemented on the study website, with multiple versions of quizzes on each module available to account for those participants who will acquire the information more slowly than others. Participants will have access to traditional paper and pencil self-monitoring, and consistent with current implementation of BT, after self-monitoring skill is acquired, participants can choose to use traditional or technology-based recording. Participants will have access to the study website for feedback, and interventionists will have access to the website to assess patient progress, assist with problem solving and to communicate with participants to structure solutions. The website will also contain password protected sections that are for internal use by study personnel. This section will be a repository for study documents and a communications hub for the study. The website will not contain protected health information. Participants in both groups will meet with an interventionist to review progress. One group will be trained to implement EFT using the ecological momentary intervention (EMI) computer based program that the investigators have developed. This program can be accessed by smartphone, tablet or computer. This application stores self-generated EFT cues, prompts their use, asks questions about use, and records their use. EFT training will include developing individualized future event cues to use in implementing EFT in the natural environment. In the control group, participants may use non-future cues, recall previous events, and not use prospection. Cues are stimuli that prompt engaging in EFT. Cues can be signs, reminder cards, audio cues, or physical cues. Subjects will practice using these cues and learn to envision that the future is now when making decisions in the laboratory as they are engaged in a variety of DD and food decision training tasks such as the opportunity to have a very enticing snack now or larger portions of healthier food later, earning a small amount of money now or more later, etc. In this way, participants will learn to generate episodic future cues and practice EFT skills in situations where they usually would choose the more immediate reward. Episodic future cues may include audio and written cues that can be accessed during tempting situations in the natural environment. During individual sessions interventionists will review habit changes and medication adherence, and use of EFT. In the control group participants be asked to log into the MAMRT web-app at the same frequency as the EFT group, but will see no cues prior to their daily questions. Participants in both groups will be weighed at the beginning of each session, and height will also be collected at baseline. Data to be collected at baseline, 3 and 6 months include delay discounting tasks, working memory, measures of medication and behavioral adherence, weight, glycemic control, blood pressure and cholesterol, eating and activity.

Interventions

Participants will practice using these cues when making decisions about health choices. Participants will implement EFT while using The Traffic Light Diet, The Traffic Light Activity Program, and a variety of behavioral techniques including stimulus control, self-monitoring, goal setting, problem solving, resetting rewarding mechanisms by reducing need for immediate gratification, finding behavioral substitutes for highly reinforcing food.

BEHAVIORALDaily Check in

Participants will be asked to access the electronic app at the same rate as the experimental group (e.g. daily). Participants will receive behavioral weight loss treatment including The Traffic Light Diet, The Traffic Light Activity Program, and a variety of behavioral techniques including stimulus control, self-monitoring, goal setting, problem solving, and finding behavioral substitutes for highly reinforcing food.

Sponsors

Virginia Polytechnic Institute and State University
CollaboratorOTHER
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
CollaboratorNIH
State University of New York at Buffalo
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
SINGLE (Outcomes Assessor)

Masking description

Research personnel who will be conducting assessment sessions, including weight measurements, will not be informed of participant's group assignment

Intervention model description

Participants will engage in EFT or control while using The Traffic Light Diet, The Traffic Light Activity Program, and a variety of behavioral techniques including stimulus control, self-monitoring, goal setting, problem solving, resetting rewarding mechanisms by reducing need for immediate gratification, finding behavioral substitutes for highly reinforcing food.

Eligibility

Sex/Gender
ALL
Age
18 Years to 74 Years
Healthy volunteers
No

Inclusion criteria

* Overweight or obese (BMI ≥ 25) * Prediabetes (HbA1c between 5.7 - 6.4%; 39-40 mmol/mol)

Exclusion criteria

* Type 2 Diabetes * Use of diabetic drugs * Pregnancy * Not ambulatory * Intellectual impairment * Unmanaged mood disorders * Current substance use disorder (excluding nicotine and caffeine) * History of eating disorders (Except binge eating disorder) * Abnormal blood glucose related to medications

