Popular Nutrition-Related Mobile Apps: An Agreement Assessment Against a UK Reference Method

The effectiveness of personalised food choice advice tailored to an individual's socio-demographic, cognitive characteristics, and sensory preferences

Personalised dietary advice has become increasingly popular, currently however most approaches are based on an individual's genetic and phenotypic profile whilst largely ignoring other determinants such as socio economic and cognitive variables. This paper provides novel insights by testing the effectiveness of personalised healthy eating advice concurrently tailored to an individual's socio-demographic group, cognitive characteristics, and sensory preferences. We first used existing data to build a synthetic dataset based on information from 3654 households (Study 1a), and then developed a cluster model to identify individuals characterised by similar socio-demographic, cognitive, and sensory aspects (Study 1b). Finally, in Study 2 we used the characteristics of 8 clusters to build 8 separate personalised food choice advice and assess their ability to motivate the increased consumption of fruit and vegetables and decreased intakes of saturated fat and sugar. We presented 218 participants with either generic UK Government "EatWell" advice, advice that was tailored to their allocated cluster (matched personalised), or advice tailored to a different cluster (unmatched personalised). Results showed that, when compared to generic advice, participants that received matched personalised advice were significantly more likely to indicate they would change their diet. Participants were similarly motivated to increase vegetable consumption and decrease saturated fat intake when they received unmatched personalised advice, potentially highlighting the power of providing alternative food choices. Overall, this study demonstrated that the power of personalizing food choice advice, based on a combination of individual characteristics, can be more effective than current approaches in motivating dietary change. Our study also emphasizes the viability of addressing population health through automatically delivered web-based personalised advice.

Evaluating the Quality and Comparative Validity of Manual Food Logging and Artificial Intelligence-Enabled Food Image Recognition in Apps for Nutrition Care

For artificial intelligence (AI) to support nutrition care, high quality and accuracy of its features within smartphone applications (apps) are essential. This study evaluated popular apps' features, quality, behaviour change potential, and comparative validity of dietary assessment via manual logging and AI. The top 200 free and paid nutrition-related apps from Australia's Apple App and Google Play stores were screened (n = 800). Apps were assessed using MARS (quality) and ABACUS (behaviour change potential). Nutritional outputs from manual food logging and AI-enabled food-image recognition apps were compared with food records for Western, Asian, and Recommended diets. Among 18 apps, Noom scored highest on MARS (mean = 4.44) and ABACUS (21/21). From 16 manual food-logging apps, energy was overestimated for Western (mean: 1040 kJ) but underestimated for Asian (mean: -1520 kJ) diets. MyFitnessPal and Fastic had the highest accuracy (97% and 92%, respectively) out of seven AI-enabled food image recognition apps. Apps with more AI integration demonstrated better functionality, but automatic energy estimations from AI-enabled food image recognition were inaccurate. To enhance the integration of apps into nutrition care, collaborating with dietitians is essential for improving their credibility and comparative validity by expanding food databases. Moreover, training AI models are needed to improve AI-enabled food recognition, especially for mixed dishes and culturally diverse foods.

Smartphone applications for nutrition Support: A systematic review of the target outcomes and main functionalities

INTRODUCTION

A proper nutrition is essential for human life. Recently, special attention on this topic has been given in relation to three health statuses: obesity, malnutrition and specific diseases that can be related to food or treated with specific diets. Mobile technology is often used to assist users that wish to regulate their eating habits, and identifying which fields of application have been explored the most by the app developers and which main functionalities have been adopted can be useful in view of future app developments.

METHODS

We selected 322 articles mentioning nutrition support apps through a literature database search, all of which have undergone an initial screening. After the exclusion of papers that were already reviews, not presenting apps or not focused on nutrition, not relevant or not developed for human subjects, 100 papers were selected for subsequent analyses that aimed at identifying the main treated conditions, outcome measures and functionalities implemented in the Apps.

RESULTS

Of the selected studies, 33 focus on specific diseases, 24 on obesity, 2 on malnutrition and 41 on other targets (e.g., weight/diet control). Type 2 diabetes is the most targeted disease, followed by gestational diabetes, hypertension, colorectal cancer and CVDs which all were targeted by more than one app. Most Apps include self-monitoring and coaching functionalities, educational content and artificial intelligence (AI) tools are slightly less common, whereas counseling, gamification and questionnaires are the least implemented. Body weight and calories/nutrients were the most common general outcome measures, while glycated hemoglobin (HbA1c) was the most common clinical outcome. No statistically significant differences in the effectiveness of the different functionalities were found.

CONCLUSION

The use of mobile technology to improve nutrition has been widely explored in the last years, especially for weight control and specific diseases like diabetes; however, other food-related conditions such as Irritable Bowel Diseases appear to be less targeted by newly developed smartphone apps and their related studies. All different kinds of functionalities appear to be equally effective, but further specific studies are needed to confirm the results.

A Comprehensive Mixed-Method Approach to Characterize the Source of Diurnal Tacrolimus Exposure Variability in Children: Systematic Review, Meta-analysis, and Application to an Existing Data Set

Tacrolimus is widely reported to display diurnal variation in pharmacokinetic parameters with twice-daily dosing. However, the contribution of chronopharmacokinetics versus food intake is unclear, with even less evidence in the pediatric population. The objectives of this study were to summarize the existing literature by meta-analysis and evaluate the impact of food composition on 24-hour pharmacokinetics in pediatric kidney transplant recipients. For the meta-analysis, 10 studies involving 253 individuals were included. The pooled effect sizes demonstrated significant differences in area under the concentration-time curve from time 0 to 12 hours (standardized mean difference [SMD], 0.27; 95% confidence interval [CI], 0.03-0.52) and maximum concentration (SMD, 0.75; 95% CI, 0.35-1.15) between morning and evening dose administration. However, there was significant between-study heterogeneity that was explained by food exposure. The effect size for minimum concentration was not significantly different overall (SMD, -0.09; 95% CI, -0.27 to 0.09) or across the food exposure subgroups. A 2-compartment model with a lag time, linear clearance, and first-order absorption best characterized the tacrolimus pharmacokinetics in pediatric participants. As expected, adding the time of administration and food composition covariates reduced the unexplained within-subject variability for the first-order absorption rate constant, but only caloric composition significantly reduced variability for lag time. The available data suggest food intake is the major driver of diurnal variation in tacrolimus exposure, but the associated changes are not reflected by trough concentrations alone.

