Special Article

Who’s calling for weight loss? A systematic review of mobile phone weight loss programs for adolescents Catherine A. Wickham and Elena T. Carbone Context: Adolescent overweight and obesity are ongoing public health concerns, and innovative weight loss interventions are needed to reach this age group. Objective: The objective of this systematic review was to assess and synthesize the literature on adolescent weight loss programs that utilize cell phones as an intervention component to reduce weight, as measured by body mass index or body mass index z-score. Data Sources: A systematic review of the literature, consistent with PRISMA guidelines, was undertaken using 11 databases. Study Selection: Studies of weight loss interventions published in peer-reviewed journals in English during the last 10 years were eligible for inclusion if they examined an adolescent population, used validated measures for pre- and post-test weight, identified weight loss as a primary or secondary outcome, and specified use of cell phones to deliver a component of the program. Data Synthesis: While within-group weight loss results were noted, no significant between-group differences were found across the majority of studies reviewed. Cell phone components were embedded within larger weight loss programs, making it difficult to determine their true effect. Conclusions: Cell phone use is ubiquitous and, as such, may offer an interesting addition or alternative to current weight loss programs, particularly for adolescents who are considered digital natives. Future research in this area should be systematic in design so that the true effect of the individual components (i.e., cell phones) can be detected.

INTRODUCTION Adolescent overweight and obesity are significant public health concerns in the United States. Healthy People 2020 identifies childhood and adolescent obesity as leading health indicators and sets a target for improvement of 10% by the year 2020.1 The Centers for Disease Control and Prevention defines adolescent overweight as a body mass index (BMI) greater than or equal to the 85th percentile and less than the 95th percentile for age

and sex and defines obesity as a BMI greater than or equal to the 95th percentile for age and sex.2 The most recent National Health and Nutrition Examination Survey data from 2009 to 2010 indicate that in the United States 19.6% of boys and 17.1% of girls aged 12–19 years were obese.3 Overall, 33.6% of adolescents in the United States have a BMI greater than or equal to the 85th percentile and 18.4% have a BMI greater than

Affiliation: C.A. Wickham and E.T. Carbone are with the Department of Nutrition, University of Massachusetts, Amherst, Massachusetts, USA. Correspondence: E.T. Carbone, Department of Nutrition, University of Massachusetts, Chenoweth Lab, Holdsworth Way, Amherst, MA, 01003, USA. E-mail: [email protected]. Phone: þ1-413-545-1071. Key words: adolescence, cell phone, weight loss C The Author(s) 2015. Published by Oxford University Press on behalf of the International Life Sciences Institute. All rights reserved. For V

Permissions, please e-mail: [email protected]. 386

doi: 10.1093/nutrit/nuu018 Nutrition ReviewsV Vol. 73(6):386–398 R

or equal to the 95th percentile.4 Equally concerning is the portion of adolescents (13.9%) who meet the definition of adult obesity, i.e., BMI 30 kg/m2.4 Young children who are obese are more likely to develop conditions that were once only seen in adults, including high blood pressure,2,5–8 hyperlipidemia,2,6–8 impaired glucose tolerance,2,5–8 type 2 diabetes,2,5,7 sleep apnea,2,5,7 joint problems,2,5 and fatty liver disease.2,5,7 In addition, children may be at risk for a variety of social and psychological problems such as low self-esteem, bullying, and discrimination.2,6,7 Adolescents who are overweight or obese may be at risk for adult overweight/obesity.9,10 A recent systematic review reported that between 22% and 58% of overweight or obese adolescents become overweight or obese as adults.10 Obesity in adults is linked to comorbidities including type 2 diabetes, hypertension, coronary heart disease, and some types of cancer.11 More effective weight loss and weight maintenance programs are needed to help address this problem. Numerous weight loss interventions have been tried with children and adolescents.12–15 The success of these programs varies; however, certain types of programs seem to promote better weight loss outcomes, such as those with behavioral lifestyle components that focus on aspects of dietary intake and physical activity.13–15 In children and adolescents, behavioral interventions of medium (26–75 h/week) to high intensity (>75 h/week) may provide the best results in the short term (defined as 12 months or less).16 Long-term results for weight loss and maintenance are inconsistent and do not show as much promise.16 In young adults aged 18–25 years, behavioral weight loss interventions have also been shown to have a positive impact on self-efficacy and other psychosocial factors.17 Feedback and social interaction are two factors that have been identified as important components of weight loss interventions that may contribute to adherence and better outcomes.18–20 Cell phones provide two-way contact by way of a phone call or text message, increasing access to feedback. In addition, cell phones are gateways to social networking sites, potentially offering an acceptable means of increasing social contact with a young audience. The Pew Internet and American Life Project estimated that 95% of teens aged 12–17 years use the Internet and 93% have access to the Internet at home.21 In addition, 78% of 12- to 17-year-olds have a cell phone, 47% of which are smartphones.21 One in four members of this age group are considered “cell mostly,” meaning they access the Internet primarily through their cell phones.21 Adolescents aged 12–17 years are Nutrition ReviewsV Vol. 73(6):386–398 R

