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

Mitigating errors in mobile-based dietary assessments: Effects of a data modification process on the validity of an image-assisted food and nutrition app

OBJECTIVE

Mobile nutrition applications (apps) provide a simple way for individuals to record their diet, but the validity and inherent errors need to be carefully evaluated. The aim of this study was to assess the validity and clarify the sources of measurement errors of image-assisted mobile nutrition apps.

METHODS

This was a cross-sectional study with 98 students recruited from School of Nutrition and Health Sciences, Taipei Medical University. A 3-d nutrient intake record by Formosa Food and Nutrient Recording App (FoodApp) was compared with a 24-h dietary recall (24-HDR). A two-stage data modification process, manual data cleaning, and reanalyzing of prepackaged foods were employed to address inherent errors. Nutrient intake levels obtained by the two methods were compared with the recommended daily intake (DRI), Taiwan. Paired t test, Spearman's correlation coefficients, and Bland-Altman plots were used to assess agreement between the FoodApp and 24-HDR.

RESULTS

Manual data cleaning identified 166 food coding errors (12%; stage 1), and 426 food codes with missing micronutrients (32%) were reanalyzed (stage 2). Positive linear trends were observed for total energy and micronutrient intake (all Ptrend < 0.05) after the two stages of data modification, but not for dietary fat, carbohydrates, or vitamin D. There were no statistical differences in mean energy and macronutrient intake between the FoodApp and 24-HDR, and this agreement was confirmed by Bland-Altman plots. Spearman's correlation analyses showed strong to moderate correlations (r = 0.834 ∼ 0.386) between the two methods. Participants' nutrient intake tended to be lower than the DRI, but no differences in proportions of adequacy/inadequacy for DRI values were observed between the two methods.

CONCLUSIONS

Mitigating errors significantly improved the accuracy of the Formosa FoodApp, indicating its validity and reliability as a self-reporting mobile-based dietary assessment tool. Dietitians and health professionals should be mindful of potential errors associated with self-reporting nutrition apps, and manual data cleaning is vital to obtain reliable nutrient intake data.

The Nutritional Online sUrvey for pRegnancy Induced Sickness & Hyperemesis (NOURISH) study: results from the first trimester

BACKGROUND

Hyperemesis gravidarum (HG) is severe pregnancy sickness, often leading to dehydration, weight loss and electrolyte disturbances. Little is known about nutritional intake and its consequences in those affected. The aim of this study was to explore the first trimester nutritional intake and clinical characteristics in those with severe sickness.

METHOD

Recruitment was via the social media accounts of national pregnancy charities. The eligibility criteria were as follows: between 6 and 11 weeks pregnant, age ≥18 years and residing in the UK. Participants completed a self-report online questionnaire including the Pregnancy Unique Quantification of Emesis 24 (PUQE24) score and a 3-day online diet diary. Groups were compared by PUQE24 categories. Nutritional intakes were compared to dietary reference values.

RESULTS

One hundred sixty-six participants took part in the study: 36 categorised with mild, 109 with moderate and 21 with severe symptoms at a median gestation of 8.1 (interquartile range [IQR] 3) weeks. Those in the severe category had significantly higher weight loss (3.0 kg, IQR 3.5) than the mild category (0.0 kg, IQR 0.9). In those who completed the diet diary (n = 70), intakes of energy, carbohydrate, protein, fat, fibre, calcium, iron, zinc, thiamine, riboflavin, folate and vitamin C were all significantly lower in the severe category (p < 0.05). The severe group consumed only 39.5% and 41.6% of energy and protein needs, respectively, and were more likely to stop taking micronutrient supplements (p < 0.05).

CONCLUSION

Nutritional and supplement intake in those with severe pregnancy sickness was poor; however, intake across all participants was suboptimal. Future research should investigate how to improve nutritional intake across all categories of pregnancy sickness.

