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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-track...
Contexts in source publication
Context 1
... 1 illustrates both the positive and negative aspects of usability from the SUS. Detailed usability subscores (averages of the reviewers) and the aggregate SUS score for each app are reported in Table 2 of Appendix 1. The users' usability scores were consistent and positively correlated with each other, and ranged from moderate to high correlation (Pearson's correlations of 0.66, 0.84, and 0.89). ...Context 2
... app that tended to be most accurate in coding calorie and macronutrients relative to the USDA reference was MyFitnessPal, while the least was LifeSum. As an additional assessment of coding accuracy, a ripe medium banana, a plain Nature Valley granola bar, and a Big Mac from McDonald's were coded using each app and the USDA database (Table 2). We found high consistency for caloric content of a medium banana across the apps and the USDA database with average difference of 3.7 kcal (3.5%) compared to USDA. ...Similar publications
Although meal replacement can lead to weight reduction, there is uncertainty whether this dietary approach implemented into a lifestyle programme can improve long-term dietary intake. In this subanalysis of the Almased Concept against Overweight and Obesity and Related Health Risk (ACOORH) study (n = 463), participants with metabolic risk factors w...
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...
Citations
... Nutrition applications (apps) are excellent tools for tracking progress because they permit the storage of continuous food records on a single device [8][9][10]. The performance of apps varies according to their photo analysis function, barcode analysis function, and the number of foods referenced [9][10][11]. ...
... Nutrition applications (apps) are excellent tools for tracking progress because they permit the storage of continuous food records on a single device [8][9][10]. The performance of apps varies according to their photo analysis function, barcode analysis function, and the number of foods referenced [9][10][11]. It has previously been reported that, compared to an FFQ, the GB HealthWatch (San Diego, CA, USA) mobile app underestimates the 3-day average intake of most macro-and micronutrients (by 5% for total calories, 19% for cobalamin, and 33% for vitamin E) [12], and the mean correlations between the values generated using these two methods were found to be 0.87 for macronutrients and 0.84 for micronutrients [12]. ...
In Japan, nutritional guidance based on food-recording apps and food frequency questionnaires (FFQs) is becoming popular. However, it is not always recognized that different dietary assessment methods have different nutritional values. Here, we compared the compatibility of dietary intake data obtained from an app with those obtained from FFQs in 59 healthy individuals who recorded information regarding their diet for at least 7 days per month using an app developed by Asken (Tokyo, Japan). The diurnal coefficient of variation in total energy and protein intake was 20%, but those for vitamins B12 and D were >80%, reflecting the importance of 7 days of recording rather than a single day of recording for dietary intake analyses. Then, we compared the results of two FFQs—one based on food groups and one based on a brief self-administered diet history questionnaire—for 7 days, as recorded by the app. There was a correlation coefficient of >0.4 for all the items except salt. Regarding the compatibility between the app and FFQs, the percentage errors for total energy and nutrients were >40–50%, suggesting no agreement between the app and the two FFQs. In conclusion, careful attention should be paid to the impact of different dietary assessment methods on nutrient assessment.
... Occupational Therapist Mobile App Directory Providing Home Modification [56] The Pregnancy and Work Application to provide advice on adjustment of work during pregnancy [57] Calories Applications to help individuals lose weight [58] ICmed Applications to support parental independence and family care [59] The Medication Error Reporting App (MERA.) ...
... Translating and validating the SUS questionnaire into Danish (SUS-DK) 4 [52] Develop and validate the new m-Health app usability questionnaire [79] Performa systematic assessment of the 11 mHealth applications to assist in the self-management of T2DM. [82] Develop a comprehensive set of usability guidelines for mHealth applications 2 Compared method/apps [5] Comparing three usability benchmarking instruments 3 [57] Reviewing the usability of the current iPhone operating system (iOS) and Android app's diet tracking, the e tent to which app features align with behavior change constructs [15] Design and evaluate two innovative mobile voice enhancement applications 3 Apps improvement [25] Improve usability and acceptance of system Apps 15 [65] Develop a cancer pain assessment mobile application prototype [37] Develop and evaluate the usability of mobile applications [40] Develop a mobile application for behavior monitoring and test its usability [44] Presenting the development of heuristic evaluation for m-Health (HE4EH) applications [8] Describe the development and usability of mobile applications [48] Develop an image-based smartphone app, SurgCare [49] To develop a medical document monitoring system [15] Design and evaluate two innovative mobile voice enhancement applications [63] Developing mobile applications for public education [66] Describe the development and assess the usefulness of HOCOS [70] Develop, design, and test the acceptability, learning ability, heuristics, and usability of MindClimb [72] Develop a mobile application and e plore the patient's perceived usefulness of the application [74] Design and develop Squire and evaluate the use 4 [22] Evaluation of usability aspects of Swedish PAEHR users 52 ...
