A Focused Review of Smartphone Diet-Tracking Apps: Usability, Functionality, Coherence With Behavior Change Theory, and Comparative Validity of Nutrient Intake and Energy Estimates

Background Smartphone diet-tracking apps may help individuals lose weight, manage chronic conditions, and understand dietary patterns; however, the usabilities and functionalities of these apps have not been well studied. Objective The aim of this study was to review the usability of current iPhone operating system (iOS) and Android diet-tracking apps, the degree to which app features align with behavior change constructs, and to assess variations between apps in nutrient coding. Methods The top 7 diet-tracking apps were identified from the iOS iTunes and Android Play online stores, downloaded and used over a 2-week period. Each app was independently scored by researchers using the System Usability Scale (SUS), and features were compared with the domains in an integrated behavior change theory framework: the Theoretical Domains Framework. An estimated 3-day food diary was completed using each app, and food items were entered into the United States Department of Agriculture (USDA) Food Composition Databases to evaluate their differences in nutrient data against the USDA reference. Results Of the apps that were reviewed, LifeSum had the highest average SUS score of 89.2, whereas MyDietCoach had the lowest SUS score of 46.7. Some variations in features were noted between Android and iOS versions of the same apps, mainly for MyDietCoach, which affected the SUS score. App features varied considerably, yet all of the apps had features consistent with Beliefs about Capabilities and thus have the potential to promote self-efficacy by helping individuals track their diet and progress toward goals. None of the apps allowed for tracking of emotional factors that may be associated with diet patterns. The presence of behavior change domain features tended to be weakly correlated with greater usability, with R2 ranging from 0 to .396. The exception to this was features related to the Reinforcement domain, which were correlated with less usability. Comparing the apps with the USDA reference for a 3-day diet, the average differences were 1.4% for calories, 1.0% for carbohydrates, 10.4% for protein, and −6.5% for fat. Conclusions Almost all reviewed diet-tracking apps scored well with respect to usability, used a variety of behavior change constructs, and accurately coded calories and carbohydrates, allowing them to play a potential role in dietary intervention studies.

[1]  Juliana Chen,et al.  The Most Popular Smartphone Apps for Weight Loss: A Quality Assessment , 2015, JMIR mHealth and uHealth.

[2]  Sandra Capra,et al.  A Framework to Assist Health Professionals in Recommending High-Quality Apps for Supporting Chronic Disease Self-Management: Illustrative Assessment of Type 2 Diabetes Apps , 2015, JMIR mHealth and uHealth.

[3]  B. Thomas,et al.  Usability Evaluation In Industry , 1996 .

[4]  Alison M Darcy,et al.  Development of a smartphone application for eating disorder self-monitoring. , 2015, The International journal of eating disorders.

[5]  R. Halfens,et al.  The Effects of Dietary Mobile Apps on Nutritional Outcomes in Adults with Chronic Diseases: A Systematic Review and Meta-Analysis. , 2019, Journal of the Academy of Nutrition and Dietetics.

[6]  W. Velicer,et al.  The Transtheoretical Model of Health Behavior Change , 1997, American journal of health promotion : AJHP.

[7]  W. James,et al.  WHO recognition of the global obesity epidemic , 2008, International Journal of Obesity.

[8]  Cameron S. Carter,et al.  Chronic stress exposure may affect the brain's response to high calorie food cues and predispose to obesogenic eating habits , 2013, Physiology & Behavior.

[9]  Brian Cugelman Gamification: What It Is and Why It Matters to Digital Health Behavior Change Developers , 2013, JMIR serious games.

[10]  Buzhou Tang,et al.  Usability Study of Mainstream Wearable Fitness Devices: Feature Analysis and System Usability Scale Evaluation , 2018, JMIR mHealth and uHealth.

[11]  R. Mansell From Digital Divides to Digital Entitlements in Knowledge Societies , 2002 .

[12]  D. G. da Silva,et al.  The relative validity of a food record using the smartphone application MyFitnessPal , 2018, Nutrition & dietetics: the journal of the Dietitians Association of Australia.

[13]  Hannah E Payne,et al.  Health Behavior Theory in Popular Calorie Counting Apps: A Content Analysis , 2016, JMIR mHealth and uHealth.

[14]  J. B. Brooke,et al.  SUS: a retrospective , 2013 .

[15]  K. Flegal,et al.  Prevalence of childhood and adult obesity in the United States, 2011-2012. , 2014, JAMA.

