Future Directions for Integrative Objective Assessment of Eating Using Wearable Sensing Technology

AI nutritionist: Intelligent software as the next generation pioneer of precision nutrition

With the rapid development of information technology and artificial intelligence (AI), people have acquired the abilities and are encouraged to develop intelligent tools and software, which begins to shed light on intelligent and precise food nutrition. Despite the rapid development of such software, disparities still exist in terms of methodology, contents, and implementation strategies. Hence, a set of panoramic profiles is urgently needed to elucidate their values and guide their future development. Here a comprehensive review was conducted aiming to summarize and compare the objects, contents, intelligent algorithms, and functions realized by the already released software in current research. Consequently, 177 AI nutritionists in recent years were collected and analyzed. The advantages, limitations, and trends concerning their application scenarios were analyzed. It was found that AI nutritionists have been gradually advancing the production modes and efficiency of food recognition, dietary recording/monitoring, nutritional assessment, and nutrient/recipe recommendation. Most AI nutritionists have a relatively low level of intelligence. However, new trends combining advanced AI algorithms, intelligent sensors and big data are coming with new applications in real-time and precision nutrition. AI models concerning molecular-level behaviors are becoming the new focus to drive AI nutritionists. Multi-center and multi-level studies have also gradually been realized to be necessary.

Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review

To monitor adherence to diets and to design and evaluate nutritional interventions, it is essential to obtain objective knowledge about eating behavior. In most research, measures of eating behavior are based on self-reporting, such as 24-h recalls, food records (food diaries) and food frequency questionnaires. Self-reporting is prone to inaccuracies due to inaccurate and subjective recall and other biases. Recording behavior using nonobtrusive technology in daily life would overcome this. Here, we provide an up-to-date systematic overview encompassing all (close-to) publicly or commercially available technologies to automatically record eating behavior in real-life settings. A total of 1328 studies were screened and, after applying defined inclusion and exclusion criteria, 122 studies were included for in-depth evaluation. Technologies in these studies were categorized by what type of eating behavior they measure and which type of sensor technology they use. In general, we found that relatively simple sensors are often used. Depending on the purpose, these are mainly motion sensors, microphones, weight sensors and photo cameras. While several of these technologies are commercially available, there is still a lack of publicly available algorithms that are needed to process and interpret the resulting data. We argue that future work should focus on developing robust algorithms and validating these technologies in real-life settings. Combining technologies (e.g., prompting individuals for self-reports at sensed, opportune moments) is a promising route toward ecologically valid studies of eating behavior.

Exploring the Applicability of General Dietary Recommendations for People Affected by Obesity

(1) Obesity has emerged as a major public health challenge with increasing prevalence globally. The General Dietary Behavior Inventory (GDBI) was developed based on official dietary recommendations. However, little is known about whether general dietary recommendations also apply to people affected by obesity and whether the GDBI can be used appropriately. (2) A cross-sectional study was conducted. A total of 458 people meeting the inclusion criteria participated in the study. The assessment consisted of the GDBI and behavioral, dietary, and health-related variables. We used descriptive analysis to examine the item characteristics of the GDBI and inferential statistics to investigate the associations between the GDBI score and behavioral, dietary, and health-related outcomes. (3) Several items of the GDBI were concerned by ceiling effects. A higher GDBI score (indicating a higher adherence to dietary recommendations) was related to higher age, higher nutrition knowledge, more restrained eating behavior, lower impulsivity, and higher body mass index. There were no associations between the GDBI score and reported physical and mental health or quality of life. (4) The GDBI showed inconsistent relationships with the study outcomes. General dietary recommendations do not appear to be applicable to people with obesity. Hence, there is an urgent need for specific recommendations and subsequent assessments of behavioral adherence for people affected by obesity.

Are commonly used lab-based measures of food value and choice predictive of self-reported real-world snacking? An ecological momentary assessment study

OBJECTIVES

While the assessment of actual food intake is essential in the evaluation of behaviour change interventions for weight-loss, it may not always be feasible to collect this information within traditional experimental paradigms. For this reason, measures of food preference (such as measures of food value and choice) are often used as more accessible alternatives. However, the predictive validity of these measures (in relation to subsequent food consumption) has not yet been studied. Our aim was to investigate the extent to which three commonly used measures of preference for snack foods (explicit food value, unhealthy food choice and implicit preference) predicted self-reported real-world snacking occasions.

DESIGN

Ecological Momentary Assessment (EMA) design.

