Body Weight Changes, Obesity
Conditions
Keywords
artificial intelligence, obesity, weight loss
Brief summary
The goal of this observational study is to learn about the personality attributes and values of people living with obesity that are part of the Latino community, and how these personality attributes and values can help to predict success during a weight loss program. The main questions it aims to answer are: * What are the personality attributes and values of people living with obesity that sign up to the LCSS-Latino Crossover Semaglutide Study trial? * Can behavioral artificial intelligence (a computer formula) predict which patients will complete the LCSS-Latino Crossover Semaglutide Study trial? * How do behavioral artificial Intelligence predictions (a computer formula) compare to clinician predictions of patient success? * Can behavioral artificial intelligence (a computer formula) predict patient weight loss, calorie consumption and physical activity levels during the LCSS-Latino Crossover Semaglutide Study trial? Participants will be recorded in English and Spanish while responding to a question regarding participation in a weight loss study.
Interventions
BEHAVIORALVoice data
Recorded response to a question about their participation in a weight loss study.
Sponsors
Loma Linda University
Scaled Insights
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 74 Years
Inclusion criteria
* Participation in the LCSS-Latino Crossover Semaglutide Study
Exclusion criteria
* Not a participant of the LCSS-Latino Crossover Semaglutide Study at the point of data collection
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Predicted patient weight change success | The voice data measurement will take place at baseline and take about 10-15 minutes for collection to take place. | Predicted patient weight change as determined by Scaled Insights Behavioural Artificial Intelligence based on subject voice data. Weight loss exceeding 5-10 pounds over 6 months will be considered to be successful. Predicted weight change will be compared to the weight change measured in a separate clinical trial \[Latino Crossover Semaglutide Study (LCSS) NCT05087342\]. Similar weight change values between the predicted and measured outcomes will indicate that the Scaled Insights Behavioural Artificial Intelligence is good predictor. |
| Clinician predictions | The clinician judgement will be measured during the second month of the subject's weight loss study. | Clinician (physician) judgement of patient weight loss success during a weight loss study. |
| Predicated patient calorie intake | The voice data measurement will take place during the subject's initial clinic visit and take about 10-15 minutes for collection to take place. | Predicted patient calorie intake as determined by Scaled Insights Behavioural Artificial Intelligence based on subject voice data. Predicted calorie intake will be compared to the calorie intake measured in a separate clinical trial \[Latino Crossover Semaglutide Study (LCSS) NCT05087342\]. Similar calorie values between the predicted and measured outcomes will indicate that the Scaled Insights Behavioural Artificial Intelligence is good predictor. |
| Predicated patient physical activity level | The voice data measurement will take at baseline and take about 10-15 minutes for collection to take place. | Predicted patient physical activity level as determined by Scaled Insights Behavioural Artificial Intelligence based on subject voice data. Predicted physical activity will be compared to the physical activity measured in a separate clinical trial \[Latino Crossover Semaglutide Study (LCSS) NCT05087342\]. Similar physical activity level values between the predicted and measured outcomes will indicate that the Scaled Insights Behavioural Artificial Intelligence is good predictor. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Personality attributes and values | The voice data measurement will take place at baseline and take about 10-15 minutes for collection to take place. | Extrapolated personality attributes and values as determined by Scaled Insights Behavioural Artificial Intelligence based on subject voice data. These are qualitative non-numerical descriptors. |
| Predicted patient attrition rate | The voice data measurement will take place at baseline and take about 10-15 minutes for collection to take place. | Predicted patient attrition rate from the weight loss study as determined by Scaled Insights Behavioural Artificial Intelligence based on subject voice data. Predicted patient attrition rate will be compared to the attrition rate occurring during the weight loss study. Similar attrition rates between the predicted and actual rates will indicate that the Scaled Insights Behavioural Artificial Intelligence is good predictor. |
Countries
United States
Contacts
PRINCIPAL_INVESTIGATORCeline Heskey, DrPH
Loma Linda University
Outcome results
None listed