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Review

Validation of Dietary Intake Estimated by Web-Based Dietary Assessment Methods and Usability Using Dietary Records or 24-h Dietary Recalls: A Scoping Review

by
Utako Murai
1,
Ryoko Tajima
1,
Mai Matsumoto
1,
Yoko Sato
2,
Saki Horie
3,
Aya Fujiwara
1,4,
Emiko Koshida
1,
Emiko Okada
1,
Tomoko Sumikura
1,
Tetsuji Yokoyama
5,
Midori Ishikawa
5,
Kayo Kurotani
6 and
Hidemi Takimoto
1,*
1
Department of Nutritional Epidemiology and Shokuiku, National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Kento Innovation Park NK Building, 3-17 Shinmachi, Settsu City, Osaka 566-0002, Japan
2
Department of the Science of Living, Kyoritsu Women’s Junior College, Tokyo 101-8437, Japan
3
Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo 160-8582, Japan
4
Department of Epidemiology and Prevention, Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo 162-8655, Japan
5
Department of Health Promotion, National Institute of Public Health, Wako 351-0197, Japan
6
Faculty of Food and Health Sciences, Showa Women’s University, Tokyo 154-8533, Japan
*
Author to whom correspondence should be addressed.
Nutrients 2023, 15(8), 1816; https://doi.org/10.3390/nu15081816
Submission received: 2 March 2023 / Revised: 29 March 2023 / Accepted: 5 April 2023 / Published: 8 April 2023
(This article belongs to the Section Nutrition Methodology & Assessment)
Browse Figure
Review Reports Versions Notes

Abstract

:
The goal was to summarize studies comparing the accuracy of web-based dietary assessments with those of conventional face-to-face or paper-based assessments using 24-h dietary recall or dietary record methods in the general population. Using two databases, mean differences and correlation coefficients (CCs) for intakes of energy, macronutrients, sodium, vegetables, and fruits were extracted from each study independently by the authors. We also collected information regarding usability from articles reporting this. From 17 articles included in this review, the mean dietary intake differences in the web-based dietary assessment compared to conventional methods, were −11.5–16.1% for energy, −12.1–14.9% for protein, −16.7–17.6% for fat, −10.8–8.0% for carbohydrates, −11.2–9.6% for sodium, −27.4–3.9% for vegetables, and −5.1–47.6% for fruits. The CC was 0.17–0.88 for energy, protein, fat, carbohydrates, and sodium, and 0.23–0.85 for vegetables and fruits. In three out of four studies reporting usability, more than half of the participants preferred the web-based dietary assessment. In conclusion, % difference and CC of dietary intake were acceptable in both web-based dietary records and 24-h dietary recalls. The findings from this review highlight the possibility of wide-spread application of the web-based dietary assessment in the future.

1. Introduction

Accurate measurements of dietary intake are fundamental for health and nutrition research. To date, the dietary record or 24-h dietary recall methods have been used for national nutrition surveys worldwide [1,2,3,4]. New technologies in dietary intake assessment, including web-based dietary assessments, have been shown to reduce issues associated with the conventional collection of dietary data (i.e., paper-based or face-to-face), such as cost, participation rates and accuracy of data collected [5,6]. Recently, the internet has become widely used [7,8], and there has been increasing interest in developing web-based dietary assessment methods [9]. Therefore, it is important to examine the possibility of applying web-based dietary assessment.
Moreover, it has become difficult to conduct in-person interviews due to the COVID-19 pandemic in 2019. In Japan, the National Health and Nutrition Survey has been annually conducted using paper-based dietary records, accompanied by in-person interviews to check information [10], but the survey was suspended in 2020 and 2021 for the first time in its history due to the pandemic [11]. On the other hand, in the UK, the national dietary survey was able to be conducted in the middle of the pandemic, since a web-based 24-h dietary recall tool was in place from 2019 [12]. Considering this situation, there is a need to examine applying web-based dietary assessment methods to the national dietary survey in Japan.
Regarding web-based 24-h dietary recalls, a previous review focused on online tools including methodology or registering foods [13]. Another review reported the validity of various mobile phone applications or web-based 24-h dietary recalls and dietary records in childhood and adolescence and concluded that those assessments may be comparably valid with respect to conventional methods [14]. However, the representative value of dietary intake was not summarized in these reviews. In dietary records of mobile phone applications, the intakes of micronutrients and food groups were underestimated compared with conventional dietary assessments in a systematic review [15]. However, that review did not systematically evaluate the validity of web-based dietary records using the internet. The validity of web-based dietary assessments has been especially limited for the elderly population [13,14,15]. The usability of web-based dietary assessment methods compared to conventional methods should be also considered, since it may affect the participation rate of the survey. Regarding usability of web-based 24-h dietary recalls, satisfaction may be considered good. On the other hand, it mentioned several comments such as difficulties in logging in or identifying correct foods in the previous review [13]. However, there was limited information about usability of web-based dietary records.
Therefore, this scoping review aimed to summarize the comparative validity of representative dietary intake estimated from web-based and conventional dietary assessment methods using dietary records or 24-h dietary recall, which are mainly used in international surveys worldwide, in the general population including children, adolescents, adults, the elderly, or those who are not used to the computer. When articles reported on usability, we collected this information.

2. Materials and Methods

This systematic review was planned by authors (HT, UM) with the support of authors (RT, MM) and conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) guidelines [16,17], as described below.

2.1. Search Strategy

To search the validation study of dietary intake estimated by web-based dietary assessment methods, we included words related to validation, dietary assessment, and web-based assessment methods as search terms. In addition, to examine the validity of the web-based dietary assessment methods in Japan, we included articles written in Japanese. An electronic literature search was conducted by UM on 26 February 2021 using PubMed and Web of Science without limiting the publication date. The following search terms, which were determined based on Mesh terms, were used (in English): (“food record” OR “diet record” OR “food diary” OR “dietary record” OR “recall method” OR “dietary recall” OR “diet recall” OR “24-h recall” OR “24-h recall” OR “dietary assessment”) AND (web OR internet OR automated OR mobile OR online OR digital OR “computer assisted” OR computerized) AND (validity OR validated OR validation OR comparison OR reliability) AND (English[LA] OR Japanese[LA]).

2.2. Inclusion Criteria

Articles eligible for inclusion: (1) were peer-reviewed original articles written in English or Japanese; (2) evaluated the validity of web-based dietary assessment compared to conventional methods (paper-based or face-to-face) as a gold standard using dietary records or 24-h dietary recalls; (3) assessed whole day intake of energy, protein, fat, carbohydrates, sodium, vegetables, and fruit; and (4) were conducted on individuals aged ≥1 years consuming a usual diet. We included studies in which parents responded on behalf of their children, and those where participants were different between web-based and conventional dietary assessments but belonged to the general population.
Articles excluded were: (1) reviews, case studies, conference reports, or abstracts; (2) focusing on specific meal occasions (e.g., breakfast); (3) using biomarkers as a gold standard; (4) focusing on patients with any diseases; (5) focusing on participants who did not eat usual diets, such as obesity, athletes, soldiers, vegetarians, and pregnant or lactating women. We included all eligible studies regardless of the study design.
This review scoped the validity of the estimated intake of energy, macronutrients, and sodium because these nutrients were important for health conditions, and vegetable and fruit intake were suggested to be important to prevent noncommunicable diseases by the World Health Organization [18]. We excluded studies using biomarkers as a gold standard because these assessment methods could not estimate representative values of dietary intake. The validity of web-based dietary assessment summarized in this study was defined as (1) an ability to estimate representative values (mean or median) of dietary intakes, and (2) an ability to rank individuals based on dietary intakes.

2.3. Study Selection

The titles and abstracts of all articles were screened (UM, AF, AN). After removing duplicate articles, the full texts of the articles were assessed for eligibility (UM, AF, YS, SH, EK, AN). Any disagreements were discussed and resolved by consensus or by another reviewer, if necessary (HT, RT). The reference lists of the articles identified were manually searched and screened (UM, YS, SH). The reference lists of the articles identified during this process were also examined by hand search (UM) to further identify potentially relevant articles and previous review articles [5,6,14,19,20,21,22,23,24,25,26,27,28,29,30,31]. All articles were screened or evaluated independently by at least two reviewers.

2.4. Data Collection

Data were extracted by two independent reviewers using an electronic spreadsheet. The information extracted included author names, publication year, study year, country, characteristics of participants (sex, age, sample size, and exclusion criteria), number of survey days, tool name, food, recipe, or dish database incorporated into the tool, and devices for web-based dietary assessment method, number of survey days of conventional dietary assessment method, mean or median intake of total energy, protein, fat, carbohydrate, sodium, and vegetables and fruit, and their correlation coefficients (CCs) both of web-based and conventional dietary assessments. When articles reported on usability such as accessibility of web-based dietary assessments, we collected this information.