Health applications have unique characteristics compared to other applications. This application is needed to support the health of users/families with various facilities provided according to their functions and objectives. Usability measurement is carried out to evaluate the successful use of the application using various usability criteria. This study aimed to identify, analyze, and synthesize the usability evaluation of a mobile health application. The review was carried out on 65 selected papers from 799 usability papers from the Web of Science and Scopus in the 2013 to 2023 time period. The Systematic Literature Review approach used is the Preferred Reporting Item for Systematic Reviews and Meta-Analysis (PRISMA). Based on the review results, it was identified that usability measurement on mobile health applications aims to validate system design, compare usability methods, improve application performance, and evaluate usability. Meanwhile, mHealth apps mostly function for treatment and self-care/self-management. Most of the reviewed papers used the general public as respondents. The respondents or participants in these studies are diverse and can be categorized into five groups: patients, healthcare professionals, older adults, experts, and the general public. Most of the research aims to evaluate usability with the most widely used method, the System Usability Scale, which is equipped with other supporting methods.
... Smartphone diet-tracking apps are effective in monitoring daily eating patterns, weight changes, and chronic conditions [9,10]. A recent literature review indicates that these applications are highly user-friendly and accurately track daily calorie and nutrient consumption [9]. ...
... Smartphone diet-tracking apps are effective in monitoring daily eating patterns, weight changes, and chronic conditions [9,10]. A recent literature review indicates that these applications are highly user-friendly and accurately track daily calorie and nutrient consumption [9]. They also demonstrated a positive impact on dietary behavior [11]. ...
... Recent studies suggest that applications of deep learning for image classification and object detection could enhance dietary assessment by increasing efficiency and reducing inaccuracies associated with self-reported food intake [15,16]. The rapid increase in diet-tracking apps [9,10] indicates a growing demand from nutrition professionals and the public for automated diet monitoring. This demand should be met by data acquisition and model building. ...
Nuts are nutrient-dense foods and can be incorporated into a healthy diet. Artificial intelligence-powered diet-tracking apps may promote nut consumption by providing real-time, accurate nutrition information but depend on data and model availability. Our team developed a dataset comprising 1380 photographs, each in RGB color format and with a resolution of 4032 × 3024 pixels. These images feature 11 types of nuts that are commonly consumed. Each photo includes three nut types; each type consists of 2–4 nuts, so 6–9 nuts are in each image. Rectangular bounding boxes were drawn using a visual geometry group (VGG) image annotator to facilitate the identification of each nut, delineating their locations within the images. This approach renders the dataset an excellent resource for training models capable of multi-label classification and object detection, as it was meticulously divided into training, validation, and test subsets. Utilizing transfer learning in Python with the IceVision framework, deep neural network models were adeptly trained to recognize and pinpoint the nuts depicted in the photographs. The ultimate model exhibited a mean average precision of 0.7596 in identifying various nut types within the validation subset and demonstrated a 97.9% accuracy rate in determining the number and kinds of nuts present in the test subset. By integrating specific nutritional data for each type of nut, the model can precisely (with error margins ranging from 0.8 to 2.6%) calculate the combined nutritional content—encompassing total energy, proteins, carbohydrates, fats (total and saturated), fiber, vitamin E, and essential minerals like magnesium, phosphorus, copper, manganese, and selenium—of the nuts shown in a photograph. Both the dataset and the model have been made publicly available to foster data exchange and the spread of knowledge. Our research underscores the potential of leveraging photographs for automated nut calorie and nutritional content estimation, paving the way for the creation of dietary tracking applications that offer real-time, precise nutritional insights to encourage nut consumption.
... Today, these apps have gained more popularity with the widespread use of smartphones (Bhuyan et al., 2016). Popularity of these apps is evidenced by a large number of mobile health apps that can be downloaded to smartphones (Ferrara et al., 2019). Commonly used mobile health apps include pedometers, heart rate monitors, calorie counters, diet apps, and exercise apps (Seto et al., 2012). ...
