European Review for Medical and Pharmacological Sciences

2023; 27: 8829-8841

Predicting habitual water intake fromlifestyle questions
H. TANAKA1, K. MIZUMA2, Y. NAKAMURA3, A. HIRATA4, J. MIYAZAKI4,
K. SUZUKI2, H. SETA2, H. WATANABE3, T. SUZUKI3, R. WATANABE2,
N. MURAYAMA3, T. OKAMURA4, S. NAKAMURA1
Division of Information Science, Nara Institute of Science and Technology, Ikoma, Nara, Japan
Suntory Global Innovation Center Limited, Institute for Water Science, Seika-cho, Soraku-gun,Kyoto, Japan
3
Suntory Global Innovation Center Limited, Research Institute, Seika-cho, Soraku-gun, Kyoto, Japan
4
Department of Preventive Medicine and Public Health, Keio University School of Medicine,
Shinjuku-ku, Tokyo, Japan
1
2

Abstract. – OBJECTIVE: Previous studies

have used selective recall and descriptive dietary record methods, requiring considerable
effort for assessing food and water intake. This
study created a simplified lifestyle questionnaire to predict habitual water intake (SQW), accurately and quickly assessing the habitual water intake. We also evaluated the validity using
descriptive dietary records as a cross-sectional study.
SUBJECTS AND METHODS: First, we used
crowdsourcing and machine learning to collectdata, predict water intake records, and create
questionnaires. We collected 305 lifestyle-related questions as predictor variables and selective recall methods for assessing water intake
as an outcome variable. Random forests wereused for the machine learning models becauseof their interpretability and accurate estimation.
Random forest and single regression correlation analysis were augmented by the synthetic
minority oversampling that trained the model.
We separated the data by sex and evaluated ourmodel using unseen hold-out testing data, predicting the individual and overall habitual water
intake from various sources, including non-alcoholic beverages, alcohol, and food.
RESULTS: We found a 0.60 Spearman’s correlation coefficient for total water intake between the predicted and the selective recall
method values, reflecting the target value to beachieved. This question set was then used forfeasibility tests. The descriptive dietary record
method helped to obtain a ground-truth value.
We categorized the data by gender, season, andsource: non-alcoholic beverages, alcohol, food,and total water intake, and the correlation was
confirmed. Consequently, our results showed aPearson’s correlation coefficient of 0.50 for total

water intake between the predicted and the selective recall method values.
CONCLUSIONS: We hypothesize that dissemination of SQW can lead to better health management by easily determining the habitual water intake.
Key Words:
Habitual water intake, Random forests, Questionnaire, Lifestyle

Introduction
Water, a vital life component, constitutes 60%of the human body1,2. Water critically maintains
homeostasis and is a medium for delivering oxygen, nutrients, hormones, and other substances
throughout the body, and helps remove waste
products and excess substances3. Moreover, sufficient water intake is critical for human health,creating an association between water intake
and health status. Inadequate water intake increases the risk of renal and cardiovascular diseases, contributes to metabolic disorders4, and isassociated with the onset of cerebral infarction5.
In addition, studies from physical and cognitive
perspectives highlight the association between
dehydration and many health issues, like loss ofattention, concentration, cognitive, mood, andmotor functions, fatigue, and headaches6,7. For
example, Secher and Ritz8 established a clear
link between dehydration and reduced cognitive performance. Other studies7-10 showed thatdrinking more water may improve performance

Corresponding Author: Hiroki Tanaka, Ph.D; e-mail: hiroki-tan@is.naist.jp8829

H. Tanaka, K. Mizuma, Y. Nakamura, A. Hirata, J. Miyazaki, et alin an attention test. Therefore, consistent andadequate daily water intake could reduce thedisease risk and maintain mental health. Successful prediction of habitual water intake mightenable building a conversational agent that recommends drinking water based on monitoring
the current water intake11,12.
However, measuring water intake requires a
substantial effort, which includes the descriptive dietary record method and food frequency
questionnaires obtained via interviews with many questions. Moreover, most of these studies
have focused on the limited aspects of behavior
and lifestyle. For example, a self-administered
food frequency questionnaire was analyzed andvalidated for assessing food13,14 and specific amino acid intake15. Creating new questionnaires
is expensive and time-consuming, requiring theevaluation of validation and reliability. Recent
advances in data-driven approaches have enableddealing with large amounts of data. In addition,
crowdsourcing is helpful for efficiently collecting
data from human participants to reduce the number of questions16,17. Partial least squares (PLS)
regression and factor analysis were used in aprevious study18 to predict age and body massindex and to observe question weights. Anotherprevious study19 used a multi-layer perceptron topredict seasonal water consumption.
Our present study aimed to create a new questionnaire using a simplified lifestyle-based one topredict habitual water intake (SQW) with only ten
questions and to evaluate the validity by exploring the relationship between SQW values and descriptive dietary records20,21. We first constructedensemble trees to predict water intake records tocreate a new SQW. Then, our SQW was used formodel validation with different regions, seasons,and populations, including those participating in
epidemiological studies and those recruited forthis study, and descriptive dietary records wereobtained as a ground-truth value. The following
sections describe the model construction, results,
and model validation.