2.5. Evaluation of the Relative Validity of Web-Based Dietary Assessment

Using the extracted information, the percentage difference of mean or median intake was calculated as the following formula: (mean or median dietary intake calculated by the web-based—mean or median dietary intake calculated by the conventional)/mean or median dietary intake calculated by the conventional × 100. According to the criteria proposed by Lombard et al. [32], the percent differences were categorized ≤10 as good; 11.0–20.0 as acceptable; >20.0 as poor, and the CCs between dietary intake estimated using the web-based and the conventional dietary assessment methods was evaluated: ≥0.50 was categorized as good; 0·20–0·49 as acceptable; <0.20 as poor. We assessed the quality score (points) of each validation study using the definition of the previous study [33]. A non-homogeneous sample (e.g., sex) was 0.5, sample size over 100 was 0.5, comparison of dietary intake was 1, correlations (0.5 for crude, 1 for energy-adjusted, and 1.5 for deattenuated or intraclass), 0.5 for agreement of dietary intake (e.g., Bland–Altman plot), 1 for data correction (face-to-face interview), 0.5 for seasonality, and 1.5 for considering supplement intake. Scores ranged from 0 to 7, and validation studies were classified as very good (>5.0), good (5.0–3.5), acceptable (3.0–2.5), and poor (<2.5). We did not conduct an adjustment/weighting of the correlation coefficient according to the quality score and a rating of the adjusted/weighted correlation, because we assessed the crude dietary intake as possible.

3. Results

3.1. Screening and Characteristics of Included Studies

Of the 856 identified articles, 562 were screened based on their titles and abstracts. Of these, 77 full-text articles were assessed for eligibility and 14 were included in this review [34,35,36,37,38,39,40,41,42,43,44,45,46,47]. In addition, we identified three studies by hand search [48,49,50] (Figure 1). Details of the included articles are presented in Table 1. All studies included this review were written in English. Out of 17 papers in this review, since the same tool was reported in two different studies, seven web-based dietary record tools and eight web-based 24-h dietary recall methods were included in this review. The included studies were conducted in nine countries: three studies in Canada [34,38,43], the UK [41,42,45], and the US [44,46,47] two in Ireland [36,40] and Brazil [49,50], and one in Belgium [48], France [37], Japan [35], and Sweden [39] (Table 1). The sample size ranged from 19 to 1147, with 10 studies conducted on <100 participants. The range of participants’ ages varied from 3 to 82 years. In addition, nine studies included participants who were recruited by email or were accustomed to the internet [34,35,36,37,38,40,41,43,45,48]. On the other hand, some studies excluded participants who were not ability to use a computer [41,44]. Except for one study conducted on nursery school children [48], participants reported their dietary intake themselves. Although the number of survey days was different between web-based and conventional methods, the most common survey period was 3 days in both methods [34,36,38,43,44,46,48,50]. The databases based on foods, recipes, and dishes were used. The range of total quality scores was 2 to 5 (Table S2). In quality of including studies, sixteen studies were moderate (2.5 to 5 of a total of 7 points) [34,35,36,37,38,39,40,41,42,43,44,45,46,47,49,50]. the remaining one study was low (2 points) [48].
Among the seven studies which used the web-based dietary record method [35,37,38,44,46,48,49], there was only one study [44] that applied a 24-h dietary recall as the conventional method. Among the 10 studies which used the web-based 24-h dietary recall method [34,36,39,40,41,42,43,45,47,50], there were 3 studies [34,36,47] that applied the dietary record as the conventional method.

3.2. Difference of Dietary Intake between the Web-Based and the Conventional Dietary Assessment

Table 2 showed the mean (or median) difference in dietary intake estimated by the web-based dietary assessment method compared to those estimated by the conventional method. In web-based dietary records, the range of difference was −68.0 to 125.4 kcal (−3.1% to 6.6% difference) for energy intake, −10.4 to 3.1 g (−11.8 to 3.9% difference) for protein intake, −10.1 to 11.2g (−16.7 to 17.6% difference) for fat intake, −35.0 to 19.9 g (−10.7 to 7.8% difference) for carbohydrate intake, and 43 to 400mg (1.4 to 5.5% difference) for sodium intake. One study reported vegetable and fruit intake, and the difference was −8.0 g (−14.0% difference) for vegetable intake and 1.0 g (0.8% difference) for fruit intake. In web-based 24-h dietary recall, the range of differences was −241.0 to 342.0 kcal (−11.5% to 16.1% difference) for energy intake, −11.5 to 11.0 g (−12.1 to 14.9% difference) for protein intake, −15.0 to 10.0 g (−15.4 to 13.2% difference) for fat intake, −21.0 to 18.0 g (−7.9 to 8.0% difference) for carbohydrate intake, and −287 to 305 mg (−11.2 to 9.6% difference) for sodium intake. The range of difference was −65.0 to 3.3 g (−27.4 to 3.9% difference) for vegetable intake and −14.0 to 120.0 g for fruit intake (−5.1 to 47.6% difference).
Among the seven studies which used the web-based dietary record method [35,37,38,44,46,48,49], the one study [44] which compared to the 24-h dietary recall had the largest difference regarding fat intake (17.6%). Among the three studies [34,36,47] that compared the web-based 24-h dietary recall to the dietary record, the differences between intakes of energy, protein, fat, or carbohydrates were similar to the differences observed between web-based and conventional 24-h dietary recalls. One study [36] showed relatively large differences regarding vegetable (−27.4%) and fruits (47.6%) intakes, compared with the three studies [39,40,41] that applied the conventional 24-h dietary recall for comparisons.

3.3. Correlation Coefficients between the Web-Based and the Conventional Dietary Assessment

Table 3 shows the Spearman, Pearson, or Intraclass CC between dietary intake estimated by the web-based and the conventional dietary assessment methods. Overall, 12 out of 17 studies reported Spearman, Pearson, or Intraclass CC between dietary intakes. In the web-based dietary records, the CC of dietary intake was 0.37 to 0.87 for energy, 0.41 to 0.78 for protein, 0.33 to 0.75 for fat, 0.31 to 0.82 for carbohydrates, and 0.59 for sodium. There were no reports about CC of vegetable and fruit intake. In the web-based 24-h dietary recalls, the CC of dietary intake was 0.44 to 0.88 for energy, 0.41 to 0.83 for protein, 0.33 to 0.75 for fat, 0.36 to 0.82 for carbohydrate, and 0.17 and 0.75 for sodium. In food groups, there were two reported studies: the CCs were 0.23 to 0.84 for vegetable intake, and 0.56 to 0.85 for fruit intake.
Among the four studies which used the web-based dietary record method [35,38,44,49], the CCs for energy, protein, fat, or carbohydrate were low in one study [38] which applied different number of survey days for web-based and conventional methods. Among the three studies [34,36,47] that compared the web-based 24-h dietary recall to the dietary record, one study [47] showed the lowest CCs for energy, protein, carbohydrate, and sodium intakes.

3.4. Usability of the Web-Based Dietary Assessment Methods

Of 17 articles, 5 reported the usability of web-based dietary assessment methods [36,37,48,49], although details were not described in one study (Table 4) [44]. More than half of the participants in the three studies preferred the web-based dietary assessment [36,37,49]. However, according to Vereecken et al. [48], in the study where 58 parents were asked to either complete a web-based dietary assessment or a conventional (paper-based) dietary record, only 5 of them chose the web-based assessment. On the other hand, in the same study [48], the majority of 164 parents who were requested to answer the web-based dietary assessment indicated that the web-based dietary assessment was user-friendly (79%), attractive (68%), well-liked (66%), provided adequate instructions (92%), clear information (93%), clear pictures (91%), and easy-to-find food items (88%). Regarding registration in the dietary intake using web-based dietary assessments, participants in two of three studies answered the web-based dietary assessment was easy to report their dietary intakes on site compared with the conventional method [36,37], while a study reported that participants preferred the conventional method (paper-based) [49], and one study did not collect that information [48]. In the number of survey days, only one study reported the period for mastering the web-based dietary assessment and the most common (48.4%) answer was taking two days [37].

4. Discussion

This scoping review of 17 studies summarized the representative values and the CC of the intakes of energy, macronutrients, sodium, vegetables, and fruits between the web-based and conventional dietary assessments in the general population. Although the % difference in energy, protein and sodium intake was slightly smaller in web-based dietary records than in those 24-h dietary recalls, the mean difference in dietary intake estimated from both methods was within approximately ± 20% difference for energy and nutrient intakes. The CC between the web-based dietary records and conventional methods were acceptable (0.37–0.87 for energy and 0.31–0.82 for nutrients and sodium, but NA for food groups), and slightly better for web-based 24-h dietary recalls regarding energy (0.44–0.88) but not for nutrients and sodium (0.17–0.83).
We evaluated the representative dietary intakes for a whole day using web-based dietary records, and our results supported the previous study [15]. In a previous study, web-based dietary records underestimated dietary intake compared with the conventional method [15]. However, this review assessed only mobile phone applications and included studies of not assessing dietary intake for the whole day, face-to-face conventional dietary assessments, or compared with biomarkers as conventional methods [15]. In addition, we showed that the validity of dietary intake using web-based 24-h dietary recalls was also reasonable. A previous review evaluated the web-based 24-h dietary recall methods [13], and another review reported the validity of mobile phone applications or web-based dietary assessments in childhood and adolescence [14]. As this scoping review focused on the representative value of dietary intake in the general population, we excluded several studies included in previous studies [13,14]. The reasons for the excluded studies were listed in Table S1 [5,6,14,19,20,21,22,23,24,25,26,27,28,29,30,31,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103].

