Review and evaluation of innovative technologies for measuring diet in nutritional epidemiology.

INTRODUCTION The use of innovative technologies is deemed to improve dietary assessment in various research settings. However, their relative merits in nutritional epidemiological studies, which require accurate quantitative estimates of the usual intake at individual level, still need to be evaluated. OBJECTIVE To report on the inventory of available innovative technologies for dietary assessment and to critically evaluate their strengths and weaknesses as compared with the conventional methodologies (i.e. Food Frequency Questionnaires, food records, 24-hour dietary recalls) used in epidemiological studies. METHODS A list of currently available technologies was identified from English-language journals, using PubMed and Web of Science. The search criteria were principally based on the date of publication (between 1995 and 2011) and pre-defined search keywords. RESULTS Six main groups of innovative technologies were identified ('Personal Digital Assistant-', 'Mobile-phone-', 'Interactive computer-', 'Web-', 'Camera- and tape-recorder-' and 'Scan- and sensor-based' technologies). Compared with the conventional food records, Personal Digital Assistant and mobile phone devices seem to improve the recording through the possibility for 'real-time' recording at eating events, but their validity to estimate individual dietary intakes was low to moderate. In 24-hour dietary recalls, there is still limited knowledge regarding the accuracy of fully automated approaches; and methodological problems, such as the inaccuracy in self-reported portion sizes might be more critical than in interview-based applications. In contrast, measurement errors in innovative web-based and in conventional paper-based Food Frequency Questionnaires are most likely similar, suggesting that the underlying methodology is unchanged by the technology. CONCLUSIONS Most of the new technologies in dietary assessment were seen to have overlapping methodological features with the conventional methods predominantly used for nutritional epidemiology. Their main potential to enhance dietary assessment is through more cost- and time-effective, less laborious ways of data collection and higher subject acceptance, though their integration in epidemiological studies would need additional considerations, such as the study objectives, the target population and the financial resources available. However, even in innovative technologies, the inherent individual bias related to self-reported dietary intake will not be resolved. More research is therefore crucial to investigate the validity of innovative dietary assessment technologies.

[1]  Elaine R Monsen,et al.  Nutrition in the prevention and treatment of disease , 2013 .

[2]  R. Carroll,et al.  Taking advantage of the strengths of 2 different dietary assessment instruments to improve intake estimates for nutritional epidemiology. , 2012, American journal of epidemiology.

[3]  L. Arab,et al.  Validity of a multipass, web-based, 24-hour self-administered recall for assessment of total energy intake in blacks and whites. , 2011, American journal of epidemiology.

[4]  Victor Kipnis,et al.  Dealing with dietary measurement error in nutritional cohort studies. , 2011, Journal of the National Cancer Institute.

[5]  E. Trolle,et al.  Rationale and methods of the European Food Consumption Validation (EFCOVAL) Project , 2011, European Journal of Clinical Nutrition.

[6]  E. Trolle,et al.  The standardized computerized 24-h dietary recall method EPIC-Soft adapted for pan-European dietary monitoring , 2011, European Journal of Clinical Nutrition.

[7]  V. Beral,et al.  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 , 2011, Public Health Nutrition.

[8]  A. Metspalu,et al.  Feasibility of innovative dietary assessment in epidemiological studies using the approach of combining different assessment instruments , 2011, Public Health Nutrition.

[9]  M. Sevick,et al.  The Effect of Electronic Self‐Monitoring on Weight Loss and Dietary Intake: A Randomized Behavioral Weight Loss Trial , 2011, Obesity.

[10]  A. Frood Technology: A flavour of the future , 2010, Nature.

[11]  D. Boggs,et al.  Use of a web-based questionnaire in the Black Women's Health Study. , 2010, American journal of epidemiology.

[12]  Jim Kaput,et al.  Web-enabled and improved software tools and data are needed to measure nutrient intakes and physical activity for personalized health research. , 2010, The Journal of nutrition.

[13]  M. Touvier,et al.  Comparison between an interactive web-based self-administered 24 h dietary record and an interview by a dietitian for large-scale epidemiological studies , 2010, British Journal of Nutrition.

[14]  Hafza Dadabhoy,et al.  Children’s accuracy of portion size estimation using digital food images: effects of interface design and size of image on computer screen , 2010, Public Health Nutrition.

[15]  Nicholas Chen,et al.  Toward Dietary Assessment via Mobile Phone Video Cameras. , 2010, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[16]  I. Tetens,et al.  Evaluation of a digital method to assess evening meal intake in a free-living adult population , 2010, Food & nutrition research.