also fervent users of text messaging also referred to as short messaging service (SMS) as a means of communication.22 In 2011, the median number of text messages sent by this group was 60 per day, which is up from 50 just 2 years earlier.22 A Nielsen 2011 data usage report indicated that teens aged 13–17 years sent an average of 3,417 text messages monthly (112/day), with females sending more than 1,000 more messages per month than males.23 The top three reasons for using text messaging were speed, ease, and fun. A 2010 systematic review of technology-based weight loss/weight maintenance programs conducted by the American Heart Association indicated that Internet-based programs may show promise for encouraging weight loss.20 Limitations of the use of technology in weight loss interventions include the following: high attrition rates (>20%); exclusion of data from people who do not finish the trial (failure to use intention-to-treat principle, which biases the data by only including information from those who complete the study); and failure to acknowledge or report adherence to intervention components.18,20 When examining text messaging specifically, it is often difficult to determine a true effect, as texting is often only a single component of a larger intervention.24 Adolescents are at a unique developmental stage socially and cognitively as they are neither biologically children nor fully matured as adults.25,26 During this stage of the life cycle, there is a developmental transition in behaviors, particularly in the area of decision making.26 Behaviors that involve social and cognitive abilities become more established, including processes such as goal-setting.26 Innovative programs are needed to engage this age group more effectively and address their social and cognitive needs and abilities. Cell phones may be an unconventional way to fill this gap by providing adolescents with acceptable ways to remain socially connected to people, messages, and even Web sites and apps. This instant connection may help with self-monitoring of behaviors such as dietary intake and weight and may be used as a motivational tool to encourage and remind participants of a particular behavior. The high rate of cell phone use among adolescents naturally leads to the question: do weight loss programs that use cell phones as the tool for delivery lead to weight loss in adolescents who are obese or overweight, as measured by BMI or BMI z-score? The present systematic review was performed to answer that question, with the specific goal to assess and synthesize the literature on weight loss programs for obese or overweight adolescents aged 12–18 years who utilize cell phones as a tool to reduce weight, as measured by BMI or BMI z-score (Table 1). 387

Table 1 Description of the PICO criteria used in the present review Parameter Population Intervention Comparison Outcomes

Description Overweight or obese adolescents 12–18 y Weight loss program that utilizes a cell phone component as part of the intervention Pre- and post-test weight loss design Reduced BMI or BMI z-score

METHODS Literature sources and search strategy A systematic review of the literature was undertaken from September 2013 through March 2014 using the following 11 databases: Academic Search Premier, CINAHL, Cochrane Library, Google Scholar, Medline Plus, Nursing@Ovid, Physical Education Index, PsycINFO, PubMed/Medline, SPORTDiscus, and Web of Science. Key words used to search the databases included varying combinations of the following: adolescence, adolescent, cell phone, mobile phone, weight loss, weight loss program, overweight, obese, text, text messaging, SMS, and physical activity. The search strategy employed 3 main approaches: 1) literature search using 11 databases relevant to the field of nutrition and physical activity; 2) backward search of articles, i.e., references in relevant works were reviewed; and 3) hand search of literature cited in reviews. In addition, a reference librarian was consulted to verify research procedures and database sources. The systematic review process and manuscript development are consistent with the guidelines of PRISMA (see Appendix S1 in the Supporting Information available in the online version of this article).27

Study selection Inclusion criteria were as follows: articles published in the last 10 years in peer-reviewed journals, English language, abstract and full text, and adolescents (aged 12–18 years) identified as the primary study population. Studies also had to specify use of validated measures for pre- and post-test weight (BMI or BMI z-score), identify weight loss as a primary or secondary outcome, and specify the use of cell phones to deliver a component of the program. The titles and abstracts of 299 articles were reviewed. Of these, 19 articles appeared to meet the inclusion criteria. The full text of these studies was examined further, resulting in the elimination of 12 articles: 4 did not include a weight loss outcome, 2 focused on adults, 2 did not include a cell phone intervention component, 2 studied children aged < 11 years, 1 represented a baseline assessment rather than an assessment of 388

intervention components, and 1 was eliminated based on poor design quality and the fact that human participant approval was noted as not being required despite the study’s clear involvement of humans. One study included telephone calls as part of the weight loss program but did not specify the use of cell phones28. The corresponding author of that study was contacted and verified that cell phones were among the study components examined; therefore, this study was included for review. The reference lists of all included articles were also reviewed, and 1 additional study that met the inclusion criteria was discovered.