4.1. Characteristics of Included Studies

In this review, only one study included participants over 70 years old [45]. In addition, included studies mainly recruited individuals who regularly used the internet or computers [34,35,36,37,40,41,43,44,45,50], although most of the included studies did not describe devices. In the national dietary survey in the UK conducted using a web-based 24-h dietary recall, participants who were not able to access through the internet or felt uncomfortable using it, or unconfident completing the dietary recalls, were supported by the survey team via telephone or internet video (Zoom) conferencing [12]. Therefore, when conducting a web-based dietary assessment for the general population including a wide range of age groups or people without internet access, it may be necessary to consider a support system. Ten studies used databases based on foods or meals [34,35,36,39,40,41,42,46,48,49]. Although two of the remaining studies used food composition tables [45,50] instead of food items as the database, five studies did not mention the database [37,38,43,44,47]. Because the database is important to estimate dietary intakes, it needed to be described in the validation studies.
As a result of this review study, the majority of studies were conducted in developed countries and no studies involving immigrants or ethnic language minority groups were included. As the number of immigrants and people living overseas increases and racial diversity expands, web-based dietary survey methods for researching these groups will also be needed. In the UK, for participants who were not able to access through the internet or felt uncomfortable using it, for example the elderly, they were helped by using devices to assist [12]. Similarly, the National Health and Nutrition Examination Survey in the US hired interpreters of various languages when necessary [104]. It is important to pay attention to the diversification of languages or emigrations when conducting web-based national dietary surveys in the future.

4.2. The Differences and Correlation Coefficients between Dietary Intake between the Web-Based and the Conventional Dietary Assessment

Overall, the mean or median nutrient intake calculated from the web-based dietary assessment method was underestimated compared to those from the conventional method, and it was more frequent with the web-based 24-h dietary recall method than with the web-based dietary records. Although the number of studies that described the database was limited, the number of food and beverage items was larger in the web-based dietary records than in web-based 24-h dietary recalls, and it could affect accuracy when participants registered food or beverage items.
The mean difference in dietary intake estimated by the web-based dietary assessment compared to those estimated by the conventional method was within approximately ± 20% difference for energy and nutrient intake except for fat intake in both web-based dietary records and those 24-h dietary recalls (Table 2). Regarding fat intake, sources of error between the web-based food record and the conventional method suggested that food portion estimation was the greatest source of error (50%) [44]. Another reason may be the differences in food selection, such as selecting similar but not exactly the same foods [44]. In another study [49], the database using web-based dietary assessment included 3 million items, and it seemed difficult for the subject to select the exact foods. On the other hand, Timon et al. [36] indicated a small number of databases (751 food and beverage items) as a reason for less agreement. This should be taken into account when constructing databases for web-based dietary assessments. CC of dietary intake was similar results to % difference (Table 3). Additionally, CC for sodium was lower compared to other nutrients. The web-based dietary surveys extracted in this study included dish-based dietary records [35]. In the case of the dish-based survey, the differences from the dietary records may be observed because the detailed seasonings were not checked. In addition, some systems recruited a method of selecting several portion sizes for intake [40,41]. These would have the potential to affect the estimating of the intake of salt and other seasonings.
Vegetable or fruit intake has been reported in only one study in web-based dietary records [48] and five studies in those 24-h dietary recalls. In the web-based 24-h dietary recalls, intakes of vegetables and fruits were mean different by over 20% [36], considering that dietary intakes over two different time points have been noted. CCs of vegetable and fruit intake were not reported in the web-based dietary record and only three studies were represented in those 24-h dietary recalls. Therefore, further studies are needed to confirm this association.
In the two studies, the comparison between the web-based and conventional dietary assessment methods was based on the two different populations [43,48]. Although CCs were not reported in these studies [43,48], the difference in energy intake was as small as 5% between web-based and conventional methods. As the validity of the representative values was reasonable even when the subjects of the web-based and conventional dietary assessments were different, web-based dietary assessments may be useful for a dietary assessment for the general population.

4.3. Usability of the Web-Based Dietary Assessment

Participants, recruited through the internet or e-mail, reported the web-based dietary assessment method made it easy to register their dietary intake on-site compared with the conventional method [36,37]. The web-based dietary assessments may be useful for people who are used to the internet which may be possible to improve the response rates of dietary assessments by using web-based dietary assessments. In a study of participants who preferred the conventional method (paper-based) [49], participants needed to register the web-based dietary record from over 3 million food items and in portion size details (e.g., grams and milliliters), which was difficult to complete, and estimate portion sizes. In the previous study, it was also noted to be difficult to identify the correct food in web-based 24-h dietary recalls [13]. According to Vereecken et al. [48], only 8% of parents who were asked to either complete a web-based or paper-based dietary assessment chose the web-based dietary assessment compared to the conventional method. The authors considered that parents did not want to spend time on the new web-based dietary assessment tools. However, in the same study [48], the majority of parents who were requested to answer the web-based dietary assessment indicated that they were favorable to the web-based method. Participants do not prefer to use the web-based dietary assessment for the first time. However, they may find it easy when they use it. Therefore, it should be considered for the recruitment and collecting of information for each generation or people who are unfamiliar with the internet. In addition, for accurate estimated dietary intake in web-based dietary assessments, careful database construction is required, such as the number of foods, dish composition, and portion size setting in both web-based dietary records and 24-h dietary recalls.

4.4. Strength and Limitation

The strengths of this scoping review are its investigation of the validity of representative dietary intake using the web-based dietary assessment methods compared to the conventional methods by dietary records or 24-h dietary recalls, which are used worldwide, and % difference and CC of dietary intake were reasonable in the web-based dietary records and 24-h dietary recalls. Although discussion for some nutrients is warranted, this result indicated that web-based dietary assessments may be effective as dietary assessments for the general population, especially for people who are familiar with the internet or e-mail. However, there are several limitations to this scoping review. First, despite our use of wide search terms and hand-search of reference lists, we could not capture all relevant publications. In addition, we used only two independent literature searches and searched in only two languages. Second, although we considered including the latest study, only one study included participants over 70 years old [45]. Further studies on dietary assessment methods for older people and people without familiarity with the internet are needed. Since some eligible studies were recruiting participants on the internet or who could use e-mail, we could not consider such as the participation rate of participants who do not use the internet and their usability of them. Finally, we used the criteria employed by Lombard et al. [32] to evaluate the difference between the web-based and conventional methods. This criterion also used by Luevano-Contreras et al. [105,106], is used to identify differences between two survey instruments when examining the validity and reliability of the FFQ. However, caution may need to be exercised in its interpretation, as it is not a criterion developed to compare between web-based and conventional methods.

5. Conclusions

The validity of web-based dietary assessment methods compared to conventional methods using dietary records and 24-h dietary recalls for dietary intake was reasonable. Although the databases and web tools used differed for most studies, the representative values of dietary intakes were consistent to some extent between the web-based and conventional dietary assessment, and it may be possible to use the web-based dietary assessment. Three of four studies reported usability, and more than half of the participants preferred the web-based dietary assessment. The results of this review provided the information needed to examine the validity of a web-based dietary evaluation method compared to conventional methods. Moreover, our results serve as both a reference and an indication for further research, as well as for the development of non-face-to-face dietary assessment methods. This review highlights findings that may be applied in designing and investigating the performance of the web-based dietary survey in the future.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu15081816/s1, Table S1: List of excluded studies and reason for exclusion; Table S2: Score of validity study.

