Assessing the Validity and Usability of a Speech Recognition Dietary Assessment Tool for Older Adults

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By 2050, the Asia-Pacific region is expected to see unprecedented ageing, with 10 countries classified as 'ageing societies' (over 7% of its population aged 65 and over), five as 'aged societies' (over 14% aged 65 and over), and 11 as 'super-aged societies' (over 21% aged 65 and over) with South Korea as anticipated to be among the latter, projected to have over 40% of its population aged 65 and older. Effective nutritional assessment for this demographic remains challenging due to limitations in conventional methods, exacerbated by factors like cognitive decline and low literacy. Despite efforts to address these challenges with new technological solutions like web, scanners, and mobile-based tools, issues of accuracy, usability, and cost persist. Therefore, this study aims to introduce and evaluate a speech-based dietary assessment tool as a promising alternative for older adults. Method In a randomised cross-over design, 18 participants aged 65 and older were divided into two groups, each using both a pen-and-paper food diary (FD) method and a speech recognition dietary assessment (SR) method over two weeks. Dietary intake was recorded and analysed over non-consecutive 3-day periods each week while usability was assessed via the Systems Usability Scale (SUS). Results The results of the nutrient intake analysis indicated small mean differences between methods, with a 0.49% difference in energy and an average of 5.02% in essential nutrients. Significant correlation coefficients for nutrient intake between methods were observed, with the highest for fat (0.740), protein (0.738), and dietary fibre (0.671). Bland-Altman analysis indicated no substantial bias in energy or essential nutrient intakes, except for cholesterol which showed a discernible proportional bias. SUS scores between the SR and FD methods showed no statistical significance; however, the SR method received a higher standardised rating, characterised as 'acceptable' with a score of 72.77, compared to the FD method, rated as 'marginally acceptable’ with a score of 66.25. Conclusion These findings suggest that the SR method is adequately comparable to the traditional FD method for dietary intake assessment in older adults and is on par in terms of usability, indicating its potential as a valid and acceptable alternative. Trial registration KCT0009677 Older adults Speech recording Dietary intake assessment Validity Usability Figures Figure 1 Figure 2 Figure 3 Figure 4 Background The demographic landscape of South Korea is undergoing a notable transformation with projections indicating that by 2060, individuals over 65 will constitute approximately 43.9% of the total population ( 1 ). This demographic shift underscores the urgent need to address the nutritional needs of older adults. Despite this, malnutrition among older adults has been on the rise since 2014, primarily due to the lack of appropriate routine dietary assessment methods suitable to their unique characteristics, such as sensory, cognitive impairments, and literacy challenges ( 2 ). Consequently, there is a compelling need for an enhanced dietary assessment tool specifically designed to accurately evaluate the overall nutritional status of the older population. Nutritional assessment involves evaluating various factors such as anthropometric measurements, biochemical indicators, clinical history, and dietary intake. Accurate documentation and analysis of dietary habits are crucial for diagnosing malnutrition and implementing effective interventions ( 3 ). Conventional methods like self-reported food records and food frequency questionnaires (FFQs) are labour-intensive and impractical for those with varying literacy levels and cognitive impairments ( 4 ). Technological advancements have introduced new dietary assessment methods, such as web-based 24-hour recall systems, mobile food diary apps, and camera-based sensors ( 5 ). However, these methods face challenges related to accuracy, accessibility, and literacy ( 6 ). Among the new innovative tools available, speech recording has emerged as a promising method for efficient data collection, noted for its simplicity and user-friendliness. This technique enables individuals to capture dietary details through natural speech, thereby reducing participant burden and minimizing the likelihood of omitting essential details ( 7 ). It holds particular value for populations with literacy and sensory impairments, thereby broadening the scope of inclusion to a more diverse demographic ( 8 ). Advances in speech recognition technology have further automated data processing, enhancing accuracy and reducing the need for manual labour ( 9 ). Speech recognition technology functions by converting audio-recorded speech into text, identifying keywords, and facilitating nutritional analysis ( 10 ). Initially, the speech recognition food search engine converts the audio-recorded speech to text, facilitating the identification of keywords such as the names of consumed foods and their respective quantities. This process aids in the subsequent calculation and analysis of the nutritional value of the meal. With the widespread availability of smartphones equipped with speech-recording capabilities, data collection for speech-based dietary assessment has become more accessible than ever before ( 11 ). With advancements in speech recognition technology, there has been a surge in research interest surrounding the utilisation of speech technology to enhance the process of nutritional assessment, offering a novel approach for a wide range of users ( 12 ). This study, therefore, aims to assess the validity and usability of a speech-recognition dietary assessment tool as a viable alternative to the traditional pen-and-paper food diary among older participants. The study focused on assessing the agreement between the SR and FD methods, validating the accuracy of the SR tool and examining its practical usability. Due to the impracticality of precisely measuring an individual’s self-reported diet over extended periods, studies often compare new methods with existing dietary assessment tools to assess their relative despite their inherent limitations ( 13 ). Unlike prior research conducted in Europe and America where dietary intake is recorded through a food-based, single-ingredient intake format, this study focuses on assessing the intake of dish-based meals, prevalent in Asian countries like South Korea. Notably, there has been no validation study conducted on the dietary intake assessment via speech recognition specifically for dish-based meals. Given that many Korean dishes are complex combinations of various ingredients and cooking techniques, examining nutrient sources based on dishes provides a more comprehensive understanding of dietary patterns within populations ( 14 ). Methods Study Design This study was conducted at Kyung-Hee University from February 2024 to March 2024 after review and approval by the Institutional Review Boards of Kyung-Hee University (No. KHGRIB-24-040). This study employed a randomised cross-over (AB/BA) design to compare the effectiveness of two dietary assessment methods: the pen-and-paper food diary (FD) method and the speech recognition (SR) method over a two-week period. (Fig. 1 .) Participants alternated between using the FD and SR methods to record their dietary intake, with the order of methods randomly assigned to each group. Dietary intake assessments were conducted over non-consecutive 3-day periods each week. Study Participants Eligible participants were older adults aged 65 years or older of both genders, who owned smartphones, and demonstrated the capability to accurately record their food intake for three non-consecutive days over a two-week period. Participants were also screened for basic digital literacy proficiencies using a tailored survey comprising 19 questions focused on digital familiarity, digital efficacy and basic digital skills ( 15 ). Inclusion criteria required participants to score 3 or higher for digital familiarity and efficacy and 2 or higher on basic digital skills indicating above-average digital competency, to be eligible for the study. Participants diagnosed with mild cognitive impairment or dementia, as well as those with significant visual or hearing impairments, were excluded from the study. Additionally, individuals deemed ineligible for participation based on the researcher’s judgment were also excluded from the study. Participants were recruited through various channels including posters, word-of-mouth referrals, and social networking platforms over a one-month period. The participant’s general characteristics such as age, gender, education level, marital status, current living status, comorbidities, and financial status were investigated using constructive self-reported questionnaires, administered with the guidance of an interviewer. (Supplementary file 1) Data Collection Anthropometric Measurements Anthropometric data including height, weight, BMI, skeletal muscle mass, body fat percentage, mid-upper arm circumference (MAC), and calf circumference (CC) were collected. Body composition was measured using the Inbody 920, and body measurements such as MAC and CC were taken with a standard tape measure. Nutritional Status Assessments The nutritional status of participants was evaluated using the Mini Nutrition Assessment (MNA) and the Nutrition Quotient-Elderly (NQ-E) questionnaires. The MNA assesses malnutrition risks through various criteria including body mass index, weight loss, mobility, psychological stress, and other factors, categorising scores as normal nutritional status (24–30 points), risk of malnutrition (17-23.5 points), and malnourishment (0-16.5 points) ( 16 ). The NQ-E assesses nutritional status and meal quality through 20 questions on food intake frequency and eating behaviours, with higher scores indicating better nutritional status ( 17 ). Nutritional assessments were conducted to identify discrepancies between nutritional status and nutrient intake, revealing potential validity limitations, such as poor nutritional health despite normal nutrient intake levels or vice versa ( 18 ). Dietary Intake Assessments Dietary intake was assessed using both a pen-and-paper food diary and a speech-recording food log. Participants recorded their dietary intake for three non-consecutive days each week using both methods. Nutrient intakes were analysed using CAN-Pro 6.0 software (Korean Nutritional Society, Seoul, Korea). Pen-and-Paper Food Diary (FD) Participants documented their dietary intake manually on provided sheets, detailing the date, time, location, and food items for each meal and snack. Speech Recording of Dietary Intake (SR) Participants used their mobile smartphones to verbally record dietary intake, which was transcribed via CLOVA (Naver, Seongnam, Korea), the Cloud Virtual Assistant speech recognition engine widely utilised for transcribing verbal input into text format. Transcription accuracy was validated by comparing machine-generated transcripts with human transcriptions. Participants also used standardised measures or food scales for accurate reporting and took photographs of their meals before and after consumption. To minimise self-reporting errors, a 24-hour dietary recall assessment was conducted via phone interviews within 24 hours of meal recording on both occasions. Usability Evaluation The usability of each dietary assessment method was evaluated using the Systems Usability Scale (SUS) questionnaire ( 19 ). The SUS includes 10 items on a Likert scale, with scores ranging from 0 to 100, where higher scores indicate better usability. SUS scores were interpreted through acceptability ranges (0–50 considered ‘not acceptable’, 50–70 ‘marginally acceptable’, and 70–100 were considered ‘acceptable’), descriptive adjectives ("Excellent" for scores above 85, "Good" around 71, "OK" around 51, to "Poor" for lower scores) and a grading system from A (superior) to F (failing). Participants completed the SUS questionnaire weekly, after a week of using each dietary assessment method, to provide feedback on user experience. Statistical Analysis All data was analysed with the statistical analysis program, Statistical Package for the Social Sciences (IBM, Korea) version 29.0 for macOS. The normality of data distribution was assessed using the Shapiro-Wilk test. A comparison of both dietary intake assessment methods was performed through the independent t-test or Mann-Whitney U test. Pearson’s correlation coefficient and Spearman’s rank correlation coefficient were computed to test the significance of the relationship between intake estimates derived from the two dietary assessment methods. A Bland-Altman Plot was used to depict the levels of agreement between the two methods visually. Continuous variables were expressed as mean and standard deviation (SD), and categorical variables were expressed as counts and percentages (%). Statistical significance was determined by a p-value of < 0.05. Results General characteristics of the study population A total of 18 participants (13 females, 5 males) completed the study, each assessed for 3 days. (Table. 