Hostname: page-component-594f858ff7-hd6rl Total loading time: 0 Render date: 2023-06-08T06:37:06.532Z Has data issue: false Feature Flags: { "corePageComponentGetUserInfoFromSharedSession": true, "coreDisableEcommerce": false, "corePageComponentUseShareaholicInsteadOfAddThis": true, "coreDisableSocialShare": false, "useRatesEcommerce": true } hasContentIssue false

Personalised nutrition: the role of new dietary assessment methods

Published online by Cambridge University Press:  02 June 2015

Hannah Forster
Affiliation:
UCD Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Republic of Ireland
Marianne C. Walsh
Affiliation:
UCD Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Republic of Ireland
Michael J. Gibney
Affiliation:
UCD Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Republic of Ireland
Lorraine Brennan
Affiliation:
UCD Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Republic of Ireland UCD Conway Institute, University College Dublin, Belfield, Dublin 4, Republic of Ireland
Eileen R. Gibney*
Affiliation:
UCD Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Republic of Ireland
*
*Corresponding author: Dr Eileen R. Gibney, email eileen.gibeny@ucd.ie
Rights & Permissions[Opens in a new window]

Abstract

Food records or diaries, dietary recalls and FFQ are methods traditionally used to measure dietary intake; however, advancing technologies and growing awareness in personalised health have heightened interest in the application of new technologies to assess dietary intake. Dietary intake data can be used in epidemiology, dietary interventions and in the delivery of personalised nutrition advice. Compared with traditional dietary assessment methods, new technologies have many advantages, including their ability to automatically process data and provide personalised dietary feedback advice. This review examines the new technologies presently under development for the assessment of dietary intakes, and their utilisation and efficacy for personalising dietary advice. New technology-based methods of dietary assessment can broadly be categorised into three key areas: online (web-based) methods, mobile methods and sensor technologies. Several studies have demonstrated that utilising new technologies to provide tailored advice can result in positive dietary changes and have a significant impact on selected nutrient and food group intakes. However, comparison across studies indicates that the magnitude of change is variable and may be influenced by several factors, including the frequency and type of feedback provided. Future work should establish the most effective combinations of these factors in facilitating dietary changes across different population groups.

Type
Irish Postgraduate Winners
Copyright
Copyright © The Authors 2015 

Worldwide, the prevalence of overweight and obesity has escalated by 27·5 % in adults from 1980 to 2013( Reference Ng, Fleming and Robinson 1 ), with more than 1·6 billion adults presently considered to be overweight or obese globally( 2 ). Poor dietary profiles characterised by energy dense, high-fat diets, with low fruit and vegetable intakes, are associated with increased risk of diet-related non-communicable diseases such as CVD, type 2 diabetes and some cancers( Reference Celis-Morales, Livingstone and Marsaux 3 ), and are modifiable factors for reducing obesity risk( Reference Wright, Sherriff and Dhaliwal 4 ). Face-to-face behavioural change programmes or intervention studies, which aim to alter dietary intake and/or increase physical activity have been shown to result in significant weight loss( Reference Brandt, Brandt and Pedersen 5 ). However, the feasibility of these programmes on a large scale, required to stimulate widespread changes in diet and physical activity, and help reduce the rising prevalence of obesity is limited, indicating that there is a growing need for alternative effective strategies.

Advances in the field of nutritional science have facilitated the shift from the ‘one size fits all’ general population-based guidelines to more personalised dietary recommendations( Reference Joost, Gibney and Cashman 6 , Reference Ronteltap, van Trijp and Berezowska 7 ). Existing nutrient intake recommendations are presently differentiated for subgroups of the population based on factors including age, sex and physiological status( Reference Joost, Gibney and Cashman 6 , Reference de Roos 8 , 9 ). However, growing evidence has demonstrated that providing individuals with more specific personalised feedback advice can be more effective than generic information in improving a variety of dietary outcomes, including reducing fat intakes and increasing fruit and vegetable intakes( Reference Lustria, Cortese and Noar 10 Reference Ezendam, Brug and Oenema 12 ). To provide personalised advice, an appropriate method for measuring and assessing dietary intake is required.

Traditionally dietary intakes have been assessed using weighed or non-weighed food diaries, paper-based FFQ and telephone or in-person administered 24 h recalls( Reference Biro, Hulshof and Ovesen 13 , Reference Thompson, Subar and Loria 14 ). However, with advances in technology and growth of consumer interest in personal health, there is increasing research into the application of new technology-based dietary assessment methods for providing personalised dietary advice more efficiently( Reference Celis-Morales, Livingstone and Marsaux 3 , Reference Kerr, Pollard and Howat 15 Reference Springvloet, Lechner and de Vries 17 ). Compared with traditional dietary assessment methods, these instruments can collect dietary intake data more easily across large geographically dispersed populations( Reference Illner, Freisling and Boeing 18 ), and therefore have greater potential to provide personalised dietary advice on the large scale. Furthermore, in contrast with the traditional methods of dietary assessment, technology-based methods are less expensive, can be completed at the participants’ convenience and are associated with reduced researcher burden as nutrient intakes can be generated automatically( Reference Labonte, Cyr and Baril-Gravel 19 Reference Touvier, Kesse-Guyot and Méjean 21 ). This has the further advantage of enabling feedback advice to be pre-programmed and potentially delivered in real-time( Reference Hutchesson, Rollo and Callister 22 ). The objectives of the present review are to (i) examine the range of new technologies developed for the assessment of dietary intakes and (ii) assess the utilisation and efficacy of new technologies for providing personalised dietary advice.

New technologies for dietary assessment

The digital revolution has evolved the process in which dietary assessment methods are administered and dietary intakes are measured, resulting in a rise in the development of new technology-based instruments for assessing dietary intakes( Reference Ngo, Engelen and Molag 23 Reference Schap, Zhu and Delp 28 ). These new instruments can be broadly categorised into three key areas: online (web-based) methods; mobile methods; sensor technologies. Evidence has demonstrated several of these newer methods of dietary assessment to be preferred over traditional methods by an array of population groups, including adolescents and adults( Reference Touvier, Kesse-Guyot and Méjean 21 , Reference Hutchesson, Rollo and Callister 22 , Reference Boushey, Kerr and Wright 29 , Reference Fallaize, Forster and Macready 30 ). Furthermore, the feasibility of utilising technology-based methods to collect dietary intake data within specific population groups is also being explored with novel tools being developed for both children and older adults( Reference Foster, Hawkins and Delve 31 , Reference Timon, Astell and Hwang 32 ). As new technologies for dietary assessment are frequently used in nutrition research and increasingly utilised to provide personalised dietary advice, it is important that they are designed to facilitate fundamental updates, such as the updating of nutritional composition data, to prevent them from becoming swiftly redundant and replaced by improved versions.

