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The use of apps to tackle overweight and obesity by tracking physical and dietary patterns and providing recommendations and motivation strategies to achieve personalized goals has increased over recent years. However, evidence of the efficacy, effectiveness, and safety of these apps is severely lacking.
The aim of this study was to identify efficacy, safety, and effectiveness criteria used to assess weight control, overweight, and obesity management in mobile health (mHealth) interventions through a systematic review.
PubMed, PsycINFO, Scopus, UK Trial Database, ClinicalTrials.gov, and the Cochrane Library were surveyed up to May 2018. All types of clinical studies were considered. A total of 2 independent reviewers assessed quality using Scottish Intercollegiate Guidelines Network (SIGN) criteria. Ratings were used to provide an overall score for each study (low, moderate, or high). Data were synthesized in evidence tables.
From 233 potentially relevant publications, only 28 studies were included. Of these, 13 (46%) were randomized control trials, 11 were single-arm studies (39%), 3 were nonrandomized controlled trials (11%), and 1 study was a cluster randomized trial (4%). The studies were classified as low (15), high (7), and moderate (6) quality according to SIGN criteria. All studies focused on efficacy, with only 1 trial mentioning safety and another 1 effectiveness. In 11 studies, the apps were used as stand-alone interventions, the others were multicomponent studies that included other tools for support such as sensors or websites. The main management tool included in the apps was feedback messaging (24), followed by goal-setting mechanisms (20) and self-monitoring (19). The majority of studies took weight or body mass index loss as the main outcome (22) followed by changes in physical activity (14) and diet (12). Regarding outputs, usability, adherence, and engagement (17) were the most reported, followed by satisfaction (7) and acceptability (4).
There is a remarkable heterogeneity among these studies and the majority have methodological limitations that leave considerable room for improvement. Further research is required to identify all relevant criteria for assessing the efficacy of mHealth interventions in the management of overweight and obesity.
PROSPERO CRD42017056761; https://tinyurl.com/y2zhxtjx
Obesity and overweight are considered major public health concerns because of their high prevalence and association with various health complications including cardiovascular disease, type 2 diabetes, and cancer [
In recent years, we have witnessed a revolution in the use of apps within personal health care, as they are fast, flexible, handy, versatile, manageable, and illustrative tools that can empower patients. Hence, mobile health (mHealth) can play an important adjuvant role in the prevention and treatment of overweight and obesity by tracking physical activity (PA), enabling self-reporting of dietary patterns, providing recommendations to achieve healthier habits, guidance, advice, tips, and motivational strategies to achieve personalized goals; all are relevant aspects for the prevention and treatment of obesity, as recognized in numerous guidelines [
The Global Observatory for eHealth of the World Health Organization (WHO) defines mHealth as “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices” [
There are several initiatives attempting to define how apps should be evaluated. However, all of these consider only partial aspects of evaluation [
This lack of rigorous evaluation is an increasing concern for health authorities. A number of recommendations to ensure a minimum quality of mHealth interventions have already been defined by the WHO Technical Evidence Review Group [
The aim of this paper was to undertake a systematic review of efficacy, safety, and effectiveness assessment criteria in use, including both outputs and outcomes, to assess weight control, overweight, and obesity management in mHealth interventions. These criteria will later be included in a tool for assessing mHealth interventions intended to manage overweight and obesity.
