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ARTICLE IN PRESSG ModelJB-3231; No. of Pages 12

International Journal of Medical Informatics xxx (2015) xxx–xxx

Contents lists available at ScienceDirect

International Journal of Medical Informatics

journa l homepage: www. i jmi journa l .com

eview article

romising approaches of computer-supported dietary assessment andanagement—Current research status and available applications

ndreas G. Arens-Volland a,∗, Lübomira Spassova a, Torsten Bohn b

Luxembourg Institute of Science and Technology, IT for Innovative Services (ITIS) Department, 5, avenue des Hauts-Fourneaux, L-4362 Esch/Alzette,uxembourgLuxembourg Institute of Science and Technology, Environmental Research and Innovation (ERIN) Department, 41, rue du Brill, L-4422 Belvaux,uxembourg

r t i c l e i n f o

rticle history:eceived 17 November 2014eceived in revised form 11 August 2015ccepted 14 August 2015vailable online xxx

eywords:ietary recordsood diarieself-management

a b s t r a c t

Purpose: The aim of this review was to analyze computer-based tools for dietary management (includingweb-based and mobile devices) from both scientific and applied perspectives, presenting advantages anddisadvantages as well as the state of validation.Methods: For this cross-sectional analysis, scientific results from 41 articles retrieved via a medline searchas well as 29 applications from online markets were identified and analyzed.Results: Results show that many approaches computerize well-established existing nutritional conceptsfor dietary assessment, e.g., food frequency questionnaires (FFQ) or dietary recalls (DR). Both food recordsand barcode scanning are less prominent in research but are frequently offered by commercial applica-tions. Integration with a personal health record (PHR) or a health care workflow is suggested in the

ood intakeersonal health recordsbiquitous and mobile devices

literature but is rarely found in mobile applications.Conclusions: It is expected that employing food records for dietary assessment in research settings will beincreasingly used when simpler interfaces, e.g., barcode scanning techniques, and comprehensive fooddatabases are applied, which can also support user adherence to dietary interventions and follow-upphases of nutritional studies.

© 2015 Elsevier Ireland Ltd. All rights reserved.

ontents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 002. Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00

2.1. Data sources and search terms employed for article and app search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 002.2. Selection and exclusion criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 002.3. Data extraction and analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00

3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .003.1. General aspects of computer-supported dietary management and state of validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 003.2. Computer-supported dietary management for overweight, obesity, and weight-loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00

3.2.1. Scientific approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 003.2.2. Computer programs and mobile apps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .00

Please cite this article in press as: A.G. Arens-Volland, et

assessment and management—Current research status andhttp://dx.doi.org/10.1016/j.ijmedinf.2015.08.006

3.3. Computer-supported dietary management for diabetes . . . . . . . . . . .3.3.1. Scientific approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.3.2. Mobile apps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Abbreviations: 24-HDR, 24-hour dietary recall; DHQ, dietary history questionnaire;

ionnaire; FNDDS, food and nutrient database for dietary studies; FR, food records; ICT, inancer Institute; NICE, National Institute for Health and Clinical Excellence; PDA, person1DM, type 1 diabetes mellitus; T2DM, type 2 diabetes mellitus; TADA, technology assist∗ Corresponding author. Fax: +352 42 59 91 333.

E-mail address: [email protected] (A.G. Arens-Volland).

ttp://dx.doi.org/10.1016/j.ijmedinf.2015.08.006386-5056/© 2015 Elsevier Ireland Ltd. All rights reserved.

al., Promising approaches of computer-supported dietary available applications, Int. J. Med. Inform. (2015),

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00

DR, dietary recall; FDA, food and drug administration; FFQ, food frequency ques-formation and communication technology; mHealth, mobile health; NCI, Nationalal digital assistant; PHR, personal health record; RCT, randomized controlled trial;ed diet assessment; USDA, United States Department of Agriculture.

dx.doi.org/10.1016/j.ijmedinf.2015.08.006


http://www.sciencedirect.com/science/journal/13865056

http://www.ijmijournal.com

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ARTICLE IN PRESSG ModelIJB-3231; No. of Pages 12

2 A.G. Arens-Volland et al. / International Journal of Medical Informatics xxx (2015) xxx–xxx

4. Integrative summary and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 004.1. Principal results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 004.2. Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00

5. Conclusions and perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00Conflicts of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00Funding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00Authors’ contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00

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References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. Introduction

Diet-related chronic health complications, such as obesity, dia-etes, or food hypersensitivities are major public health burdens.ccording to a WHO report from 2004, diseases with major nutri-

ional determinants make up 41% of disability-adjusted life yearsmong all diagnosed diseases in Europe [1]. A healthy diet is aey component of a healthy lifestyle that can prevent the onsetf chronic diseases or mitigate their severity. However, despiteany efforts by national and international nutrition organizations

o promote healthy eating behavior, the prevalence of e.g. obe-ity, cardiovascular diseases and diabetes is still increasing in mostesternized but also in developing countries, an observation that

as been related to a too high consumption of total calories [2], tooany sugars [3], high sodium intake [4], and an insufficient intake

f dietary fiber [2,5], among others.In general, tackling the problem of being overweight and

bese is perceived as a difficult target, typically requiring complexifestyle changes with multi-dimensional support with respect tosychological, social, and clinical aspects, including dietary support6]. A lot of research has thus been carried out on means promotingehavioral changes, including personalized strategies such as goaletting and self-monitoring [7]. In addition to recording physicalctivity, self-monitoring involves the capturing of dietary intake toelp individuals to become aware of their current behavior. Earlyomputer-tailored dietary behavior interventions were introducedn the 1990s [8] and have become increasingly popular during theast decade [9]. The advent of portable technologies such as per-onal digital assistants and smartphones has particularly propelledesearch activities applying mobile health (mHealth) approachesn the field of diet management.

Although evidence for the efficacy of mHealth is generally sparse10], research has indicated that the use of hand-held devicesan improve the dietary intake of healthy food groups such ashole grains and vegetables [11]. The use of mHealth technology

lso has the potential to reduce health care costs and to improveell-being in numerous ways, for example through continuous

ealth monitoring, encouraging healthy patterns, and supportingelf-management [10,12,13]. In their systematic review, Kroezet al. [14] concluded that there is strong evidence in favor ofomputer-tailored interventions for improving dietary behavior.hese findings have also been supported by Long et al. in their 2010eview on technology employed for dietary assessment [15].

In 2009, Ngo et al. systematically reviewed the literature fortudies applying information and communication technology toietary assessment [16]. The authors found that most often food

requency questionnaires (FFQ), 24-hour diet recalls (24-HDR) andiet histories have been applied in ICT. To a lesser extent, foodecords (FR) or taking photos of foods were used. Rusin et al. lookedt logging techniques for measuring food intake, such as typingn or selecting a food type from a database [17]. They concluded



hat very few barcode-based solutions are available and that mostystems share information via e-mail, which cannot be seen as anntegrated solution. Their review, however, neglected input typesther than textual, such as photo documentation, which has also

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00

been used in dietary assessment [18,19] or self-monitoring tools[20]. The majority of scientific reviews have focused on specificdiseases, such as obesity [21–25] or diabetes [26–33], or on partic-ular application areas, such as nutritional epidemiology [9,34–37].No cross-sectional analysis of computer-based tools and appliedfunctions for dietary management exists in the literature.

In this article, we review the different fields in which computer-aided dietary assessment has been employed, aiming to givean overview of the state-of-the-art possibilities of computer-supported dietary management techniques from both scientific andapplied perspectives. The specific questions that are addressed inthis review are: (1) What current scientific evidence exists for theefficacy of computer-supported diet management approaches? (2)Which functionalities are offered by diet-related mobile apps? (3)Which similarities and differences between scientific approachesand available apps are there in terms of requirements concern-ing specific diseases? and (4) Which gaps exist between scientificresearch and commercially available applications in the respectiveareas?

It needs to be stressed that an analysis of any psychologicalaspects, such as social interactions or stress, which undoubtedlyplay an important role in computer-supported diet management,is beyond the scope of this article.