Online (web-based) methods of dietary assessment

Global increases in Internet usage and availability over the past decade have resulted in online (web-based) methods of dietary assessment becoming popular for assessing dietary intakes for both research and commercial purposes( Reference Forster, Fallaize and Gallagher 33 ). Unlike the traditional paper-based methods of dietary assessment, online methods possess many benefits: they can be pre-programmed with plausibility and completeness checks, probing questions and can be developed to incorporate food photographs to enhance food recognition and portion size estimation( Reference Illner, Freisling and Boeing 18 , Reference Labonte, Cyr and Baril-Gravel 19 , Reference Touvier, Kesse-Guyot and Méjean 21 ). As illustrated in Table 1, the majority of tools developed to date are centred around 24 h recalls and FFQ, with fewer examples of food diaries/records( Reference Hutchesson, Rollo and Callister 22 ). Nonetheless, studies have shown online food records to be comparable with paper-based records in their ability to estimate energy intake and subsequent associated levels of underreporting( Reference Hutchesson, Rollo and Callister 22 , Reference Hutchesson, Truby and Callister 34 ).

Table 1. Online (web-based) dietary assessment tools for the collection of dietary intake data in adults

ASA24, automated self-administered 24-h recall; AMPM, automated multiple-pass method; DHQ, diet history questionnaire.

To date, numerous online (web-based) 24 h recalls have been developed for the collection of dietary intake data both within adults and children( Reference Touvier, Kesse-Guyot and Méjean 21 , Reference Douglass, Islam and Baranowski 35 Reference Arab, Tseng and Ang 39 ). As outlined in Table 1, ASA24, DietDay, NutriNet Sante and the Oxford WebQ are web-based 24 h recalls developed for the assessment of dietary intake data for adults, which have been shown to be comparable with other methods of dietary assessment( Reference Touvier, Kesse-Guyot and Méjean 21 , Reference Subar, Kirkpatrick and Mittl 38 Reference Kirkpatrick, Subar and Douglass 41 ). ASA24 is a web-based 24 h recall based on the automated multiple-pass method that is available for use by clinicians, educators and researchers( Reference Subar, Kirkpatrick and Mittl 38 ). The tool has a dynamic user interface which features an animated guide and audio and visual cues( Reference Subar, Kirkpatrick and Mittl 38 ), and has been demonstrated to have reasonable agreement with an interview-administered automated multiple-pass method for selected nutrients and food groups( Reference Kirkpatrick, Subar and Douglass 41 ). DietDay is similar to ASA24, as it also applies multipasses analogous to the automated multiple-pass method( Reference Arab, Wesseling-Perry and Jardack 42 ). DietDay consists of 9349 food items and more than 7000 food images( Reference Arab, Wesseling-Perry and Jardack 42 ), and has been shown to have greater validity than a paper-based FFQ using the doubly labelled water method( Reference Arab, Tseng and Ang 39 ).

Several studies have also been published regarding the development and use of online FFQ for the estimation of dietary intake, with the majority of online FFQ based on previously validated FFQ or country-specific food guides( Reference Labonte, Cyr and Baril-Gravel 19 , Reference Matthys, Pynaert and De Keyzer 20 , Reference Kristal, Kolar and Fisher 25 , Reference Fallaize, Forster and Macready 30 , Reference Forster, Fallaize and Gallagher 33 , Reference González Carrascosa, García Segovia and Martínez Monzó 43 Reference Vereecken, De Bourdeaudhuij and Maes 45 ). Like the traditional paper-based FFQ, online FFQ vary in their population of interest, inclusion of photographs and number of food items. Key features for several of the online FFQ developed for collecting dietary intake data within adults are presented in Table 1. The online Food4Me FFQ was recently developed for the collection of dietary intake data across seven European countries and has been translated into six languages( Reference Forster, Fallaize and Gallagher 33 ). This FFQ lists food items (157 in the English-language version) and requires a response regarding the frequency and portion size consumption of each individual food item over the past month( Reference Forster, Fallaize and Gallagher 33 ). Although the Food4Me FFQ is similar in format to traditional FFQ, online FFQ also have the flexibility to differ from this conventional list-style format. For example, GraFFS is a recently developed algorithm-driven FFQ which presents users with thumbnail illustrations of food items within a food category; from these users then select the food items which they have consumed at least once in the past month, and only these food items are further questioned( Reference Kristal, Kolar and Fisher 25 ). Thus far, evidence examining the validity of online FFQ to other dietary assessment methods has been varied. Several studies have found online FFQ to be highly correlated with 24 h recalls and comparable with food records( Reference Labonte, Cyr and Baril-Gravel 19 , Reference Kristal, Kolar and Fisher 25 ). For example, the Food4Me FFQ has been shown to have good agreement with a paper-based FFQ and moderate agreement with a 4-d weighed food record for estimating both nutrient and food group intakes( Reference Fallaize, Forster and Macready 30 , Reference Forster, Fallaize and Gallagher 33 ). Meanwhile, other studies have shown online FFQ to result in poorer estimation of food groups when compared with traditionally administered dietary recalls and weighed food records( Reference Matthys, Pynaert and De Keyzer 20 , Reference Vereecken, De Bourdeaudhuij and Maes 45 ). This variability is commonly observed with FFQ regardless of their mode of administration, and usability analyses have indicated that individuals would be more willing to complete online methods of dietary assessment over the traditional methods( Reference Touvier, Kesse-Guyot and Méjean 21 , Reference Fallaize, Forster and Macready 30 ).

Mobile methods of dietary assessment

Initially, personal digital assistants (PDA) were the leading mobile method for assessing dietary intakes, being utilised from the mid-1990s. These devices contained an integrated pre-defined drop-down list of foods, typically ranging from 180 to >4000 items( Reference Illner, Freisling and Boeing 18 ), and have been demonstrated to be comparable with traditional methods of dietary assessment( Reference Beasley, Riley and Jean-Mary 46 Reference Fukuo, Yoshiuchi and Ohashi 48 ). Progressions in technology have however resulted in smartphone use surpassing PDA as the foremost mobile method of dietary assessment( Reference Recio-Rodríguez, Martín-Cantera and González-Viejo 16 , Reference Carter, Burley and Nykjaer 26 , Reference Sharp and Allman-Farinelli 49 ), with growing research emerging into the use of smartphones to measure dietary intakes using both applications (app) and image-based methods( Reference Weiss, Stumbo and Divakaran 50 ). Smartphones have huge potential in the measurement of dietary intake and delivery of low-cost interventions to large population groups( Reference Free, Phillips and Galli 51 ). Statistics show their use/ownership is increasing among a variety of age groups and they are usually carried by the individual, enabling users to record data conveniently and discretely in real-time( Reference Carter, Burley and Nykjaer 26 , Reference Kong and Tan 52 , 53 ).

