Validation of a Web-Based, Time-Use Application to Assess Children’s School Meal Intakes: My E-Diary for Activities and Lifestyle (MEDAL)
Abstract
:1. Introduction
2. Materials and Methods
2.1. Participants
2.2. Study Procedures
2.2.1. Demonstration Session
2.2.2. Recess Meal Photography
2.2.3. The MEDAL Application for Diet Assessment
2.2.4. Demographic Data Collection
2.3. Data Processing
2.4. Statistical Analyses
3. Results
3.1. Food Item Reporting Accuracy
3.2. Portion Size Reporting Accuracy
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Food Group | Food Item | Portion Size | ||
---|---|---|---|---|
n | κ 2 | n | κ 2 | |
Overall | 92 | 0.85 | 81 | 0.70 |
Drinks | 16 | 0.55 | 13 | NA 3 |
Meat and fish | 28 | 0.90 | 27 | 0.62 |
Noodles, pasta and potatoes | 12 | 0.88 | 11 | 0.62 |
Rice and porridge | 9 | 0.42 | 9 | NA 3 |
All others4 | 27 | 0.83 | 21 | 0.90 |
Included (n = 33) | Excluded (n = 23) | p-Value 1 | |
---|---|---|---|
Age, years (interquartile range) | 11.0 (10.0–11.0) | 11.0 (10.0–11.0) | 0.586 |
School, % | |||
School A | 72.7 | 52.2 | 0.114 |
School B | 27.3 | 47.8 | |
Sex, % | |||
Females | 54.5 | 47.8 | 0.621 |
Males | 45.5 | 52.2 | |
BMI-for-age, % | |||
Underweight | 26.1 | 4.3 | 0.377 |
Healthy range | 78.8 | 87.0 | |
Overweight | 15.2 | 8.7 | |
Internet access, % | |||
Yes | 90.9 | 82.6 | 0.355 |
No | 9.1 | 17.4 |
Food Item Match | Omission 4 (%) | Intrusion 5 (%) | |||
---|---|---|---|---|---|
Strict 2 (%) | Loose 3 (%) | Total (%) | |||
By sex | |||||
Males (n = 15) | 50.0 (0.0–80.0) | 0.0 (0.0–0.0) | 60.0 (25.0–100.0) | 0.0 (0.0–40.0) | 0.0 (0.0–40.0) |
Females (n = 18) | 50.0 (33.3–75.0) | 0.0 (0.0–25.0) | 75.0 (44.4–100.0) | 0.0 (0.0–50.0) | 0.0 (0.0–0.0) |
p-value1 | 0.985 | 0.112 | 0.435 | 0.692 | 0.230 |
By school | |||||
School A (n = 24) | 50.0 (23.6–55.0) | 0.0 (0.0–21.1) | 55.0 (38.9–100.0) | 0.0 (0.0–50.0) | 0.0 (0.0–25.0) |
School B (n = 9) | 66.7 (50.0–80.0) | 0.0 (0.0–0.0) | 80.0 (60.0–100.0) | 20.0 (0.0–33.3) | 0.0 (0.0–0.0) |
p-value1 | 0.175 | 0.680 | 0.361 | 0.965 | 0.551 |
By day of recording | |||||
First day (n = 12) | 50.0 (26.7–100.0) | 0.0 (0.0–0.0) | 75.0 (33.3–100.0) | 0.0 (0.0–50.0) | 0.0 (0.0–0.0) |
Second day (n = 12) | 70.8 (29.2–100.0) | 0.0 (0.0–25.0) | 87.5 (58.3–100.0) | 0.0 (0.0–29.2) | 0.0 (0.0–0.0) |
p-value1 | 0.367 | 0.564 | 0.469 | 0.352 | 0.951 |
References
- Kelder, S.H.; Perry, C.L.; Klepp, K.I.; Lytle, L.L. Longitudinal tracking of adolescent smoking, physical activity, and food choice behaviors. Am. J. Public Health 1994, 84, 1121–1126. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Baxter, S.D.; Hitchcock, D.B.; Royer, J.A.; Smith, A.F.; Guinn, C.H. Fourth-grade children’s dietary reporting accuracy by meal component: Results from a validation study that manipulated retention interval and prompts. Appetite 2017, 113, 106–115. [Google Scholar] [CrossRef]