2. Methods

2.1. Data sources and search terms employed for article and appsearch

PubMed was searched to retrieve articles written in English andrelated to computer-supported dietary management approachesamong adults and children (Fig. 1). There were no boundaries setfor the time interval, as diet-related research involving computer-based technologies was expected to be rather novel. The searchwas performed between September 2013 and April 2014, andtitles and abstracts of articles were evaluated. Different searchterms were selected to represent information and communica-tion technology (ICT): “mobile Health”, “PDA”, “mobile computer”,“smartphone”, “handheld”, “cell phone”, “Internet”, “computer”,“web-based”, “website”, target domains of nutrition and health:“diet”, “healthy eating”, “eating”, “nutrition”, “food”, and nutrition-related diseases or conditions: “obesity”, “overweight”, “weightloss”, and “diabetes”. Using the PubMed advanced search inter-face, terms describing ICT were OR-combined and joined withOR-combined terms describing the target domains. The retrievedarticles were then evaluated for additional referenced sources. Inaddition to PubMed, online markets for iOS and Android applica-tions were searched, using similar terms as mentioned above forthe diet-related conditions and nutrition domains.

2.2. Selection and exclusion criteria


Only articles published in scientific peer reviewed journals andfull papers from conference proceedings were included. Themati-cally, any publication related to some form of dietary management



A.G. Arens-Volland et al. / International Journal of Medical Informatics xxx (2015) xxx–xxx 3

41 articles r ema ining

- 10 r eviews

- 31 original r esea rch

Researc h stu dies :

- Pub med- All years until present- Scientific (peer

reviewed) journals + full paper proceedings

Applicati ons ( comme rcially

available):

- Apple iPhone apps

- Android apps

“Co mmunication techno log y”: mob ile H ealth, PDA,

mob ile compu ter, smartpho ne, han dhe ld, cell

phone, Interne t, computer, web-ba sed , web-site

“Target domains”: diet, healthy eating, eating,

nutrition, food

“Di et-related diseases”: obesity, overweigh t,

weight-loss, diabetes

370 art icles rema ining

Filter on title: only individu als. No

institutions, not merely edu cationa l

2602 articles

- with abstracts

Filter: further abstract and manu script

screen ing; exploration of referen ces

29 mobile applicati ons

tions u

uppr

eibfmwwe

Fig. 1. Selection process of studies and applica

sing ICT offered to individual end users was included. Thus, allublications irrespective of their types of study design, partici-ant selection, and outcome measures have been considered in thiseview.

Approaches targeted to worksite or school settings werexcluded. Publications claiming to use Internet resources were alsoncluded, as recent mHealth approaches are often built upon web-ased approaches. Any applications including diet managementunctionalities, such as diet assessment, dietary advice, diet and

enu planning, social interaction and integration into health care



orkflow, i.e. communication with a counselor or synchronizationith personal health records (PHR), were considered, while merely

ducational applications were left out.

tilizing computer-aided dietary management.

2.3. Data extraction and analysis

The PubMed search retrieved 2602 articles with availableabstracts. After a first screening of the titles, we rejected papers thatwere clearly not related to diet management as described above, sothat 370 articles remained for further review. Based on an evalua-tion of the corresponding abstracts, 36 articles finally remained forin-depth evaluation, for which full text documents were retrievedand analyzed. Through exploring their reference lists, 5 additionalarticles were identified, resulting in a total number of 41 articles, of


which 10 were reviews and the remaining 31 were original researcharticles. A summary of these articles’ characteristics can be seen inTable 1.



4 A.G. Arens-Volland et al. / International Journal of Medical Informatics xxx (2015) xxx–xxx

Table 1Overview of studies employing ICT for dietary management.

Area No. of researcharticles reviewed

Type and no. of applied dietmanagement approach

Type and no. of appliedtechnology

References

General dietaryassessment

10 Dietary recall: 4 PDA: 2 [11,18,19,24,38–43]Food record: 6 Mobile phone: 5

Web-based: 3Obesity, overweight,weight-loss

12 Dietary recall: 2 PDA: 4 [21–23,37,44–51]Food record: 5 Mobile phone: 4Self-management: 5 Web-based: 4

Diabetes (type 1 and 2) 3 Food record: 2 PDA: 1 [19,26,28]Menu planning:1 Mobile phone: 1

Web-based: 1Validation/Epidemiology 5 Dietary recall: 3 Web-based: 4 [35–37,52,53]

Food record: 2 Mobile phone: 1

Abbreviations: PDA: personal digital assistant.

Table 2Characteristics of reviewed commercially available mobile applications in the area of dietary management.

Area No. Input techniques Diet recommendation/menuplanning

Integration with socialnetwork, HCP, or data export

Major missing aspects/shortcomings

Obesity, 20 Barcode scan: 6 Meal planning: 4 Social network: 11 No comprehensive underlyingfood databases

overweight, Picture taking: 6 Recipes: 1 HCP: 5weight-loss Typing in/selection form a list: 14

Speech input: 2Diabetes 9 Barcode scan: 2 Recipes: 2 Social network: 4 Most apps not approved as

medical applications;Picture taking: 2 HCP/data export: 8 Diet recommendations and

menu planning functionalitiesare missing;

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Typing in/selection form a list: 9

bbreviations: PHR: personal health record; HCP: health care professional.

In addition, we reviewed a total of 29 mobile applications (apps)elated to diet management in the relevant fields as describedbove, which were available in application stores. Table 2 describeshe characteristics of the evaluated apps.

. Results

.1. General aspects of computer-supported dietary managementnd state of validation

In a structured review on dietary assessment technologies inutritional epidemiology by Illner et al. [9] published in 2012, theuthors identified the real-time food recording capability as theain advantage of smartphones in the context of eating events,

.e., during meals. However, the validity of dietary intake assessedith this technology remains uncertain. Predominant advantages

nclude the cost- and time-effectiveness as well as a decreasedffort in terms of data collection and a high user acceptance. Accord-ng to the authors, many epidemiological studies have favoredelf-administered FFQs, which are poorly validated and include aigh number of systematic and random measurement errors, suchs no quantification or an imprecise estimation of portion sizes.owever, self-administered FFQs have the advantage of being less

ime consuming, and they are easier to integrate into the individualifestyle without perturbing the personal eating patterns. On thether hand, 24-HDRs are known for their high validity and goodeasurement properties, but they are quite expensive when used

s a main instrument, and due to their short time period covered9] they need some repetitions or a large number of participants



n order to balance out possible fluctuations. As a consequence,omputer- and web-based technologies have emerged to facilitatehe application of 24-HDRs to large populations in a cost-saving

anner.

Integration of PHR is missing

In a 2007 systematic review by Norman and Zabinski [54], inwhich eHealth interventions aiming at improving physical activityand/or healthy eating were reviewed, the most prominent solutionto assess dietary behavior was the use of self-report FFQ or dietaryrecalls. This finding is supported by the 2006 work of Kroeze et al.[14], which further emphasized the fact that FR were not widelyused in the mid of the last decade as compared to FFQ. Finally,Norman et al. identified an improved dietary behavior resultingin significant weight loss of subjects that were allowed to sharetheir collected data with health professionals and were able toreceive timely and personalized feedback. Unfortunately, concretenumbers were not reported.

Leatherdale and Laxer performed a validation study [53] to testfor the reliability and validity of the web-based FFQ eaTracker,developed by the Canadian national professional association fordietitians. For this purpose, 178 students in Ontario (Canada) usedthe eaTracker consumption diary [55] on a daily basis for a periodof one week. The authors found that the dietary intake measureswere accurate, thus supporting its potential use in research studieswhere other objective measures are not possible due to large-scalecohorts.

In a 2009 validation study of a web-based, pictorial version ofthe US National Cancer Institute (NCI) paper-based diet historyquestionnaire (DHQ) developed by Beasley et al. [36], the authorsfound that the web-based version yielded similar repeatability andvalidity compared to the paper-based version, when used by 218participants in randomized order. The study revealed a stable rela-tionship between DHQ and other food intake measurement tools,such as FR or 24-HDR. As a consequence, the practical advantages ofa web-based DHQ, such as remote administration, immediate nutri-


ent analysis or a potential reduction of missing responses, may leadto its further use in research.

Subar and colleagues [39,40] developed the web-based Auto-mated Self Administered 24-HDR (ASA24) for adults. The


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ARTICLEJB-3231; No. of Pages 12

A.G. Arens-Volland et al. / International Jou

espondents reported their meals through searching or browsingor foods in a hierarchical list, and afterwards, the portion sizes

ere estimated using digital food images. This approach has beenontinued in a version targeted at children [56], who are an impor-ant target group as eating behavior may be best influenced at earlyge, and children have difficulties articulating their eating patternsy means usually applied to adults, such as dietary records.