Subjects and Methods
Constructing Models for
Predicting Water Intake
Data collection
We recruited participants three times using
CrowdWork (https://crowdworks.jp) to obtain
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data from sufficient participants, the primarydata collection period was from January 14
to 25, 2020. CrowdWork is one of the largest
crowdsourcing platforms in Japan, and a previous study18 obtained data from the platforms.
More than eight hundred thousand people areregistered in CrowdWorks, and 797 participants
applied for our data collection and were used forthe cross-sectional analysis. Eligibility criteria
included men and women between the ages of
18 and 85, who were members of CrowdWorks,
and those who understood and agreed to participate in this study. We first obtained informed
consent from all participants. Over one week,
the participants answered questions about theirdietary recall. We have attached the corresponding images to represent food and drink amounts
that were easy to understand in questionnaires.
The selective dietary recall questionnaires required approximately 30 min to complete. The
crowd workers were allowed to contact the firstauthor if they faced difficulties. Our questionnaire included two dummy questions regardingsex (male or female) and experience of acupuncture or moxibustion (yes or no). Due to thechallenging tasks requiring significant effort toanswer all questions, we eliminated participants
who failed to concentrate on this by observing
the consistency of answers to the two dummy
questions (Criteria 3).
Lifestyle questions collection
We prepared 305 multidimensional questionsin Japanese as predictor variables. The questions
were either already validated or manually created
and relevant to water intake habits (Figure 1a).
The questions were carefully selected by consulting a dietitian. It also included a food frequency
questionnaire (FFQg) obtained with permission
from a previous study22 since the frequency offood intake may be affected. Other questions
were from the simplified nutritional appetite
questionnaire (SNAQ-J)23 and Pittsburgh Sleep
Assessment24, which calibrates sleep quality andsmoking behaviors. We also obtained questionnaires from the National Health and Nutrition
Survey25 and the Japanese version of the Constitution in the Chinese Medicine Questionnaire(CCMQ)26. We also adopted the Ten Item Personality Inventory (TIPI-J)27,28. We defined a category, a physical constitution based on the CCMQ, as
an individual’s body condition formed by interactions between genetic and environmental factors.
In addition to the validated questionnaires, wePredicting habitual water intake from lifestyle questions

Figure 1. Flow diagram of this study. Development of a simplified lifestyle questionnaire for predicting water intake (SQW)(a) and validation flow of SQW (b).

created 73 new questions regarding dietary anddrinking behaviors. A complete list of the 305
questionnaires is available upon request.
Selective recall method
The selective recall method is a survey-stylequestionnaire for assessing water intake from foodand beverage, in which respondents answer thetype and amount of food and beverages consumed
at each time of the day. Our previous study29
showed that the correlation coefficient between
descriptive dietary recall as a gold standard andselective recall methods was greater than 0.90.
To construct the model, we collected daily water
intake data based on the selective recall of 215
questions. After the lifestyle questionnaires, theparticipants addressed the questions on water intake, alcohol use, and food consumption the day
before the questionnaire. Since one-day answers
were highly biased, they completed a four-day dietary record, which included three weekdays andone weekend day, and used it as an outcome variable. The participants were questioned about theirprevious day’s water and food intake in the morning, afternoon, evening, and night. For example,
we collected data on daily water intake from tea,water, coffee, milk, and soft drinks in nine timezones in one day. We then transformed the recalled
dietary information into water intake values basedon the predicted amount of water in each food anddrink. Habitual water intake was averaged overfour days, as in the previous study30.

