Abstract

INTRODUCTION

The prevalence of overweight or obesity in the USA is of high public health concern [1, 2]. Maintaining a healthy weight can reduce one’s risk for a number of chronic diseases [3]. First lines of treatment for many individuals attempting to lose weight are self-help programs. These can include a number of different methods, such as books, “how-to” manuals, and commercial programs [4, 5]. Advances in communication technologies have pushed these self-help and commercial programs to Internet platforms and more recently to mobile smartphones [6, 7].

As of 2010, there were an estimated 293 million mobile phone subscribers in the USA [8]. About one third (31%) of these subscribers are smartphone users, and this group of smartphone users is fairly diverse with respect to racial/ethnic minority status [9]. Blackberry RIM, Apple, and Google each have about 29% of the US smartphone market share [9]. There are numerous health-related applications (“apps”) available for smartphones that are marketed for helping individuals manage or improve their health, such as for diet, physical activity, or weight loss.

Smartphone apps may be promising for helping people improve their health [10]. However, research on apps for these and other health promotion purposes has not kept pace with technological innovations, and their efficacy is yet to be determined [11]. While little is known about the efficacy of smartphone apps, a systematic review of the literature on Internet-based approaches concluded that Internet-based approaches are efficacious for improving behavioral outcomes, including in the arena of weight loss [12, 13]. Many of the reasons that make Internet approaches promising may apply to smartphone apps. The treatment effect sizes of Internet-based approaches are not large, but their potential to reach a large audience could result in a meaningful public health impact [14]. Some of the reasons that Internet platforms are efficacious may be due to ability to provide a tailored experience along with the ease of self-monitoring [12, 15, 16]. Mobile apps have the additional appeal of being portable and available to a person regardless of their location or setting [17]. Tailored feedback and self-monitoring are both relevant in behavior change interventions. Apps have the potential to further increase the ability to receive tailored feedback and self-monitor because they run on mobile devises allowing for easy access and use. Unfortunately, little is known about the types of apps that are available, the features they contain, and the degree to which apps incorporate evidence-informed practices for healthy weight loss and weight management.

Certain strategies and practices for weight loss have received empirical support. For instance, moderate caloric restriction with reduction in fat intake and increases in fruit and vegetable intake are generally accepted and effective strategies [18, 19]. Controlling portion size and self-monitoring of diet and weight are also established weight management practices [20, 21]. Maintaining regular physical activity especially subsequent to weight loss is also recommended because studies have shown physical activity to be associated with weight loss success [22]. Finally, social influences (e.g., social networks, social norms, and social support) are also thought to be relevant and influential in weight loss programs [23, 24]. Based on the evidence, a number of leading governmental agencies have provided recommendations for individuals who want to manage their weight.

Being able to compare apps based on concordance with evidence-informed practices would be helpful for consumers and practitioners alike. A review of the available apps would also provide researchers wishing to develop apps with insights into the types of apps that potential participants might like and find useful as well as how future apps could be improved and expanded. The overall purpose of this study was to review and summarize the content of the first generation of iPhone apps for weight loss and management available as of September 25, 2009. This was done by first determining the degree to which an app adhered to evidence-informed practices for weight loss and weight management. Additionally, using a latent class analysis, this study aimed to evaluate the different types of apps that were available on the market. The user ratings and price of these apps were also described.

METHODS

RESULTS

The 204 apps were first reviewed to determine if they generally contained information and content that would be considered standard or conventional weight loss/management information or recommendations. Nearly all were in this category (89%). However, some contained unconventional strategies or information. These strategies included the use of colors and sounds to help facilitate appetite restraint, placing the phone on one’s stomach while the vibrate function was active in order to break up fat cells, “detox” diets (e.g., eating apples frequently or drinking only lemonade), or provided positive affirmations to encourage staying on one’s diet.

Of the 204 apps, 6% (n=12) did not have any of the 13 practices, and none of the apps had all 13. The percentages for the remaining were as follows: 1=25% (n=50), 2=30% (n=62), 3=19% (n=38), 4=6% (n=12), 5=4% (n=9), 6=2% (n=4), 7=3% (n=7), 8=0.9% (n=2), 9=1% (n=3), 10=0.9% (n=2), 11=0.9% (n=2), and 12=0.5% (n=1). Table 1 reviews the presence of the 13 evidence-informed practices across the 204 apps. Forty-three percent of the apps recommended or provided a tool for keeping a food diary (43%). Many of the apps also recommended or provided a tool to calculate one’s body mass index (BMI) (36%) and recommended or provided a tool to track one’s weight (34%). Less than 10% of the apps offered advice on meal planning, offered the advice to drink water instead of soda or juice, or provided the recommendation to aim for a 1- to 2-lb loss per week.