- Lu, A.S.; Baranowski, J.; Islam, N.; Baranowski, T. How to engage children in self-administered dietary assessment programmes. J. Hum. Nutr. Diet. 2014, 27 (Suppl. 1), 5–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- de Vlieger, N.M.; Weltert, M.; Molenaar, A.; McCaffrey, T.A.; Rollo, M.E.; Truby, H.; Livingstone, B.; Kirkpatrick, S.I.; Boushey, C.J.; Kerr, D.A.; et al. A systematic review of recall errors associated with portion size estimation aids in children. Appetite 2020, 147, 104522. [Google Scholar]
- Illner, A.K.; Freisling, H.; Boeing, H.; Huybrechts, I.; Crispim, S.P.; Slimani, N. Review and evaluation of innovative technologies for measuring diet in nutritional epidemiology. Int. J. Epidemiol. 2012, 41, 1187–1203. [Google Scholar] [CrossRef] [Green Version]
- Baranowski, T.; Islam, N.; Baranowski, J.; Cullen, K.W.; Myres, D.; Marsh, T. The food intake recording software system is valid among fourth-grade children. J. Am. Diet. Assoc. 2002, 102, 380–385. [Google Scholar] [CrossRef]
- Moore, H.J.; Ells, L.J.; McLure, S.A.; Crooks, S.; Cumbor, D.; Summerbell, C.D.; Batterham, A.M. The development and evaluation of a novel computer program to assess previous-day dietary and physical activity behaviours in school children: The Synchronised Nutrition and Activity Program (SNAP). Br. J. Nutr. 2008, 99, 1266–1274. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Storey, K.E.; McCargar, L.J. Reliability and validity of Web-SPAN, a web-based method for assessing weight status, diet and physical activity in youth. J. Hum. Nutr. Diet. 2012, 25, 59–68. [Google Scholar] [CrossRef] [PubMed]
- Biltoft-Jensen, A.; Bysted, A.; Trolle, E.; Christensen, T.; Knuthsen, P.; Damsgaard, C.T.; Andersen, L.F.; Brockhoff, P.; Tetens, I. Evaluation of web-based dietary assessment software for children: Comparing reported fruit, juice and vegetable intakes with plasma carotenoid concentration and school lunch observations. Br. J. Nutr. 2013, 110, 186–195. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Diep, C.S.; Hingle, M.; Chen, T.A.; Dadabhoy, H.R.; Beltran, A.; Baranowski, J.; Subar, A.F.; Baranowski, T. The automated self-administered 24-hour dietary recall for children, 2012 version, for youth aged 9 to 11 years: A validation study. J. Acad. Nutr. Diet. 2015, 115, 1591–1598. [Google Scholar] [CrossRef] [Green Version]
- Davies, V.F.; Kupek, E.; de Assis, M.A.; Natal, S.; Di Pietro, P.F.; Baranowski, T. Validation of a web-based questionnaire to assess the dietary intake of Brazilian children aged 7–10 years. J. Hum. Nutr. Diet. 2015, 28 (Suppl. 1), 93–102. [Google Scholar] [CrossRef] [PubMed]
- Sugianto, R.; Chan, M.J.; Wong, S.F.; Shek, L.P.C.; Tan, K.H.; Chong, Y.S.; Godfrey, K.M.; Tai, B.C.; Chong, M.F.F. Evaluation of a quantitative food frequency questionnaire for 5-year-old children in an asian population. J. Acad. Nutr. Diet. 2020, 120, 437–444. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Health Promotion Board. Students’ Health Survey. Available online: https://data.gov.sg/dataset/students-health-survey (accessed on 15 October 2021).
- Health Promotion Board. National Nutrition Survey 2010. Available online: https://www.hpb.gov.sg/docs/default-source/pdf/nns-2010-report.pdf?sfvrsn=18e3f172_2 (accessed on 15 October 2021).