Design outcomes

Primary

MeasureTime frameDescription
Change From Baseline in Glycemic ControlBaseline (0 weeks), 12 weeks and 24 weeksGlycemic control will be measured as hemoglobin A1c (HbA1c), which is the percentage of glycated hemoglobin within total hemoglobin. Change is assessed using repeated measures.
Change From Baseline in Delay DiscountingBaseline (0 weeks), 12 weeks and 24 weeksDelay Discounting will be assessed using an adjusting amount task where choices will be present between a larger, delayed amount of money ($100) and a smaller, immediate amount. The smaller, immediate amount will begin at $50 on the first trial and will be adjusted following each trial. Participants cues created during treatment will be displayed during the task. To calculate discount rates hyperbolic discounting model will be used V=A/1+kD where V is discounted value, A is reward amount, D is delay and k is a free parameter that indexes the rate of discounting. k values are transformed using natural log. Higher scores indicate more choices for immediate reward. Change is assessed using repeated measures.
Change From Baseline in WeightBaseline (0 weeks), 12 weeks, and 24 weeksWeight measured in kilograms. Change is assessed using repeated measures.

Secondary

MeasureTime frameDescription
Change in Total CaloriesBaseline (0 weeks), 12 weeks and 24 weeksDietary intake, as an index of behavioral health and a target of the treatment, was measured using 3 automated self-administered 24-hour multi-pass food recalls. Total calories were averaged across the three sessions for each timepoint. Change was assessed using repeated measures.
Changes in Working MemoryBaseline (0 weeks), 12 weeks and 24 weeksVisuospatial working memory will be measured using the Backwards Corsi block-tapping task. The total score, or (number of trials completed correctly (out of 14 trials) x longest correctly reported block of items (2 - 8 items) ). Possible scores range from 0 (minimum) - (112) maximum. Higher scores indicate better working memory. Change is assessed using repeated measures
Change in Medication AdherenceBaseline (0 weeks), 12 weeks and 24 weeksAdherence to prescribed medication for co-morbid hypertension and/or hyperlipidemia will be assessed using pill counts. Experimenter will count pills 2x and record number of pills, medication, dosage and fill date. Adherence percentage is calculated \[(Quantity of pills dispensed - remaining)/(quantity prescribed per day\*days since last refill)\] \*100. Change is assessed using repeated measures.
Changes in Physical ActivityBaseline (0 weeks), 12 weeks and 24 weeksPhysical activity, as one index of behavioral health and a target of the behavioral weight loss treatment, was measured using an Actigraph Accelerometer. Participants will be asked to wear an Actigraph Accelerometer for at least 10 hours per day for approximately one week. Accelerometer data was filtered using ActiLife, for 90 minutes consecutive non-wear and by participants wear time diaries. The main outcome measure was percent of time engaged in moderate to vigorous activity (MVPA) (MET\>3.00). Change is assessed using repeated measures.

Other

MeasureTime frameDescription
Changes in Relative Reinforcing Efficacy of Unhealthy FoodBaseline (0 weeks), 12 weeks and 24 weeksRelative Reinforcing efficacy of food is measured with a hypothetical purchasing task, in which number two foods are available and number of portions of food purchased at various prices ($0 - $20) is measured. Foods used were considered unhealthy snack foods, e.g. cookies, potato chips, etc. Intensity, the number of portions of food requested when the price is $0, was used as the outcome measure. Significant non-normality of the data required a log base 10 transformation (log (food portions + 1). Larger numbers represent more food portions, higher intensity and higher reinforcing efficacy. Change is assessed using repeated measures.

Countries

United States

Participant flow

Recruitment details

Participants with prediabetes (HbA1c) between 5.7 to 6.4% Participants had no prior or current diagnosis of diabetes, were not pregnant and were not taking medications that influenced their blood glucose.

Pre-assignment details

Participants (n=933) completed a prescreen internet survey and n=294 were screened in our laboratory. N=72 participated in the initial weight loss and 8 participants declined or did not qualify to enroll in the randomized study. 64 participants were randomized between intervention and control groups.