Data analysis All included studies were reviewed using the Scottish Intercollegiate Guidelines Network29 critical appraisal checklists, which are used to assess internal validity and potential sources of bias. Differences in pre- and posttest BMI or BMI z-score were reviewed and noted. Study quality was assessed based on a 9-category scoring system adapted from a methodology developed by Downs and Black30 (Table 2) and used in previous systematic reviews.31,32 Studies were first separated into either randomized control trials (RCTs) or cohort studies. Scoring categories for RCTs included the following: 1) individual randomization; 2) control group; 3) technology isolated in the intervention components so that outcome measures could be attributed to technology alone (isolate technology); 4) pre- and post-test design; 5) retention 80%; 6) baseline groups equivalent; 7) missing data reported; 8) sample size calculated; and 9) validated measures used. For cohort studies, individual randomization and control group categories were replaced with more relevant concepts as follows: 1) population source was comparable in all respects other than the factor under investigation and 2) likelihood that some participants might have the outcome of investigation at the time of enrollment was taken into account. Each of the 9 categories was given a score of 11.11 (100 points/9 categories), with a total possible score for all categories combined of 99.99 (11.11  9 categories). A “yes” response resulted in an award of the total point value; a “no” resulted in zero points. Responses of “unknown” also received zero points. The quality of studies was Nutrition ReviewsV Vol. 73(6):386–398 R

Table 2 Criteria used to determine the quality rating of studies Scoring criteria Individual randomization

Control group Isolate technology Pre-test/posttest design Retention

Baseline groups equivalent Missing data

Sample size calculation Validated measures

Description Were participants randomly assigned to study conditions? If so, was randomization at the individual level? Stratified and blocked randomization is acceptable. Studies that used individual randomization combined with a small proportion of randomized matched pairs are also considered “yes”. Appropriately designed and powered group randomization would also be acceptable if group was also unit of analysis. Individual randomization is “no” when the authors fail to mention randomization, to specify that another method of assigning group status was used, or to randomize at the group level and analyze at the individual level Did the study include a comparison group? Comparison group could be a no-treatment, treatment-as-usual, or alternate-treatment group To isolate the technology, the authors had to test the technology alone and test the technology alone and compare with a group with no technology (yes). Packaged intervention in which the technological components cannot be parsed out are coded as not isolating the technology (no) Was assessment of behavior completed pre- and postintervention? Was study retention at least 80% of participants who initially agreed to participate in the study? Retention is calculated for the entire sample and not by group. For studies that did not report retention or dropout rates, retention can be calculated using the sample sizes used for analysis (e.g., 300 randomized but only 250 included in analyses ¼ 83.3% retention) Were tests conducted to determine whether groups were equivalent at baseline regarding important variables (e.g., gender, age, weight)? If no tests mentioned, then unknown/unclear. If subset of tests indicated any group differences at baseline, then ¼ N. Were analyses conducted with consideration for missing data that maintain the fidelity of the randomization (e.g., intent to treat, imputation)? Likewise, case deletion (completer analysis) ¼ N if only analysis conducted. If 100% retention, then completer analysis is appropriate ¼ Y. If authors compared the “dropped subgroup” with the selected or randomized sample but did not consider the impact of the dropped subgroup on randomization (e.g., intent to treat or imputation), then code as N. Was power analysis reported to determine study sample size? If a feasibility or exploratory study for which sample size cannot be calculated beforehand, then N/A. Did the description of measures include reliability and validity information? If reference or coefficients, then Y. If well-established measure known to be validated, then Y. For objective measures without validity evidence, if the objective measure is used as a proxy (e.g., food receipts for nutrition intake), then N. If the objective measure is used as a direct measure of behavior (e.g., food receipts for food purchase), then Y. If validity not reported and measure unknown, then unknown/unclear.