Calorie Compensation Patterns Observed in App-Based Food Diaries

Self-regulation of food intake is necessary for maintaining a healthy body weight. One of the characteristics of self-regulation is calorie compensation. Calorie compensation refers to adjusting the current meal's energy content based on the energy content of the previous meal(s). Preload test studies measure a single instance of compensation in a controlled setting. The measurement of calorie compensation in free-living conditions has largely remained unexplored. This paper proposes a methodology that leverages extensive app-based observational food diary data to measure an individual's calorie compensation profile in free-living conditions. Instead of a single compensation index followed in preload-test studies, we present the compensation profile as a distribution of days a user exhibits under-compensation, overcompensation, non-compensation, and precise compensation. We applied our methodology to the public food diary data of 1622 MyFitnessPal users. We empirically established that four weeks of food diaries were sufficient to characterize a user's compensation profile accurately. We observed that meal compensation was more likely than day compensation. Dinner compensation had a higher likelihood than lunch compensation. Precise compensation was the least likely. Users were more likely to overcompensate for missing calories than for additional calories. The consequences of poor compensatory behavior were reflected in their adherence to their daily calorie goal. Our methodology could be applied to food diaries to discover behavioral phenotypes of poor compensatory behavior toward forming an early behavioral marker for weight gain.

Effectiveness of a Smartphone Application for Dietary Sodium Intake Measurement

Accurate estimation of sodium intake is a key requirement for evaluating the efficacy of interventional strategies to reduce salt intake. The effectiveness of a smartphone application in measuring dietary sodium intake was assessed. This study included 46 participants who consented to register in Noom's food-logging program. All participants were followed up for six months from the day of enrollment. The mean age of the participants was 40.2 ± 12.3 years, and 22 (48%) participants were male. The average number of times/weeks the meals were logged was 16.2 ± 10.3. At baseline, the mean 24-h urine sodium was 124.3 mmol/24 h. The mean sodium intake measured by the smartphone application and calculated using the 24-h urine sodium was 2020.9 mg/24 h and 2857.6 mg/24 h, respectively. During the second visit, the mean 24-h urine sodium was 117.4 mmol/24 h. The mean sodium intake measured by the smartphone application and calculated using the 24-h urine sodium was 1456.0 mg/24 h and 2698.3 mg/24 h, respectively. Sodium intake measured using the smartphone application positively correlated with that calculated using the 24-h urine sodium at baseline (r = 0.464; p < 0.001) and follow-up (r = 0.334; p= 0.023). Dietary sodium intake measured using a smartphone application correlated well with that estimated using 24-h urine sodium level.

A Novel Mobile App for Personalized Dietary Advice Leveraging Persuasive Technology, Computer Vision, and Cloud Computing: Development and Usability Study

BACKGROUND

The Australian Dietary Guidelines (ADG) translate the best available evidence in nutrition into food choice recommendations. However, adherence to the ADG is poor in Australia. Given that following a healthy diet can be a potentially cost-effective strategy for lowering the risk of chronic diseases, there is an urgent need to develop novel technologies for individuals to improve their adherence to the ADG.

OBJECTIVE

This study describes the development process and design of a prototype mobile app for personalized dietary advice based on the ADG for adults in Australia, with the aim of exploring the usability of the prototype. The goal of the prototype was to provide personalized, evidence-based support for self-managing food choices in real time.

METHODS

The guidelines of the design science paradigm were applied to guide the design, development, and evaluation of a progressive web app using Amazon Web Services Elastic Compute Cloud services via iterations. The food layer of the Nutrition Care Process, the strategies of cognitive behavioral theory, and the ADG were translated into prototype features guided by the Persuasive Systems Design model. A gain-framed approach was adopted to promote positive behavior changes. A cross-modal image-to-recipe retrieval model under an Apache 2.0 license was deployed for dietary assessment. A survey using the Mobile Application Rating Scale and semistructured in-depth interviews were conducted to explore the usability of the prototype through convenience sampling (N=15).

RESULTS

The prominent features of the prototype included the use of image-based dietary assessment, food choice tracking with immediate feedback leveraging gamification principles, personal goal setting for food choices, and the provision of recipe ideas and information on the ADG. The overall prototype quality score was "acceptable," with a median of 3.46 (IQR 2.78-3.81) out of 5 points. The median score of the perceived impact of the prototype on healthy eating based on the ADG was 3.83 (IQR 2.75-4.08) out of 5 points. In-depth interviews identified the use of gamification for tracking food choices and innovation in the image-based dietary assessment as the main drivers of the positive user experience of using the prototype.