Two apps in the sample were listed in the Apple App Store’s list of top 20 paid apps: (1) Tap & Track—Calorie, Weight, and Exercise Tracker and (2) Calorie Tracker by LIVESTRONG.COM. Tap & Track—Calorie, Weight, and Exercise Tracker had eight of the evidence-informed practices and a user rating of 3.5, and Calorie Tracker had nine of the evidence-informed practices also with a user rating of 3.5. One app was listed among the top 20 free (weight watchers) and had seven of the evidence-informed practices and an average user rating of 2.5.

Latent class enumeration and profiles

The goodness-of-fit indices for the four latent class models are presented in Table 2. These indices indicate a three-class solution as the best-fitting model, as the AIC, BIC, and SSABIC were all lower compared to earlier models. Only small decreases were observed in these indices with a four-class model, and the LMR-LRT indicated that the four-class model is not a better fit than the three-class model (p value=0.25). The average conditional probabilities indicated that the three classes were all well-defined: 0.955 for class 1, 0.944 for class 2, and 0.929 for class 3. Figure 1 depicts the endorsement probabilities across the 13 evidence-informed practices. These indicate that the apps differed in their coverage of the 13 practices. The first class (labels diet, physical activity, and weight journals) comprised of 19% (n=40) of the apps and was characterized by a high probability of the following 13 practices: advocated maintaining a calorie balance (1.0), allowed for keeping a physical activity journal (0.94), allowed for keeping a food diary (0.93), advocated regular physical activity (0.67), allowed for tracking weight (0.65), and had a BMI calculator for assessing weight (0.46). The remaining criteria had endorsement probabilities <0.37. The second class (labels dietary advice and journals) comprised of 34% (n=70) of the apps and was characterized by a high probability of advocating portion control (0.50), allowed for keeping a food diary (0.49), and reading nutritional labels (0.48). The remaining criteria had endorsement probabilities below 0.2. The third class (labeled weight assessment and tracking) comprised of 46% (n=94) of the apps and was characterized by a high probability of having a BMI calculator for assessing weight (0.61) and allowed for tracking weight (0.42). The remaining 11 criteria had probabilities below 0.16.

Table 2

Fit indices for a one-class model through to a four-class model

Model	Fit statistics
	AIC	BIC	SSABIC	Entropy	LMR-LRT
1 class	2,479.297	2,522.432	2,481.245	N/A	N/A
2 class	2,274.08	2,363.669	2,278.125	0.936	0.0002
3 class	2,161.574	2,297.617	2,167.716	0.87	<0.0001
4 class	2,108.591	2,291.088	2,116.832	0.886	0.2504

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Best-fitting model in bold type

AIC Akaike’s information criteria, BIC Bayesian information criteria, SSABIC sample-size adjusted Bayesian information criteria, LRT-LMR p value for the Lo–Mendel–Rubin likelihood ration test

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Fig 1

Latent class profile plot of the probability of having each of the 13 evidence-informed practices across the three latent classes

DISCUSSION

In this study, we reviewed the content of 204 apps that were available through iTunes in September 2009 to determine the extent to which these apps would be consistent with 13 evidence-informed practices for weight control. Our main finding was that a majority of these apps adhered to one to two evidence-informed practices. Our findings also suggest that of these first generation apps, there were three main types. Apps used for assessing weight (BMI) and tracking weight comprised a majority followed by dietary journals or apps that offered dietary advice and in the minority were apps that would be considered the most comprehensive of the 13 best practices. These more comprehensive apps allowed for keeping journals for weight, diet, and physical activity.

It was notable from the LCA that many of these first generation apps had sparse or limited coverage of the 13 evidence-informed practices. For instance, apps with weight-related tools, like BMI calculators, or methods for tracking weight were less likely to also include other helpful strategies with respect to weight, such advocating a reasonable weekly weight loss goal of 1 to 2 lb. Also, another large proportion of these first generation apps were tools for tracking diet. These apps were less likely to also include information about physical activity which could be useful to more accurately estimate calorie balance. Notably, there were some first generation apps with notable strengths, following many of the evidence-informed practices for weight loss. These apps that adhered to multiple guidelines had useful tools, such as food and exercise diaries, recipes that could be easily accessed, personalized weight graphs and charts, nutritional databases for looking up calorie content, and the ability for the user to synchronize the app with an Internet website. However, apps with numerous features like this were not common. Subsequent studies are needed to determine if second-generation apps are able to expand the coverage of evidence-informed practices.