1) Participants' ages ranged from 65 to 85 years, with a mean age of 72 ± 4.89 years (males: 71.80 ± 1.79; females: 72.30 ± 5.72). The average Body Mass Index (BMI) was 24.92 ± 2.70 kg/m². Males had higher skeletal muscle mass (28.8 ± 6.4 kg) compared to females (20.9 ± 2.7 kg). Regarding education, 33.3% had university-level education, with a higher proportion of males (60%) than females (23.1%). All participants were married, with most living with their partners (61.1%). The primary source of living expenses was through salary or pensions of the individual or their partner (all males, 84.6% of females). Most participants felt secure in their ability to afford groceries (83.3%). Table 1 Anthropometrics and general characteristics of study participants Variables Total (n = 18) Male (n = 5) Female (n = 13) Age, mean ± SD 72.17 ± 4.89 71.80 ± 1.79 72.30 ± 5.72 Height (cm 2 ) 159.31 ± 10.41 167.0 ± 11.2 156.4 ± 8.8 Weight (kg) 63.86 ± 10.01 72.6 ± 12.5 60.5 ± 6.9 Body Mass Index (kg/m 2 ) 24.92 ± 2.70 25.9 ± 2.6 24.5 ± 2.7 Skeletal Muscle Mass (kg) 23.14 ± 5.25 28.8 ± 6.3 20.9 ± 2.7 Body Fat Mass (kg) 21.12 ± 4.85 20.6 ± 4.6 21.3 ± 5.1 Body Fat Percentage (%) 32.21 ± 6.46 28.58 ± 5.55 34.99 ± 6.04 Education level ≤ Middle school 5(27.8) 1(20.0) 4 (30.8) High school 7(38.9) 1(20.0) 6 (46.2) ≥ University 6(33.3) 3(60.0) 3 (23.1) Marital status Married 18(100.0) 5(100.0) 13(100.0) Single 0(0.0) 0(0.0) 0 (0.0) Living arrangements Living alone 1(5.6) 0(0.0) 1(7.7) Living with partner 11(61.1) 4(80.0) 7(53.8) Living with children 6(33.3) 1(20.0) 5(38.5) Other 0(0.0) 0(0.0) 0(0.0) Living expenses Salary or pension 16(88.9) 5(100.0) 11(84.6) Children or relatives 1(5.6) 0(0.0) 1(7.7) Governmental support 0(0.0) 0(0.0) 0(0.0) Part-time job 0(0.0) 0(0.0) 0(0.0) Other 1(5.6) 0(0.0) 1(7.7) Food Security Yes, secure 15(83.3) 5(100.0) 10(76.9) No, not secure 3(16.7) 0(0.0) 3(23.1) Values are presented as the mean ± standard deviations or number Nutritional assessment The Mini Nutritional Assessment (MNA) scores indicated normal nutritional status for all participants, with a mean score of 27.06 ± 2.27 (males: 26.60 ± 2.27; females: 27.23 ± 2.34). The Nutrition Quotient for Elderly (NQ-E) score was 62.92 ± 13.96, higher in male participants (70.79 ± 10.68) than female participants (59.89 ± 14.22). Male participants scored higher across NQ-E sub-scores in Balance (62.02 ± 13.79), Moderation (60.28 ± 13.61), and Practice (66.94 ± 11.75) compared to female participants (Balance: 60.98 ± 18.15; Moderation: 59.59 ± 17.81; Practice: 55.54 ± 14.51). Dietary intake assessment An assessment was conducted to evaluate the accuracy and reliability of the speech recognition tool employed to transcribe speech recordings of participants capturing details such as date, time and meal type (e.g., breakfast, lunch, dinner, and snacks), as well as the name of the dish and estimated portions using either standard household object or food scales. The mean accuracy of transcription across participants was calculated to be 95.40 ± 0.02%, indicating a high level of fidelity in transcribing and discerning dietary speech records. Each participant’s speech transcription accuracy was individually assessed for all three days of recorded speech, with each day’s transcription compared to manual transcriptions. This analysis process further reinforced confidence in the reliability of the SR tool for dietary intake assessment and the findings substantiate the viability of utilising speech recognition technology as a reliable means for capturing and transcribing detailed dietary intake data among older adults. Table 2 Mean energy, macronutrient, and micronutrient intakes measured by FD and SR FD 1) SR 2) FD-SR p -value Correlation coefficient Nutrient Mean ± SD Mean ± SD Mean Difference (%) Pearson Spearman Macronutrient Energy (kcal) 1949.44 ± 497.59 1941.69 ± 385.58 0.49 0.698† 4) 0.518* 0.567* Carbohydrate (g) 272.40 ± 74.42 254.65 ± 50.36 6.97 0.408† 0.178 0.240 Protein (g) 82.84 ± 16.00 84.39 ± 17.05 -1.84 0.645 0.738** 0.709** Fat (g) 55.86 ± 19.98 56.03 ± 16.51 -0.32 0.820 0.740** 0.777** C: P: F (%) 3) 55.9:17.0:25.8 52.5:17.4:26.0 - - - - Dietary Fibre (g) 34.09 ± 10.49 31.53 ± 7.51 8.12 0.806 0.671* 0.608** Total Sugar (g) 42.74 ± 24.79 40.57 ± 19.57 5.33 0.717 0.163 0.467 Cholesterol (mg) 261.86 ± 116.61 302.18 ± 69.13 -13.35 0.182 0.178 0.461 Minerals (g) 21.3513 ± 3.57 21.24 ± 5.05 0.51 0.233 0.656** 0.459 Fat-soluble vitamin Vitamin A (RAE) 337.99 ± 90.51 336.38 ± 198.82 0.48 0.656 3) -0.146 -0.140 Vitamin D (µg) 0.95 ± 0.70 1.31 ± 0.84 -28.06 0.503 0.057 0.126 Vitamin E (mg) 19.48 ± 4.49 19.97 ± 2.95 -2.44 0.276† 0.907** 0.725** Vitamin K (µg) 241.43 ± 81.48 244.34 ± 81.78 -1.19 0.777 0.701** 0.616** Water-soluble vitamin Vitamin C (mg) 86.09 ± 31.25 76.26 ± 16.92 12.89 0.717 0.416 0.225 Thiamine (mg) 1.31 ± 0.17 1.29 ± 0.27 1.87 0.667 0.690** 0.696** Riboflavin (mg) 2.12 ± 1.67 1.53 ± 0.23 38.76 0.740 0.871** 0.872** Niacin (mg) 13.03 ± 1.93 13.85 ± 2.90 -5.93 0.376 0.703** 0.295 Vitamin B6 (mg) 0.98 ± 0.39 0.63 ± 0.22 56.45 0.614 0.653** 0.496* Folate (DFE) (µg) 355.89 ± 52.68 352.47 ± 67.26 0.97 0.468 0.503* 0.527* Vitamin B12 (µg) 3.29 ± 1.85 2.99 ± 3.26 10.12 0.308 0.498* 0.531* Calcium (mg) 653.20 ± 202.81 644.32 ± 210.43 1.38 0.931 3) 0.469* 0.474* Phosphorous (mg) 1408.69 ± 551.93 1254.29 ± 295.47 12.31 0.720† 0.763** 0.785** Sodium (mg) 4145.07 ± 1403.47 4305.08 ± 1327.20 -3.72 0.291 0.577* 0.432 Potassium (mg) 3350.30 ± 1136.41 3263.26 ± 791.54 2.67 0.140 0.449 0.385 Magnesium (mg) 342.23 ± 110.26 326.51 ± 96.59 4.82 0.057 0.709** 0.785** Iron (mg) 14.76 ± 2.73 15.44 ± 4.02 -4.42 0.676 0.568* 0.546* Values are presented as the mean ± standard deviations. 1) FD, Food Diary 2) SR, Speech Recording 3) Energy ratio of carbohydrate, protein, and fat 4) No significant difference by Mann-Whitney U test at p < 0.05 † No significant difference by independent t-test at p < 0.05 Percentage of the mean difference between FD and SR in each nutrient (calculated as % of the difference = (mean amount from FD-mean amount from SR)/ mean amount from SR) *100) * Intakes by two methods, which food diary and speech recording, are significantly correlated by Pearson’s in quantity and Spearman’s in ranking (*p < 0.05, **p < 0.01) Table 2 presents the mean daily energy and nutrient intakes using the SR and FD methods, the mean difference, the significance of the mean difference, and the correlation between the methods. No statistically significant differences were observed for macronutrients, vitamins, or minerals. The FD method estimated an energy intake of 1949.44 ± 497.59 kcal, compared to 1941.69 ± 385.58 kcal with the SR method, resulting in a mean difference of 0.49% (p = 0.698). Carbohydrate intake was 272.40 ± 74.42 g for FD and 254.65 ± 50.36 g for SR, with a mean difference of 6.97% (p = 0.408). Protein intake was 82.84 ± 16.00 g for FD and 55.86 ± 19.98 g for SR, showing a mean difference of -1.84% (p = 0.645). Fat intake had a smaller mean difference of -0.32% (p = 0.820), with FD estimating 55.86 ± 19.98 g and SR estimating 56.03 ± 16.51 g. Mean differences for energy and essential nutrients averaged around 5.02%, while mean differences for vitamins and minerals (calcium, phosphorus, sodium, potassium, magnesium, and iron) averaged around 14.90% and 4.89%, respectively. The SR method tended to underestimate intake compared to the FD method, except for nutrients like fat, protein, cholesterol, minerals, and certain vitamins. The Pearson correlation coefficient for nutrient intake between the two methods was statistically significant for most nutrients, ranging from 0.498 to 0.907, except for carbohydrates, total sugar, cholesterol, vitamins A, D, C, and potassium. Among essential nutrients and energy intake, the highest correlations were observed for fat (r = 0.740), protein (r = 0.738), dietary fibre (r = 0.671), minerals (r = 0.656), and energy intake (r = 0.518). High correlations were shown for vitamins E, K, thiamine, and riboflavin, and were notably high for phosphorus and magnesium among minerals. Vitamins A, D, C, and potassium showed no significant correlation. Examining correlation ranks using Spearman’s correlation coefficient further nuanced the comparative efficacy of the methods. The Spearman’s correlation coefficient for energy was 0.567, protein was 0.709, fat was 0.777, and dietary fibre was 0.608. These results were generally consistent with Pearson’s correlation coefficient, which ranged from 0.474 to 0.872. However, for minerals and niacin, no statistically significant correlation was observed with Spearman’s rank coefficient despite visible correlations with Pearson’s coefficient. The energy and macronutrient intake data were further analysed using Bland-Altman analysis to evaluate the level of agreement between the two methods. Figure 2 shows the Bland-Altman plots of the mean differences between the two recording methods for total energy, carbohydrate, protein, fat, total sugar, dietary fibre, cholesterol, and minerals. Energy intake had a mean difference of 7.75 kcal with lower and upper limits of agreement (2SD) of -863.39 to 878.89 kcal, showing no significant differences. The mean difference for fat intake was − 1.55 g with lower and upper limits of agreement of -24.98 to 21.87, protein showed a mean difference of -0.18 g with lower and upper limits of agreement of -26.83 to 26.47 g, and carbohydrates showed a mean difference of -1.55 g with lower and upper limits of agreement of -24.98 to 21.87, all similar or within the limits of comparable studies. The Bland-Altman analysis demonstrated relatively good agreement between the SR and FD methods. The mean differences for most nutrients were close to zero, except for cholesterol, which exhibited a proportional bias with increasing intake, shown by a funnel-shaped distribution. The limits of agreement for cholesterol were also wider than those reported in previous studies, indicating greater inconsistency between the methods. Nonetheless, the overall agreement and distribution of standard deviations suggest that both methods are reliable for most nutrients, with only minor discrepancies. Usability evaluation The Systems Usability Scale (SUS) scores were obtained from all 18 participants after they had utilized both FD and SR methods. The SUS score for the pen-and-paper food diary method was 66.25 ± 11.83, while the speech recording dietary assessment tool scored 72.77 ± 10.77 out of 100 points. Although the scores showed no statistically significant difference (p = 0.586), in this study, the SR method had the score of being ‘Acceptable’ (70–100 points) whilst the FD fell into the ‘marginally acceptable’ range (50–70 points). Furthermore, the SD method was