[16]  Charles Abraham,et al.  A review and content analysis of engagement, functionality, aesthetics, information quality, and change techniques in the most popular commercial apps for weight management , 2016, International Journal of Behavioral Nutrition and Physical Activity.

[17]  F. Tao,et al.  Effects of emotional symptoms and life stress on eating behaviors among adolescents , 2013, Appetite.

[18]  Lora E Burke,et al.  Mobile applications for weight management: theory-based content analysis. , 2013, American journal of preventive medicine.

[19]  Ali Idri,et al.  Empirical Studies on Usability of mHealth Apps: A Systematic Literature Review , 2015, Journal of Medical Systems.

[20]  K. Flegal,et al.  Prevalence of Childhood and Adult Obesity in the United States, 2011–2012 , 2014 .

[21]  Joshua H. West,et al.  Health Behavior Theories in Diet Apps , 2013 .

[22]  A. Bandura Self-efficacy: toward a unifying theory of behavioral change. , 1977, Psychology Review.

[23]  Wen-Hao Huang,et al.  The use of mobile apps to improve nutrition outcomes: A systematic literature review , 2015, Journal of telemedicine and telecare.

[24]  Cheri A. Levinson,et al.  My Fitness Pal calorie tracker usage in the eating disorders. , 2017, Eating behaviors.

[25]  J. Bernhardt,et al.  Behavioral Functionality of Mobile Apps in Health Interventions: A Systematic Review of the Literature , 2015, JMIR mHealth and uHealth.

[26]  A. Bauman,et al.  The use of smartphone health apps and other mobile health (mHealth) technologies in dietetic practice: a three country study , 2017, Journal of human nutrition and dietetics : the official journal of the British Dietetic Association.

[27]  Margaret Allman-Farinelli,et al.  The use of a food logging app in the naturalistic setting fails to provide accurate measurements of nutrients and poses usability challenges. , 2019, Nutrition.

[28]  M. Boguniewicz,et al.  Evaluation of food allergy in patients with atopic dermatitis. , 2013, The journal of allergy and clinical immunology. In practice.

[29]  Christina Cheng,et al.  Evaluating mobile phone applications for health behaviour change: A systematic review , 2018, Journal of telemedicine and telecare.

[30]  Matthew Plow,et al.  Using mHealth Technology in a Self-Management Intervention to Promote Physical Activity Among Adults With Chronic Disabling Conditions: Randomized Controlled Trial , 2017, JMIR mHealth and uHealth.

[31]  P. Wilson,et al.  Carbohydrate nutrition, insulin resistance, and the prevalence of the metabolic syndrome in the Framingham Offspring Cohort. , 2004, Diabetes care.

[32]  C. Nederkoorn,et al.  Happy eating. The underestimated role of overeating in a positive mood , 2013, Appetite.

[33]  B. Popkin,et al.  Global nutrition transition and the pandemic of obesity in developing countries. , 2012, Nutrition reviews.

[34]  P. Elliott Observational Studies of Salt and Blood Pressure , 1991, Hypertension.

[35]  M. Nestle,et al.  The contribution of expanding portion sizes to the US obesity epidemic. , 2002, American journal of public health.

[36]  S. Michie,et al.  Validation of the theoretical domains framework for use in behaviour change and implementation research , 2012, Implementation Science.

[37]  S. Dawe,et al.  Does negative mood drive the urge to eat? The contribution of negative mood, exposure to food cues and eating style , 2011, Appetite.

[38]  Treating Irritable Bowel Syndrome with a Food Elimination Diet Followed by Food Challenge and Probiotics , 2006 .

[39]  A. Bandura Self-efficacy: toward a unifying theory of behavioral change. , 1977, Psychological review.

[40]  Mark A Pereira,et al.  Assessment of the accuracy of nutrient calculations of five popular nutrition tracking applications , 2018, Public Health Nutrition.

[41]  S. Camille Peres,et al.  Validation of the System Usability Scale (SUS) , 2013 .

[42]  Madhu C. Reddy,et al.  "It's Definitely Been a Journey": A Qualitative Study on How Women with Eating Disorders Use Weight Loss Apps , 2017, CHI.

[43]  Laurence S Freedman,et al.  Addressing Current Criticism Regarding the Value of Self-Report Dietary Data. , 2015, The Journal of nutrition.

[44]  Ari Haukkala,et al.  Emotional eating, depressive symptoms and self-reported food consumption. A population-based study , 2010, Appetite.

[45]  T. Perry,et al.  Diet App Use by Sports Dietitians: A Survey in Five Countries , 2015, JMIR mHealth and uHealth.