CONCLUSIONS

A novel evidence-based prototype mobile app was successfully developed by leveraging a cross-disciplinary collaboration. A detailed description of the development process and design of the prototype enhances its transparency and provides detailed insights into its creation. This study provides a valuable example of the development of a novel, evidence-based app for personalized dietary advice on food choices using recent advancements in computer vision. A revised version of this prototype is currently under development.

Weight Estimation of Mediterranean Food Images using Random Forest Regression Algorithm. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-4. [PMID: 38082778 DOI: 10.1109/embc40787.2023.10340040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

The daily nutrition management is one of the most important issues that concern individuals in the modern lifestyle. Over the years, the development of dietary assessment systems and applications based on food images has assisted experts to manage people's nutritional facts and eating habits. In these systems, the food volume estimation is the most important task for calculating food quantity and nutritional information. In this study, we present a novel methodology for food weight estimation based on a food image, using the Random Forest regression algorithm. The weight estimation model was trained on a unique dataset of 5,177 annotated Mediterranean food images, consisting of 50 different foods with a reference card placed next to the plate. Then, we created a data frame of 6,425 records from the annotated food images with features such as: food area, reference object area, food id, food category and food weight. Finally, using the Random Forest regression algorithm and applying nested cross validation and hyperparameters tuning, we trained the weight estimation model. The proposed model achieves 22.6 grams average difference between predicted and real weight values for each food item record in the data frame and 15.1% mean absolute percentage error for each food item, opening new perspectives in food image-based volume and nutrition estimation models and systems.Clinical Relevance- The proposed methodology is suitable for healthcare systems and applications that monitor an individual's malnutrition, offering the ability to estimate the energy and nutrients consumed using an image of the meal.

Home-based cooking intervention with a smartphone app to improve eating behaviors in children aged 7-9 years: a feasibility study

Objective

To develop and evaluate the feasibility of a mobile application in Swiss households and assess its impact on dietary behavior and food acceptability between children who cooked with limited parental support (intervention group) with children who were not involved in cooking (control group).

Methods

A ten-week randomized controlled trial was conducted online in 2020. Parents were given access to a mobile-app with ten recipes. Each recipe emphasized one of two generally disliked foods (Brussels sprouts or whole-meal pasta). Parents photographed and weighed the food components from the child's plate and reported whether their child liked the meal and target food. The main outcome measures were target food intake and acceptability analyzed through descriptive analysis for pre-post changes.

Results

Of 24 parents who completed the baseline questionnaires, 18 parents and their children (median age: 8 years) completed the evaluation phase. Mean child baseline Brussel sprouts and whole-meal pasta intakes were 19.0 ± 24.2 g and 86.0 ± 69.7 g per meal, respectively. No meaningful differences in intake were found post-intervention or between groups. More children reported a neutral or positive liking towards the whole-meal pasta in the intervention group compared to those in the control group. No change was found for liking of Brussel sprouts.

Conclusions for practice

The intervention was found to be feasible however more studies on larger samples are needed to validate feasibility. Integrating digital interventions in the home and promoting meal preparation may improve child reported acceptance of some healthy foods. Using such technology may save time for parents and engage families in consuming healthier meals.

Mobile apps used for people living with multiple sclerosis: A scoping review

BACKGROUND

Multiple Sclerosis (MS) is a chronic neurodegenerative disorder. People living with MS (plwMS) require long-term, multidisciplinary care in both clinical and community settings. MS-specific mHealth interventions have advanced in the form of clinical treatments, rehabilitation, disease monitoring and self-management of disease. However, mHealth interventions for plwMS appear to have limited proof of clinical efficacy. As native mobile apps target specific mobile operating systems, they tend to have better interactive designs leveraging platform-specific guidelines. Thus, to improve such efficacy, it is pivotal to explore the design characteristics of native mobile apps used for plwMS.

OBJECTIVES

This study aimed to explore the design characteristics of native mobile apps used for adults living with MS in academic settings.