Preprocessing
We recorded the participants’ answers using
Google Forms, which exported the gathered data to a CSV text format and processed this in
Python (version 3) using the Numpy library31.
Some features were transformed into binary variables because some answers could not handle
the nominal scale of the variables. We obtained
797 participant applications from crowdsourcing
and chose 434 individuals (184 males and 250
females) based on the following three selection
criteria (Figure 1a). We accepted 56% of the data
and rejected 44% of them.
Criteria 1: We removed users who answered morethan twice because the Google form accepts
multiple answers, which could introduce noise.
Criteria 2: Exact matches of crowdsourcing user
IDs that occurred five times: one questionnaireand four diet records.
Criteria 3: We removed participants who gavedifferent answers to the two dummy questions
regarding gender and experienced acupuncture
or moxibustion.
Random forests training
Before our main study, we attempted several models, including regression trees, PLS
regression, and random forests. We used the R
packages32 for statistical and correlation analyses and machine learning algorithms: regression
trees, PLS regression, and random forests. One
advantage of regression trees is that they use
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H. Tanaka, K. Mizuma, Y. Nakamura, A. Hirata, J. Miyazaki, et al

every observation and produce consistent results
among trials, enabling an efficient selection ofquestions. However, regression trees had lower
predictive capability than complicated models,
including PLS and random forests. Predictive
capability is our primary outcome in selecting
models for accurately predicting the water intake. We also attempted PLS regression, which
generally produces better predictive capability
than regression trees. However, the originalidea of PLS included finding a good axis representing multiple variables for predicting target
values. PLS is not an appropriate prediction
model because it uses only a few fixed questions. Random forest performed the best withhigh interpretability among the above models.
The basic concept of the random forest model
involves using an ensemble of regression trees.
The algorithm has four steps: (1) it draws arandom bootstrap sample of size n, (2) grows
a regression tree from the bootstrap sample (ateach node: randomly select d features without
replacements, splits the node using the feature
for the best split, based on an objective function,such as maximizing the information gain), (3)repeats these steps m times, (4) aggregates theprediction by each tree to assign the class label
by averaging the outcomes.
We separated the data as follows: male training,
110; development, 29; test, 45; female training,
150; development, 40; and test, 60. This separation was performed based on random samplingfrom all data. We ran multiple random forest constructions because they rely on random sampling
to validate the stability of the selected features.
We attempted numerous separation combinations
that showed no significant differences. Our plans
to use this trained model in future validation
studies led us to fix the training data to be tested.
We performed mean age interpolation to avoid
unavailable values for training the random forest
because of incorrect birth dates typed by someparticipants. Because we confirmed that water
intake is not uniformly distributed (the water
intake was imbalanced), we adopted SMOTER33,
an extension of the synthetic minority oversampling technique (SMOTE), for the regression of
our models. As shown in Figure 2, the total water
intake was imbalanced, especially for outliers’
males. Testing our prediction model without suchdata augmentation led to worse performance than
the augmentation. This paper presents the results
obtained using SMOTER. We used the following parameters: an indicator of the number of
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nearest neighbors (=3) and a number indicating
the relevance threshold above which a case wasconsidered to belong to the rare class (=0.50). We
added them back to our original data to double
the number of samples in the training set over
the original size. We also performed feature
selection based on each of their weight values
as a result of the random forest fitting on all thetraining data. We retained the order of importantfeatures (questions) and ran the training, development, and testing again to confirm whether
the reduced questions could predict water intake.
We attempted to observe the estimation performance between the top 1 and top 15 features
because we considered more than 15 questions,which was a substantial number. A random forest
has few tuning parameters34; therefore, we useda grid search approach to find the appropriate
hyperparameters. Based on the grid search, we
also determined a grid search for a development
dataset with the following values: number oftrees: m {100,200,300,400,500}; several featuresin each tree: dpost ({2,3,4,5,10,15} for post-feature
selection), and dpre ({10,20,30,40,50,60,70} forpre-feature selection).
Evaluation metrics
We evaluated our models using testing data.
First, we built a model of the training data to
determine feature weights and used the selected
top m features for retraining. The final evaluation
was performed on the test dataset: 45 samples

Figure 2. Histograms of total water intake (mL) of males
and females in crowdsourcing. Water intake is a 4-day
average obtained using a selective recall questionnaire on
water intake.

