Higher Servings Of Fruit Consumption Predicts Very Slightly Lower Lack Of Motivation for Population

Higher Servings Of Fruit Consumption Predicts Very Slightly Lower Lack Of Motivation for Population

Contents

Variables

A
Servings of Fruit 94
A
Lack of Motivation 870

Categories

A
Foods 13415
A
Symptoms 13336

Actions

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Your Data

Tags

Low Confidence
Very Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 7.1% average decrease in Lack of Motivation following above average Servings of Fruit Consumption.

Abstract

Lack of Motivation was generally 20.4% lower than average after 12 serving of Servings of Fruit per 14 days.

Aggregated data from 2 study participants suggests with a LOW degree of confidence (p=0.283, 95% CI -1.184 to 1.023) that Servings of Fruit has a very weakly negative predictive relationship (R=-0.0803) with Lack of Motivation.

The highest quartile of Lack of Motivation measurements were observed following an average 5.61 serving Servings of Fruit per day.

The lowest quartile of Lack of Motivation measurements were observed following an average 6.27 serving of Servings of Fruit per day.

After an onset delay of 30 minutes, Lack of Motivation is typically 10% lower than average over the 14 days following around 6.27 serving of Servings of Fruit Servings of Fruit.

Keywords: Servings of Fruit, Lack of Motivation, 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 19 paired observations from 2 participants revealed a moderate reduction in Lack of Motivation following above-average Servings of Fruit exposure.

-20.4%
Change from Baseline
Moderate effect on Lack of Motivation
0.01
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
-0.080
Correlation (r)
p = 0.002
Significance
z = 0.46
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Servings of Fruit:

  • Lack of Motivation decreased by 20.4% on average
  • Temporal analysis supports Servings of Fruit as the predictor (not the outcome)
  • This relationship is statistically significant (p = 0.002)

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

2.5 serving
Value Predicting Higher Lack of Motivation
Average Servings of Fruit when Lack of Motivation exceeded its mean
12.0 serving
Value Predicting Lower Lack of Motivation
Average Servings of Fruit when Lack of Motivation was below its mean

What This Suggests

Lack of Motivation tended to be lowest (best) when Servings of Fruit was around 12.0 serving.

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

Servings of Fruit Distribution

Lack of Motivation Distribution

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Servings of Fruit Consumption
Effect Variable Name Lack of Motivation
Sinn Predictive Coefficient 0.014555921124246
Confidence Level LOW
Confidence Interval 1.103421943288
Forward Pearson Predictive Coefficient -0.0803
Critical T Value 1.828
Total Servings of Fruit Consumption Over Previous 14 days Before ABOVE Average Lack of Motivation 5.61 serving
Total Servings of Fruit Consumption Over Previous 14 days Before BELOW Average Lack of Motivation 6.27 serving
Duration of Action 14 days
Effect Size very weakly negative
Number of Paired Measurements 19
Optimal Pearson Product 0.019373138731988
P Value 0.28306939280099
Statistical Significance 0.002
Strength of Relationship 1.103421943288
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 2

Servings of Fruit Info

Property Value
Variable Name Servings of Fruit
Aggregation Method SUM
Analysis Performed At 2020-09-15
Duration of Action 14 days
Filling Value 0
Kurtosis 13.704898842561
Maximum Allowed Value 40 serving
Mean 0.74259222033898 serving
Median 0.53389830508475 serving
Minimum Allowed Value 0 serving
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 94
Number of Measurements 411
Number of Measurements (including those generated by tagged, joined, or child variables) 410
Public true
Onset Delay 30 minutes
Standard Deviation 0.49500862537374
Unit Serving
User Variables 115
UPC 819354010616
Variable Category Foods
Variable ID 1715
Variance 0.68573595754334

Lack of Motivation Info

Property Value
Variable Name Lack of Motivation
Aggregation Method MEAN
Analysis Performed At 2020-09-15
Duration of Action 24 hours
Kurtosis 2.040505438469
Maximum Allowed Value 5 out of 5
Mean 3.3661459459459 out of 5
Median 3.3524452724453 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 712
Number of Aggregate Outcomes 158
Number of Measurements 3871
Number of Measurements (including those generated by tagged, joined, or child variables) 3784
Public true
Onset Delay 0 seconds
Standard Deviation 0.42179765402052
Unit 1 to 5 Rating
User Variables 746
UPC 0
Variable Category Symptoms
Variable ID 89387
Variance 0.44483689943851

Introduction

Background

Servings of Fruit (Foods) and Lack of Motivation (Symptoms) 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 Servings of Fruit affect Lack of Motivation?

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 Servings of Fruit for maximizing Lack of Motivation?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Servings of Fruit and Lack of Motivation. Additionally, we attempt to determine the Servings of Fruit values most likely to produce optimal Lack of Motivation 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 20.4% reduction in Lack of Motivation following above-average Servings of Fruit exposure. The Predictor Impact Score (PIS) of 0.01 indicates insufficient evidence for a causal relationship. This finding is statistically significant (p = 0.002).

Statistical Significance

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

After treatment, a 7.1% decrease (-0.517 out of 5) from the mean baseline 2.76 out of 5 was observed. The relative standard deviation at baseline was 41.7%. The observed change was 0.45614 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: 0.826
Critical t-value: 1.828

Since t = 0.83 < 1.83, 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 Servings of Fruit might influence Lack of Motivation.

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 Servings of Fruit 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 Servings of Fruit
  • 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 Servings of Fruit was associated with a 20.4% reduction in Lack of Motivation—a moderate 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 20.4% 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 Servings of Fruit may influence Lack of Motivation 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 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 Servings of Fruit would produce an observable change in Lack of Motivation.
  • Duration of Action: It was assumed that Servings of Fruit could produce an observable change in Lack of Motivation for as much as 14 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Servings of Fruit values and subsequent Lack of Motivation 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

Servings of Fruit 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.

Lack of Motivation 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 Servings of Fruit Affect Lack of Motivation?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-1715-effect-89387-population-study
BibTeX 
@misc{sinn_cause_1715_effect_89387_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Servings of Fruit Affect Lack of Motivation?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-1715-effect-89387-population-study},
  note = {Accessed: January 7, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Servings of Fruit Affect Lack of Motivation?." The Journal of Citizen Science. Accessed January 7, 2026. https://studies.crowdsourcingcures.org/study/cause-1715-effect-89387-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Servings of Fruit Affect Lack of Motivation?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-1715-effect-89387-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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