NURS 6208-90L Cell Phone Intervention for you POWER POINT: Cell Phone Intervention for You (CITY): A Randomized, Controlled Trial of Behavioral Weight Los

NURS 6208-90L Cell Phone Intervention for you POWER POINT:

Cell Phone Intervention for You (CITY): A Randomized, Controlled Trial of Behavioral Weight Loss Intervention for Young Adults Using Mobile Technology



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Total Points


Score Cell Phone Intervention for You
(CITY): A Randomized, Controlled
Trial of Behavioral Weight Loss
Intervention for Young Adults Using
Mobile Technology
Laura P. Svetkey1,2, Bryan C. Batch3, Pao-Hwa Lin1,2, Stephen S. Intille4,5, Leonor Corsino3,
Crystal C. Tyson1, Hayden B. Bosworth6,7,8,9, Steven C. Grambow10, Corrine Voils6,9, Catherine
Loria11, John A. Gallis10, Jenifer Schwager1,2, and Gary B. Bennett12,13
Objective: To determine the effect on weight of two mobile technology-based (mHealth) behavioral
weight loss interventions in young adults. Methods: Randomized, controlled comparative
effectiveness trial in 18- to 35-year-olds with BMI25 kg/m2 (overweight/obese), with participants
randomized to 24 months of mHealth intervention delivered by interactive smartphone application on
a cell phone (CP); personal coaching enhanced by smartphone self-monitoring (PC); or Control.
Results: The 365 randomized participants had mean baseline BMI of 35 kg/m2. Final weight was
meas ured in 86% of participants. CP was not superior to Control at any measurement point. PC
participants lost significantly more weight than Controls at 6 months (net effect 21.92 kg [CI 23.17,
20.67], P 5 0.003), but not at 12 and 24 months. Conclusions: Despite high intervention engagement
and study retention, the inclusion of behavioral princi ples and tools in both interventions, and weight
loss in all treatment groups, CP did not lead to weight loss, and PC did not lead to sustained weight
loss relative to Control. Although mHealth solutions offer broad dissemination and scalability, the
CITY results sound a cautionary note concerning intervention deliv ery by mobile applications.
Effective intervention may require the efficiency of mobile technology, the social support and human
interaction of personal coaching, and an adaptive approach to intervention design.
Obesity (2015) 23, 2133-2141. doi:10.1002/oby.21226
Obesity is present in 35% of young adults (defined as age 18-35 years) in the US (1) and deserves
attention: Weight gain is most rapid during these years (2,3); increasing body mass index (BMI) in
young adulthood increases the risk of developing metabolic syndrome over the subsequent 15 years
almost 20-fold (4); and weight gain in early adulthood is also associated with increased coronary
calcification in middle age (5), forecasting future cardio vascular disease (CVD) events. These data
suggest the need for effective and sustainable weight control strategies early in adult life (6).
1 Division of Nephrology, Department of Medicine, Duke University Medical Center, Durham, North
Carolina, USA. Correspondence: Laura P. Svetkey ( 2 Sarah W. Stedman
Nutrition and Metabolism Center, Duke Molecular Physiology Institute, Durham, North Carolina, USA
3 Division of Endocrinology, Metabolism, and Nutrition, Department of Medicine, Duke University
Medical Center, Durham, North Carolina, USA 4 College of Computer and Information Science,
Northeastern University, Boston, Massachusetts, USA 5 Bouve College of Health Sciences,
Northeastern University, Boston, Massachusetts, USA 6 Division of General Internal Medicine,
Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA 7
Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North
Carolina, USA 8 School of Nursing, Duke University Medical Center, Durham, North Carolina, USA 9
Center for Health Services Research in Primary Care, Durham Veterans Affairs Medical Center,
Durham, North Carolina, USA 10 Department of Biostatistics and Bioinformatics, Duke University
Medical Center, Durham, North Carolina, USA 11 Division of Cardiovascular Sciences, National
Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA 12
Department of Psychology & Neuroscience, Duke University, Durham, North Carolina, USA 13 Duke
Obesity Prevention Program, Duke University Medical Center,
Durham, North Carolina, USA.
Funding agencies: The CITY study was sponsored by grant number U01HL096720 from the
National Heart, Lung, and Blood Institute, a component of the National Institutes of Health (NIH).
Disclosure: Dr. Svetkey is a consultant to Oregon Center for Applied Science (ORCAS; Eugene,
Oregon), a health innovation company that creates self-management programs to improve physical
and emotional well-being. Dr. Grambow is a consultant to Gilead Sciences as a member of multiple
DSMBs. Although the relationship is not perceived to represent a conflict with the present work, it
has been included in the spirit of full disclosure. Dr. Bennett is a member of the scientific advisory
board at Nutrisystem and owns shares in Scale Down, a digital weight loss vendor. Additional
Supporting Information may be found in the online version of this article. Received: 21 March 2015;
Accepted: 15 June 2015; Published online 4 November 2015. doi:10.1002/oby.21226
Effective behavioral weight loss strategies involve regular personal contact with a trained
interventionist using behavioral techniques such as self-monitoring and goal setting (6). Evidencebased obesity treatment recommendations endorse high-intensity intervention:
14 in-person interventionist sessions over 6 months (6). However, the optimal behavioral “dose” is
unclear (7), and a smaller effect with lower intensity intervention might be offset by the potential for
increased scalability. In addition, trials testing comprehensive behav ioral approaches have primarily
included middle-aged adults and suggest that intervention is more effective as age increases (8,9).