Studies include Down and Black (1998),30 Shaw and Bosworth (2012),31 and Cole-Lewis and Kershaw (2010).32 Abbreviations: N/A, not applicable; N, no; Y, yes.

stratified with high-quality (þþ) studies receiving scores of 99.99–66.67 points, intermediate quality (þ) 66.66–33.34 points, and low quality (0) 33.33–0 points. For example, Nguyen et al.33 received a total score of 88.88 and a rating of þþ after meeting the qualifications for “yes” in 8 of the 9 categories. Tables 1, 2 and 3 illustrate the rating system and provide further details of the categories and criteria. Two independent reviewers (C.A.W. and E.T.C.) examined and rated the articles. Results were compared, discrepancies were discussed, and consensus was reached. RESULTS Design quality and ratings Eight studies (6 RCTs28,33–37 and 2 cohort studies38,39) were included in the final analysis (Figure 1). The overall average rating for the 8 studies28,33–39 was 74%, with an average score of 80% for the RCTs and Nutrition ReviewsV Vol. 73(6):386–398 R

55% for cohorts. Five studies met the criteria of high quality,33–37 with 1 RCT28 and 2 cohort studies38,39 meeting the criteria for intermediate quality. Sample sizes ranged from 30 to 357 (median, n ¼ 147). Four of the 6 RCTs33,35–37 used individual randomization, while 2 of the RCTs28,34 randomized at the group level and could, therefore, not be classified as individual randomization. Five studies33–35,37,38 had retention rates of 80%. Two studies28,36 had retention rates of 50% female participation.28,33–37,39 Six studies enrolled participants who were overweight or obese at baseline.28,33,36–39 The 2 remaining studies did not identify weight as an inclusion criteria.34,35 Seven studies

Inial Search (n=299) Google Scholar (n=1153 of which n=121 reviewed) Web of Science (n=59) Medline Plus (n=41) CINAHL (n=40) PubMed/MEDLINE (n=34) Cochrane Library (n=3) Academic Search Premier (n=1)

Excluded (n=280) Reasons for exclusion: • Not related (n=156) • Adult Populaon (n=40) • Reviews/Meta-Analysis (n=31) • Duplicates (n=26) • Not a weight loss program or did not include cell phone (n=22) • Qualitave Study (n=3) • Full Text Not Available (n=1) • Child Populaon (n=1) • • •

Fully Reviewed (n=19) PubMed/MEDLINE (n=11) Google Scholar (n=7) Academic Search Premier (n=1)

Excluded (n=12) Reasons for exclusion: • No weight loss outcome (n=4) • Adult populaon (n= 2) • No cell phone (n=2) • Child populaon (n=2) • Did not obtain IRB approval (n=1) • Study represented a baseline assessment (n=1)

Reviewed References • Included (n=1)

• • • •

Final Sample (n=8) PubMed/MEDLINE (n=4) Google Scholar (n=2) Academic Search Premier (n=1) Reference (n=1)

Randomized Controlled Trial (n=6)

Cohort Studies (n=2)

Figure 1 Flow chart of search and selection process for studies included in the review Abbreviation: IRB, institutional review board. Nutrition ReviewsV Vol. 73(6):386–398 R

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included a defined weight loss/weight reduction component,28,33,34,36–39 and all 8 studies reported pre- and postintervention BMI or BMI z-scores.28,33–39 Although the study by Newton et al.35 did not have a defined weight loss component, it was included in this review for the following reasons: the intervention focused on physical activity, which is a component of weight loss; it included BMI z-score as an outcome measure; and it had a pre- and postmeasure design. Additional weight-related indicators were used in 7 of the studies.28,33,34,36–39 Five measured percent body fat,28,34,37–39 3 measured hip and/or waist circumference,28,33,36 and 2 measured waist-to-height ratio.33,36 Five studies included physical activity components such as number of steps taken and intensity level of activity,34,35,37–39 and 4 included clinical measures such as blood pressure, hemoglobin A1C, blood glucose, lipid levels, and cardiovascular fitness.28,33,35,36 Psychosocial measures were reported in 4 studies and included items such as perceived body image, self-esteem, and wellbeing.33,34,36,37 A control group was used in all 6 of the RCTs; however, none of the studies used a no-treatment group. Control group participants in 3 studies28,33,36 received a weight loss intervention without a cell phone component; controls in 3 other studies34,35,37 received standard care,35 usual care,37 or, in the case of the school-based program,34 the typical physical activity and nutrition curriculum. The 2 cohort studies38,39 did not use a comparison group. Duration of the RCTs varied from 12 weeks to 2 years, with the cell phone phase varying in duration and frequency.28,33–37 The cell phone component used by Lubans et al.34 took place over 9 months, with weekly text messages sent for a 6month period and biweekly messages sent for the remaining 3 months. In the study by Newton et al.,35 weekly text messages were sent over the entire 12-week intervention. In the intervention reported by Nguyen et al. biweekly telephone coaching was used for 10 months33 and 22 months,36 respectively. Two of the 4arms in the year-long intervention of Patrick et al.37 included either brief bimonthly follow-up phone calls or weekly text messages related to behavioral elements. The cell phone component in Resnicow et al.28 consisted of 4–6 telephone calls made to participants over the 6-month intervention. The duration of the intervention in the 2 cohort studies was 35–36.5 days; however, the use of cell phones was limited to only 1–4 days per participant.38,39 All studies employed a behavioral approach in their intervention and 3 used a theoretical model to guide the development of all34,37 or part28 of the intervention. For instance, the Nutrition and Enjoyable Activity for Teen Girls program by Lubans et al.34 was based on social 392

