Higher Electronics Activities Predicts Slightly Lower Distress for Population

Higher Electronics Activities Predicts Slightly Lower Distress for Population

Contents

Variables

A
Electronics Activities 137
A
Distress 1349

Categories

A
Activities 1637
A
Emotions 2028

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Your Data

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Low Confidence
Very Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 19.3% average decrease in Distress following above average Electronics Activities.

Abstract

Distress was generally 20.55% lower than average after 4 minutes of Electronics Activities per 7 days.

Aggregated data from 3 study participants suggests with a LOW degree of confidence (p=0.272, 95% CI -1.373 to 1.162) that Electronics Activities has a weakly negative predictive relationship (R=-0.106) with Distress.

The highest quartile of Distress measurements were observed following an average 36 seconds Electronics Activities per day.

The lowest quartile of Distress measurements were observed following an average 4 minutes of Electronics Activities per day.

After an onset delay of 0 seconds, Distress is typically 15% lower than average over the 7 days following around 4 minutes of Electronics Activities Electronics Activities.

Keywords: Electronics Activities, Distress, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 3 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 12 paired observations from 3 participants revealed a moderate reduction in Distress following above-average Electronics Activities exposure.

-20.6%
Change from Baseline
Moderate effect on Distress
0.03
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

Medium
Confidence
-0.106
Correlation (r)
p = 0.036
Significance
z = 0.39
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Electronics Activities:

  • Distress decreased by 20.6% on average
  • Temporal analysis supports Electronics Activities as the predictor (not the outcome)
  • This relationship is statistically significant (p = 0.036)

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 3 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 3 participants and 12 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

0.0 h
Value Predicting Higher Distress
Average Electronics Activities when Distress exceeded its mean
0.1 h
Value Predicting Lower Distress
Average Electronics Activities when Distress was below its mean

What This Suggests

Distress tended to be lowest (best) when Electronics Activities was around 0.1 h.

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

Electronics Activities Distribution

Distress Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Electronics Activities
Effect Variable Name Distress
Sinn Predictive Coefficient 0.027343677131038
Confidence Level LOW
Confidence Interval 1.2679
Forward Pearson Predictive Coefficient -0.1055
Critical T Value 1.87
Total Electronics Activities Over Previous 7 days Before ABOVE Average Distress 36 seconds
Total Electronics Activities Over Previous 7 days Before BELOW Average Distress 4 minutes
Duration of Action 7 days
Effect Size weakly negative
Number of Paired Measurements 12
Optimal Pearson Product 0.034075702501351
P Value 0.27192
Statistical Significance 0.0362
Strength of Relationship 1.2679
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 3

Electronics Activities Info

Property Value
Variable Name Electronics Activities
Aggregation Method SUM
Analysis Performed At 2020-10-11
Duration of Action 7 days
Filling Value 0
Kurtosis 266.71402927004
Maximum Allowed Value 7 days
Mean 90 seconds
Median 0 seconds
Minimum Allowed Value 0 seconds
Number of Aggregate Predictors 24
Number of Aggregate Outcomes 113
Number of Measurements 1154
Number of Measurements (including those generated by tagged, joined, or child variables) 813
Public true
Onset Delay 0 seconds
Standard Deviation 0.19954214989109
Unit Hours
User Variables 42
Variable Category Activities
Variable ID 5956941
Variance 0.28735402690496

Distress Info

Property Value
Variable Name Distress
Aggregation Method MEAN
Analysis Performed At 2020-09-17
Duration of Action 24 hours
Kurtosis 2.8522483497534
Maximum Allowed Value 5 out of 5
Mean 2.4777342286981 out of 5
Median 2.4229733332357 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 1209
Number of Aggregate Outcomes 140
Number of Measurements 33096
Number of Measurements (including those generated by tagged, joined, or child variables) 32968
Public true
Onset Delay 0 seconds
Standard Deviation 0.53719668856315
Unit 1 to 5 Rating
User Variables 1486
UPC 647297398818
Variable Category Emotions
Variable ID 1305
Variance 0.6312526863217

Introduction

Background

Electronics Activities (Activities) and Distress (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

Do Electronics Activities affect Distress?

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 Electronics Activities for maximizing Distress?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Electronics Activities and Distress. Additionally, we attempt to determine the Electronics Activities values most likely to produce optimal Distress 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 20.6% reduction in Distress following above-average Electronics Activities exposure. The Predictor Impact Score (PIS) of 0.03 indicates insufficient evidence for a causal relationship. This finding is statistically significant (p = 0.036).

Statistical Significance

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

After treatment, a 19.3% decrease (-0.511 out of 5) from the mean baseline 2.53 out of 5 was observed. The relative standard deviation at baseline was 52.55%. The observed change was 0.39349 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: 0.868
Critical t-value: 1.870

Since t = 0.87 < 1.87, 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 Electronics Activities might influence Distress.

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

Conclusion

📊 Preliminary Findings: With 3 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 Electronics Activities was associated with a 20.6% reduction in Distress—a moderate effect. The Predictor Impact Score of 0.03 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.03 and a 20.6% 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 Electronics Activities may influence Distress 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 3 participants. Thus, the study design is equivalent to the aggregation of 3 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 Electronics Activities would produce an observable change in Distress.
  • Duration of Action: It was assumed that Electronics Activities could produce an observable change in Distress for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Electronics Activities values and subsequent Distress 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

Electronics Activities data was primarily collected using RescueTime. Detailed reports show which applications and websites you spent time on. Activities are automatically grouped into pre-defined categories with built-in productivity scores covering thousands of websites and applications. You can customize categories and productivity scores to meet your needs.

Distress 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 Electronics Activities Affect Distress?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5956941-effect-1305-population-study
BibTeX 
@misc{sinn_cause_5956941_effect_1305_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Electronics Activities Affect Distress?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5956941-effect-1305-population-study},
  note = {Accessed: January 9, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Electronics Activities Affect Distress?." The Journal of Citizen Science. Accessed January 9, 2026. https://studies.crowdsourcingcures.org/study/cause-5956941-effect-1305-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Electronics Activities Affect Distress?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5956941-effect-1305-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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