METHODS

A scoping review of studies was conducted. A literature search was performed through PubMed, CINAHL, MEDLINE and Cochrane Library. Per native mobile apps, characteristics, persuasive technology elements and evaluations were summarized.

RESULTS

A total of 14 native mobile apps were identified and 43% of the identified apps were used for data collection (n=6). Approximately 70% of the included apps involved users (plwMS) whilst developing (n=10). A total of three apps utilized embedded sensors. Videos or photos were used for physical activity interventions (n=2) and gamification principles were applied for cognitive and/or motor rehabilitation interventions (n=3). Behavior change theories were integrated into the design of the apps for fatigue management and physical activity. Regarding persuasive technology, the design principles of primary support were applied across all identified apps. The elements of dialogue support and social support were the least applied. The methods for evaluating the identified apps were varied.

CONCLUSION

The findings suggest that the identified apps were in the early stages of development and had a user-centered design. By applying the persuasive systems design model, interaction design qualities and features of the identified mobile apps in academic settings were systematically evaluated at a deeper level. Identifying the digital functionality and interface design of mobile apps for plwMS will help researchers to better understand interactive design and how to incorporate these concepts in mHealth interventions for improvement of clinical efficacy.

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.

Characteristics of smartphone-based dietary assessment tools: a systematic review

Smartphones have become popular in assessing eating behaviour in real-life and real-time. This systematic review provides a comprehensive overview of smartphone-based dietary assessment tools, focusing on how dietary data is assessed and its completeness ensured. Seven databases from behavioural, social and computer science were searched in March 2020. All observational, experimental or intervention studies and study protocols using a smartphone-based assessment tool for dietary intake were included if they reported data collected by adults and were published in English. Out of 21,722 records initially screened, 117 publications using 129 tools were included. Five core assessment features were identified: photo-based assessment (48.8% of tools), assessed serving/ portion sizes (48.8%), free-text descriptions of food intake (42.6%), food databases (30.2%), and classification systems (27.9%). On average, a tool used two features. The majority of studies did not implement any features to improve completeness of the records. This review provides a comprehensive overview and framework of smartphone-based dietary assessment tools to help researchers identify suitable assessment tools for their studies. Future research needs to address the potential impact of specific dietary assessment methods on data quality and participants' willingness to record their behaviour to ultimately improve the quality of smartphone-based dietary assessment for health research.

National Food Intake Assessment: Technologies to Advance Traditional Methods

National dietary surveillance produces dietary intake data used for various purposes including development and evaluation of national policies in food and nutrition. Since 2000, What We Eat in America, the dietary component of the National Health and Nutrition Examination Survey, has collected dietary data and reported on the dietary intake of the US population. Continual innovations are required to improve methods of data collection, quality, and relevance. This review article evaluates the strengths and limitations of current and newer methods in national dietary data collection, underscoring the use of technology and emerging technology applications. We offer four objectives for national dietary surveillance that serve as guiding principles in the evaluation. Moving forward, national dietary surveillance must take advantage of new technologies for their potential in enhanced efficiency and objectivity in data operations while continuing to collect accurate dietary information that is standardized, validated, and publicly transparent.

GlucoseML Mobile Application for Automated Dietary Assessment of Mediterranean Food

Over the years and with the help of technology, the daily care of type 1 diabetes has been improved significantly. The increased adoption of continuous glucose monitoring, the continuous subcutaneous insulin injection and the accurate behavioral monitoring mHealth solutions have contributed to this phenomenon. In this study we present a mobile application for automated dietary assessment of Mediterranean food images as part of the GlucoseML system. Based on short-term predictive analysis of the glucose trajectory, GlucoseML is a type-1 diabetes self-management system. A computer vision approach is used as main part of the GlucoseML dietary assessment system calculating food carbohydrates, fats and proteins, relying on: (i) a deep learning subsystem for food image classification, and (ii) a 3D food image reconstruction subsystem for the volume estimation of food. The deep learning subsystem achieves 82.4% and 97.5% top-1 and top-5 accuracy, respectively, for food image classification while the subsystem for volume estimation of food achieves a mean absolute percentage error 10.7% for the four main categories of MedGRFood dataset.