Predicting habitual water intake from lifestyle questions

from the males and 60 from the females. We also
confirmed the highly weighted features producedby random forests. To clarify this ranking, wethen interpreted our model’s predictions as reasonable and the chosen combination of features
as appropriate to reduce the number of questions
to 15, which could be answered quickly.
Ethics Approval
This study was conducted following the guidelines of the Helsinki Declaration (revised by the
Fortaleza General Meeting of the World Medical
Association). All procedures involving humanparticipants were approved by the Ethics Committee of Suntory Holdings Limited (IRB No.
15000139) and Nara Institute of Science andTechnology (IRB No. 2019-I-19). This study also
followed the Ethical Guidelines for Medical Research Involving Human Subjects (2014 Ministryof Education, Culture, Sports, Science and Technology and the Ministry of Health, Labour andWelfare Ministerial Notification No. 3). Written
informed consent was obtained from all the participants.
Model Validation
Data collection
We validated each random forest model using
data collected from the Kansai area in Japanbetween January 2020 and November 2021. For
the model validation, we involved different inclusion criteria. Eligibility criteria included men
and women over 50 and those who understoodand agreed to participate in this study. The recruited participants included 100 males [mean:
58.5, (standard deviation; SD=5.6) years] and176 females [mean age: 58.1 (SD=4.8) years].
The breakdown is as follows: The KOBE study35
included 37 male and 121 female subjects. The
other, 63 male and 55 female, subjects were fromthe Oneness Support Co., Ltd.’s panel of subjects.
However, there is a possibility that the subjects ofthis study do not represent the general Japanese
population as a selection bias. We collected SQW
together with the acquisition of descriptive dietary records for the cross-sectional analysis. The
study size was designed with reference to a similar case study15,36 conducted in the past on the validation of a questionnaire on dietary intake. The
model was validated throughout the study period.
We assumed that temperature might be a factor
influencing water intake, and hence, categorized
the three seasons as follows: winter (December,

January, February, and March as low-temperature seasons), spring and autumn (April, May,
October, and November as mid-temperature seasons), and summer (June, July, August, and September as high-temperature seasons). We eliminated outlier participants based on dietary recall
values by using repeated Smirnov-Grubbs tests
until no more participants were at p<0.05 after
confirming the normality of the distribution. For
outlier elimination, we did not include the casewhere water intake was from alcohol because alarge amount of data had a value of 0. Overall,three male participants were excluded from thevalidation analysis. We performed under-sampling of the data in the winter season using random sampling because the winter season has amore significant number of data samples than theother seasons.
Descriptive dietary records
A descriptive dietary record is a method of
writing down all food and drinks, and water
intake is calculated from the contents21. We collected data on a web form or physically printed
paper, depending on the participant’s preference.
We used the descriptive dietary record method
used in a previous study21,29 as the question weused was the ground truth, which most accurately
measured water intake. However, some informational bias in recalling water intake was likely
present.
Validation analysis
The outcome of this study is represented by
Pearson’s correlations of descriptive dietary records and SQW. The amount of water intake bydescriptive dietary record method was analyzed
as the dependent variable and SQW as the independent variable. Each data set was analyzed
considering the factor of sex.
We validated the constructed models for 276
participants interviewed by dietitians (Figure 1b),
following the previously reported ethical guidelines.
Statistical Analysis
Using the selective recall method, we calculated Spearman’s correlation coefficient between
the predicted and water intake values. We tested
their correlation coefficient to find no correlation
with an alpha level of 0.05. Pearson’s correlations
were calculated between descriptive dietary records and SQW. We used the R packages32 (TheR Foundation for Statistical Computing, Vienna,
Austria) for statistical and correlation analyses.
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Results
Participant’s Characteristics
Preprocessing determined the ages of our participants (males, mean: 40.4, SD=11.1; females,
mean: 37.3, SD=8.9), with ages ranging from 18
to 73. Figure 2 shows a histogram of the total water intake by all our participants, most of whomdrank over 400 mL and less than 2,000 mL.
Males drank more than females and showed more
physiological outliers, such as those drinking
more than 10,000 mL. Alcohol and food-based
water intakes are presented in Table I. Alcohol
consumption showed high deviations among individuals as the SD was nearly 2-3 times the mean.
Moreover, food also contributed to the total water
intake, approximately half the amount.

We performed the same evaluation for beverage-based, alcohol-based, and food-based total
water intake, which revealed similar weighted
features. However, some of these features weredifferent depending on the source. Other derivations are listed by the following top questions infemales:
Water intake from beverage: How many times
do you drink tea daily? How much do you usuallydrink something (mL) in the afternoon?
Water intake from alcohol: The number of alcoholic drinks per week; how often do you drink
mineral water instead of tap water?
Water intake from food: How often do you
eat vegetables and mushrooms during breakfast?
How many cups of miso soup do you drink per
week? How many eggs do you eat per week?