App for tracking dietary intake can be solely dietary based or integrated into other app such as physical activity app e.g. My Fitness Pal( Reference Jospe, Fairbairn and Green 54 ). Although many smartphone app are available for tracking dietary intake, publications regarding the validity and accuracy of many of these app are limited( Reference Jospe, Fairbairn and Green 54 ). ‘My Meal Mate’ is an exception; this electronic food diary smartphone app is designed to facilitate weight loss and consists of a 40 000 food item database containing both generic and branded food items( Reference Carter, Burley and Nykjaer 26 ). Although this app has been shown to be well correlated with two 24 h recalls for estimating energy and macronutrient intakes, it was demonstrated to have large variability in estimating individual energy intakes( Reference Carter, Burley and Nykjaer 26 ). Therefore, considering many smartphone app are used and targeted to the individual, there is a need for future studies to establish the validity of utilising smartphone app for the assessment of dietary intakes at the individual level. Notwithstanding their ability to accurately capture dietary intake data, evidence examining use of mobile app have found them to be more effective than paper diaries in reducing energy intakes after 6 months( Reference Turner-McGrievy, Beets and Moore 55 ), highlighting their potential for dietary interventions.

Advances in technology have also led to the utilisation of smartphones for image-based methods of dietary assessment( Reference Gemming, Utter and Ni Mhurchu 56 ). These image-based methods require the user to photograph their foods/meals along with a fiducial marker before and after eating, and therefore have the potential to diminish the burden to the user that is commonly associated with other methods of dietary assessment( Reference Schap, Zhu and Delp 28 ). Presently, two approaches for image-based assessment have been described in the literature: those requiring human input (e.g. remote food photography method) and those not requiring human input (e.g. the technology assisted dietary assessment mobile phone food record and DietCam)( Reference Schap, Zhu and Delp 28 , Reference Kong and Tan 52 , Reference Khanna, Boushey and Kerr 57 , Reference Martin, Nicklas and Gunturk 58 ). With the remote food photography method images are sent wirelessly to a Food Photography Application where trained analysts compare the sent images with a catalogue of images of foods of known portion sizes to estimate food intake( Reference Martin, Nicklas and Gunturk 58 , Reference Martin, Correa and Han 59 ). Evidence has demonstrated that this method underestimated energy intake by only 3·7 % compared with the doubly labelled water method( Reference Martin, Correa and Han 59 ). Furthermore, comparisons of nutrient intake estimates from the remote food photography method and two laboratory-based weighed meals showed no significant differences for energy, macro or micronutrient intakes with the exception of vitamin A and cholesterol (P < 0·01)( Reference Martin, Correa and Han 59 ). In contrast, the technology assisted dietary assessment mobile phone food record uses built in integrated image classification, analysis and visualisation tools to automatically estimate the amount of food and nutrients consumed at each meal( Reference Schap, Zhu and Delp 28 , Reference Zhu, Bosch and Woo 60 ). This method has been shown to be more limited in its ability to distinguish between similarly shaped and coloured food items e.g. brownies and chocolate cake( Reference Zhu, Bosch and Woo 60 ). However, image-based methods are being designed to incorporate developments such as voice recognition, to overcome many errors associated with automated food item classification( Reference Weiss, Stumbo and Divakaran 50 ).

Sensor technologies

In recent years, use of wearable sensor technologies for measurement of dietary intake has emerged( Reference Sun, Burke and Mao 27 ). These devices take photographic images while being worn, which capture a range of data and health activities, and can be used to estimate nutritional intake without self-reporting( Reference Stumbo 24 ). Some of these devices, such as the Microsoft SenseCam, have been used in conjunction with other methods of dietary assessment to help improve their accuracy by capturing information that may be incorrectly estimated or unreported e.g. left overs, portion sizes and unrecalled food items( Reference O'Loughlin, Cullen and McGoldrick 61 ). Evidence has shown that the application of a paper diary together with the Microsoft SenseCam resulted in more accurate estimations of portion size data and subsequent energy intakes when compared with using the diary alone( Reference O'Loughlin, Cullen and McGoldrick 61 ). In this study, forty-seven participants were recruited and requested to wear the SenseCam for 1 d while simultaneously recording a 1-d food diary( Reference O'Loughlin, Cullen and McGoldrick 61 ). Data from the SenseCam and food diary were reviewed by a dietitian and any discrepancies between the methods were identified( Reference O'Loughlin, Cullen and McGoldrick 61 ). Moving forward from this concept, the eButton is another sensor device that has been recently developed, which can automatically estimate the nutritional composition of food items through integrated food segmentation, volume estimation, modelling and fitting processes( Reference Sun, Burke and Mao 27 ). This device is a small lightweight decorative button, worn on the chest, which encompasses a camera and passively photographs foods every 2 s to record the eating process ( Reference Sun, Burke and Mao 27 ) . Although there is future potential for such devices in estimating dietary intake, the ability to automatically estimate the nutritional composition of the wide variety of foods and meals consumed on a daily basis from food photographs is a complex process. Recent accuracy evaluations for the eButton demonstrated its error rate to be about 30 % for regularly shaped foods, and significantly larger for irregularly shaped or occluded food items( Reference Sun, Burke and Mao 27 ), indicating that further development is required before these devices will be applied for automatically estimating nutritional intakes.

Application of new technology-based dietary assessment tools for personalised nutrition

These technology-based dietary assessment instruments can be developed into or integrated with tools which provide personalised or tailored dietary feedback advice, and therefore have the potential to help individuals to make positive sustainable changes to their dietary behaviours. These instruments are being increasingly utilised in intervention studies to provide personalised or tailored feedback, with numerous randomised controlled trials demonstrating this personalised advice to be more effective than generic advice in improving selected dietary outcomes( Reference Springvloet, Lechner and de Vries 17 , Reference Sternfeld, Block and Quesenberry 62 , Reference Ambeba, Ye and Sereika 63 ).

