K Patrick1, S J Marshall2, E P Davila2, J K Kolodziejczyk3, J H Fowler4, K J Calfas5, J S Huang6, C L Rock5, W G Griswold7, A Gupta8, G Merchant3, G J Norman2, F Raab2, M C Donohue5, B J Fogg9, T N Robinson10. 1. Center for Wireless and Population Health Systems (CWPHS), Qualcomm Institute/Calit2, University of California, San Diego, La Jolla, CA 92093-0628, United States; Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA 92093, United States. Electronic address: kpatrick@ucsd.edu. 2. Center for Wireless and Population Health Systems (CWPHS), Qualcomm Institute/Calit2, University of California, San Diego, La Jolla, CA 92093-0628, United States; Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA 92093, United States. 3. Center for Wireless and Population Health Systems (CWPHS), Qualcomm Institute/Calit2, University of California, San Diego, La Jolla, CA 92093-0628, United States; Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA 92093, United States; Graduate School of Public Health, San Diego State University, San Diego, CA 92182, United States. 4. Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA 92093, United States; Medical Genetics Division and Political Science Department, University of California, San Diego, La Jolla, CA 92093, United States. 5. Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA 92093, United States. 6. Center for Wireless and Population Health Systems (CWPHS), Qualcomm Institute/Calit2, University of California, San Diego, La Jolla, CA 92093-0628, United States; Rady Children's Hospital, San Diego, CA 92123, United States; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, United States. 7. Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, United States. 8. Center for Wireless and Population Health Systems (CWPHS), Qualcomm Institute/Calit2, University of California, San Diego, La Jolla, CA 92093-0628, United States. 9. Behavior Design Lab, Human Sciences and Technologies Advanced Research Institute, Stanford University, Stanford, CA 94305, United States. 10. Division of General Pediatrics, Department of Pediatrics and Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA 94305, United States.
Abstract
PURPOSE: To describe the theoretical rationale, intervention design, and clinical trial of a two-year weight control intervention for young adults deployed via social and mobile media. METHODS: A total of 404 overweight or obese college students from three Southern California universities (M(age) = 22( ± 4) years; M(BMI) = 29( ± 2.8); 70% female) were randomized to participate in the intervention or to receive an informational web-based weight loss program. The intervention is based on behavioral theory and integrates intervention elements across multiple touch points, including Facebook, text messaging, smartphone applications, blogs, and e-mail. Participants are encouraged to seek social support among their friends, self-monitor their weight weekly, post their health behaviors on Facebook, and e-mail their weight loss questions/concerns to a health coach. The intervention is adaptive because new theory-driven and iteratively tailored intervention elements are developed and released over the course of the two-year intervention in response to patterns of use and user feedback. Measures of body mass index, waist circumference, diet, physical activity, sedentary behavior, weight management practices, smoking, alcohol, sleep, body image, self-esteem, and depression occur at 6, 12, 18, and 24 months. Currently, all participants have been recruited, and all are in the final year of the trial. CONCLUSION: Theory-driven, evidence-based strategies for physical activity, sedentary behavior, and dietary intake can be embedded in an intervention using social and mobile technologies to promote healthy weight-related behaviors in young adults.
RCT Entities:
PURPOSE: To describe the theoretical rationale, intervention design, and clinical trial of a two-year weight control intervention for young adults deployed via social and mobile media. METHODS: A total of 404 overweight or obese college students from three Southern California universities (M(age) = 22( ± 4) years; M(BMI) = 29( ± 2.8); 70% female) were randomized to participate in the intervention or to receive an informational web-based weight loss program. The intervention is based on behavioral theory and integrates intervention elements across multiple touch points, including Facebook, text messaging, smartphone applications, blogs, and e-mail. Participants are encouraged to seek social support among their friends, self-monitor their weight weekly, post their health behaviors on Facebook, and e-mail their weight loss questions/concerns to a health coach. The intervention is adaptive because new theory-driven and iteratively tailored intervention elements are developed and released over the course of the two-year intervention in response to patterns of use and user feedback. Measures of body mass index, waist circumference, diet, physical activity, sedentary behavior, weight management practices, smoking, alcohol, sleep, body image, self-esteem, and depression occur at 6, 12, 18, and 24 months. Currently, all participants have been recruited, and all are in the final year of the trial. CONCLUSION: Theory-driven, evidence-based strategies for physical activity, sedentary behavior, and dietary intake can be embedded in an intervention using social and mobile technologies to promote healthy weight-related behaviors in young adults.
Keywords:
API; Health promotion; Internet; Obesity; PA; RCT; SCT; SMS; Short Message Service; Social support; Weight loss; Young adult; application programming interface; physical activity; randomized controlled trial; social cognitive theory
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