Objectives
The interest in mobile health apps is increasing day by day. This study aimed to examine young people’s preferences for mobile health apps, their physical activity levels, and health perceptions.
Materials and Methods
The study was conducted with 283 students from two vocational schools of higher education. Participants were asked to complete the International Physical Activity Questionnaire Short Form and the Perception of Health Scale, as well as questions about their mobile health application preferences. The data were evaluated at p<0.05 significance level.
Results
The study was conducted with 283 participants with a mean age of 20.6±2 years. The number of participants using health-related apps on their smartphones was 179 (63.3%). Of the 104 participants who did not use mobile health apps, 71.1% reported that they did not prefer them because they thought they would not use them regularly. 11 participants did not know about mobile apps. 41.9% had been using mobile health apps for about one year, and the most used app was pedometers (n=147). Both health perceptions and physical activity scores of individuals who used mobile health apps were higher than those who did not (respectively; p=0.003, p<0.001), and the health perception of physically active individuals was higher than the others (p=0.044).
Conclusion
Currently, the use of mobile health applications is associated with both health perception and physical activity. However, constant use of the apps cannot be ensured. Therefore, it is important to provide the necessary promotion and motivation to people.
... There is some evidence that individuals who read food labels are more likely to seek nutrition facts and engage in eating healthy foods (Bui et al., 2021;Ollberding et al., 2011;Roark et al., 2022). Furthermore, tracking the consumption of food has been found to improve an individual's understanding of their dietary patterns and help with self-monitoring and dietary self-efficacy (Fakih El Khoury et al., 2019;Ferrara et al., 2019). Alternatively, dietary monitoring and food tracking have been linked to disordered eating (Messer et al., 2021). ...
Purpose/Objective: Nutrition knowledge, beliefs, and behaviors have important implications for managing and preventing chronic and injury-related secondary conditions in persons with spinal cord injuries and disorders (SCI/D). Yet, the unique dietary and nutritional needs and recommendations specific to individuals with SCI/D and their eating beliefs and behaviors have been understudied. Aim is to describe nutrition and eating beliefs and behaviors from the perspectives of individuals with SCI/D. Research Method/Design: Descriptive qualitative design using in-depth semistructured interviews with a national sample of veterans with SCI/D (n = 33). Audio-recorded and transcribed verbatim transcripts were coded and analyzed using thematic analysis. Results: Participants were male (61%), aged 29–84 years, and 55% had tetraplegia. Five key themes were identified: extreme fasting/caloric restriction, perceived healthy eating behaviors, perceived unhealthy eating behaviors, modified eating behaviors due to SCI/D-related symptoms, and food/preparation choices based on abilities/independence and access. Conclusions/Implications: Nutrition among veterans with SCI/D may be impacted by many factors, such as nutrition knowledge and beliefs/behaviors about “healthy” and “unhealthy” nutrition, fasting, caloric restriction, imbalanced intake of macro- and micronutrients, overconsumption relative to energy needs, injury-related secondary complications, postinjury body composition and function changes, impairments related to satiety and hunger signals, and difficulty in obtaining and preparing food. Study findings provide many areas that would benefit from intervention. Findings can be used to inform ideal nutrition and healthy eating beliefs and behaviors which are important because nutritional inadequacies can lead to diet-related diseases, may exacerbate SCI secondary conditions, and lead to poor overall health.
... Dietary adherence was measured by self-reported food tracking through Daily Nutrition Goals using the MyFitnessPal app, a database of nutritional information for over 11 million foods that allows users to capture their real dietary intake by manually adding individual ingredients, scanning barcodes, or searching for whole meals from a list of recipes [50]. MyFitnessPal has been shown to have the best coding accuracy among other dietary tracking apps on the market [55]. Participants were instructed to enter all food consumed throughout the trial period; an instructional user guide was provided (Appendix B-4). ...