Participants by arm

ArmCount
Episodic Future Thinking (EFT)
Participants will generate positive future cues that will be accessed via an electronic app to engage in EFT. Episodic Future Thinking: Participants will practice using these cues when making decisions about health choices. Participants will implement EFT while using The Traffic Light Diet, The Traffic Light Activity Program, and a variety of behavioral techniques including stimulus control, self-monitoring, goal setting, problem solving, resetting rewarding mechanisms by reducing need for immediate gratification, finding behavioral substitutes for highly reinforcing food.
31
Daily Check in (DCI)
Participants will be asked to access an electronic app daily, but will receive no cues. Daily Check in: Participants will be asked to access the electronic app at the same rate as the experimental group (e.g. daily). Participants will receive behavioral weight loss treatment including The Traffic Light Diet, The Traffic Light Activity Program, and a variety of behavioral techniques including stimulus control, self-monitoring, goal setting, problem solving, and finding behavioral substitutes for highly reinforcing food.
33
Total64

Withdrawals & dropouts

PeriodReasonFG000FG001
Overall StudyLost to Follow-up10
Overall StudyWithdrawal by Subject03

Baseline characteristics

CharacteristicTotalDaily Check in (DCI)Episodic Future Thinking (EFT)
Age, Continuous54.6 Years
STANDARD_DEVIATION 9.8
54.1 Years
STANDARD_DEVIATION 9.3
55.0 Years
STANDARD_DEVIATION 10.4
Annual Household Income43090 US$
STANDARD_DEVIATION 31892
48889 US$
STANDARD_DEVIATION 35608
37500 US$
STANDARD_DEVIATION 27335
Body Mass Index (BMI)37.5 kg/m^2
STANDARD_DEVIATION 7
38.0 kg/m^2
STANDARD_DEVIATION 6.9
37.0 kg/m^2
STANDARD_DEVIATION 7.2
Delay Discounting-6.01 ln (k)
STANDARD_DEVIATION 2.73
-6.11 ln (k)
STANDARD_DEVIATION 3.14
-5.92 ln (k)
STANDARD_DEVIATION 2.33
Education15.6 years
STANDARD_DEVIATION 2.2
15.5 years
STANDARD_DEVIATION 2.3
15.7 years
STANDARD_DEVIATION 2.2
HbA1c (%)5.90 percentage of glycosylated hemoglobin
STANDARD_DEVIATION 0.29
5.91 percentage of glycosylated hemoglobin
STANDARD_DEVIATION 0.29
5.90 percentage of glycosylated hemoglobin
STANDARD_DEVIATION 0.29
Race (NIH/OMB)
American Indian or Alaska Native
0 Participants0 Participants0 Participants
Race (NIH/OMB)
Asian
2 Participants2 Participants0 Participants
Race (NIH/OMB)
Black or African American
10 Participants4 Participants6 Participants
Race (NIH/OMB)
More than one race
4 Participants2 Participants2 Participants
Race (NIH/OMB)
Native Hawaiian or Other Pacific Islander
0 Participants0 Participants0 Participants
Race (NIH/OMB)
Unknown or Not Reported
3 Participants1 Participants2 Participants
Race (NIH/OMB)
White
45 Participants24 Participants21 Participants
Region of Enrollment
United States
64 participants33 participants31 participants
Sex: Female, Male
Female
51 Participants26 Participants25 Participants
Sex: Female, Male
Male
13 Participants7 Participants6 Participants
Site Enrollment
University at Buffalo
33 Participants17 Participants16 Participants
Site Enrollment
Virginia Tech Carillon
31 Participants16 Participants15 Participants
Timing of treatment with COVID-19 by cohort
Cohort 1; prior to COVID, in person
25 Participants13 Participants12 Participants
Timing of treatment with COVID-19 by cohort
Cohort 2; before and during COVID; 50% in person 50% remote
21 Participants11 Participants10 Participants
Timing of treatment with COVID-19 by cohort
Cohort 3 - during COVID, remote
18 Participants9 Participants9 Participants

Adverse events

Event typeEG000
affected / at risk
EG001
affected / at risk
deaths
Total, all-cause mortality
0 / 310 / 33
other
Total, other adverse events
0 / 310 / 33
serious
Total, serious adverse events
1 / 310 / 33

Outcome results

Primary

Change From Baseline in Delay Discounting

Delay Discounting will be assessed using an adjusting amount task where choices will be present between a larger, delayed amount of money ($100) and a smaller, immediate amount. The smaller, immediate amount will begin at $50 on the first trial and will be adjusted following each trial. Participants cues created during treatment will be displayed during the task. To calculate discount rates hyperbolic discounting model will be used V=A/1+kD where V is discounted value, A is reward amount, D is delay and k is a free parameter that indexes the rate of discounting. k values are transformed using natural log. Higher scores indicate more choices for immediate reward. Change is assessed using repeated measures.