Author Contributions

Conceptualization: H.T., U.M, R.T. and M.M.; Methodology: U.M., Y.S., S.H., A.F., E.K. and E.O.; Writing—original draft preparation: U.M.; Writing—review and editing: U.M., R.T., M.M., T.S., A.F. and H.T.; Funding Acquisition: H.T., T.Y., M.I. and K.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by part of basic research to secure and improve the quality of national health and nutrition surveys project, grant number 20FA1901.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We would like to thank Akemi Nishide for this review.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Chen, T.C.; Clark, J.; Riddles, M.K.; Mohadjer, L.K.; Fakhouri, T.H.I. National Health and Nutrition Examination Survey, 2015–2018: Sample Design and Estimation Procedures; Vital and Health Statistics Series 2; U.S. Department of Health and Human Services: Washington, DC, USA, 2020; Volume 184, pp. 1–35.
  2. Public Health England. National Diet and Nutrition Survey. Available online: https://www.gov.uk/government/collections/national-diet-and-nutrition-survey (accessed on 18 October 2022).
  3. Public Health England. National Diet and Nutrition Survey: Results from Years 7 and 8 (Combined) of the Rolling Programme (2014/2015–2015/2016). Available online: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/699241/NDNS_results_years_7_and_8.pdf (accessed on 18 October 2022).
  4. Australian Health Survey. Available online: https://www.abs.gov.au/ausstats/[email protected]/Lookup/D1A83EE0703D6E8FCA257BBB0014C443?opendocument (accessed on 18 October 2022).
  5. Ngo, J.; Engelen, A.; Molag, M.; Roesle, J.; García-Segovia, P.; Serra-Majem, L. A review of the use of information and communication technologies for dietary assessment. Br. J. Nutr. 2009, 101 (Suppl. 2), S102–S112. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Timon, C.M.; van den Barg, R.; Blain, R.J.; Kehoe, L.; Evans, K.; Walton, J.; Flynn, A.; Gibney, E.R. A review of the design and validation of web- and computer-based 24-h dietary recall tools. Nutr. Res. Rev. 2016, 29, 268–280. [Google Scholar] [CrossRef] [PubMed]
  7. Ministry of Internal Affairs and Communications. Basic Data and Policy Trends (Japanese). Available online: https://www.soumu.go.jp/johotsusintokei/whitepaper/ja/r02/html/nd252120.html (accessed on 18 October 2022).
  8. Committed to Connecting the World International Telecommunication Union. Individuals Using the Internet. Available online: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx (accessed on 18 October 2022).
  9. Thompson, F.E.; Subar, A.F.; Loria, C.M.; Reedy, J.L.; Baranowski, T. Need for technological innovation in dietary assessment. J. Am. Diet. Assoc. 2010, 110, 48–51. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  10. Ministry of Health, Labour and Welfare. The National Health and Nutrition Survey 2019 (Japanese). Available online: https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/kenkou_iryou/kenkou/eiyou/r1-houkoku_00002.html (accessed on 18 October 2022).
  11. Ministry of Health, Labour and Welfare. The National Health and Nutrition Survey (Japanese). Available online: https://www.mhlw.go.jp/bunya/kenkou/kenkou_eiyou_chousa.html (accessed on 18 October 2022).
  12. Public Health England. NDNS: Diet and Physical Activity—A Follow-Up Study during COVID-19. Available online: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1019663/Follow_up_stud_2020_main_report.pdf (accessed on 18 October 2022).
  13. Gazan, R.; Vieux, F.; Mora, S.; Havard, S.; Dubuisson, C. Potential of existing online 24-h dietary recall tools for national dietary surveys. Public Health Nutr. 2021, 24, 5361–5386. [Google Scholar] [CrossRef] [PubMed]
  14. Kouvari, M.; Mamalaki, E.; Bathrellou, E.; Poulimeneas, D.; Yannakoulia, M.; Panagiotakos, D.B. The validity of technology-based dietary assessment methods in childhood and adolescence: A systematic review. Crit. Rev. Food Sci. Nutr. 2021, 61, 1065–1080. [Google Scholar] [CrossRef] [PubMed]
  15. Zhang, L.; Misir, A.; Boshuizen, H.; Ocké, M. A Systematic Review and Meta-Analysis of Validation Studies Performed on Dietary Record Apps. Adv. Nutr. 2021, 12, 2321–2332. [Google Scholar] [CrossRef] [PubMed]
  16. Arksey, H.; O’Malley, L. Scoping studies: Towards a methodological framework. Int. J. Soc. Res. Methodol. 2005, 8, 19–32. [Google Scholar] [CrossRef] [Green Version]
  17. Tricco, A.C.; Lillie, E.; Zarin, W.; O’Brien, K.K.; Colquhoun, H.; Levac, D.; Moher, D.; Peters, M.D.J.; Horsley, T.; Weeks, L.; et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann. Intern. Med. 2018, 169, 467–473. [Google Scholar] [CrossRef] [Green Version]
  18. World Health Organization. Healthy Diet. Available online: https://www.who.int/news-room/fact-sheets/detail/healthy-diet (accessed on 18 October 2022).
  19. Pisa, P.T.; Landais, E.; Margetts, B.; Vorster, H.H.; Friedenreich, C.M.; Huybrechts, I.; Martin-Prevel, Y.; Branca, F.; Lee, W.T.K.; Leclercq, C.; et al. Inventory on the dietary assessment tools available and needed in Africa: A prerequisite for setting up a common methodological research infrastructure for nutritional surveillance, research, and prevention of diet-related non-communicable diseases. Crit. Rev. Food Sci. Nutr. 2018, 58, 37–61. [Google Scholar] [CrossRef] [PubMed]
  20. Cade, J.E. Measuring diet in the 21st century: Use of new technologies. Proc. Nutr. Soc. 2017, 76, 276–282. [Google Scholar] [CrossRef]
  21. Pendergast, F.J.; Leech, R.M.; McNaughton, S.A. Novel Online or Mobile Methods to Assess Eating Patterns. Curr. Nutr. Rep. 2017, 6, 212–227. [Google Scholar] [CrossRef] [Green Version]
  22. Dao, M.C.; Subar, A.F.; Warthon-Medina, M.; Cade, J.E.; Burrows, T.; Golley, R.K.; Forouhi, N.G.; Pearce, M.; Holmes, B.A. Dietary assessment toolkits: An overview. Public Health Nutr. 2019, 22, 404–418. [Google Scholar] [CrossRef] [Green Version]
  23. Hooson, J.J.; Hutchinson, J.J.; Warthon-Medina, M.; Hancock, N.; Greathead, K.; Knowles, B.; Vargas-Garcia, E.; Gibson, L.E.; Bush, L.A.; Margetts, B.; et al. A systematic review of reviews identifying UK validated dietary assessment tools for inclusion on an interactive guided website for researchers: www.nutritools.org. Crit. Rev. Food Sci. Nutr. 2020, 60, 1265–1289. [Google Scholar] [CrossRef] [Green Version]
  24. Archundia Herrera, M.C.; Chan, C.B. Narrative Review of New Methods for Assessing Food and Energy Intake. Nutrients 2018, 10, 1064. [Google Scholar] [CrossRef] [Green Version]
  25. Schembre, S.M.; Liao, Y.; O’Connor, S.G.; Hingle, M.D.; Shen, S.E.; Hamoy, K.G.; Huh, J.; Dunton, G.F.; Weiss, R.; Thomson, C.A.; et al. Mobile Ecological Momentary Diet Assessment Methods for Behavioral Research: Systematic Review. JMIR Mhealth Uhealth 2018, 6, e11170. [Google Scholar] [CrossRef] [Green Version]
  26. 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] [PubMed] [Green Version]
  27. Conrad, J.; Koch, S.A.J.; Nöthlings, U. New approaches in assessing food intake in epidemiology. Curr. Opin. Clin. Nutr. Metab. Care 2018, 2, 343–351. [Google Scholar] [CrossRef]
  28. Sharp, D.B.; Allman-Farinelli, M. Feasibility and validity of mobile phones to assess dietary intake. Nutrition 2014, 30, 1257–1266. [Google Scholar] [CrossRef] [PubMed]
  29. Lieffers, J.R.; Hanning, R.M. Dietary assessment and self-monitoring with nutrition applications for mobile devices. Can. J. Diet. Pract. Res. 2012, 73, e253–e260. [Google Scholar] [CrossRef]
  30. Arens-Volland, A.G.; Spassova, L.; Bohn, T. Promising approaches of computer-supported dietary assessment and management-Current research status and available applications. Int. J. Med. Inform. 2015, 84, 997–1008. [Google Scholar] [CrossRef] [PubMed]
  31. Höchsmann, C.; Martin, C.K. Review of the validity and feasibility of image-assisted methods for dietary assessment. Int. J. Obes. 2020, 44, 2358–2371. [Google Scholar] [CrossRef] [PubMed]
  32. Lombard, M.J.; Steyn, N.P.; Charlton, K.E.; Senekal, M. Application and interpretation of multiple statistical tests to evaluate validity of dietary intake assessment methods. Nutr. J. 2015, 14, 40. [Google Scholar] [CrossRef] [Green Version]
  33. Serra-Majem, L.; Andersen, L.F.; Henríque-Sánchez, P.; Doreste-Alonso, J.; Sánchez-Villegas, A.; Ortiz-Andrelluchi, A.; Negri, E.; La Vecchia, C. Evaluating the quality of dietary intake validation studies. Br. J. Nutr. 2009, 102 (Suppl. 1), S3–S9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Lafrenière, J.; Laramée, C.; Robitaille, J.; Lamarche, B.; Lemieux, S. Assessing the relative validity of a new, web-based, self-administered 24 h dietary recall in a French-Canadian population. Public Health Nutr. 2018, 21, 2744–2752. [Google Scholar] [CrossRef] [Green Version]
  35. Matsuzaki, E.; Michie, M.; Kawabata, T. Validity of Nutrient Intakes Derived from an Internet Website Dish-Based Dietary Record for Self-Management of Weight among Japanese Women. Nutrients 2017, 9, 1058. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Timon, C.M.; Blain, R.J.; McNulty, B.; Kehoe, L.; Evans, K.; Walton, J.; Flynn, A.; Gibney, E.R. The Development, Validation, and User Evaluation of Foodbook24: A Web-Based Dietary Assessment Tool Developed for the Irish Adult Population. J. Med. Internet Res. 2017, 19, e158. [Google Scholar] [CrossRef] [Green Version]
  37. Monnerie, B.; Tavoularis, L.G.; Guelinckx, I.; Hebel, P.; Boisvieux, T.; Cousin, A.; Le Bellego, L. A cross-over study comparing an online versus a paper 7-day food record: Focus on total water intake data and participant’s perception of the records. Eur. J. Nutr. 2015, 54 (Suppl. 2), 27–34. [Google Scholar] [CrossRef] [Green Version]
  38. 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]
  39. Lindroos, A.K.; Petrelius Sipinen, J.; Axelsson, C.; Nyberg, G.; Landberg, R.; Leanderson, P.; Arnemo, M.; Warensjö Lemming, E. Use of a Web-Based Dietary Assessment Tool (RiksmatenFlex) in Swedish Adolescents: Comparison and Validation Study. J. Med. Internet Res. 2019, 21, e12572. [Google Scholar] [CrossRef] [Green Version]