MyFitnessPal smartphone application: relative validity and intercoder reliability among dietitians in assessing energy and macronutrient intakes of selected Filipino adults with obesity

This study aimed to explore the validity of energy and macronutrient intake estimates provided by a popular nutrition tracking smartphone application. 37 obese Filipino adults and 3 nutritionist-dietitians participated in this study. Participants used MyFitnessPal to log their food intake for 5 days. They also completed paper-based food record forms at the same time. Dietitians then referred to each of the participants' completed food record forms to log the participants' food intakes and generated estimates of energy and nutrient intake using the same app. The researcher also referred to the participants' completed food record forms and generated energy and nutrient intake data using the Food Composition Tables (FCT)-the Philippine reference standard for estimating calorie and nutrient intakes. T-tests showed no statistical difference in energy and macronutrient data generated between participants and dietitians using MyFitnessPal app but Bland-Altman plots showed very weak to moderate agreements. T-tests revealed statistically significant difference between using the MyFitnessPal app and FCT in estimating energy, protein and fat intakes and Bland-Altman plots showed very weak to moderate agreement between MyFitnessPal and FCT. MyFitnessPal was found to underestimate the values for energy, carbohydrates and fat and overestimate values for protein when compared with estimates using FCT. Analysis of variance showed good intercoder reliability among dietitians, with the exception of fat intake estimates. The Goldberg approach showed very low likelihood of misreporting energy intake among the participants in this study. In this study, MyFitnessPal showed poor validity among Filipinos with obesity but with good reliability when used by dietitians. It also showed poor validity relative to the FCT. Prior nutrition knowledge is a factor in ensuring the accuracy of energy and nutrient intake data generated using MyFitnessPal app. It is recommended that users consult with dietitians for guidance on how to use these apps in weight management interventions.

The effect of pre-exercise protein intake on substrate metabolism, energy expenditure, and energy intake: a dose-response study

BACKGROUND

Pre-exercise protein consumption does not seem to influence substrate metabolism during exercise compared to fasted exercise, however it is unclear if the protein dose impacts on this effect.

METHODS

In a randomized, double-blinded within-subject design trial, healthy, active males and females (n = 15, 25 ± 5 yrs, O2peak: 47.5 ± 8.8 ml/kg/min) completed 1 h of cycling exercise at 60% peak power output 30 min after having consumed either 0, 20, or 40 g of whey protein. Indirect calorimetry was used to measure substrate oxidation during exercise and baseline and post-exercise resting energy expenditure. Blood samples were taken throughout the trials to measure metabolic responses. Free-living food intake post-trial was collected using food diaries.

RESULTS

Fat oxidation rates during exercise did not differ between the three conditions (p = 0.19) with small effect sizes between conditions (Cohen's dz: 0 vs. 20 g = 0.22, 0 vs. 40 g = 0.47, 20 vs. 40 g = 0.27). Serum insulin was higher in the protein groups vs. 0 g (p < 0.05), whereas non-esterified fatty acids were higher in the 0 g compared to 20 and 40 g (p < 0.05). Glucose was significantly lower after 15 min of exercise in 20 and 40 g vs. 0 g (p = 0.01). Resting energy expenditure was elevated post-exercise (p < 0.001), without an interaction for protein dose (p = 0.90). Post-trial free-living energy intake was not different between conditions (p = 0.31), but 24-h energy intake was significantly higher in 40 vs. 0 g (p = 0.04).

CONCLUSION

Protein doses up to 40 g do not seem to impair fat oxidation rates during exercise compared to fasted exercise and could be considered as a nutritional strategy for exercising individuals who struggle to include fasted exercise in their training.

Dietary Patterns and Their Associations with Symptom Levels Among People with Multiple Sclerosis: A Real-World Digital Study

INTRODUCTION

The objective of the study was to investigate long-term food intake patterns and establish possible associations between the inferred dietary habits and levels of reported symptoms among people with multiple sclerosis (MS) in Denmark.

METHODS

The present study was designed as a prospective cohort study. Participants were invited to register daily food intake and MS symptoms and were observed during a period of 100 days. Dropout and inclusion probabilities were addressed using generalized linear models. Dietary clusters were identified among 163 participants using hierarchical clustering on principal component scores. Associations between the dietary clusters and the levels of self-assessed MS symptoms were estimated using inverse probability weighting. Furthermore, the effect of a person's position on the first and second principal dietary component axis on symptom burden was investigated.

RESULTS

Three dietary clusters were identified: a Western dietary cluster, a plant-rich dietary cluster and a varied dietary cluster. Analyses further indicated a vegetables-fish-fruit-whole grain axis and a red-meat-processed-meat axis. The plant-rich dietary cluster showed reduction in symptom burden in nine pre-defined MS symptoms compared to the Western dietary cluster (between 19 and 90% reduction). This reduction was significant for pain and bladder dysfunction as well as across all nine symptoms (pooled p value = 0.012). Related to the two dietary axes, high intake of vegetables resulted in 32-74% reduction in symptom burden compared to low levels of vegetable intake. Across symptoms, this was significant (pooled p value = 0.015), also regarding walking difficulty and fatigue.

CONCLUSIONS

Three dietary clusters were identified. Compared to levels of self-assessed MS-related symptoms, and adjusted for potential confounders, the results suggested less symptom burden with increased intake of vegetables. Although the research design limits the possibilities of establishing causal inference, the results indicate that general guidelines for healthy diet may be relevant as a tool in coping with MS symptoms.