labelled with a descriptive adjective of “Good”, whereas the conventional FD method was labelled “OK”. Despite these differences in acceptability and adjectives, both methods received a C grade, indicating a comparable level of usability within the two methods. The response to each SUS item of the two assessment methods is presented in Figs. 3 and 4. Positive items (Fig. 3 ) for the SR method showed strong agreement, though confidence was lower (27.8%). Negative responses (Fig. 4) were fewer for the SR method (16.7%) compared to the FD method (27.8%). The FD method showed lower favourability on positive items (33.3% ease of use, 27.8% confidence) and higher unfavourable responses on negative items (50% needed support, 44.4% found it inconsistent). Discussion This study compared nutritional intake using the traditional pen-and-paper food diary (FD) method and a new speech-recognition (SR) dietary assessment tool. While most studies have focused on web-based 24-hour recalls, online food frequency questionnaires, and AI-based image recognition tools, this research evaluates SR as a potentially simpler and more accurate technology-based recording method ( 20 ). The primary finding is that SR showed comparable results to FD, with relatively small mean differences between the two methods across most nutrients, supported by moderate to good correlation coefficients and Bland-Altman plots. The mean difference in essential nutrients averaged 5.02%, vitamins 14.47%, and micronutrients 4.89%. This is consistent with a previous review where web-based and conventional methods showed a mean energy intake difference of 5.31% (0.6%-16.1%) compared to 0.49% in this study. The differences for protein and fat intake were also lower in this study, at 1.84% and 0.32% respectively, compared to 5.00% and 7.73% in the reviewed studies ( 21 ). Luevano-Contreras et al.'s criteria categorise mean difference percentages of 0.0-10.9% as ‘good,’ 11.0–20.0% as ‘acceptable,’ and > 20.0% as ‘poor’ ( 22 ), with most mean nutrient differences in this study falling within the ‘good’ range. SR showed lower mean intake levels for most nutrients except for fat, protein, cholesterol, vitamins D, E, K, and niacin. The Bland-Altman analysis indicated relatively good agreement between FD and SR methods, with small mean differences, narrow limits of agreement, and few outliers. Carbohydrate intake showed comparable results despite higher mean differences and fat and protein intake differences were small and consistent. However, in the case of cholesterol, as the average intake increased, the discrepancy between the two methods also increased, exhibiting a funnel-shaped proportional bias. The limit values of the data were also broader than those reported in previous studies, indicating greater inconsistency between the two methods. Correlations between FD and SR methods were fair to moderate, with Pearson and Spearman coefficients indicating notable agreement for total energy intake (0.518 and 0.567), essential nutrients (0.478 and 0.523), vitamins (0.559 and 0.477), and minerals (0.589 and 0.568). To contextualise these findings, the interpretations of Chan YH et al. were employed wherein a correlation of 1 indicates a perfect association, 0.8–0.9 indicates a very strong association, 0.6–0.7 a moderate association, 0.3–0.5 a fair association, and 0.1–0.2 a poor association ( 23 ). Certain dietary parameters, such as carbohydrates, total sugar, cholesterol, and vitamins A, D, C, and potassium, exhibited weaker correlations, likely due to the diverse range of foods containing these nutrients and day-to-day dietary variability ( 24 – 27 ). Previous studies have similarly reported low correlations for vitamins and minerals in dietary assessments. Kim et al. (2008) found correlations for vitamins A and C to be 0.18 and 0.48 respectively, which aligns with our findings of -0.146 and 0.416 ( 28 ). While this study underscores the comparability of SR for micronutrient intake assessment, it is important to recognize the limitations of the 3-day food record method, especially for micronutrient measurement due to underreporting and variability in food consumption ( 29 ). According to Kwon et al. ( 30 ), accurately estimating dietary intakes within 20% of the true mean with 90% confidence may require 25–29 days of food records for certain vitamins. Usability was assessed using the Systems Usability Scale (SUS) survey after each method. The SUS is frequently used to assess and compare the usability of various systems, including in nutrition-related contexts. In this study, the SR method scored 6.5 points higher than the FD method. According to Bangor et al., SUS scores of 68 or above are considered “good” and “acceptable” regarding usability ( 31 ), a criterion met by the SR method but not by the FD method in this older adult demographic. Previous research evaluating web-based dietary recall systems, such as the Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) ( 32 ) and EatWellQ8 ( 33 ), a food frequency questionnaire system and other mobile dietary intake applications ( 34 ) reported SUS scores ranging from 58 to 75 points, aligning with the SR method's score. Positive responses (odd-numbered questions) shown in Fig. 3 to SR were highly correlated, with over 51% agreeing or strongly agreeing, and no strong disagreement except for one question about quick usability. One participant found the SR method overly complicated (Fig. 4), possibly due to the initial learning curve associated with digital health tools. For the FD method, participants reported difficulties in learning and needing technical support (11.1%), with over 50% strongly agreeing on the complexity of learning the method. This difficulty can be attributed to the necessity for participants to meticulously detail ingredients and cooking methods as required for accurate food records ( 35 ). Additionally, the FD method was found cumbersome due to the necessity of immediate documentation after consumption, contributing to its overall inconvenience and time commitment. The strengths of this study lie in its evaluation practices, particularly the utilisation of a randomised cross-over design, which effectively mitigated sequence bias and enhanced the internal validity. This design ensured that each participant experienced both the speech recognition (SR) tool and the pen-and-paper food diary (FD), reducing the impact of potential confounding variables associated with individual differences ( 36 ). Furthermore, the high transcription accuracy of the speech recognition tool underscored its precision in transcribing dietary data, thereby contributing to the validity of the study’s findings. However, it is essential to acknowledge certain limitations inherent in the study design and methodology. While the crossover design minimised sequence bias, the difference in dietary intake due to the application of different time points may have affected the data leading to a need for a longer study period ( 37 ). The study was designed to ensure the impartiality of the order in which the dietary assessments would be executed ( 38 ), thereby resulting in differences in food consumption at different times contributing to discrepancies in nutrient intake. Additionally, the focus on older adult participants with varying levels of digital proficiency may limit the generalisability of our findings due to potential variations in participant compliance or engagement with the dietary assessment methods ( 39 ). A key limitation is the comparison with the traditional pen-and-paper food diary method, which, like any dietary assessment tool, is subject to inherent biases and limitations. In the absence of a true measure of habitual dietary intake, our study, similar to previous validity studies, was constrained to comparing the relative validity of the SR method against the FD method ( 13 ). Another notable limitation is the multi-step process of the SR method, involving transcription through an automated speech recognition tool followed by manual analysis. Although this complexity did not significantly impact user acceptance, future advancements in speech recognition technology should aim to integrate this tool into a unified digital health platform. Conclusion This study investigated the validity and usability of a novel speech recognition dietary assessment tool compared to a traditional pen-and-paper food diary among older adult participants. The findings suggest that the specific recording method shows promise as a valid alternative with higher user acceptance. Notably, the study demonstrated comparable levels of dietary intake between speech-recording and traditional techniques, with moderate to strong correlations observed for various nutrients. Moreover, the Systems Usability Scale survey indicated favourable user perceptions of the speech-recording tool, highlighting its potential as a user-friendly option for dietary assessment in older adult populations. Overall, this study contributes insight into the validity and utility of speech-recording technology for dietary assessment, particularly among older adults. Abbreviations CAN-Pro Computer-Aided Nutritional Analysis Program FD Pen-and-Paper Food Diary KNS The Korean Nutrition Society MNA Mini Nutritional Assessment NQ-E Nutrition Quotient for the Elderly SR Speech Recognition SUS Systems Usability Scale Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Kyung Hee University (KHGIRB-24-040 on the 21st of February 2024). All participants were fully informed on the purpose and procedures of the study and provided written consent prior to their participation. Consent for publication Not applicable. Availability of data and materials The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Competing interests The authors declare no competing interests. Funding This study was supported by a grant from the National Research Council of Science and Technology (NST) Aging Convergence Research Center (CRC22013-600). Authors' contributions Conceptualisation, Y.K.P. and Y.S.; methodology, Y.K.P. and Y.S.; recruitment, Y.S., and S.J.; formal analysis, Y.S., S.J.; investigation, Y.S., H.J.K. and S.J.; data curation, Y.S., H.J.K., and S.J.; writing—original draft preparation, Y.S.; writing—review and editing, Y.S. and H.J.K.; supervision, Y.K.P.; project administration, Y.K.P.; funding acquisition, Y.K.P. and H.J.K.; administrative support, S.Y.M., J.H.J. and S.H.C.; critical review of the manuscript, S.Y.M., J.H.J., and S.H.C. All authors have read and agreed to the published version of the manuscript. Acknowledgements The authors express their gratitude to the National Research Council of Science and Technology (NST) Aging Convergence Research Center for their support and resources.[PH1] [MOU2] [MOU3] Acknowledgements We would like to thank Bahir Dar University for allowing us to perform this research. Our sincere thanks go to all of our study participants and counsellors who participated in the research. Funding The research was supported by Bahir Dar University. The funders had no role in study design, data collection and analysis, decision to publish, or prepara- tion of the manuscript. References Statistics Korea. Population projections for Korea: 2010–2060. http://kostat.go.kr . Accessed 4 June 2024. Agarwalla R, Saikia AM, Baruah R. Assessment of the nutritional status of the elderly and its correlates. J Fam Commun Med. 2015;22(1):39–43. https://doi.org/10.4103/2230-8229.149588 . Domenech-Briz V, Gea-Caballero V, Czapla M, Chover-Sierra E, Juárez-Vela R, Santolalla Arnedo I, Villanueva-Blasco VJ, Sánchez-González JL, Martínez-Sabater A. Importance of nutritional assessment tools in the critically ill patient: A systematic review. Front Nutr. 2023;9:1073782. https://doi.org/10.3389/fnut.2022.1073782 . Zuniga K, McAuley E. Considerations in the selection of diet assessment methods for examining the effect of nutrition on cognition. J Nutr Health Aging. 