In 2011, Arab et al. validated the web-based DietDay 24-hourecall against the established NCI DHQ, using the doubly labeledater method with 233 healthy adults aged 21–69 years and

ound that the web-based 24-HDR could provide cost-effectivealid dietary intake reports [35]. The authors found that the validityf web-administered recalls was superior to paper-based FFQ withespect to delivering reproducible results across different ethnicroups.

In the framework of the technology assisted dietary assess-ent (TADA) project [38] of the Purdue University, the research

roup around Carol Boushey developed methods for food iden-ification and portion estimation [41,42] using pictures taken on

obile phones. The image analysis consisted of segmentation, fea-ure extraction, classification, volume estimation of portion size,nd finally, calorie and nutrient estimation using the food and nutri-nt database for dietary studies (FNDDS) curated by the Unitedtates Department of Agriculture. Early pilot trials [18,24] sug-ested good usability of this mobile phone food record, althoughhe authors admitted that further research is needed in order toncrease the accuracy of volume estimation of the approach. Unsur-risingly, it was found that mobile phone FR may be most likelydopted by adolescents, as these are the most enthusiastic usersnd require the least training to provide accurate diet assessments compared to adults, who are less efficient, i.e. taking more timentil reaching the same skill level.

In summary, many diverse approaches for computerized dietanagement are being pursued: first, well established and orig-

nally paper-based research tools such as FFQ, DHQ, 24-HDR areranslated into their respective electronic counterparts, whereashe application of electronic FR is still on a quite low level. Recallsnd FFQs are useful in population-based studies, but in clinical stud-es, the preferred dietary assessment method is FR [38]. Throughhone- and picture-based approaches, such as those developed inhe TADA project, electronic FR might replace the currently usedraditional FR methods.

.2. Computer-supported dietary management for overweight,besity, and weight-loss

.2.1. Scientific approachesApplications targeting weight monitoring and a balanced diet

onstitute the predominant part of computer-aided diet manage-ent. Bacigalupo et al. [57] systematically reviewed randomized

ontrolled trials (RCT) applying mobile technologies for self-onitoring activities in overweight and obese subjects. The

eviewed seven trials showed consistent evidence for short andedium-term weight-loss through the use of mobile technology

s part of the intervention delivery.Hutchessen et al. observed in a 2013 small-scale pilot study [58]

ith nine participants that the estimated energy intake obtainedy web-based FR is consistent with other published dietary intakeethods, such as total energy expenditure measured by the doubly

abeled water method, reaching an accuracy of 79.6% (SD = 14.1%).n a retrospective analysis [48] with 2979 women and 642 men,



ohnson et al. were already able to show in 2011 that participantsn the top third of engagement with electronic food diaries were

ore likely to achieve clinically significant weight losses, i.e., over% of initial body weight.

PRESSf Medical Informatics xxx (2015) xxx–xxx 5

Self-monitoring is a crucial and recognized factor for obesitytreatment. Regular interaction with a counselor (human or auto-matic) has shown to improve the results of weight loss programs[59]. Research has demonstrated that electronic self-monitoring,i.e., recording food intake and physical activity, is more effectivein terms of weight-loss than the more cumbersome paper diaries[45,51], as it is easier to use and less time consuming. Burke et al.were able to show in a 24-month RCT with 210 subjects aged18–59 years that a combination of electronic self-monitoring anddaily feedback tailored to the captured data and providing positivestimulations in form of motivational messages, resulted in the high-est user adherence levels (90%) and achieved weight-loss (63%),compared to groups with no intervention (46% and 55%) or onlyelectronic self-monitoring (80% and 49%). In another 2011 reviewby Burke et al. on self-monitoring activities for weight-loss, ana-lyzing US-based studies [7], the authors found that through dateand time stamping, an objective validation of the self-monitoringbehavior could be achieved. Extensive databases compiling infor-mation about foods and restaurant dishes eliminated the necessityto look up and calculate the sum of nutrients and calories. Inaddition, the possibility to store frequently consumed food itemseliminated the need for repeatedly searching identical entries.

Already in 2007, Yon et al. [50] tried to confirm the advan-tage of personal digital assistants (PDA) in a 24-week behavioralweight-loss study with 61 obese and overweight subjects usingCalorie King’s Diet Diary software on a PDA, compared to 115 sim-ilar subjects equipped with paper-based food diaries. Almost halfof the participants (44%) complained about the PDA and the pro-vided software due to shortcomings when trying to find commonlyconsumed foods. However, as no significant differences in weight-loss or diet self-monitoring (measured in% of weekly FR submitted)between the two groups were found, the authors concluded thatPDAs were at least comparable to traditional diaries.

In a 2010 review of efficient technology-based weight-lossinterventions [60], Khaylis et al. identified five key components thateffectively drive technology-supported weight loss and determinedits successful use: (1) self-monitoring, (2) frequent counselor feed-back and communication, (3) social support, (4) use of a structuredprogram, and (5) use of an individually tailored program allowingto adjust to the personal lifestyle.

In this respect, Krukowski et al. [61] recognized the “feedback”factor (progress charts, physiological calculators, and past journals)as the best predictor for efficient weight loss during the interven-tion time, here of up to 12 months. The “social support” factor onthe other hand was the best predictor for maintaining weight-lossafter the intervention, which may explain the somewhat mixedresults for long-term studies, in case social support may not bepresent. Table 3 summarizes the research body on successful useof computer-supported diet management.

3.2.2. Computer programs and mobile appsA large number of the food-related health and fitness apps that

are commercially available in different app stores, such as iTunes orGoogle Play, are related to calorie counting and weight-loss. Foodand calorie trackers, such as “MyFitnessPal”, “Lose It!”, or “CalorieCount” etc., allow users to log their daily food intake, define per-sonal weight loss goals and review and analyze the gathered data.One of the critical issues in this context is the entry of new itemsinto the food diary. Given the huge amount of possible food items, itis a particular challenge to implement an easy-to-use interface forfood logging. For this purpose, many apps, such as “Food Scanner”,“FitDay” or “Foodzy”, have incorporated custom food databases


containing nutritional information about a number of food prod-ucts and offering different options to access this data, such as viamanual search by typing in product names or hierarchical searchthrough food categories. Some apps, such as “Calorie Count” or


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xxx–xxxTable 3Studies demonstrating the successful use of computer-supported diet management with respect to weight management.

Reference and country of origin Type of computer basedintervention

Study design/analysis approach Subject characteristics Results

Acharya [21], 2011, USA PDA-based dietary record 6-month RCT; comparison between PDA- andpaper-based approaches; measures: dietary intakethrough 24-HDR, calculated calorie intake, bodyweight

192 white obese subjects aged18–59

PDA group significantly increased fruit (P = 0.02)and vegetable (P = 0.04) consumption compared topaper-based group;PDA group significantly decreased consumption ofrefined grains (P = 0.02) compared to paper-basedgroup;Both groups had significant reductions in weight,energy intake and calories (P < 0.001)

Anton [22], 2012, USA Web-based computerizedtracking system for FR,feedback and messaging

Within a 2 year RCT testing the efficacy of fourmacronutrient diets, an evaluation of the usageand effects of the web-based system has beenperformed.

811 healthy, obese/overweightmen and women aged 30–70

Participants with higher usage of the systemshowed higher weight loss (−8.7% of initial bodyweight) as compared to those with lower usage(−5.5%) (P < 0.001)

Arab [35], 2011, USA Web-based 24-HDR Validation study using the doubly labeled watermethod; comparison to FFQ results. Measures:body weight, dietary intake and total energyexpenditure.

115 black and 118 whitehealthy adults aged 21–69

Web-based dietary recalls offer an inexpensive andaccessible solution for dietary assessment; validityof web-administered recall was superior topaper-based FFQ with respect to delivering stableresults across different ethnic groups

Atienza [11], 2008, USA PDA-based dietary recall andeducation

8 week pilot RCT; intervention group monitoredtheir vegetable and whole-grain intake using aPDA; control group received written educationalmaterial related to nutrition in middle-aged andolder adults; measures: dietary intake assessed viaBlock FFQ.

27 subjects aged 50 or older Intervention participants reported significantlyhigher increased vegetable intake (1-5-2.5servings/day; P = 0.02) and greater intake of dietaryfiber from grains (3.7–4.5 servings/day; P = 0.10) ascompared to control.