  40. Timon, C.M.; Evans, K.; Kehoe, L.; Blain, R.J.; Flynn, A.; Gibney, E.R.; Walton, J. Comparison of a Web-Based 24-h Dietary Recall Tool (Foodbook24) to an Interviewer-Led 24-h Dietary Recall. Nutrients 2017, 9, 425. [Google Scholar] [CrossRef] [Green Version]
  41. Albar, S.A.; Alwan, N.A.; Evans, C.E.; Greenwood, D.C.; Cade, J.E. Agreement between an online dietary assessment tool (myfood24) and an interviewer-administered 24-h dietary recall in British adolescents aged 11–18 years. Br. J. Nutr. 2016, 115, 1678–1686. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Bradley, J.; Simpson, E.; Poliakov, I.; Matthews, J.N.; Olivier, P.; Adamson, A.J.; Foster, E. Comparison of INTAKE24 (an Online 24-h Dietary Recall Tool) with Interviewer-Led 24-h Recall in 11–24 Year-Old. Nutrients 2016, 8, 358. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Brassard, D.; Laramée, C.; Robitaille, J.; Lemieux, S.; Lamarche, B. Differences in Population-Based Dietary Intake Estimates Obtained From an Interviewer-Administered and a Self-Administered Web-Based 24-h Recall. Front. Nutr. 2020, 7, 137. [Google Scholar] [CrossRef]
  44. Beasley, J.; Riley, W.T.; Jean-Mary, J. Accuracy of a PDA-based dietary assessment program. Nutrition 2005, 21, 672–677. [Google Scholar] [CrossRef] [PubMed]
  45. Liu, B.; Young, H.; Crowe, F.L.; Benson, V.S.; Spencer, E.A.; Key, T.J.; Appleby, P.N.; Beral, V. 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. 2011, 14, 1998–2005. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Raatz, S.K.; Scheett, A.J.; Johnson, L.K.; Jahns, L. Validity of electronic diet recording nutrient estimates compared to dietitian analysis of diet records: Randomized controlled trial. J. Med. Internet Res. 2015, 17, e21. [Google Scholar] [CrossRef]
  47. Frankenfeld, C.L.; Poudrier, J.K.; Waters, N.M.; Gillevet, P.M.; Xu, Y. Dietary intake measured from a self-administered, online 24-h recall system compared with 4-day diet records in an adult US population. J. Acad. Nutr. Diet. 2012, 112, 1642–1647. [Google Scholar] [CrossRef]
  48. Vereecken, C.A.; Covents, M.; Haynie, D.; Maes, L. Feasibility of the Young Children’s Nutrition Assessment on the Web. J. Am. Diet. Assoc. 2009, 109, 1896–1902. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Teixeira, V.; Voci, S.M.; Mendes-Netto, R.S.; da Silva, D.G. The relative validity of a food record using the smartphone application MyFitnessPal. Nutr. Diet. 2018, 75, 219–225. [Google Scholar] [CrossRef] [PubMed]
  50. Mescoloto, S.B.; Caivano, S.; Domene, S. Evaluation of a mobile application for estimation of food intake Avaliação de um aplicativo móvel para a estimativa de ingestão alimentar. Rev. Nutr. Campinas 2017, 30, 91–98. [Google Scholar] [CrossRef] [Green Version]
  51. Pérez-Llamas, F.; Garaulet, M.; Torralba, C.; Zamora, S. Desarrollo de una versión actualizada de una aplicación informática para investigación y práctica en nutrición humana (GRUNUMUR 2.0) [Development of a current version of a software application for research and practice in human nutrition (GRUNUMUR 2.0)]. Nutr. Hosp. 2012, 27, 1576–1582. (In Spanish) [Google Scholar] [CrossRef]
  52. Meroni, A.; Jualim, N.; Fuller, N. ‘Boden Food Plate’: Novel Interactive Web-based Method for the Assessment of Dietary Intake. J. Vis. Exp. 2018, 139, 57923. [Google Scholar] [CrossRef] [Green Version]
  53. Fallaize, R.; Zenun Franco, R.; Pasang, J.; Hwang, F.; Lovegrove, J.A. Popular Nutrition-Related Mobile Apps: An Agreement Assessment Against a UK Reference Method. JMIR Mhealth Uhealth 2019, 7, e9838. [Google Scholar] [CrossRef]
  54. Thompson, F.E.; Kirkpatrick, S.I.; Subar, A.F.; Reedy, J.; Schap, T.E.; Wilson, M.M.; Krebs-Smith, S.M. The National Cancer Institute’s Dietary Assessment Primer: A Resource for Diet Research. J. Acad. Nutr. Diet. 2015, 115, 1986–1995. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Baranowski, T.; Islam, N.; Baranowski, J.; Martin, S.; Beltran, A.; Dadabhoy, H.; Adame, S.H.; Watson, K.B.; Thompson, D.; Cullen, K.W.; et al. Comparison of a Web-based versus traditional diet recall among children. J. Acad. Nutr. Diet. 2012, 112, 527–532. [Google Scholar] [CrossRef] [Green Version]
  56. Lancaster, R.; Radd-Vagenas, S.; Fiatarone Singh, M.; Noble, Y.; Daniel, K.; Mavros, Y.; Sachdev, P.S.; Lautenschlager, N.; Cox, K.; Brodaty, H.; et al. Electronic food records among middle-aged and older people: A comparison of self-reported and dietitian-assisted information. Nutr. Diet. 2021, 78, 145–153. [Google Scholar] [CrossRef] [PubMed]
  57. Foster, E.; Hawkins, A.; Simpson, E.; Adamson, A.J. Developing an interactive portion size assessment system (IPSAS) for use with children. J. Hum. Nutr. Diet. 2014, 27 (Suppl. 1), 18–25. [Google Scholar] [CrossRef]
  58. Hughes, A.R.; Summer, S.S.; Ollberding, N.J.; Benken, L.A.; Kalkwarf, H.J. Comparison of an interviewer-administered with an automated self-administered 24 h (ASA24) dietary recall in adolescents. Public Health Nutr. 2017, 20, 3060–3067. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Shinozaki, N.; Murakami, K. Evaluation of the Ability of Diet-Tracking Mobile Applications to Estimate Energy and Nutrient Intake in Japan. Nutrients 2020, 12, 3327. [Google Scholar] [CrossRef]
  60. Subar, A.F.; Potischman, N.; Dodd, K.W.; Thompson, F.E.; Baer, D.J.; Schoeller, D.A.; Midthune, D.; Kipnis, V.; Kirkpatrick, S.I.; Mittl, B.; et al. Performance and Feasibility of Recalls Completed Using the Automated Self-Administered 24-h Dietary Assessment Tool in Relation to Other Self-Report Tools and Biomarkers in the Interactive Diet and Activity Tracking in AARP (IDATA) Study. J. Acad. Nutr. Diet. 2020, 120, 1805–1820. [Google Scholar] [CrossRef] [PubMed]
  61. Fontana, J.M.; Higgins, J.A.; Schuckers, S.C.; Bellisle, F.; Pan, Z.; Melanson, E.L.; Neuman, M.R.; Sazonov, E. Energy intake estimation from counts of chews and swallows. Appetite 2015, 85, 14–21. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Daniel, C.R.; Kapur, K.; McAdams, M.J.; Dixit-Joshi, S.; Devasenapathy, N.; Shetty, H.; Hariharan, S.; George, P.S.; Mathew, A.; Sinha, R. Development of a field-friendly automated dietary assessment tool and nutrient database for India. Br. J. Nutr. 2014, 111, 160–171. [Google Scholar] [CrossRef] [Green Version]
  63. Wark, P.A.; Hardie, L.J.; Frost, G.S.; Alwan, N.A.; Carter, M.; Elliott, P.; Ford, H.E.; Hancock, N.; Morris, M.A.; Mulla, U.Z.; et al. Validity of an online 24-h recall tool (myfood24) for dietary assessment in population studies: Comparison with biomarkers and standard interviews. BMC Med. 2018, 16, 136. [Google Scholar] [CrossRef] [PubMed]
  64. Timon, C.M.; Astell, A.J.; Hwang, F.; Adlam, T.D.; Smith, T.; Maclean, L.; Spurr, D.; Forster, S.E.; Williams, E.A. The validation of a computer-based food record for older adults: The Novel Assessment of Nutrition and Ageing (NANA) method. Br. J. Nutr. 2015, 113, 654–664. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Comrie, F.; Masson, L.F.; McNeill, G. A novel online Food Recall Checklist for use in an undergraduate student population: A comparison with diet diaries. Nutr. J. 2009, 8, 13. [Google Scholar] [CrossRef] [Green Version]
  66. Freese, J.; Feller, S.; Harttig, U.; Kleiser, C.; Linseisen, J.; Fischer, B.; Leitzmann, M.F.; Six-Merker, J.; Michels, K.B.; Nimptsch, K.; et al. Development and evaluation of a short 24-h food list as part of a blended dietary assessment strategy in large-scale cohort studies. Eur. J. Clin. Nutr. 2014, 68, 324–329. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Du, S.S.; Jiang, Y.S.; Chen, Y.; Li, Z.; Zhang, Y.F.; Sun, C.H.; Feng, R.N. Development and Applicability of an Internet-Based Diet and Lifestyle Questionnaire for College Students in China: A Cross-Sectional Study. Medicine 2015, 94, e2130. [Google Scholar] [CrossRef]
  68. Bruening, M.; van Woerden, I.; Todd, M.; Brennhofer, S.; Laska, M.N.; Dunton, G. A Mobile Ecological Momentary Assessment Tool (devilSPARC) for Nutrition and Physical Activity Behaviors in College Students: A Validation Study. J. Med. Internet. Res. 2016, 18, e209. [Google Scholar] [CrossRef]
  69. Ambrosini, G.L.; Hurworth, M.; Giglia, R.; Trapp, G.; Strauss, P. Feasibility of a commercial smartphone application for dietary assessment in epidemiological research and comparison with 24-h dietary recalls. Nutr. J. 2018, 17, 5. [Google Scholar] [CrossRef] [PubMed]
  70. Thompson, F.E.; Dixit-Joshi, S.; Potischman, N.; Dodd, K.W.; Kirkpatrick, S.I.; Kushi, L.H.; Alexander, G.L.; Coleman, L.A.; Zimmerman, T.P.; Sundaram, M.E.; et al. Comparison of Interviewer-Administered and Automated Self-Administered 24-h Dietary Recalls in 3 Diverse Integrated Health Systems. Am. J. Epidemiol. 2015, 181, 970–978. [Google Scholar] [CrossRef] [Green Version]
  71. Wellard-Cole, L.; Chen, J.; Davies, A.; Wong, A.; Huynh, S.; Rangan, A.; Allman-Farinelli, M. Relative Validity of the Eat and Track (EaT) Smartphone App for Collection of Dietary Intake Data in 18-to-30-Year Olds. Nutrients 2019, 11, 621. [Google Scholar] [CrossRef] [Green Version]
  72. Bucher Della Torre, S.; Carrard, I.; Farina, E.; Danuser, B.; Kruseman, M. Development and Evaluation of e-CA, an Electronic Mobile-Based Food Record. Nutrients 2017, 9, 76. [Google Scholar] [CrossRef] [Green Version]
  73. Rangan, A.M.; Tieleman, L.; Louie, J.C.; Tang, L.M.; Hebden, L.; Roy, R.; Kay, J.; Allman-Farinelli, M. Electronic Dietary Intake Assessment (e-DIA): Relative validity of a mobile phone application to measure intake of food groups. Br. J. Nutr. 2016, 115, 2219–2226. [Google Scholar] [CrossRef] [Green Version]
  74. Rangan, A.M.; O’Connor, S.; Giannelli, V.; Yap, M.L.; Tang, L.M.; Roy, R.; Louie, J.C.; Hebden, L.; Kay, J.; Allman-Farinelli, M. Electronic Dietary Intake Assessment (e-DIA): Comparison of a Mobile Phone Digital Entry App for Dietary Data Collection With 24-h Dietary Recalls. JMIR Mhealth Uhealth 2015, 3, e98. [Google Scholar] [CrossRef]