Validation of Dietary Intake Estimated by Web-Based Dietary Assessment Methods and Usability Using Dietary Records or 24-h Dietary Recalls: A Scoping Review

The goal was to summarize studies comparing the accuracy of web-based dietary assessments with those of conventional face-to-face or paper-based assessments using 24-h dietary recall or dietary record methods in the general population. Using two databases, mean differences and correlation coefficients (CCs) for intakes of energy, macronutrients, sodium, vegetables, and fruits were extracted from each study independently by the authors. We also collected information regarding usability from articles reporting this. From 17 articles included in this review, the mean dietary intake differences in the web-based dietary assessment compared to conventional methods, were -11.5-16.1% for energy, -12.1-14.9% for protein, -16.7-17.6% for fat, -10.8-8.0% for carbohydrates, -11.2-9.6% for sodium, -27.4-3.9% for vegetables, and -5.1-47.6% for fruits. The CC was 0.17-0.88 for energy, protein, fat, carbohydrates, and sodium, and 0.23-0.85 for vegetables and fruits. In three out of four studies reporting usability, more than half of the participants preferred the web-based dietary assessment. In conclusion, % difference and CC of dietary intake were acceptable in both web-based dietary records and 24-h dietary recalls. The findings from this review highlight the possibility of wide-spread application of the web-based dietary assessment in the future.

Stance4Health Nutritional APP: A Path to Personalized Smart Nutrition

Access to good nutritional health is one of the principal objectives of current society. Several e-services offer dietary advice. However, multifactorial and more individualized nutritional recommendations should be developed to recommend healthy menus according to the specific user's needs. In this article, we present and validate a personalized nutrition system based on an application (APP) for smart devices with the capacity to offer an adaptable menu to the user. The APP was developed following a structured recommendation generation scheme, where the characteristics of the menus of 20 users were evaluated. Specific menus were generated for each user based on their preferences and nutritional requirements. These menus were evaluated by comparing their nutritional content versus the nutrient composition retrieved from dietary records. The generated menus showed great similarity to those obtained from the user dietary records. Furthermore, the generated menus showed less variability in micronutrient amounts and higher concentrations than the menus from the user records. The macronutrient deviations were also corrected in the generated menus, offering a better adaptation to the users. The presented system is a good tool for the generation of menus that are adapted to the user characteristics and a starting point to nutritional interventions.

Nutrition-related mobile applications - Should they be used for dietary prevention and treatment of cardiovascular diseases?

BACKGROUND AND AIMS

There is no prior research on the usefulness that popular nutrition-related mobile applications would have in assessing fatty acids intake. In this study, we examine these applications through their utilization in the assessment of consumption of saturated (SFAs) and polyunsaturated (PUFAs) fatty acids against the Polish reference method (RM, Dieta 6.0). This report does also include the information about monounsaturated fatty acids and cholesterol intake.

METHODS AND RESULTS

SFAs and PUFAs intake was assessed using two-day dietary recalls obtained from 120 individuals by 3 selected mobile applications (App1 = Yazio, App2 = MyFitnessPal, App3 = Fitatu) and compared with RM. Despite strong (SFAs by App1 and App3) and moderate (SFAs by App2 and PUFAs by App1, App2, App3) correlations with RM, Bland-Altman analyses showed relevant biases and wide range between limits of agreement. Considering SFAs and MUFAs intake, App1 had the best agreement. App1 had high sensitivity (94.6%) in recognition of subjects with SFAs intake >10% with moderate specificity (67.9%), while App2 had poor sensitivity (27.2%) and high specificity (100%). App3 showed moderate sensitivity and specificity (77.2% and 75%, respectively).

CONCLUSIONS

Mobile applications are not accurate tools in SFAs and PUFAs assessment when compared to the RM. Nonetheless, their ability to recognize SFAs intake >10% energy intake may suggest that further development of mobile applications could potentially become an attractive tool in clinical practice.

Using Household Expenditure Surveys for Comparable and Replicable Nutritional Analysis: Evidence from México

In this study, we explore how to use household expenditures and income surveys (HEIS) to provide replicable and comparable measures of nutrients availability at the population level. Our method formalizes the common practice in the literature and consists of three steps: identification of relevant food categories, pairing of food contents food groups in HEIS data, and calculation of the typical amount of nutrients by food group. We illustrate the usage of the method with Mexican data and provide a publicly available data set to readily convert food purchases into six nutrients: calories, proteins, vitamins A and C, iron, and zinc. We perform a descriptive analysis of the evolution of nutrients intake among Mexican households between 2008 and 2020, considering differences by income level. Our results reflect the effect of the COVID-19 pandemic on nutrient availability in Mexican households, mainly driven by a substantial reduction in the expenditure in food consumed away from home, although for most nutrients the trend was stable over most of the period.

The Assessment of a Personalized Nutrition Tool (eNutri) in Germany: Pilot Study on Usability Metrics and Users’ Experiences

Background

To address the epidemic burden of diet-related diseases, adequate dietary intake assessments are needed to determine the actual nutrition intake of a population. In this context, the eNutri web app has been developed, providing online automated personalized dietary advice, based on nutritional information recorded via an integrated and validated food frequency questionnaire (FFQ). Originally developed for a British population and their dietary habits, the eNutri tool has specifically been adapted to the German population, taking into account national eating habits and dietary recommendations.

Objective

The primary aim of this study is to evaluate the system usability and users’ experience and feedback on the eNutri app in a small-scale preliminary study. The secondary aim is to investigate the efficacy of personalized nutrition (PN) recommendations versus general dietary advice in altering eating habits.

Methods

The app was piloted for 4 weeks by 106 participants from across Germany divided into a PN group and a control group. The groups differed according to the degree of personalization of dietary recommendations obtained.

Results

An overall System Usability Scale (SUS) score of 78.4 (SD 12.2) was yielded, indicating an above average user experience. Mean completion time of the FFQ was 26.7 minutes (SD 10.6 minutes). Across subgroups (age, sex, device screen sizes) no differences in SUS or completion time were found, indicating an equal performance for all users independent of the assigned experimental group. Participants’ feedback highlighted the need for more personalized dietary advice for controls, while personalized nutritional recommendations improved the awareness of healthy eating behavior. Further improvements to the eNutri app were suggested by the app users.

Conclusions

In total, the eNutri app has proven to be a suitable instrument to capture the dietary habits of a German population sample. Regarding functionality, system usability, and handling, direct user feedback was quite positive. Nutritional advice given was rated ambivalent, pointing to several weaknesses in the eNutri app, minimizing the system’s full potential. A higher level of personalization within nutritional advice subjectively improved the app’s usability. The insights gained will be used as a basis to further develop and improve this digital diet assessment tool.