METHOD

Over a seven-day study period, participants (N = 49) completed three daily assessments where they reported their healthy and unhealthy snack food consumption and completed the three measures of preference (explicit food value, unhealthy food choice and implicit preference).

RESULTS

Our findings demonstrated some weak evidence that unhealthy Visual Analogue Scale scores predicted between-subject increases in unhealthy snacking frequency (OR = 1.018 [1.006, 1.030], p = .002). No other preference measures significantly predicted self-reported healthy or unhealthy snacking occasions (ps > .05).

CONCLUSIONS

These findings raise questions in relation to the association between measures of preference and self-reported real-world snack food consumption. Future research should further evaluate the predictive and construct validity of these measures in relation to food behaviours and explore the development of alternative assessment methods within eating behaviour research.

Precision nutrition: Maintaining scientific integrity while realizing market potential

Precision Nutrition (PN) is an approach to developing comprehensive and dynamic nutritional recommendations based on individual variables, including genetics, microbiome, metabolic profile, health status, physical activity, dietary pattern, food environment as well as socioeconomic and psychosocial characteristics. PN can help answer the question “What should I eat to be healthy?”, recognizing that what is healthful for one individual may not be the same for another, and understanding that health and responses to diet change over time. The growth of the PN market has been driven by increasing consumer interest in individualized products and services coupled with advances in technology, analytics, and omic sciences. However, important concerns are evident regarding the adequacy of scientific substantiation supporting claims for current products and services. An additional limitation to accessing PN is the current cost of diagnostic tests and wearable devices. Despite these challenges, PN holds great promise as a tool to improve healthspan and reduce healthcare costs. Accelerating advancement in PN will require: (a) investment in multidisciplinary collaborations to enable the development of user-friendly tools applying technological advances in omics, sensors, artificial intelligence, big data management, and analytics; (b) engagement of healthcare professionals and payers to support equitable and broader adoption of PN as medicine shifts toward preventive and personalized approaches; and (c) system-wide collaboration between stakeholders to advocate for continued support for evidence-based PN, develop a regulatory framework to maintain consumer trust and engagement, and allow PN to reach its full potential.

Combining passive eating monitoring and ecological momentary assessment to characterize dietary lapses from a lifestyle modification intervention

Dietary lapses (i.e., specific instances of nonadherence to recommended dietary goals) contribute to suboptimal weight loss outcomes during lifestyle modification programs. Passive eating monitoring could enhance lapse measurement via objective assessment of eating characteristics that could be markers for lapse (e.g., more bites consumed). The purpose of this study was to evaluate if passively-inferred eating characteristics (i.e., bites, eating duration, and eating rate), measured via wrist-worn device, could distinguish dietary lapses from non-lapse eating. Adults (n = 25) with overweight/obesity received a 24-week lifestyle modification intervention. Participants completed ecological momentary assessment (EMA; repeated smartphone surveys) biweekly to self-report on dietary lapses and non-lapse eating episodes. Participants wore a wrist device that captured continuous wrist motion. Previously-validated algorithms inferred eating episodes from wrist data, and calculated bite count, duration, and rate (seconds per bite). Mixed effects logistic regressions revealed no simple effects of bite count, duration, or eating rate on the likelihood of dietary lapse. Moderation analyses revealed that eating episodes in the evening were more likely to be lapses if they involved fewer bites (B = -0.16, p < .05), were shorter (B = -0.54, p < .05), or had a slower rate (B = 1.27, p < .001). Statistically significant interactions between eating characteristics (Bs = -0.30 to -0.08, ps < .001) revealed two distinct patterns. Eating episodes that were 1. smaller, slower, and shorter than average, or 2. larger, quicker, and longer than average were associated with increased probability of lapse. This study is the first to use objective eating monitoring to characterize dietary lapses throughout a lifestyle modification intervention. Results demonstrate the potential of sensors to identify non-adherence using only patterns of passively-sensed eating characteristics, thereby minimizing the need for self-report in future studies. CLINICAL TRIALS REGISTRY NUMBER: NCT03739151.

A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation

Knowing the amounts of energy and nutrients in an individual's diet is important for maintaining health and preventing chronic diseases. As electronic and AI technologies advance rapidly, dietary assessment can now be performed using food images obtained from a smartphone or a wearable device. One of the challenges in this approach is to computationally measure the volume of food in a bowl from an image. This problem has not been studied systematically despite the bowl being the most utilized food container in many parts of the world, especially in Asia and Africa. In this paper, we present a new method to measure the size and shape of a bowl by adhering a paper ruler centrally across the bottom and sides of the bowl and then taking an image. When observed from the image, the distortions in the width of the paper ruler and the spacings between ruler markers completely encode the size and shape of the bowl. A computational algorithm is developed to reconstruct the three-dimensional bowl interior using the observed distortions. Our experiments using nine bowls, colored liquids, and amorphous foods demonstrate high accuracy of our method for food volume estimation involving round bowls as containers. A total of 228 images of amorphous foods were also used in a comparative experiment between our algorithm and an independent human estimator. The results showed that our algorithm overperformed the human estimator who utilized different types of reference information and two estimation methods, including direct volume estimation and indirect estimation through the fullness of the bowl.