As illustrated in Table 2, PDA, online FFQ and web-based dietary questionnaires have all been utilised for providing personalised dietary advice, with studies in recent years also examining the potential for smartphone app to provide tailored or personalised feedback advice( Reference Kerr, Pollard and Howat 15 , Reference Recio-Rodríguez, Martín-Cantera and González-Viejo 16 ). A smartphone app that can capture dietary and physical activity information and provide personalised recommendations has been developed for use in the EVIDENT II trial( Reference Recio-Rodríguez, Martín-Cantera and González-Viejo 16 ). This app encompasses a food diary to enable users to record their daily food intake, evaluates the user's food intake, and provides personalised recommendations to help facilitate changes in dietary habits( Reference Recio-Rodríguez, Martín-Cantera and González-Viejo 16 ). In addition, smartphones applying image-based assessment (requiring input from trained analysts) have recently been used in the Connecting Health and Technology project to assess nutritional intake and provide tailored feedback advice and text messages to young adults to promote changes in consumption of fruit, vegetables and junk food( Reference Kerr, Pollard and Howat 15 ). Results regarding the efficacy of utilising these smartphone-based tools to provide personalised advice are yet to be published.

Table 2. Summary of randomised controlled trials utilising new technology-based dietary assessment instruments to provide personalised/tailored advice

PDA, personal digital assistant.

Efficacy of technology-based tools in providing personalised dietary advice

To date, several studies have utilised online FFQ to assess dietary intake to aid the provision of personalised dietary advice (Table 2). In 2011, Maes et al. demonstrated that using an online FFQ, consisting of 137 food items, to collect dietary data and provide tailored advice for fibre, vitamin C, calcium, iron and fat, to adolescents (n 713), only resulted in significant improvements in fat intake in overweight adolescents( Reference Vereecken, De Bourdeaudhuij and Maes 45 , Reference Maes, Cook and Ottovaere 64 ). Online FFQ have also recently been used to measure dietary intake and provide tailored advice as part of a nutrition education intervention within adults, as illustrated in Table 2 ( Reference Springvloet, Lechner and de Vries 17 ). However, this study showed similar variation in the efficacy of tailored advice for improving nutrient and food group intakes. Following the intervention, participants receiving tailored advice reported significantly lower saturated fat and high-energy snack intakes, compared with participants receiving non-tailored advice. However, the tailored advice had no effect on vegetable intakes and only limited impact on changes in fruit intake( Reference Springvloet, Lechner and de Vries 17 ). The validated FFQ utilised to assess fruit, vegetable, fat and high-fat snack intakes used six, four, thirty-five and twenty-one food items to measure intakes of each of these dietary outcomes, respectively. Therefore, it is possible that the ability of these instruments to identify changes in fruit and vegetable intakes between the control and intervention groups was limited by the low number of food items used to assess fruit and vegetable intakes. However, Sternfeld et al.( Reference Sternfeld, Block and Quesenberry 62 ) have previously demonstrated that using an online dietary questionnaire, consisting of only thirty-five food items, to assess dietary intake and provide tailored feedback advice for fruit and vegetables and fat and sugar, resulted in significant improvements to saturated fat, trans fat and fruit and vegetable intakes both immediately and 4 months post-intervention. This would therefore indicate that numerous other factors, relating more directly to the feedback itself and the target population, also influence the efficacy of utilising technology-based instruments to provide effective personalised advice, as illustrated in Fig. 1.

Fig. 1. Factors influencing the efficacy of new technology-based instruments in providing personalised/tailored advice to improve dietary intake.

As detailed in Table 2, in the study by Sternfeld et al.( Reference Sternfeld, Block and Quesenberry 62 ), the intervention was a 16-week email program during which intervention participants firstly chose the area they wanted to target and secondly chose one to two goals to achieve each week. Providing individuals with the opportunity to choose the goals they want to target may be an important factor influencing the efficacy of tailored/personalised dietary feedback and magnitude of behaviour change. This is because individuals will tend to choose goals that are of more interest to them and are subsequently more likely to achieve. Another factor, however, that also differed between this study and the two previously discussed, was the frequency in which feedback was provided. The study where feedback was provided more frequently was associated with greater changes in a variety of dietary outcomes. Concurrent with this, recent evidence has shown the application of PDA to provide daily tailored advice to have a considerable impact on dietary behaviours( Reference Ambeba, Ye and Sereika 63 ). Participants who received daily tailored energy and fat feedback messages delivered remotely on a PDA in real-time significantly reduced their energy (P = 0·03) and saturated fat intake (P = 0·04) after 24 months compared with participants receiving no daily feedback messages( Reference Ambeba, Ye and Sereika 63 ). Mean differences in energy and fat intake at 24 months were −9·3 and −12·7 % greater, respectively, for those who received the daily tailored feedback messages compared with those receiving no daily feedback messages( Reference Ambeba, Ye and Sereika 63 ).

In addition to the frequency of feedback, the mode of delivery of personalised advice is another element that could influence the efficacy of personalised dietary advice. Personalised feedback advice can be delivered to users in a variety of formats, such as onscreen messages or detailed reports. The format in which advice is delivered is often dependent on the method of assessment: with smartphones or PDA feedback advice is customarily delivered as onscreen messages( Reference Ambeba, Ye and Sereika 63 ), whereas with online tools feedback advice could either be delivered in the form of a report or made available on a personalised homepage( Reference Sternfeld, Block and Quesenberry 62 ). An earlier study examining the impact of delivering interactive (designed to mimic web-based tailored feedback), print-delivered computer-tailored feedback advice and generic feedback information on saturated fat intake found the two tailored feedback conditions to have similar short-term reduction effects( Reference Kroeze, Oenema and Campbell 65 ). However, after 6 months, reduction effects were maintained only in participants receiving the print-delivered tailored feedback. One possible explanation is that it is potentially easier and less burdensome to read and reread printed information more thoroughly than on-screen information( Reference Kroeze, Oenema and Campbell 65 , Reference Oenema, Tan and Brug 66 ). However, latter studies have shown the Internet to be a successful method for delivering tailored or personalised advice( Reference Springvloet, Lechner and de Vries 17 , Reference Sternfeld, Block and Quesenberry 62 ). Furthermore, using the Internet to administer personalised advice can enable greater interactivity with the feedback provided as there is the potential to encompass additional tools including homepages and discussion forums( Reference Kroeze, Oenema and Dagnelie 67 , Reference Brug 68 ).