cognitive theory and included behavioral aspects such as goal-setting and self-monitoring. Goal-setting for steps taken was also included in the intervention designed by Newton et al.35 The Loozit program reported on after 12 and 24 months by Nguyen et al.33,36 was a lifestyle intervention that monitored self-reported behavioral data but it did not specify if self-monitoring activities were part of the program. Patrick et al.37 used a behavioral approach for the intervention guided by both the behavioral determinants model and the transtheoretical model. The Go Girls program by Resnicow et al.28 involved behavioral changes targeted to goals set by the participants. Phone calls in the Go Girls program utilized motivational interviewing and focused on participants’ personal goals; however, self-monitoring behaviors were not specifically mentioned.28 The 2 cohort studies by Schiel et al.38,39 included self-reported physical activity and food intake. In these studies, personal goals (activity and energy intake) were tailored to participants, and cell phone technology provided real-time monitoring of physical activity.38,39 How cell phone components were actually used in the intervention varied across studies. Patrick et al.,37 Nguyen et al.,33,35 Newton et al.,35 and Lubans et al.34 reported the use of cell phones for text messages; 4 studies28,33,36,37 used cell phones to provide coaching or counseling. The 2 cohort studies38,39 used cell phones to track food intake and physical activity. The 4 studies that used cell phone calls for coaching or counseling28,33,36,37 were the only ones that clearly provided direct 2-way communication between researchers or a health professional and participants. In general, there was a lack of sufficient data on the actual components of cell phone intervention components. In addition, the number of messages (by day/week/program), time of day, duration of contact, and content of messages/contact were infrequently reported. Five studies reported the number of text messages sent or phone calls made over the course of the program.28,33–36 No study reported the duration of contact for either text messages or phone calls, and only 328,34,35 made any mention of the actual content of the messages or contact. Weight loss All studies measured and tracked BMI or BMI z-score in a pre- and post-test format. A number of additional weight-related indicators were used in 7 of the 8 studies, including waist and/or hip circumference,28,33,36 waist-to-height ratio,33,36 and body fat percentage.28,34,37–39 In all cases, validated and reliable measures were used. Weight loss, reduced BMI, and/or reduced BMI z-score were reported in all studies except that of Newton et al.35 Lubans et al.34 reported a Nutrition ReviewsV Vol. 73(6):386–398 R

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Sample

BMI mean (standard error) for baseline, 6 and 12 months Web only 2.2 (0.07), 2.1 (0.08), 2.1 (0.09); Web site and SMS 2.2 (0.07), 2.1 (0.08), 2.1 (0.09); Web site and group 2.2 (0.07), 2.2 (0.08), 2.0 (0.09); usual care 2.2 (0.07), 2.2 (0.08), 2.2 (0.09) Moderate-intensity Primary: BMI, BMI mean (standard deviation) group received 6 inweight, waist for baseline and 6 months; person behavioral ac- and hip moderate intensity 33.2 (7.3) tivity sessions circumference and 33.6 (7.8), high intensity 32.0 (5.8) and 31.9 (5.5)

Intervention group inTelephone coaching, e- Loozit group standard cluded Loozit group pro- mails, and/or SMS program gram plus additional text messages therapeutic contact that included telephone coaching, e-mails, and text messages Web only, Web þ 90 min Phone/cell phone Received print materials Primary: BMI zmonthly group session, and encouraged to score Web þ text messages attend 3 group sent weekly, usual care session

Two-group RCT, preHigh-intensity group rePhone/cell phone and post-test, 6 ceived motivational inmonths, follow-up at terviewing calls by 1 year from baseline telephone/cell phone and used two-way paging device to receive and send messages Abbreviations: BMI, body mass index; CI, confidence interval; RCT, randomized control trial; SMS, short message service.

