Predicting habitual water intake from lifestyle questions

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.

First, we used crowdsourcing and machine learning to collect data, 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 were used for the machine learning models because of 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 our model using unseen hold-out testing data, predicting the individual and overall habitual water intake from various sources, including non-alcoholic beverages, alcohol, and food.

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 be achieved. This question set was then used for feasibility tests. The descriptive dietary record method helped to obtain a ground-truth value. We categorized the data by gender, season, and source: non-alcoholic beverages, alcohol, food, and total water intake, and the correlation was confirmed. Consequently, our results showed a Pearson's correlation coefficient of 0.50 for total water intake between the predicted and the selective recall method values.

We hypothesize that dissemination of SQW can lead to better health management by easily determining the habitual water intake.