2015;19(3):333–40. https://doi.org/10.1007/s12603-014-0566-5 . Eldridge AL, Piernas C, Illner AK, Gibney MJ, Gurinović MA, de Vries JHM, Cade JE. Evaluation of New Technology-Based Tools for Dietary Intake Assessment-An ILSI Europe Dietary Intake and Exposure Task Force Evaluation. Nutrients. 2018;11(1). 10.3390/nu11010055 . FAO. 2018. Dietary Assessment: A resource guide to method selection and application in low resource settings. Rome. Dodd C, Adam M, Rollo M. 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IEEE J biomedical health Inf. 2018;22(1):252–64. https://doi.org/10.1109/JBHI.2017.2709333 . Korpusik M, Glass J. Spoken Language Understanding for a Nutrition Dialogue System, in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 25, no. 7, pp. 1450–1461, July 2017, 10.1109/TASLP.2017.2694699 . keywords: {Semantics;Tagging;Neural networks;Speech;Speech recognition;Hidden Markov models;Speech processing;Conditional random field;crowdsourcing;neural networks;semantic tagging;word vectors}. Astell AJ, Hwang F, Brown LJ, Timon C, Maclean LM, Smith T, Adlam T, Khadra H, Williams EA. Validation of the NANA (Novel Assessment of Nutrition and Ageing) touch screen system for use at home by older adults. Exp Gerontol. 2014;60:100–7. https://doi.org/10.1016/j.exger.2014.10.008 . Shinozaki N, Yuan X, Murakami K, Sasaki S. Development, validation and utilisation of dish-based dietary assessment tools: a scoping review. Public Health Nutr. 2021;24(2):223–42. https://doi.org/10.1017/S136898002000172X . Gil H. Development of a diagnostic tool for tailored digital competence of the elderly. Seoul Digital Foundation; 2022. Holvoet E, Vanden Wyngaert K, Van Craenenbroeck AH, Van Biesen W, Eloot S. The screening score of Mini Nutritional Assessment (MNA) is a useful routine screening tool for malnutrition risk in patients on maintenance dialysis. PLoS ONE. 2020;15(3):e0229722. https://doi.org/10.1371/journal.pone.0229722 . Chung M-J, Kwak T-K, Kim H-Y, Kang M-H, Lee J-S, Chung HR, Choi Y-S. Development of NQ-E, Nutrition Quotient for Korean elderly: item selection and validation of factor structure. J Nutr Health Korean Nutr Soc. 2018. https://doi.org/10.4163/jnh.2018.51.1.87 . Lee JS, Frongillo EA Jr. Nutritional and health consequences are associated with food insecurity among U.S. elderly persons. J Nutr. 2001;131(5):1503–9. 10.1093/jn/131.5.1503 . Hyzy M, Bond R, Mulvenna M, Bai L, Dix A, Leigh S, Hunt S. System Usability Scale Benchmarking for Digital Health Apps: Meta-analysis. JMIR mHealth uHealth. 2022;10(8):e37290. https://doi.org/10.2196/37290 . Eldridge AL, Piernas C, Illner AK, Gibney MJ, Gurinović MA, de Vries JHM, Cade JE. Evaluation of New Technology-Based Tools for Dietary Intake Assessment-An ILSI Europe Dietary Intake and Exposure Task Force Evaluation. Nutrients. 2018;11(1):55. https://doi.org/10.3390/nu11010055 . 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. Validation of Dietary Intake Estimated by Web-Based Dietary Assessment Methods and Usability Using Dietary Records or 24-h Dietary Recalls. Scoping Rev Nutrients. 2023;15(8):1816. https://doi.org/10.3390/nu15081816 . 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(8):1030–5. Akoglu H. User's guide to correlation coefficients. Turkish J Emerg Med. 2018;18(3):91–3. https://doi.org/10.1016/j.tjem.2018.08.001 . Bush LA, Hutchinson J, Hooson J, Warthon-Medina M, Hancock N, Greathead K, et al. Measuring energy, macro and micronutrient intake in UK children and adolescents: a comparison of validated dietary assessment tools. BMC Nutr. 2019;5(1):53. 10.1186/s40795-019-0312-9 . Gibson S, Francis L, Newens K, Livingstone B. Associations between free sugars and nutrient intakes among children and adolescents in the UK. Br J Nutr. 2016;116(7):1265–74. 10.1017/s0007114516003184 . Tabacchi G, Amodio E, Di Pasquale M, Bianco A, Jemni M, Mammina C. Validation and reproducibility of dietary assessment methods in adolescents: a systematic literature review. Public Health Nutr. 2014;17(12):2700–14. 10.1017/S1368980013003157 . Cao Y, Yu Y. Associations between Cholesterol Intake, Food Sources and Cardiovascular Disease in Chinese Residents. Nutrients. 2024;16(5):716. https://doi.org/10.3390/nu16050716 . Kim MJ. (2008): A comparative analysis of nutrient intake of Jeju seniors using 24-hour recall method and the food frequency questionnaire method. Masters degree thesis. Cheju National University. pp.22–23. Yang YJ, Kim MK, Hwang SH, Ahn Y, Shim JE, Kim DH. Relative validities of 3-day food records and the food frequency questionnaire. Nutr Res Pract. 2010;4(2):142–8. https://doi.org/10.4162/nrp.2010.4.2.142 . Smeuninx B, Greig CA, Breen L. Amount, Source and Pattern of Dietary Protein Intake Across the Adult Lifespan: A Cross-Sectional Study. Front Nutr. 2020;7:25. https://doi.org/10.3389/fnut.2020.00025 . Bangor A, Kortum P, Miller J. Determining what individual SUS scores mean: adding an adjective rating scale. J Usability Stud. 2009;4(3):114–23. Subar AF, Kirkpatrick SI, Mittl B, Zimmerman TP, Thompson FE, Bingley C, Willis G, Islam NG, Baranowski T, McNutt S, Potischman N. The Automated Self-Administered 24-hour dietary recall (ASA24): a resource for researchers, clinicians, and educators from the National Cancer Institute. J Acad Nutr Dietetics. 2012;112(8):1134–7. https://doi.org/10.1016/j.jand.2012.04.016 . Alawadhi B, Fallaize R, Franco RZ, Hwang F, Lovegrove J. Web-Based Dietary Intake Estimation to Assess the Reproducibility and Relative Validity of the EatWellQ8 Food Frequency Questionnaire: Validation Study. JMIR formative Res. 2021;5(3):e13591. https://doi.org/10.2196/13591 . Gioia S, Vlasac IM, Babazadeh D, Fryou NL, Do E, Love J, Robbins R, Dashti HS, Lane JM. Mobile Apps for Dietary and Food Timing Assessment: Evaluation for Use in Clinical Research. JMIR formative Res. 2023;7:e35858. https://doi.org/10.2196/35858 . Roe BE, Qi D, Beyl RA, Neubig KE, Martin CK, Apolzan JW. (2020). The Validity, Time Burden, and User Satisfaction of the FoodImage™ Smartphone App for Food Waste Measurement Versus Diaries: A Randomized Crossover Trial. Resources, conservation, and recycling, 160, 104858. https://doi.org/10.1016/j.re sconrec.2020.104858 Lim CY, In J. Considerations for crossover design in clinical study. Korean J anesthesiology. 2021;74(4):293–9. https://doi.org/10.4097/kja.21165 . Monnerie B, Tavoularis LG, 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 54 Suppl. 2015;2(Suppl 227–34. https://doi.org/10.1007/s00394-015-0945-7 . Margetts BM, Nelson M. Design Concepts in Nutritional Epidemiology. Oxford University Press; 1997. Kang H, Baek J, Chu SH, Choi J. Digital literacy among Korean older adults: A scoping review of quantitative studies. Digit health. 2023;9:20552076231197334. https://doi.org/10.1177/20552076231197334 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryFileGeneralCharacteristicsQuestionnaire.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6335141","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463649946,"identity":"73cce26f-5cc0-48d2-9440-8a0bf50ae699","order_by":0,"name":"Yoonjee 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participants\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.FlowChartofParticipants.png","url":"https://assets-eu.researchsquare.com/files/rs-6335141/v1/2a2d90ddfb3492e01716c0b7.png"},{"id":83752261,"identity":"58e42321-99ec-4e7f-8a04-646b33c316ce","added_by":"auto","created_at":"2025-06-02 07:13:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":135490,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBland-Altman plot of the FD and SR method for essential nutrient intake\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBland–Altman plots of the difference between intakes recorded by the FD and the SR method against the mean intakes for the two reporting methods for energy, carbohydrate, protein, fat, total sugar, dietary fibre, cholesterol, and mineral.\u003c/p\u003e","description":"","filename":"Figure2.BlandAltmanplotoftheFDandSRmethodforessentialnutrientintake.png","url":"https://assets-eu.researchsquare.com/files/rs-6335141/v1/0ef647eb9cb21864b4983a18.png"},{"id":83752263,"identity":"715a252a-ce96-497d-9ad6-adb23bae20e9","added_by":"auto","created_at":"2025-06-02 07:13:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":112208,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of the response to SUS items (odd) after completion of FD and\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.DistributionoftheresponsetoSUSitemsoddaftercompletionofFDandSR.png","url":"https://assets-eu.researchsquare.com/files/rs-6335141/v1/045fbfa8d2158fecf909ce7a.png"},{"id":83752387,"identity":"cafb34f0-6e60-4dea-b3b8-41d14cea8736","added_by":"auto","created_at":"2025-06-02 07:21:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":105287,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of the response to SUS items (even) after completion of FD and SR\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.DistributionoftheresponsetoSUSitemsevenaftercompletionofFDandSR.png","url":"https://assets-eu.researchsquare.com/files/rs-6335141/v1/5e578f44b3003aea6dee6198.png"},{"id":86648703,"identity":"29a11d22-e9a5-4a13-b726-8c294ed02218","added_by":"auto","created_at":"2025-07-14 09:17:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1764703,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6335141/v1/03d721ef-f272-4e51-83f7-d3ddeddec995.pdf"},{"id":83752262,"identity":"f749b2c2-4e67-40dd-a087-6552e9aa06b8","added_by":"auto","created_at":"2025-06-02 07:13:32","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":65579,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFileGeneralCharacteristicsQuestionnaire.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6335141/v1/c85b96310c39519a53726fa9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the Validity and Usability of a Speech Recognition Dietary Assessment Tool for Older Adults","fulltext":[{"header":"Background","content":"\u003cp\u003eThe demographic landscape of South Korea is undergoing a notable transformation with projections indicating that by 2060, individuals over 65 will constitute approximately 43.9% of the total population (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This demographic shift underscores the urgent need to address the nutritional needs of older adults. Despite this, malnutrition among older adults has been on the rise since 2014, primarily due to the lack of appropriate routine dietary assessment methods suitable to their unique characteristics, such as sensory, cognitive impairments, and literacy challenges (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Consequently, there is a compelling need for an enhanced dietary assessment tool specifically designed to accurately evaluate the overall nutritional status of the older population.