The Open Nursing Journal




ISSN: 1874-4346 ― Volume 13, 2019
REVIEW ARTICLE

Wearable Devices for Caloric Intake Assessment: State of Art and Future Developments



Maria Laura Magrini1, Clara Minto1, Francesca Lazzarini1, Matteo Martinato2, Dario Gregori1, *
1 Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic and Vascular Sciences, University of Padova, Padova, Italy
2 Department of Gastroenterology, University of Padova, Padova, Italy

Abstract

Background:

The self-monitoring of caloric intake is becoming necessary as the number of pathologies related to eating increases. New wearable devices may help people to automatically record energy assumed in their meals.

Objective:

The present review collects the released articles about wearable devices or method for automatic caloric assessments.

Method:

A literature research has been performed with PubMed, Google Scholar, Scopus and ClinicalTrials.gov search engines, considering released articles regarding applications of wearable devices in eating environment, from 2005 onwards.

Results:

Several tools allow caloric assessment and food registration: wearable devices counting the number of bites ingested by the user, instruments detecting swallows and chewings, methods that analyse food with digital photography. All of them still require more validation and improvement.

Conclusion:

Automatic recording of caloric intake through wearable devices is a promising method to monitor body weight and eating habits in clinical and non-clinical settings, and the research is still going on.

Keywords: Wearable devices, Caloric intake monitoring, Non-communicable diseases, Body Mass Index, Bite, Armband.


Article Information


Identifiers and Pagination:

Year: 2017
Volume: 11
Issue: Suppl-1, M7
First Page: 232
Last Page: 240
Publisher Id: TONURSJ-11-232
DOI: 10.2174/1874434601711010232

Article History:

Received Date: 15/02/2017
Revision Received Date: 15/05/2017
Acceptance Date: 07/07/2017
Electronic publication date: 31/10/2017
Collection year: 2017

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© 2017 Magrini et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


* Address correspondence to this authors at the Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic and Vascular Sciences, Via Loredan 18, 35121 Padova, Italy; Tel: +390498275384; Fax: +3902700445089; E-mail: dario.gregori@unipd.it




1. INTRODUCTION

Nowadays, worldwide health care systems are focused on the creation of new management strategies to prevent worsening of chronic diseases and reduce associated economic costs. Non-communicable diseases (NCDs) are responsible for almost 38 million of deaths each year due to cardiovascular events, respiratory pathologies, cancer and diabetes. In high and middle income countries, NCDs are mostly related to unhealthy diets, sedentary lifestyle, tobacco smoke and alcohol abuse. Particularly, the excess of body weight, defined as a Body Mass Index higher than 25 kg/m2, the low consumption of fruits and vegetables and the consumption of foods with high content of fats and sugar, are main concerns of current health policies [1Goris AH, Meijer EP, Westerterp KR. Repeated measurement of habitual food intake increases under-reporting and induces selective under-reporting. Br J Nutr 2001; 85(5): 629-34.
[http://dx.doi.org/10.1079/BJN2001322] [PMID: 11348579]
].