Correlation Analysis
Table II presents the Spearman’s correlationof the water intake values by the selective recall
method and predicted values. Based on the valuesof total, beverage, alcohol, and food intake, we
separated the results, which changed by approximately 0.05 based on each trial of the random
forest. We plotted the model results and calculated Spearman’s correlation coefficient (Figure3), representing our predictions in all the cases.
The results showed a significantly higher correlation than no correlation (p<0.05). Our predictions were accurate for people consuming a large
amount (above 3,500 mL) and in the mid-range(approximately 2,500 mL). In almost all cases,
the correlation coefficients exceeded 0.6, showing
a high correlation. In this study, we predicted thehabitual water intake from water-, alcohol-, andfood-based sources.
Also, Table II shows that the first one of threequestions was unstable, but ten questions werestable, with similar values for all 305 inputs. Water intake from food value was relatively difficult
to predict at 0.50 for males and 0.37 for females,
which confirmed that water intake from food predictions could be reached at all values using up to
30-50 questions.

Rechecks of Understandability andAmbiguity of Questions
Before conducting model validation, we further considered whether the SQW question set
applies to the general population in terms of understandability and ambiguity. We also reduced
the number of questions because some questions had ambiguous answers. We eliminatedquestions regarding (1) life events (2) whether
the participant lived in the Kanto area, and (3)whether the participant was married, as suchquestions were unrelated to eating, drinking,and life habits. We tested cases in which thesequestions were removed from the crowdsourced
data. The correlation coefficients, excluding thequestions, are summarized in Table III. We confirmed the absence of any large difference in thecorrelations of total water intake by excludingthese questions compared with the correlations
reported in Table II. Furthermore, the value ofwater intake of each source was also subjected
to re-machine learning with top 9 or top 10 bydeleting irrelevant question items, as was thecase with total water intake. The correlation
coefficient was 0.62 for top 10, 0.78 for top 10,
and 0.51 for top 9 for age-derived, alcohol-derived, and food-derived males, respectively, andTable I. Summary of water intake with mean and standard deviation. Water intake is a 4-day average obtained using a selective
recall questionnaire on water intake.
Total
Males	  2,998.5 (1,017.7)
Females
2,566.1 (735.7)

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Beverage

Alcohol

Food

1,658.3 (773.3)
1,439.8 (578.5)

123.2 (221.7)
56.5 (141.1)

1,217.0 (479.1)
1,069.7 (319.2)

Predicting habitual water intake from lifestyle questions
Table II. All data and top-rank features were measured using Spearman’s correlation: the bold value represents the best correlation after feature reduction with minimal features.
All

Top 1 Top 2

Top 3

Top 4

Top 5

Top 6

Top 7

Top 8

Top 9

Top 10

Top 11

Top 12

Top 13

Top 14 Top 15

Total
Males
Females

0.68
0.63

0.14
0.32

0.47
0.30

0.51
0.48

0.47
0.39

0.43
0.48

0.47
0.54

0.52
0.53

0.54
0.60

0.57
0.63

0.63
0.53

0.66
0.57

0.61
0.50

0.66
0.52

0.64
0.54

0.62
0.46

Beverage
Males
Females

0.70
0.68

0.38
-0.22

0.05
0.04

0.58
0.45

0.55
0.61

0.55
0.55

0.46
0.61

0.44
0.56

0.54
0.59

0.57
0.53

0.62
0.57

0.57
0.60

0.61
0.61

0.66
0.60

0.66
0.65

0.63
0.61

Alcohol
Male
Females

0.80
0.73

0.03
-0.16

0.73
0.70

0.73
0.71

0.73
0.64

0.71
0.65

0.75
0.67

0.76
0.72

0.75
0.69

0.78
0.70

0.78
0.70

0.79
0.68

0.78
0.66

0.77
0.67

0.76
0.63

0.77
0.67

Food
Males
Females

0.68
0.47

0.29
0.26

0.24
0.11

0.30
0.18

0.22
0.12

0.35
0.21

0.43
0.31

0.45
0.33

0.37
0.32

0.42
0.37

0.50
0.33

0.48
0.32

0.52
0.29

0.50
0.33

0.49
0.35

0.5
0.31

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H. Tanaka, K. Mizuma, Y. Nakamura, A. Hirata, J. Miyazaki, et al

Figure 3. Prediction results were measured using the correlation coefficient of the best model of predicted and actual water
intake values by the selective recall method in testing participants: water intake from total (a), beverage (b), alcohol (c), andfood (d).