Validating Accuracy of an Internet-Based Application against USDA Computerized Nutrition Data System for Research on Essential Nutrients among Social-Ethnic Diets for the E-Health Era

Internet-based applications (apps) are rapidly developing in the e-Health era to assess the dietary intake of essential macro-and micro-nutrients for precision nutrition. We, therefore, validated the accuracy of an internet-based app against the Nutrition Data System for Research (NDSR), assessing these essential nutrients among various social-ethnic diet types. The agreement between the two measures using intraclass correlation coefficients was good (0.85) for total calories, but moderate for caloric ranges outside of <1000 (0.75) and >2000 (0.57); and good (>0.75) for most macro- (average: 0.85) and micro-nutrients (average: 0.83) except cobalamin (0.73) and calcium (0.51). The app underestimated nutrients that are associated with protein and fat (protein: −5.82%, fat: −12.78%, vitamin B12: −13.59%, methionine: −8.76%, zinc: −12.49%), while overestimated nutrients that are associated with carbohydrate (fiber: 6.7%, B9: 9.06%). Using artificial intelligence analytics, we confirmed the factors that could contribute to the differences between the two measures for various essential nutrients, and they included caloric ranges; the differences between the two measures for carbohydrates, protein, and fat; and diet types. For total calories, as an example, the source factors that contributed to the differences between the two measures included caloric range (<1000 versus others), fat, and protein; for cobalamin: protein, American, and Japanese diets; and for folate: caloric range (<1000 versus others), carbohydrate, and Italian diet. In the e-Health era, the internet-based app has the capacity to enhance precision nutrition. By identifying and integrating the effects of potential contributing factors in the algorithm of output readings, the accuracy of new app measures could be improved.

Weight Loss Trajectories and Related Factors in a 16-Week Mobile Obesity Intervention Program: Retrospective Observational Study

Background

In obesity management, whether patients lose ≥5% of their initial weight is a critical factor in clinical outcomes. However, evaluations that take only this approach are unable to identify and distinguish between individuals whose weight changes vary and those who steadily lose weight. Evaluation of weight loss considering the volatility of weight changes through a mobile-based intervention for obesity can facilitate understanding of an individual’s behavior and weight changes from a longitudinal perspective.

Objective

The aim of this study is to use a machine learning approach to examine weight loss trajectories and explore factors related to behavioral and app use characteristics that induce weight loss.

Methods

We used the lifelog data of 13,140 individuals enrolled in a 16-week obesity management program on the health care app Noom in the United States from August 8, 2013, to August 8, 2019. We performed k-means clustering with dynamic time warping to cluster the weight loss time series and inspected the quality of clusters with the total sum of distance within the clusters. To identify use factors determining clustering assignment, we longitudinally compared weekly use statistics with effect size on a weekly basis.

Results

The initial average BMI value for the participants was 33.6 (SD 5.9) kg/m², and it ultimately reached 31.6 (SD 5.7) kg/m². Using the weight log data, we identified five clusters: cluster 1 (sharp decrease) showed the highest proportion of participants who reduced their weight by >5% (7296/11,295, 64.59%), followed by cluster 2 (moderate decrease). In each comparison between clusters 1 and 3 (yo-yo) and clusters 2 and 3, although the effect size of the difference in average meal record adherence and average weight record adherence was not significant in the first week, it peaked within the initial 8 weeks (Cohen d>0.35) and decreased after that.

Conclusions

Using a machine learning approach and clustering shape-based time series similarities, we identified 5 weight loss trajectories in a mobile weight management app. Overall adherence and early adherence related to self-monitoring emerged as potential predictors of these trajectories.

Comparative Validity of Mostly Unprocessed and Minimally Processed Food Items Differs Among Popular Commercial Nutrition Apps Compared with a Research Food Database

BACKGROUND

Commercial nutrition apps are increasingly used to evaluate diet. Evaluating the comparative validity of nutrient data from commercial nutrition app databases is important to determine the merits of using these apps for dietary assessment.

OBJECTIVE

Nutrient data from four commercial nutrition apps were compared with a research-based food database, Nutrition Data System for Research (NDSR) (version 2017).

DESIGN

Comparative validation study.

PARTICIPANTS/SETTING

An investigator identified the 50 most frequently consumed foods (22% of total reported foods) from a weight-loss study in Chicago, IL, during 2017. Nutrient data were compared between four commercial databases with NDSR.

MAIN OUTCOME MEASURES

Comparative validity of energy, macronutrients, and other nutrient data (ie, total sugars, fiber, saturated fat, cholesterol, calcium, and sodium).

STATISTICAL ANALYSES PERFORMED

Intraclass correlation coefficients (ICCs) evaluated agreement between commercial databases with the NDSR for foods that were primarily un- and minimally processed and by the three most frequently consumed food groups. Bland-Altman plots determined degree of bias for calories between commercial databases and NDSR.

RESULTS

This study observed excellent agreement between NDSR and CalorieKing (ICC range = 0.90 to 1.00). Compared with NDSR, agreement for Lose It! and MyFitnessPal ranged from good to excellent (ICC range = 0.89 to 1.00), with the exception of fiber in MyFitnessPal (ICC = 0.67). Fitbit showed the widest variability with NDSR (ICC range = 0.52 to 0.98). When evaluating by food group, Fitbit had poor agreement for all food groups, with the lowest agreement observed for fiber within the vegetable group (ICC = 0.16). Bland-Altman plots confirmed ICC energy results but also found that MyFitnessPal had the poorest agreement to NDSR (mean 8.35 [SD 133.31] kcal) for all food items.

CONCLUSIONS

Degree of agreement varied by commercial nutrition app. CalorieKing and Lose It! had mostly excellent agreement with NDSR for all investigated nutrients. Fitbit showed the widest variability in agreement with NDSR for most nutrients, which may reflect how well the app can accurately capture diet.