- Brownlee, I.A.; Low, J.; Duriraju, N.; Chun, M.; Ong, J.X.Y.; Tay, M.E.; Hendrie, G.A.; Santos-Merx, L. Evaluation of the proximity of singaporean children’s dietary habits to food-based dietary guidelines. Nutrients 2019, 11, 2615. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chia, A.; Chew, M.N.J.S.; Tan, S.Y.X.; Chan, M.J.; Colega, M.T.; Toh, J.Y.; Natarajan, P.; Lança, C.; Shek, L.P.; Saw, S.M.; et al. A web-based time-use application to assess diet and movement behavior in asian schoolchildren: Development and usability study of my e-diary for activities and lifestyle (MEDAL). J. Med. Internet Res. 2021, 23, e25794. [Google Scholar] [PubMed]
- Poslusna, K.; Ruprich, J.; de Vries, J.H.M.; Jakubikova, M.; van’t Veer, P. Misreporting of energy and micronutrient intake estimated by food records and 24 hour recalls, control and adjustment methods in practice. Br. J. Nutr. 2009, 101, S73–S85. [Google Scholar] [CrossRef] [Green Version]
- Baxter, S.D.; Smith, A.F.; Litaker, M.S.; Baglio, M.L.; Guinn, C.H.; Shaffer, N.M. Children’s social desirability and dietary reports. J. Nutr. Educ. Behav. 2004, 36, 84–89. [Google Scholar] [CrossRef] [Green Version]
- Baranowski, T.; Domel, S.B. A cognitive model of children’s reporting of food intake. Am. J. Clin. Nutr. 1994, 59, 212S–217S. [Google Scholar] [CrossRef] [PubMed]
- Foster, E.; Bradley, J. Methodological considerations and future insights for 24-hour dietary recall assessment in children. Nutr. Res. 2018, 51, 1–11. [Google Scholar] [CrossRef] [PubMed]
- Lillegaard, I.T.; Overby, N.C.; Andersen, L.F. Can children and adolescents use photographs of food to estimate portion sizes? Eur. J. Clin. Nutr. 2005, 59, 611–617. [Google Scholar] [CrossRef] [PubMed]
- Krehbiel, C.F.; DuPaul, G.J.; Hoffman, J.A. A Validation study of the automated self-administered 24-hour dietary recall for children, 2014 version, at school lunch. J. Acad. Nutr. Diet. 2017, 117, 715–724. [Google Scholar] [CrossRef] [PubMed]
- Graziose, M.M.; Wolf, R.L.; Koch, P.A.; Gray, H.L.; Contento, I.R. Validation of a questionnaire to measure fruits and vegetables selected and consumed at school lunch among second- and third-grade students. J. Acad. Nutr. Diet. 2018, 118, 1700–1710.e2. [Google Scholar] [CrossRef] [PubMed]
- Biltoft-Jensen, A.; Damsgaard, C.T.; Andersen, R.; Ygil, K.H.; Andersen, E.W.; Ege, M.; Christensen, T.; Sørensen, L.B.; Stark, K.D.; Tetens, I.; et al. Accuracy of self-reported intake of signature foods in a school meal intervention study: Comparison between control and intervention period. Br. J. Nutr. 2015, 114, 635–644. [Google Scholar] [CrossRef] [Green Version]
- Tugault-Lafleur, C.N.; Black, J.L.; Barr, S.I. A Systematic review of methods to assess children’s diets in the school context. Adv. Nutr. 2017, 8, 63–79. [Google Scholar] [CrossRef] [Green Version]
- Swanson, M. Digital photography as a tool to measure school cafeteria consumption. J. Sch. Health 2008, 78, 432–437. [Google Scholar] [CrossRef]
- Lyng, N.; Fagt, S.; Davidsen, M.; Hoppe, C.; Holstein, B.; Tetens, I. Reporting accuracy of packed lunch consumption among Danish 11-year-olds differ by gender. Food Nutr. Res. 2013, 57, 19621. [Google Scholar]
- Sabinsky, M.S.; Toft, U.; Andersen, K.K.; Tetens, I. Validation of a digital photographic method for assessment of dietary quality of school lunch sandwiches brought from home. Food Nutr. Res. 2013, 57, 20243. [Google Scholar] [CrossRef] [PubMed]
- Winzer, E.; Luger, M.; Schindler, K. Using digital photography in a clinical setting: A valid, accurate, and applicable method to assess food intake. Eur. J. Clin. Nutr. 2018, 72, 879–887. [Google Scholar] [CrossRef]
- Health Promotion Board. Body Mass Index (BMI)-for-Age-Children and Youth Aged 6 to 18 Years Old. Available online: https://bukitbatoksec.moe.edu.sg/qql/slot/u537/Total%20Curriculum/Curricular%20Areas/PE/BMI_chart.pdf (accessed on 15 October 2021).
