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| Name | Class |
|---|---|
| Scaled Insights | INDUSTRY |
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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:
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Story-LCSS |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Voice data | Behavioral | Recorded response to a question about their participation in a weight loss study. |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Personality attributes and values | Extrapolated personality attributes and values as determined by Scaled Insights Behavioural Artificial Intelligence based on subject voice data. These are qualitative non-numerical descriptors. | The voice data measurement will take place at baseline and take about 10-15 minutes for collection to take place. |
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Inclusion Criteria:
Exclusion Criteria:
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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.
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| Name | Affiliation | Role |
|---|---|---|
| Celine Heskey, DrPH | Loma Linda University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Nutrition Research Center, School of Public Health, Loma Linda University | Loma Linda | California | 92350 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 27826445 | Background | 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. | |
| 26444382 | 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. |
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There is not plan currently to share IPD outside of the collaborators that are part of the study. It is possible that requests may be made by students later on to use data for secondary data analysis as part of their academic research projects, and the research collaborators will decide if unidentified IPD would be shared at such a time.
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| The voice data measurement will take at baseline and take about 10-15 minutes for collection to take place. |
| Predicted patient attrition rate | 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. | The voice data measurement will take place at baseline and take about 10-15 minutes for collection to take place. |
| 34585130 | 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. |
| 33472547 | 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. |
| 23537492 | 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. |
| 26807008 | 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. |
| 33567185 | 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. |
| ID | Term |
|---|---|
| D009765 | Obesity |
| D001836 | Body Weight Changes |
| D015431 | Weight Loss |
| ID | Term |
|---|---|
| D050177 | Overweight |
| D044343 | Overnutrition |
| D009748 | Nutrition Disorders |
| D009750 | Nutritional and Metabolic Diseases |
| D001835 | Body Weight |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
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