A Pilot Study on the Prevalence of Micronutrient Imbalances in a Dutch General Population Cohort and the Effects of a Digital Lifestyle Program

Maintaining an adequate micronutrient status can be achieved by following a complete, diverse diet. Yet, food trends in Western countries show suboptimal consumption of healthy nutrients. In this study, we explored the prevalence of vitamin and mineral imbalances in a general population cohort of Dutch adults and evaluated the effect of a digital lifestyle program on the nutritional status and nutrition health behaviors of these individuals. A micronutrient panel was measured in 348 participants, alongside a dietary assessment. One hundred users subsequently underwent a remeasurement. We identified at least one nutritional imbalance in 301 individuals (86.5%). A total of 80% improved and normalized B6, 67% improved folate, 70% improved B12, and 86% improved vitamin D. Iron abnormalities were corrected in 75% of the participants. In conclusion, this study found that micronutrient deficiencies of easily obtainable vitamins through diet or supplementation such as B vitamins and vitamin D were more prevalent than expected in a Dutch population. This can partly be explained by insufficient consumption of food groups rich in B vitamins. Our preliminary results in those remeasured after a digitally enabled lifestyle intervention show these imbalances can be corrected with adequate behavioral support complemented with supplementation where needed.

Nutrition-Related Mobile Apps in the French App Stores: Assessment of Functionality and Quality

BACKGROUND

The global burden of disease attributes 20% of deaths to poor nutrition. Although hundreds of nutrition-related mobile apps have been created, and these have been downloaded by millions of users, the effectiveness of these technologies on the adoption of healthy eating has had mixed.

OBJECTIVE

The aim of this study was to review which nutrition-related mobile apps are currently available on the French market and assess their quality.

METHODS

We screened apps on the Google Play Store and the French Apple App Store, from March 10 to 17, 2021, to identify those related to nutritional health. A shortlist of 15 apps was identified, and each was assessed using the French version of the Mobile App Rating Scale: 8 dietitians and nutritionists assessed 7 apps, and the remaining apps were randomly allocated to ensure 4 assessments per app. Intraclass correlation was used to evaluate interrater agreement. Means and standard deviations of scores for each section and each item were calculated.

RESULTS

The top scores for overall quality were obtained by Yazio - Régime et Calories (mean 3.84, SD 0.32), FeelEat (mean 3.71, SD 0.47), and Bonne App (mean 3.65, SD 0.09). Engagement scores ranged from a mean of 1.95 (SD 0.5) for iEatBetter: Journal alimentaire to a mean of 3.85 (SD 0.44) for FeelEat. Functionality scores ranged from a mean of 2.25 (SD 0.54) for Naor to a mean of 4.25 (SD 0.46) for Yazio. Aesthetics scores ranged from a mean of 2.17 (SD 0.34) for Naor to a mean of 3.88 (SD 0.47) for Yazio. Information scores ranged from a mean of 2.38 (SD 0.60) for iEatBetter to a mean of 3.73 (SD 0.29) for Yazio. Subjective quality scores ranged from a mean of 1.13 (SD 0.25) for iEatBetter to a mean of 2.28 (SD 0.88) for Compteur de calories FatSecret. Specificity scores ranged from a mean of 1.38 (SD 0.64) for iEatBetter to a mean of 3.50 (SD 0.91) for FeelEat. The app-specific score was always lower than the subjective quality score, which was always lower than the quality score, which was lower than the rating from the iOS or Android app stores.

CONCLUSIONS

Although prevention and information messages in apps regarding nutritional habits are not scientifically verified before marketing, we found that app quality was good. Subjective quality and specificity were associated with lower ratings. Further investigations are needed to assess whether information from these apps is consistent with recommendations and to determine the long-term impacts of these apps on users.

Measurement Properties of Smartphone Approaches to Assess Diet, Alcohol Use, and Tobacco Use: Systematic Review

BACKGROUND

Poor diet, alcohol use, and tobacco smoking have been identified as strong determinants of chronic diseases, such as cardiovascular disease, diabetes, and cancer. Smartphones have the potential to provide a real-time, pervasive, unobtrusive, and cost-effective way to measure these health behaviors and deliver instant feedback to users. Despite this, the validity of using smartphones to measure these behaviors is largely unknown.

OBJECTIVE

The aim of our review is to identify existing smartphone-based approaches to measure these health behaviors and critically appraise the quality of their measurement properties.

METHODS

We conducted a systematic search of the Ovid MEDLINE, Embase (Elsevier), Cochrane Library (Wiley), PsycINFO (EBSCOhost), CINAHL (EBSCOHost), Web of Science (Clarivate), SPORTDiscus (EBSCOhost), and IEEE Xplore Digital Library databases in March 2020. Articles that were written in English; reported measuring diet, alcohol use, or tobacco use via a smartphone; and reported on at least one measurement property (eg, validity, reliability, and responsiveness) were eligible. The methodological quality of the included studies was assessed using the Consensus-Based Standards for the Selection of Health Measurement Instruments Risk of Bias checklist. Outcomes were summarized in a narrative synthesis. This systematic review was registered with PROSPERO, identifier CRD42019122242.

RESULTS

Of 12,261 records, 72 studies describing the measurement properties of smartphone-based approaches to measure diet (48/72, 67%), alcohol use (16/72, 22%), and tobacco use (8/72, 11%) were identified and included in this review. Across the health behaviors, 18 different measurement techniques were used in smartphones. The measurement properties most commonly examined were construct validity, measurement error, and criterion validity. The results varied by behavior and measurement approach, and the methodological quality of the studies varied widely. Most studies investigating the measurement of diet and alcohol received very good or adequate methodological quality ratings, that is, 73% (35/48) and 69% (11/16), respectively, whereas only 13% (1/8) investigating the measurement of tobacco use received a very good or adequate rating.

CONCLUSIONS

This review is the first to provide evidence regarding the different types of smartphone-based approaches currently used to measure key behavioral risk factors for chronic diseases (diet, alcohol use, and tobacco use) and the quality of their measurement properties. A total of 19 measurement techniques were identified, most of which assessed dietary behaviors (48/72, 67%). Some evidence exists to support the reliability and validity of using smartphones to assess these behaviors; however, the results varied by behavior and measurement approach. The methodological quality of the included studies also varied. Overall, more high-quality studies validating smartphone-based approaches against criterion measures are needed. Further research investigating the use of smartphones to assess alcohol and tobacco use and objective measurement approaches is also needed.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-https://doi.org/10.1186/s13643-020-01375-w.