Burke [7,45], 2011, USA PDA-based self-monitoringapplying FR and feedback

6-month RCT; three groups: (a) PDAself-monitoring, (b) Paper diary/record; (c) PDAself-monitoring plus feedback; measures:weight-change after 6 months and adherence overtime

210 healthy adults aged 18-59with mean BMI of 34.0 kg/m2

Combined approach (PDA + feedback) achieved >5%weight loss as compared to paper based records(P = 0.05) or electronic approach without feedback(P = 0.09); A greater proportion of PDA groups,compared to paper diary group, was adherent>60% of time (P = 0.03)

Cadmus-Bertram [62], 2013, USA Web-based self-monitoring foroverweight/obese women atincreased breast cancer risk

12-week RCT; intervention group (n = 33) usedSparkPeople website for self-monitoring (goalsetting, tracking diet and exercise); control group(n = 17) received dietary information only

50 overweight/obese womenat increased breast cancer risk

Intervention group lost 3.3 ± 4.0 kg, comparisongroup gained 0.9 ± 3.4 kg (P < 0.0001).

Carter [37], 2012, UK Smartphone-based dietaryassessment applying FR

1-week validation trial; used 7 days smartphoneapp; conducted twice a 24-HDR for reference;

50 healthy adults; mean age35; mean BMI 24

High correlation of recorded energy intakebetween both approaches: day 1: r 0.77 (95% CI0.62, 0.86), day 2: r 0.85 (95% CI 0.74, 0.91)

Carter [63], 2013, UK Smartphone-based dietaryassessment applying FR

6-month RCT; Smartphone group; Web-basedgroup; Paper-based group

128 healthy overweight (BMI>27 kg/m2) adults (aged 18–65)

Adherence was significantly higher in thesmartphone group compared with the websitegroup and the diary group (P < 0.001); Smartphonegroup showed the highest decrease in weight, BMI,and body fat compared to the two otherapproaches.

Thomas [49], 2013, USA Smartphone-basedself-monitoring applying FRkeeping and feedback

Pilot study, 12–24 weeks; measures: weight,adherence, physical activity, and satisfaction;compared to results from other primary literature.

20 overweight/obese(25–50 kg/m2) adults (aged18–70)

Weight-loss monitored was substantially largerthan the loss of 3–5% of initial body weightobtained with text message only-basedinterventions. Adherence to the self-monitoringprotocol was 91% (SE 3.3%) and 85% (SE 4.0%) at 12and 24 weeks, respectively. This was substantiallyhigher than rates seen in other trials of behavioralweight-loss treatment using paper diaries (e.g.55%)

Abbreviations: PDA, personal digital assistant; RCT, randomized controlled trial; FR, food record; 24-HDR 24-hour dietary recall; FFQ, Food frequency questionnaire; BMI, body mass index


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Bon’App” even enable voice input. More elaborated food trackerpps (e.g., “MyFitnessPal”, “Lose It!”, “Calorie Count”, “Fooducate”,FoodScanner”) provide barcode scanning possibilities using themartphone camera, which is supposed to facilitate the identifica-ion of branded food items.

However, the usability of these food entry options depends onhe completeness of the underlying food databases, i.e., data aboutood products can only be retrieved given their listing in the foodatabase used by the app. Thus, the quality of a food and calorie-racking app highly depends on the quality and quantity of datavailable in its food database. This is why some food tracking apps,ncluding “Foodzy”, “FoodScanner” and “FatSecret”, provide thepportunity for users to extend their food databases with customroducts, which are in some cases automatically included into thenderlying food database and thus made available for other users asell. Nevertheless, even the largest current food databases are still

ar from being complete and often contain only country-specificroducts. To evade this problem of lacking food-related data, somepps that implement photo-based food diaries have emerged. Fornstance, with “PhotoCalorie” or “MealSnap”, users only need toake a photo of their meal and provide a brief corresponding textualescription in order to create a food log entry, upon which corre-ponding nutritional values are automatically estimated. However,ser reports show that these values are often inaccurate.

Some food-related health apps aim at personalized meal plan-ing, taking into account the user’s health and weight-loss goalss well as previously defined food preferences. “Pocket Dietitian”ffers automated meal planning with regard to individual dailyutrient levels. The “intelli-Diet” app creates a personal well-alanced diet based on a list of favorite foods specified by the usernd an eating plan for each day of the week, and it even automati-ally generates a corresponding shopping list.

In 2009, Breton et al. [64] reviewed 204 apps available in theTunes app store for compliance with evidence-informed practicesn weight-loss. Of the reviewed apps, 43% provided tools for keeping

food diary but less than 10% offered advice on meal planning.ood nutritional databases were applied in one third of the appsn = 67), and only 15% of the apps (n = 30) were designed to be usedn conjunction with a website. A small fraction of 3% (n = 7) hadome type of social network integrated. Based on this study, it cane concluded that only a small portion of commercially availablepps allowed individual meal planning based on food databases.

In a recent 2013 pilot study with 20 overweight participantsver 12–24 weeks [49], Thomas and Wing showed that a smart-hone application offering self-monitoring functionalities achieveduch better effects with respect to weight-loss than only send-

ng supportive text messages (9% weight-loss on average insteadf only 3–5%). Furthermore, they found higher adherence ratesor an app-based self-monitoring protocol (91%) as compared to

paper-based diary (55%). For their study, the authors used theommercially available “DailyBurn” app for tracking food intake,eight, and physical activity combined with the self-developed

Health-E-Call” app for texting, providing supportive videos andther material, as well as for setting behavioral goals. This studylso highlights the need for integrated solutions, in which a mereracking of food intake is enhanced by additional support to furtherncrease the users’ motivation to adhere to the intervention. Thisdditional support appears to play a crucial role, similar to the sup-ort in conventional weight-loss programs accompanied by regulareetings with physicians and nurses. Many food and diet track-

rs offer forum groups and enable their users to share their foodiaries with friends in order to receive support and encouragement



e.g., through Twitter or Facebook). For example, the “SparkPeople”pp applies a game-like approach using leaderboards, prizes andwards to encourage a “friendly competition” concerning fitnessnd weight-loss. In some other cases, apps provide the opportunity


to connect to a health care professional. With “My Dietitian”, userscan receive customized daily feedback on their food journal froma personal registered dietitian. Other apps, e.g. “Pocket Dietitian”,allow users to export and email dietary reports to their physicians.

In a web-based computer-tailored intervention named FATaint-PHAT to promote energy balance among 883 overweightadolescents [47], Ezendam et al. did not find the expected reduc-tion of BMI and waist circumference. The two-group RCT showedonly minor positive dietary behavioral effects in the short term(4-month follow-up) but not in the long term (2-year follow-up).The intervention concept was a non-commercial web-based educa-tional platform that used FFQ and 24-HDR for capturing food intakeand provided goal setting, action planning and behavioral feedback,but this approach may have lacked sufficient support and motiva-tion tools as integrated by some of the other programs mentionedabove. Similarly, Bauer et al. [44] could not demonstrate a statisti-cally significant reduction in BMI Standard Deviation Scores for anSMS-based maintenance treatment with weekly self-monitoring ofdata on eating, exercise and emotions in a 12-week study compris-ing 40 overweight subjects.

In contrast, in 2012 Anton and his colleagues were able to showin a study with 811 participants that overweight subjects with ahigh usage of a web-based tracking system for dietary assessmentand feedback lost significantly greater amounts of weight than par-ticipants with low usage (8.7% versus 5.5% of initial body weight)[22]. The authors attributed the system’s success to its immedi-ate feedback on reported behaviors and dietary intake, includingassessment of key behavioral indicators of adherence that may notbe available in many other current applications.

In a 2012 validation study [37], Carter et al. compared 24-HDRconducted via phone interviews to smartphone-based food anddrink records employing a database comprising 40,000 commer-cial food items including generic and branded items [65] in anobese population of 50 subjects. On an individual level, large dis-agreement between both approaches could be monitored, but on agroup level, taking FR on mobile phones appeared to bear poten-tial as a diary assessment method, yielding results comparable tothe dietary recall approach. In a later pilot study in 2013 [63] with128 overweight subjects, the authors reported significantly higheradherence to their smartphone-based approach (92 days) as com-pared to web-based (35 days) and paper-based (29 days) methods.

In 2012, Lieffers et al. reviewed available studies on mobiledevices for food intake recording in healthy adult populations inrelation to general weight-loss approaches [25]. The authors differ-entiated between applications by means of record selection fromfood databases (e.g. USDA National Nutrient Database for StandardReference) and picture taking in conjunction with reference objectsand annotation through text or voice input. The authors found goodcorrelations for both methods regarding energy and nutrient intakein comparison with conventional methods (24-HDR, paper-basedFR).