Time frame: Baseline (0 weeks), 12 weeks and 24 weeks

Population: All participants were analyzed using mixed model methods, 5 participants did not complete DD measures at 12 weeks, and 5 participants did not complete DD measures at 24 weeks

ArmMeasureGroupValue (MEAN)Dispersion
Episodic Future Thinking (EFT)Change From Baseline in Delay DiscountingBaseline Delay Discounting-5.92 ln (k)Standard Deviation 2.33
Episodic Future Thinking (EFT)Change From Baseline in Delay DiscountingChange from baseline in Delay discounting at 12 weeks-3.09 ln (k)Standard Deviation 2.63
Episodic Future Thinking (EFT)Change From Baseline in Delay DiscountingChange from baseline in Delay discounting at 24 weeks-2.79 ln (k)Standard Deviation 2.31
Daily Check in (DCI)Change From Baseline in Delay DiscountingBaseline Delay Discounting-6.11 ln (k)Standard Deviation 3.14
Daily Check in (DCI)Change From Baseline in Delay DiscountingChange from baseline in Delay discounting at 12 weeks-0.96 ln (k)Standard Deviation 2.18
Daily Check in (DCI)Change From Baseline in Delay DiscountingChange from baseline in Delay discounting at 24 weeks-1.33 ln (k)Standard Deviation 2.04
Comparison: A mixed model ANCOVA with unstructured covariance and random effects of participant. Treatment group and cohort were the between subject variables, weeks as the repeated measure and site as a covariate.p-value: 0.0035Mixed Models Analysis
Comparison: A mixed model ANCOVA with unstructured covariance and random effects of participant. Treatment group and cohort were the between subject variables, weeks as the repeated measure and site as a covariatep-value: <0.001Mixed Models Analysis
Primary

Change From Baseline in Glycemic Control

Glycemic control will be measured as hemoglobin A1c (HbA1c), which is the percentage of glycated hemoglobin within total hemoglobin. Change is assessed using repeated measures.

Time frame: Baseline (0 weeks), 12 weeks and 24 weeks

Population: Analyses were Intention to Treat (ITT) and included all randomized participants. 5 participants did not complete 12 week measures, and 4 participants did not complete 24 week measures.

ArmMeasureGroupValue (MEAN)Dispersion
Episodic Future Thinking (EFT)Change From Baseline in Glycemic ControlBaseline hBa1c5.90 Percentage of glycosylated hemoglobinStandard Deviation 0.29
Episodic Future Thinking (EFT)Change From Baseline in Glycemic ControlChange from baseline hBa1c at 24 weeks-0.35 Percentage of glycosylated hemoglobinStandard Deviation 0.35
Episodic Future Thinking (EFT)Change From Baseline in Glycemic ControlChange from baseline hBa1c at 12 weeks-0.22 Percentage of glycosylated hemoglobinStandard Deviation 0.36
Daily Check in (DCI)Change From Baseline in Glycemic ControlBaseline hBa1c5.91 Percentage of glycosylated hemoglobinStandard Deviation 0.29
Daily Check in (DCI)Change From Baseline in Glycemic ControlChange from baseline hBa1c at 12 weeks-0.27 Percentage of glycosylated hemoglobinStandard Deviation 0.28
Daily Check in (DCI)Change From Baseline in Glycemic ControlChange from baseline hBa1c at 24 weeks-0.39 Percentage of glycosylated hemoglobinStandard Deviation 0.31
Comparison: A mixed model ANCOVA with unstructured covariance and random effects of participant. Group and cohort were the between subject variables, weeks as the repeated measure and site as a covariatep-value: 0.79Mixed Models Analysis
Comparison: A mixed model ANCOVA with unstructured covariance and random effects of participant. Group and cohort were the between subject variables, weeks as the repeated measure and site as a covariatep-value: <0.001Mixed Models Analysis
Primary

Change From Baseline in Weight

Weight measured in kilograms. Change is assessed using repeated measures.