This systematic review was prospectively registered with PROSPERO (CRD42017056761) [
Any trial that assessed the efficacy and/or safety and/or effectiveness of mHealth-based interventions for overweight or obesity management was considered. No restrictions in terms of target population were foreseen. We define efficacy as changes in lifestyles on the basis of diet and PA in a controlled population; effectiveness in the general population; and safety as a lack of adverse effects resulting from mHealth interventions. Studies carried out with less than 10 individuals were excluded. We assessed the quality of trials according to the Scottish Intercollegiate Guidelines Network (SIGN) criteria [
A systematic search was conducted in the following databases: MEDLINE, EMBASE, PsycINFO, The Cochrane Library (Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials [CENTRAL]), UK Trial Database, and Scopus. This survey was supplemented through the snowballing technique to identify relevant articles in the references of those returned by the search. A manual search was also conducted on the indices of the following publications:
The search strategy included both controlled vocabulary and free-text terms. The terms used were apps, mHealth, eHealth, overweight, obesity, efficacy, security, safety, effectiveness, and evaluation (see
All identified references were imported into Mendeley v1.18 (Elsevier) and duplicates eliminated. A total of 6 researchers undertook the review process, which was conducted in 2 stages. First, each article identified was randomly assigned to 2 reviewers to independently review the title and abstract. Articles that met the inclusion criteria were full-text reviewed and quality-assessed by 2 independent reviewers. In cases of disagreement, a third reviewer made the final decision. Study features and outcomes were entered into a database specifically designed for this review. Risk of bias was assessed according to SIGN codes for study assessment [
A total of 233 potentially relevant publications (17 from a manual search) were identified as eligible. From these, 19.7% (46/233) were identified as duplicates. From the remaining (187), only 49.2% (92/187) were accepted for abstract review. Out of these, 47% (44/92) were excluded for not following inclusion criteria. A full-text review was conducted on 48 studies. After peer review, 30 articles corresponding to 28 different studies (62.5% from the total included for full-text review) were finally included in this nonquantitative review. The exclusion criteria were as follows: published study protocols (n=8), out of scope studies that were not using an mHealth intervention (n=4), or those studies in which final outcomes were other than efficacy or safety (n=6;
The main characteristics of the 28 studies included are detailed in
In total, 46% of the studies (13/28) are RCTs, 1 is a cluster-randomized trial [
The number of participants ranged from 10 to 1012, but most studies (17) covered less than 100 people. One trial [
Apart from one 24-month trial [
Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flow diagram of selection of papers for inclusion in the review.
In regard to the specificities of mHealth interventions (
Although their main aim was to measure the efficacy of mHealth interventions, most of the selected studies also measured other outputs that might be relevant to determine primary outcome measures (23 out of 28, 82%).
A total of 4 studies out of 28 (14%) attempted to measure participants’ acceptance of the intervention, using mixed methods (survey, focus groups, and data performance tracking) [
These 3 dimensions have been considered together, as the main analysis strategies used (data tracking and surveys) integrate all 3 aspects. Only 1 study used a validated questionnaire to assess usability [
Only 7 studies analyzed the satisfaction rate of users [
Few studies [
Out of the 7 studies dealing with peer support, only 2 attempted to assess the perception of this support [
The end point outcomes of the selected studies were as follows: reduction of weight and body mass index (BMI) as well as fat mass and waist and hip circumferences; changes in dietary habits, PA, and screen time patterns; biochemical measurements; and blood pressure (
Most of the studies (22/28, 78%) considered reduction of weight and/or BMI as the main outcome with which to assess intervention efficacy. Devices used to measure weight and/or height were detailed in 17 trials, and only a few relied on self-reported data [
Fat mass reduction was measured in 3 studies [
Fukuoka et al [
We identified 12 trials that assessed changes in dietary patterns [
In total, 14 of the 28 studies (50%) had PA pattern as a main end point. Various strategies were used to measure PA: (1) data tracking through accelerometers [
All studies except 4 [
As the intervention assessed was based on an addiction treatment approach, Pretlow et al [
Nollen et al [
As blood fasting lipids and glucose levels are usually high among people with overweight and obesity; 5 studies included these as secondary outcomes. Only Block et al [
Fukuoka [
One high-quality trial [
Only 1 study was targeted at the general population. He et al [
In this systematic review, we have identified the range of dimensions and tools used to assess the efficacy of mHealth interventions intended to manage overweight and obesity. We have provided a descriptive analysis of 28 clinical trials along with an account of the components and elements included in each intervention. Outputs and outcomes used for the evaluation of trials have also been identified. No specific criteria for assessing safety or effectiveness have been identified due to the small number of studies focused on these aspects.