3.3. Computer-supported dietary management for diabetes

3.3.1. Scientific approachesSimilar to obesity management, computer-aided diabetes man-

agement mainly consists of self-monitoring and education. The UKNational Institute for Health and Clinical Excellence (NICE) guide-lines for management of type 2 diabetes [66] “encourage high-fiber,low-glycemic-index sources of carbohydrate in the diet, such asfruit, vegetables, whole grains and pulses; include low-fat dairyproducts and oily fish; and control the intake of foods containing


saturated and trans fatty acids.”In a pilot study by Arsand et al. [26], five important points were

highlighted for IT devices designed for diabetes type I and II: (1)a complete food pick-list, (2) a smartphone touchscreen concept,


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entioned design concepts were viewed as useful and potentiallyowerful tools in both self-care and provider-supported care set-ings. The food pick-list should be constantly re-ordered based onrequency of item selection, and it should allow for specifying por-ion and serving sizes. Participants found both positive and negativeeinforcement cues appealing and rewarding in respect of theirrogress.

In a 2011 literature review and comparison of available mobileype I and II diabetes application features against evidence-baseduidelines [67], Chomutare et al. reviewed features includingelf-monitoring, social media integration, data export and commu-ication as well as synchronization with PHR systems or patientortals. Their literature research comprising 26 studies showed thatost approaches included some kind of PHR synchronization (69%),

nsulin and medication recording (65%), diet recording (65%), andata export and communication (62%). Most notably, Chomutaret al. revealed that PHR synchronization was present in only 17%f the applications available on the online markets (n = 101). Here,he most prominent features were insulin and medication record-ng (62%), data export and communication (60%), diet recording47%), and weight management (43%). It turned out that captur-ng consumed food items was a highly manual task, as the usersither had to estimate carbohydrates or navigate through an exten-ive food hierarchy or through a menu. The authors concluded thatost approaches introduced in the literature comprised PHR inte-

ration, which was not true for most apps available on the market,ighlighting the gap between scientifically reasonable approachesnd practically available strategies. In a 2013 short review by Goyalnd Cafazzo [68], the authors concluded that a significant potentialies in direct, real time communication between health profession-ls and individuals in order to be able to capture data electronically,nd thus to provide decision support more easily.

Menu planning and tools aiding in choosing dishes have showno be important features for diabetes patients. In a pilot study [28]ith 33 type II diabetes mellitus (T2DM) adults, Bader et al. were

ble to show that web-based menu planning during a 24 weekeriod had the potential to lead to clinically important weighteductions (above 5%) in more than 25% of the adherent partici-ants.

.3.2. Mobile appsMobile apps in the diabetes domain have become increas-

ngly abundant. Important drivers for successful app developmentre consumer expectations. In a 2011 review on diabetes-relatedelemedicine approaches by Franc et al. [69], the authors sum-

arized the patient expectations into three concepts: (1) Anasy to use mobile and pocket-sized system to improve theompliance as compared to systems involving desktop com-uters; (2) Systems should respond immediately to patients’uestions and provide automatic assessment of carbohydrate con-ents through using a reliable food database, while in addition,he devices should also guide food choices through an onboardatabase; (3) Interactions with a known caregiver as a keyomponent for the success of telemedical systems for diabetesare.

Besides the possibility to log food intake (carbohydrates),ost apps targeted at diabetes management allow the logging

f other relevant parameters, such as blood glucose, dosagef insulin, blood pressure, pulse, weight, and sport activities.MyNetDiary Diabetes Tracker” provides a tool for the daily and



eekly analysis of the logged data to support users in improv-ng their diet. Furthermore, it offers diet planning, allowingsers to define their individual macronutrient targets. With “Dia-etes In Check”, users have access to diabetes-friendly recipes,

PRESSf Medical Informatics xxx (2015) xxx–xxx

sample meal plans and customized daily menus, and they receivetips and constructive feedback on a regular basis as a moti-vation to improve their medical condition. Such motivationalaspects also play an important role in the “mySugr Diabetes Com-panion” app. In this tool, users receive immediate feedback ontheir entries through a virtual character called “diabetes mon-ster”.

With respect to data management, which is important for shar-ing with other health professionals, most apps offer the possibilityto export a report as a printable PDF or Excel file, or even a directtransmission of the report to a physician via e-mail. Reminders tomeasure blood glucose, to take medication, or to track food andexercise are also implemented in many diabetes management apps.Social interaction with a community seems to be less importantthan with weight-loss apps, perhaps as the personal motivation ofsubjects with a disease is stronger, and the link to health profes-sionals is usually given. Nevertheless, some diabetes apps integratesocial media interfaces. The “Glucose Buddy Diabetes Log” appoffers Facebook and Twitter functionalities, while “Diabetes InCheck” provides access to community message boards for postingpersonal questions, sharing success stories and providing supportto others.

In a recent review by Eng and Lee [70], available iPhone apps(n = 492) related to diabetes management were scrutinized basedon their summary descriptions. Most of the apps (33%) focused onhealth tracking, such as blood sugar, insulin doses, and carbohy-drates, involving manual entry, but only 8% of the analyzed appsprovided food reference databases. Only two apps allowed captur-ing blood sugar levels through glucometers directly attached to thesmartphone. Additional features were teaching/training (8%), socialblogs/forums (5%) and physician-directed apps (8%). The authorshighlighted that only the “WellDoc” system appeared suitable fordirect integration into health care workflows or Electronic MedicalRecord systems. Further, only “WellDoc”, “Glooko”, and “IGBStar”have received clearance from the US Food and Drug Administration(FDA). The authors pointed out safety concerns about the majorityof the apps, which were non-FDA certified, although they should beso according to safety regulations. In line with these observations,El-Gayar and colleagues concluded in a 2013 review of commer-cial applications for diabetes self-management [31] that mobileapplications have the potential to positively impact diabetes self-management, but also identified limitations, such as the lack ofpersonalized feedback, usability issues, such as difficult data entry,and missing integration with PHR.

Arsand et al. concluded in a review article on mobile healthapplications assisting patients with diabetes [27] that using mobilephone picture diaries is useful for the identification of treatmentobstacles for type 1 diabetes mellitus (T1DM) patients. It was fur-ther suggested that the food information on phones for T2DMshould not be too fine-grained, as too much detailed informationmay result in user discouragement and little user friendliness. Leeet al. concluded in their review on mobile terminal-based tools fordiabetes diet management [33] that in order to make such mobiletools feasible for diet management, these should enable the record-ing of food intake in an easy but accurate manner, and suggestedthat photographs could be a meaningful strategy.

4. Integrative summary and discussion


This review aimed at providing a descriptive overview of thecurrent status of computer-supported diet management, integrat-ing scientific evidence as well as highlighting important aspects ofcommercially available applications and developments in this field.



A.G. Arens-Volland et al. / International Journal of Medical Informatics xxx (2015) xxx–xxx 9

Table 4Summary of tools in computerized diet management and associated advantages and disadvantages and scientific evidence across application areas.

Type of dietaryassessment &self-management

Dietarymanagementtechniques applied

Associatedadvantages

Associateddrawbacks

Scientific evidence References

Web-based 24-HDR;self-monitoring;FR; goal setting;feedback;

Inexpensive;widely accessible;

Tedious orcumbersome FRinput due to userinterfacelimitations;

RCT (n = 3), pilot (n = 3), usability (n = 3) trialsand one retrospective analysis in areas ofoverweight/obesity, weight-loss, and diabetes.Additionally, four validation studies have beenperformed.Study measures included dietary intake,physical activity (minutes/week), energyexpenditure, body weight and height.Sample sizes ranged from 9 (pilot validationstudy) to 3621 subjects (retrospectiveanalysis), age range 18–70.

[22,26,28,35,36,39,40,43,46–48,52,53,58,62,63,72]

Mobile FR; 24-HDR;self-monitoring;feedback; foodpicture diary

High validitycompared totraditionalmethods; high useracceptance anduser adherence;effective for

Expensive; onlypreliminaryevidence ofeffectiveness offood picture diary.