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Predicting habitual water intake from lifestyle questions
Table III. Spearman’s correlation for total water intake after reducing ambiguity questions in crowdsourcing. The bold value
represents the best correlation.
Top 1 Top 2 Top 3 Top 4 Top 5 Top 6 Top 7 Top 8 Top 9 Top 10 Top 11 Top 12 Top 13 Top 14
Males
0.14
Females 0.31

0.47
0.48

0.51
0.35

0.43
0.48

0.50
0.46

0.46
0.50

0.48
0.57

0.62
0.50

0.52
0.58

0.62
0.64

0.61
0.61

0.54
0.65

0.53
0.58

0.53
0.55

0.61 for top 9, 0.67 for top 9, and 0.33 for top
10 for beverage-derived, alcohol-derived, andfood-derived respectively, for females. Thus,
there were no noticeable differences between theconditions before and after excluding the questions. We proposed that the maximum numberof questions for validation should be 15 for total
water intake and 10 for other sources. Thus,
after reducing the three questions, we finallyanalyzed a maximum of 9 to 14 questions as the
SQW for total water intake and a maximum of 9
or 10 questions as the SQW for others (Table IV,
Supplementary Table I).

males. There was uncertainty about whether the
study subjects represented the general Japanese
population. However, although the obtained results contain some bias, they are likely to be
somewhat generalizable because the data werecollected on a sufficient sample size and withseasonality taken into account. SQW has beenshown to provide a simple way to determine
habitual water intake. It would help understandthe distribution of water intake in epidemiology
and elderly facilities, and so on.

Data Collection for Descriptive Dietary
Records and Validation
The target of the validation analysis was 97
males (age, mean: 58.6, SD=5.5) and 101 females(ages, M: 57.8, SD=5.4). We also obtained thedatasets of winter (33 males and 33 females),spring and autumn (31 males and 33 females), andsummer (33 males and 35 females).
Table V presents Pearson’s correlations foreach season among males and females. We
confirmed correlation coefficients of more than
0.50, although this is slightly lower than thecrowdsourcing results. For total water intake
from SQW, the top 11 for males and the 14
for females showed the highest correlation. We
confirmed that water intake from alcohol was
relatively easier to predict, corroborating thecrowdsourcing data. Since Spearman’s correlation was used during the development of SQW,
we also checked the correlation coefficient using
Spearman’s correlation coefficient, and there
was almost no difference between Spearman’sand Pearson’s. Furthermore, correlations in thequintile value were confirmed for use in epidemiological analyses. Pearson’s correlation coefficients for water intake to descriptive recall
methods when the SQW values were in quantiles are 0.49, 0.54, 0.55, 0.49, 0.49, and 0.50,
from top 9 to top 14 for males and 0.39, 0.42,
0.55, 0.51, 0.55, and 0.59 top 9 to top 14 for fe-

Using the selective recall method, we predicted the habitual water intake based on a four-day
average dietary recall, which revealed a goodcorrelation between the predicted and water intake values. We further tested the models in thevalidation study and confirmed a Pearson’s correlation of 0.5 in most cases. Our SQW took only
2-3 minutes for a total water intake or 8-9 minutes
for each source and total.
The trained models in random forests can beunderstood in terms of their weighted features,
which makes our question set interpretable.
For example, drinking tea and water are questions that predict habitual water intake. These
trained models and questionnaires can be usedin validation studies with dietary records. Ourdetailed question lists for water intake from
non-alcoholic beverages, alcohol, and food(other than total water intake) are available
upon request. The food results showed a lower
prediction range because the question set waslimited. Food includes various intakes, including noodles, fruits, and vegetables. Therefore,
having fewer questions complicates the estimation of water intake from food because itis necessary to cover a variety of foods. To
predict water intake from food more precisely,
we must broadly design new questions that specifically ask about food amount consumption.
In this task, we confirmed that random forests

Discussion

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H. Tanaka, K. Mizuma, Y. Nakamura, A. Hirata, J. Miyazaki, et al
Table IV. Simplified lifestyle questionnaire for predicting habitual water intake (SQW) for total water intake. All questions
were translated into English.
Males

Question

Answer1
How many times a day do you eat a meal?
2
How much do you usually drink in the morning*1?
		3
Of the staple foods, how many times a week do you eat
sushi, takikomi-gohan, donburi, or okonomiyaki?
4
How much light-colored vegetables and mushrooms doyou eat for breakfast?
5
How many soybeans/soy products do you eat for
breakfast at once?
6
How many pickles do you eat at once?
7
How much tea do you usually drink in a day?
1 cup: approximately 200 mL
8
How much do you usually drink in the afternoon?*2
		