Data from observational studies reveal that the global phenomenon of obesity has nearly doubled between 1980 and 2008: percentage of obese men increased from 5% to 10%, while percentage of obese women increased from 8% to 14% [2Thompson FE, Subar AF, Loria CM, Reedy JL, Baranowski T. Need for technological innovation in dietary assessment. J Am Diet Assoc 2010; 110(1): 48-51.
[http://dx.doi.org/10.1016/j.jada.2009.10.008] [PMID: 20102826]
]. According to the CUORE project, between 1998 and 2002, 17% of Italian men and 21% of Italian women were obese [3Lopez-Meyer P, Patil Y, Tiffany T, Sazonov E. Detection of hand-to-mouth gestures using a RF operated proximity sensor for monitoring cigarette smoking. Open Biomed Eng J 2013; 9: 41-9.
[http://dx.doi.org/10.2174/1874120701307010041] [PMID: 23723954]
]. The World Health Organization (WHO) suggests the importance of healthy food choices to reduce consequences of food-related pathologies such as obesity, diabetes, cancer and cardiovascular diseases [4Scisco JL, Muth ER, Hoover AW. Examining the utility of a bite-count-based measure of eating activity in free-living human beings. J Acad Nutr Diet 2014; 114(3): 464-9.
[http://dx.doi.org/10.1016/j.jand.2013.09.017] [PMID: 24231364]
]. These recommendations are widely supported by scientific literature on dietary style: intake of healthy foods has positive effects on health conditions by reducing risks of recurrent coronary heart disease [5Desendorf J, Bassett DR Jr, Raynor HA, Coe DP. Validity of the Bite Counter device in a controlled laboratory setting. Eat Behav 2014; 15(3): 502-4.
[http://dx.doi.org/10.1016/j.eatbeh.2014.06.013] [PMID: 25064306]
], positively modulating markers of inflammation in patients with diabetes [6Salley JN, Hoover AW, Wilson ML, Muth ER. Comparison between human and bite-based methods of estimating caloric intake. J Academy Nutrition Dietetics 2016; 31(10): 1568-77.] and improving health status of subjects with obstructive sleep apnea syndrome [7Amft O, Kusserow M, Tröster G. Bite weight prediction from acoustic recognition of chewing. IEEE Trans Biomed Eng 2009; 56(6): 1663-72.
[http://dx.doi.org/10.1109/TBME.2009.2015873] [PMID: 19272978]
]. The maintenance of a positive energy balance is ensured by the complementary action of energy taken from nutrients (energy intake) and energy expended through physical activity and resting metabolic rate (energy expenditure) [8Kalantarian H, Alshurafa N, Le T, Sarrafzadeh M. Monitoring eating habits using a piezoelectric sensor-based necklace. Comput Biol Med 2015; 58: 46-55.
[http://dx.doi.org/10.1016/j.compbiomed.2015.01.005] [PMID: 25616023]
]. Several factors such as gender, age, environmental temperature, general health status and physical activity can influence energy intake and energy expenditure, suggesting the necessity of an energy balance calibrated for each subject [9Fontana JM, Higgins JA, Schuckers SC, et al. Energy intake estimation from counts of chews and swallows. Appetite 2015; 85: 14-21.
[http://dx.doi.org/10.1016/j.appet.2014.11.003] [PMID: 25447016]
].

As regards economic expenditure of obesity, an annual medical cost of 3613$ pro-capita has been estimated for women, and 1152$ pro-capita for men in United States [10Dong Y, Hoover A, Muth E. A device for detecting and counting bites of food taken by a person during eating. Bioinformatics and Biomedicine, 2009 BIBM'09 IEEE International Conference on: IEEE 2009.
[http://dx.doi.org/10.1109/BIBM.2009.29]
]. A similar trend is observable in Italy, where costs related to health-care system are strongly influenced by clinical status of individuals: compared with normal-weighted subjects, annual pro-capita expenditure rises to 65€ in overweight subjects and to 105€ in obese subjects [3Lopez-Meyer P, Patil Y, Tiffany T, Sazonov E. Detection of hand-to-mouth gestures using a RF operated proximity sensor for monitoring cigarette smoking. Open Biomed Eng J 2013; 9: 41-9.
[http://dx.doi.org/10.2174/1874120701307010041] [PMID: 23723954]
].

Because of the importance of preventing chronic diseases that may result from excess of body weight and unhealthy diet, clinical trials and epidemiological studies have developed different research instruments in order to collect reliable data on dietary patterns of large populations. Classic tools for food assessment include 24-hours dietary recall, dietary record, dietary history and food-frequency questionnaire. All these methods give subjective measure using a predefined or open-ended, self- or interviewer-administered format [11Turner McGrievy B Using Bite Counter For Weight Loss. A One-Month Usability Trial to Test The Effectiveness of Using the Bite Counter (Bites) ClinicalTrialsgov 2016.]. Despite several advantages, such as low cost, suitability and accuracy, food questionnaires are not free from potential errors: the inclusion of open-ended questions can be time-consuming, patients cannot be able to remind daily diet, and predefined answers can be inaccurate. Moreover, the long-term monitoring increases the risk of underestimating the energy intake and return of incorrect or incomplete information about the quality and type of nutrient intake [12O Neil P. Assessing the Bite Counter. Available at: ClinicalTrialsgov ].

For these reasons, the assessment of caloric intake and energy expenditure is a crucial challenge for the maintenance of individual and public health. An accurate monitor-activity is essential to obtain information about the eating habits of subjects at risk (e.g. obese and diabetic), subjects whose dietary assessment is difficult and often inaccurate (e.g. elderly and children) and also of healthy subjects. This is even more important considering that a diet-therapy requires long period of treatment and often involves subjects at home.