[17]  L. Andersen,et al.  Two non-consecutive 24 h recalls using EPIC-Soft software are sufficiently valid for comparing protein and potassium intake between five European centres – results from the European Food Consumption Validation (EFCOVAL) study , 2010, British Journal of Nutrition.

[18]  A. Schatzkin,et al.  Gains in Statistical Power From Using a Dietary Biomarker in Combination With Self-reported Intake to Strengthen the Analysis of a Diet-Disease Association: An Example From CAREDS , 2010, American journal of epidemiology.

[19]  Lenore Arab,et al.  Automated camera-phone experience with the frequency of imaging necessary to capture diet. , 2010, Journal of the American Dietetic Association.

[20]  L. Arab,et al.  Eight self-administered 24-hour dietary recalls using the Internet are feasible in African Americans and Whites: the energetics study. , 2010, Journal of the American Dietetic Association.

[21]  David S. Ebert,et al.  The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation , 2010, IEEE Journal of Selected Topics in Signal Processing.

[22]  I. White,et al.  Dietary fiber and colorectal cancer risk: a nested case-control study using food diaries. , 2010, Journal of the National Cancer Institute.

[23]  Carol J. Boushey,et al.  Assessment of dietary intake: NuGO symposium report , 2010, Genes & Nutrition.

[24]  I. Bourdeaudhuij,et al.  The HELENA online food frequency questionnaire: reproducibility and comparison with four 24-h recalls in Belgian–Flemish adolescents , 2010, European Journal of Clinical Nutrition.

[25]  L. Arab,et al.  Using the web for recruitment, screen, tracking, data management, and quality control in a dietary assessment clinical validation trial. , 2010, Contemporary clinical trials.

[26]  L. Maes,et al.  How accurate are adolescents in portion-size estimation using the computer tool Young Adolescents' Nutrition Assessment on Computer (YANA-C)? , 2010, British Journal of Nutrition.

[27]  U. Nöthlings,et al.  The Assessment of Individual Usual Food Intake in Large-Scale Prospective Studies , 2010, Annals of Nutrition and Metabolism.

[28]  Victor Kipnis,et al.  Can we use biomarkers in combination with self-reports to strengthen the analysis of nutritional epidemiologic studies? , 2010, Epidemiologic perspectives & innovations : EP+I.

[29]  Raymond J Carroll,et al.  Modeling Data with Excess Zeros and Measurement Error: Application to Evaluating Relationships between Episodically Consumed Foods and Health Outcomes , 2009, Biometrics.

[30]  A. Subar,et al.  Challenges in converting an interviewer-administered food probe database to self-administration in the National Cancer Institute Automated Self-administered 24-Hour Recall (ASA24). , 2009, Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems.

[31]  Katherine L Tucker,et al.  Validation of a web-based dietary questionnaire designed for the DASH (Dietary Approaches to Stop Hypertension) diet: the DASH Online Questionnaire , 2009, Public Health Nutrition.

[32]  Lea Maes,et al.  Feasibility of the Young Children's Nutrition Assessment on the Web. , 2009, Journal of the American Dietetic Association.

[33]  Lora E Burke,et al.  SMART trial: A randomized clinical trial of self-monitoring in behavioral weight management-design and baseline findings. , 2009, Contemporary clinical trials.

[34]  R. Bender,et al.  Longitudinal changes in energy expenditure in an elderly German population: a 12-year follow-up , 2009, European Journal of Clinical Nutrition.

[35]  Lori D. Sigrist,et al.  Monitoring energy intake: a hand-held personal digital assistant provides accuracy comparable to written records. , 2009, Journal of the American Dietetic Association.

[36]  L. Serra-Majem,et al.  A review of the use of information and communication technologies for dietary assessment , 2009, British Journal of Nutrition.

[37]  K. Ohashi,et al.  Development of a hand-held personal digital assistant-based food diary with food photographs for Japanese subjects. , 2009, Journal of the American Dietetic Association.

[38]  W. Riley,et al.  Evaluation of a web-based, pictorial diet history questionnaire , 2009, Public Health Nutrition.

[39]  S. Bingham,et al.  Biomarkers in nutritional epidemiology: applications, needs and new horizons , 2009, Human Genetics.

[40]  S. Jaeger,et al.  A quantitative characterisation of meals and their contexts in a sample of 25 to 49-year-old Spanish people , 2009, Appetite.

[41]  A. Schatzkin,et al.  Observational Epidemiologic Studies of Nutrition and Cancer: The Next Generation (with Better Observation) , 2009, Cancer Epidemiology Biomarkers & Prevention.

[42]  Andrew J Milat,et al.  Computer-tailored weight reduction interventions targeting adults: a narrative systematic review. , 2009, Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals.

