Higher Spending On DHA Predicts Moderately Lower Alertness for Population

Higher Spending On DHA Predicts Moderately Lower Alertness for Population

Contents

Variables

A
Spending on DHA 13
A
Alertness 1377

Categories

A
Treatments 9356
A
Emotions 2028

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Low Confidence
Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 30.3% average decrease in Alertness following above average Spending on DHA.

Abstract

Alertness was generally 6% higher than average after a total of $0 of Spending On DHA over the previous 30 days.

Aggregated data from 2 study participants suggests with a LOW degree of confidence (p=0.298, 95% CI -2.202 to 1.596) that Spending On DHA has a moderately negative predictive relationship (R=-0.303) with Alertness.

The highest quartile of Alertness measurements were observed following an average $0.14 Spending On DHA per day.

The lowest quartile of Alertness measurements were observed following an average $0.25 of Spending On DHA per day.

After an onset delay of 0 seconds, Alertness is typically 25% lower than average over the 30 days following around $0.25 of Spending On DHA Spending On DHA.

Keywords: Spending On DHA, Alertness, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 2 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 11 paired observations from 2 participants revealed a substantial reduction in Alertness following above-average Spending On DHA exposure.

-28.7%
Change from Baseline
Substantial effect on Alertness
0.05
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
-0.303
Correlation (r)
p = 0.001
Significance
z = 0.78
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Spending On DHA:

  • Alertness decreased by 28.7% on average
  • Temporal analysis supports Spending On DHA as the predictor (not the outcome)
  • This relationship is statistically significant (p = 0.001)

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. With only 2 participants, these scores are preliminary and will become more reliable as additional data is collected.

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 2 participants and 11 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

0.0 $
Value Predicting Higher Alertness
Average Spending On DHA when Alertness exceeded its mean
1.0 $
Value Predicting Lower Alertness
Average Spending On DHA when Alertness was below its mean

What This Suggests

Alertness tended to be lowest (best) when Spending On DHA was around 1.0 $.

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

Spending On DHA Distribution

Alertness Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Spending on DHA
Effect Variable Name Alertness
Sinn Predictive Coefficient 0.054924582379205
Confidence Level LOW
Confidence Interval 1.8987
Forward Pearson Predictive Coefficient -0.303
Critical T Value 1.796
Total Spending on DHA Over Previous 30 days Before ABOVE Average Alertness $0.14
Total Spending on DHA Over Previous 30 days Before BELOW Average Alertness $0.25
Duration of Action 30 days
Effect Size moderately negative
Number of Paired Measurements 11
Optimal Pearson Product 0.080253862199642
P Value 0.29842
Statistical Significance 0.0012
Strength of Relationship 1.8987
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 2

Spending on DHA Info

Property Value
Variable Name Spending On DHA
Aggregation Method SUM
Analysis Performed At 2020-10-09
Duration of Action 30 days
Filling Value 0
Kurtosis 126.84111716285
Mean $0.46668533333333
Median $0.33333333333333
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 13
Number of Measurements 0
Number of Measurements (including those generated by tagged, joined, or child variables) 5
Public true
Onset Delay 0 seconds
Standard Deviation 1.6192482050032
Unit Dollars
User Variables 4
Variable Category Treatments
Variable ID 6023484
Variance 7.1203290619512

Alertness Info

Property Value
Variable Name Alertness
Aggregation Method MEAN
Analysis Performed At 2020-09-15
Duration of Action 24 hours
Kurtosis 3.4645816890262
Maximum Allowed Value 5 out of 5
Mean 2.7616584422685 out of 5
Median 2.7577906518121 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 1205
Number of Aggregate Outcomes 172
Number of Measurements 25994
Number of Measurements (including those generated by tagged, joined, or child variables) 25832
Public true
Onset Delay 0 seconds
Standard Deviation 0.49012595054405
Unit 1 to 5 Rating
User Variables 1597
UPC 794504377927
Variable Category Emotions
Variable ID 1258
Variance 0.50617576344301

Introduction

Background

Spending On DHA (Treatments) and Alertness (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 Spending On DHA affect Alertness?

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 Spending On DHA for maximizing Alertness?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Spending On DHA and Alertness. Additionally, we attempt to determine the Spending On DHA values most likely to produce optimal Alertness 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 28.7% reduction in Alertness following above-average Spending On DHA exposure. The Predictor Impact Score (PIS) of 0.05 indicates insufficient evidence for a causal relationship. This finding is statistically significant (p = 0.001).

Statistical Significance

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

After treatment, a 30.3% decrease (-0.806 out of 5) from the mean baseline 2.81 out of 5 was observed. The relative standard deviation at baseline was 36.7%. The observed change was 0.78278 times the standard deviation.

A common rule of thumb considers a change greater than twice the baseline standard deviation on two separate pre-post experiments may be considered significant. This occurrence would have only a 5% likelihood of resulting from random fluctuation (a p-value < 0.05).

T-Test Details
Observed t-value: 0.762
Critical t-value: 1.796

Since t = 0.76 < 1.80, 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.

Community feedback on the biological plausibility of this relationship is still being collected. Consider the known mechanisms by which Spending On DHA might influence Alertness.

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 Spending On DHA 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 Spending On DHA
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 2 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 Spending On DHA was associated with a 28.7% reduction in Alertness—a substantial effect. The Predictor Impact Score of 0.05 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.05 and a 28.7% effect size, this relationship currently lacks sufficient evidence. Continue monitoring as more data becomes available. Note: These conclusions may strengthen or change direction as more data is collected.

These findings contribute to our understanding of how Spending On DHA may influence Alertness in real-world conditions. The within-subject design and temporal analysis provide confidence in these relationships, though observational limitations remain.

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Methods

Study Design

This study is based on data donated by 2 participants. Thus, the study design is equivalent to the aggregation of 2 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 0 seconds would pass before a change in Spending On DHA would produce an observable change in Alertness.
  • Duration of Action: It was assumed that Spending On DHA could produce an observable change in Alertness for as much as 30 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Spending On DHA values and subsequent Alertness 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

Spending On DHA 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.

Alertness 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 Spending On DHA Affect Alertness?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-6023484-effect-1258-population-study
BibTeX 
@misc{sinn_cause_6023484_effect_1258_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Spending On DHA Affect Alertness?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-6023484-effect-1258-population-study},
  note = {Accessed: January 9, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Spending On DHA Affect Alertness?." The Journal of Citizen Science. Accessed January 9, 2026. https://studies.crowdsourcingcures.org/study/cause-6023484-effect-1258-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Spending On DHA Affect Alertness?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-6023484-effect-1258-population-study [Accessed January 9, 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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