\u003c/p\u003e \u003cp\u003eNutritional assessment involves evaluating various factors such as anthropometric measurements, biochemical indicators, clinical history, and dietary intake. Accurate documentation and analysis of dietary habits are crucial for diagnosing malnutrition and implementing effective interventions (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Conventional methods like self-reported food records and food frequency questionnaires (FFQs) are labour-intensive and impractical for those with varying literacy levels and cognitive impairments (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Technological advancements have introduced new dietary assessment methods, such as web-based 24-hour recall systems, mobile food diary apps, and camera-based sensors (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, these methods face challenges related to accuracy, accessibility, and literacy (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the new innovative tools available, speech recording has emerged as a promising method for efficient data collection, noted for its simplicity and user-friendliness. This technique enables individuals to capture dietary details through natural speech, thereby reducing participant burden and minimizing the likelihood of omitting essential details (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). It holds particular value for populations with literacy and sensory impairments, thereby broadening the scope of inclusion to a more diverse demographic (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Advances in speech recognition technology have further automated data processing, enhancing accuracy and reducing the need for manual labour (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpeech recognition technology functions by converting audio-recorded speech into text, identifying keywords, and facilitating nutritional analysis (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Initially, the speech recognition food search engine converts the audio-recorded speech to text, facilitating the identification of keywords such as the names of consumed foods and their respective quantities. This process aids in the subsequent calculation and analysis of the nutritional value of the meal. With the widespread availability of smartphones equipped with speech-recording capabilities, data collection for speech-based dietary assessment has become more accessible than ever before (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith advancements in speech recognition technology, there has been a surge in research interest surrounding the utilisation of speech technology to enhance the process of nutritional assessment, offering a novel approach for a wide range of users (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This study, therefore, aims to assess the validity and usability of a speech-recognition dietary assessment tool as a viable alternative to the traditional pen-and-paper food diary among older participants. The study focused on assessing the agreement between the SR and FD methods, validating the accuracy of the SR tool and examining its practical usability. Due to the impracticality of precisely measuring an individual\u0026rsquo;s self-reported diet over extended periods, studies often compare new methods with existing dietary assessment tools to assess their relative despite their inherent limitations (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Unlike prior research conducted in Europe and America where dietary intake is recorded through a food-based, single-ingredient intake format, this study focuses on assessing the intake of dish-based meals, prevalent in Asian countries like South Korea. Notably, there has been no validation study conducted on the dietary intake assessment via speech recognition specifically for dish-based meals. Given that many Korean dishes are complex combinations of various ingredients and cooking techniques, examining nutrient sources based on dishes provides a more comprehensive understanding of dietary patterns within populations (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003e This study was conducted at Kyung-Hee University from February 2024 to March 2024 after review and approval by the Institutional Review Boards of Kyung-Hee University (No. KHGRIB-24-040). This study employed a randomised cross-over (AB/BA) design to compare the effectiveness of two dietary assessment methods: the pen-and-paper food diary (FD) method and the speech recognition (SR) method over a two-week period. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.) Participants alternated between using the FD and SR methods to record their dietary intake, with the order of methods randomly assigned to each group. Dietary intake assessments were conducted over non-consecutive 3-day periods each week.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Participants\u003c/h3\u003e\n\u003cp\u003eEligible participants were older adults aged 65 years or older of both genders, who owned smartphones, and demonstrated the capability to accurately record their food intake for three non-consecutive days over a two-week period. Participants were also screened for basic digital literacy proficiencies using a tailored survey comprising 19 questions focused on digital familiarity, digital efficacy and basic digital skills (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Inclusion criteria required participants to score 3 or higher for digital familiarity and efficacy and 2 or higher on basic digital skills indicating above-average digital competency, to be eligible for the study. Participants diagnosed with mild cognitive impairment or dementia, as well as those with significant visual or hearing impairments, were excluded from the study. Additionally, individuals deemed ineligible for participation based on the researcher\u0026rsquo;s judgment were also excluded from the study. Participants were recruited through various channels including posters, word-of-mouth referrals, and social networking platforms over a one-month period. The participant\u0026rsquo;s general characteristics such as age, gender, education level, marital status, current living status, comorbidities, and financial status were investigated using constructive self-reported questionnaires, administered with the guidance of an interviewer. (Supplementary file 1)\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eAnthropometric Measurements\u003c/strong\u003e \u003cp\u003eAnthropometric data including height, weight, BMI, skeletal muscle mass, body fat percentage, mid-upper arm circumference (MAC), and calf circumference (CC) were collected. Body composition was measured using the Inbody 920, and body measurements such as MAC and CC were taken with a standard tape measure.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNutritional Status Assessments\u003c/strong\u003e \u003cp\u003eThe nutritional status of participants was evaluated using the Mini Nutrition Assessment (MNA) and the Nutrition Quotient-Elderly (NQ-E) questionnaires. The MNA assesses malnutrition risks through various criteria including body mass index, weight loss, mobility, psychological stress, and other factors, categorising scores as normal nutritional status (24\u0026ndash;30 points), risk of malnutrition (17-23.5 points), and malnourishment (0-16.5 points) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The NQ-E assesses nutritional status and meal quality through 20 questions on food intake frequency and eating behaviours, with higher scores indicating better nutritional status (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Nutritional assessments were conducted to identify discrepancies between nutritional status and nutrient intake, revealing potential validity limitations, such as poor nutritional health despite normal nutrient intake levels or vice versa (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDietary Intake Assessments\u003c/strong\u003e \u003cp\u003eDietary intake was assessed using both a pen-and-paper food diary and a speech-recording food log. Participants recorded their dietary intake for three non-consecutive days each week using both methods. Nutrient intakes were analysed using CAN-Pro 6.0 software (Korean Nutritional Society, Seoul, Korea).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePen-and-Paper Food Diary (FD)\u003c/strong\u003e \u003cp\u003eParticipants documented their dietary intake manually on provided sheets, detailing the date, time, location, and food items for each meal and snack.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSpeech Recording of Dietary Intake (SR)\u003c/strong\u003e \u003cp\u003e Participants used their mobile smartphones to verbally record dietary intake, which was transcribed via CLOVA (Naver, Seongnam, Korea), the Cloud Virtual Assistant speech recognition engine widely utilised for transcribing verbal input into text format. Transcription accuracy was validated by comparing machine-generated transcripts with human transcriptions. Participants also used standardised measures or food scales for accurate reporting and took photographs of their meals before and after consumption.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eTo minimise self-reporting errors, a 24-hour dietary recall assessment was conducted via phone interviews within 24 hours of meal recording on both occasions.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eUsability Evaluation\u003c/strong\u003e \u003cp\u003eThe usability of each dietary assessment method was evaluated using the Systems Usability Scale (SUS) questionnaire (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The SUS includes 10 items on a Likert scale, with scores ranging from 0 to 100, where higher scores indicate better usability. SUS scores were interpreted through acceptability ranges (0\u0026ndash;50 considered \u0026lsquo;not acceptable\u0026rsquo;, 50\u0026ndash;70 \u0026lsquo;marginally acceptable\u0026rsquo;, and 70\u0026ndash;100 were considered \u0026lsquo;acceptable\u0026rsquo;), descriptive adjectives (\"Excellent\" for scores above 85, \"Good\" around 71, \"OK\" around 51, to \"Poor\" for lower scores) and a grading system from A (superior) to F (failing). Participants completed the SUS questionnaire weekly, after a week of using each dietary assessment method, to provide feedback on user experience.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll data was analysed with the statistical analysis program, Statistical Package for the Social Sciences (IBM, Korea) version 29.0 for macOS. The normality of data distribution was assessed using the Shapiro-Wilk test. A comparison of both dietary intake assessment methods was performed through the independent t-test or Mann-Whitney U test. Pearson\u0026rsquo;s correlation coefficient and Spearman\u0026rsquo;s rank correlation coefficient were computed to test the significance of the relationship between intake estimates derived from the two dietary assessment methods. A Bland-Altman Plot was used to depict the levels of agreement between the two methods visually. Continuous variables were expressed as mean and standard deviation (SD), and categorical variables were expressed as counts and percentages (%). Statistical significance was determined by a p-value of \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGeneral characteristics of the study population\u003c/h2\u003e \u003cp\u003eA total of 18 participants (13 females, 5 males) completed the study, each assessed for 3 days. (Table. 1) Participants' ages ranged from 65 to 85 years, with a mean age of 72\u0026thinsp;\u0026plusmn;\u0026thinsp;4.89 years (males: 71.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79; females: 72.30\u0026thinsp;\u0026plusmn;\u0026thinsp;5.72). The average Body Mass Index (BMI) was 24.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2.70 kg/m\u0026sup2;. Males had higher skeletal muscle mass (28.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4 kg) compared to females (20.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7 kg). Regarding education, 33.3% had university-level education, with a higher proportion of males (60%) than females (23.1%). All participants were married, with most living with their partners (61.1%). The primary source of living expenses was through salary or pensions of the individual or their partner (all males, 84.6% of females). Most participants felt secure in their ability to afford groceries (83.3%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnthropometrics and general characteristics of study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale (n\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.17\u0026thinsp;\u0026plusmn;\u0026thinsp;4.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.30\u0026thinsp;\u0026plusmn;\u0026thinsp;5.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeight (cm\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159.31\u0026thinsp;\u0026plusmn;\u0026thinsp;10.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.86\u0026thinsp;\u0026plusmn;\u0026thinsp;10.