Time frame: Baseline (0 weeks), 12 weeks, and 24 weeks

Population: Analyses were Intent to Treat (ITT) and included all randomized participants, using mixed models. 5 participants did not complete week 12 measures and 4 participants did not complete 24 week height and weight measures

ArmMeasureGroupValue (MEAN)Dispersion
Episodic Future Thinking (EFT)Change From Baseline in WeightBaseline Weight102.3 kilogramsStandard Deviation 22.3
Episodic Future Thinking (EFT)Change From Baseline in WeightChange from baseline in weight at 12 weeks-6.8 kilogramsStandard Deviation 4.3
Episodic Future Thinking (EFT)Change From Baseline in WeightChange from baseline in weight at 24 weeks-9.8 kilogramsStandard Deviation 6.5
Daily Check in (DCI)Change From Baseline in WeightBaseline Weight104.2 kilogramsStandard Deviation 23.2
Daily Check in (DCI)Change From Baseline in WeightChange from baseline in weight at 12 weeks-7.2 kilogramsStandard Deviation 3.8
Daily Check in (DCI)Change From Baseline in WeightChange from baseline in weight at 24 weeks-9.8 kilogramsStandard Deviation 5.6
Comparison: A mixed model ANCOVA with unstructured covariance and random effects of participant. Group and cohort were the between subject variables, weeks as the repeated measure and site as a covariatep-value: 0.85Mixed Models Analysis
Comparison: A mixed model ANCOVA with unstructured covariance and random effects of participant. Group and cohort were the between subject variables, weeks as the repeated measure and site as a covariatep-value: <0.001Mixed Models Analysis
Secondary

Change in Medication Adherence

Adherence to prescribed medication for co-morbid hypertension and/or hyperlipidemia will be assessed using pill counts. Experimenter will count pills 2x and record number of pills, medication, dosage and fill date. Adherence percentage is calculated \[(Quantity of pills dispensed - remaining)/(quantity prescribed per day\*days since last refill)\] \*100. Change is assessed using repeated measures.

Time frame: Baseline (0 weeks), 12 weeks and 24 weeks

Population: Analyses were Intention to Treat (ITT) and included all randomized participants, using mixed model analyses. 3 participants did not completed baseline (0) medication adherence measurements, an additional 5 participants did not complete week 12 medication adherence measurements (total missing for change score n =8) and an additional 6 participants did not complete 24 week medication adherence measures (total missing for change score n=9)

ArmMeasureGroupValue (MEAN)Dispersion
Episodic Future Thinking (EFT)Change in Medication AdherenceBaseline medication adherence84.3 percent medication adherenceStandard Deviation 21
Episodic Future Thinking (EFT)Change in Medication AdherenceChange from baseline in medication adherence at 12 weeks5.33 percent medication adherenceStandard Deviation 17.3
Episodic Future Thinking (EFT)Change in Medication AdherenceChange from baseline in medication adherence at 24 weeks5.23 percent medication adherenceStandard Deviation 16.49
Daily Check in (DCI)Change in Medication AdherenceBaseline medication adherence91.7 percent medication adherenceStandard Deviation 17.2
Daily Check in (DCI)Change in Medication AdherenceChange from baseline in medication adherence at 12 weeks-0.25 percent medication adherenceStandard Deviation 19.5
Daily Check in (DCI)Change in Medication AdherenceChange from baseline in medication adherence at 24 weeks0.09 percent medication adherenceStandard Deviation 9.5
Comparison: A mixed model ANCOVA with unstructured covariance and random effects of participant. Group and cohort were the between subject variables, weeks as the repeated measure and site as a covariatep-value: 0.201Mixed Models Analysis
Comparison: A mixed model ANCOVA with unstructured covariance and random effects of participant. Group and cohort were the between subject variables, weeks as the repeated measure and site as a covariatep-value: 0.315Mixed Models Analysis
Secondary

Change in Total Calories

Dietary intake, as an index of behavioral health and a target of the treatment, was measured using 3 automated self-administered 24-hour multi-pass food recalls. Total calories were averaged across the three sessions for each timepoint. Change was assessed using repeated measures.

Time frame: Baseline (0 weeks), 12 weeks and 24 weeks

Population: Analyses were Intent to Treat (ITT) and included all randomized participants, using mixed models. 2 participants did not complete baseline measures, 9 participants did not complete week 12 measures and 10 participants did not complete week 24 measures. One participant did not complete measures at any timepoint.

