Higher Advantedge Carb Control French Vanilla Protein Drink Consumption Predicts Very Slightly Lower Body Weight for Population

Higher Advantedge Carb Control French Vanilla Protein Drink Consumption Predicts Very Slightly Lower Body Weight for Population

Contents

Variables

A
Advantedge Carb Control French Vanilla Protein Drink 26
A
Body Weight 1140

Categories

A
Foods 13415
A
Physique 41

Tags

Medium Confidence
Very Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 2.1% average decrease in Body Weight following above average Advantedge Carb Control French Vanilla Protein Drink Consumption.

Abstract

Body Weight was generally 0% higher than average after a total of 0 serving of Advantedge Carb Control French Vanilla Protein Drink over the previous 14 days.

Aggregated data from 1 study participants suggests with a MEDIUM degree of confidence (p=0.0304, 95% CI -1.668 to 1.612) that Advantedge Carb Control French Vanilla Protein Drink has a very weakly negative predictive relationship (R=-0.0277) with Body Weight.

The highest quartile of Body Weight measurements were observed following an average 0.0341 serving Advantedge Carb Control French Vanilla Protein Drink per day.

The lowest quartile of Body Weight measurements were observed following an average 0.0364 serving of Advantedge Carb Control French Vanilla Protein Drink per day.

After an onset delay of 30 minutes, Body Weight is typically 1% higher than average over the 14 days following around 0.0364 serving of Advantedge Carb Control French Vanilla Protein Drink Advantedge Carb Control French Vanilla Protein Drink.

Keywords: Advantedge Carb Control French Vanilla Protein Drink, Body Weight, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 1 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 169 paired observations from 1 participants revealed a minimal improvement in Body Weight following above-average Advantedge Carb Control French Vanilla Protein Drink exposure.

+1.3%
Change from Baseline
Minimal effect on Body Weight
0.05
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

Medium
Confidence
-0.028
Correlation (r)
p = 0.059
Significance
z = 0.36
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Advantedge Carb Control French Vanilla Protein Drink:

  • Body Weight increased by 1.3% on average
  • Temporal analysis supports Advantedge Carb Control French Vanilla Protein Drink 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 1 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 Advantedge Carb Control French Vanilla Protein Drink 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

Advantedge Carb Control French Vanilla Protein Drink Distribution

Body Weight Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Advantedge Carb Control French Vanilla Protein Drink Consumption
Effect Variable Name Body Weight
Sinn Predictive Coefficient 0.048899292718966
Confidence Level MEDIUM
Confidence Interval 1.6400246458285
Forward Pearson Predictive Coefficient -0.0277
Critical T Value 1.646
Total Advantedge Carb Control French Vanilla Protein Drink Consumption Over Previous 14 days Before ABOVE Average Body Weight 0.0341 serving
Total Advantedge Carb Control French Vanilla Protein Drink Consumption Over Previous 14 days Before BELOW Average Body Weight 0.0364 serving
Duration of Action 14 days
Effect Size very weakly negative
Number of Paired Measurements 169
Optimal Pearson Product 8.6374326829726E-5
P Value 0.030395527440622
Statistical Significance 0.0593
Strength of Relationship 1.6400246458285
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 1

Advantedge Carb Control French Vanilla Protein Drink Info

Property Value
Variable Name Advantedge Carb Control French Vanilla Protein Drink
Aggregation Method SUM
Analysis Performed At 2020-09-15
Duration of Action 14 days
Filling Value 0
Kurtosis 37.121991348747
Maximum Allowed Value 40 serving
Mean 0.519355 serving
Median 0 serving
Minimum Allowed Value 0 serving
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 26
Number of Measurements 12
Number of Measurements (including those generated by tagged, joined, or child variables) 6
Public true
Onset Delay 30 minutes
Standard Deviation 0.22268720397529
Unit Serving
User Variables 2
Variable Category Foods
Variable ID 1650
Variance 0.050439882697947

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

Advantedge Carb Control French Vanilla Protein Drink (Foods) 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 Advantedge Carb Control French Vanilla Protein Drink 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 Advantedge Carb Control French Vanilla Protein Drink for maximizing Body Weight?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Advantedge Carb Control French Vanilla Protein Drink and Body Weight. Additionally, we attempt to determine the Advantedge Carb Control French Vanilla Protein Drink 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 1.3% improvement in Body Weight following above-average Advantedge Carb Control French Vanilla Protein Drink exposure. The Predictor Impact Score (PIS) of 0.05 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 0.0593 indicates there is less than a 5.93% probability that this result occurred by chance.

After treatment, a 2.1% decrease (2.07 pounds) from the mean baseline 160 pounds was observed. The relative standard deviation at baseline was 3.6%. The observed change was 0.36 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: 2.269
Critical t-value: 1.646

Since t = 2.27 > 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.

Based on community responses so far, 1 person feels that there is a plausible mechanism of action and 0 feel that any relationship observed between Advantedge Carb Control French Vanilla Protein Drink and Body Weight is coincidental.

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 Advantedge Carb Control French Vanilla Protein Drink 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 Advantedge Carb Control French Vanilla Protein Drink
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 1 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 Advantedge Carb Control French Vanilla Protein Drink was associated with a 1.3% improvement in Body Weight—a minimal effect. The Predictor Impact Score of 0.05 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.05 and a 1.3% 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 Advantedge Carb Control French Vanilla Protein Drink 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.

Help End Unnecessary Suffering

Current clinical trials are 82x more expensive than necessary and take 17 years to bring treatments to market. Pragmatic trials integrated into standard healthcare could reduce costs from $41,000 to $500 per participant and compress timelines to just 2 years. Learn how redirecting just 1% of global military spending could accelerate cures for the 2 billion people suffering from treatable diseases.

Make Suffering Optional

Methods

Study Design

This study is based on data donated by 1 participants. Thus, the study design is equivalent to the aggregation of 1 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 Advantedge Carb Control French Vanilla Protein Drink would produce an observable change in Body Weight.
  • Duration of Action: It was assumed that Advantedge Carb Control French Vanilla Protein Drink could produce an observable change in Body Weight for as much as 14 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Advantedge Carb Control French Vanilla Protein Drink 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

Advantedge Carb Control French Vanilla Protein Drink data was primarily collected using MyFitnessPal. Lose weight with MyFitnessPal, the fastest and easiest-to-use calorie counter for iPhone and iPad. With the largest food database of any iOS calorie counter (over 3,000,000 foods), and amazingly fast food and exercise entry.

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 Advantedge Carb Control French Vanilla Protein Drink Affect Body Weight?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-1650-effect-1486-population-study
BibTeX 
@misc{sinn_cause_1650_effect_1486_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Advantedge Carb Control French Vanilla Protein Drink Affect Body Weight?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-1650-effect-1486-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Advantedge Carb Control French Vanilla Protein Drink Affect Body Weight?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-1650-effect-1486-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Advantedge Carb Control French Vanilla Protein Drink Affect Body Weight?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-1650-effect-1486-population-study [Accessed January 10, 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

LinkedIn Reddit