Some examples of apps that were more innovative included Food Scanner, SparkPeople, and Fast Food Calorie Hunter. Food Scanner allows users to scan barcodes with their phone’s camera in order to view and upload the nutritional information directly into their personal food diary. SparkRecipes, a website that contains hundreds of healthy recipes uploaded from online users is linked with the SparkPeople app so that users can easily access the recipes remotely. For people who fast food frequently, Fast Food Calorie Hunter provides calories and nutritional content for many common fast food items. Education about healthy fast food options has been used as a key component of Internet-based weight loss programs [33–35]. Fast Food Calorie Hunter provides this information and is also linked with a location tracker to view fast food venues that are closest in proximity to one’s location. These apps are innovative in that they appear to reduce the burden of self-monitoring and planning. Recent research has shown that electronic self-monitoring is more effective at changing weight outcomes than the more cumbersome paper diaries [36]. It is conceivable that apps which allow for self-monitoring and planning will improve weight loss adherence and ultimately be more effective at inducing weight change than those without such features. Research on the efficacy for these types of apps is needed.

A notable limitation of these first generation apps was that very few incorporated methods for maximizing social support. Ongoing social support from family and friends, as well as other sources (e.g., professional help), may help to sustain weight loss efforts [24] and group-based treatment approaches improves efficacy over individual treatment approaches [37]. For technology interventions, this may be especially crucial, since users of the intervention are likely to be geographically dispersed and thus lack access to others who are going through the same treatment. Further, a social support component to a technology-based intervention may also be useful in that the online support community may foster engagement in the intervention—users may return to the technology if there is the benefit of a supportive online community [38, 39]. In our sample, only 7 of the 204 apps provided any means to support or interact with others. Two of these apps additionally had the ability to link users with an online community (e.g., message boards or Twitter). The inclusion of a social community in these apps will likely change rapidly, as social networking has become a key feature of the online experience. In fact, some of the apps we reviewed which did not have the capacity to link with others at the time now do (e.g., Weight Watchers recently updated their app to include access to their new online beta community).

To date, we are not aware of any efficacy studies of commercially available smartphone apps. However, several studies have demonstrated that text messaging for weight loss on regular mobile phones (feature phones) is feasible and effective. For example, one recent study found that a research-designed mobile phone intervention providing automated and interactive text messages with personalized feedback was more effective at inducing short-term weight loss than print-based self-help materials [40]. The growing evidence for the efficacy of text messaging implies that until we have evidence on what is effective with smartphone apps, smartphone apps should be designed to include proactive and interactive text messages as a feature of the app. It is noteworthy that none of the apps examined for this study used proactive alerts or text messages to promote behavior change.

This study highlights the many shortcomings of the first generation apps. However, as in any study, the results should be considered with respect to the limitations. First, we based our review on the description that the app developer provided on the iPhone store. We did not download each of the 204 apps and confirm that this information accurately described the apps features and tools. Thus, it is possible that an app might have had more or fewer of the recommended evidence-informed practices than included in the description. While Apple executes a rigorous review process that tests all submitted apps for software bugs, instability on the iPhone platform, and use of unauthorized protocols, they do not review apps for content validity. Another limitation is that our reliance on user ratings as an estimate of likeability may reflect the biases of users who choose to rate an app and also we did not account for the number of ratings. Despite limitations, this measure has been used in previous studies [41]. Collecting additional information about the number of raters or the number of downloads may have improved our precision, and such similar future studies would be advised to collect this type of information. Although not necessarily, a limitation of this study in particular, but of these types of studies in general, is that the technological advances often outpace research efforts. In this study, we sampled and reviewed the available apps at a certain point in time. Many of these apps could have been improved or possibly discontinued since our data collection time point. A second cross-sectional study could help confirm some of our findings here. Finally, our study examines app content in relation to evidence-informed practices as a first step within this field of study. What is clearly needed as research progresses in this area is information about the effectiveness of these apps in assisting in successful weight loss and weight management.

The strength of this study is that it is the first of its kind to evaluate the degree to which the content of commercially available weight control apps adhere to evidence-informed practices. While similar studies exist for diabetes management and smoking cessation, no known studies have looked at smartphone apps for weight control [10]. The study of smoking cessation apps also found that they contain serious omissions in the adherence to established guidelines. Such findings highlight the vast chasm between the wide availability of health apps and their adherence to evidence-based practices.

As mobile computing technology improves and availability of smartphones becomes more ubiquitous, specialized apps for helping individuals manage their daily health and adhere to treatment recommendations are becoming more popular. Until research catches up with the pace of technological development, users of such apps may be well advised to seek additional sources of self-help, preferably those that have received professional input and evaluation.

Electronic supplementary material

Acknowledgments

Footnotes

References