In summary, although several studies have demonstrated that technology-based dietary assessment tools can be used to provide tailored/personalised advice that is more effective in changing dietary behaviours than generic information, evaluation across studies would indicate that the magnitude of change is variable and may be affected by several factors as summarised in Fig. 1. This is in agreement with a systematic review which identified several factors, such as target population, target behaviours and mode of delivery, as potential contributors to the variable effectiveness of e-learning interventions on improving dietary behaviours.( Reference Harris, Felix and Miners 69 )

Translating nutrient intake data into personalised advice

Although progressively more dietary assessment instruments are being used to provide tailored or personalised dietary advice, literature regarding the processes in which personalised advice is generated by these tools is largely limited. A simplified process for generating computer-tailored dietary messages was outlined in 1999: results from a baseline questionnaire are entered into a data file which is subsequently linked by computer software (using pre-programmed algorithms) to an appropriate feedback message(s)( Reference Brug 68 ). The appropriate message(s) are selected from a message archive and then assembled into a predefined format. However since then, developments in technology have advanced this process; nutritional intake data are automatically generated from new technology-based dietary assessment instruments and algorithms can be used to automatically generate feedback advice. Several studies have briefly referred to the use of decision tree algorithms to generate appropriate feedback advice from nutritional intake data( Reference Ambeba, Ye and Sereika 63 , Reference Maes, Cook and Ottovaere 64 ). The web-based computer-tailored nutrition intervention, developed for adolescents in the HELENA study, consisted of three key components: a validated FFQ, a food composition database and a decision tree algorithm to provide tailored advice( Reference Maes, Cook and Ottovaere 64 ). The decision tree algorithm was designed to compare reported nutrient intakes with recommendations and provide food-based advice when intakes differed from recommendations( Reference Maes, Cook and Ottovaere 64 ). More complex systems have since been developed which enable feedback advice to be transmitted in real-time. For example, an automated investigator-developed algorithm linking dietary intake data from PDA devices to feedback messages was recently constructed in four time-related categories which were further differentiated into five sections resembling goal attainment, enabling feedback messages to be delivered in real-time( Reference Ambeba, Ye and Sereika 63 ). Largely as illustrated in Table 2, the majority of studies utilising technology-based dietary assessment tools have been developed to provide tailored or personalised advice for a small selection of nutrients/food groups only( Reference Kerr, Pollard and Howat 15 , Reference Springvloet, Lechner and de Vries 17 , Reference Sternfeld, Block and Quesenberry 62 , Reference Ambeba, Ye and Sereika 63 ), with limited studies personalising advice for extended lists of nutrients( Reference Celis-Morales, Livingstone and Marsaux 3 , Reference Maes, Cook and Ottovaere 64 ). Personalised advice is most commonly provided for nutrients and food groups considered to be important constituents of a healthy/unhealthy diet, predominately saturated fat, fruit and vegetables( Reference Capacci, Mazzocchi and Shankar 70 ). The Food4Me study is one of the few exceptions that developed a multi-step system for personalising advice for a larger range of nutrients. In this study, an online FFQ was used to assess dietary intake. Decision trees were developed to link nutritional intake data from the FFQ to feedback messages, and provide participants with personalised feedback advice for three nutrient-related goals( Reference Celis-Morales, Livingstone and Marsaux 3 ). The three nutrient-related goals were derived based on their risk status (deviations from recommendations) and selected from a possible seventeen nutrients( Reference Celis-Morales, Livingstone and Marsaux 3 ).

Furthermore, given that behaviour change is considered to be a multi-stage process, strategies to help increase motivation and stimulate changes in dietary behaviour may often be incorporated in both the process of generating personalised feedback and in the content of feedback( Reference Noar, Harrington and Van Stee 71 , Reference Block, Sternfeld and Block 72 ). For example identifying an individual's stage of change may function as a diagnostic variable for determining specific messages to be given in tailored feedback( Reference Kroeze, Oenema and Dagnelie 67 , Reference Noar, Harrington and Van Stee 71 , Reference Brug and van Assema 73 ). Feedback that is tailored based on both present dietary intakes and stage of change may be considered to be more motivational and may be more effective in promoting change than advice that is tailored based on dietary intakes only( Reference Brug and van Assema 73 ). To date elements from a variety of behavioural models have been applied in personalising dietary advice, including goal-setting, social marketing and the transtheoretical model( Reference Sternfeld, Block and Quesenberry 62 ). Evidence has shown that interventions incorporating specific behaviour change techniques, such as goal setting and use of prompts, demonstrated greater effects on fruit and vegetable intakes when compared with studies without these techniques( Reference Celis-Morales, Lara and Mathers 74 ). In tandem with this concept, another important factor which could be central to the efficacy of new technology-based tools in providing personalised advice is whether individuals respond differently to varying types of feedback (content, delivery and frequency). For example, some individuals may prefer and respond greater to receiving brief feedback more frequently, whereas others may benefit more from detailed feedback sent less frequently.

Content of personalised dietary advice

Evidently a fundamental feature of any tailored feedback report is that it is personalised to the individual. Research suggests that addressing the individual's name and other recognisable personal characteristics leads to increased involvement with the feedback messages( Reference Brug 68 ). Specific feedback provided in tailored or personalised interventions is often varied. Many studies to date have included graphs to compare intakes with recommended levels; with intakes of peers of same age/sex; and to display improvements from previously attained levels( Reference Sternfeld, Block and Quesenberry 62 , Reference Oenema, Tan and Brug 66 , Reference Kroeze, Oenema and Dagnelie 67 , Reference Patrick, Calfas and Norman 75 ). The combination of providing an individual with personal feedback on their own fat intake along with normative feedback (ones level in relation to their peers or average national intakes) and actions for how to change their intake was previously shown to be beneficial to improve awareness of and induce changes in fat intake( Reference Kroeze, Oenema and Dagnelie 67 ). However, it has also been demonstrated that a combination of comparing intakes with guideline amounts and providing recommendations for improvement can help towards promoting changes in dietary behaviours( Reference Sternfeld, Block and Quesenberry 62 ). Randomised controlled trials have delivered feedback advice consisting of suggestions for improvement, including top sources of problematic nutrients( Reference Sternfeld, Block and Quesenberry 62 ), food-based messages to help improve nutrient-intakes( Reference Celis-Morales, Livingstone and Marsaux 3 ) and information relating to potential barriers to change( Reference Springvloet, Lechner and de Vries 17 ). Although the specific content of messages is important, another aspect that must be considered, particularly when feedback is delivered frequently, relates to how often message content is renewed. Previous studies have shown that feedback message content and structure need to be refreshed at regular intervals to prevent individuals becoming desensitised to feedback messages, and therefore sustain long-term changes( Reference Ambeba, Ye and Sereika 63 , Reference Tate, Jackvony and Wing 76 ).