[43]  L. Neville,et al.  Computer-tailored dietary behaviour change interventions: a systematic review , 2009, Health education research.

[44]  G. Mcneill,et al.  A novel online Food Recall Checklist for use in an undergraduate student population: a comparison with diet diaries , 2009, Nutrition journal.

[45]  E J Delp,et al.  Use of technology in children’s dietary assessment , 2009, European Journal of Clinical Nutrition.

[46]  Carol J. Stevens,et al.  Validity and reliability of photographic diet diaries for assessing dietary intake among young children. , 2009 .

[47]  Holly Blake,et al.  Mobile phone technology in chronic disease management. , 2008, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[48]  E. Fowles,et al.  The feasibility of personal digital assistants (PDAs) to collect dietary intake data in low-income pregnant women. , 2008, Journal of nutrition education and behavior.

[49]  Y. Manios,et al.  Development and evaluation of a self-administered computerized 24-h dietary recall method for adolescents in Europe , 2008, International Journal of Obesity.

[50]  A. Galante,et al.  Desenvolvimento e aplicação de um questionário semiquantitativo de freqüência alimentar on-line para estimar a ingestão de cálcio e ferro , 2008 .

[51]  Mark Swanson,et al.  Digital photography as a tool to measure school cafeteria consumption. , 2008, The Journal of school health.

[52]  Corby K. Martin,et al.  A novel method to remotely measure food intake of free-living individuals in real time: the remote food photography method. , 2008, The British journal of nutrition.

[53]  Audie A Atienza,et al.  Using hand-held computer technologies to improve dietary intake. , 2008, American journal of preventive medicine.

[54]  Teresa J. Sakraida,et al.  Design, feasibility, and acceptability of an intervention using personal digital assistant-based self-monitoring in managing type 2 diabetes. , 2008, Contemporary clinical trials.

[55]  R. Carroll,et al.  Performance of a food-frequency questionnaire in the US NIH–AARP (National Institutes of Health–American Association of Retired Persons) Diet and Health Study , 2008, Public Health Nutrition.

[56]  G. Tröster,et al.  Recognition of dietary activity events using on-body sensors , 2008, Artif. Intell. Medicine.

[57]  J. Matthews,et al.  Children's estimates of food portion size: the development and evaluation of three portion size assessment tools for use with children , 2007, British Journal of Nutrition.

[58]  T. Baranowski,et al.  Formative research of a quick list for an automated self-administered 24-hour dietary recall. , 2007, Journal of the American Dietetic Association.

[59]  Jan-Eric Litton,et al.  New times, new needs; e-epidemiology , 2007, European Journal of Epidemiology.

[60]  Christophe Matthys,et al.  Validity and reproducibility of an adolescent web-based food frequency questionnaire. , 2007, Journal of the American Dietetic Association.

[61]  Da‐hong Wang,et al.  The application of a handheld personal digital assistant with camera and mobile phone card (Wellnavi) to the general population in a dietary survey. , 2007, Journal of nutritional science and vitaminology.

[62]  Siew Sun Wong,et al.  Evaluation of a computerized food frequency questionnaire to estimate calcium intake of Asian, Hispanic, and non-Hispanic white youth. , 2007, Journal of the American Dietetic Association.

[63]  S. Schinke,et al.  Criterion validity of the Healthy Eating Self-monitoring Tool (HEST) for black adolescents. , 2007, Journal of the American Dietetic Association.

[64]  Jan-Eric Litton,et al.  Optimizing the design of web-based questionnaires – experience from a population-based study among 50,000 women , 2007, European Journal of Epidemiology.

[65]  Da‐hong Wang,et al.  Development of a new instrument for evaluating individuals' dietary intakes. , 2006, Journal of the American Dietetic Association.

[66]  T. Aoki,et al.  Estimation of dietary nutritional content using an online system with ability to assess the dieticians' accuracy , 2006, Journal of telemedicine and telecare.

[67]  D. Midthune,et al.  The food propensity questionnaire: concept, development, and validation for use as a covariate in a model to estimate usual food intake. , 2006, Journal of the American Dietetic Association.

[68]  Linda Van Horn Assessing dietary intake: new ideas and better approaches. , 2006, Journal of the American Dietetic Association.

[69]  W. Willett,et al.  Not the Time to Abandon the Food Frequency Questionnaire: Point , 2006, Cancer Epidemiology Biomarkers & Prevention.

[70]  J. Potter,et al.  Not the Time to Abandon the Food Frequency Questionnaire: Counterpoint , 2006, Cancer Epidemiology Biomarkers & Prevention.