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody Mass Index (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSkeletal Muscle Mass (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.14\u0026thinsp;\u0026plusmn;\u0026thinsp;5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody Fat Mass (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.12\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody Fat Percentage (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.21\u0026thinsp;\u0026plusmn;\u0026thinsp;6.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.58\u0026thinsp;\u0026plusmn;\u0026thinsp;5.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.99\u0026thinsp;\u0026plusmn;\u0026thinsp;6.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le; Middle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (30.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (46.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (23.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving arrangements\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(7.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(53.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(38.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving expenses\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalary or pension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(84.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren or relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(7.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernmental support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePart-time job\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(7.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFood Security\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes, secure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(76.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo, not secure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(23.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eValues are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations or number\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNutritional assessment\u003c/h3\u003e\n\u003cp\u003eThe Mini Nutritional Assessment (MNA) scores indicated normal nutritional status for all participants, with a mean score of 27.06\u0026thinsp;\u0026plusmn;\u0026thinsp;2.27 (males: 26.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2.27; females: 27.23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34). The Nutrition Quotient for Elderly (NQ-E) score was 62.92\u0026thinsp;\u0026plusmn;\u0026thinsp;13.96, higher in male participants (70.79\u0026thinsp;\u0026plusmn;\u0026thinsp;10.68) than female participants (59.89\u0026thinsp;\u0026plusmn;\u0026thinsp;14.22). Male participants scored higher across NQ-E sub-scores in Balance (62.02\u0026thinsp;\u0026plusmn;\u0026thinsp;13.79), Moderation (60.28\u0026thinsp;\u0026plusmn;\u0026thinsp;13.61), and Practice (66.94\u0026thinsp;\u0026plusmn;\u0026thinsp;11.75) compared to female participants (Balance: 60.98\u0026thinsp;\u0026plusmn;\u0026thinsp;18.15; Moderation: 59.59\u0026thinsp;\u0026plusmn;\u0026thinsp;17.81; Practice: 55.54\u0026thinsp;\u0026plusmn;\u0026thinsp;14.51).\u003c/p\u003e\n\u003ch3\u003eDietary intake assessment\u003c/h3\u003e\n\u003cp\u003e An assessment was conducted to evaluate the accuracy and reliability of the speech recognition tool employed to transcribe speech recordings of participants capturing details such as date, time and meal type (e.g., breakfast, lunch, dinner, and snacks), as well as the name of the dish and estimated portions using either standard household object or food scales. The mean accuracy of transcription across participants was calculated to be 95.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02%, indicating a high level of fidelity in transcribing and discerning dietary speech records. Each participant\u0026rsquo;s speech transcription accuracy was individually assessed for all three days of recorded speech, with each day\u0026rsquo;s transcription compared to manual transcriptions. This analysis process further reinforced confidence in the reliability of the SR tool for dietary intake assessment and the findings substantiate the viability of utilising speech recognition technology as a reliable means for capturing and transcribing detailed dietary intake data among older adults.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean energy, macronutrient, and micronutrient intakes measured by FD and SR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFD \u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSR \u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFD-SR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCorrelation coefficient\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Difference (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ePearson\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eSpearman\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacronutrient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy (kcal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1949.44\u0026thinsp;\u0026plusmn;\u0026thinsp;497.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1941.69\u0026thinsp;\u0026plusmn;\u0026thinsp;385.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.698\u0026dagger; \u003csup\u003e4)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.518*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.567*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbohydrate (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e272.40\u0026thinsp;\u0026plusmn;\u0026thinsp;74.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e254.65\u0026thinsp;\u0026plusmn;\u0026thinsp;50.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.408\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.84\u0026thinsp;\u0026plusmn;\u0026thinsp;16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.39\u0026thinsp;\u0026plusmn;\u0026thinsp;17.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.738**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.709**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.86\u0026thinsp;\u0026plusmn;\u0026thinsp;19.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.03\u0026thinsp;\u0026plusmn;\u0026thinsp;16.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.740**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.777**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC: P: F (%)\u003csup\u003e3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.9:17.0:25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.5:17.4:26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary Fibre (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.09\u0026thinsp;\u0026plusmn;\u0026thinsp;10.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.53\u0026thinsp;\u0026plusmn;\u0026thinsp;7.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.671*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.608**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Sugar (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.74\u0026thinsp;\u0026plusmn;\u0026thinsp;24.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.57\u0026thinsp;\u0026plusmn;\u0026thinsp;19.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e261.86\u0026thinsp;\u0026plusmn;\u0026thinsp;116.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e302.18\u0026thinsp;\u0026plusmn;\u0026thinsp;69.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-13.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinerals (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.3513\u0026thinsp;\u0026plusmn;\u0026thinsp;3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.24\u0026thinsp;\u0026plusmn;\u0026thinsp;5.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.656**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFat-soluble vitamin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin A (RAE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e337.99\u0026thinsp;\u0026plusmn;\u0026thinsp;90.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e336.38\u0026thinsp;\u0026plusmn;\u0026thinsp;198.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.656\u003csup\u003e3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin D (\u0026micro;g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-28.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin E (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.97\u0026thinsp;\u0026plusmn;\u0026thinsp;2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.276\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.907**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.725**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin K (\u0026micro;g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e241.43\u0026thinsp;\u0026plusmn;\u0026thinsp;81.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e244.34\u0026thinsp;\u0026plusmn;\u0026thinsp;81.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.701**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.616**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWater-soluble vitamin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin C (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.09\u0026thinsp;\u0026plusmn;\u0026thinsp;31.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.26\u0026thinsp;\u0026plusmn;\u0026thinsp;16.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThiamine (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.690**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.696**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRiboflavin (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.871**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.872**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNiacin (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.703**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B6 (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.653**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.496*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFolate (DFE) (\u0026micro;g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e355.89\u0026thinsp;\u0026plusmn;\u0026thinsp;52.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e352.47\u0026thinsp;\u0026plusmn;\u0026thinsp;67.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.503*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.527*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B12 (\u0026micro;g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.498*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.531*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e653.20\u0026thinsp;\u0026plusmn;\u0026thinsp;202.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e644.32\u0026thinsp;\u0026plusmn;\u0026thinsp;210.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.931\u003csup\u003e3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.469*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.474*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorous (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1408.69\u0026thinsp;\u0026plusmn;\u0026thinsp;551.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1254.29\u0026thinsp;\u0026plusmn;\u0026thinsp;295.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.720\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.763**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.785**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4145.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1403.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4305.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1327.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.577*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3350.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1136.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3263.26\u0026thinsp;\u0026plusmn;\u0026thinsp;791.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e342.23\u0026thinsp;\u0026plusmn;\u0026thinsp;110.