RCT (n = 4), pilot (n = 5) or usability studies(n = 2) available in areas of overweight/obesity,weight-loss. Only one RCT for diabetes and onevalidation study. Study measures includeddietary intake, calculated calorie intake, bodyweight change, adherence and satisfaction.Sample sizes ranged from 27 to 365 subjects,

[11,18,19,21,23,24,37,38,41,42,44,45,49–51]

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bbreviations: 24-HDR: 24-hour dietary recall; FR: food record; RCT: randomized c

.1. Principal results

From a scientific viewpoint, it can be assumed that web-basednly solutions for tackling obesity in young adults are not effective47]. Mobile solutions are at least equally effective as traditionalaper-based methods [50], and they appear superior to traditionalpproaches when allowing for personalized feedback [45]. Further-ore, Burke et al. demonstrated that with suitable functionality

access to food database) and integration into the health care work-ow (giving feedback with respect to patient actions), a high levelf adherence and weight-loss can be achieved [7,45]. This is sup-orted by the findings of Yon et al. [50], who argued that suitable

ood databases are required to achieve a satisfying user experience.enerally, in research, FFQs and 24-HDRs are the most commonlysed tools for food intake monitoring; occasionally FRs are offered,nd barcode-scanning is hardly ever used for food record input. Thiss clearly related to the fact that the commonly used food data in theeviewed settings were most often derived from food compositionatabases and only rarely from food product databases that con-ain information on branded food products. The end-users’ demandor suitable food databases and patient-caregiver interaction haslso been identified by Franc et al. [69]. Existing scientific evidenceor web-based and mobile dietary management is summarized inable 4, also highlighting associated advantages and drawbacks.

In this respect, it is noteworthy to emphasize the general limi-ations of employing food composition databases for determiningutritional and caloric composition of the finally consumed prod-ct, which may differ from the values captured in the database dueo “factory to fork” losses, i.e., following storage and kitchen proce-ures applied, such as freezing/thawing, mixing, chopping, heatingtc. [71]. However, this applies to all underlying databases and isot limited to computer aided dietary assessment. Additional limi-ations are the natural variations of the listed food items as well ashe difficulty of the consumer to judge serving sizes.

With respect to the used app features, commercially availablepps show increased innovative functionalities, such as keepingR via barcode scanning and diet documentation through pho-ographs. Promising approaches for picture-based identification of



ood as well as for calorie and nutrient estimation exist in research18,38,41,42], but apps that implement these features are oftennaccurate. This is due to the discrepancy between the limited lab-ratory settings in research as compared to the varying real-life

including children, adolescents, adults andelderly.

lled trial.

environments. The opportunity to share information and experi-ences and to receive feedback and support from a community oflike-minded people seems to be an important aspect concerningapps targeting weight-loss.

It was noticed that an integration with a PHR or in the health careworkflow is present in the literature [30,70,73], while in contrast,applications accessible in the app stores generally do not providesuch features. At most, the apps allow for data export (e.g., viaemail) to health care professionals, however, they fail to activelyengage them. The “WellDoc” app [70] is a notable exception in thiscontext.

With the aim to promote health-related apps of high quality,the British National Health Service (NHS) offers an online HealthApps Library [74]. It contains health apps from different domains(e.g. diabetes, nutrition, heart, cancer) that have been reviewedand approved by the NHS. Doctors are encouraged to prescribesuch apps to their patients in order to facilitate and improve theirtreatment.

4.2. Limitations

As this review has integrated data from scientific publications,the described functionalities and findings were taken only from thepublished articles and were not further tested or verified. Due to thehuge number of applications already available and the extremelyrapid development of the market, combined with time constraints,we were unable to take into account all available applications in thepresent review, and we were far from being able to test all theseapplications. We thus had to rely on the descriptions made in thecatalogues of the suppliers and in the corresponding test reports.

A lot of scientific and practical work with respect to computerdiet management has been carried out in the field of obesity anddiabetes. Some of the publications reviewed in this article are highlevel clinical studies, but others report the results of observationalor usability studies. Scientific studies concerning diet managementin the areas of other medical conditions, such as food hypersen-sitivities, cancer or CVD, are rather sparse, and they are thus notconsidered in this review.


5. Conclusions and perspectives

The need for well-established, reliable and affordable tech-niques for monitoring food intake (FFQ, 24-HDR, FR) is evident,


ARTICLE ING ModelIJB-3231; No. of Pages 12

10 A.G. Arens-Volland et al. / International Journal o

Summary pointsWhat was already known on the topic

• Computer-supported dietary assessment and managementtechnologies support the user and health care professionalin food intake monitoring.

• Various techniques are employed in different applicationareas, such as weight-loss, diabetes etc., as well as in sci-entific and commercial settings.

What this study added to our knowledge

• Many computer-based approaches implement well-established nutritional concepts for dietary assessment.

• Both food records and barcode scanning are less promi-nent in research but are frequently offered within commercialapplications.

• Integration with a personal health record (PHR) or a healthcare workflow is suggested in the literature but is rarelyfound in commercial applications.

• Major challenges in the context of computer-supported dietmanagement:

• Simple, intuitive and robust user interfaces for input of foodrecords.

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• Comprehensive and reliable food databases for packed food.

oth for collecting large data with a scientific purpose, but also forndividuals controlling their own dietary behavior. Innovative input

ethods (barcode scanning, FR) that are largely available in com-ercial apps have so far been used only rarely in science. However,

everal studies have indicated that computer-supported diet man-gement has many advantages, including efficacy and efficiency asell as the possibility to collect detailed nutrition-related data and

o offer in-time communication and feedback.In our opinion, there are two major factors that will drive the

sability and acceptance of computer-supported FR: (1) the devel-pment of simple, intuitive and robust user interfaces for the inputf food records, especially for mobile diet-related apps; and (2) thevailability of comprehensive food databases for packed food thatrovide reliable data. The first challenge might be tackled through

urther developments in the area of picture-based food recognition,eading to a more accurate identification of food type and serv-ng sizes, or through the incorporation of novel approaches, suchs spectrometer-based nutrient recognition (e.g., TellSpec [75]) ornobtrusive sensors, such as the ear-worn device “BitBite”, whichses a microphone to recognize chewing patterns and to allow voice

nput of food records [76].Concerning the second challenge, we believe that a combina-

ion of established food composition databases and food productatabases might be a favorable solution for achieving a compre-ensive food database suitable for use in computer-supported dietanagement applications. On the other hand, the lacking integra-

ion and financing of available mobile apps in the health care sectorith a clear legal status is a major disadvantage that should be over-

ome in order to facilitate access of a broader population to suchealth-supporting tools.

In summary, electronic means for dietary management havehown to offer some advantages over traditional ones, i.e. paper-ased approaches. However, it should be kept in mind thatomputer-supported dietary assessment merely presents onetrategy of dietary support, and it should be ideally combined withther means, including e.g. psychological and social support, to suc-



essfully motivate healthy behavioral changes toward e.g. weightoss. In this context, further validation studies evaluating the effec-iveness of computer-supported dietary management are required.

[


Conflicts of interest

None declared.

Funding

This work was supported by the ERDF (European Research andDevelopment Fund).

Authors’ contributions

Arens-Volland was responsible for the organization and creationof the manuscript. He performed literature search and evaluation ofarticles. Spassova reviewed available mobile apps and web-basedsolutions and contributed to the development and content of themanuscript. Bohn reviewed analyses and scientific findings of sci-entific articles and aided in overall manuscript structuring. Allauthors reviewed and contributed to the preparation of the finalmanuscript.

References

[1] A. Robertson, C. Tirado, T. Lobstein, M. Jermini, C. Knai, J.H. Jensen, et al., Foodand health in Europe: a new basis for action, WHO Reg. Publ. Eur. Ser. i–xvi(2004) 1–385.

[2] B.A. Swinburn, I. Caterson, J.C. Seidell, W.P.T. James, Diet, nutrition and theprevention of excess weight gain and obesity, Public Health Nutr. 7 (2004)123–146.

[3] F.B. Hu, Resolved: there is sufficient scientific evidence that decreasingsugar-sweetened beverage consumption will reduce the prevalence ofobesity and obesity-related diseases, Obes. Rev. 14 (2013) 606–619.

[4] C.A. Grimes, L.J. Riddell, K.J. Campbell, C.A. Nowson, Dietary salt intake,sugar-sweetened beverage consumption, and obesity risk, Pediatrics 131(2013) 14–21.

[5] J. Salas-Salvadó, M.Á. Martinez-González, M. Bulló, E. Ros, The role of diet inthe prevention of type 2 diabetes, Nutr. Metab. Cardiovasc. Dis. 21 (Suppl 2)(2011) B32–B48.

[6] C. Robertson, D. Archibald, A. Avenell, F. Douglas, P. Hoddinott, E. vanTeijlingen, et al., Systematic reviews of and integrated report on thequantitative, qualitative and economic evidence base for the management ofobesity in men, Health Technol. Assess. 18 (2014) 1–424, v–vi, xxiii–xxix.

