Higher Minutes Asleep Predicts Very Slightly Lower Body Weight for Population

Higher Minutes Asleep Predicts Very Slightly Lower Body Weight for Population

Contents

Variables

A
Minutes Asleep 707
A
Body Weight 1140

Categories

A
Sleep 111
A
Physique 41

Tags

High Confidence
Very Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 0.4% average decrease in Body Weight following above average Minutes Asleep.

Abstract

Body Weight was generally 0% higher than average after a total of 6 hours of Minutes Asleep over the previous 24 hours.

Aggregated data from 10 study participants suggests with a HIGH degree of confidence (p=0.23, 95% CI -3.556 to 3.477) that Minutes Asleep has a very weakly negative predictive relationship (R=-0.0395) with Body Weight.

The highest quartile of Body Weight measurements were observed following an average 7 hours Minutes Asleep per day.

The lowest quartile of Body Weight measurements were observed following an average 7 hours of Minutes Asleep per day.

After an onset delay of 0 seconds, Body Weight is typically 0% lower than average over the 24 hours following around 7 hours of Minutes Asleep Minutes Asleep.

Keywords: Minutes Asleep, Body Weight, 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 702 paired observations from 10 participants revealed a minimal reduction in Body Weight following above-average Minutes Asleep exposure.

-0.2%
Change from Baseline
Minimal effect on Body Weight
0.01
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
-0.040
Correlation (r)
p = 0.635
Significance
z = 1.50
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Minutes Asleep:

  • Body Weight decreased by 0.2% on average
  • Temporal analysis supports Minutes Asleep 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

No clear dose-response relationship detected. The Minutes Asleep values associated with high and low Body Weight 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

Minutes Asleep Distribution

Body Weight Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Minutes Asleep
Effect Variable Name Body Weight
Sinn Predictive Coefficient 0.012484381611443
Confidence Level HIGH
Confidence Interval 3.5165418467183
Forward Pearson Predictive Coefficient -0.0395
Critical T Value 1.6986
Total Minutes Asleep Over Previous 24 hours Before ABOVE Average Body Weight 7 hours
Total Minutes Asleep Over Previous 24 hours Before BELOW Average Body Weight 7 hours
Duration of Action 24 hours
Effect Size very weakly negative
Number of Paired Measurements 702
Optimal Pearson Product 0.0261801822424
P Value 0.22987898751593
Statistical Significance 0.6345
Strength of Relationship 3.5165418467183
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 10

Minutes Asleep Info

Property Value
Variable Name Minutes Asleep
Aggregation Method SUM
Analysis Performed At 2020-10-11
Duration of Action 24 hours
Filling Value 0
Kurtosis 9.5200753036668
Maximum Allowed Value 7 days
Mean 4 hours
Median 3 hours
Minimum Allowed Value 0 seconds
Number of Aggregate Predictors 600
Number of Aggregate Outcomes 107
Number of Measurements 7101
Number of Measurements (including those generated by tagged, joined, or child variables) 943
Public true
Onset Delay 0 seconds
Standard Deviation 172.08438112251
Unit Minutes
User Variables 53
UPC 0
Variable Category Sleep
Variable ID 5964699
Variance 33896.625095035

Body Weight Info

Property Value
Variable Name Body Weight
Aggregation Method MEAN
Analysis Performed At 2020-09-23
Duration of Action 7 days
Kurtosis 29.271534088526
Maximum Allowed Value 1000 pounds
Mean 168.9619340574 pounds
Median 168.27481272906 pounds
Minimum Allowed Value 0 pounds
Number of Aggregate Predictors 883
Number of Aggregate Outcomes 257
Number of Measurements 108822
Number of Measurements (including those generated by tagged, joined, or child variables) 21092
Public true
Onset Delay 0 seconds
Standard Deviation 8.7190661755282
Unit Pounds
User Variables 417
UPC 875011003902
Variable Category Physique
Variable ID 1486
Variance 594.35417755402

Introduction

Background

Minutes Asleep (Sleep) and Body Weight (Physique) 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 Minutes Asleep affect Body Weight?

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 Minutes Asleep for maximizing Body Weight?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Minutes Asleep and Body Weight. Additionally, we attempt to determine the Minutes Asleep values most likely to produce optimal Body Weight 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 0.2% reduction in Body Weight following above-average Minutes Asleep exposure. The Predictor Impact Score (PIS) of 0.01 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 Body Weight is not statistically significant at a 95% confidence interval. This suggests that the Minutes Asleep value may not have a significant influence on the Body Weight value, or that more data is needed to detect an effect.

After treatment, a 0.4% decrease (-0.008 pounds) from the mean baseline 175 pounds was observed. The relative standard deviation at baseline was 3.25%. The observed change was 1.5042 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: 1.051
Critical t-value: 1.699

Since t = 1.05 < 1.70, 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 Minutes Asleep might influence Body Weight.

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 Minutes Asleep 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 Minutes Asleep
  • 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 Minutes Asleep was associated with a 0.2% reduction in Body Weight—a minimal effect. The Predictor Impact Score of 0.01 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.01 and a 0.2% 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 Minutes Asleep may influence Body Weight 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 Minutes Asleep would produce an observable change in Body Weight.
  • Duration of Action: It was assumed that Minutes Asleep could produce an observable change in Body Weight for as much as 24 hours after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Minutes Asleep values and subsequent Body Weight 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

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

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

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 Minutes Asleep Affect Body Weight?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5964699-effect-1486-population-study
BibTeX 
@misc{sinn_cause_5964699_effect_1486_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Minutes Asleep Affect Body Weight?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5964699-effect-1486-population-study},
  note = {Accessed: January 11, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Minutes Asleep Affect Body Weight?." The Journal of Citizen Science. Accessed January 11, 2026. https://studies.crowdsourcingcures.org/study/cause-5964699-effect-1486-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Minutes Asleep Affect Body Weight?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5964699-effect-1486-population-study [Accessed January 11, 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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