... , range =[10,63]) at baseline, 36.5 (IQR =[25.25, 40.25], range =[20,55]) at 5 weeks, and 28.5 (IQR =[20,40], range =[13,57]) at 9 weeks. Mean reported severity across all 89 symptoms, as rated on a 0-5 scale, were 1.15 (SD = 0.63) at baseline, 0.96 (SD = 0.45) at week 5, and 0.77 (SD = 0.44) at week 9. Median numbers of symptoms rated as at least moderate severity were 18 (IQR = [16, 25], range = [6, 53]) at baseline, 14 (IQR = [9.5, 21], range = [4, 35]) at 5 weeks, and 12 (IQR = [4, 13.25], range = [0, 30]) at 9 weeks. ...
Purpose: Hypermobility spectrum disorders (HSD) and Ehlers-Danlos syndromes (EDS) are multisystem
conditions marked by dysfunctional connective tissue. This feasibility study evaluated a 9-week
integrative medicine program in this population.
Methods: Using a single-arm study design, adults with HSD or EDS were given recommendations for
an anti-inflammatory Mediterranean diet and self-management with additional behavioral and
psychosocial support. Preliminary data on feasibility based on recruitment and retention, adherence to
the diet, mobile app tracking, changes to perceived well-being via health outcomes, and satisfaction
with care were obtained.
Results: Thirteen participants were enrolled within a 4-month timeframe. Eight participants completed
the study. Three participants met dietary tracking requirement in at least 4 of 8 intervention weeks
and met the macronutrient requirements in at least half of the weeks tracked. No decreases in VAS
pain scores after 5 and 9 weeks were noted; however, 62.5% (n
=
5) of participants had decreased pain
at 9 weeks, compared to baseline. There were significant improvements (p<.05) in six of twelve
measurements of satisfaction with care at the end of the intervention.
Conclusion: This study provides a foundation for future research on patient experience and introduces
a novel treatment paradigm focused on nutrition and self-management.
Supplementary material available at: https://doi.org/10.6084/m9.figshare.c.6486154.v3
... These apps have been studied in different countries and can potentially help individuals with obesity with socioeconomic restrictions in self-monitoring their food and nutrient intake as part of behavioural approach to weight management. [1][2][3][4][5] Accuracy and reliability of these apps, however, should be considered when nutrition interventions are based on the assessment of dietary intake of individuals. 6 Unfortunately, these apps currently lack scientific validation especially since these apps are developed and studied within the context of Western societies and other developed countries. ...
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.
... 17 For example, a recent systematic review in oral health including 15 randomised clinical trials using mHealth interventions to improve oral hygiene, incorporated a variety of BCTs and found significant improvement in plaque and gingivitis outcomes. 18 While there are many apps for general diet tracking in relation to weight loss and diabetes management, 19 emphasising various aspects of the diet (e.g. saturated fat, total calories intake, etc.), very few apps focus on free sugars intake and the goal of improving oral health through dietary change in adults. ...
... Ease of data entry is an important factor affecting the adherence to diet tracking, and these limitations may potentially influence user compliance. 19,42 Nevertheless, a systematic review showed image-based methods are well received by users and preferred over traditional methods, 43,44 and active research to address these known challenges of image-based sensing monitoring of the diet is underway. 13 For example, image segmentation of mixed meals, or ingredient recognition, has been suggested to improve the accuracy of the automated photo recognition. ...
Objective
Diet significantly contributes to dental decay (caries) yet monitoring and modifying patients’ diets is a challenge for many dental practitioners. While many oral health and diet-tracking mHealth apps are available, few focus on the dietary risk factors for caries. This study aims to present the development and key features of a dental-specific mobile app for diet monitoring and dietary behaviour change to prevent caries, and pilot data from initial user evaluation.
Methods
A mobile app incorporating a novel photo recognition algorithm and a localised database of 208,718 images for food item identification was developed. The design and development process were iterative and incorporated several behaviour change techniques commonly used in mHealth. Pilot evaluation of app quality was assessed using the end-user version of the Mobile Application Rating Scale (uMARS).
Results
User feedback from the beta-testing of the prototype app spurred the improvement of the photo recognition algorithm and addition of more user-centric features. Other key features of the final app include real-time prompts to drive actionable behaviour change, goal setting, comprehensive oral health education modules, and visual metrics for caries-related dietary factors (sugar intake, meal frequency, etc.). The final app scored an overall mean (standard deviation) of 3.6 (0.5) out of 5 on the uMARS scale.
Conclusion
We developed a novel diet-tracking mobile app tailored for oral health, addressing a gap in the mHealth landscape. Pilot user evaluations indicated good app quality, suggesting its potential as a useful clinical tool for dentists and empowering patients for self-monitoring and behavioural management.