[71]  L. Mccargar,et al.  School Region Socio-economic Status and Geographic Locale is Associated with Food Behaviour of Ontario and Alberta Adolescents , 2006, Canadian journal of public health = Revue canadienne de sante publique.

[72]  R. Carroll,et al.  A comparison of two dietary instruments for evaluating the fat-breast cancer relationship. , 2006, International journal of epidemiology.

[73]  Rachel K. Johnson,et al.  The use of a personal digital assistant for dietary self-monitoring does not improve the validity of self-reports of energy intake. , 2006, Journal of the American Dietetic Association.

[74]  W. Riley,et al.  Evaluation of a PDA-based Dietary Assessment and Intervention Program: A Randomized Controlled Trial , 2006, Journal of the American College of Nutrition.

[75]  Ulrike Peters,et al.  Is It Time to Abandon the Food Frequency Questionnaire? , 2005, Cancer Epidemiology Biomarkers & Prevention.

[76]  M. Robinson,et al.  Using smart card technology to monitor the eating habits of children in a school cafeteria: 1. Developing and validating the methodology. , 2005, Journal of human nutrition and dietetics : the official journal of the British Dietetic Association.

[77]  J. Zoellner,et al.  Comparative validation of a bilingual interactive multimedia dietary assessment tool. , 2005, Journal of the American Dietetic Association.

[78]  Lora E Burke,et al.  Self-monitoring dietary intake: current and future practices. , 2005, Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation.

[79]  M. Sevick,et al.  A preliminary study of PDA-based dietary self-monitoring in hemodialysis patients. , 2005, Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation.

[80]  Jeannette Beasley,et al.  Accuracy of a PDA-based dietary assessment program. , 2005, Nutrition.

[81]  S. Henauw,et al.  Validity and reproducibility of a computerised tool for assessing the iron, calcium and vitamin C intake of Belgian women , 2004, European Journal of Clinical Nutrition.

[82]  H. Allen,et al.  Digital photography: A new method for estimating food intake in cafeteria settings , 2004, Eating and weight disorders : EWD.

[83]  Raymond J Carroll,et al.  A comparison of a food frequency questionnaire with a 24-hour recall for use in an epidemiological cohort study: results from the biomarker-based Observing Protein and Energy Nutrition (OPEN) study. , 2003, International journal of epidemiology.

[84]  F. Clavel-Chapelon,et al.  Group level validation of protein intakes estimated by 24-hour diet recall and dietary questionnaires against 24-hour urinary nitrogen in the European Prospective Investigation into Cancer and Nutrition (EPIC) calibration study. , 2003, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[85]  N. Day,et al.  Are imprecise methods obscuring a relation between fat and breast cancer? , 2003, The Lancet.

[86]  D. Midthune,et al.  Using intake biomarkers to evaluate the extent of dietary misreporting in a large sample of adults: the OPEN study. , 2003, American journal of epidemiology.

[87]  F. Clavel-Chapelon,et al.  European Prospective Investigation into Cancer and Nutrition (EPIC) calibration study: rationale, design and population characteristics , 2002, Public Health Nutrition.

[88]  Da‐hong Wang,et al.  Validity and reliability of a dietary assessment method: the application of a digital camera with a mobile phone card attachment. , 2002, Journal of nutritional science and vitaminology.

[89]  Janice Baranowski,et al.  The food intake recording software system is valid among fourth-grade children. , 2002, Journal of the American Dietetic Association.

[90]  C. Pullen,et al.  Use and reliability of the World Wide Web version of the Block Health Habits and History Questionnaire with older rural women. , 2002, Journal of nutrition education and behavior.

[91]  J. Jobe,et al.  Cognitive research enhances accuracy of food frequency questionnaire reports: results of an experimental validation study. , 2002, Journal of the American Dietetic Association.

[92]  A F Subar,et al.  Design and serendipity in establishing a large cohort with wide dietary intake distributions : the National Institutes of Health-American Association of Retired Persons Diet and Health Study. , 2001, American journal of epidemiology.

[93]  N E Day,et al.  Nutritional methods in the European Prospective Investigation of Cancer in Norfolk , 2001, Public Health Nutrition.

[94]  W. Willett Commentary: Dietary diaries versus food frequency questionnaires-a case of undigestible data. , 2001, International journal of epidemiology.

[95]  H. S. Bayley,et al.  Four-day multimedia diet records underestimate energy needs in middle-aged and elderly women as determined by doubly-labeled water. , 2000, The Journal of nutrition.

[96]  D O Stram,et al.  A multiethnic cohort in Hawaii and Los Angeles: baseline characteristics. , 2000, American journal of epidemiology.