ArmMeasureGroupValue (MEAN)Dispersion
Episodic Future Thinking (EFT)Change in Total CaloriesBaseline total daily calorie intake1802.2 kilocaloriesStandard Deviation 591.5
Episodic Future Thinking (EFT)Change in Total CaloriesChange from baseline in total daily calorie intake at week 12-542.5 kilocaloriesStandard Deviation 599.3
Episodic Future Thinking (EFT)Change in Total CaloriesChange from baseline in total daily calorie intake at week 24-298.4 kilocaloriesStandard Deviation 458
Daily Check in (DCI)Change in Total CaloriesBaseline total daily calorie intake1886.3 kilocaloriesStandard Deviation 594.7
Daily Check in (DCI)Change in Total CaloriesChange from baseline in total daily calorie intake at week 12-504.7 kilocaloriesStandard Deviation 465
Daily Check in (DCI)Change in Total CaloriesChange from baseline in total daily calorie intake at week 24-568.3 kilocaloriesStandard Deviation 363.5
Comparison: A mixed model ANCOVA with unstructured covariance and random effects of participant. Group and cohort were the between subject variables, weeks as the repeated measure and site as a covariatep-value: 0.007Mixed Models Analysis
Comparison: A mixed model ANCOVA with unstructured covariance and random effects of participant. Group and cohort were the between subject variables, weeks as the repeated measure and site as a covariatep-value: <0.0001Mixed Models Analysis
Secondary

Changes in Physical Activity

Physical activity, as one index of behavioral health and a target of the behavioral weight loss treatment, was measured using an Actigraph Accelerometer. Participants will be asked to wear an Actigraph Accelerometer for at least 10 hours per day for approximately one week. Accelerometer data was filtered using ActiLife, for 90 minutes consecutive non-wear and by participants wear time diaries. The main outcome measure was percent of time engaged in moderate to vigorous activity (MVPA) (MET\>3.00). Change is assessed using repeated measures.

Time frame: Baseline (0 weeks), 12 weeks and 24 weeks

Population: Analyses were Intention to Treat (ITT) and included all randomized participants, using mixed model analysis.

ArmMeasureGroupValue (MEAN)Dispersion
Episodic Future Thinking (EFT)Changes in Physical ActivityBaseline percent MVPA5.61 Percent of timeStandard Deviation 2.4
Episodic Future Thinking (EFT)Changes in Physical ActivityChange from baseline in percent MVPA at 12 weeks1.16 Percent of timeStandard Deviation 1.95
Episodic Future Thinking (EFT)Changes in Physical ActivityChange from baseline in percent MVPA at 24 weeks0.91 Percent of timeStandard Deviation 2.71
Daily Check in (DCI)Changes in Physical ActivityBaseline percent MVPA5.67 Percent of timeStandard Deviation 2.65
Daily Check in (DCI)Changes in Physical ActivityChange from baseline in percent MVPA at 24 weeks0.22 Percent of timeStandard Deviation 1.54
Daily Check in (DCI)Changes in Physical ActivityChange from baseline in percent MVPA at 12 weeks0.82 Percent of timeStandard Deviation 1.77
p-value: 0.345Mixed Models Analysis
Comparison: A mixed model ANCOVA with unstructured covariance and random effects of participant. Group and cohort were the between subject variables, weeks as the repeated measure and site as a covariatep-value: 0.0003Mixed Models Analysis
Secondary

Changes in Working Memory

Visuospatial working memory will be measured using the Backwards Corsi block-tapping task. The total score, or (number of trials completed correctly (out of 14 trials) x longest correctly reported block of items (2 - 8 items) ). Possible scores range from 0 (minimum) - (112) maximum. Higher scores indicate better working memory. Change is assessed using repeated measures

Time frame: Baseline (0 weeks), 12 weeks and 24 weeks

Population: Analyses were Intent to Treat (ITT) and included all randomized participants, using mixed models. 8 participants did not complete week 12 measures and 5 participants did not complete 24 week measures

