Katerina Dounavi & Olga Tsoumani
The present study consisted in a systematic literature review that aimed to gather evidence on the efficacy of mobile health technology in facilitating weight management behaviors. The population at risk of overweight or obesity is constantly growing throughout the world raising public health concerns. Through implementation of strategies at an individual level, behavioral analysis focuses on making substantial improvements in socially significant behaviors. Combining behavioral science with advances in information technology has resulted in mobile health applications (mHealth apps), with the most prominent of these focusing on monitoring physical activity and diet.
The review included 39 peer-reviewed journal articles (17 non-randomized studies and 22 Randomized Controlled Trials-RCT) published between 2012 and 2017. The studies included adult participants with typical cognitive capacities and measured their weight management behaviors using mobile technology. Extracted data were coded in the categories of participant characteristics, intervention, outcomes, certainty of evidence and social validity.
Nonrandomized studies were mainly defined as feasibility or acceptability trials and included single-armed qualitative, correlational or secondary data analyses. Treatment fidelity was not reported in any of the studies. The apps assessed across the studies were either tailor-made or existed before the study. Additional treatments of intervention packages included personal coaching. MHealth apps proved effective for managing weight as well as for making decisions on medical treatment and fostering contact with health providers.
RCTs involved two-, three-, and four-arm parallel groups and were assessed based on the Cochrane rigorous standards. Participants were randomly assigned to either control or intervention groups. The effectiveness of mHealth apps was assessed by conducting prospective or post hoc analyses. Treatment fidelity was reported in only two studies. Results showed that weight management behaviors significantly changed toward the desired direction as far as health and well-being is considered. MHealth apps were also characterized by strong social validity. However, high levels of bias were monitored across key domains. Both nonrandomized studies and RCTs reported patient satisfaction with the apps regardless of their age, although a considerable number of patients stopped engaging with them due to technical issues.
MHealth apps can therefore be effective in promoting weight self-management and improving health indicators. Effortless self-monitoring of diet and physical activity, tailored feedback, reminders for app use, and interaction with peers increase engagement and therefore facilitate weight management. After all, mHealth apps incorporate behavioral components, which have repeatedly shown to successfully achieve a change in habits. Continuous behavioral support is required for successful lifestyle changes, hence continuous use of mHealth apps could accomplish this cost-effectively. In relation to social validity, the fact that fewer than half of the studies assessed it leads to the conclusion that further research, which would incorporate measures of intervention acceptability and technology satisfaction, is necessary.
Regarding research quality limitations, the authors reported that only two RCTs documented treatment fidelity as a validity and reliability indicator. Given clinical decision making should be based on strong research evidence and patient needs, with RCTs serving as a trusted source of information for policy makers, it is of paramount importance that future research assesses behavior management strategies used in apps following a solid behavior analytical system (Michie, Richardson, Johnston, et al., 2013). Additionally, given only 23 % of eligible studies showed no probability of bias across key domains, it is crucial that research quality is warranted by following a standardized set of criteria, such as the Cochrane rigor standards, especially with regards to data reporting outcomes, participant and staff blinding, and random sequence generation.
As far as the limitations of the current review are concerned, it should be acknowledged that searches were performed only in English, no meta-analysis was conducted due to the nature and heterogeneity of the included studies and analyses were restricted to mobile apps, without specifying whether a sensor was also used. In future research, it would be worth investigating whether sensors, such as pedometers, could enhance patient commitment, refine data collection and optimize treatment effectiveness.
In sum, mhealth apps are widely regarded as efficient, easy to use, and helpful in achieving patients’ weight loss goals through behavioral changes in eating habits and levels of physical activity. In particular, they assist weight loss by enhancing commitment to treatment through techniques such as self-monitoring. There is a clear need to obtain more empirical data on social validity and weight maintenance and generalization with larger samples. Compliance with evidence-based medicine standards will ensure scientific rigor.
Michie, S., Richardson, M., Johnston, M., Abraham, C., Francis, J., Hardeman, W., … Wood, C. E. (2013). The Behavior Change Technique Taxonomy (v1) of 93 Hierarchically Clustered Techniques: Building an International Consensus for the Reporting of Behavior Change Interventions. Annals of Behavioral Medicine, 46(1), 81–95. https://doi.org/10.1007/s12160-013-9486-6
Summary by Valentina Triantafyllidoy and Katerina Dounavi
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