Perspective: A Framework for Addressing Dynamic Food Consumption Processes

The study of food consumption, diet, and related concepts is motivated by diverse goals, including understanding why food consumption impacts our health, and why we eat the foods we do. These varied motivations can make it challenging to define and measure consumption, as it can be specified across nearly infinite dimensions-from micronutrients to carbon footprint to food preparation. This challenge is amplified by the dynamic nature of food consumption processes, with the underlying phenomena of interest often based on the nature of repeated interactions with food occurring over time. This complexity underscores a need to not only improve how we measure food consumption but is also a call to support theoreticians in better specifying what, how, and why food consumption occurs as part of processes, as a prerequisite step to rigorous measurement. The purpose of this Perspective article is to offer a framework, the consumption process framework, as a tool that researchers in a theoretician role can use to support these more robust definitions of consumption processes. In doing so, the framework invites theoreticians to be a bridge between practitioners who wish to measure various aspects of food consumption and methodologists who can develop measurement protocols and technologies that can support measurement when consumption processes are clearly defined. In the paper we justify the need for such a framework, introduce the consumption process framework, illustrate the framework via a use case, and discuss existing technologies that enable the use of this framework and, by extension, more rigorous study of consumption. This consumption process framework demonstrates how theoreticians could fundamentally shift how food consumption is defined and measured towards more rigorous study of what, how, and why food is eaten as part of dynamic processes and a deeper understanding of linkages between behavior, food, and health.

Exploring the factors influencing adoption of health-care wearables among generation Z consumers in India

Purpose

The purpose of this study is to identify the major factors influencing the adoption of health-care wearables in generation Z (Gen Z) customers in India. A conceptual framework using push pull and mooring (PPM) adoption theory was developed.

Design/methodology/approach

Data was collected from 208 Gen Z customers based on 5 constructs related to the adoption of health-care wearables. Confirmatory factor analysis and structural equation modelling was used to analyse the responses. The mediation paths were analysed using bootstrapping method and examination of the standardized direct and indirect effects in the model.

Findings

The study results indicated that the antecedent factors consisted of push (real-time health information availability), pull (normative environment) and mooring (decision self-efficacy) factors. The mooring factor (MOOR) was related to the push factor but not the pull factor. The MOOR, in turn, was related to the switching intention of Gen Z customers for health wearables adoption.

Research limitations/implications

The research study extended the literature related to the PPM theory in the context of the adoption of health wearables among Gen Z customers in India.

Practical implications

The study outcome would enable managers working in health wearable organizations to understand consumer behaviour towards health wearables.

Social implications

The use of health wearables among Gen Z individuals would lead to future generations adopting a healthy lifestyle resulting in an effective workforce and better economy.

Originality/value

This was one of the few studies which have explored the PPM theory to explore the factors for the adoption of health wearables among Gen Z customers in India.

[Web search data as health data? : Geographic differences, temporal trends, and topics of interest from internet search engine analyses in Germany]

Hintergrund

Die eingeschränkt zeitnahe, kostengünstige und regionale Verfügbarkeit von Daten im Gesundheitswesen gilt als einer der limitierenden Faktoren für zeitgerechte Analysen im Bereich der Versorgungsforschung und damit für die Gesundheitsversorgung der allgemeinen Bevölkerung. Deshalb geraten zunehmend Internetsuchmaschinenanalysen in den Fokus.

Fragestellung

Welchen Beitrag können Daten zum Internetsuchvolumen zu verschiedenen Erkrankungen in der Gesundheitsversorgung der deutschen Bevölkerung leisten? Wo liegt das Potenzial, und wo gibt es Grenzen?

Material und Methoden

Es erfolgten die Aufbereitung der aktuellen Literatur sowie eine selektive Übersicht der in den letzten 4 Jahren veröffentlichten Suchmaschinenanalysen zu dermatologischen, allergologischen und infektiologischen Erkrankungen in Deutschland.

