Higher Adderall Intake Predicts Slightly Higher Activeness for Population

Higher Adderall Intake Predicts Slightly Higher Activeness for Population

Contents

Variables

A
Adderall 283
A
Activeness 1326

Categories

A
Treatments 9356
A
Emotions 2028

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Low Confidence
Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 1.6% average increase in Activeness following above average Adderall Intake.

Abstract

Activeness was generally 3% higher than average after a total of 35.4 milligrams of Adderall over the previous 18 days.

Aggregated data from 5 study participants suggests with a LOW degree of confidence (p=0.235, 95% CI -0.52 to 1.081) that Adderall has a weakly positive predictive relationship (R=0.281) with Activeness.

The highest quartile of Activeness measurements were observed following an average 233 milligrams Adderall per day.

The lowest quartile of Activeness measurements were observed following an average 224 milligrams of Adderall per day.

After an onset delay of 30 minutes, Activeness is typically 18% lower than average over the 18 days following around 224 milligrams of Adderall Adderall.

Keywords: Adderall, Activeness, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 5 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 59 paired observations from 5 participants revealed a modest improvement in Activeness following above-average Adderall exposure.

+7.0%
Change from Baseline
Modest effect on Activeness
0.11
Predictor Impact Score
Weak evidence for causal relationship

Supporting Statistics

Medium
Confidence
0.281
Correlation (r)
p = 0.037
Significance
z = 0.58
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Adderall:

  • Activeness increased by 7.0% on average
  • Temporal analysis supports Adderall as the predictor (not the outcome)
  • This relationship is statistically significant (p = 0.037)

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

Optimal Daily Values

No clear dose-response relationship detected. The Adderall values associated with high and low Activeness are too similar to provide meaningful dosing guidance. This may indicate a threshold effect (any amount works equally well), no effect, or insufficient data variance. With more participants, a clearer pattern may emerge.

Population Correlation

Adderall Distribution

Activeness Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Adderall Intake
Effect Variable Name Activeness
Sinn Predictive Coefficient 0.11040750135001
Confidence Level LOW
Confidence Interval 0.80061271699234
Forward Pearson Predictive Coefficient 0.2806
Critical T Value 1.8154
Total Adderall Intake Over Previous 18 days Before ABOVE Average Activeness 233 milligrams
Total Adderall Intake Over Previous 18 days Before BELOW Average Activeness 224 milligrams
Duration of Action 18 days
Effect Size weakly positive
Number of Paired Measurements 59
Optimal Pearson Product 0.019534791465475
P Value 0.23450903576789
Statistical Significance 0.0368
Strength of Relationship 0.80061271699234
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 5

Adderall Info

Property Value
Variable Name Adderall
Aggregation Method SUM
Analysis Performed At 2022-12-05
Duration of Action 21 days
Filling Value 0
Kurtosis 21.957848139755
Mean 25.09509177907 milligrams
Median 23.395348837209 milligrams
Minimum Allowed Value 0 milligrams
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 283
Number of Measurements 137
Number of Measurements (including those generated by tagged, joined, or child variables) 2504
Public true
Onset Delay 30 minutes
Standard Deviation 8.861967666185
Unit Milligrams
User Variables 136
UPC 637769766061
Variable Category Treatments
Variable ID 1253
Variance 291.35352900516

Activeness Info

Property Value
Variable Name Activeness
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 24 hours
Kurtosis 1.8468106022057
Maximum Allowed Value 5 out of 5
Mean 2.3430371584699 out of 5
Median 2.3108746584699 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 1200
Number of Aggregate Outcomes 126
Number of Measurements 30704
Number of Measurements (including those generated by tagged, joined, or child variables) 30582
Public true
Onset Delay 0 seconds
Standard Deviation 0.52428579928588
Unit 1 to 5 Rating
User Variables 1510
UPC 0
Variable Category Emotions
Variable ID 1252
Variance 0.56911364809229

Introduction

Background

Adderall (Treatments) and Activeness (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 Adderall affect Activeness?

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 Adderall for maximizing Activeness?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Adderall and Activeness. Additionally, we attempt to determine the Adderall values most likely to produce optimal Activeness 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 7.0% improvement in Activeness following above-average Adderall exposure. The Predictor Impact Score (PIS) of 0.11 indicates weak evidence for a causal relationship. This finding is statistically significant (p = 0.037).

Statistical Significance

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

After treatment, a 1.6% increase (0.012 out of 5) from the mean baseline 2.31 out of 5 was observed. The relative standard deviation at baseline was 45.58%. The observed change was 0.580211 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: 1.683
Critical t-value: 1.815

Since t = 1.68 < 1.82, 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 Adderall might influence Activeness.

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 Adderall 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 Adderall
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 5 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 Adderall was associated with a 7.0% improvement in Activeness—a modest effect. The Predictor Impact Score of 0.11 indicates this relationship is warranting continued monitoring.

Bottom Line: Based on a PIS of 0.11 and a 7.0% effect size, this relationship shows weak evidence. Additional observational data is recommended before investing in experimental validation. Note: These conclusions may strengthen or change direction as more data is collected.

These findings contribute to our understanding of how Adderall may influence Activeness 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 5 participants. Thus, the study design is equivalent to the aggregation of 5 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 Adderall would produce an observable change in Activeness.
  • Duration of Action: It was assumed that Adderall could produce an observable change in Activeness for as much as 18 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Adderall values and subsequent Activeness 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

Adderall 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.

Activeness 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 Adderall Affect Activeness?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-1253-effect-1252-population-study
BibTeX 
@misc{sinn_cause_1253_effect_1252_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Adderall Affect Activeness?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-1253-effect-1252-population-study},
  note = {Accessed: January 7, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Adderall Affect Activeness?." The Journal of Citizen Science. Accessed January 7, 2026. https://studies.crowdsourcingcures.org/study/cause-1253-effect-1252-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Adderall Affect Activeness?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-1253-effect-1252-population-study [Accessed January 7, 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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