Mapping the digital food environment: A systematic scoping review

Food environments are directly linked to diets and health outcomes such as overweight, obesity, and noncommunicable diseases. The digitalization of food environments is becoming a central issue in public health, yet little is known about this emerging field. We performed a systematic scoping review to map the research on the digital food environment and investigate how the eight dimensions of the food environment, according to an established framework (availability, prices, vendor and product properties, marketing and regulation, accessibility, affordability, convenience, and desirability), might be shifting in the context of a digital society. We searched MEDLINE, EMBASE, Scopus, and Web of Science for studies published between 2000 and 2019, using search terms covering digital technology and food environment, which yielded 13,580 unique records. Our analysis of 357 studies shows that digitalization is taking place in all dimensions of the food environment, and enabling the emergence of new forms of buying and selling food, such as online grocery shopping and online food delivery, which may be changing availability of foods and affecting the physical distance to shops and time allocated for shopping. Systematic reviews identified indicated that digital food marketing and social media can influence food choices, preferences and consumption. Our findings suggest that digital and physical food environments are interconnected and influencing one another, but the impact of the digital on health and nutrition is yet unclear.

Digitization and Health in Germany: Cross-sectional Nationwide Survey

Background

Digital technologies are shaping medicine and public health.

Objective

The aim of this study was to investigate the attitudes toward and the use of digital technologies for health-related purposes using a nationwide survey.

Methods

We performed a cross-sectional study using a panel sample of internet users selected from the general population living in Germany. Responses to a survey with 28 items were collected using computer-assisted telephone interviews conducted in October 2020. The items were divided into four topics: (1) general attitudes toward digitization, (2) COVID-19 pandemic, (3) physical activity, and (4) perceived digital health (eHealth) literacy measured with the eHealth Literacy Scale (eHEALS; sum score of 8=lowest to 40=highest perceived eHealth literacy). The data were analyzed in IBM-SPSS24 using relative frequencies. Three univariate multiple regression analyses (linear or binary logistic) were performed to investigate the associations among the sociodemographic factors (age, gender, education, and household income) and digital technology use.

Results

The participants included 1014 internet users (n=528, 52.07% women) aged 14 to 93 years (mean 54, SD 17). Among all participants, 66.47% (674/1014) completed up to tertiary (primary and secondary) education and 45.07% (457/1017) reported a household income of up to 3500 Euro/month (1 Euro=US $1.18). Over half (579/1014, 57.10%) reported having used digital technologies for health-related purposes. The majority (898/1014, 88.56%) noted that digitization will be important for therapy and health care, in the future. Only 25.64% (260/1014) reported interest in smartphone apps for health promotion/prevention and 42.70% (433/1014) downloaded the COVID-19 contact-tracing app. Although 52.47% (532/1014) reported that they come across inaccurate digital information on the COVID-19 pandemic, 78.01% (791/1014) were confident in their ability to recognize such inaccurate information. Among those who use digital technologies for moderate physical activity (n=220), 187 (85.0%) found such technologies easy to use and 140 (63.6%) reported using them regularly (at least once a week). Although the perceived eHealth literacy was high (eHEALS mean score 31 points, SD 6), less than half (43.10%, 400/928) were confident in using digital information for health decisions. The use of digital technologies for health was associated with higher household income (odds ratio [OR] 1.28, 95% CI 1.11-1.47). The use of digital technologies for physical activity was associated with younger age (OR 0.95, 95% CI 0.94-0.96) and more education (OR 1.22, 95% CI 1.01-1.46). A higher perceived eHealth literacy score was associated with younger age (β=–.22, P<.001), higher household income (β=.21, P<.001), and more education (β=.14, P<.001).

Conclusions

Internet users in Germany expect that digitization will affect preventive and therapeutic health care in the future. The facilitators and barriers associated with the use of digital technologies for health warrant further research. A gap exists between high confidence in the perceived ability to evaluate digital information and low trust in internet-based information on the COVID-19 pandemic and health decisions.

An Italian Case Study for Assessing Nutrient Intake through Nutrition-Related Mobile Apps

National food consumption surveys are crucial for monitoring the nutritional status of individuals, defining nutrition policies, estimating dietary exposure, and assessing the environmental impact of the diet. The methods for conducting them are time and resource-consuming, so they are usually carried out after extended periods of time, which does not allow for timely monitoring of any changes in the population’s dietary patterns. This study aims to compare the results of nutrition-related mobile apps that are most popular in Italy, with data obtained with the dietary software Foodsoft 1.0, which was recently used in the Italian national dietary survey IV SCAI. The apps considered in this study were selected according to criteria, such as popularity (downloads > 10,000); Italian language; input characteristics (daily dietary recording ability); output features (calculation of energy and macronutrients associated with consumption), etc. 415 apps in Google Play and 226 in the iTunes Store were examined, then the following five apps were selected: YAZIO, Lifesum, Oreegano, Macro and Fitatu. Twenty 24-hour recalls were extracted from the IV SCAI database and inputted into the apps. Energy and macronutrient intake data were compared with Foodsoft 1.0 output. Good agreement was found between the selected apps and Foodsoft 1.0 (high correlation index), and no significant differences were found in the mean values of energy and macronutrients, except for fat intakes. In conclusion, the selected apps could be a suitable tool for assessing dietary intake.

Four Weeks of 16/8 Time Restrictive Feeding in Endurance Trained Male Runners Decreases Fat Mass, without Affecting Exercise Performance

Background: Time restricted Feeding (TRF) is a dietary pattern utilized by endurance athletes, but there is insufficient data regarding its effects on performance and metabolism in this population. The purpose of this investigation was to examine the effects of a 16/8 TRF dietary pattern on exercise performance in trained male endurance runners. Methods: A 4-week randomized crossover intervention was used to compare an 8-h TRF to a 12-h normal diet (ND) feeding window. Exercise training and dietary intake were similar across interventions. Runners completed a dual-energy X-ray absorptiometry (DXA) scan to assess body composition, a graded treadmill running test to assess substrate utilization, and ran a 10 km time trial to assess performance. Results: There was a significant decrease in fat mass in the TRF intervention (−0.8 ± 1.3 kg with TRF (p = 0.05), vs. +0.1 ± 4.3 kg with ND), with no significant change in fat-free mass. Exercise carbon dioxide production (VCO₂) and blood lactate concentration were significantly lower with the TRF intervention (p ≤ 0.02). No significant changes were seen in exercise respiratory exchange ratio or 10 km time trial performance (−00:20 ± 3:34 min:s TRF vs. −00:36 ± 2:57 min:s ND). Conclusion: This investigation demonstrated that adherence to a 4-week 16/8 TRF dietary intervention decreased fat mass and maintained fat-free mass, while not affecting running performance, in trained male endurance runners.