  75. Touvier, M.; Kesse-Guyot, E.; Méjean, C.; Pollet, C.; Malon, A.; Castetbon, K.; Hercberg, S. 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. 2011, 105, 1055–1064. [Google Scholar] [CrossRef] [PubMed]
  76. Chen, J.; Berkman, W.; Bardouh, M.; Ng, C.Y.K.; Allman-Farinelli, M. The use of a food logging app in the naturalistic setting fails to provide accurate measurements of nutrients and poses usability challenges. Nutrition 2019, 57, 208–216. [Google Scholar] [CrossRef] [PubMed]
  77. Carter, M.C.; Burley, V.J.; Nykjaer, C.; Cade, J.E. ‘My Meal Mate’ (MMM): Validation of the diet measures captured on a smartphone application to facilitate weight loss. Br. J. Nutr. 2013, 109, 539–546. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  78. Erkilic, T.O.; Pekcan, G. Evaluation of validity of digital photograph based dietary intake in school children. Prog. Nutr. 2020, 22, e2020012. [Google Scholar]
  79. Erkilic, T.O.; Rakicioglu, N. Comparison of different food consumption methods to digital food photography method of elderly in nursing home. Prog. Nutr. 2020, 22, e2020009. [Google Scholar]
  80. Higgins, J.A.; LaSalle, A.L.; Zhaoxing, P.; Kasten, M.Y.; Bing, K.N.; Ridzon, S.E.; Witten, T.L. Validation of photographic food records in children: Are pictures really worth a thousand words? Eur. J. Clin. Nutr. 2009, 63, 1025–1033. [Google Scholar] [CrossRef] [Green Version]
  81. Rumpler, W.V.; Kramer, M.; Rhodes, D.G.; Moshfegh, A.J.; Paul, D.R. Identifying sources of reporting error using measured food intake. Eur. J. Clin. Nutr. 2008, 62, 544–552. [Google Scholar] [CrossRef] [PubMed]
  82. Landig, J.; Erhardt, J.G.; Bode, J.C.; Bode, C. Validation and comparison of two computerized methods of obtaining a diet history. Clin. Nutr. 1998, 17, 113–117. [Google Scholar] [CrossRef] [PubMed]
  83. Kretsch, M.J.; Fong, A.K.H. Validity and reproducibility of a new computerized dietary assessment method: Effects of gender and educational level. Nutr. Res. 1993, 13, 133–146. [Google Scholar] [CrossRef]
  84. Gregorič, M.; Zdešar Kotnik, K.; Pigac, I.; Gabrijelčič Blenkuš, M. A Web-Based 24-H Dietary Recall Could Be a Valid Tool for the Indicative Assessment of Dietary Intake in Older Adults Living in Slovenia. Nutrients 2019, 11, 2234. [Google Scholar] [CrossRef] [Green Version]
  85. Fukuo, W.; Yoshiuchi, K.; Ohashi, K.; Togashi, H.; Sekine, R.; Kikuchi, H.; Sakamoto, N.; Inada, S.; Sato, F.; Kadowaki, T.; et al. Development of a hand-held personal digital assistant-based food diary with food photographs for Japanese subjects. J. Am. Diet. Assoc. 2009, 109, 1232–1236. [Google Scholar] [CrossRef]
  86. Kretsch, M.J.; Fong, A.K. Validation of a new computerized technique for quantitating individual dietary intake: The Nutrition Evaluation Scale System (NESSy) vs the weighed food record. Am. J. Clin. Nutr. 1990, 51, 477–484. [Google Scholar] [CrossRef] [PubMed]
  87. Vereecken, C.A.; Covents, M.; Sichert-Hellert, W.; Alvira, J.M.; Le Donne, C.; De Henauw, S.; De Vriendt, T.; Phillipp, M.K.; Béghin, L.; Manios, Y.; et al. Development and evaluation of a self-administered computerized 24-h dietary recall method for adolescents in Europe. Int. J. Obes. 2008, 32, S26–S34. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  88. Mensink, G.B.; Haftenberger, M.; Thamm, M. Validity of DISHES 98, a computerised dietary history interview: Energy and macronutrient intake. Eur. J. Clin. Nutr. 2001, 55, 409–417. [Google Scholar] [CrossRef] [Green Version]
  89. Vereecken, C.A.; Covents, M.; Matthys, C.; Maes, L. Young adolescents’ nutrition assessment on computer (YANA-C). Eur. J. Clin. Nutr. 2005, 59, 658–667. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  90. Benedik, E.; Seljak, B.K.; Simčič, M.; Rogelj, I.; Bratanič, B.; Ding, E.L.; Orel, R.; Mis, N.F. Comparison of paper- and web-based dietary records: A pilot study. Ann. Nutr. Metab. 2014, 64, 156–166. [Google Scholar] [CrossRef]
  91. Griffiths, C.; Harnack, L.; Pereira, M.A. Assessment of the accuracy of nutrient calculations of five popular nutrition tracking applications. Public Health Nutr. 2018, 21, 1495–1502. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  92. Engel, R.; de Assis, M.A.A.; Lobo, A.S.; Leal, D.B.; Vieira, F.G.K.; Di Pietro, P.F. Validation of the Online version of the Previous Day Food Questionnaire for schoolchildren. Rev. Nutr. 2017, 30, 627–637. [Google Scholar] [CrossRef] [Green Version]
  93. Kong, K.; Zhang, L.; Huang, L.; Tao, Y. Validity and practicability of smartphone-based photographic food records for estimating energy and nutrient intake. Asia. Pac. J. Clin. Nutr. 2017, 26, 396–401. [Google Scholar] [CrossRef] [PubMed]
  94. Kirkpatrick, S.I.; Subar, A.F.; Douglass, D.; Zimmerman, T.P.; Thompson, F.E.; Kahle, L.L.; George, S.M.; Dodd, K.W.; Potischman, N. Performance of the Automated Self-Administered 24-h Recall relative to a measure of true intakes and to an interviewer-administered 24-h recall. Am. J. Clin. Nutr. 2014, 100, 233–240. [Google Scholar] [CrossRef] [Green Version]
  95. Lemacks, J.L.; Adams, K.; Lovetere, A. Dietary Intake Reporting Accuracy of the Bridge2U Mobile Application Food Log Compared to Control Meal and Dietary Recall Methods. Nutrients 2019, 11, 199. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  96. Béjar, L.M.; García-Perea, M.D.; Reyes, Ó.A.; Vázquez-Limón, E. Relative Validity of a Method Based on a Smartphone App (Electronic 12-Hour Dietary Recall) to Estimate Habitual Dietary Intake in Adults. JMIR Mhealth Uhealth 2019, 7, e11531. [Google Scholar] [CrossRef] [Green Version]
  97. 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-h 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]
  98. Lee, J.E.; Song, S.; Ahn, J.S.; Kim, Y.; Lee, J.E. Use of a Mobile Application for Self-Monitoring Dietary Intake: Feasibility Test and an Intervention Study. Nutrients 2017, 9, 748. [Google Scholar] [CrossRef]
  99. Astell, A.J.; Hwang, F.; Brown, L.J.; Timon, C.; Maclean, L.M.; Smith, T.; Adlam, T.; Khadra, H.; Williams, E.A. Validation of the NANA (Novel Assessment of Nutrition and Ageing) touch screen system for use at home by older adults. Exp. Gerontol. 2014, 60, 100–107. [Google Scholar] [CrossRef] [Green Version]
  100. Hillier, F.C.; Batterham, A.M.; Crooks, S.; Moore, H.J.; Summerbell, C.D. The development and evaluation of a novel Internet-based computer program to assess previous-day dietary and physical activity behaviours in adults: The Synchronised Nutrition and Activity Program for Adults (SNAPA™). Br. J. Nutr. 2012, 107, 1221–1231. [Google Scholar] [CrossRef] [Green Version]
  101. Martin, C.K.; Han, H.; Coulon, S.M.; Allen, H.R.; Champagne, C.M.; Anton, S.D. A novel method to remotely measure food intake of free-living individuals in real time: The remote food photography method. Br. J. Nutr. 2009, 101, 446–456. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  102. Baranowski, T.; Islam, N.; Baranowski, J.; Cullen, K.W.; Myres, D.; Marsh, T.; de Moor, C. The food intake recording software system is valid among fourth-grade children. J. Am. Diet. Assoc. 2002, 102, 380–385. [Google Scholar] [CrossRef] [PubMed]
  103. De Keyzer, W.; Huybrechts, I.; De Vriendt, V.; Vandevijvere, S.; Slimani, N.; Van Oyen, H.; De Henauw, S. Repeated 24-h recalls versus dietary records for estimating nutrient intakes in a national food consumption survey. Food Nutr. Res. 2011, 55, 7307. [Google Scholar] [CrossRef] [Green Version]
  104. Zipf, G.; Chiappa, M.; Porter, K.S.; Ostchega, Y.; Lewis, B.G.; Dostal, J. National Health and Nutrition Examination Survey: Plan and Operations, 1999–2010; Vital and Health Statistics Series 1; U.S. Department of Health and Human Services: Washington, DC, USA, 2013; Volume 56, pp. 1–37.
  105. Luevano-Contreras, C.; Durkin, T.; Pauls, M.; Chapman-Novakofski, K. Development, relative validity, and reliability of a food frequency questionnaire for a case-control study on dietary advanced glycation end products and diabetes complications. Int. J. Food Sci. Nutr. 2013, 64, 1030–1035. [Google Scholar] [CrossRef] [PubMed]
  106. Harmouche-Karaki, M.; Mahfouz, M.; Obeyd, J.; Salameh, P.; Mahfouz, Y.; Helou, K. Development and validation of a quantitative food frequency questionnaire to assess dietary intake among Lebanese adults. Nutr. J. 2020, 19, 65. [Google Scholar] [CrossRef]
Nutrients 15 01816 g001 550
Figure 1. Flow diagram of the article selection process.
Figure 1. Flow diagram of the article selection process.
Nutrients 15 01816 g001
Table
Table 1. Characteristics of the web-based dietary records or 24-h dietary recalls.
Table 1. Characteristics of the web-based dietary records or 24-h dietary recalls.
StudyCountrySurvey YearParticipantsWeb-Based Dietary AssessmentConventional Dietary Assessment
Sample Size (M, F)Age, Means ± SD (Range)Exclusion Criteria (Regarding the Internet or E-Mails)No. of Survey DaysTool NamesDatabaseDevicesDietary Assessment MethodsNo. of Survey Days
Web-based dietary records
Matsuzaki E et al. (2017) [35]Japan2013–2014163 (F: 100%)39.3 ± 10.3Not using e-mails1Internet website dish-based dietary recordsApproximately 100,000 dishesNADietary Record1
Monnerie B et al. (2015) [37]France2010246 (F: 59%)(18–60)Not having the internet connection at home and not being accustomed to using the internet7MXS-EpidemioNANADietary Record7
Vereecken CA et al. (2009) [48] aBelgium2008Web-based: 216 (NA)3.5 ± 0.4Not providing e-mail addresses3Young Children’s Nutrition Assessment on the WebApproximately 800 different food itemsNADietary Record3
Conventional: 39 (NA)
Raatz SK et al. (2015) [46]US2010–201119 (F: 58%)51.6 ± 1.5NA3NutrihandUSDA-NND for Standard Reference (Release 21 as of 9/2013)PCDietary Record3
Storey KE et al. (2012) [38]Canada2005459 (F: 51%, missing = 1)12.8 (11–15)Not contacting by e-mails2On-line Web-SPANNANADietary Record3
Beasley J et al. (2005) [44]USNA39 (F: 54%)53 ± 1.7 (19–69)Lacked familiarity with personal computers3DietMatePro programNANA24-h Dietary Recall1
Teixeira V et al. (2017) [49]BrazilNA30 (F: 73%)22.8 ± 2.6 (18–30)NA2MyFitnessPalMore than three million food itemsSmartphone, PCDietary Record2