Conclusion

Numerous new technology-based instruments have been established for the assessment of dietary intakes, and are presently under varying stages of development. These instruments have considerable advantages for the assessment of dietary intakes, including reduced burden to both users and researchers. Although evidence has shown several of these instruments, predominately online methods, to be reliable approaches for assessing nutrient intakes, there is a need to further examine the validity and usability of the wider variety of these instruments e.g. mobile methods and sensor technologies, across different population groups. New technology-based instruments have large potential for providing personalised nutrition advice, and improving dietary and lifestyle choices on a widespread level. Presently several randomised controlled trials have examined the use of these instruments for providing tailored or personalised advice, and demonstrated varying levels of effectiveness. Comparisons across studies would however indicate that the efficacy of utilising new technology-based dietary assessment tools to provide effective personalised advice is dependent on a number of factors, including feedback content, frequency and delivery, population group, sensitivity of the assessment instrument to changes in dietary outcomes, and the processes involved in personalising feedback. Future work should ascertain the most effective combinations of these factors in promoting sustained dietary changes across different population groups.

Financial Support

This work was supported by the Food4Me Study which funded the PhD scholarship for H. F. Food4Me is funded by the European Commission under the Food, Agriculture, Fisheries and Biotechnology Theme of the Seventh Framework Programme for Research and Technological Development (grant number 265494).

Conflicts of Interest

None.

Authorship

H. F. drafted the manuscript. M. C. W., M. J. G., L. B. and E. R. G. critically evaluated the manuscript. All authors approved the final version.

