Background: There is growing interest in the use of information communication technologies to treat obesity. An intervention delivered by smartphone could be a convenient, potentially cost-effective, and wide-reaching weight management strategy. Although there have been studies of texting-based interventions and smartphone applications (apps) used as adjuncts to other treatments, there are currently no randomized controlled trials (RCT) of a stand-alone smartphone application for weight loss that focuses primarily on self-monitoring of diet and physical activity. Objective: The aim of this pilot study was to collect acceptability and feasibility outcomes of a self-monitoring weight management intervention delivered by a smartphone app, compared to a website and paper diary. Methods: A sample of 128 overweight volunteers were randomized to receive a weight management intervention delivered by smartphone app, website, or paper diary. The smartphone app intervention, My Meal Mate (MMM), was developed by the research team using an evidence-based behavioral approach. The app incorporates goal setting, self-monitoring of diet and activity, and feedback via weekly text message. The website group used an existing commercially available slimming website from a company called Weight Loss Resources who also provided the paper diaries. The comparator groups delivered a similar self-monitoring intervention to the app, but by different modes of delivery. Participants were recruited by email, intranet, newsletters, and posters from large local employers. Trial duration was 6 months. The intervention and comparator groups were self-directed with no ongoing human input from the research team. The only face-to-face components were at baseline enrollment and brief follow-up sessions at 6 weeks and 6 months to take anthropometric measures and administer questionnaires. Results: Trial retention was 40/43 (93%) in the smartphone group, 19/42 (55%) in the website group, and 20/43 (53%) in the diary group at 6 months. Adherence was statistically significantly higher in the smartphone group with a mean of 92 days (SD 67) of dietary recording compared with 35 days (SD 44) in the website group and 29 days (SD 39) in the diary group ( P <.001). Self-monitoring declined over time in all groups. In an intention-to-treat analysis using baseline observation carried forward for missing data, mean weight change at 6 months was -4.6 kg (95% CI –6.2 to –3.0) in the smartphone app group, –2.9 kg (95% CI –4.7 to –1.1) in the diary group, and –1.3 kg (95% CI –2.7 to 0.1) in the website group. BMI change at 6 months was –1.6 kg/m 2 (95% CI –2.2 to –1.1) in the smartphone group, –1.0 kg/m 2 (95% CI –1.6 to –0.4) in the diary group, and –0.5 kg/m 2 (95% CI –0.9 to 0.0) in the website group. Change in body fat was –1.3% (95% CI –1.7 to –0.8) in the smartphone group, –0.9% (95% CI –1.5 to –0.4) in the diary group, and –0.5% (95% CI –0.9 to 0.0) in the website group. Conclusions: The MMM app is an acceptable and feasible weight loss intervention and a full RCT of this approach is warranted. Trial Registration: ClinicalTrials.gov NCT01744535; http://clinicaltrials.gov/ct2/show/NCT01744535 (Archived by WebCite at http://www.webcitation.org/6FEtc3PVB) [J Med Internet Res 2013;15(4):e32]

Adherence to a Smartphone Application for Weight Loss Compared to Website and Paper Diary: Pilot Randomized Controlled Trial

Although mortality for cardiovascular disease (CVD) has declined for several decades, heart disease and stroke continue to be the leading causes of death, disability, and high healthcare costs. Unhealthy behaviors related to CVD risk (eg, smoking, sedentary lifestyle, and unhealthful eating habits) remain highly prevalent. The high rates of overweight, obesity, and type 2 diabetes mellitus (T2DM); the persistent presence of uncontrolled hypertension; lipid levels not at target; and the ≈18% of adults who continue to smoke cigarettes pose formidable challenges for achieving improved cardiovascular health.1,2 It is apparent that the performance of healthful behaviors related to the management of CVD risk factors has become an increasingly important facet of the prevention and management of CVD.3

In 2010, the American Heart Association (AHA) made a transformative shift in its strategic plan and added the concept of cardiovascular health.2 To operationalize this concept, the AHA targeted 4 health behaviors in the 2020 Strategic Impact Goals: reduction in smoking and weight, healthful eating, and promotion of regular physical activity. Three health indicators also were included: glucose, blood pressure (BP), and cholesterol. On the basis of the AHA Life’s Simple 7 metrics for improved cardiovascular health, 30% have not reached the target levels for lipids or BP. National Health and Nutrition Examination Survey (NHANES) data revealed that people who met ≥6 of the cardiovascular health metrics had a significantly better risk profile (hazard ratio for all-cause mortality, 0.49) compared with individuals who had achieved only 1 metric or none.2 The studies reviewed in this statement targeted these behaviors (ie, smoking, physical activity, healthful eating, and maintaining a healthful weight) and cardiovascular health indicators (ie, blood …

Current Science on Consumer Use of Mobile Health for Cardiovascular Disease Prevention: A Scientific Statement from the American Heart Association