Web-based 24-h dietary recalls
Lafrenière J et al. (2018) [34]Canada2015107 (F: 53%)47.4 ± 13.3 (18–65)No recruited other than e-mails and e-newsletters3R24W2568 food items and 687 recipesNADietary Record3
Timon CM et al. (2017) [36]IrelandNA39 (F: 51%)32.2 ± 13.4 (18–64)Recruited other than via e-mails, and posters3Foodbook24751 food and beverage itemsNADietary Record4
Lindroos AK et al. (2019) [39]Sweden2016–201778 (F: 69%)(11–18)NA2RiksmatenFlexDiet761 core food and approximately 2300 possible food item combinationsPC, tablet, smartphone24-h Dietary Recall2
Timon CM et al. (2017) [40]Ireland201579 (F: 51%)33.2 ± 12.5 (18–60)Recruited other than by e-mails, posters, and social media and not having regular access to the internet2Foodbook24751 food and beverage itemsNA24-h Dietary Recall2
Albar SA et al. (2016) [41]UKNA75 (F: 51%)14.6 (11–18)Having any limitation that could inhibit the adolescent’s ability to use a computer2Measure Your Food on One Day (myfood24)approximately 50,000 food itemsPC24-h Dietary Recall2
Bradley J et al. (2016) [42]UK2013–201411–16 years old: 52 (F: 63%)(11–16)Recruited through e-mail advertisements and snowballing techniques (aged 17–24 years).4INTAKE24Over 3000 food photographs (the NDNS Nutrient Databank)NA24-h Dietary Recall4
17–24 years old: 116 F: 53%)(17–24)
Brassard D et al. (2020) [43] aCanada2015–2017Web-based (PREDISE study): 1147 (F: 50%)(18–65)Not having a computer, access to the internet, and a valid e-mail address3R24WNANA24-h Dietary Recall1
Conventional (CCHS): 875 (F: 50%)
Frankenfeld CL et al. (2012) [47]US201093 (F: 65%)27 ± 11 (18–62)Recruited other than using flyers and web posting2Automated Self-Administered 24-h Dietary RecordsNANADietary Record4
Mescoloto SB et al. (2017) [50]BrazilNA40 (F: 85%)21 (20–24)Not owing a smartphone, people who had used the Nutrabem app before the start of the study3Nutrabem appNANA24-h Dietary Recall3
Liu B et al. (2011) [45]UK2008116 (F: 72%)42 (19–82)Recruited other than through e-mails using the mailing lists1Oxford WebQNANA24-h Dietary Recall1
SD, standard deviation; NA, not applicable; United States Department of Agriculture National Nutrient Database, USDA-NND; National Diet and Nutrition Survey, NDNS. a Participants were different for web-based and conventional dietary assessment.
Table
Table 2. Intakes of energy, macronutrient, sodium, vegetables and fruits using the web-based and conventional dietary assessment methods.
Table 2. Intakes of energy, macronutrient, sodium, vegetables and fruits using the web-based and conventional dietary assessment methods.
StudyEnergy (kcal/day)Protein (g/day)Fat (g/day)
Web-Based
(Mean)		Conventional
(Mean)		Difference% DifferenceWeb-Based
(Mean)		Conventional
(Mean)		Difference% DifferenceWeb-Based
(Mean)		Conventional
(Mean)		Difference% Difference
Web-based dietary records
Dietary records as the conventional method
Matsuzaki E et al. (2017) [35] a1554147282.05.661.361.6−0.3−0.545.745.9−0.2−0.4
Monnerie B et al. (2015) [37]18251836−11.0−0.675.277.1−1.9−2.573.273.8−0.6−0.8
Vereecken CA et al. (2009) [48]12941329−35.0−2.651.051.00.00.045.045.00.00.0
Storey KE et al. (2012) [38] b20191893125.46.667.973.0−5.1−6.971.568.03.45.1
Raatz SK et al. (2015) [46]1961187685.34.582.179.03.13.979.977.42.53.2
Teixeira V et al. (2017) [49]1820 c1834 c−14.0−0.877.788.1−10.4−11.850.260.3−10.1−16.7
24-h dietary recalls as the conventional method
Beasley J et al. (2005) [44]20912159−68.0−3.172.071.01.01.474.963.711.217.6
Web-based 24-h dietary recalls
24-h dietary recalls as the conventional method
Lindroos AK et al. (2019) [39] d2131 e1920 e210.210.985.074.011.014.986.076.010.013.2
Timon CM et al. (2017) [40] f1st18882168−241.0−11.577.088.0−11.0−10.373.088.0−15.0−15.4
2nd18172019−202.0−10.079.086.0−7.0−8.170.081.0−11.0−13.6
Albar SA et al. (2016) [41] d19351989−54.8−2.868.170.1−2.0−2.868.371.3−3.0−4.2
Bradley J et al. (2016) [42] b11–16 y15971631−34.0−2.152.452.40.00.052.355.8−3.5−6.3
17–24 y17711796−25.7−1.464.262.91.32.163.162.70.40.6
Brassard D et al. (2020) [43]24602118342.016.1--------
Mescoloto SB et al. (2017) [50]18041950−145.1−7.488.786.62.12.465.176.3−11.2−14.7
Liu B et al. (2011) [45]2082 c2080 c2.60.174.375.3−1.0−1.079.375.83.55.0
Dietary records as the conventional method
Lafrenière J et al. (2018) [34]25952408187.07.8104.399.74.64.6105.595.89.710.1
Timon CM et al. (2017) [36]19712100−129.0−6.183.595.0−11.5−12.178.485.7−7.3−8.5
Frankenfeld CL et al. (2012) [47]18311850−19.0−1.075.872.43.44.769.769.00.71.0
StudyCarbohydrate (g/day), MeanSodium (mg/day), MeanVegetables (g/day), MeanFruits (g/day), Mean
Web-BasedConventionalDifference% differenceWeb-BasedConventionalDifference% DifferenceWeb-BasedConventionalDifference% DifferenceWeb-BasedConventionalDifference% Difference
Web-based dietary records
Dietary records as the conventional method
Matsuzaki E et al. (2017) [35] a215.6208.17.53.5770073004005.5--------
Monnerie B et al. (2015) [37]202.0199.03.01.526982641572.2--------
Vereecken CA et al. (2009) [48]171.0180.0−9.0−5.0----4957−8.0−14.01251241.00.8
Storey KE et al. (2012) [38] b273.8253.819.97.8------------
Raatz SK et al. (2015) [46]224.6209.115.57.431503107431.4----- --
Teixeira V et al. (2017) [49]207.8232.9−25.1−10.8------------
24-h dietary recalls as the conventional method
Beasley J et al. (2005) [44]292.0327.0−35.0−10.7------------
Web-based 24-h dietary recalls
24-h dietary recalls as the conventional method
Lindroos AK et al. (2019) [39] d243.0225.018.08.0----137139−2.0−1.48788−1.0−1.1
Timon CM et al. (2017) [40] f1st226.0247.0−21.0−7.925662583−17−4.2142150−8.0−5.3259273−14.0−5.1
2nd216.0233.0−17.0−7.321682358−190−8.1151168−17.0−10.1269249208
Albar SA et al. (2016) [41] d264.4275.5−11.1−4.026502700−50−1.989863.33.91591581.30.8
Bradley J et al. (2016) [42] b11–16 y234.2236.0−1.8−0.8------------
17–24 y229.1230.3−1.2−0.5------------
Brassard D et al. (2020) [43]----347031653059.6 -------
Mescoloto SB et al. (2017) [50]217.5230.0−12.5−5.4----1.02 g1.12 g−0.1−0.10.69 g0.73 g−0.04−0.1
Liu B et al. (2011) [45]261.9267.3−5.4−2.0------------
Dietary records as the conventional method
Lafrenière J et al. (2018) [34]290.6277.712.94.6345531553019.5--------
Timon CM et al. (2017) [36]221.0238.0−17.0−7.122652552−287−11.2172237−65.0−27.4372252120.047.6
Frankenfeld CL et al. (2012) [47]233.0233.00.00.033403500−160−4.6--------
a Dietary intakes were median intake. b Dietary intakes were mean value of two times of web-based and three times of conventional dietary assessments. c Energy intake (kcal) calculated by KJ × 0.239. d Dietary intakes were mean value in 2 days both of web-based and conventional dietary assessments. e Energy intake (kcal) calculated by MJ × 238.846. f Dietary intakes were collected a web-based and conventional dietary assessment on two separate occasions over a one-month period. g Portion size.
Table
Table 3. Correlation coefficients for intakes of energy, macronutrient, vegetable, and fruit between web-based and conventional dietary assessment methods.
Table 3. Correlation coefficients for intakes of energy, macronutrient, vegetable, and fruit between web-based and conventional dietary assessment methods.
Correlation Coefficients
EnergyProteinFatCarbohydrateSodiumVegetableFruit
Web-based dietary records
Dietary records as the conventional method
Matsuzaki E et al. (2017) [35] a0.870.780.750.820.59--
Storey KE et al. (2012) [38] b0.370.410.330.31---
Teixeira V et al. (2017) [49] a0.670.530.590.58---
24-h dietary recalls as the conventional method
Beasley J et al. (2005) [44]0.710.620.510.80---
Web-based 24-h dietary recalls
24-h dietary recalls as the conventional method
Lindroos AK et al. (2019) [39] b, e0.530.570.57--0.230.56
Timon CM et al. (2017) [40]1st0.620.770.750.650.750.84 a0.76 a
2nd0.720.790.610.800.630.84 a0.85 a
Albar SA et al. (2016) [41] d, e0.880.770.750.810.460.470.67
Mescoloto SB et al. (2017) [50]0.770.830.710.82-0.43 f0.78 f
Liu B et al. (2011) [45] a0.580.590.570.66---
Dietary records as the conventional method
Lafrenière J et al. (2018) [34]0.570.610.540.530.55--
Timon CM et al. (2017) [36] c0.540.750.330.530.30--
Frankenfeld CL et al. (2012) [47]0.440.410.460.360.17--
Data were not available from Monnerie B et al. (2015) [37], Vereecken CA et al. (2009) [48], Raatz SK et al. [46], Bradley J et al. (2016) [42] and Brassard D et al. (2020) [43]. a Spearman coefficient correlation. Otherwise, Pearson’s correlation coefficient. b Mean value of dietary intake in 2 days of web-based and 3 days of conventional dietary assessment. c Energy-adjusted dietary intake. d Intraclass correlation coefficients. e Mean value of dietary intake in 2 days both of web-based and conventional dietary assessments. f Portion size.
Table
Table 4. Summary of usability of the web-based dietary assessment methods.
Table 4. Summary of usability of the web-based dietary assessment methods.
Web-Based Dietary Assessments
Negative PointsPositive Points
  • Difficult to record
  • Difficult in estimating food portions
  • Pressure to use
  • Did not change the usual diet
  • User-friendliness (perception of the data collection tool was conventional, quick and easy, and appreciation of the general layout of the questionnaire)
  • Preferred method
  • Practicality
  • Self-consciousness of food habits
  • Time required to respond to the dietary assessment was short
  • Provided adequate instructions
  • provided clear information, clear pictures, and easy-to-find food items
Summarized by the authors, from descriptions in Timon CM et al. [36], Monnerie B et al. [37], Vereecken CA et al. [48], Teixeira V et al. [49].
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Murai, U.; Tajima, R.; Matsumoto, M.; Sato, Y.; Horie, S.; Fujiwara, A.; Koshida, E.; Okada, E.; Sumikura, T.; Yokoyama, T.; et al. Validation of Dietary Intake Estimated by Web-Based Dietary Assessment Methods and Usability Using Dietary Records or 24-h Dietary Recalls: A Scoping Review. Nutrients 2023, 15, 1816. https://doi.org/10.3390/nu15081816