Optimal Design of Clinical Trials of Dietary Interventions in Disorders of Gut-Brain Interaction

There is accumulating evidence for the fundamental role of diet in the integrated care of disorders of gut-brain interaction. Food is a complex mixture of components with individual, synergistic, and antagonistic effects, compared with the relative purity of a pharmaceutical. Food is also an inherent part of individuals' daily lives, and food choice is strongly tied to food preferences, personal beliefs, cultural and religious practices, and economic status, which can influence its ability to function as a therapeutic intervention. Hence, randomized controlled trials of dietary interventions carry unique methodological complexities that are not applicable to pharmaceutical trials that if disregarded can pose significant risk to trial quality. The challenges of designing and delivering the dietary intervention depend on the type of intervention (i.e., nutrient vs food supplementation or whole-diet intervention). Furthermore, there are multiple modes of delivery of dietary interventions, each with their own advantages (e.g., the high precision of feeding trials and the strong clinical applicability of dietary counseling trials). Randomized placebo-controlled trials of dietary interventions are possible with sufficient attention to their design and methodological nuances. Collaboration with experts in nutrition and dietetics is essential for the planning phase; however, even with expert input, not all challenges can be overcome. Researchers undertaking future dietary trials must be transparent in reporting these challenges and approaches for overcoming them. This review aims to provide guiding principles and recommendations for addressing these challenges to facilitate the conduct and reporting of high-quality trials that inform and improve clinical practice.

Attitudes and Expectations of Clinical Research Participants Toward Digital Health and Mobile Dietary Assessment Tools: Cross-Sectional Survey Study

Background

The adoption of health technologies is key to empower research participants and collect quality data. However, the acceptance of health technologies is usually evaluated in patients or healthcare practitioners, but not in clinical research participants.

Methods

A 27-item online questionnaire was provided to the 11,695 members of a nutrition clinical research participant database from the Nantes area (France), to assess (1) participants' social and demography parameters, (2) equipment and usage of health apps and devices, (3) expectations in research setting and (4) opinion about the future of clinical research. Each item was described using frequency and percentage overall and by age classes. A global proportion comparison was performed using chi-square or Fisher-exact tests.

Results

A total of 1529 respondents (81.0% women, 19.0% men) completed the survey. Main uses of health apps included physical activity tracking (54.7%, age-related group difference, p < 0.001) and food quality assessment (45.7%, unrelated to age groups). Overall, 20.4% of respondents declared owning a connected wristband or watch. Most participants (93.8%) expected the use of connected devices in research. However, protection of personal data (37.5%), reliability (35.5%) and skilled use of devices (28.5%) were perceived as the main barriers. Most participants (93.3%) would agree to track their food intake using a mobile app, and 80.5% would complete it for at least a week while taking part in a clinical study. Only 13.2% would devote more than 10 min per meal to such record. A majority (60.4%) of respondents would accept to share their social media posts in an anonymous way and most (82.2%) of them would accept to interact with a chatbot for research purposes.

Conclusions

Our cross-sectional study suggests that clinical study participants are enthusiastic about all forms of digital health technologies and participant-centered studies but remain concerned about the use of personal data. Repeated assessments are suggested to evaluate the research participant's interest in technologies following the increase in use and demand for innovative health services during the pandemic of COVID-19.

The role of body mass index, healthy eating-related apps and educational activities on eating motives and behaviours among women during the COVID-19 pandemic: A cross sectional study