[97]  D O Stram,et al.  Calibration of the dietary questionnaire for a multiethnic cohort in Hawaii and Los Angeles. , 2000, American journal of epidemiology.

[98]  A. Bartolucci,et al.  Comparison of a computer-based food frequency questionnaire for calcium intake with 2 other assessment tools. , 1999, Journal of the American Dietetic Association.

[99]  Elio Riboli,et al.  The EPIC Project: Rationale and study design , 1997 .

[100]  K. Bättig,et al.  Comparison of an electronic food diary with a nonquantitative food frequency questionnaire in male and female smokers and nonsmokers. , 1996, Journal of the American Dietetic Association.

[101]  A F Subar,et al.  Improving food frequency questionnaires: a qualitative approach using cognitive interviewing. , 1995, Journal of the American Dietetic Association.

[102]  F. Thompson,et al.  Dietary assessment resource manual. , 1994, The Journal of nutrition.

[103]  N E Day,et al.  Comparison of dietary assessment methods in nutritional epidemiology: weighed records v. 24 h recalls, food-frequency questionnaires and estimated-diet records , 1994, British Journal of Nutrition.

[104]  D Feskanich,et al.  Computerized collection and analysis of dietary intake information. , 1989, Computer methods and programs in biomedicine.

[105]  M. Sevick,et al.  Self-monitoring in weight loss: a systematic review of the literature. , 2011, Journal of the American Dietetic Association.

[106]  D. Schoeller,et al.  American Journal of Epidemiology Practice of Epidemiology Evaluation and Comparison of Food Records, Recalls, and Frequencies for Energy and Protein Assessment by Using Recovery Biomarkers , 2022 .

[107]  N. Roeleveld,et al.  American Journal of Epidemiology Practice of Epidemiology Web-based Questionnaires: the Future in Epidemiology? , 2022 .

[108]  Ajay Divakaran,et al.  Automatic food documentation and volume computation using digital imaging and electronic transmission. , 2010, Journal of the American Dietetic Association.

[109]  Tom Greene,et al.  Validation of a dietary history questionnaire for American Indian and Alaska Native people. , 2010, Ethnicity & disease.

[110]  Jennifer L. Crafts,et al.  Assessment of the accuracy of portion size reports using computer-based food photographs aids in the development of an automated self-administered 24-hour recall. , 2010, Journal of the American Dietetic Association.

[111]  H. Eyles,et al.  Use of household supermarket sales data to estimate nutrient intakes: a comparison with repeat 24-hour dietary recalls. , 2010, Journal of the American Dietetic Association.

[112]  Mingui Sun,et al.  A wearable electronic system for objective dietary assessment. , 2010, Journal of the American Dietetic Association.

[113]  E. Delp,et al.  Evidence-based development of a mobile telephone food record. , 2010, Journal of the American Dietetic Association.

[114]  David S. Ebert,et al.  Personal dietary assessment using mobile devices , 2009, Electronic Imaging.

[115]  R. Hanning,et al.  Web-based Food Behaviour Questionnaire: validation with grades six to eight students. , 2009, Canadian journal of dietetic practice and research : a publication of Dietitians of Canada = Revue canadienne de la pratique et de la recherche en dietetique : une publication des Dietetistes du Canada.

[116]  Roger L. Edwards,et al.  Development, implementation, and evaluation of a computerized self-administered diet history questionnaire for use in studies of American Indian and Alaskan native people. , 2008, Journal of the American Dietetic Association.

[117]  O. Dale,et al.  Despite technical problems personal digital assistants outperform pen and paper when collecting patient diary data. , 2007, Journal of clinical epidemiology.

[118]  Roger L. Edwards,et al.  Practice of Epidemiology Development and Use of Touch-Screen Audio Computer-assisted Self-Interviewing in a Study of American Indians , 2007 .

[119]  William J. Long,et al.  Natural Language Processing of Spoken Diet Records (SDRs) , 2006, AMIA.

[120]  M. Goran,et al.  Use of tape-recorded food records in assessing children's dietary intake. , 2000, Obesity research.

[121]  E Riboli,et al.  The EPIC Project: rationale and study design. European Prospective Investigation into Cancer and Nutrition. , 1997, International journal of epidemiology.

[122]  S. Gadowsky,et al.  An interactive 24-h recall technique for assessing the adequacy of trace mineral intakes of rural Malawian women; its advantages and limitations. , 1995, European journal of clinical nutrition.

[123]  S A Bingham,et al.  Limitations of the various methods for collecting dietary intake data. , 1991, Annals of nutrition & metabolism.