[7] L.E. Burke, J. Wang, M.A. Sevick, Self-monitoring in weight loss: a systematicreview of the literature, J. Am. Diet Assoc. 111 (2011) 92–102.

[8] L.M. Neville, B. O’Hara, A.J. Milat, Computer-tailored dietary behaviour changeinterventions: a systematic review, Health Educ. Res. 24 (2009) 699–720.

[9] A.-K. Illner, H. Freisling, H. Boeing, I. Huybrechts, S.P. Crispim, N. Slimani,Review and evaluation of innovative technologies for measuring diet innutritional epidemiology, Int. J. Epidemiol. 41 (2012) 1187–1203.

10] S. Kumar, W.J. Nilsen, A. Abernethy, A. Atienza, K. Patrick, M. Pavel, et al.,Mobile health technology evaluation, Am. J. Prev. Med. 45 (2013) 228–236.

11] A.A. Atienza, A.C. King, B.M. Oliveira, D.K. Ahn, C.D. Gardner, Using hand-heldcomputer technologies to improve dietary intake, Am. J. Prev. Med. 34 (2008)514–518.

12] C.K.L. Or, D. Tao, Does the use of consumer health information technologyimprove outcomes in the patient self-management of diabetes? Ameta-analysis and narrative review of randomized controlled trials, Int. J.Med. Inf. 83 (2014) 320–329.

13] O. El-Gayar, P. Timsina, N. Nawar, W. Eid, A systematic review of IT fordiabetes self-management: are we there yet? Int. J. Med. Inf. 82 (2013)637–652.

14] W. Kroeze, A. Werkman, J. Brug, A systematic review of randomized trials onthe effectiveness of computer-tailored education on physical activity anddietary behaviors, Ann. Behav. Med. 31 (2006) 205–223.

15] J.D. Long, L.A. Littlefield, G. Estep, H. Martin, T.J. Rogers, C. Boswell, et al.,Evidence review of technology and dietary assessment, Worldviews Evid.Based Nurs. 7 (2010) 191–204.

16] J. Ngo, A. Engelen, M. Molag, J. Roesle, P. García-Segovia, L. Serra-Majem, Areview of the use of information and communication technologies for dietaryassessment, Br. J. Nutr. 101 (Suppl) (2009) 102–112.

17] M. Rusin, E. Arsand, G. Hartvigsen, Functionalities and input methods forrecording food intake: a systematic review, Int. J. Med. Inf. 82 (2013) 653–664.

18] B.L. Six, T.E. Schap, F.M. Zhu, A. Mariappan, M. Bosch, E.J. Delp, et al.,Evidence-based development of a mobile telephone food record, J. Am. Diet.


19] W. Fukuo, K. Yoshiuchi, K. Ohashi, H. Togashi, R. Sekine, H. Kikuchi, et al.,Development of a hand-held personal digital assistant-based food diary withfood photographs for Japanese subjects, J. Am. Diet. Assoc. 109 (2009)1232–1236.


http://refhub.elsevier.com/S1386-5056(15)30030-7/sbref0005










































































































































































































































































































































































































































































































































































































ING ModelI

rnal o

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[



20] N. Ramanathan, D. Swendeman, W.S. Comulada, D. Estrin, M.J.Rotheram-Borus, Identifying preferences for mobile health applications forself-monitoring and self-management: focus group findings fromHIV-positive persons and young mothers, Int. J. Med. Inf. 82 (2013) e38–e46.

21] S.D. Acharya, O.U. Elci, S.M. Sereika, M.A. Styn, L.E. Burke, Using a personaldigital assistant for self-monitoring influences diet quality in comparison to astandard paper record among overweight/obese adults, J. Am. Diet. Assoc. 111(2011) 583–588.

22] S.D. Anton, E. LeBlanc, H.R. Allen, C. Karabetian, F. Sacks, G. Bray, et al., Use of acomputerized tracking system to monitor and provide feedback on dietarygoals for calorie-restricted diets: the POUNDS LOST study, J. Diabetes Sci.Technol. 6 (2012) 1216–1225.

23] B.C. Batch, C. Tyson, J. Bagwell, L. Corsino, S. Intille, P.-H. Lin, et al., Weight lossintervention for young adults using mobile technology: design and rationaleof a randomized controlled trial-Cell phone Intervention for You (CITY),Contemp. Clin. Trials 37 (March (2)) (2014) 333–341.

24] B.L. Daugherty, T.E. Schap, R. Ettienne-Gittens, F.M. Zhu, M. Bosch, E.J. Delp,et al., Novel technologies for assessing dietary intake: evaluating the usabilityof a mobile telephone food record among adults and adolescents, J. Med.Internet Res. 14 (2012) e58.

25] J.R.L. Lieffers, R.M. Hanning, Dietary assessment and self-monitoring withnutrition applications for mobile devices, Can. J. Diet. Pr. Res. 73 (2012)e253–e260.

26] E. Arsand, J.T. Tufano, J.D. Ralston, P. Hjortdahl, Designing mobile dietarymanagement support technologies for people with diabetes, J. Telemed.Telecare 14 (2008) 329–332.

27] E. Årsand, D.H. Frøisland, S.O. Skrøvseth, T. Chomutare, N. Tatara, G.Hartvigsen, et al., Mobile health applications to assist patients with diabetes:lessons learned and design implications, J. Diabetes Sci. Technol. 6 (2012)1197–1206.

28] A. Bader, R. Gougeon, L. Joseph, D. Da Costa, K. Dasgupta, Nutritionaleducation through internet-delivered menu plans among adults with type 2diabetes mellitus: pilot study, JMIR Res. Protoc. 2 (2013) e41.

29] F. Cameron, G. Niemeyer, B.A. Buckingham, Probabilistic evolving mealdetection and estimation of meal total glucose appearance, J. Diabetes Sci.Technol. 3 (2009) 1022–1030.

30] T. Chomutare, L. Fernandez-Luque, E. Arsand, G. Hartvigsen, Features ofmobile diabetes applications: review of the literature and analysis of currentapplications compared against evidence-based guidelines, J. Med. InternetRes. 13 (2011) e65.

31] O. El-Gayar, P. Timsina, N. Nawar, W. Eid, Mobile applications for diabetesself-management: status and potential, J. Diabetes Sci. Technol. 7 (2013)247–262.

32] S. Franc, A. Daoudi, S. Mounier, B. Boucherie, D. Dardari, H. Laroye, et al.,Telemedicine and diabetes: achievements and prospects, Diabetes Metab. 37(2011) 463–476.

33] E. Lee, N. Tatara, E. Arsand, G. Hartvigsen, Review of mobile terminal-basedtools for diabetes diet management, Stud. Health Technol. Inf. 169 (2011)23–27.

34] A.-K. Illner, U. Harttig, G. Tognon, D. Palli, S. Salvini, E. Bower, et al., Feasibilityof innovative dietary assessment in epidemiological studies using theapproach of combining different assessment instruments, Public Health Nutr.14 (2011) 1055–1063.

35] L. Arab, C.-H. Tseng, A. Ang, P. Jardack, Validity of a multipass, web-based,24-hour self-administered recall for assessment of total energy intake inblacks and whites, Am. J. Epidemiol. 174 (2011) 1256–1265.

36] J.M. Beasley, A. Davis, W.T. Riley, Evaluation of a web-based, pictorial diethistory questionnaire, Public Health Nutr. 12 (2009) 651–659.

37] M.C. Carter, V.J. Burley, C. Nykjaer, J.E. Cade, My meal mate (MMM): validationof the diet measures captured on a smartphone application to facilitateweight loss, Br. J. Nutr. (2012) 1–8.

38] N. Khanna, C.J. Boushey, D. Kerr, M. Okos, D.S. Ebert, E.J. Delp, An overview ofthe technology assisted dietary assessment project at Purdue University, ISM(2010) 290–295.

39] A.F. Subar, J. Crafts, T.P. Zimmerman, M. Wilson, B. Mittl, N.G. Islam, et al.,Assessment of the accuracy of portion size reports using computer-basedfood photographs aids in the development of an automated self-administered24-hour recall, J. Am. Diet. Assoc. 110 (2010) 55–64.

40] A.F. Subar, F.E. Thompson, N. Potischman, B.H. Forsyth, R. Buday, D. Richards,et al., Formative research of a quick list for an automated self-administered24-hour dietary recall, J. Am. Diet. Assoc. 107 (2007) 1002–1007.

41] F. Zhu, M. Bosch, I. Woo, S. Kim, C.J. Boushey, D.S. Ebert, et al., The use ofmobile devices in aiding dietary assessment and evaluation, IEEE J. Sel. TopSignal Process 4 (2010) 756–766.

