Measuring A1C and Weight Changes in Consistent Food Trackers

Gabriel W. · 2025-10-14 09:15:19.745412+00

I want to measure whether there’s a decrease in A1C and/or weight over time among users who consistently track their food. My main event is Daily User Behavior. Relevant properties on this event are: meal_count → number of meals tracked that day a1c_reading → user’s daily or latest A1C reading weight_reading → user’s daily or latest weight reading I define “consistent trackers” as users who track at least 5 days in a week. I want to understand, using Mixpanel’s best-practice method: How to measure if these users’ a1c_reading and weight_reading values decrease over time. Whether Mixpanel recommends doing this through Insights, Impact, or Signal, given my goal is to see within-user improvement (not comparison with a control group). If cohorting is necessary, how to handle the issue that new users joining later could distort the trend. Please explain the most reliable approach using Mixpanel’s existing reports and features to answer this question accurately.

To measure whether there’s a decrease in A1C and/or weight over time among users who consistently track their food, here’s how you can approach this in Mixpanel using best practices: ---

- Cohorting is necessary to focus your analysis on users who meet your “consistent tracker” criteria (at least 5 days of tracking per week). - Use the Cohort Builder to define users who did the Daily User Behavior event on at least 5 unique days within a 7-day window. This can be set up as: “Users who did Daily User Behavior on ≥5 days in the last 7 days” Save this as your “Consistent Trackers” cohort. How to create cohorts and frequency-based cohort examples.---

- Use the Insights report for this analysis, as it is designed to visualize trends and aggregate property values over time for cohorts or user segments. Insights overview - In Insights: - Set the event to Daily User Behavior. - Filter or break down by your “Consistent Trackers” cohort. - For measurement, select the property a1c_reading and use the Average aggregation (or Median, if you want to reduce the impact of outliers). Repeat for weight_reading. - Plot these metrics over time (e.g., by week or month) to visualize trends in A1C and weight among consistent trackers. - This approach shows whether the average (or median) A1C and weight readings for your cohort are decreasing over time, indicating within-user improvement. How to use property aggregations in Insights---

- Cohort drift (new users joining the cohort over time) can distort trends. To minimize this: - Consider creating a fixed cohort: For example, users who qualified as “consistent trackers” during a specific baseline period (e.g., the first month of your analysis window). Then, track their A1C and weight over subsequent periods. - Alternatively, use a rolling cohort but be aware that the cohort’s composition will change as new users qualify, which can affect the trend line. Cohort creation and time-based cohorting---

- Insights is recommended for this use case because: - It allows you to track property values (like A1C and weight) over time for a specific cohort. - It supports aggregations (average, median, etc.) and time-based trend visualization. - Impact and Signal are more suited for measuring the effect of a feature launch or identifying properties/events that correlate with a target event, often involving comparison groups or causal inference, which is not your primary goal here. Insights report use cases, Cohorts in analysis---

1. Create a “Consistent Trackers” cohort using the cohort builder with your frequency criteria. 2. Use the Insights report to plot the average (or median) a1c_reading and weight_reading for this cohort over time. 3. Mitigate cohort drift by either fixing your cohort at a baseline period or being mindful of rolling cohort effects. 4. Interpret the trend lines to determine if there is a decrease in A1C and/or weight among consistent trackers.This approach leverages Mixpanel’s strengths in cohorting and time-based property analysis, and is the most reliable method based on the available features and documentation.---References: - Cohorts: Group users by demographic and behavior - Using Cohorts in Analysis - Insights: Visualize trends and compositions within your data - Insights: Measurements

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Measuring A1C and Weight Changes in Consistent Food Trackers

23 comments

1. Define the “Consistent Trackers” Cohort

2. Analyze A1C and Weight Trends Over Time

3. Handling New Users Joining Later

4. Why Use Insights (Not Impact or Signal)?

Summary of Steps

1. Dynamic Cohort: “Users who did Daily User Behavior on ≥5 days in the last 7 days”

2. Fixed Cohort: Users Who Qualified During a Baseline Period