The COVID-19 related lockdown made it much more difficult for people to control their eating behaviours and body weight with the methods and means they had used before. This is reflected in reports that show that eating behaviours deteriorated significantly during the COVID-19 pandemic (including in Poland). Therefore, it is important to determine what factors may be conducive to healthy eating behaviours among people with different BMI. As previous studies show, the use of healthy eating related-apps and training programs may be a protective factor against the development of unhealthy eating behaviours. Therefore, it is worth checking whether their action will be a protective factor during COVID-19. The aim of this cross sectional study was to analyse whether the current use of healthy eating-related apps and previous participation in training in this field (educational activities) as well as body mass index may play a role in eating motives and behaviours among women during COVID-19. Our final sample included 1,447 women (age: M = 31.34 ± 11.05). Participants completed: the Eating Motivation Survey, the Emotional Overeating Questionnaire, the Mindful Eating Questionnaire, socio-demographic survey and questions about healthy eating-related apps and training (educational activities). Referring to the selected significant results, our study shows that during COVID-19, the use of healthy eating-related apps alone, as well as the use of apps and prior training participation promote healthy eating motives and behaviours. It suggests that promoting the use of healthy eating applications and the acquisition of knowledge and skills in this field could be one way of shaping resources that can be effectively used to deal with crisis situations.

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.

Synergizing Mouse and Human Studies to Understand the Heterogeneity of Obesity

Obesity is routinely considered as a single disease state, which drives a "one-size-fits-all" approach to treatment. We recently convened the first annual University of North Carolina Interdisciplinary Nutrition Sciences Symposium to discuss the heterogeneity of obesity and the need for translational science to advance understanding of this heterogeneity. The symposium aimed to advance scientific rigor in translational studies from animal to human models with the goal of identifying underlying mechanisms and treatments. In this review, we discuss fundamental gaps in knowledge of the heterogeneity of obesity ranging from cellular to population perspectives. We also advocate approaches to overcoming limitations in the field. Examples include the use of contemporary mouse genetic reference population models such as the Collaborative Cross and Diversity Outbred mice that effectively model human genetic diversity and the use of translational models that integrate -omics and computational approaches from pre-clinical to clinical models of obesity. Finally, we suggest best scientific practices to ensure strong rigor that will allow investigators to delineate the sources of heterogeneity in the population with obesity. Collectively, we propose that it is critical to think of obesity as a heterogeneous disease with complex mechanisms and etiologies, requiring unique prevention and treatment strategies tailored to the individual.

Mapping and Modeling of Discussions Related to Gastrointestinal Discomfort in French-Speaking Online Forums: Results of a 15-Year Retrospective Infodemiology Study

BACKGROUND

Gastrointestinal (GI) discomfort is prevalent and known to be associated with impaired quality of life. Real-world information on factors of GI discomfort and solutions used by people is, however, limited. Social media, including online forums, have been considered a new source of information to examine the health of populations in real-life settings.

OBJECTIVE

The aims of this retrospective infodemiology study are to identify discussion topics, characterize users, and identify perceived determinants of GI discomfort in web-based messages posted by users of French social media.

METHODS

Messages related to GI discomfort posted between January 2003 and August 2018 were extracted from 14 French-speaking general and specialized publicly available online forums. Extracted messages were cleaned and deidentified. Relevant medical concepts were determined on the basis of the Medical Dictionary for Regulatory Activities and vernacular terms. The identification of discussion topics was carried out by using a correlated topic model on the basis of the latent Dirichlet allocation. A nonsupervised clustering algorithm was applied to cluster forum users according to the reported symptoms of GI discomfort, discussion topics, and activity on online forums. Users' age and gender were determined by linear regression and application of a support vector machine, respectively, to characterize the identified clusters according to demographic parameters. Perceived factors of GI discomfort were classified by a combined method on the basis of syntactic analysis to identify messages with causality terms and a second topic modeling in a relevant segment of phrases.

RESULTS

A total of 198,866 messages associated with GI discomfort were included in the analysis corpus after extraction and cleaning. These messages were posted by 36,989 separate web users, most of them being women younger than 40 years. Everyday life, diet, digestion, abdominal pain, impact on the quality of life, and tips to manage stress were among the most discussed topics. Segmentation of users identified 5 clusters corresponding to chronic and acute GI concerns. Diet topic was associated with each cluster, and stress was strongly associated with abdominal pain. Psychological factors, food, and allergens were perceived as the main causes of GI discomfort by web users.

CONCLUSIONS

GI discomfort is actively discussed by web users. This study reveals a complex relationship between food, stress, and GI discomfort. Our approach has shown that identifying web-based discussion topics associated with GI discomfort and its perceived factors is feasible and can serve as a complementary source of real-world evidence for caregivers.

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.