Low Comparability of Nutrition-Related Mobile Apps against the Polish Reference Method-A Validity Study

Nutrition-related mobile applications (apps) are commonly used to provide information about the user’s dietary intake, however, limited research has been carried out to assess to what extent their results agree with those from the reference method (RM). The main aim of this study was to evaluate the agreement of popular nutrition-related apps with the Polish RM (Dieta 6.0). The dietary data from two days of dietary records previously obtained from adults (60 males and 60 females) were compared with values calculated in five selected apps (FatSecret, YAZIO, Fitatu, MyFitnessPal, and Dine4Fit). The selection of apps was performed between January and February 2021 and based on developed criteria (e.g., availability in the Polish language, access to the food composition database, and the number of downloads). The data was entered by experienced clinical dietitians and checked by one more researcher. The mean age of study participants was 41.7 ± 14.8. We observed that all the apps tended to overestimate the energy intake, however, when considering the macronutrient intake, over- and underestimation were observed. According to our assumed criterion (±5% as perfect agreement, ±10% as sufficient agreement), none of the apps can be recommended as a replacement for the reference method both for scientific as well as clinical use. According to the Bland-Altman analysis, the smallest bias was observed in Dine4Fit in relation to energy, protein, and fat intake (respectively: −23 kcal; −0.7 g, 3 g), however, a wide range between the upper and lower limits of agreement were reported. According to the carbohydrate intake, the lowest bias was observed when FatSecret and Fitatu were used. These results indicate that the leading nutrition-related apps present a critical issue in the assessment of energy and macronutrient intake. Therefore, the implementation of validation studies for quality assessment is crucial to develop apps with satisfying quality.

User Perspectives of Diet-Tracking Apps: Reviews Content Analysis and Topic Modeling

Background

The availability and use of mobile apps in health and nutrition management are increasing. Ease of access and user friendliness make diet-tracking apps an important ally in their users’ efforts to lose and manage weight. To foster motivation for long-term use and to achieve goals, it is necessary to better understand users’ opinions and needs for dietary self-monitoring.

Objective

The aim of this study was to identify the key topics and issues that users highlight in their reviews of diet-tracking apps on Google Play Store. Identifying the topics that users frequently mention in their reviews of these apps, along with the user ratings for each of these apps, allowed us to identify areas where further improvement of the apps could facilitate app use, and support users’ weight loss and intake management efforts.

Methods

We collected 72,084 user reviews from Google Play Store for 15 diet-tracking apps that allow users to track and count calories. After a series of text processing operations, two text-mining techniques (topic modeling and topical n-grams) were applied to the corpus of user reviews of diet-tracking apps.

Results

Using the topic modeling technique, 11 separate topics were extracted from the pool of user reviews. Most of the users providing feedback were generally satisfied with the apps they use (average rating of 4.4 out of 5 for the 15 apps). Most topics referred to the positive evaluation of the apps and their functions. Negatively rated topics mostly referred to app charges and technical difficulties encountered. We identified the positive and negative topic trigrams (3-word combinations) among the most frequently mentioned topics. Usability and functionality (tracking options) of apps were rated positively on average. Negative ratings were associated with trigrams related to adding new foods, technical issues, and app charges.

Conclusions

Motivating users to use an app over time could help them better achieve their nutrition goals. Although user reviews generally showed positive opinions and ratings of the apps, developers should pay more attention to users’ technical problems and inform users about expected payments, along with their refund and cancellation policies, to increase user loyalty.

Medical Food Assessment Using a Smartphone App With Continuous Glucose Monitoring Sensors: Proof-of-Concept Study

Background

Novel wearable biosensors, ubiquitous smartphone ownership, and telemedicine are converging to enable new paradigms of clinical research. A new generation of continuous glucose monitoring (CGM) devices provides access to clinical-grade measurement of interstitial glucose levels. Adoption of these sensors has become widespread for the management of type 1 diabetes and is accelerating in type 2 diabetes. In parallel, individuals are adopting health-related smartphone-based apps to monitor and manage care.

Objective

We conducted a proof-of-concept study to investigate the potential of collecting robust, annotated, real-time clinical study measures of glucose levels without clinic visits.

Methods

Self-administered meal-tolerance tests were conducted to assess the impact of a proprietary synbiotic medical food on glucose control in a 6-week, double-blind, placebo-controlled, 2×2 cross-over pilot study (n=6). The primary endpoint was incremental glucose measured using Abbott Freestyle Libre CGM devices associated with a smartphone app that provided a visual diet log.

Results

All subjects completed the study and mastered CGM device usage. Over 40 days, 3000 data points on average per subject were collected across three sensors. No adverse events were recorded, and subjects reported general satisfaction with sensor management, the study product, and the smartphone app, with an average self-reported satisfaction score of 8.25/10. Despite a lack of sufficient power to achieve statistical significance, we demonstrated that we can detect meaningful changes in the postprandial glucose response in real-world settings, pointing to the merits of larger studies in the future.

Conclusions

We have shown that CGM devices can provide a comprehensive picture of glucose control without clinic visits. CGM device usage in conjunction with our custom smartphone app can lower the participation burden for subjects while reducing study costs, and allows for robust integration of multiple valuable data types with glucose levels remotely.

Trial Registration

ClinicalTrials.gov NCT04424888; http://clinicaltrials.gov/ct2/show/NCT04424888.

