Higher Water Intake Predicts Very Slightly Lower Overall Mood for Population

Higher Water Intake Predicts Very Slightly Lower Overall Mood for Population

Contents

Variables

A
Water 228
A
Overall Mood 7137

Categories

A
Foods 13415
A
Emotions 2028

Tags

Medium Confidence
Very Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 3.3% average decrease in Overall Mood following above average Water Intake.

Abstract

Overall Mood was generally 5% higher than average after a total of 1220 milliliters of Water over the previous 14 days.

Aggregated data from 52 study participants suggests with a MEDIUM degree of confidence (p=0.206, 95% CI -0.549 to 0.412) that Water has a very weakly negative predictive relationship (R=-0.0682) with Overall Mood.

The highest quartile of Overall Mood measurements were observed following an average 3 milliliters Water per day.

The lowest quartile of Overall Mood measurements were observed following an average 4120 milliliters of Water per day.

After an onset delay of 30 minutes, Overall Mood is typically 4% lower than average over the 14 days following around 4120 milliliters of Water Water.

Keywords: Water, Overall Mood, N-of-1 trials, real-world evidence, causal inference, observational study

High Confidence: With 52 participants, these findings have strong statistical power.

Results

Primary Findings

Analysis of 124 paired observations from 52 participants revealed a minimal reduction in Overall Mood following above-average Water exposure.

0.0%
Change from Baseline
Minimal effect on Overall Mood
0.57
Predictor Impact Score
Strong evidence for causal relationship

Supporting Statistics

Medium
Confidence
-0.068
Correlation (r)
p = 0.109
Significance
z = 0.00
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Water:

  • Overall Mood decreased by 0.0% on average
  • The Predictor Impact Score of 0.57 suggests this relationship warrants high priority for experimental validation
  • Temporal analysis supports Water as the predictor (not the outcome)

Interpreting the Predictor Impact Score

The Predictor Impact Score (PIS) integrates multiple Bradford Hill causal criteria into a single metric. Use this guide to interpret the score:

PIS Range Interpretation Recommended Action
≥ 0.5 Strong evidence High priority for RCT validation
0.3 - 0.5 Moderate evidence Consider for experimental investigation
0.1 - 0.3 Weak evidence Monitor for additional data
< 0.1 Insufficient evidence Low priority; may be noise

Note: PIS is a prioritization heuristic, not proof of causation. High scores indicate relationships worth investigating, not confirmed causal effects.

Optimal Daily Values (Precision Dosing)

Based on the observed relationship, we can estimate the predictor values associated with the best and worst outcomes. These values enable personalized dosing recommendations.

⚠️ Preliminary Data: With 52 participants and 124 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

1,220.7 mL
Value Predicting Higher Overall Mood
Average Water when Overall Mood exceeded its mean
1,372.8 mL
Value Predicting Lower Overall Mood
Average Water when Overall Mood was below its mean

What This Suggests

Overall Mood tended to be lowest (best) when Water was around 1,372.8 mL.

Important: These values reflect correlations, not guaranteed causal effects. Individual responses may vary. Use as a starting point for personal experimentation, not as a definitive prescription. Consult healthcare providers before making treatment decisions.

Population Correlation

Water Distribution

Overall Mood Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Water Intake
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.56506548772861
Confidence Level MEDIUM
Confidence Interval 0.48031820180587
Forward Pearson Predictive Coefficient -0.0682
Critical T Value 1.7203653846154
Total Water Intake Over Previous 14 days Before ABOVE Average Overall Mood 3 milliliters
Total Water Intake Over Previous 14 days Before BELOW Average Overall Mood 4120 milliliters
Duration of Action 14 days
Effect Size very weakly negative
Number of Paired Measurements 124
Optimal Pearson Product 0.10968199977923
P Value 0.20637657276546
Statistical Significance 0.1091
Strength of Relationship 0.48031820180587
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 52