Ergebnisse

Durch die Analyse von Suchmaschinendaten konnten zeitliche Entwicklungen wie Saisonalität, Monate mit dem höchsten Suchinteresse und geografische Unterschiede auf nationaler, Bundesländer- und Städteebene abgebildet und Interessenschwerpunkte bezüglich eines die Krankheit betreffenden Themas (z. B. Hautkrebs am Auge oder analer Juckreiz) aufgezeigt werden. Darüber hinaus fanden manche Studien einen Zusammenhang zwischen dem Suchvolumen und externen Faktoren (z. B. Temperatur, ärztliche Versorgungsstruktur) sowie zu registrierten Fällen (z. B. Hautkrebs, Borreliose).

Schlussfolgerung

Internetsuchmaschinendaten liefern als nahezu in Echtzeit verfügbare Datenquelle unter Berücksichtigung der aufgezeigten Fallstricke ein räumlich-zeitliches Abbild hinsichtlich der Bedürfnisse der internetnutzenden Bevölkerung. Sie können besonders nützlich in Situationen sein, in denen traditionelle Gesundheitsdaten begrenzt oder nicht vorhanden sind.

Zusatzmaterial online

Die Online-Version dieses Beitrags (10.1007/s00105-021-04918-x) enthält eine zusätzliche Tabelle. Beitrag und Zusatzmaterial stehen Ihnen im elektronischen Volltextarchiv auf https://www.springermedizin.de/der-hautarzt zur Verfügung. Sie finden das Zusatzmaterial am Beitragsende unter „Supplementary Information“.

Wearable and Mobile Sensors for Personalized Nutrition

While wearable and mobile chemical sensors have experienced tremendous growth over the past decade, their potential for tracking and guiding nutrition has emerged only over the past three years. Currently, guidelines from doctors and dietitians represent the most common approach for maintaining optimal nutrition status. However, such recommendations rely on population averages and do not take into account individual variability in responding to nutrients. Precision nutrition has recently emerged to address the large heterogeneity in individuals' responses to diet, by tailoring nutrition based on the specific requirements of each person. It aims at preventing and managing diseases by formulating personalized dietary interventions to individuals on the basis of their metabolic profile, background, and environmental exposure. Recent advances in digital nutrition technology, including calories-counting mobile apps and wearable motion tracking devices, lack the ability of monitoring nutrition at the molecular level. The realization of effective precision nutrition requires synergy from different sensor modalities in order to make timely reliable predictions and efficient feedback. This work reviews key opportunities and challenges toward the successful realization of effective wearable and mobile nutrition monitoring platforms. Non-invasive wearable and mobile electrochemical sensors, capable of monitoring temporal chemical variations upon the intake of food and supplements, are excellent candidates to bridge the gap between digital and biochemical analyses for a successful personalized nutrition approach. By providing timely (previously unavailable) dietary information, such wearable and mobile sensors offer the guidance necessary for supporting dietary behavior change toward a managed nutritional balance. Coupling of the rapidly emerging wearable chemical sensing devices-generating enormous dynamic analytical data-with efficient data-fusion and data-mining methods that identify patterns and make predictions is expected to revolutionize dietary decision-making toward effective precision nutrition.

Salivary Protein Profile and Food Intake: A Dietary Pattern Analysis

Saliva research has gained interest due to its potential as a source of biomarkers. One of the factors inducing changes in saliva, in the short term, is food intake, and evidence exist about changes in salivary proteome induced by some food components. Since this topic of research is in its early stages, it was hypothesized that saliva protein composition could be associated with different levels of adherence to dietary patterns that contain higher amounts of plant products. The aim of the present study was to test this hypothesis, in adults, by comparing salivary protein electrophoretic profiles of individuals with different diet characteristics, particularly dietary patterns (DP) that exhibit different proportions of animal and plant-based products. Dietary habits were assessed in 122 adults (61 from each sex, with ages ranging from 20 to 59 years) using Food Frequency Questionnaires. To identify the dietary patterns, a principal component analysis was used. Individual's non-stimulated saliva was evaluated for flow rate, pH, protein concentration, α-amylase activity, and electrophoretic protein profiles. Seven dietary patterns (DP) were identified. Salivary amylase enzymatic activity was positively associated with animal-based and starchy foods DP, and with plant-based fatty foods without wine DP. At the same time, protein bands containing amylase and type S cystatins were positively associated with the cheese/yoghurt and wine DP. Our results support the association of salivary proteomics and different dietary patterns and highlight the need of considering food consumption habits in studies using saliva, since this is a factor associated with variations in the composition of this fluid.