9How many eggs do you eat per week?
10
How many times a week do you eat salty foods?
11
How much soup do you drink when you eat noodles?
12
How much green and yellow vegetables do you eat at atime for breakfast?
13
How much soup is left at a time when you eat noodles?
14
How often do you usually eat noodles?
		Femalesl.t. once/once/twice/three times/m.t. four times
l.t. 200 mL/m.t. 200 mL/m.t. 350 mL/m.t. 500 mL/m.t.
700 mL/m.t. 1,000 mL/m.t. 1,500 mL(Describe the number of times)
Never/Small/Normal/Large
Never/Small/Normal/Large
Never/Small/Normal/Large
Never/l.t.1 cup/1 cup/2 cups/3 cups/4-5 cups/6-7 cups/
m.t. 8 cups
l.t. 200 mL/ m.t. 200 mL/m.t. 350 mL/m.t. 500 mL/m.t.
700 mL/m.t. 1,000 mL/m.t. 1,500 mL(Describe the number of eggs)(Describe the number of times)
Almost all/80%/40-60%/20%/Almost none
Never/Small/Normal/Large
Almost all/Approximately half/Almost none
Everyday/5-6 days a week/3-4 days a week/1-2 days aweek/1-3 day a month/Hardly

Question

Answer1
How much do you usually drink in the afternoon*2?
		2
How much do you typically drink in the morning*1?
		3
How much carbonated beverages, fruit juices, andsoft drinks do you usually drink daily?
1 cup: approximately 200 mL
4
How much tea do you usually drink in a day?
1 cup: approximately 200 mL
5
Do you think you drink at a faster pace compared to others?
6
How much miso soup do you usually eat every day?
1 cup: about one bowl
7
How many cups of miso soup do you drink in a week?
8
How many times a week do you eat light-coloredvegetables and mushrooms for breakfast?
9
Do you drink at mealtime?
10
Do you think you drink more than others?
11
How much light-colored vegetables and mushrooms
do you eat at a time for lunch?
12
Do you feel sick from cold food?
13
How much meat/processed meat do you eat for
breakfast at one time?
14
Choose one that is similar to your normal state
regarding bowel movements.

l.t. 200 mL/m.t. 200 mL/m.t. 350 mL/m.t. 500 mL/m.t.
700 mL/m.t. 1,000 mL/m.t. 1,500 mL
l.t. 200 mL/m.t.200 mL/m.t. 350 mL/m.t. 500 mL/m.t.
700 mL/m.t. 1,000 mL/m.t. 1,500 mL
Never/l.t.1 cup/1 cup/2 cups/3 cups/4-5 cups/6-7
cups/m.t. 8 cups
Never/l.t.1 cup/1 cup/2 cups/3 cups/4-5 cups/6-7
cups/m.t. 8 cups
Faster/Somewhat faster/Same/Somewhat slower/Slower
Never/l.t.1 cup/1 cup/2 cups/3 cups/4-5 cups/6-7
cups/m.t. 8 cups
(Describe the number of cups)(Describe the number of times)
Always/Almost always/Rarely/NeverMore/Somewhat more/Same/Somewhat less/Less
Never/Small/Normal/Large
Never/Rarely/Sometimes/Often/Always
Never/Small/Normal/Large
Prone to diarrhea/somewhat diarrhea/Normal/
sometimes constipated/Constipatedl.t.: less than, m.t.: more than. *1: morning is the time from waking up to before lunch. *2: afternoon is the time from lunch todinner.

outperformed other regression algorithms interms of interpretability and prediction. We
also hypothesize that deep learning models canimprove the accuracy of a model (high correlations).
8838

Limitations
The study includes a few limitations. The number of data samples was too small, despite augmenting the data. We believe that random forest
and other algorithms are potentially helpful forPredicting habitual water intake from lifestyle questions
Table V. Pearson’s correlations in seasons for validation: bold values represent the best correlation for total water intake.
			

N

Top 9

Top 10

Top 11

Top 12

Top 13

Top 14

Total
Average of a year		Winter		
Spring or Autumn		Summer		
Beverage Average of a year
		Alcohol
Average of a year		
Food
Average of a year
		

97
101
33
33
31
33
33
35
97
101
97
101
97
101

0.53
0.40
0.46
0.39
0.62
0.43
0.57
0.47
0.55
0.76
0.34
-

0.55
0.45
0.55
0.38
0.65
0.48
0.55
0.53
0.39
0.61
0.34

0.58
0.53
0.52
0.47
0.65
0.46
0.63
0.67
-

0.56
0.53
0.51
0.45
0.69
0.49
0.50
0.67
-

0.56
0.54
0.47
0.46
0.72
0.50
0.54
0.66
-

0.51
0.58
0.43
0.49
0.66
0.57
0.52
0.71
-

Males
Females
Males
Females
Males
Females
Males
Females
Males
Females
Males
Females
Males
Females- Indicates not available.