Control theory and other frameworks for understanding self-regulation suggest that monitoring goal progress is a crucial process that intervenes between setting and attaining a goal, and helps to ensure that goals are translated into action. However, the impact of progress monitoring interventions on rates of behavioral performance and goal attainment has yet to be quantified. A systematic literature search identified 138 studies (N = 19,951) that randomly allocated participants to an intervention designed to promote monitoring of goal progress versus a control condition. All studies reported the effects of the treatment on (a) the frequency of progress monitoring and (b) subsequent goal attainment. A random effects model revealed that, on average, interventions were successful at increasing the frequency of monitoring goal progress (d+ = 1.98, 95% CI [1.71, 2.24]) and promoted goal attainment (d+ = 0.40, 95% CI [0.32, 0.48]). Furthermore, changes in the frequency of progress monitoring mediated the effect of the interventions on goal attainment. Moderation tests revealed that progress monitoring had larger effects on goal attainment when the outcomes were reported or made public, and when the information was physically recorded. Taken together, the findings suggest that monitoring goal progress is an effective self-regulation strategy, and that interventions that increase the frequency of progress monitoring are likely to promote behavior change.

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Does monitoring goal progress promote goal attainment? A meta-analysis of the experimental evidence.

Summary
A systematic review of randomized controlled trials was conducted to evaluate the effectiveness of eHealth interventions for the prevention and treatment of overweight and obesity in adults. Eight databases were searched for studies published in English from 1995 to 17 September 2014. Eighty-four studies were included, with 183 intervention arms, of which 76% (n = 139) included an eHealth component. Sixty-one studies had the primary aim of weight loss, 10 weight loss maintenance, eight weight gain prevention, and five weight loss and maintenance. eHealth interventions were predominantly delivered using the Internet, but also email, text messages, monitoring devices, mobile applications, computer programs, podcasts and personal digital assistants. Forty percent (n = 55) of interventions used more than one type of technology, and 43.2% (n = 60) were delivered solely using eHealth technologies. Meta-analyses demonstrated significantly greater weight loss (kg) in eHealth weight loss interventions compared with control (MD −2.70 [−3.33,−2.08], P < 0.001) or minimal interventions (MD −1.40 [−1.98,−0.82], P < 0.001), and in eHealth weight loss interventions with extra components or technologies (MD 1.46 [0.80, 2.13], P < 0.001) compared with standard eHealth programmes. The findings support the use of eHealth interventions as a treatment option for obesity, but there is insufficient evidence for the effectiveness of eHealth interventions for weight loss maintenance or weight gain prevention.

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eHealth interventions for the prevention and treatment of overweight and obesity in adults: a systematic review with meta-analysis.

Importance Overweight and obesity have been associated with adverse health effects. Objective To systematically review evidence on benefits and harms of behavioral and pharmacotherapy weight loss and weight loss maintenance interventions in adults to inform the US Preventive Services Task Force. Data Sources MEDLINE, PubMed Publisher-Supplied Records, PsycINFO, and the Cochrane Central Register of Controlled Trials for studies published through June 6, 2017; ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry Platform for ongoing trials through August 2017; and ongoing surveillance in targeted publications through March 23, 2018. Studies from previous reviews were reevaluated for inclusion. Study Selection Randomized clinical trials (RCTs) focusing on weight loss or weight loss maintenance in adults. Data Extraction and Synthesis Data were abstracted by one reviewer and confirmed by another. Random-effects meta-analyses were conducted for weight loss outcomes in behavior-based interventions. Main Outcomes and Measures Health outcomes, weight loss or weight loss maintenance, reduction in obesity-related conditions, and adverse events. Results A total of 122 RCTs (N = 62 533) and 2 observational studies (N = 209 993) were identified. Compared with controls, participants in behavior-based interventions had greater mean weight loss at 12 to 18 months (−2.39 kg [95% CI, −2.86 to −1.93]; 67 studies [n = 22065]) and less weight regain (−1.59 kg [95% CI, −2.38 to −0.79]; 8 studies [n = 1408]). Studies of medication-based weight loss and maintenance interventions also reported greater weight loss or less weight regain in intervention compared with placebo groups at 12 to 18 months (range, −0.6 to −5.8 kg; no meta-analysis). Participants with prediabetes in weight loss interventions had a lower risk of developing diabetes compared with controls (relative risk, 0.67 [95% CI, 0.51 to 0.89]). There was no evidence of other benefits, but most health outcomes such as mortality, cardiovascular disease, and cancer were infrequently reported. Small improvements in quality of life in some medication trials were noted but were of unclear clinical significance. There was no evidence of harm such as cardiovascular disease from behavior-based interventions; higher rates of adverse events were associated with higher dropout rates in medication groups than in placebo groups. Conclusions and Relevance Behavior-based weight loss interventions with or without weight loss medications were associated with more weight loss and a lower risk of developing diabetes than control conditions. Weight loss medications, but not behavior-based interventions, were associated with higher rates of harms. Long-term weight and health outcomes data, as well as data on important subgroups, were limited.

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Behavioral and Pharmacotherapy Weight Loss Interventions to Prevent Obesity-Related Morbidity and Mortality in Adults: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force.

Jennifer R. Shapiro

Papers

Text4Diet: a randomized controlled study using text messaging for weight loss behaviors.