AMA Style

Murai U, Tajima R, Matsumoto M, Sato Y, Horie S, Fujiwara A, Koshida E, Okada E, Sumikura T, Yokoyama T, et al. Validation of Dietary Intake Estimated by Web-Based Dietary Assessment Methods and Usability Using Dietary Records or 24-h Dietary Recalls: A Scoping Review. Nutrients. 2023; 15(8):1816. https://doi.org/10.3390/nu15081816

Chicago/Turabian Style

Murai, Utako, Ryoko Tajima, Mai Matsumoto, Yoko Sato, Saki Horie, Aya Fujiwara, Emiko Koshida, Emiko Okada, Tomoko Sumikura, Tetsuji Yokoyama, and et al. 2023. "Validation of Dietary Intake Estimated by Web-Based Dietary Assessment Methods and Usability Using Dietary Records or 24-h Dietary Recalls: A Scoping Review" Nutrients 15, no. 8: 1816. https://doi.org/10.3390/nu15081816

APA Style

Murai, U., Tajima, R., Matsumoto, M., Sato, Y., Horie, S., Fujiwara, A., Koshida, E., Okada, E., Sumikura, T., Yokoyama, T., Ishikawa, M., Kurotani, K., & Takimoto, H. (2023). Validation of Dietary Intake Estimated by Web-Based Dietary Assessment Methods and Usability Using Dietary Records or 24-h Dietary Recalls: A Scoping Review. Nutrients, 15(8), 1816. https://doi.org/10.3390/nu15081816

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