A constant and accurate monitoring of calories and nutrient quality is essential both for the healthcare provider and the patient, to improve therapy effectiveness and compliance to diet. New wearable devices for caloric intake seem to be the future of scientific research on nutrition: easy to use, accurate, free of subjective bias, these new electronic tools are able to collect large amount of data.

These wearable devices can be divided into three macro categories according to the object or action captured by the device:

  1. Devices capturing the gestures related to nutrition, such as arm and wrist movements;
  2. Devices capturing sounds and vibrations from chewing and/or swallowing;
  3. Devices that identify the kind and the amount of food analyzing food digital images.

The aim of the present paper is to review wearable devices or methods for automatic caloric assessment which are currently on a research and development process, reporting for each tool a brief description, a case study and some consideration about pros and cons.

2. MATERIALS AND METHODS

The literature search started from PubMed search engine, using the string: (“device”[Title] OR “bite”[Title] OR “armband”[Title]) AND (“caloric intake”[Title] OR “intake”[Title]). The results allowed the extraction of the names of the devices described in this review. These names were searched through Google Scholar, PubMed, Scopus and ClinicalTrials.gov search engines. Papers published from 2005 onwards and dealing with application of the cited wearable devices in eating environment were considered. Articles regarding the same devices in other applications (i.e. physical movement, hydraulic, veterinary) were excluded. No literature reviews about this topic were found. A flowchart of the papers’ selection process is represented in Fig. (1).

3. RESULTS

Results are shown in Table 1, a list of each device/method with its description, experimental studies related to the specific device and method, and consideration about pros and cons. Four devices (Bite-counter, Autodietary, E-button, piezoelectric sensor-based necklace) and one method for caloric assessment which requires a personal smartphone (digital photography) are described. The devices or methods were tested in experimental studies to perform a process of validation, by asking participants to eat different kinds of food. The relative results provided an estimation of the accuracy of each tool, with consideration about limits and future developments.

Table 1
Devices description, experimental studies. Pros and cons.


Fig. (1)
Flowchart of the review method.


Table 2
Accuracy of the Bite counter in bite detection [5Desendorf J, Bassett DR Jr, Raynor HA, Coe DP. Validity of the Bite Counter device in a controlled laboratory setting. Eat Behav 2014; 15(3): 502-4.
[http://dx.doi.org/10.1016/j.eatbeh.2014.06.013] [PMID: 25064306]
].



Table 3
Accuracy of Autodietary in food recognition [13Bi Y, Lv M, Song C, Xu W, Guan N, Yi W. AutoDietary: A wearable acoustic sensor system for food intake recognition in daily life. IEEE Sens J 2016; 16: 806-16.
[http://dx.doi.org/10.1109/JSEN.2015.2469095]
].



Table 4
Accuracy of piezoelectric sensor-based necklace with accelerometer in food recognition [8Kalantarian H, Alshurafa N, Le T, Sarrafzadeh M. Monitoring eating habits using a piezoelectric sensor-based necklace. Comput Biol Med 2015; 58: 46-55.
[http://dx.doi.org/10.1016/j.compbiomed.2015.01.005] [PMID: 25616023]
].



4. DISCUSSION

Among devices containing inertial sensors (accelerometers and gyroscopes), capable of detecting the gesture of a body portion (arm or wrist) in a given space, the bite-counter measures both the vertical movement of the arm from the top downwards and the rotational movement of wrist. The gesture is translated into number of bites and these are translated into energy intake through the use of algorithms whose rational is based on the existence of a relationship between the number of bites and the number of calories eaten. In fact, although the instrument is not able to detect the type of food and the quality of the nutrients, and the calories per bite vary according to the food taken, the number of bites remains positively correlated to individually introduced calories [4Scisco JL, Muth ER, Hoover AW. Examining the utility of a bite-count-based measure of eating activity in free-living human beings. J Acad Nutr Diet 2014; 114(3): 464-9.
[http://dx.doi.org/10.1016/j.jand.2013.09.017] [PMID: 24231364]
].