... Another commonly reported burden of diet SM was the extensive yet confusing built-in food database. Similar to previous research, common issues associated with the food database include the inaccurate nutritional information, the presence of multiple entries for the same food item, and the requirement for precise search terms to locate desired food products (29,30,31). Thus, additional effort is required on the part of app developers to improve the food database and assure the quality and accuracy of information in the food database. ...
Background: Data-driven trajectory modeling is a promising approach for identifying meaningful participant subgroups with various self-monitoring (SM) responses in digital lifestyle interventions. However, there is limited research investigating factors that underlie different subgroups. This qualitative study aimed to investigate factors contributing to participant subgroups with distinct SM trajectory in a digital lifestyle intervention over 6 months.
Methods: Data were collected from a subset of participants (n = 20) in a 6-month digital lifestyle intervention. Participants were classified into Lower SM Group (n = 10) or a Higher SM (n = 10) subgroup based on their SM adherence trajectories over 6 months. Qualitative data were obtained from semi-structured interviews conducted at 3 months. Data were thematically analyzed using a constant comparative approach.
Results: Participants were middle-aged (52.9 ± 10.2 years), mostly female (65%), and of Hispanic ethnicity (55%). Four major themes with emerged from the thematic analysis: Acceptance towards SM Technologies, Perceived SM Benefits, Perceived SM Barriers, and Responses When Facing SM Barriers. Participants across both subgroups perceived SM as positive feedback, aiding in diet and physical activity behavior changes. Both groups cited individual and technical barriers to SM, including forgetfulness, the burdensome SM process, and inaccuracy. The Higher SM Group displayed positive problem-solving skills that helped them overcome the SM barriers. In contrast, some in the Lower SM Group felt discouraged from SM. Both subgroups found diet SM particularly challenging, especially due to technical issues such as the inaccurate food database, the time-consuming food entry process in the Fitbit app.
Conclusions: This study complements findings from our previous quantitative research, which used data-drive trajectory modeling approach to identify distinct participant subgroups in a digital lifestyle based on individuals’ 6-month SM adherence trajectories. Our results highlight the potential of enhancing action planning problem solving skills to improve SM adherence in the Lower SM Group. Our findings also emphasize the necessity of addressing the technical issues associated with current diet SM approaches. Overall, findings from our study may inform the development of practical SM improvement strategies in future digital lifestyle interventions.
Trial registration: The study was pre-registered at ClinicalTrials.gov (NCT05071287) on April 30, 2022.
... This is not an easy task in practical AP applications, since it would pose a heavy burden on the patients. However, it is worth noting that ongoing research to develop applications for automatic identification of food composition [71], [72] could help gather information on nutrients. Another aspect that should be addressed relates the input features used for the prediction of postprandial glycemic levels: in fact, these have been selected based on theoretical and physiological considerations [23], [61], and available data, but may not capture all relevant variables. ...
Type 1 diabetes mellitus (T1DM) is characterized by insulin deficiency and blood sugar control issues. The state-of-the-art solution is the artificial pancreas (AP), which integrates basal insulin delivery and glucose monitoring. However, APs are unable to manage postprandial glucose response (PGR) due to limited knowledge of its determinants, requiring additional information for accurate bolus delivery, such as estimated carbohydrate intake. This study aims to quantify the influence of various meal-related factors on predicting postprandial blood glucose levels (BGLs) at different time intervals (15 min, 60 min, and 120 min) after meals by using deep neural network (DNN) models. The prediction models incorporate preprandial blood glucose values, insulin dosage, and various meal-related nutritional factors such as intake of energy, carbohydrates, proteins, lipids, fatty acids, fibers, glycemic index, and glycemic load as input variables. The impact of input features was assessed by exploiting eXplainable Artificial Intelligence (XAI) methodologies, specifically SHapley Additive exPlanations (SHAP), which provide insights into each feature's contribution to the model predictions. By leveraging XAI methodologies, this study aims to enhance the interpretability and transparency of BGL prediction models and validate clinical literature hypotheses. The findings can aid in the development of decision-support tools for individuals with T1DM, facilitating PGR management and reducing the risks of adverse events. The improved understanding of PGR determinants may lead to advancements in AP technology and improve the overall quality of life for T1DM patients.