Resnicow n ¼ 147, age range 12–16 y, all female, et al. mean BMI 33 (2005)28

n ¼ 101, age range Four-group RCT, prePatrick 12–16 y, 63% female, and post-test, 12 et al. mean BMI ¼ 97.6 months (2013)37 percentile

n ¼ 151, age range Two-group RCT, preNguyen 13–16 y, 51.7% feand post-test, 24 et al. male, mean BMI 30.8 months (2013)36

Standard care

Results

Mean change in BMI (adjusted mean difference: 0.19; 95% CI: 0.70 to 0.33), BMI z-score (mean: 0.08; 95% CI: 0.20 to 0.04), and body fat % (mean: 1.09; 95% CI: 2.88 to 0.70) Secondary: BMI Mean changes in BMI z-score z-score between baseline and followup; intervention 0.006 (95% CI: 0.07 to 0.09), control 0.16 (95% CI:0.08 to 0.11) Primary: BMI, Mean group difference between BMI z-score, baseline and 12 months BMI weight, waist (0.1; 95% CI: 1.2 to 1.3); BMI circumference z-score (0.00; 95% CI: 0.11 to 0.10); waist circumference (1.7; 95% CI: 1.4 to 4.8); waist-to-height ratio (0.01; 95% CI: 0.01 to 0.22). Primary: BMI z Mean group difference between –score, weight, baseline and 24 months BMI waist-to(0.01; 95% CI: 0.11 to height ratio 0.10)

Weight-related outcomes and measures Standard nutrition and Primary: BMI, physical activity body fat % curriculum

Control

Intervention group inTelephone coaching, Loozit group standard cluded Loozit group pro- e-mails, and/or SMS program gram plus additional text messages therapeutic contact that included telephone coaching, e-mails, and text messages

Received a motivational Text message text message each week to wear pedometer and be active

Intervention

Delivery method for cell phone component Two-group RCT, preNutrition workshops, physi- Text messages and post-test, with 12 cal activity sessions, pemonth follow-up dometers, and text messaging for social support

Research design/ duration

n ¼ 78, age range Two-group RCT, preNewton 11–18 y, 54% female, and post-test, 12 et al. mean BMI z-score: weeks (2009)35 0.64 for control, 0.62 for intervention n ¼ 151, age range Two-group RCT, preNguyen 13–16 y, 51.7% feand post-test, 12 et al. male, mean BMI 30.8 months (2012)33

n ¼ 357, age range Lubans 12–14 y, all female, et al. mean BMI 22.64 (2012)34

Reference

Table 5 Characteristics of randomized controlled trials examining weight loss interventions utilizing cell phones for adolescents

Table 6 Characteristics of cohort studies examining weight loss interventions utilizing cell phones for adolescents Reference

Sample

Research design/ Intervention duration

Delivery method for WeightResults technology related outcomponent comes and measures Schiel et al. n ¼ 30, mean age Quasi-experimen- Patients enrolled in a Mobile motion sen- Primary: BMI Mean weight reduc14 y, 47% fetal study with (2010)38 structured treatment sor board embedtion of 8.1 kg; sigmale, mean BMI cohort design; and teaching ded in cell phone nificant decrease in 32.7 technology com- program to track physical BMI, BMI–standard ponent lasted activity; in addideviation score, 1–4 days per tion, cell phones body fat mass, and participant used to take picpercentage of body tures of foods fat (all P < 0.001); eaten these results cannot be attributed to technology component Schiel et al. n ¼ 124, mean age Quasi-experimen- Patients enrolled in a Mobile motion sen- Primary: BMI Mean weight reduc13.5 y, 56% fetal study with structured treatment sor board embedtion of 7.1 kg; sig(2012)39 male, mean BMI cohort design; and teaching ded in cell phone nificant decreases 31.3 technology com- program to track physical (P < 0.01) also ponent lasted activity; in addifound in BMI and 1–4 days per tion, cell phones BMI–standard deviparticipant used to take pication score tures of foods eaten Abbreviations: BMI, body mass index.

nonsignificant reduction in BMI (mean: 0.19; 95% confidence interval [CI]: 0.70 to 0.33), BMI z-score (mean: 0.08; 95% CI: 0.20 to 0.04), and body fat percentage (mean: 1.09; 95% CI: 2.88 to 0.70) between the intervention and the control groups at 12 months. Despite the nonsignificant results in this study, changes in BMI, BMI z-score, and body fat percentage were reported in favor of the intervention group. At 12 months in the Loozit trial, Nguyen et al.33 found overall significant but modest time difference reductions in BMI zscore (mean: 0.09; 95% CI: 0.12 to 0.06) and waist-to-height ratio (mean: 0.02; 95% CI: -0.03 to 0.01) and at 24 months the significant reductions in BMI z-score and waist-to-height ratio were maintained in both the intervention (mean: 0.13; 95% CI: 0.20 to 0.06) and the control groups (mean: 0.02; 95% CI: 0.03 to 0.01).36 No between-group effect was detected in the Loozit trial at 12 or 24 months.33,36 Newton et al.35 showed no reduction in BMI z-score within groups and a nonsignificant between-group difference (mean: 0.009; 95% CI: 0.13 to 0.12). Participants in the study of Patrick et al.37 had a very small reduction in mean BMI z-scores (Web site, 0.1; Web site and short messaging service, 0.1; Web site and group sessions, 2.0) and mean BMI percentile (Web site, 0.9; Web site and SMS, 0.8; Web site and group session, 0.2) from baseline to 12 months in the 3 intervention groups. When intervention arms were analyzed based on a group by time interaction against the usual 394