Bite counter is one of the most promising self-monitoring tools for energy intake detection, both in healthy subjects and patients, thanks to its convenience and simplicity. Its use is already widespread and implemented in clinical and non-clinical setting [6Salley JN, Hoover AW, Wilson ML, Muth ER. Comparison between human and bite-based methods of estimating caloric intake. J Academy Nutrition Dietetics 2016; 31(10): 1568-77., 11Turner McGrievy B Using Bite Counter For Weight Loss. A One-Month Usability Trial to Test The Effectiveness of Using the Bite Counter (Bites) ClinicalTrialsgov 2016., 12O Neil P. Assessing the Bite Counter. Available at: ClinicalTrialsgov ]. Nevertheless, some limitations of the bite-counter must be acknowledged. First, bite-counter is susceptible to similar movements to the typical nutrition gestures and tends to register as bites movements of different nature. Second, it requires an active role of the subject in activating the tool and placing it on the dominant hand during the meal. Last, bite-counter does not provide any information about the quality of the food introduced [5Desendorf J, Bassett DR Jr, Raynor HA, Coe DP. Validity of the Bite Counter device in a controlled laboratory setting. Eat Behav 2014; 15(3): 502-4.
[http://dx.doi.org/10.1016/j.eatbeh.2014.06.013] [PMID: 25064306]
, 6Salley JN, Hoover AW, Wilson ML, Muth ER. Comparison between human and bite-based methods of estimating caloric intake. J Academy Nutrition Dietetics 2016; 31(10): 1568-77.].

Given its high potential for use in health research, bite-counter needs some improvements for a more accurate and reliable detection of caloric intake.

The electronic devices that detect swallowing and/or chewing sounds are often very accurate in the identification of the type of food, although they are able to recognize only a limited number of foods [7Amft O, Kusserow M, Tröster G. Bite weight prediction from acoustic recognition of chewing. IEEE Trans Biomed Eng 2009; 56(6): 1663-72.
[http://dx.doi.org/10.1109/TBME.2009.2015873] [PMID: 19272978]
] and do not provide information on the caloric intake. In more recent studies they are associated with other devices, such as motion and proximity sensors [8Kalantarian H, Alshurafa N, Le T, Sarrafzadeh M. Monitoring eating habits using a piezoelectric sensor-based necklace. Comput Biol Med 2015; 58: 46-55.
[http://dx.doi.org/10.1016/j.compbiomed.2015.01.005] [PMID: 25616023]
, 9Fontana JM, Higgins JA, Schuckers SC, et al. Energy intake estimation from counts of chews and swallows. Appetite 2015; 85: 14-21.
[http://dx.doi.org/10.1016/j.appet.2014.11.003] [PMID: 25447016]
], which are useful to reduce potential errors associated with movements of different nature and to obtain additional information. The biggest drawback of these tools is that they are uncomfortable to wear: in most cases, they must be worn as collars, and require careful use by the subject which has to minimize noise and accessory movements.

Finally, devices that capture digital images of foods before and after eating, allow the estimate of caloric intake from the analysis of pictures. Considering calories monitoring in free-living conditions, the Remote Food Photography Method (RFPM) is a semi-automatic method and its evolution is still in place: its function is based on the association of similar foods images by type and portion. Calculation software then allows the calculation of the calories and the analysis of the quality of nutrients introduced, providing a wide range of useful information for the subject and the researcher. The image technique takes advantage of common technologies, such as Smartphone, crossing data from multiple different database containing essential information on foods. However, detection is often affected by the quality of the image, by the position of food, and an important collaboration is requested from the subject.

CONCLUSION

Self-monitoring of caloric intake becomes more urgent as wrong eating habits of developed countries increase. Moreover, health problems like obesity, diabetes, or other diseases with a high need for dietary assistance require instruments for a major control of caloric intake. New technologies based on wearable devices allow people to monitor their energy and caloric intake of their meals. Different methods are available, but all of them are still in progress. Wrist devices based on the arm movement and bite counting seem to be the most comfortable, cheap and easy to use: they still need a correlation between the number of bites and calories ingested. Collars with sensors for the detection of chewing and swallowing showed high accuracy, but they lack in comfort and still do not give information about caloric intake. Methods of food photography showed accuracy in caloric content of foods, and are very accessible with instruments of common use as smartphones, but they are strictly bounded to the user’s intervention and images qualities. For these reasons, further research is needed on this field.

LIST OF ABBREVIATIONS

(NCDs)  = Non-communicable diseases
(WHO)  = World Health Organization

CONSENT FOR PUBLICATION

Not applicable.

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

ACKNOWLEDGEMENTS

Declared none.

REFERENCES

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