References

1. Ng, M, Fleming, T, Robinson, M et al. (2014) Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 384, 766781.CrossRefGoogle ScholarPubMed
2. World Health Organisation (2015) Obesity and overweight fact sheet. http://www.who.int/mediacentre/factsheets/fs311/en/ (accessed March 2015).Google Scholar
3. Celis-Morales, C, Livingstone, KM, Marsaux, CF et al. (2015) Design and baseline characteristics of the Food4Me study: a web-based randomised controlled trial of personalised nutrition in seven European countries. Genes Nutr 10, 450.CrossRefGoogle ScholarPubMed
4. Wright, JL, Sherriff, JL, Dhaliwal, SS et al. (2011) Tailored, iterative, printed dietary feedback is as effective as group education in improving dietary behaviours: results from a randomised control trial in middle-aged adults with cardiovascular risk factors. Int J Behav Nutr Phys Act 8, 43.CrossRefGoogle ScholarPubMed
5. Brandt, CJ, Brandt, V, Pedersen, M et al. (2014) Long-term effect of interactive online dietician weight Loss Advice in General Practice (LIVA) protocol for a randomized controlled trial. Int J Family Med. Available at http://dx.doi.org/10.1155/2014/245347 CrossRefGoogle ScholarPubMed
6. Joost, HG, Gibney, MJ, Cashman, KD et al. (2007) Personalised nutrition: status and perspectives. Br J Nutr 98, 2631.CrossRefGoogle Scholar
7. Ronteltap, A, van Trijp, H, Berezowska, A et al. (2013) Nutrigenomics-based personalised nutritional advice: in search of a business model?. Genes Nutr 8, 153163.CrossRefGoogle ScholarPubMed
8. de Roos, B (2013) Personalised nutrition: ready for practice? Proc Nutr Soc 72, 4852.CrossRefGoogle ScholarPubMed
9. Department of Health (1991) Dietary Reference Values for Food Energy and Nutrients for the United Kingdom: Report of the Panel on Dietary Reference Values of the Committee on Medical Aspects of Food Policy. London: The Stationary Office.Google Scholar
10. Lustria, ML, Cortese, J, Noar, SM et al. (2009) Computer-tailored health interventions delivered over the Web: review and analysis of key components. Patient Educ Couns 74, 156173.CrossRefGoogle ScholarPubMed
11. Lustria, ML, Noar, SM, Cortese, J et al. (2013) A meta-analysis of web-delivered tailored health behavior change interventions. J Health Commun 18, 10391069.CrossRefGoogle ScholarPubMed
12. Ezendam, NP, Brug, J & Oenema, A (2012) Evaluation of the Web-based computer-tailored FATaintPHAT intervention to promote energy balance among adolescents: results from a school cluster randomized trial. Arch Pediatr Adolesc Med 166, 248255.CrossRefGoogle ScholarPubMed
13. Biro, G, Hulshof, KF, Ovesen, L et al. (2002) Selection of methodology to assess food intake. Eur J Clin Nutr 56, S25S32.CrossRefGoogle ScholarPubMed
14. Thompson, FE, Subar, AF, Loria, CM et al. (2010) Need for technological innovation in dietary assessment. J Am Diet Assoc 110, 4851.CrossRefGoogle ScholarPubMed
15. Kerr, DA, Pollard, CM, Howat, P et al. (2012) Connecting Health and Technology (CHAT): protocol of a randomized controlled trial to improve nutrition behaviours using mobile devices and tailored text messaging in young adults. BMC Public Health 12, 477.CrossRefGoogle ScholarPubMed
16. Recio-Rodríguez, JI, Martín-Cantera, C, González-Viejo, N et al. (2014) Effectiveness of a smartphone application for improving healthy lifestyles, a randomized clinical trial (EVIDENT II): study protocol. BMC Public Health 14, 254.CrossRefGoogle ScholarPubMed
17. Springvloet, L, Lechner, L, de Vries, H et al. (2015) Short- and medium-term efficacy of a web-based computer-tailored nutrition education intervention for adults including cognitive and environmental feedback: randomized controlled trial. J Med Internet Res 17, e23.CrossRefGoogle Scholar
18. Illner, AK, Freisling, H, Boeing, H et al. (2012) Review and evaluation of innovative technologies for measuring diet in nutritional epidemiology. Int J Epidemiol 41, 11871203.CrossRefGoogle ScholarPubMed
19. Labonte, ME, Cyr, A, Baril-Gravel, L et al. (2012) Validity and reproducibility of a web-based, self-administered food frequency questionnaire. Eur J Clin Nutr 66, 166173.CrossRefGoogle ScholarPubMed
20. Matthys, C, Pynaert, I, De Keyzer, W et al. (2007) Validity and reproducibility of an adolescent web-based food frequency questionnaire. J Am Diet Assoc 107, 605610.CrossRefGoogle ScholarPubMed
21. Touvier, M, Kesse-Guyot, E, Méjean, C et al. (2011) Comparison between an interactive web-based self-administered 24 h dietary record and an interview by a dietitian for large-scale epidemiological studies. Br J Nutr 105, 10551064.CrossRefGoogle ScholarPubMed
22. Hutchesson, MJ, Rollo, ME, Callister, R et al. (2015) Self-monitoring of dietary intake by young women: online food records completed on computer or smartphone are as accurate as paper-based food records but more acceptable. J Acad Nutr Diet 115, 8794.CrossRefGoogle ScholarPubMed
23. Ngo, J, Engelen, A, Molag, M et al. (2009) A review of the use of information and communication technologies for dietary assessment. Br J Nutr 101, S102S112.CrossRefGoogle ScholarPubMed
24. Stumbo, PJ (2013) New technology in dietary assessment: a review of digital methods in improving food record accuracy. Proc Nutr Soc 72, 7076.CrossRefGoogle ScholarPubMed
25. Kristal, AR, Kolar, AS, Fisher, JL et al. (2014) Evaluation of web-based, self-administered, graphical food frequency questionnaire. J Acad Nutr Diet 114, 613621.CrossRefGoogle ScholarPubMed
26. Carter, MC, Burley, VJ, Nykjaer, C et al. (2013) ‘My Meal Mate’ (MMM): validation of the diet measures captured on a smartphone application to facilitate weight loss. Br J Nutr 109, 539546.CrossRefGoogle ScholarPubMed
27. Sun, M, Burke, L, Mao, Z et al. (2014) eButton: a wearable computer for health monitoring and personal assistance. Proc Des Autom Conf 2014, 16.Google ScholarPubMed
28. Schap, TE, Zhu, F, Delp, EJ et al. (2014) Merging dietary assessment with the adolescent lifestyle. J Hum Nutr Diet 27, S82S88.CrossRefGoogle ScholarPubMed
29. Boushey, CJ, Kerr, DA, Wright, J et al. (2009) Use of technology in children's dietary assessment. Eur J Clin Nutr 63, S50S57.CrossRefGoogle ScholarPubMed
30. Fallaize, R, Forster, H, Macready, AL et al. (2014) Online dietary intake estimation: reproducibility and validity of the food4me food frequency questionnaire against a 4-day weighed food record. J Med Internet Res 16, e190.CrossRefGoogle ScholarPubMed
31. Foster, E, Hawkins, A, Delve, J et al. (2014) Reducing the cost of dietary assessment: self-completed recall and analysis of nutrition for use with children (SCRAN24). J Hum Nutr Diet 27, S26S35.CrossRefGoogle Scholar
32. Timon, CM, Astell, AJ, Hwang, F et al. (2015) The validation of a computer-based food record for older adults: the Novel Assessment of Nutrition and Ageing (NANA) method. Br J Nutr 113, 654664.CrossRefGoogle ScholarPubMed
33. Forster, H, Fallaize, R, Gallagher, C et al. (2014) Online dietary intake estimation: the Food4Me food frequency questionnaire. J Med Internet Res 16, e150.CrossRefGoogle ScholarPubMed
34. Hutchesson, MJ, Truby, H, Callister, R et al. (2013) Can a web-based food record accurately assess energy intake in overweight and obese women? A pilot study. J Hum Nutr Diet 26, S140S144.CrossRefGoogle ScholarPubMed
35. Douglass, D, Islam, N, Baranowski, J et al. (2013) Simulated adaptations to an adult dietary self-report tool to accommodate children: impact on nutrient estimates. J Am Coll Nutr 32, 9297.CrossRefGoogle Scholar
36. Biltoft-Jensen, A, Trolle, E, Christensen, T et al. (2014) WebDASC: a web-based dietary assessment software for 8–11-year-old Danish children. J Hum Nutr Diet 27, S43S53.CrossRefGoogle ScholarPubMed
37. Moore, HJ, Hillier, FC, Batterham, AM et al. (2014) Technology-based dietary assessment: development of the Synchronised Nutrition and Activity Program (SNAP). J Hum Nutr Diet 27, S36S42.CrossRefGoogle Scholar
38. Subar, AF, Kirkpatrick, SI, Mittl, B et al. (2012) The Automated Self-Administered 24-hour dietary recall (ASA24): a resource for researchers, clinicians, and educators from the National Cancer Institute. J Acad Nutr Diet 112, 11341137.CrossRefGoogle ScholarPubMed