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e326.51\u0026thinsp;\u0026plusmn;\u0026thinsp;96.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.709**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.785**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.76\u0026thinsp;\u0026plusmn;\u0026thinsp;2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.44\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.568*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.546*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eValues are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations.\u003c/p\u003e \u003cp\u003e1) FD, Food Diary\u003c/p\u003e \u003cp\u003e2) SR, Speech Recording\u003c/p\u003e \u003cp\u003e3) Energy ratio of carbohydrate, protein, and fat\u003c/p\u003e \u003cp\u003e4) No significant difference by Mann-Whitney U test at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003cp\u003e\u0026dagger; No significant difference by independent t-test at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003cp\u003ePercentage of the mean difference between FD and SR in each nutrient (calculated as % of the difference = (mean amount from FD-mean amount from SR)/ mean amount from SR) *100)\u003c/p\u003e \u003cp\u003e* Intakes by two methods, which food diary and speech recording, are significantly correlated by Pearson\u0026rsquo;s in quantity and Spearman\u0026rsquo;s in ranking (*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the mean daily energy and nutrient intakes using the SR and FD methods, the mean difference, the significance of the mean difference, and the correlation between the methods. No statistically significant differences were observed for macronutrients, vitamins, or minerals. The FD method estimated an energy intake of 1949.44\u0026thinsp;\u0026plusmn;\u0026thinsp;497.59 kcal, compared to 1941.69\u0026thinsp;\u0026plusmn;\u0026thinsp;385.58 kcal with the SR method, resulting in a mean difference of 0.49% (p\u0026thinsp;=\u0026thinsp;0.698). Carbohydrate intake was 272.40\u0026thinsp;\u0026plusmn;\u0026thinsp;74.42 g for FD and 254.65\u0026thinsp;\u0026plusmn;\u0026thinsp;50.36 g for SR, with a mean difference of 6.97% (p\u0026thinsp;=\u0026thinsp;0.408). Protein intake was 82.84\u0026thinsp;\u0026plusmn;\u0026thinsp;16.00 g for FD and 55.86\u0026thinsp;\u0026plusmn;\u0026thinsp;19.98 g for SR, showing a mean difference of -1.84% (p\u0026thinsp;=\u0026thinsp;0.645). Fat intake had a smaller mean difference of -0.32% (p\u0026thinsp;=\u0026thinsp;0.820), with FD estimating 55.86\u0026thinsp;\u0026plusmn;\u0026thinsp;19.98 g and SR estimating 56.03\u0026thinsp;\u0026plusmn;\u0026thinsp;16.51 g. Mean differences for energy and essential nutrients averaged around 5.02%, while mean differences for vitamins and minerals (calcium, phosphorus, sodium, potassium, magnesium, and iron) averaged around 14.90% and 4.89%, respectively. The SR method tended to underestimate intake compared to the FD method, except for nutrients like fat, protein, cholesterol, minerals, and certain vitamins.\u003c/p\u003e \u003cp\u003eThe Pearson correlation coefficient for nutrient intake between the two methods was statistically significant for most nutrients, ranging from 0.498 to 0.907, except for carbohydrates, total sugar, cholesterol, vitamins A, D, C, and potassium. Among essential nutrients and energy intake, the highest correlations were observed for fat (r\u0026thinsp;=\u0026thinsp;0.740), protein (r\u0026thinsp;=\u0026thinsp;0.738), dietary fibre (r\u0026thinsp;=\u0026thinsp;0.671), minerals (r\u0026thinsp;=\u0026thinsp;0.656), and energy intake (r\u0026thinsp;=\u0026thinsp;0.518). High correlations were shown for vitamins E, K, thiamine, and riboflavin, and were notably high for phosphorus and magnesium among minerals. Vitamins A, D, C, and potassium showed no significant correlation.\u003c/p\u003e \u003cp\u003eExamining correlation ranks using Spearman\u0026rsquo;s correlation coefficient further nuanced the comparative efficacy of the methods. The Spearman\u0026rsquo;s correlation coefficient for energy was 0.567, protein was 0.709, fat was 0.777, and dietary fibre was 0.608. These results were generally consistent with Pearson\u0026rsquo;s correlation coefficient, which ranged from 0.474 to 0.872. However, for minerals and niacin, no statistically significant correlation was observed with Spearman\u0026rsquo;s rank coefficient despite visible correlations with Pearson\u0026rsquo;s coefficient.\u003c/p\u003e \u003cp\u003eThe energy and macronutrient intake data were further analysed using Bland-Altman analysis to evaluate the level of agreement between the two methods. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the Bland-Altman plots of the mean differences between the two recording methods for total energy, carbohydrate, protein, fat, total sugar, dietary fibre, cholesterol, and minerals. Energy intake had a mean difference of 7.75 kcal with lower and upper limits of agreement (2SD) of -863.39 to 878.89 kcal, showing no significant differences. The mean difference for fat intake was \u0026minus;\u0026thinsp;1.55 g with lower and upper limits of agreement of -24.98 to 21.87, protein showed a mean difference of -0.18 g with lower and upper limits of agreement of -26.83 to 26.47 g, and carbohydrates showed a mean difference of -1.55 g with lower and upper limits of agreement of -24.98 to 21.87, all similar or within the limits of comparable studies.\u003c/p\u003e \u003cp\u003eThe Bland-Altman analysis demonstrated relatively good agreement between the SR and FD methods. The mean differences for most nutrients were close to zero, except for cholesterol, which exhibited a proportional bias with increasing intake, shown by a funnel-shaped distribution. The limits of agreement for cholesterol were also wider than those reported in previous studies, indicating greater inconsistency between the methods. Nonetheless, the overall agreement and distribution of standard deviations suggest that both methods are reliable for most nutrients, with only minor discrepancies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eUsability evaluation\u003c/h2\u003e \u003cp\u003eThe Systems Usability Scale (SUS) scores were obtained from all 18 participants after they had utilized both FD and SR methods. The SUS score for the pen-and-paper food diary method was 66.25\u0026thinsp;\u0026plusmn;\u0026thinsp;11.83, while the speech recording dietary assessment tool scored 72.77\u0026thinsp;\u0026plusmn;\u0026thinsp;10.77 out of 100 points. Although the scores showed no statistically significant difference (p\u0026thinsp;=\u0026thinsp;0.586), in this study, the SR method had the score of being \u0026lsquo;Acceptable\u0026rsquo; (70\u0026ndash;100 points) whilst the FD fell into the \u0026lsquo;marginally acceptable\u0026rsquo; range (50\u0026ndash;70 points). Furthermore, the SD method was labelled with a descriptive adjective of \u0026ldquo;Good\u0026rdquo;, whereas the conventional FD method was labelled \u0026ldquo;OK\u0026rdquo;. Despite these differences in acceptability and adjectives, both methods received a C grade, indicating a comparable level of usability within the two methods. The response to each SUS item of the two assessment methods is presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and 4. Positive items (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) for the SR method showed strong agreement, though confidence was lower (27.8%). Negative responses (Fig.\u0026nbsp;4) were fewer for the SR method (16.7%) compared to the FD method (27.8%). The FD method showed lower favourability on positive items (33.3% ease of use, 27.8% confidence) and higher unfavourable responses on negative items (50% needed support, 44.4% found it inconsistent).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study compared nutritional intake using the traditional pen-and-paper food diary (FD) method and a new speech-recognition (SR) dietary assessment tool. While most studies have focused on web-based 24-hour recalls, online food frequency questionnaires, and AI-based image recognition tools, this research evaluates SR as a potentially simpler and more accurate technology-based recording method (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e The primary finding is that SR showed comparable results to FD, with relatively small mean differences between the two methods across most nutrients, supported by moderate to good correlation coefficients and Bland-Altman plots. The mean difference in essential nutrients averaged 5.02%, vitamins 14.47%, and micronutrients 4.89%. This is consistent with a previous review where web-based and conventional methods showed a mean energy intake difference of 5.31% (0.6%-16.1%) compared to 0.49% in this study. The differences for protein and fat intake were also lower in this study, at 1.84% and 0.32% respectively, compared to 5.00% and 7.73% in the reviewed studies (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Luevano-Contreras et al.'s criteria categorise mean difference percentages of 0.0-10.9% as \u0026lsquo;good,\u0026rsquo; 11.0\u0026ndash;20.0% as \u0026lsquo;acceptable,\u0026rsquo; and \u0026gt;\u0026thinsp;20.0% as \u0026lsquo;poor\u0026rsquo; (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), with most mean nutrient differences in this study falling within the \u0026lsquo;good\u0026rsquo; range. SR showed lower mean intake levels for most nutrients except for fat, protein, cholesterol, vitamins D, E, K, and niacin.\u003c/p\u003e \u003cp\u003eThe Bland-Altman analysis indicated relatively good agreement between FD and SR methods, with small mean differences, narrow limits of agreement, and few outliers. Carbohydrate intake showed comparable results despite higher mean differences and fat and protein intake differences were small and consistent. However, in the case of cholesterol, as the average intake increased, the discrepancy between the two methods also increased, exhibiting a funnel-shaped proportional bias. The limit values of the data were also broader than those reported in previous studies, indicating greater inconsistency between the two methods.\u003c/p\u003e \u003cp\u003eCorrelations between FD and SR methods were fair to moderate, with Pearson and Spearman coefficients indicating notable agreement for total energy intake (0.518 and 0.567), essential nutrients (0.478 and 0.523), vitamins (0.559 and 0.477), and minerals (0.589 and 0.568). To contextualise these findings, the interpretations of Chan YH et al. were employed wherein a correlation of 1 indicates a perfect association, 0.8\u0026ndash;0.9 indicates a very strong association, 0.6\u0026ndash;0.7 a moderate association, 0.3\u0026ndash;0.5 a fair association, and 0.1\u0026ndash;0.2 a poor association (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Certain dietary parameters, such as carbohydrates, total sugar, cholesterol, and vitamins A, D, C, and potassium, exhibited weaker correlations, likely due to the diverse range of foods containing these nutrients and day-to-day dietary variability (\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies have similarly reported low correlations for vitamins and minerals in dietary assessments. Kim et al. (2008) found correlations for vitamins A and C to be 0.18 and 0.48 respectively, which aligns with our findings of -0.146 and 0.416 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). While this study underscores the comparability of SR for micronutrient intake assessment, it is important to recognize the limitations of the 3-day food record method, especially for micronutrient measurement due to underreporting and variability in food consumption (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). According to Kwon et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), accurately estimating dietary intakes within 20% of the true mean with 90% confidence may require 25\u0026ndash;29 days of food records for certain vitamins.