Higher Lack Of Motivation Predicts Very Slightly Higher Fear for Population

Higher Lack Of Motivation Predicts Very Slightly Higher Fear for Population

Contents

Variables

A
Lack of Motivation 870
A
Fear 1115

Categories

A
Symptoms 13336
A
Emotions 2028

Tags

Medium Confidence
Very Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 1.6% average increase in Fear following above average Lack of Motivation.

Abstract

Fear was generally 6.54% higher than average after 2.99 out of 5 of Lack of Motivation per 5 days.

Aggregated data from 15 study participants suggests with a MEDIUM degree of confidence (p=0.281, 95% CI -0.685 to 0.69) that Lack of Motivation has a very weakly positive predictive relationship (R=0.0026) with Fear.

The highest quartile of Fear measurements were observed following an average 3.08 out of 5 Lack of Motivation.

The lowest quartile of Fear measurements were observed following an average 3.2 out of 5 of Lack of Motivation.

After an onset delay of 0 seconds, Fear is typically 4% lower than average over the 5 days following around 3.2 out of 5 of Lack of Motivation Lack of Motivation.

Keywords: Lack of Motivation, Fear, N-of-1 trials, real-world evidence, causal inference, observational study

Moderate Confidence: Based on 15 participants. More data would increase certainty.

Results

Primary Findings

Analysis of 275 paired observations from 15 participants revealed a modest improvement in Fear following above-average Lack of Motivation exposure.

+6.5%
Change from Baseline
Modest effect on Fear
0.00
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

Medium
Confidence
0.003
Correlation (r)
p = 0.064
Significance
z = 0.63
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Lack of Motivation:

  • Fear increased by 6.5% on average
  • Temporal analysis supports Lack of Motivation 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 15 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 Lack of Motivation values associated with high and low Fear 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

Lack of Motivation Distribution

Fear Distribution

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Lack of Motivation
Effect Variable Name Fear
Sinn Predictive Coefficient 0.0020198615891128
Confidence Level MEDIUM
Confidence Interval 0.68763393630759
Forward Pearson Predictive Coefficient 0.0026
Critical T Value 1.7926666666667
Average Lack of Motivation Over Previous 5 days Before ABOVE Average Fear 3.08 out of 5
Average Lack of Motivation Over Previous 5 days Before BELOW Average Fear 3.2 out of 5
Duration of Action 5 days
Effect Size very weakly positive
Number of Paired Measurements 275
Optimal Pearson Product 0.037114783800869
P Value 0.28106320017578
Statistical Significance 0.0635
Strength of Relationship 0.68763393630759
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 15

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

Fear Info

Property Value
Variable Name Fear
Aggregation Method MEAN
Analysis Performed At 2020-09-17
Duration of Action 24 hours
Kurtosis 3.4116931250559
Maximum Allowed Value 5 out of 5
Mean 2.2840202686203 out of 5
Median 2.2359612572647 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 1002
Number of Aggregate Outcomes 113
Number of Measurements 22861
Number of Measurements (including those generated by tagged, joined, or child variables) 22709
Public true
Onset Delay 0 seconds
Standard Deviation 0.47483589524388
Unit 1 to 5 Rating
User Variables 1670
UPC 0
Variable Category Emotions
Variable ID 1313
Variance 0.56609015151219

Introduction

Background

Lack of Motivation (Symptoms) and Fear (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

Does Lack of Motivation affect Fear?

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 Lack of Motivation for maximizing Fear?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Lack of Motivation and Fear. Additionally, we attempt to determine the Lack of Motivation values most likely to produce optimal Fear 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 6.5% improvement in Fear following above-average Lack of Motivation exposure. The Predictor Impact Score (PIS) of 0.00 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 Fear is not statistically significant at a 95% confidence interval. This suggests that the Lack of Motivation value may not have a significant influence on the Fear value, or that more data is needed to detect an effect.

After treatment, a 1.6% increase (0.202 out of 5) from the mean baseline 2.61 out of 5 was observed. The relative standard deviation at baseline was 18.62%. The observed change was 0.628972 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: 0.836
Critical t-value: 1.793

Since t = 0.84 < 1.79, 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 Lack of Motivation might influence Fear.

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 Lack of Motivation 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 Lack of Motivation
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 15 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 Lack of Motivation was associated with a 6.5% improvement in Fear—a modest effect. The Predictor Impact Score of 0.00 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.00 and a 6.5% 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 Lack of Motivation may influence Fear 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 15 participants. Thus, the study design is equivalent to the aggregation of 15 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 Lack of Motivation would produce an observable change in Fear.
  • Duration of Action: It was assumed that Lack of Motivation could produce an observable change in Fear for as much as 5 days after the stimulus event.

Statistical Methods

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

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.

Fear 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 Lack of Motivation Affect Fear?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-89387-effect-1313-population-study
BibTeX 
@misc{sinn_cause_89387_effect_1313_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Lack of Motivation Affect Fear?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-89387-effect-1313-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Lack of Motivation Affect Fear?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-89387-effect-1313-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Lack of Motivation Affect Fear?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-89387-effect-1313-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

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