Our results show that researchers use the following primary end points to measure a study’s success: (1) reduction in weight and/or BMI; (2) reduction in fat mass; (3) reduction in waist and hip circumference; (4) improvement in dietary habits/patterns; (5) increase in PA; (6) increase in emotional well-being; (7) decrease in screen time patterns; (8) improvement in biochemical measures; and (9) decrease in blood pressure. All these factors are closely linked to obesity and overweight and are risk factors for future chronic disease. Although the main aim of most of the studies was to measure the efficacy of mHealth interventions, they also measured other outputs that might be relevant for determining the success of the intervention, such as (1) acceptability, (2) adherence, usability, and engagement, (3) satisfaction, (4) motivation, (5) intention to continue, and (6) perceived support. All these aspects appear to affect whether an intervention will be successful. Tests and questionnaires are the most prevalent tools used for assessment, whether existing and previously validated or devised for the situation. Objective data tracking of PA performance through the mHealth-based intervention, when possible, was a common strategy for avoiding self-reported data. It appears to be highly important to gather objective data and use standardized protocols when assessing the usability and efficacy of mHealth interventions. The mHealth strategies considered to be more sophisticated usually include a higher number of elements. Although the recent strategies of peer support and gamification appear to improve efficacy by increasing engagement and motivation, there is as yet not enough evidence to state this definitively.
The acknowledgement and evaluation of comprehensive sociodemographic differences, such as race/ethnicity, socioeconomic status, and sex, are severely lacking. Future analyses of mHealth interventions should consider and, whenever possible, include eHealth literacy aspects in an effort to reduce communication inequalities across groups [
Previously published reviews have concluded that despite a lack of evidence concerning the best use of technology in weight loss interventions, when the optimal combination of technological components is determined, technology-based interventions will be a valid tool for weight loss [
Currently, most apps used or prescribed in daily clinical practices have only received technical verification or partial clinical validation on the basis of a small group of patients.
Future research is necessary to better assess mHealth interventions in development and before clinical application. It is important to find a balance between the necessary development of mHealth, which should be characterized as disruptive, innovative, and rapid, and the imperative need to validate mHealth interventions. From a Public Health point of view, it is necessary to avoid or minimize the potential problems a new mHealth intervention might create without accurate evaluation. It has been argued that the app market regulates itself: the good persist; the bad disappear. However, in such a potentially harmful field as mHealth, there is a need for new approaches and tools, and a multidisciplinary assessment process [
One of the main limitations of this review is publication bias. References from other sources such as conferences and meetings have not been included. Although the number of scientific journals that publish mHealth-related articles has increased in recent years, there is a lot of gray literature surrounding this field that we may have missed. Moreover, only studies published in English, French, or Spanish have been included. The heterogeneity of interventions and populations and the low number of participants in many studies have made it difficult to synthesize results. Most of the studies included were deemed to be of moderate-low quality, and consequently findings need to be considered with caution. In total, 11 studies lacked a control group and therefore results cannot be attributable to the technology-based intervention alone. One must also take into account the established fact that individuals who agree to participate in intervention studies have greater motivation to change their lifestyles than the general population.
Finally, only 1 study was identified with the primary aim of assessing the safety and effectiveness of an mHealth intervention. Given awareness of safety-related issues such as a possible increase in anxiety and stress due to the use of mHealth intervention and the possible promotion of eating disorders, this is rather surprising. Furthermore, the studies reviewed largely assessed dietary habits and PA, ignoring other possible outcomes relating to body weight such as sleeping behavior. This also needs to be addressed in future research.
The potential for apps to positively help users manage their obesity or overweight has yet to be attained. Studies assessing the success of mHealth interventions are remarkably heterogeneous and most have methodological limitations that leave significant room for improvement regarding quality. Further research is needed to identify all relevant criteria for assessing the efficacy of mHealth interventions in the prevention and management of overweight and obesity.
Search strategy.
Excluded publications.
Characteristics of the selected studies.
Elements included in the mobile health interventions of the selected studies.
Output tools and results from the selected studies.
Main outcome results from the selected studies.
body mass index
electronic health
International Physical Activity Questionnaire
mobile health
physical activity
randomized control trial
Scottish Intercollegiate Guidelines Network
World Health Organization
All authors contributed equally. The research for this paper was fully funded by the Instituto de Salud Carlos III from the Spanish Ministry of Science, Innovation and Universities, grant number PI16/01764 co-funded by FEDER.
None declared.