- Pérez-Rodrigo, C.; Artiach Escauriaza, B.; Artiach Escauriaza, J.; Polanco Allúe, I. Dietary assessment in children and adolescents: Issues and recommendations. Nutr. Hosp. 2015, 31 (Suppl. 3), 76–83. [Google Scholar]
- Raffoul, A.; Hobin, E.P.; Sacco, J.E.; Lee, K.M.; Haines, J.; Robson, P.J.; Dodd, K.W.; Kirkpatrick, S.I. School-age children can recall some foods and beverages consumed the prior day using the automated self-administered 24-hour dietary assessment tool (ASA24) without assistance. J. Nutr. 2019, 149, 1019–1026. [Google Scholar] [CrossRef] [PubMed]
- Willett, W. Nutritional Epidemiology; Oxford University Press: Oxford, UK, 1998. [Google Scholar]
- Baxter, S.D.; Hitchcock, D.B.; Guinn, C.H.; Vaadi, K.K.; Puryear, M.P.; Royer, J.A.; McIver, K.L.; Dowda, M.; Pate, R.R.; Wilson, D.K. A validation study concerning the effects of interview content, retention interval, and grade on children’s recall accuracy for dietary intake and/or physical activity. J. Acad. Nutr. Diet. 2014, 114, 1902–1914. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Baxter, S.D.; Royer, J.A.; Guinn, C.H.; Hardin, J.W.; Smith, A.F. Origins of intrusions in children’s dietary recalls: Data from a validation study concerning retention interval and information from school food-service production records. Public Health Nutr. 2009, 12, 1569–1575. [Google Scholar] [CrossRef]
- Smith, A.F.; Baxter, S.D.; Hardin, J.W.; Royer, J.A.; Guinn, C.H. Some intrusions in dietary reports by fourth-grade children are based on specific memories: Data from a validation study of the effect of interview modality. Nutr. Res. 2008, 28, 600–608. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Guinn, C.H.; Baxter, S.D.; Hardin, J.W.; Royer, J.A.; Smith, A.F. Intrusions in children’s dietary recalls: The roles of BMI, sex, race, interview protocol, and social desirability. Obesity 2008, 16, 2169–2174. [Google Scholar] [CrossRef] [Green Version]
- Champagne, C.M.; Baker, N.B.; DeLany, J.P.; Harsha, D.W.; Bray, G.A. Assessment of energy intake underreporting by doubly labeled water and observations on reported nutrient intakes in children. J. Am. Diet. Assoc. 1998, 98, 426–433. [Google Scholar] [CrossRef]
- Smith, A.F.; Baxter, S.D.; Hitchcock, D.B.; Finney, C.J.; Royer, J.A.; Guinn, C.H. Cognitive ability, social desirability, body mass index and socioeconomic status as correlates of fourth-grade children’s dietary-reporting accuracy. Eur. J. Clin. Nutr. 2016, 70, 1028–1033. [Google Scholar] [CrossRef] [Green Version]
- Klesges, L.M.; Baranowski, T.; Beech, B.; Cullen, K.; Murray, D.M.; Rochon, J.; Pratt, C. Social desirability bias in self-reported dietary, physical activity and weight concerns measures in 8-to 10-year-old African-American girls: Results from the girls health enrichment multisite studies (GEMS). Prev. Med. 2004, 38, 78–87. [Google Scholar] [CrossRef]
- Carvalho, M.A.; Baranowski, T.; Foster, E.; Santos, O.; Cardoso, B.; Rito, A.; Pereira Miguel, J. Validation of the Portuguese self-administered computerised 24-hour dietary recall among second-, third- and fourth-grade children. J. Hum. Nutr. Diet. 2015, 28, 666–674. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Total (n = 33) | School A (n = 24) | School B (n = 9) | p-Value 1 | |
---|---|---|---|---|
Age, years (interquartile range) | 11.0 (11.0–11.0) | 10.0 (10.0–11.0) | 11.0 (11.0–11.0) | 0.103 |
Sex, % | 0.943 | |||
Females | 54.5 | 54.2 | 55.6 | |
Males | 45.5 | 45.8 | 44.4 | |
BMI-for-age % | 0.720 | |||
Underweight | 6.1 | 4.2 | 11.1 | |
Healthy weight | 78.8 | 79.2 | 77.8 | |
Overweight | 15.1 | 16.6 | 11.1 | |
