Eat a Yesbar and record status.

Two Hour Follow-up.

The study titled "Testing YesBar Experience Approaches" is an exploratory investigation designed to evaluate two distinct methodologies for data collection concerning YesBar consumption. As a single-arm observational trial, this study allows participants to serve as their own control, which is an efficient method to gather initial insights without the complexities of a control group. At Efforia, such studies are termed "Signal Phase" studies, as they aid in providing directional indications of the intervention's effectiveness—in this case, the YesBar. This approach is particularly useful for obtaining preliminary data that can suggest potential positive outcomes, which can then be further explored through more extensive and controlled trials.

In terms of study description, the trial explores two approaches for data collection. The first approach involves a singular data entry form, originally conceived as the straightforward method for gathering data. The second approach dissects the information into two distinct components, potentially providing a more nuanced understanding of the data points. This study is intended for internal purposes, and it has not sought Institutional Review Board (IRB) approval, underscoring its preliminary nature. The findings from this Signal Phase trial are crucial, as they will not only indicate the effectiveness of the YesBar but also furnish much-needed safety data, which currently remains limited.

Participants in this study will follow a structured experience that includes specific tasks and follow-up activities. The study experience involves a "Two Hour Follow-up," which likely serves as a critical time point for assessing the immediate effects and participant feedback. Participants are instructed to "Eat a Yesbar and Record Status," thus engaging directly with the product and providing real-time data on their experiences. This hands-on interaction, along with the structured follow-up, is designed to yield actionable insights into the YesBar's impact. Should the results suggest a positive signal, indicating that the YesBar is effective, the study would lay the groundwork for considering more robust and comprehensive study designs in the future. For further information on this study's methodology, the "Minimal Risk Umbrella protocol" provides more detailed insights.

To determine the minimum sample size required for detecting a statistically significant effect in the Efforia's sub-studies, we need to conduct a power analysis considering the assumptions and constraints provided. Here is how we can approach this:

Sample Size Calculations

Minimum Sample Size Estimate (No Stratification)

For the unstratified analysis, we will use the paired t-test as it's a single-arm observational trial. The assumptions are:

• Effect Size (Cohen's d): 0.1
• Significance Level (α): 0.05 and robustness check at 0.10
• Power (1 - β): 0.80
• Dropout Rate: 60%

Using these parameters, the initial sample size before accounting for dropout can be calculated with standard power analysis formulas for a paired t-test. Given the small effect size, the initial sample size would be large.

For a Cohen's d of 0.1, α = 0.05, and power = 0.80, the initial sample size is approximately 1572 participants. Adjusting for a 60% dropout rate, the sample size required is:

[ n = \frac{1572}{1 - 0.60} \approx 3930 ]

Middle Sample Size Estimate (Stratified by Treatment Expectancy)

When stratifying by treatment expectancy (high vs. low), we need to account for the stratification factor in the analysis. This increases the complexity and potentially the sample size required.

Assuming equal distribution of participants across strata, we divide the sample size by the number of strata (2 in this case: high and low treatment expectancy). However, to maintain power within each stratum, we multiply the calculated sample size by 1.2 due to the stratification:

[ n_{stratified} = 3930 \times 1.2 \approx 4716 ]

Maximum Sample Size Estimate (Stratified by Various Health and Fitness Measures)

For maximum stratification, considering additional factors (sex, physical activity levels, etc.), we assume approximately 6 different strata. This further increases the sample size requirement due to the need to maintain adequate power within each stratum. We multiply the initial sample size by 1.5 to account for the increased number of strata:

[ n_{max_stratified} = 3930 \times 1.5 \approx 5895 ]

Justification for Sample Sizes

• Minimum Sample Size: This calculation provides the base number of participants needed to detect a small effect size in a single-group setup without accounting for potential biases introduced by varying participant characteristics.

• Middle Sample Size: Stratifying by treatment expectancy helps control for biases related to participants' beliefs and expectations about the intervention's effectiveness. This approach provides a more precise estimate of the effect within these sub-groups.

• Maximum Sample Size: By stratifying across multiple self-reported measures, we capture a wide range of variables that could influence outcomes. This approach ensures robustness and generalizability of the findings across different participant profiles but requires a larger sample to maintain statistical power across all strata.

These calculations are essential for designing a study that is both statistically robust and capable of addressing the varied research questions posed by the exploratory nature of Efforia's interventions.

In conducting this single-arm observational trial under the Minimal Risk Citizen Science Umbrella Protocol, we must acknowledge inherent limitations, particularly the potential for bias due to the lack of a control group. This limitation is especially pertinent given the study's objective to evaluate different data collection methods for the YesBar, where participants essentially serve as their own controls. To mitigate this bias, an expectations questionnaire will be utilized to quantify and understand participant expectations, providing insights into how these expectations might influence the outcomes. This approach helps to contextualize the results within the framework of participant perceptions, offering a degree of control over the subjective variability inherent in single-arm trials.

Another limitation of this study is the potential for sample size constraints, which can affect the statistical power and generalizability of the findings. To address this, we have carefully designed our statistical methodology, potentially stratifying the data by participant demographics to ensure a more nuanced analysis. This stratification is critical given the trial's objectives to internally compare data collection approaches for the YesBar. By accounting for demographic variables, we aim to minimize confounding factors and enhance the reliability of our findings. Furthermore, this trial serves as a signal detection study, where positive results could justify more robust, controlled studies to replicate and validate initial findings, ensuring the reliability and applicability of the outcomes.

This study exemplifies Efforia's commitment to democratizing clinical research through the Minimal Risk Umbrella Protocol. By supporting research questions and researchers that might otherwise be overlooked, this protocol facilitates the exploration of innovative tools like the YesBar within the constraints of ethical and practical considerations. Although the study is designed for internal use, its implications extend to the broader research community by highlighting the potential of alternative data collection methods. The trial's focus on signal detection rather than definitive conclusions allows for a responsible approach to emerging technologies, with the potential to inform future, more controlled studies. For more information, individuals are encouraged to refer to the Minimal Risk Umbrella Protocol.