Evaluation of the Ability of Diet-Tracking Mobile Applications to Estimate Energy and Nutrient Intake in Japan

We evaluated the energy and nutrient intake estimates of popular Japanese diet-tracking mobile applications (apps). We identified five diet-tracking apps in the iTunes store during August 2020. A researcher entered the dietary data from a one-day paper-based dietary record (DR) previously obtained from apparently healthy free-living adults (15 males and 15 females; 22-65 years) into each app. The energy and nutrient intakes estimated by the apps were compared with those calculated using the Standard Tables of Food Composition in Japan based on the paper-based DR (reference method). The number of dietary variables available ranged from one (energy in Mogutan) to 17 (FiNC). Compared to the DR-based estimates, the median energy intake was significantly overestimated by MyFitnessPal, Asken, Calomiru, and Mogutan. Moreover, the intakes of many nutrients were overestimated by Asken and Calomiru and underestimated by MyFitnessPal. For energy intake, the Spearman correlation coefficient between the DR and the apps was lowest for Mogutan (0.76) and highest for FiNC (0.96). The median correlation coefficient for nutrient intakes was lower in MyFitnessPal (0.50) than in the other three apps (0.80 in Asken, 0.87 in FiNC, and 0.88 in Calomiru). These results suggest that intake calculations differ among apps. Further evaluation is needed in free-living settings, where users input their own food intake.

Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial

BACKGROUND

Carbohydrate counting is an important component of diabetes management, but it is challenging, often performed inaccurately, and can be a barrier to optimal diabetes management. iSpy is a novel mobile app that leverages machine learning to allow food identification through images and that was designed to assist youth with type 1 diabetes in counting carbohydrates.

OBJECTIVE

Our objective was to test the app's usability and potential impact on carbohydrate counting accuracy.

METHODS

Iterative usability testing (3 cycles) was conducted involving a total of 16 individuals aged 8.5-17.0 years with type 1 diabetes. Participants were provided a mobile device and asked to complete tasks using iSpy app features while thinking aloud. Errors were noted, acceptability was assessed, and refinement and retesting were performed across cycles. Subsequently, iSpy was evaluated in a pilot randomized controlled trial with 22 iSpy users and 22 usual care controls aged 10-17 years. Primary outcome was change in carbohydrate counting ability over 3 months. Secondary outcomes included levels of engagement and acceptability. Change in HbA1c level was also assessed.

RESULTS

Use of iSpy was associated with improved carbohydrate counting accuracy (total grams per meal, P=.008), reduced frequency of individual counting errors greater than 10 g (P=.047), and lower HbA1c levels (P=.03). Qualitative interviews and acceptability scale scores were positive. No major technical challenges were identified. Moreover, 43% (9/21) of iSpy participants were still engaged, with usage at least once every 2 weeks, at the end of the study.

CONCLUSIONS

Our results provide evidence of efficacy and high acceptability of a novel carbohydrate counting app, supporting the advancement of digital health apps for diabetes care among youth with type 1 diabetes. Further testing is needed, but iSpy may be a useful adjunct to traditional diabetes management.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04354142; https://clinicaltrials.gov/ct2/show/NCT04354142.

Rethinking the Use of Mobile Apps for Dietary Assessment in Medical Research

Food intake and usual dietary intake are among the key determinants of health to be assessed in medical research and important confounding factors to be accounted for in clinical studies. Although various methods are available for gathering dietary data, those based on innovative technologies are particularly promising. With combined cost-effectiveness and ease of use, it is safe to assume that mobile technologies can now optimize tracking of eating occasions and dietary behaviors. Yet, choosing a dietary assessment tool that meets research objectives and data quality standards remains challenging. In this paper, we describe the purposes of collecting dietary data in medical research and outline the main considerations for using mobile dietary assessment tools based on participant and researcher expectations.

The Quality of Mobile Apps Used for the Identification of Pressure Ulcers in Adults: Systematic Survey and Review of Apps in App Stores

Background

The increasing global use of smartphones has contributed to the growing use of apps for various health conditions, showing promising results. Through mobile apps, it is possible to perform chronological and iconographic follow-up of wounds, such as pressure ulcers, using a simple and practical tool. However, numerous surveys have pointed out issues related to the functionality, design, safety, and veracity of app information.

Objective

The objective of this study was to perform a systematic review of published studies regarding mobile apps and a systematic survey in app stores looking for apps developed to identify, evaluate, treat, and/or prevent pressure ulcers in adults, and to evaluate those apps based on software quality characteristics.

Methods

This review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The main bibliographic databases were searched between January 1, 2007 and October 15, 2018, and an app survey was performed in app stores. The selected studies were evaluated according to software quality characteristics by the International Organization for Standardization/International Electrotechnical Commission (ie, ISO/IEC 25010:2011) that involve functionality, efficiency, compatibility, usability, reliability, safety, maintenance, and portability.

Results

The search in databases and web-based app stores returned a total of 2075 studies. After removal of duplicates and screening of titles and abstracts, 48 complete articles were evaluated for eligibility, and among these, six were included for qualitative synthesis.

Conclusions

In this review, it was observed that all studies involved the initial phase of app development or improvement, and therefore, the apps still need to be evaluated using different software quality characteristics, so that in the future, a gold standard can be approached. Therefore, the prescription of an app for the identification, evaluation, treatment, and/or prevention of pressure ulcers in adults is currently limited. However, the evaluated studies provided important insights for future research. It is of utmost importance that future surveys develop apps jointly with users, using collaborative and cocreative processes and assess patients in real-world situations across different service settings, and they should consider different ethnicities, so that apps are useful to end users, such as patients, family members, health professionals, and students, in the health area. In addition, it is necessary for studies to describe the methodological course of app development in a clear and objective way in order to ensure reproducibility of the study and to offer inputs to allow future research to approach the development of ideal apps that are geared to positively impact the health of end users.

Trial Registration

PROSPERO CRD42018114137; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=114137

15 Smartphone Apps for Older Adults to Use While in Isolation During the COVID-19 Pandemic

The maintenance of well-being, healthcare, and social connection is crucial for older adults (OA) and has become a topic of debate as much of the world faces lockdown during the coronavirus disease 2019 (COVID-19) pandemic. OAs have been advised to isolate themselves because they are at higher risk for developing serious complications from severe acute respiratory syndrome coronavirus. Additionally, nursing homes and assisted-living facilities across the country have closed their doors to visitors to protect their residents. Mobile technology such as applications (apps) could provide a valuable tool to help families stay connected, and to help OAs maintain mobility and link them to resources that encourage physical and mental well-being. Apps could address cognitive, visual, and hearing impairments. Our objective was to narratively summarize 15 apps that address physical and cognitive limitations and have the potential to improve OAs' quality of life, especially during social distancing or self-quarantine.