Clinical trial NCT02997943: SMART Weight Loss Management
Official title: SMART Weight Loss Management
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Weight Loss
Keywords: Obesity
Weight Loss
Diet
Physical Activity
Mobile Health
Lead sponsor: Northwestern University
Sponsor class: OTHER
Collaborators: name: University of Michigan
name: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
First posted: 2016-12-20
Enrollment: 400
Sex: ALL
Minimum age (years): 18
Maximum age (years): 60
Standard ages: ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: FACTORIAL
Primary purpose: TREATMENT

Brief summary: The overall objective of this study is to use an innovative experimental approach, the SMART (Sequential Multiple Assignment Randomized Trial), to determine the best way to sequence the delivery of mHealth tools and traditional treatment components in a stepped program of obesity treatments. The SMART approach is a highly efficient strategy for identifying and constructing efficacious adaptive interventions: it accommodates sequential decision-making based on the participant's response to early weight loss treatment components. The proposed treatment package begins with the least expensive components, and for participants identified as treatment non-responders, provides sequential step-up of additional treatment components. By sequentially delivering treatment components based on participant response, SMART permits achievement of the target outcome, weight loss, with least resource consumption and participant burden.

Detailed description: The proposed study seeks to develop an effective, resource-sensitive strategy to manage weight loss treatment for obesity. During the 12-week active intervention phase, participants will be randomized to one of two first-line treatments: 1) mobile app alone (APP) or 2) app plus weekly telephone coaching sessions (APP+C). Beginning at week 2, those who are identified as treatment non-responders (did not lose an average of 0.5 lbs/week) will be re-randomized to one of two augmentation tactics: 1) Modest Step-up, operationalized as the addition of an additional mHealth component, or 2) Vigorous Step-up, operationalized as the addition of an additional mHealth component plus a traditional weight loss intervention component. Treatment responders will continue with the same first-line treatment. Treatment response be re-assessed at weeks 4 and 8. Those identified as treatment non-responders will be re-randomized at that time.

Eligibility criteria: Inclusion Criteria:

* 18 through 60 years old
* BMI between 27 - 45 kg/m2
* \< 350 lbs
* Weight stable (no loss or gain \>25 lbs. for the past 6 months)
* Interested in losing weight and not enrolled in a formal weight loss program or taking medications or supplements that may cause weight change
* Own a Smartphone and be willing to install the SMART App
* Reside in the Chicago area for the duration of their participation (12 months)

Exclusion Criteria:

* Unstable medical conditions (uncontrolled hypertension, diabetes - uncontrolled or treated with insulin, uncontrolled hypothyroidism, unstable angina pectoris, transient ischemic attack, cancer undergoing active treatment, cerebrovascular accident or myocardial infarction within the past six months, or Crohn's disease)
* Pregnancy, lactation, or intended pregnancy
* Active suicidal ideation, anorexia, bulimia, binge eating disorder, current substance abuse or dependence (besides nicotine dependence)
* Require assistive device for mobility or current condition that may limit or prevent participation in moderate activity
* Use of pacemaker or other electrical implanted device
* History of bariatric (or LapBand) surgery, or considering or currently on a wait-list for bariatric or LapBand surgery
* May not live with a current or past SMART study participant

