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. 2015 Dec 16;3(4):e104.
doi: 10.2196/mhealth.4334.

The Most Popular Smartphone Apps for Weight Loss: A Quality Assessment

Juliana Chen  1 , Janet E Cade, Margaret Allman-Farinelli
Affiliations

The Most Popular Smartphone Apps for Weight Loss: A Quality Assessment

Juliana Chen et al. JMIR Mhealth Uhealth. .

Abstract

Background: Advancements in mobile phone technology have led to the development of smartphones with the capability to run apps. The availability of a plethora of health- and fitness-related smartphone apps has the potential, both on a clinical and public health level, to facilitate healthy behavior change and weight management. However, current top-rated apps in this area have not been extensively evaluated in terms of scientific quality and behavioral theory evidence base.

Objective: The purpose of this study was to evaluate the quality of the most popular dietary weight-loss smartphone apps on the commercial market using comprehensive quality assessment criteria, and to quantify the behavior change techniques (BCTs) incorporated.

Methods: The top 200-rated Health & Fitness category apps from the free and paid sections of Google Play and iTunes App Store in Australia (n=800) were screened in August 2014. To be included in further analysis, an app had to focus on weight management, include a facility to record diet intake (self-monitoring), and be in English. One researcher downloaded and used the eligible apps thoroughly for 5 days and assessed the apps against quality assessment criteria which included the following domains: accountability, scientific coverage and content accuracy of information relevant to weight management, technology-enhanced features, usability, and incorporation of BCTs. For inter-rater reliability purposes, a second assessor provided ratings on 30% of the apps. The accuracy of app energy intake calculations was further investigated by comparison with results from a 3-day weighed food record (WFR).

Results: Across the eligible apps reviewed (n=28), only 1 app (4%) received full marks for accountability. Overall, apps included an average of 5.1 (SD 2.3) out of 14 technology-enhanced features, and received a mean score of 13.5 (SD 3.7) out of 20 for usability. The majority of apps provided estimated energy requirements (24/28, 86%) and used a food database to calculate energy intake (21/28, 75%). When compared against the WFR, the mean absolute energy difference of apps which featured energy intake calculations (23/28, 82%) was 127 kJ (95% CI -45 to 299). An average of 6.3 (SD 3.7) of 26 BCTs were included.

Conclusions: Overall, the most popular commercial apps for weight management are suboptimal in quality, given the inadequate scientific coverage and accuracy of weight-related information, and the relative absence of BCTs across the apps reviewed. With the limited regulatory oversight around the quality of these types of apps, this evaluation provides clinicians and consumers an informed view of the highest-quality apps in the current popular app pool appropriate for recommendation and uptake. Further research is necessary to assess the effectiveness of apps for weight management.

Keywords: behavior change techniques; evaluation; obesity; quality; smartphone apps; weight management.

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Conflict of interest statement

Conflicts of Interest: JC declares no personal or financial conflicts of interest. JEC developed the My Meal Mate app on a grant from the National Prevention Research Initiative (grant number G0802108). It is available free of charge. MAF has developed food-based apps, but not for weight management.

Figures

Figure 1
Figure 1
Flow diagram of the sampling procedure with the number of dietary weight-loss smartphone apps included or excluded.
Figure 2
Figure 2
Regression analyses of the association between the rankings in the commercial app store (ie, popularity) versus quality assessment measures: (a) overall quality assessment score, (b) accountability, (c) scientific coverage and accuracy, (d) technology-enhanced features, (e) usability, and (f) behavior change technique (BCT). Note: ranking is numerical, with the rank of most popular apps starting from 1 and the least popular app ranked at 200.
Figure 3
Figure 3
Screenshot of sample smartphone app which provides estimates of energy requirements and searchable food databases (from Noom Weight Loss Coach by Noom, Inc).
Figure 4
Figure 4
Accuracy of dietary apps compared to the weighed food record (WFR). Differences in mean energy intake values (kJ) over 3 days for dietary apps (n=23) were compared against the 3-day WFRs analyzed on FoodWorks. The overall mean difference of all the apps from the WFRs is denoted by the black diamond, and the 95% CI is indicated by the error bars.
Figure 5
Figure 5
Incorporation of technology-enhanced features across apps. Number of total apps (n=28) incorporating each technology-enhanced feature.
Figure 6
Figure 6
Screenshots of technology-enhanced features present in smartphone apps: (a) modifiable food database (from Calorie Counter by FatSecret), (b) weight progress charts (from Noom Weight Loss Coach by Noom, Inc), (c) built-in physical activity tracking device (eg, pedometer, accelerometer, or connection to other activity monitoring apps) (from Noom Weight Loss Coach by Noom, Inc), and (d) flags for lapses in dietary goal adherence (from MyFitnessPal by MyFitnessPal, Inc).
Figure 7
Figure 7
Incorporation of behavior change techniques (BCTs) across apps. Number of apps (n=28) incorporating each of the 26 individual BCTs according to the three-phase categories: motivation enhancing, planning and preparation, and goal striving and persistence.
See this image and copyright information in PMC

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