Higher Digestive Advantage Bc 30 Predicts Slightly Lower Body Weight for Population

Higher Digestive Advantage Bc 30 Predicts Slightly Lower Body Weight for Population

Contents

Variables

A
Digestive Advantage Bc 30 19
A
Body Weight 1140

Categories

A
Treatments 9356
A
Physique 41

Tags

Medium Confidence
Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 0.7% average decrease in Body Weight following above average Digestive Advantage Bc 30.

Abstract

<h5>Not Enough Shared Data</h5>Please create a study and share it with your friends so we can collect enough data to determine the effect of Digestive Advantage Bc 30 on Body Weight. <a href="https://web.quantimo.do/#/app/study-creation" title="Create a Study" class="create-study-button"> Create a Study </a> <h5>Solution: Create a Study</h5>Please create a study and share it with your friends so we can collect enough data to determine the effect of Digestive Advantage Bc 30 on Body Weight. <a href="https://web.quantimo.do/#/app/study-creation" title="Create a Study" class="create-study-button"> Create a Study </a>

Body Weight was generally 0% higher than average after a total of 4 capsules of Digestive Advantage Bc 30 over the previous 21 days.

Aggregated data from 2 study participants suggests with a MEDIUM degree of confidence (p=0.0809, 95% CI -0.886 to 0.445) that Digestive Advantage Bc 30 has a weakly negative predictive relationship (R=-0.221) with Body Weight.

The highest quartile of Body Weight measurements were observed following an average 4.76 capsules Digestive Advantage Bc 30 per day.

The lowest quartile of Body Weight measurements were observed following an average 7.9 capsules of Digestive Advantage Bc 30 per day.

After an onset delay of 30 minutes, Body Weight is typically 0% lower than average over the 21 days following around 7.9 capsules of Digestive Advantage Bc 30 Digestive Advantage Bc 30.

Keywords: Digestive Advantage Bc 30, Body Weight, 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 136 paired observations from 2 participants revealed a minimal reduction in Body Weight following above-average Digestive Advantage Bc 30 exposure.

0.0%
Change from Baseline
Minimal effect on Body Weight
0.02
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

Medium
Confidence
-0.221
Correlation (r)
p = 0.626
Significance
z = 0.00
Effect Magnitude
φ = 0.59
Temporality

What This Means

When participants had above-average Digestive Advantage Bc 30:

  • Body Weight decreased by 0.0% on average

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 136 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

4.0 capsules
Value Predicting Higher Body Weight
Average Digestive Advantage Bc 30 when Body Weight exceeded its mean
8.0 capsules
Value Predicting Lower Body Weight
Average Digestive Advantage Bc 30 when Body Weight was below its mean

What This Suggests

Body Weight tended to be lowest (best) when Digestive Advantage Bc 30 was around 8.0 capsules.

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

Digestive Advantage Bc 30 Distribution

Body Weight Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Digestive Advantage Bc 30
Effect Variable Name Body Weight
Sinn Predictive Coefficient 0.019984935132226
Confidence Level MEDIUM
Confidence Interval 0.66579368876902
Forward Pearson Predictive Coefficient -0.2205
Critical T Value 1.646
Total Digestive Advantage Bc 30 Over Previous 21 days Before ABOVE Average Body Weight 4.76 capsules
Total Digestive Advantage Bc 30 Over Previous 21 days Before BELOW Average Body Weight 7.9 capsules
Duration of Action 21 days
Effect Size weakly negative
Number of Paired Measurements 136
Optimal Pearson Product 0.093781179238009
P Value 0.080947707726396
Statistical Significance 0.6256
Strength of Relationship 0.66579368876902
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 2

Digestive Advantage Bc 30 Info

Property Value
Variable Name Digestive Advantage Bc 30
Aggregation Method SUM
Analysis Performed At 2020-09-22
Duration of Action 21 days
Filling Value 0
Kurtosis 1.9312291001377
Maximum Allowed Value 20 capsules
Mean 0.66170666666667 capsules
Median 0 capsules
Minimum Allowed Value 0 capsules
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 19
Number of Measurements 96
Number of Measurements (including those generated by tagged, joined, or child variables) 48
Public true
Onset Delay 30 minutes
Standard Deviation 0.49939380430247
Unit Capsules
User Variables 2
UPC 823019565441
Variable Category Treatments
Variable ID 1301
Variance 0.24965079365079

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

Digestive Advantage Bc 30 (Treatments) 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 Digestive Advantage Bc 30 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 Digestive Advantage Bc 30 for maximizing Body Weight?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Digestive Advantage Bc 30 and Body Weight. Additionally, we attempt to determine the Digestive Advantage Bc 30 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 0.0% reduction in Body Weight following above-average Digestive Advantage Bc 30 exposure. The Predictor Impact Score (PIS) of 0.02 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 statistically significant at a 95% confidence interval. The p-value of 0.6256 indicates there is less than a 62.56% probability that this result occurred by chance.

T-Test Details
Observed t-value: 1.840
Critical t-value: 1.646

Since t = 1.84 > 1.65, 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 Digestive Advantage Bc 30 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

The accuracy of this study may be limited by the fact that <h5>Not Enough Shared Data</h5>Please create a study and share it with your friends so we can collect enough data to determine the effect of Digestive Advantage Bc 30 on Body Weight. <a href="https://web.quantimo.do/#/app/study-creation" title="Create a Study" class="create-study-button"> Create a Study </a> <h5>Solution: Create a Study</h5>Please create a study and share it with your friends so we can collect enough data to determine the effect of Digestive Advantage Bc 30 on Body Weight. <a href="https://web.quantimo.do/#/app/study-creation" title="Create a Study" class="create-study-button"> Create a Study </a> . A greater amount of data and more variance in the data would help to resolve this issue.

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 Digestive Advantage Bc 30 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 Digestive Advantage Bc 30
  • 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 Digestive Advantage Bc 30 was associated with a 0.0% reduction in Body Weight—a minimal effect. The Predictor Impact Score of 0.02 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.02 and a 0.0% 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 Digestive Advantage Bc 30 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 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 30 minutes would pass before a change in Digestive Advantage Bc 30 would produce an observable change in Body Weight.
  • Duration of Action: It was assumed that Digestive Advantage Bc 30 could produce an observable change in Body Weight for as much as 21 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Digestive Advantage Bc 30 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

Digestive Advantage Bc 30 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.

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 Digestive Advantage Bc 30 Affect Body Weight?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-1301-effect-1486-population-study
BibTeX 
@misc{sinn_cause_1301_effect_1486_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Digestive Advantage Bc 30 Affect Body Weight?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-1301-effect-1486-population-study},
  note = {Accessed: January 11, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Digestive Advantage Bc 30 Affect Body Weight?." The Journal of Citizen Science. Accessed January 11, 2026. https://studies.crowdsourcingcures.org/study/cause-1301-effect-1486-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Digestive Advantage Bc 30 Affect Body Weight?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-1301-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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