Higher Hourly Step Count Predicts Very Slightly Higher Pride for Population

Higher Hourly Step Count Predicts Very Slightly Higher Pride for Population

Contents

Variables

A
Hourly Step Count 304
A
Pride 999

Categories

A
Physical Activity 1719
A
Emotions 2028

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Your Data

Tags

Medium Confidence
Very Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 8.7% average increase in Pride following above average Hourly Step Count.

Abstract

Pride was generally 18% higher than average after a total of 147 count of Hourly Step Count over the previous 24 hours.

Aggregated data from 10 study participants suggests with a MEDIUM degree of confidence (p=0.177, 95% CI -0.646 to 0.834) that Hourly Step Count has a very weakly positive predictive relationship (R=0.0938) with Pride.

The highest quartile of Pride measurements were observed following an average 173 count Hourly Step Count per day.

The lowest quartile of Pride measurements were observed following an average 264 count of Hourly Step Count per day.

After an onset delay of 0 seconds, Pride is typically 14% lower than average over the 24 hours following around 264 count of Hourly Step Count Hourly Step Count.

Keywords: Hourly Step Count, Pride, N-of-1 trials, real-world evidence, causal inference, observational study

Moderate Confidence: Based on 10 participants. More data would increase certainty.

Results

Primary Findings

Analysis of 141 paired observations from 10 participants revealed a modest improvement in Pride following above-average Hourly Step Count exposure.

+8.8%
Change from Baseline
Modest effect on Pride
0.06
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

Medium
Confidence
0.094
Correlation (r)
p = 0.078
Significance
z = 0.96
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Hourly Step Count:

  • Pride increased by 8.8% on average
  • Temporal analysis supports Hourly Step Count 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. With only 10 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 10 participants and 141 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

147.1 count
Value Predicting Higher Pride
Average Hourly Step Count when Pride exceeded its mean
268.4 count
Value Predicting Lower Pride
Average Hourly Step Count when Pride was below its mean

What This Suggests

Pride tended to be highest when Hourly Step Count was around 147.1 count.

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

Hourly Step Count Distribution

Pride Distribution

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Hourly Step Count
Effect Variable Name Pride
Sinn Predictive Coefficient 0.059292908953137
Confidence Level MEDIUM
Confidence Interval 0.74009470834976
Forward Pearson Predictive Coefficient 0.0938
Critical T Value 1.8208
Total Hourly Step Count Over Previous 24 hours Before ABOVE Average Pride 173 count
Total Hourly Step Count Over Previous 24 hours Before BELOW Average Pride 264 count
Duration of Action 24 hours
Effect Size very weakly positive
Number of Paired Measurements 141
Optimal Pearson Product 0.072234772054594
P Value 0.17722519681515
Statistical Significance 0.0777
Strength of Relationship 0.74009470834976
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 10

Hourly Step Count Info

Property Value
Variable Name Hourly Step Count
Aggregation Method SUM
Analysis Performed At 2020-10-11
Duration of Action 24 hours
Filling Value 0
Kurtosis 46.60946228565
Mean 1585.3490776695 count
Median 347.80536912752 count
Minimum Allowed Value 0 count
Number of Aggregate Predictors 113
Number of Aggregate Outcomes 191
Number of Measurements 123420
Number of Measurements (including those generated by tagged, joined, or child variables) 24057
Public true
Onset Delay 0 seconds
Standard Deviation 35201.225731697
Unit Count
User Variables 151
UPC 889232179582
Variable Category Physical Activity
Variable ID 5955886
Variance 171009851577.3

Pride Info

Property Value
Variable Name Pride
Aggregation Method MEAN
Analysis Performed At 2021-01-25
Duration of Action 24 hours
Kurtosis 2.1323643437355
Maximum Allowed Value 5 out of 5
Mean 2.2469480289622 out of 5
Median 2.2071096540628 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 895
Number of Aggregate Outcomes 104
Number of Measurements 21462
Number of Measurements (including those generated by tagged, joined, or child variables) 21462
Public true
Onset Delay 0 seconds
Standard Deviation 0.50383781711499
Unit 1 to 5 Rating
User Variables 1270
UPC 0
Variable Category Emotions
Variable ID 1411
Variance 0.56713188699026

Introduction

Background

Hourly Step Count (Physical Activity) and Pride (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 Hourly Step Count affect Pride?

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 Hourly Step Count for maximizing Pride?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Hourly Step Count and Pride. Additionally, we attempt to determine the Hourly Step Count values most likely to produce optimal Pride 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 8.8% improvement in Pride following above-average Hourly Step Count exposure. The Predictor Impact Score (PIS) of 0.06 indicates insufficient evidence for a causal relationship.

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Pride is statistically significant at a 95% confidence interval. The p-value of 0.0777 indicates there is less than a 7.77% probability that this result occurred by chance.

After treatment, a 8.7% increase (0.0135 out of 5) from the mean baseline 1.93 out of 5 was observed. The relative standard deviation at baseline was 39.23%. The observed change was 0.960324 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: 2.542
Critical t-value: 1.821

Since t = 2.54 > 1.82, we 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 Hourly Step Count might influence Pride.

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 Hourly Step Count 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 Hourly Step Count
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 10 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 Hourly Step Count was associated with a 8.8% improvement in Pride—a modest effect. The Predictor Impact Score of 0.06 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.06 and a 8.8% 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 Hourly Step Count may influence Pride in real-world conditions. While preliminary, these results may inform future research directions. As more participants contribute data, the reliability and precision of these findings will improve substantially.

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Methods

Study Design

This study is based on data donated by 10 participants. Thus, the study design is equivalent to the aggregation of 10 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 Hourly Step Count would produce an observable change in Pride.
  • Duration of Action: It was assumed that Hourly Step Count could produce an observable change in Pride for as much as 24 hours after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Hourly Step Count values and subsequent Pride 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

Hourly Step Count data was primarily collected using Google Fit. Use Google Fit to import your fitness data.

Pride 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 Hourly Step Count Affect Pride?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5955886-effect-1411-population-study
BibTeX 
@misc{sinn_cause_5955886_effect_1411_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Hourly Step Count Affect Pride?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5955886-effect-1411-population-study},
  note = {Accessed: January 7, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Hourly Step Count Affect Pride?." The Journal of Citizen Science. Accessed January 7, 2026. https://studies.crowdsourcingcures.org/study/cause-5955886-effect-1411-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Hourly Step Count Affect Pride?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5955886-effect-1411-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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