this prediction task. However, since some foodfrequency questionnaires used in epidemiology
have correlation coefficients of approximately
0.3 with the gold standard15, the SQW from foodin this study can be usable in epidemiologicalanalysis.
Compared to previous cohort studies37, theresults showing Spearman’s rank correlation coefficients for the validity of the FFQ estimatedwater intake relative to water intake were 0.41
and 0.71. Furthermore, in other prior cohort studies38, Spearman’s correlation coefficients between
non-alcoholic beverage consumption and dietary
records obtained from the FFQ were 0.43 for men
and 0.28 for women in one cohort participant
and 0.56 and 0.58 for another cohort participant,respectively. Given the above, the correlation
coefficients for the study in the SQW are comparable. The SQW is considered accurate enough todetermine water intake in epidemiological studies. The SQW will make it possible to determine
the approximate water intake of the Japanese
population and deepen the analysis of the relationship between water intake and health status,
thereby building scientific evidence related to
fluid intake. In addition, by quickly ascertaining
the water intake of each facility and population, itis thought that this information can be widely deployed in educational activities on water intake.

Conclusions
This is the first study to create new questions,
SQW, and evaluate the validity of examining therelationship between selective and descriptive
recall methods and lifestyle-related questions.
This study collected large-scale data to predict
habitual water intake accurately, with at leastnine or ten questions. We further validated themodels in the validation study. We hypothesizethat dissemination of SQW can lead to better
health management by easily determining thehabitual water intake.

Conflict of Interest

The Authors declare that they have no conflict of interests.

Funding

This study was founded by Suntory Holdings, Ltd. and Suntory Global Innovation Center, Ltd., Japan.

Ethics Approval

This study was conducted following the guidelines inthe Helsinki Declaration (revised by the Fortaleza General Meeting of the World Medical Association). All procedures involving human participants were approved by the
ethics committee of Suntory Holdings Limited (IRB No.
15000139) and Nara Institute of Science and Technology(IRB No. 2019-I-19). This study also completed with the
Ethical Guidelines for Medical Research Involving Human
Subjects (2014 Ministry of Education, Culture, Sports, Science and Technology and the Ministry of Health, Labourand Welfare Ministerial Notification No. 3). All procedures
involving human participants were approved by the Ethics Committee of the Foundation for Biomedical Researchand Innovation at Kobe (IRB No. 20000109) and the ethics
committee of Miura Clinic, Medical Corporation Kanonkai(IRB No. 17000161).

8839

H. Tanaka, K. Mizuma, Y. Nakamura, A. Hirata, J. Miyazaki, et al

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Authors’ Contribution

Conceptualization: Ken Suzuki, Harumichi Seta, Hiroshi
Watanabe, Toshihide Suzuki, Reiji Watanabe, Norihito Murayama, Tomonori Okamura, Satoshi Nakamura. Data curation: Hiroki Tanaka, Aya Hirata, Junji Miyazaki. Investigation: Hiroki Tanaka, Aya Hirata, Junji Miyazaki, Keiko
Mizuma, Yumi Nakamura. Project administration: Ken Suzuki, Harumichi Seta, Hiroshi Watanabe, Toshihide Suzuki, Reiji Watanabe, Norihito Murayama, Tomonori Okamura, Satoshi Nakamspearura. Writing original draft: Hiroki
Tanaka. Writing-review and editing: Keiko Mizuma, Yumi
Nakamura, Aya Hirata, Junji Miyazaki, Ken Suzuki, Harumichi Seta, Hiroshi Watanabe, Toshihide Suzuki, Reiji Watanabe, Norihito Murayama, Tomonori Okamura, Satoshi Nakamura. Supervisor: Tomonori Okamura, Satoshi
Nakamura.

Data Availability

The datasets generated during and/or analyzed during thecurrent study are available from the corresponding author
upon reasonable request.

ORCID ID

Hiroki Tanaka: 0000-0002-0548-6252
Yumi Nakamura: 0000-0002-5915-0498
Aya Hirata: 0000-0003-1439-4242
Junji Miyazaki: 0000-0003-3634-6401
Toshihide Suzuki: 0000-0003-4796-0749
Norihito Murayama: 0000-0003-2895-7106
Tomonori Okamura: 0000-0003-0488-0351
Satoshi Nakamura: 0000-0001-6956-3803

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