Thus the potential of personal coaching for weight loss in a younger population is unknown.
Similarly, commercial mobile technology mHealth applications (“apps”) are widely downloaded for
weight loss but have not been rigorously tested for efficacy or effectiveness. Behavior change tech
niques known to produce clinically meaningful weight loss are often absent (10,11), calling into
question whether apps can have the desired effect (12,13).
Because of the potential for scalability and wide dissemination, we sought to determine the weight
loss potential of mobile technology on its own. In order to improve the efficiency of behavior change
methods known to be effective, we tested a low-intensity personal coaching intervention enhanced
by mobile technology. The Cell Phone Intervention for You (CITY) study was a three-arm random
ized trial comparing the effect on weight over 24 months of behavioral intervention that was
delivered almost entirely via a smartphone app of our design (CP) or behavioral intervention deliv
ered through personal coaching enhanced by self-monitoring via smartphone (PC), each compared
to Control. We hypothesized that CP and PC would each be superior to Control. We made no a
priori hypothesis about CP relative to PC.
The CITY study was one of seven trials in the Early Adult Reduc tion of weight through LifestYle
Intervention (EARLY) consortium, sponsored by NHLBI (1U01HL096720). Each EARLY trial was
conducted independently. However, in order to facilitate future com parison, the EARLY trials had
common eligibility criteria, measure ment methods, and primary outcome (14). The design of CITY is
reported elsewhere (15).
CITY was approved by the Duke Institutional Review Board and an NHLBI-appointed Protocol
Review Committee/Data and Safety Monitoring Board (DSMB). Enrollment occurred between
December 2010 and February 2012. Individuals were eligible if they were aged
25 kg/m2), and used 18-35 years, had overweight or obesity (BMI a mobile telephone. For logistical
reasons, participants were required to receive service from either Verizon or AT&T.
Individuals were excluded if they were taking weight loss medica tions or corticosteroids, had weight
loss surgery, weighed more than 440 lbs (the limit of study scales), or had any condition deemed
unsafe for the study. Recruitment occurred primarily by advertising and mass mailings.
Pre-screening assessment occurred by participants’ choice of tele phone, interactive voice response
(IVR), short message service (SMS), or online survey. Participants were further screened by
telephone followed by a face-to-face visit, during which all partici pants provided written informed
Randomization occurred at a separate face-to-face visit within 10 weeks of screening, at which
baseline weight was obtained. Randomization was stratified by gender and BMI (overweight [BMI 30
kg/m2]) with equal allo cation to each treatment group. Intervention lasted 24 months, with data
collection at 6, 12, and 24 months post-randomization.
25 and 10 flights climbed per day
were considered implausible and excluded from analysis.
treatment-by-time interaction. The Holm sequential testing procedure (27) maintained an overall type
I error rate of 0.05 for the analysis of the primary hypotheses.
Secondary study outcomes were analyzed similarly. Models evaluat ing effects in pre-specified race,
sex, and age subgroups as well as post hoc subgroups based on baseline BMI category, income,
and education also include the subgroup variable and its interaction with treatment, with a nominal
type I error rate of 0.05.
Missing data was addressed in our primary statistical modeling approach by maximum likelihood
methods (28). Sensitivity analyses included multiple imputation and a “benchmark” not missing at
ran dom (NMAR) analysis that assumes that CP and PC missing values are similar to those of nonmissing Controls.
Power and sample size calculations were based on estimates from previous behavioral weight loss
trials (29,30): estimated common standard deviation of weight of 16.6 kg at baseline, 0.8 correlation
between weight measurements within individuals, and 25% attrition; and weight gain of 1.5 kg/year
in Controls (3). With these assump tions, a projected sample size of 120 participants per group (N 5
360) provided greater than 80% power at alpha 0.025 to detect a difference in weight change of 5
TABLE 2 Intervention adherence
0-6 months
7-12 months
13-24 months
CP, N (% of randomized) Self-weighing, mean times/week (SD) Number of interactionsa with CITY
PC, N (% of randomized) Self-weighing, mean times/week (SD) Number of interactionsa with CITY
Percent of contacts completedb (SD)
121 (99) 4.0 (1.7) 4.6 (3.0)
115 (96) 2.2 (1.6) 1.8 (1.5)
93.0 (16.4)
115 (94) 3.3 (1.9) 1.5 (1.4)
113 (94) 1.3 (1.4) 0.8 (1.1)
92.3 (20.8)
105 (86) 2.1 (1.7) 0.7 (0.7)
108 (90) 1.0 (1.2) 0.4 (0.6)
87.8 (21.2)
CP, cell phone intervention; PC, personal coaching intervention; SD, standard deviation.
aInteractions include any usage of the CITY app except self-weighing. Total number of app
components tracked in the CP and PC intervention was 31 and 24, respectively. b0- to 6-month data
includes completion percentage for six weekly group sessions.
A total of 365 individuals were randomized. Figure 1 shows the flow of participants through the
study. Weight was obtained at 24 months in 86%. (Those without 24-month data reported slightly
higher income and perceived stress but were otherwise similar to the overall randomized
Study population (Table 1). At entry, mean age was 29.4 years, 69.6% were women, 43.9% were
non-White race (36.2% of total were Black), and 5.8% were Hispanic ethnicity. The majority were
college-educated and employed. Mean baseline BMI was 35.2 kg/m2.
25 to One-fourth of study participants were overweight (BMI
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