Primary outcomes:
measure: Change in weight at 6 months
timeFrame: baseline, 6 months
Secondary outcomes:
Other outcomes:
measure: Change in weight at 12 months
timeFrame: baseline, 12 months
measure: Cost effectiveness
timeFrame: 12 months
Locations:
city: Chicago
country: United States
facility: Northwestern University
state: Illinois
Study officials:
affiliation: Northwestern University
name: Bonnie Spring, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: University of Michigan
name: Inbal Nahum-Shani, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Pfammatter AF, Nahum-Shani I, DeZelar M, Scanlan L, McFadden HG, Siddique J, Hedeker D, Spring B. SMART: Study protocol for a sequential multiple assignment randomized controlled trial to optimize weight loss management. Contemp Clin Trials. 2019 Jul;82:36-45. doi: 10.1016/j.cct.2019.05.007. Epub 2019 May 23.
citation: Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, Murphy SA. Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Ann Behav Med. 2018 May 18;52(6):446-462. doi: 10.1007/s12160-016-9830-8.
citation: Spring B, Pfammatter A, Alshurafa N. First Steps Into the Brave New Transdiscipline of Mobile Health. JAMA Cardiol. 2017 Jan 1;2(1):76-78. doi: 10.1001/jamacardio.2016.4440. No abstract available.
citation: Spring B, Stump T, Penedo F, Pfammatter AF, Robinson JK. Toward a health-promoting system for cancer survivors: Patient and provider multiple behavior change. Health Psychol. 2019 Sep;38(9):840-850. doi: 10.1037/hea0000760.
citation: Walton A, Nahum-Shani I, Crosby L, Klasnja P, Murphy S. Optimizing Digital Integrated Care via Micro-Randomized Trials. Clin Pharmacol Ther. 2018 Jul;104(1):53-58. doi: 10.1002/cpt.1079. Epub 2018 Apr 19.
citation: Almirall D, Kasari C, McCaffrey DF, Nahum-Shani I. Developing Optimized Adaptive Interventions in Education. J Res Educ Eff. 2018;11(1):27-34. doi: 10.1080/19345747.2017.1407136. Epub 2017 Nov 29.
citation: Wagner B 3rd, Liu E, Shaw SD, Iakovlev G, Zhou L, Harrington C, Abowd G, Yoon C, Kumar S, Murphy S, Spring B, Nahum-Shani I. ewrapper: Operationalizing engagement strategies in mHealth. Proc ACM Int Conf Ubiquitous Comput. 2017 Sep;2017:790-798. doi: 10.1145/3123024.3125612.
citation: Welch WA, Spring B, Phillips SM, Siddique J. Moderating Effects of Weather-Related Factors on a Physical Activity Intervention. Am J Prev Med. 2018 May;54(5):e83-e89. doi: 10.1016/j.amepre.2018.01.025. Epub 2018 Mar 15.
citation: Booth JN 3rd, Allen NB, Calhoun D, Carson AP, Deng L, Goff DC Jr, Redden DT, Reis JP, Shimbo D, Shikany JM, Sidney S, Spring B, Lewis CE, Muntner P. Racial Differences in Maintaining Optimal Health Behaviors Into Middle Age. Am J Prev Med. 2019 Mar;56(3):368-375. doi: 10.1016/j.amepre.2018.10.020.
citation: Nahum-Shani I, Ertefaie A, Lu XL, Lynch KG, McKay JR, Oslin DW, Almirall D. A SMART data analysis method for constructing adaptive treatment strategies for substance use disorders. Addiction. 2017 May;112(5):901-909. doi: 10.1111/add.13743. Epub 2017 Feb 18.
citation: Lu X, Nahum-Shani I, Kasari C, Lynch KG, Oslin DW, Pelham WE, Fabiano G, Almirall D. Comparing dynamic treatment regimes using repeated-measures outcomes: modeling considerations in SMART studies. Stat Med. 2016 May 10;35(10):1595-615. doi: 10.1002/sim.6819. Epub 2015 Dec 6.
citation: Spring B, Champion KE, Acabchuk R, Hennessy EA. Self-regulatory behaviour change techniques in interventions to promote healthy eating, physical activity, or weight loss: a meta-review. Health Psychol Rev. 2021 Dec;15(4):508-539. doi: 10.1080/17437199.2020.1721310. Epub 2020 Feb 17.
citation: Ertefaie A, Wu T, Lynch KG, Nahum-Shani I. Identifying a set that contains the best dynamic treatment regimes. Biostatistics. 2016 Jan;17(1):135-48. doi: 10.1093/biostatistics/kxv025. Epub 2015 Aug 3.