Does Personality Predict Patient Adherence, Health Behaviors, and Weight Loss Outcomes During the Latino Crossover Semaglutide Study (LCSS)? (Story-LCSS Project)

NCT05622045 | Active Not Recruiting

• 1 other identifier: 5220330
• Study Type: observational
• Target: 59
• Locations: 1 country
• Sites: 1

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.

Conditions

Obesity; Body Weight Changes

Keywords

artificial intelligence; obesity; weight loss

Outcome Measures

Primary Outcomes (4)

• Predicted patient weight change success

  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.

  The voice data measurement will take place at baseline and take about 10-15 minutes for collection to take place.

• Clinician predictions

  Clinician (physician) judgement of patient weight loss success during a weight loss study.

  The clinician judgement will be measured during the second month of the subject's weight loss study.

• Predicated patient calorie intake

  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.

  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.

• Predicated patient physical activity level

  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.

  The voice data measurement will take at baseline and take about 10-15 minutes for collection to take place.

Secondary Outcomes (2)

• Personality attributes and values

  The voice data measurement will take place at baseline and take about 10-15 minutes for collection to take place.

• Predicted patient attrition rate

  The voice data measurement will take place at baseline and take about 10-15 minutes for collection to take place.

Study Arms (1)

Story-LCSS

Behavioral: Voice data

Interventions

Voice data BEHAVIORAL

Recorded response to a question about their participation in a weight loss study.

Story-LCSS

Eligibility Criteria

• Age: 18 Years - 74 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Hispanic men and women, 18 to 74 years of age, with obesity, residing in Southern California, who are taking part in the LCSS-Latino Crossover Semaglutide Study.

You may qualify if:

• Participation in the LCSS-Latino Crossover Semaglutide Study

You may not qualify if:

• Not a participant of the LCSS-Latino Crossover Semaglutide Study at the point of data collection

Sponsors & Collaborators

• Loma Linda University: lead
• Scaled Insights: collaborator

Study Sites (1)

Nutrition Research Center, School of Public Health, Loma Linda University

Loma Linda, California, 92350, United States

Location

Related Publications (7)

• Alberga AS, Russell-Mayhew S, von Ranson KM, McLaren L. Weight bias: a call to action. J Eat Disord. 2016 Nov 7;4:34. doi: 10.1186/s40337-016-0112-4. eCollection 2016.

  PMID: 27826445 BACKGROUND

• Dalle Grave R, Calugi S, Compare A, El Ghoch M, Petroni ML, Tomasi F, Mazzali G, Marchesini G. Weight Loss Expectations and Attrition in Treatment-Seeking Obese Women. Obes Facts. 2015;8(5):311-8. doi: 10.1159/000441366. Epub 2015 Oct 8.

  PMID: 26444382 BACKGROUND

• Flint SW, Leaver M, Griffiths A, Kaykanloo M. Disparate healthcare experiences of people living with overweight or obesity in England. EClinicalMedicine. 2021 Sep 15;41:101140. doi: 10.1016/j.eclinm.2021.101140. eCollection 2021 Nov.

  PMID: 34585130 BACKGROUND

• Flint SW, Piotrkowicz A, Watts K. Use of Artificial Intelligence to understand adults' thoughts and behaviours relating to COVID-19. Perspect Public Health. 2022 May;142(3):167-174. doi: 10.1177/1757913920979332. Epub 2021 Jan 21.

  PMID: 33472547 BACKGROUND

• Hardcastle SJ, Taylor AH, Bailey MP, Harley RA, Hagger MS. Effectiveness of a motivational interviewing intervention on weight loss, physical activity and cardiovascular disease risk factors: a randomised controlled trial with a 12-month post-intervention follow-up. Int J Behav Nutr Phys Act. 2013 Mar 28;10:40. doi: 10.1186/1479-5868-10-40.

  PMID: 23537492 BACKGROUND

• Puhl RM, Phelan SM, Nadglowski J, Kyle TK. Overcoming Weight Bias in the Management of Patients With Diabetes and Obesity. Clin Diabetes. 2016 Jan;34(1):44-50. doi: 10.2337/diaclin.34.1.44. No abstract available.

  PMID: 26807008 BACKGROUND

• Wilding JPH, Batterham RL, Calanna S, Davies M, Van Gaal LF, Lingvay I, McGowan BM, Rosenstock J, Tran MTD, Wadden TA, Wharton S, Yokote K, Zeuthen N, Kushner RF; STEP 1 Study Group. Once-Weekly Semaglutide in Adults with Overweight or Obesity. N Engl J Med. 2021 Mar 18;384(11):989-1002. doi: 10.1056/NEJMoa2032183. Epub 2021 Feb 10.

  PMID: 33567185 BACKGROUND

MeSH Terms

Conditions

Obesity; Body Weight Changes; Weight Loss

Condition Hierarchy (Ancestors)

Overweight; Overnutrition; Nutrition Disorders; Nutritional and Metabolic Diseases; Body Weight; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Study Officials

• Celine Heskey, DrPH

  Loma Linda University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 7, 2022
• First Posted: November 18, 2022
• Study Start: February 1, 2023
• Primary Completion: May 29, 2024
• Study Completion: May 1, 2026
• Last Updated: August 7, 2025

Record last verified: 2025-08

Locations

US (1)