ArmMeasureGroupValue (MEAN)Dispersion
Episodic Future Thinking (EFT)Changes in Working MemoryBaseline Backwards Corsi Total Score44.6 scoreStandard Deviation 12
Episodic Future Thinking (EFT)Changes in Working MemoryChange from baseline in Backwards Corsi Total score at 24 weeks6.9 scoreStandard Deviation 17.6
Episodic Future Thinking (EFT)Changes in Working MemoryChange from baseline in backwards Corsi total score at 12 weeks2.1 scoreStandard Deviation 18.3
Daily Check in (DCI)Changes in Working MemoryBaseline Backwards Corsi Total Score46.0 scoreStandard Deviation 14
Daily Check in (DCI)Changes in Working MemoryChange from baseline in Backwards Corsi Total score at 24 weeks4.5 scoreStandard Deviation 13.5
Daily Check in (DCI)Changes in Working MemoryChange from baseline in backwards Corsi total score at 12 weeks1.6 scoreStandard Deviation 18.5
Comparison: A mixed model ANCOVA with unstructured covariance and random effects of participant. Group and cohort were the between subject variables, weeks as the repeated measure and site as a covariatep-value: 0.774Mixed Models Analysis
Comparison: A mixed model ANCOVA with unstructured covariance and random effects of participant. Group and cohort were the between subject variables, weeks as the repeated measure and site as a covariatep-value: 0.019Mixed Models Analysis
Other Pre-specified

Changes in Relative Reinforcing Efficacy of Unhealthy Food

Relative Reinforcing efficacy of food is measured with a hypothetical purchasing task, in which number two foods are available and number of portions of food purchased at various prices ($0 - $20) is measured. Foods used were considered unhealthy snack foods, e.g. cookies, potato chips, etc. Intensity, the number of portions of food requested when the price is $0, was used as the outcome measure. Significant non-normality of the data required a log base 10 transformation (log (food portions + 1). Larger numbers represent more food portions, higher intensity and higher reinforcing efficacy. Change is assessed using repeated measures.

Time frame: Baseline (0 weeks), 12 weeks and 24 weeks

Population: Analyses were Intent to Treat (ITT) and included all randomized participants, using mixed models. 2 participants did not complete baseline measures, 6 participants did not complete week 12 measures and 6 participants did not complete week 24 measures of relative reinforcing efficacy. Two participants did not complete any measures of relative reinforcing efficacy.

ArmMeasureGroupValue (MEAN)Dispersion
Episodic Future Thinking (EFT)Changes in Relative Reinforcing Efficacy of Unhealthy FoodBaseline relative reinforcing efficacy of unhealthy food0.926 log (food portions + 1)Standard Deviation 0.483
Episodic Future Thinking (EFT)Changes in Relative Reinforcing Efficacy of Unhealthy FoodChange from baseline in relative reinforcing efficacy of unhealthy food at 12 weeks-0.351 log (food portions + 1)Standard Deviation 0.466
Episodic Future Thinking (EFT)Changes in Relative Reinforcing Efficacy of Unhealthy FoodChange from baseline in relative reinforcing efficacy of unhealthy food at 24 weeks.-0.328 log (food portions + 1)Standard Deviation 0.545
Daily Check in (DCI)Changes in Relative Reinforcing Efficacy of Unhealthy FoodBaseline relative reinforcing efficacy of unhealthy food0.998 log (food portions + 1)Standard Deviation 0.375
Daily Check in (DCI)Changes in Relative Reinforcing Efficacy of Unhealthy FoodChange from baseline in relative reinforcing efficacy of unhealthy food at 12 weeks-0.348 log (food portions + 1)Standard Deviation 0.442
Daily Check in (DCI)Changes in Relative Reinforcing Efficacy of Unhealthy FoodChange from baseline in relative reinforcing efficacy of unhealthy food at 24 weeks.-0.247 log (food portions + 1)Standard Deviation 0.468
Comparison: A mixed model ANCOVA with unstructured covariance and random effects of participant. Group and cohort were the between subject variables, weeks as the repeated measure and site as a covariatep-value: 0.65Mixed Models Analysis
Comparison: A mixed model ANCOVA with unstructured covariance and random effects of participant. Group and cohort were the between subject variables, weeks as the repeated measure and site as a covariatep-value: <0.0001Mixed Models Analysis

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026