\u003c/p\u003e \u003cp\u003eUsability was assessed using the Systems Usability Scale (SUS) survey after each method. The SUS is frequently used to assess and compare the usability of various systems, including in nutrition-related contexts. In this study, the SR method scored 6.5 points higher than the FD method. According to Bangor et al., SUS scores of 68 or above are considered \u0026ldquo;good\u0026rdquo; and \u0026ldquo;acceptable\u0026rdquo; regarding usability (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), a criterion met by the SR method but not by the FD method in this older adult demographic. Previous research evaluating web-based dietary recall systems, such as the Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) and EatWellQ8 (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), a food frequency questionnaire system and other mobile dietary intake applications (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) reported SUS scores ranging from 58 to 75 points, aligning with the SR method's score.\u003c/p\u003e \u003cp\u003ePositive responses (odd-numbered questions) shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e to SR were highly correlated, with over 51% agreeing or strongly agreeing, and no strong disagreement except for one question about quick usability. One participant found the SR method overly complicated (Fig.\u0026nbsp;4), possibly due to the initial learning curve associated with digital health tools. For the FD method, participants reported difficulties in learning and needing technical support (11.1%), with over 50% strongly agreeing on the complexity of learning the method. This difficulty can be attributed to the necessity for participants to meticulously detail ingredients and cooking methods as required for accurate food records (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Additionally, the FD method was found cumbersome due to the necessity of immediate documentation after consumption, contributing to its overall inconvenience and time commitment.\u003c/p\u003e \u003cp\u003eThe strengths of this study lie in its evaluation practices, particularly the utilisation of a randomised cross-over design, which effectively mitigated sequence bias and enhanced the internal validity. This design ensured that each participant experienced both the speech recognition (SR) tool and the pen-and-paper food diary (FD), reducing the impact of potential confounding variables associated with individual differences (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Furthermore, the high transcription accuracy of the speech recognition tool underscored its precision in transcribing dietary data, thereby contributing to the validity of the study\u0026rsquo;s findings. However, it is essential to acknowledge certain limitations inherent in the study design and methodology. While the crossover design minimised sequence bias, the difference in dietary intake due to the application of different time points may have affected the data leading to a need for a longer study period (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The study was designed to ensure the impartiality of the order in which the dietary assessments would be executed (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), thereby resulting in differences in food consumption at different times contributing to discrepancies in nutrient intake. Additionally, the focus on older adult participants with varying levels of digital proficiency may limit the generalisability of our findings due to potential variations in participant compliance or engagement with the dietary assessment methods (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). A key limitation is the comparison with the traditional pen-and-paper food diary method, which, like any dietary assessment tool, is subject to inherent biases and limitations. In the absence of a true measure of habitual dietary intake, our study, similar to previous validity studies, was constrained to comparing the relative validity of the SR method against the FD method (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Another notable limitation is the multi-step process of the SR method, involving transcription through an automated speech recognition tool followed by manual analysis. Although this complexity did not significantly impact user acceptance, future advancements in speech recognition technology should aim to integrate this tool into a unified digital health platform.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e This study investigated the validity and usability of a novel speech recognition dietary assessment tool compared to a traditional pen-and-paper food diary among older adult participants. The findings suggest that the specific recording method shows promise as a valid alternative with higher user acceptance. Notably, the study demonstrated comparable levels of dietary intake between speech-recording and traditional techniques, with moderate to strong correlations observed for various nutrients. Moreover, the Systems Usability Scale survey indicated favourable user perceptions of the speech-recording tool, highlighting its potential as a user-friendly option for dietary assessment in older adult populations. Overall, this study contributes insight into the validity and utility of speech-recording technology for dietary assessment, particularly among older adults.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCAN-Pro\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Computer-Aided Nutritional Analysis Program\u003c/p\u003e\n\u003cp\u003eFD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Pen-and-Paper Food Diary\u003c/p\u003e\n\u003cp\u003eKNS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;The Korean Nutrition Society\u003c/p\u003e\n\u003cp\u003eMNA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mini Nutritional Assessment\u003c/p\u003e\n\u003cp\u003eNQ-E\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Nutrition Quotient for the Elderly\u003c/p\u003e\n\u003cp\u003eSR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Speech Recognition\u003c/p\u003e\n\u003cp\u003eSUS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Systems Usability Scale\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Kyung Hee University (KHGIRB-24-040 on the 21st of February\u0026nbsp;2024). All participants were fully informed on the purpose and procedures of the study and provided written consent prior to their participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by a grant from the National Research Council of Science and Technology (NST) Aging Convergence Research Center (CRC22013-600).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualisation, Y.K.P. and Y.S.; methodology, Y.K.P. and Y.S.; recruitment, Y.S., and S.J.; formal analysis, Y.S., S.J.; investigation, Y.S., H.J.K. and S.J.; data curation, Y.S., H.J.K., and S.J.; writing\u0026mdash;original draft preparation, Y.S.; writing\u0026mdash;review and editing, Y.S. and H.J.K.; supervision, Y.K.P.; project administration, Y.K.P.; funding acquisition, Y.K.P. and H.J.K.; administrative support, S.Y.M., J.H.J. and S.H.C.; critical review of the manuscript, S.Y.M., J.H.J., and S.H.C. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to the National Research Council of Science and Technology (NST) Aging Convergence Research Center for their support and resources.[PH1] [MOU2] [MOU3]\u0026nbsp;\u003c/p\u003e\n\u003cdiv id=\"_com_3\" language=\"JavaScript\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe would like to thank Bahir Dar University for allowing us to perform this research. Our sincere thanks go to all of our study participants and counsellors who participated in the research.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe research was supported by Bahir Dar University. 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Design Concepts in Nutritional Epidemiology. Oxford University Press; 1997.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang H, Baek J, Chu SH, Choi J. Digital literacy among Korean older adults: A scoping review of quantitative studies. Digit health. 2023;9:20552076231197334. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/20552076231197334\u003c/span\u003e\u003cspan address=\"10.1177/20552076231197334\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Older adults, Speech recording, Dietary intake assessment, Validity, Usability","lastPublishedDoi":"10.21203/rs.3.rs-6335141/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6335141/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWith the global population ageing rapidly, achieving healthy ageing is crucial, especially in countries like South Korea, where over 14.2% of citizens are aged 65 and older, qualifying it as an \u0026lsquo;aged society\u0026rsquo;. By 2050, the Asia-Pacific region is expected to see unprecedented ageing, with 10 countries classified as 'ageing societies' (over 7% of its population aged 65 and over), five as 'aged societies' (over 14% aged 65 and over), and 11 as 'super-aged societies' (over 21% aged 65 and over) with South Korea as anticipated to be among the latter, projected to have over 40% of its population aged 65 and older. Effective nutritional assessment for this demographic remains challenging due to limitations in conventional methods, exacerbated by factors like cognitive decline and low literacy. Despite efforts to address these challenges with new technological solutions like web, scanners, and mobile-based tools, issues of accuracy, usability, and cost persist. Therefore, this study aims to introduce and evaluate a speech-based dietary assessment tool as a promising alternative for older adults.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eIn a randomised cross-over design, 18 participants aged 65 and older were divided into two groups, each using both a pen-and-paper food diary (FD) method and a speech recognition dietary assessment (SR) method over two weeks. Dietary intake was recorded and analysed over non-consecutive 3-day periods each week while usability was assessed via the Systems Usability Scale (SUS).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results of the nutrient intake analysis indicated small mean differences between methods, with a 0.49% difference in energy and an average of 5.02% in essential nutrients. Significant correlation coefficients for nutrient intake between methods were observed, with the highest for fat (0.740), protein (0.738), and dietary fibre (0.671). Bland-Altman analysis indicated no substantial bias in energy or essential nutrient intakes, except for cholesterol which showed a discernible proportional bias. SUS scores between the SR and FD methods showed no statistical significance; however, the SR method received a higher standardised rating, characterised as 'acceptable' with a score of 72.77, compared to the FD method, rated as 'marginally acceptable\u0026rsquo; with a score of 66.25.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings suggest that the SR method is adequately comparable to the traditional FD method for dietary intake assessment in older adults and is on par in terms of usability, indicating its potential as a valid and acceptable alternative.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eKCT0009677\u003c/p\u003e","manuscriptTitle":"Assessing the Validity and Usability of a Speech Recognition Dietary Assessment Tool for Older Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-02 07:13:28","doi":"10.21203/rs.3.rs-6335141/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df485c9b-b369-42c3-8f7f-92a189e30175","owner":[],"postedDate":"June 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-14T09:09:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-02 07:13:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6335141","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6335141","identity":"rs-6335141","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}