care group, there were no statistically significant results. Resnicow et al.28 noted a favorable but not statistically significant net difference of 0.5 BMI units, 1.8 pounds, and 1.1% body fat in the intervention group at 6 months. At the 1-year follow-up, no statistically significant differences were found between groups.28 The intervention group was analyzed for a dose response at 6 months and 1 year.28 When participants were stratified based on attendance at in-person sessions, significant reductions (P < 0.01) in BMI, hip circumference, and body fat percentage were noted in those who attended at least 75% of the 20–26 weekly sessions over a 6month period.28 Overall, no significant differences between intervention and control groups were found for any weight loss measures (BMI, BMI z-score, waist and/or hip circumference, waist-to-height ratio, and body fat percentage) in the RCTs.28,33–37 In contrast, significant results were found for weight and weight-related outcomes in the cohort studies.38,39 Schiel et al.38 revealed significant reductions (P < 0.001) for pre- and post-test assessments of weight, BMI, BMI–standard deviation score, total body fat mass, and percentage body fat. Stronger personal motivation for participating in an intervention program, as reported on a self-reported questionnaire (P < 0.001), was associated with better weight loss results.38 Schiel et al.39 also found a significant reduction (P < 0.01) in body weight, BMI, BMI–standard deviation score, and body fat mass at follow-up. Nutrition ReviewsV Vol. 73(6):386–398 R

DISCUSSION Adolescents are in a unique age group, bridging childhood and adulthood. Innovative weight loss interventions that appeal to this age group are needed if obesity is to be reduced. Use of technology such as cell phones is pervasive among adolescents and several studies have examined the use of text messaging as an adjunct to weight loss programs for this population.40,41 These studies revealed that text messaging is a feasible intervention strategy that is enjoyed and accepted by adolescents40,41; however, the studies did not measure changes in BMI as a result of the use of text messaging. While only 8 studies met the very narrow inclusion criteria of the present review, there has been research on this topic in other populations. For instance, a recently conducted review of text messaging as an intervention medium for weight loss included 14 studies and showed that text messaging was a feasible and acceptable means of delivery of weight loss education for adults.31 More than three quarters (n ¼ 11) of the studies in that review reported a significant weight loss effect.31 Current weight loss guidelines for overweight or obese adolescents promote a reduction in weight or weight gained during healthy growth and development.42–44 This is achieved through a staged approach that is based on individual factors including BMI percentile and focuses on promoting behaviors that reduce energy imbalance.42–44 Because of normal growth and development, the actual weight of the individual may not always appear to be reduced. To account for this, weight studies of adolescents often utilize the BMI zscore to indicate where the individual falls relative to the mean of the group.45 Use of the BMI z-score allows for weight changes that occur as part of normal development. In the studies reviewed here, use of cell phones as a component of a weight loss intervention for adolescents showed inconsistent results. While a reduction in overall BMI and/or BMI z-score was reported in 7 studies,28,33,34,36–39 significant results were not found between groups in any study. All RCTs except that of Newton et al.35 found a reduction in BMI or BMI zscore.28,33,34,36–39 Both cohort studies showed an actual weight loss reduction; Schiel et al. reported a mean weight loss of 8.1 6 2.4 kg in 201038 and a mean weight loss of 7.1 6 3.0 kg in 2012.39 However, based on the importance of allowing for normal growth and development, the emphasis for adolescents should remain on the BMI or BMI–standard deviation score, both of which showed significant decreases in the cohort studies. No study tested the cell phone component separately, which makes it difficult to determine the true weight loss effect related to the use of cell phone Nutrition ReviewsV Vol. 73(6):386–398 R