Internet access, % | 0.108 | |||
Yes | 87.9 | 91.7 | 77.8 | |
No | 12.1 | 8.3 | 22.2 | |
Days of recording, % | 0.160 | |||
1 day | 63.6 | 70.8 | 44.4 | |
2 days | 36.4 | 29.2 | 55.6 |
Food Item Match | Omission 4 | Intrusion 5 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Strict 2 | Loose 3 | Total | |||||||||
Total n | n | % | n | % | n | % | n | % | n | % | |
Total | 118 | 58 | 49.2 | 13 | 11.0 | 71 | 60.2 | 29 | 24.6 | 18 | 15.2 |
By food group | |||||||||||
Rice and porridge | 11 | 5 | 45.5 | 4 | 36.4 | 9 | 81.8 | 2 | 18.2 | 0 | 0.0 |
Breads, spreads and cereals | 18 | 13 | 72.2 | 1 | 5.6 | 14 | 77.8 | 0 | 0.0 | 4 | 22.2 |
Noodles, pasta and potatoes | 13 | 9 | 69.2 | 1 | 7.7 | 10 | 76.9 | 1 | 7.7 | 2 | 15.4 |
Drinks | 18 | 9 | 50.0 | 2 | 11.1 | 11 | 61.1 | 1 | 5.6 | 6 | 33.3 |
Meat and fish | 20 | 9 | 45.0 | 3 | 15.0 | 12 | 60.0 | 7 | 35.0 | 1 | 5.0 |
Fruits | 4 | 2 | 50.0 | 0 | 0.0 | 2 | 50.0 | 2 | 50.0 | 0 | 0.0 |
Curry | 8 | 4 | 50.0 | 0 | 0.0 | 4 | 50.0 | 4 | 50.0 | 0 | 0.0 |
Vegetables and mushrooms | 11 | 4 | 36.4 | 1 | 9.1 | 5 | 45.5 | 6 | 54.5 | 0 | 0.0 |
Snacks and desserts | 7 | 2 | 28.6 | 1 | 14.3 | 3 | 42.9 | 1 | 14.3 | 3 | 42.9 |
Eggs and tofu | 7 | 1 | 14.3 | 0 | 0.0 | 1 | 14.3 | 4 | 57.1 | 2 | 28.6 |
Milk, cheese and yogurt | 1 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 1 | 100.0 | 0 | 0.0 |
Portion Size Match | Underestimation | Overestimation | |||||
---|---|---|---|---|---|---|---|
Total n | n | % | n | % | n | % | |
Total | 48 | 28 | 58.3 | 4 | 8.3 | 16 | 33.3 |
By food group | |||||||
Eggs and tofu | 1 | 1 | 100.0 | 0 | 0.0 | 0 | 0.0 |
Drinks | 10 | 9 | 90.0 | 1 | 10.0 | 0 | 0.0 |
Meat and fish | 9 | 7 | 77.8 | 0 | 0.0 | 2 | 22.2 |
Vegetables and mushrooms | 3 | 2 | 66.7 | 0 | 0.0 | 1 | 33.3 |
Breads, spreads and cereals | 8 | 5 | 62.5 | 1 | 12.5 | 2 | 25.0 |
Fruits | 2 | 1 | 50.0 | 1 | 50.0 | 0 | 0.0 |
Snacks and desserts | 2 | 1 | 50.0 | 1 | 50.0 | 0 | 0.0 |
Rice and porridge | 5 | 1 | 20.0 | 0 | 0.0 | 4 | 80.0 |
Noodles, pasta and potatoes | 7 | 1 | 14.3 | 0 | 0.0 | 6 | 85.7 |
Curry | 1 | 0 | 0.0 | 0 | 0.0 | 1 | 100.0 |
Milk, cheese and yogurt | 0 | 0 | - | 0 | - | 0 | - |
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Tan, S.Y.X.; Chia, A.; Sugianto, R.; Tong, H.E.; Ang, I.Y.H.; Shek, L.P.-C.; Saw, S.M.; Müller-Riemenschneider, F.; Chong, M.F.-F. Validation of a Web-Based, Time-Use Application to Assess Children’s School Meal Intakes: My E-Diary for Activities and Lifestyle (MEDAL). Nutrients 2021, 13, 3790. https://doi.org/10.3390/nu13113790
Tan SYX, Chia A, Sugianto R, Tong HE, Ang IYH, Shek LP-C, Saw SM, Müller-Riemenschneider F, Chong MF-F. Validation of a Web-Based, Time-Use Application to Assess Children’s School Meal Intakes: My E-Diary for Activities and Lifestyle (MEDAL). Nutrients. 2021; 13(11):3790. https://doi.org/10.3390/nu13113790
Chicago/Turabian StyleTan, Sarah Yi Xuan, Airu Chia, Ray Sugianto, Huiying Eunice Tong, Ian Yi Han Ang, Lynette Pei-Chi Shek, Seang Mei Saw, Falk Müller-Riemenschneider, and Mary Foong-Fong Chong. 2021. "Validation of a Web-Based, Time-Use Application to Assess Children’s School Meal Intakes: My E-Diary for Activities and Lifestyle (MEDAL)" Nutrients 13, no. 11: 3790. https://doi.org/10.3390/nu13113790
APA StyleTan, S. Y. X., Chia, A., Sugianto, R., Tong, H. E., Ang, I. Y. H., Shek, L. P. -C., Saw, S. M., Müller-Riemenschneider, F., & Chong, M. F. -F. (2021). Validation of a Web-Based, Time-Use Application to Assess Children’s School Meal Intakes: My E-Diary for Activities and Lifestyle (MEDAL). Nutrients, 13(11), 3790. https://doi.org/10.3390/nu13113790