39. Arab, L, Tseng, CH, Ang, A et al. (2011) Validity of a multipass, web-based, 24-hour self-administered recall for assessment of total energy intake in blacks and whites. Am J Epidemiol 174, 12561265.CrossRefGoogle ScholarPubMed
40. Liu, B, Young, H, Crowe, FL et al. (2011) Development and evaluation of the Oxford WebQ, a low-cost, web-based method for assessment of previous 24 h dietary intakes in large-scale prospective studies. Public Health Nutr 14, 19982005.CrossRefGoogle ScholarPubMed
41. Kirkpatrick, SI, Subar, AF, Douglass, D et al. (2014) Performance of the automated self-administered 24-hour recall relative to a measure of true intakes and to an interviewer-administered 24-h recall. Am J Clin Nutr 100, 233240.CrossRefGoogle Scholar
42. Arab, L, Wesseling-Perry, K, Jardack, P et al. (2010) Eight self-administered 24-hour dietary recalls using the Internet are feasible in African Americans and Whites: the energetics study. J Am Diet Assoc 110, 857864.CrossRefGoogle ScholarPubMed
43. González Carrascosa, R, García Segovia, P & Martínez Monzó, J (2011) Paper and pencil vs online self-administered food frequency questionnaire (FFQ) applied to university population: a pilot study. Nutr Hosp 26, 13781384.Google ScholarPubMed
44. Beasley, JM, Davis, A & Riley, WT (2009) Evaluation of a web-based, pictorial diet history questionnaire. Public Health Nutr 12, 651659.CrossRefGoogle ScholarPubMed
45. Vereecken, CA, De Bourdeaudhuij, I & Maes, L (2010) The HELENA online food frequency questionnaire: reproducibility and comparison with four 24-h recalls in Belgian-Flemish adolescents. Eur J Clin Nutr 64, 541548.CrossRefGoogle ScholarPubMed
46. Beasley, J, Riley, W & Jean-Mary, J (2005) Accuracy of a PDA-based dietary assessment program. Nutrition 21, 672677.CrossRefGoogle ScholarPubMed
47. Yon, BA, Johnson, RK, Harvey-Berino, J et al. (2007) Personal digital assistants are comparable to traditional diaries for dietary self-monitoring during a weight loss program. J Behav Med 30, 165175.CrossRefGoogle ScholarPubMed
48. Fukuo, W, Yoshiuchi, K, Ohashi, K et al. (2009) Development of a hand-held personal digital assistant-based food diary with food photographs for Japanese subjects. J Am Diet Assoc 109, 12321236.CrossRefGoogle ScholarPubMed
49. Sharp, DB & Allman-Farinelli, M (2014) Feasibility and validity of mobile phones to assess dietary intake. Nutrition 30, 12571266.CrossRefGoogle ScholarPubMed
50. Weiss, R, Stumbo, PJ & Divakaran, A (2010) Automatic food documentation and volume computation using digital imaging and electronic transmission. J Am Diet Assoc 110, 4244.CrossRefGoogle ScholarPubMed
51. Free, C, Phillips, G, Galli, L et al. (2013) The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review. PLoS Med 10, e1001362.CrossRefGoogle ScholarPubMed
52. Kong, F & Tan, J (2012) DietCam: automatic dietary assessment with mobile camera phones. Pervasive Mob. Comput. 8, 147163.CrossRefGoogle Scholar
53. Nielsen (2013) The Mobile Consumer. A Global Snapshot. http://www.nielsen.com/content/dam/corporate/uk/en/documents/Mobile-Consumer-Report-2013.pdf (accessed March 2015).Google Scholar
54. Jospe, MR, Fairbairn, KA, Green, P et al. (2015) Diet app use by sports dietitians: a survey in five countries. JMIR Mhealth Uhealth 3, e7.CrossRefGoogle ScholarPubMed
55. Turner-McGrievy, GM, Beets, MW, Moore, JB et al. (2013) Comparison of traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss program. J Am Med Inf Assoc 20, 513518.CrossRefGoogle Scholar
56. Gemming, L, Utter, J & Ni Mhurchu, C (2015) Image-assisted dietary assessment: a systematic review of the evidence. J Acad Nutr Diet 115, 6477.CrossRefGoogle Scholar
57. Khanna, N, Boushey, CJ, Kerr, D et al. (2010) An Overview of The Technology Assisted Dietary Assessment Project at Purdue University. Proceedings of the IEEE Int. Symp. on Multimedia, pp. 290–295.Google Scholar
58. Martin, CK, Nicklas, T, Gunturk, B et al. (2014) Measuring food intake with digital photography. J Hum Nutr Diet 27, S72S81.CrossRefGoogle ScholarPubMed
59. Martin, CK, Correa, JB, Han, H et al. (2012) Validity of the Remote Food Photography Method (RFPM) for estimating energy and nutrient intake in near real-time. Obesity 20, 891899.CrossRefGoogle ScholarPubMed
60. Zhu, F, Bosch, M, Woo, I et al. (2010) The use of mobile devices in aiding dietary assessment and evaluation. IEEE J Sel Top Signal Process 4, 756766.Google ScholarPubMed
61. O'Loughlin, G, Cullen, SJ, McGoldrick, A et al. (2013) Using a wearable camera to increase the accuracy of dietary analysis. Am J Prev Med 44, 297301.CrossRefGoogle ScholarPubMed
62. Sternfeld, B, Block, C, Quesenberry, CP et al. (2009) Improving diet and physical activity with ALIVE: a worksite randomized trial. Am J Prev Med 36, 475483.CrossRefGoogle ScholarPubMed
63. Ambeba, EJ, Ye, L, Sereika, SM et al. (2015) The use of mHealth to deliver tailored messages reduces reported energy and fat intake. J Cardiovasc Nurs 30, 3543.CrossRefGoogle ScholarPubMed
64. Maes, L, Cook, TL, Ottovaere, C et al. (2011) Pilot evaluation of the HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) Food-O-Meter, a computer-tailored nutrition advice for adolescents: a study in six European cities. Public Health Nutr 14, 12921302.CrossRefGoogle ScholarPubMed
65. Kroeze, W, Oenema, A, Campbell, M et al. (2008) The efficacy of Web-based and print-delivered computer-tailored interventions to reduce fat intake: results of a randomized, controlled trial. J Nutr Educ Behav 40, 226236.CrossRefGoogle ScholarPubMed
66. Oenema, A, Tan, F & Brug, J (2005) Short-term efficacy of a web-based computer-tailored nutrition intervention: main effects and mediators. Ann Behav Med 29, 5463.CrossRefGoogle ScholarPubMed
67. Kroeze, W, Oenema, A, Dagnelie, PC et al. (2008) Examining the minimal required elements of a computer-tailored intervention aimed at dietary fat reduction: results of a randomized controlled dismantling study. Health Educ Res 23, 880891.CrossRefGoogle ScholarPubMed
68. Brug, J (1999) Dutch research into the development and impact of computer-tailored nutrition education. Eur J Clin Nutr 53, S78S82.CrossRefGoogle ScholarPubMed
69. Harris, J, Felix, L, Miners, A et al. (2011) Adaptive e-learning to improve dietary behaviour: a systematic review and cost-effectiveness analysis. Health Technol Assess 15, 1160.CrossRefGoogle ScholarPubMed
70. Capacci, S, Mazzocchi, M, Shankar, B et al. (2012) Policies to promote healthy eating in Europe: a structured review of policies and their effectiveness. Nutr Rev 70, 188200.CrossRefGoogle ScholarPubMed
71. Noar, S, Harrington, N, Van Stee, S et al. (2011) Tailored health communication to change lifestyle behaviours. Am. J. Lifestyle Med 5, 112122.CrossRefGoogle Scholar
72. Block, G, Sternfeld, B, Block, CH et al. (2008) Development of Alive! (A Lifestyle Intervention Via Email), and its effect on health-related quality of life, presenteeism, and other behavioral outcomes: randomized controlled trial. J Med Internet Res 10, e43.CrossRefGoogle ScholarPubMed
73. Brug, J & van Assema, P (2000) Differences in use and impact of computer-tailored dietary fat-feedback according to stage of change and education. Appetite 34, 285293.CrossRefGoogle Scholar
74. Celis-Morales, C, Lara, J & Mathers, JC (2014) Personalising nutritional guidance for more effective behaviour change. Proc Nutr Soc 12, 19.Google Scholar
75. Patrick, K, Calfas, KJ, Norman, GJ et al. (2011) Outcomes of a 12-month web-based intervention for overweight and obese men. Ann Behav Med 42, 391401.CrossRefGoogle ScholarPubMed
76. Tate, DF, Jackvony, EH & Wing, RR (2006) A randomized trial comparing human e-mail counseling, computer-automated tailored counseling, and no counseling in an Internet weight loss program. Arch Intern Med 166, 16201625.CrossRefGoogle Scholar
Figure 0

Table 1. Online (web-based) dietary assessment tools for the collection of dietary intake data in adults

Figure 1

Table 2. Summary of randomised controlled trials utilising new technology-based dietary assessment instruments to provide personalised/tailored advice

Figure 2

Fig. 1. Factors influencing the efficacy of new technology-based instruments in providing personalised/tailored advice to improve dietary intake.