Water Info

Property Value
Variable Name Water (mL)
Aggregation Method SUM
Analysis Performed At 2020-10-11
Duration of Action 14 days
Filling Value 0
Kurtosis 168.9035754896
Maximum Allowed Value 5000 milliliters
Mean 291.96324979639 milliliters
Median 234.04866468843 milliliters
Minimum Allowed Value 0 milliliters
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 228
Number of Measurements 58290
Number of Measurements (including those generated by tagged, joined, or child variables) 5630
Public true
Onset Delay 30 minutes
Standard Deviation 222.15818986681
Unit Milliliters
User Variables 656
UPC 075720004096
Variable Category Foods
Variable ID 109592
Variance 256166.90856561

Overall Mood Info

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2020-09-12
Duration of Action 24 hours
Kurtosis 3.3832907631011
Maximum Allowed Value 5 out of 5
Mean 3.1202433341482 out of 5
Median 3.1415600073553 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 6425
Number of Aggregate Outcomes 712
Number of Measurements 617070
Number of Measurements (including those generated by tagged, joined, or child variables) 561596
Public true
Onset Delay 0 seconds
Standard Deviation 0.38176118810538
Unit 1 to 5 Rating
User Variables 9142
UPC 767674073845
Variable Category Emotions
Variable ID 1398
Variance 0.30220747449488

Introduction

Background

Water (Foods) and Overall Mood (Emotions) are both important factors in understanding human health and well-being. This study investigates the relationship between these two variables using real-world observational data.

Traditional randomized controlled trials (RCTs), while the gold standard for causal inference, are often impractical, expensive, or unethical for studying many health relationships. Aggregated N-of-1 observational studies offer a complementary approach that leverages within-subject comparisons across large populations to identify meaningful patterns.

Research Question

Does Water affect Overall Mood?

Additionally, we seek to determine:

  1. What is the direction and magnitude of any effect?
  2. How confident can we be in this relationship based on the available data?
  3. What are the optimal levels of Water for maximizing Overall Mood?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Water and Overall Mood. Additionally, we attempt to determine the Water (mL) values most likely to produce optimal Overall Mood values.

Study Overview

This is a population-level observational study using aggregated N-of-1 methodology. By aggregating individual N-of-1 experiments, we can identify population-level patterns while accounting for the substantial individual variation that exists in most health relationships. Effect sizes are reported as percent change from baseline, enabling intuitive interpretation and comparison across different measures.

Full Methodology: Framework for Real-World Evidence-Based Pharmacovigilance: Aggregated N-of-1 Trials for Quantifying Treatment Effects

Discussion

Interpretation of Findings

Participants experienced a 0.0% reduction in Overall Mood following above-average Water exposure. The Predictor Impact Score (PIS) of 0.57 indicates strong evidence for a causal relationship.

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Overall Mood is not statistically significant at a 95% confidence interval. This suggests that the Water value may not have a significant influence on the Overall Mood value, or that more data is needed to detect an effect.

T-Test Details
Observed t-value: 1.603
Critical t-value: 1.720

Since t = 1.60 < 1.72, we cannot reject the null hypothesis.

Biological Plausibility

A plausible bio-chemical mechanism between predictor and outcome is critical for interpreting observational findings. This is where human judgment excels beyond statistical analysis.

Based on community responses so far, 5 people feel that there is a plausible mechanism of action and 5 feel that any relationship observed between Water and Overall Mood is coincidental.

Bradford Hill Criteria Assessment

The Bradford Hill criteria provide a framework for assessing causality in observational studies. Our methodology operationalizes six of the nine criteria through the Predictor Impact Score (PIS):

Criterion How Addressed Metric
Strength Effect size magnitude Percent change from baseline (Δ%), z-score
Consistency Cross-participant replication Number of users (N), number of pairs (n)
Temporality Predictor precedes outcome Temporality factor (φ), onset delay (δ > 0)
Biological Gradient Dose-response relationship Gradient coefficient (φgradient)
Plausibility Biological mechanism assessment Community votes on mechanism plausibility
Specificity Category appropriateness Interest factor (finterest)

Predictor Impact Score (PIS)

The PIS integrates multiple Bradford Hill criteria into a composite metric quantifying how reliably a predictor affects an outcome. Higher scores indicate stronger evidence:

Population-Level PIS:

$$\text{PIS}_{\text{agg}} = |r_{\text{forward}}| \cdot w \cdot \phi_{\text{users}} \cdot \phi_{\text{pairs}} \cdot \phi_{\text{change}} \cdot \phi_{\text{gradient}}$$

Where φ-factors are saturation functions approaching 1 as evidence accumulates:

  • φusers = 1 - e-N/10 (user saturation)
  • φpairs = 1 - e-n/nsig (pair saturation)
  • φchange = 1 - e-Δspread/Δsig (effect spread saturation)
  • w = weighted average of plausibility votes

Temporality Assessment

We assess evidence for correct causal direction using the temporality factor:

$$\phi_{\text{temporal}} = \frac{|r_{\text{forward}}|}{|r_{\text{forward}}| + |r_{\text{reverse}}|}$$

Values approaching 1 indicate the predictor precedes the outcome (supporting causation); values near 0.5 suggest ambiguous directionality; values near 0 suggest reverse causation or confounding by indication.

Limitations

As with any observational study, correlation does not prove causation. Key limitations include:

  • Unmeasured confounders: Variables not tracked may influence results
  • Self-selection bias: Health trackers may differ from the general population
  • Measurement error: Self-reported data may contain recall bias
  • Confounding by indication: Sicker individuals may use more treatments

However, within-subject comparison and temporal precedence analysis partially mitigate these limitations. If the relationship is merely coincidental, as participants independently modify their Water values, the observed strength will decline over time. Spurious correlations naturally dissipate as more data is collected.

Future Directions

Future research should examine:

  • Subgroup analyses to identify individual differences in response
  • Potential confounders and mediators of the observed relationship
  • Optimal dosing and timing for Water
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 52 participants, these results are based on limited data. Effect sizes and confidence will improve as more participants contribute data. Consider these findings directional rather than definitive.

Above-average Water was associated with a 0.0% reduction in Overall Mood—a minimal effect. The Predictor Impact Score of 0.57 indicates this relationship is high priority for experimental validation.

Bottom Line: Based on a PIS of 0.57 and a 0.0% effect size, this relationship shows strong evidence and should be prioritized for experimental validation through randomized controlled trials. Note: These conclusions may strengthen or change direction as more data is collected.

These findings contribute to our understanding of how Water may influence Overall Mood in real-world conditions. The combination of effect size, sample size, and temporal evidence supports this as a meaningful relationship worth investigating further.

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Methods

Study Design

This study is based on data donated by 52 participants. Thus, the study design is equivalent to the aggregation of 52 separate n=1 observational natural experiments.

This within-subject design is powerful because it controls for all stable individual characteristics (genetics, baseline health status, socioeconomic factors) that might otherwise confound the relationship between variables.

Data Analysis

Temporal Assumptions

The analysis incorporates temporal assumptions about the relationship between variables:

  • Onset Delay: It was assumed that 30 minutes would pass before a change in Water would produce an observable change in Overall Mood.
  • Duration of Action: It was assumed that Water could produce an observable change in Overall Mood for as much as 14 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Water values and subsequent Overall Mood values. Individual correlations were then aggregated using Fisher's z-transformation to produce a population-level estimate:

Individual Correlation:

$$r_i = \frac{\sum(x_{ij} - \bar{x}_i)(y_{ij} - \bar{y}_i)}{\sqrt{\sum(x_{ij} - \bar{x}_i)^2 \sum(y_{ij} - \bar{y}_i)^2}}$$

Fisher's Z-Transformation:

$$z_i = \frac{1}{2} \ln\left(\frac{1 + r_i}{1 - r_i}\right)$$

Aggregated Correlation:

$$\bar{r} = \tanh(\bar{z}) \quad \text{where} \quad \bar{z} = \frac{1}{N}\sum_{i=1}^{N} z_i$$

Effect Size Calculation

Effect sizes are reported as percent change from baseline. For each participant, we compare the outcome following above-average predictor values to the overall baseline outcome:

$$\Delta\%_{\text{baseline}} = \frac{\bar{O}_{\text{follow-up}} - \bar{O}_{\text{baseline}}}{\bar{O}_{\text{baseline}}} \times 100$$

Effect Magnitude (Z-Score)

To assess effect magnitude relative to natural variability, we calculate the z-score:

$$z = \frac{|\Delta\%_{\text{baseline}}|}{\text{RSD}_{\text{baseline}}}$$

where RSDbaseline is the relative standard deviation of outcome during baseline period

A z-score > 2 indicates statistical significance (p < 0.05), meaning the observed change exceeds typical baseline fluctuation and is unlikely due to random variation.