technology. In addition, none of the studies in this review included a control group that received no treatment. This may reduce the significance of betweengroup results because all study groups took part in some type of weight loss/activity program. Few studies have examined the effect of text messaging and cell phone–mediated intervention on weight loss. One such study published in 2009 examined cell phone use among 65 adults and found statistically significant (P ¼ 0.02) between-group differences in favor of the intervention following adjustment for sex, age, and initial weight status at the 4 month follow-up.46 A slightly larger study, also published in 2009, of a weight loss program delivered by cell phone in an adult population (n ¼ 125) indicated significant (P < 0.0001) weight reduction at each follow-up time point (3, 6, 9, and 12 months) for the intervention group.47 In addition, at 12 months there was a statistically significant difference (P ¼ 0.006) in the degree of weight reduction in the intervention group versus the control group.47 Participants also reported that the use of mobile phones, in particular, was one of the most popular features.47 Authors of this study noted the importance of using theoretical models in the development of behavioral interventions and posited that use of technology may positively influence self-efficacy, thereby increasing use and adherence to weight loss programs.47 Self-monitoring as a means to increase self-efficacy may be the cornerstone of behavioral weight loss interventions among adolescents and adults. Previous research indicates that successful weight loss and longterm weight maintenance can be improved with selfmonitoring behaviors such as tracking food intake, daily weighing, and maintaining physical activity logs.48,49 Cell phone technologies may allow for easy integration of these types of activities. Therefore, behavioral weight loss interventions may be enhanced by the use of cell phones.32 A 2010 systematic review of 17 studies examined text messaging as a tool for behavior change in disease prevention and management in adolescents and adults and found that text messaging was useful in behavioral interventions, including those that involved weight loss.32 All 8 studies reviewed here28,33–39 included a behavioral approach; however, only 3 of the RCTs34,35,37 explicitly indicated any form of continuous self-monitoring, i.e., tracking physical activity. The two cohort studies38,39 did use cell phones to track physical activity in real time; however, the food photos taken by participants were used by researchers for a comparison against self-reported intake and not necessarily as a self-monitoring activity. Over- and under-reporting are often concerns in weight loss programs. It has been reported that 395

overweight/obese participants tend to underestimate weight and dietary intake.50,51 All studies in this review utilized valid and reliable clinical measures to assess weight, BMI, and additional weight loss measures.28,33–39 This would help mitigate concerns of bias that often accompany self-reports of height, weight, and BMI. Other aspects of the studies did rely on self-monitoring including tracking of physical activity and dietary intake. Lubens et al.34 noted that despite providing several methods to improve the outcomes of self-monitoring, 53.5% of participants could not provide usable accelerometer data at baseline and 24.9% could not provide these data post-test. Newton et al.35 acknowledged the potential biases of self-report of physical activity and its impact on the reliability and overestimation of actual physical activity and the overall use of pedometers. Nguyen et al.33,36 addressed the limitation of selfreported behavioral data and indicated that even if the behaviors were not fully incorporated into practice, improvements did reflect an increase in knowledge from validated food frequency questionnaires and activity surveys. Patrick et al.37 recognized that self-reported behavioral measures (diet, physical activity, etc.) could negatively impact the overall results and noted this as a limitation. Finally, the two cohort studies recognized that there are differences between self-assessments and objective measures regarding physical activity and dietary intake.38,39 To address these biases, the authors felt they developed a program to help participants provide more realistic self-assessments.38,39 Most studies in some way acknowledged the limitations of self-reporting, but no study provided specific details on actual methods incorporated to mitigate this bias; as such, this remains a methodological limitation. The effect of cell phone use, i.e., text messaging, in behavioral weight loss programs is still not clear.52 However, in an effort to keep pace with the increased use and acceptability of cell phones, continued research is warranted.52 Cell phone use may make current weight loss behaviors such as tracking weight and physical activity easier. It is estimated that 60% of US adults track weight loss–related activities including weight, diet, and exercise.53 It is further estimated that 91% of adults and 78% of adolescents own a cell phone.53 Currently, 21% of cell phone owners use their device to track health indicators.54 Cell phones may, therefore, offer an acceptable and realistic means of monitoring weight-related components such as weight, food intake, and physical activity. All 6 RCTs28,33–37 reported a power analysis on the primary outcome; however, only 528,33,34,36,37 reported a power analysis to determine a weight loss (BMI or BMI z-score) effect. Two of these studies33,36 lacked details such as standard deviation to calculate effect size. 396

Furthermore, the initial standard deviation used to calculate effect size in 2 of the studies that did report this information28,34 varied greatly from the actual standard deviation at baseline or follow-up. In addition, none of the RCTs met their sample size calculations, although 3 were only between 3 and 6 participants short of meeting enrollment goals.28,34,37 Additionally, 2 of the RCTs28,36 reported