42] F. Zhu, M. Bosch, C.J. Boushey, E.J. Delp, An image analysis system for dietaryassessment and evaluation, Proc. Int. Conf. Image Proc. (2010) 1853–1856.

43] T.P. Zimmerman, S.G. Hull, S. McNutt, B. Mittl, N. Islam, P.M. Guenther, et al.,Challenges in converting an interviewer-administered food probe database toself-administration in the National Cancer Institute AutomatedSelf-administered 24-Hour Recall (ASA24), J. Food Compos. Anal. 22 (2009)S48–S51.



44] S. Bauer, J. de Niet, R. Timman, H. Kordy, Enhancement of care throughself-monitoring and tailored feedback via text messaging and their use in thetreatment of childhood overweight, Patient Educ. Couns. 79 (2010) 315–319.

45] L.E. Burke, M.B. Conroy, S.M. Sereika, O.U. Elci, M.A. Styn, S.D. Acharya, et al.,The effect of electronic self-monitoring on weight loss and dietary intake: a

[

[


randomized behavioral weight loss trial, Obesity (Silver Spring) 19 (2011)338–344.

46] N.P.M. Ezendam, V.S.A. Noordegraaf, W. Kroeze, J. Brug, A. Oenema, Processevaluation of FATaintPHAT, a computer-tailored intervention to preventexcessive weight gain among Dutch adolescents, Health Promotion Int. 28(2013) 26–35.

47] N.P.M. Ezendam, J. Brug, A. Oenema, Evaluation of the Web-basedcomputer-tailored FATaintPHAT intervention to promote energy balanceamong adolescents: results from a school cluster randomized trial, Arch.Pediatr. Adolesc. Med. 166 (2012) 248–255.

48] F. Johnson, J. Wardle, The association between weight loss and engagementwith a web-based food and exercise diary in a commercial weight lossprogramme: a retrospective analysis, Int. J. Behav. Nutr. Phys. Act. 8 (2011) 83.

49] J.G. Thomas, R.R. Wing, Health-E-Call, a smartphone-assisted behavioralobesity treatment: pilot study, JMIR Mhealth Uhealth 1 (2013) e3.

50] B.A. Yon, R.K. Johnson, J. Harvey-Berino, B.C. Gold, A.B. Howard, Personaldigital assistants are comparable to traditional diaries for dietaryself-monitoring during a weight loss program, J. Behav. Med. 30 (2007)165–175.

51] L.E. Burke, M.A. Styn, S.M. Sereika, M.B. Conroy, L. Ye, K. Glanz, et al., UsingmHealth technology to enhance self-monitoring for weight loss: arandomized trial, Am. J. Prev. Med. 43 (2012) 20–26.

52] J. Freese, S. Feller, U. Harttig, C. Kleiser, J. Linseisen, B. Fischer, et al.,Development and evaluation of a short 24-h food list as part of a blendeddietary assessment strategy in large-scale cohort studies, Eur. J. Clin. Nutr. 68(2014) 324–329.

53] S.T. Leatherdale, R.E. Laxer, Reliability and validity of the weight status anddietary intake measures in the COMPASS questionnaire: are the self-reportedmeasures of body mass index (BMI) and Canada’s food guide servings robust?Int. J. Behav. Nutr. Phys. Act. 10 (2013) 42.

54] G. Norman, M. Zabinski, A review of eHealth interventions for physical activityand dietary behavior change, Am. J. Prev. Med. 33 (2007) 336–345.e16.

55] Dieticians of Canada, eaTracker, (2015). http://www.eatracker.ca (accessed5.8.15.).

56] T. Baranowski, N. Islam, D. Douglass, H. Dadabhoy, A. Beltran, J. Baranowski,et al., Food intake recording software system, version 4 (FIRSSt4): aself-completed 24-h dietary recall for children, J. Hum. Nutr. Diet. 27 (Suppl1) (2014) 66–71.

57] R. Bacigalupo, P. Cudd, C. Littlewood, P. Bissell, M.S. Hawley, H. BuckleyWoods, Interventions employing mobile technology for overweight andobesity: an early systematic review of randomized controlled trials, Obes.Rev. (2012).

58] M.J. Hutchesson, H. Truby, R. Callister, P.J. Morgan, P.S.W. Davies, C.E. Collins,Can a web-based food record accurately assess energy intake in overweightand obese women? A pilot study, J. Hum. Nutr. Diet. 26 (Suppl 1) (2013)140–144.

59] D.F. Tate, E.H. Jackvony, R.R. Wing, A randomized trial comparing humane-mail counseling, computer-automated tailored counseling, and nocounseling in an Internet weight loss program, Arch. Intern. Med. 166 (2006)1620–1625.

60] A. Khaylis, T. Yiaslas, J. Bergstrom, C. Gore-Felton, A review of efficacioustechnology-based weight-loss interventions: five key components, Telemed.J. E. Health 16 (2010) 931–938.

61] R.A. Krukowski, J. Harvey-Berino, T. Ashikaga, C.S. Thomas, N. Micco,Internet-based weight control: the relationship between web features andweight loss, Telemed. J. E. Health 14 (2008) 775–782.

62] L. Cadmus-Bertram, J.B. Wang, R.E. Patterson, V.A. Newman, B.A. Parker, J.P.Pierce, Web-based self-monitoring for weight loss among overweight/obesewomen at increased risk for breast cancer: the HELP pilot study,Psychooncology 22 (2013) 1821–1828.

63] M.C. Carter, V.J. Burley, C. Nykjaer, J.E. Cade, Adherence to a smartphoneapplication for weight loss compared to website and paper diary: pilotrandomized controlled trial, J. Med. Internet Res. 15 (2013) e32.

64] E.R. Breton, B.F. Fuemmeler, L.C. Abroms, Weight loss-there is an app for that!But does it adhere to evidence-informed practices? Transl. Behav. Med. 1(2011) 523–529.

65] Weight Loss Resources, (2015). http://www.weightlossresources.co.uk/(accessed 5.8.15.).

66] L. Sibal, P.D. Home, Management of type 2 diabetes: NICE guidelines, Clin.Med. (Northfield. Il 9 (2009) 353–357.

67] T. Chomutare, L. Fernandez-Luque, E. Arsand, G. Hartvigsen, Features ofmobile diabetes applications: review of the literature and analysis of currentapplications compared against evidence-based guidelines, J. Med. InternetRes. 13 (2011) e65.

68] S. Goyal, J. Cafazzo, Mobile phone health apps for diabetes management:current evidence and future developments, QJM (2013) 2–5.

69] S. Franc, A. Daoudi, S. Mounier, B. Boucherie, D. Dardari, H. Laroye, et al.,Telemedicine and diabetes: achievements and prospects, Diabetes Metab. 37(2011) 463–476.

70] D.S. Eng, J.M. Lee, The promise and peril of mobile health applications fordiabetes and endocrinology, Pediatr. Diabetes 2016 (2013) 1–8.


71] M.M. Slining, E.F. Yoon, J. Davis, B. Hollingsworth, D. Miles, S.W. Ng, Anapproach to monitor food and nutrition from factory to fork, J. Acad. Nutr.Diet. 115 (2015) 40–49.

72] M.K. Lee, H.-A. Park, Y.H. Yun, Y.J. Chang, Development and formativeevaluation of a web-based self-management exercise and diet intervention

































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































http://www.eatracker.ca















































































































































































































































































































http://www.weightlossresources.co.uk/






















































































































































































ING ModelI

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2 A.G. Arens-Volland et al. / International Jou



program with tailored motivation and action planning for cancer survivors,JMIR Res. Protoc. 2 (2013) ee11.

73] J.Y. Kim, K.H. Lee, S.H. Kim, K.H. Kim, J.H. Kim, J.S. Han, et al., Needs analysisand development of a tailored mobile message program linked with electronichealth records for weight reduction, Int. J. Med. Inf. 82 (2013) 1123–1132.

[

[



74] British National Health Service, Health Apps Library, (2015). http://apps.nhs.uk/ (accessed 5.8.15.).

75] TellSpec, (2015). http://tellspec.com/ (accessed 5.8.15.).[76 BitBite, (2015). http://www.thebitbite.com (accessed 5.8.15.).




























































http://apps.nhs.uk/

http://apps.nhs.uk/

http://apps.nhs.uk/

http://apps.nhs.uk/

http://apps.nhs.uk/

http://tellspec.com/




http://www.thebitbite.com




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