Statistical Significance

Correlation significance is assessed using a two-tailed t-test:

$$t = \frac{r\sqrt{n-2}}{\sqrt{1-r^2}}$$

We reject the null hypothesis (ρ = 0) at α = 0.05 when |t| exceeds the critical value, providing statistical evidence that the observed relationship is not due to chance.

Data Sources

Water data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

Overall Mood data was primarily collected using QuantiModo. QuantiModo allows you to easily track mood, symptoms, or any outcome you want to optimize in a fraction of a second. You can also import your data from over 30 other apps and devices. QuantiModo then analyzes your data to identify which hidden factors are most likely to be influencing your mood or symptoms.

Data Quality

Data quality measures were applied to ensure reliable results:

  • Minimum Data Requirement: Only participants with sufficient paired observations were included in the analysis.
  • Outlier Handling: Extreme values were winsorized to reduce the influence of measurement errors.
  • Missing Data: Days with missing values were handled using appropriate filling strategies based on the variable type.
  • Test User Exclusion: Test accounts and invalid users were excluded from all analyses.

Principal Investigator

Program & Methods

Mike P. Sinn
Sinn, M. P.

Designed and implemented data collection, aggregation, causal inference pipeline, and automated study generation framework. Developed the Predictor Impact Score methodology operationalizing Bradford Hill criteria for ranking causal relationships in observational data. When he tells people this at parties, they usually say they have to go check on their car.

Individual study outputs are automated, reproducible, and open to external audit. (Which I would seriously recommend.)

Contact

Cite This Study

APA Format
Sinn, M. P. (2026). Causal Analysis: Does Water (mL) Affect Overall Mood?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-109592-effect-1398-population-study
BibTeX 
@misc{sinn_cause_109592_effect_1398_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Water (mL) Affect Overall Mood?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-109592-effect-1398-population-study},
  note = {Accessed: January 14, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Water (mL) Affect Overall Mood?." The Journal of Citizen Science. Accessed January 14, 2026. https://studies.crowdsourcingcures.org/study/cause-109592-effect-1398-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Water (mL) Affect Overall Mood?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-109592-effect-1398-population-study [Accessed January 14, 2026].

Study Type: Aggregated N-of-1 Observational Mega-Study
Evidence Level: Level II (Real-World Evidence)
Methodology: Bradford Hill Criteria with Predictor Impact Score (PIS)

References

This framework was originally developed in 2013 based on the Bradford Hill criteria. Subsequent literature has independently validated similar approaches to causal inference from observational data:

  1. Hill, A.B. (1965). The environment and disease: association or causation? Proceedings of the Royal Society of Medicine, 58(5), 295-300. [Bradford Hill criteria]
  2. Lillie, E.O., et al. (2011). The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Personalized Medicine, 8(2), 161-173. [N-of-1 methodology]
  3. Pearl, J. (2009). Causality: Models, Reasoning, and Inference . Cambridge University Press. [Causal inference]
  4. Hernán, M.A., & Robins, J.M. (2020). Causal Inference: What If . Chapman & Hall/CRC. [Free textbook]
  5. FDA (2018). Framework for FDA's Real-World Evidence Program . U.S. Food and Drug Administration. [Regulatory context]
  6. Duan, N., et al. (2013). Single-patient (n-of-1) trials: a pragmatic clinical decision methodology . Journal of Clinical Epidemiology, 66(8), S21-S28.
  7. Platt, R., et al. (2018). The FDA Sentinel Initiative—an evolving national resource . New England Journal of Medicine, 379(22), 2091-2093.

This information is for research and educational purposes only, not medical advice. Consult a healthcare provider before making health decisions. Terms of Service

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