Sign in with your admin account to manage legal agreements and user roles.
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Legal Agreement Editor
Update legal agreement content without deploying code. Changes take effect after cache expires (~5 minutes).
User Role Management
Manage user roles with advanced filtering and inline editing.
Data Compliance Actions
Handle CCPA data requests from users who contact support via email.
CCPA Requirement: Users may request data export or account deletion via email.
Search for the user below and initiate the appropriate action on their behalf.
All actions are logged for compliance auditing.
App Feedback Management
Review and manage feedback submitted by users.
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Total
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New
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Bugs
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Features
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High Priority
Type
Subject
User
Area
Urgency
Status
Date
Error Logs
Monitor application errors and warnings from production.
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Total Logs
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Errors
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Warnings
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Affected Users
Time ↓
Level
Tag
Message
User
Device
Actions
App Configuration
Manage application settings. Changes take effect after app cache expires (~5 minutes).
Note: Configuration values are stored as JSON. Be careful when editing complex values.
Invalid JSON will be rejected.
Add New Configuration
Current Configuration
Key
Value
Last Updated
Actions
Waitlist Dashboard
Monitor and manage users who have signed up for the waitlist.
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Total Signups
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Beta Interested
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This Week
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This Month
Top Sources
Last 7 Days
0 signups shown
Name
Email
Beta Interest
Platform
Source
Signed Up
Actions
📋
No waitlist signups yet
When users sign up for the waitlist, they'll appear here.
Metrics Dashboard
User activity and feature adoption across the platform.
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Loading metrics...
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Total Users
?Total registered users in this cohort. Includes both active and inactive accounts.e.g. 15 total users signed up for beta
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Active (7d)
?Users who logged at least one entry (bathroom, wellness, or note) in the last 7 days. Your best signal for current engagement.e.g. 8 of 15 users active this week = 53% weekly engagement
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Onboarded
?Users who completed the onboarding flow. Low completion may signal friction in the signup experience.e.g. 12 of 15 completed = 80%. If dropping, check for onboarding UX issues
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Active (30d)
?Users active in the last 30 days. Compare with Active (7d) — a big gap means users try the app but don't stick around weekly.e.g. 10 active (30d) but only 4 active (7d) = 60% drop-off after first use
Weekly Active Users
?Unique users who logged any entry each week over the last 12 weeks. The single most important adoption metric — shows whether engagement is growing or declining.Rising bars = healthy growth. Flat or declining = retention problem that needs attention
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this week
Signups by Week
?New user registrations per week. Compare with WAU — if signups are up but WAU is flat, you have a retention problem. If both are rising, growth is healthy.e.g. 3 signups/week but WAU still at 5 = new users aren't sticking
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total signups
╱Feature Adoption Over Time
?Weekly unique users per feature over the last 12 weeks. Shows which features are gaining momentum vs. being abandoned. A feature with rising lines is worth investing in.If Bathroom rises from 3 to 8 users/week while Notes stays at 2, double down on bathroom UX
Bathroom
Wellness
Notes
Episodes
☰ Feature Adoption
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Which features are being used, by how many people, and how deeply. Sort mentally by Adoption % to find underperforming features.
Feature
?Each tracked feature in the app with a sub-detail row showing the breakdown (e.g. urination vs bowel, full vs quick check-ins).e.g. "Bathroom Logging — 120 urination, 45 bowel"
Total Entries
?The total number of logged entries across all users for this feature. Higher counts indicate heavier overall usage.e.g. 342 total bathroom events logged by everyone combined
Unique Users
?How many distinct users have used this feature at least once. Compare against Total Users to see breadth of adoption.e.g. 8 of 12 total users have logged at least one bathroom event
Adoption
?Percentage of active users (30d) who have used this feature. Shows how widely a feature has been discovered and tried.e.g. 67% means 8 out of 12 active users have tried it
Per User
?Average entries per user who has used this feature (Total Entries ÷ Unique Users). Measures depth of engagement — are adopters actually using it regularly?e.g. 5.3 means each user who adopted this feature logged ~5 entries on average
❤ Wellness Check-in Breakdown
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Deep dive into wellness check-in variants. Compare Active (7d) to Users to spot variants losing steam.
No wellness check-in data available
Variant
?The type of wellness check-in. "Full" check-ins capture all dimensions (discomfort, stress, energy, mood). "Quick" captures a single dimension for fast logging.e.g. "Morning" = full check-in with sleep data, "Quick: Stress" = single stress-only entry
Entries
?Total number of check-ins of this variant across all users. Compare across variants to see which check-in styles are most popular.e.g. 42 Morning check-ins total — if this is much higher than Evening, users may prefer logging in the AM
Users
?Number of distinct users who have used this variant at least once. Shows breadth — how many people discovered and tried this check-in style.e.g. 8 users tried Morning check-ins vs. 3 for Evening — Morning is more widely adopted
Active (7d)
?Users who logged this variant in the last 7 days. A recency signal — is this variant gaining traction or was it a one-time experiment?e.g. 5 of 8 Morning users active recently = healthy retention. 1 of 6 Evening users = variant may be dying
Per User
?Average entries per user for this variant (Entries ÷ Users). Measures how deeply engaged adopters are with this specific check-in style.e.g. 5.3 per user for Morning vs 2.1 for Quick: Energy — Morning users are more committed
╱Daily Activity (Last 30 Days)
?Total entries per day broken down by type. Look for consistent daily usage vs. sporadic bursts. Gaps indicate days with zero engagement across all users.Steady 5-10 entries/day = healthy habit. Spikes then silence = users trying then abandoning
Bathroom
Wellness
Notes
★Engagement Depth
?Quality signals beyond just "are they logging." Provider tags mean users are preparing for appointments. Streaks mean daily habits are forming. Time-to-first-entry shows onboarding friction.
Episode completion rate
?% of started episodes that were completed. Low rates may mean episodes are too long or users don't see value in finishing them.e.g. 40% completion = users start tracking bad days but give up halfway-
Total notes
?Total notes logged by users. Notes capture free-form observations about symptoms, contributors, and questions for providers.e.g. 25 notes = users actively documenting their experiences-
Avg days to first entry
?Average days between signup and first logged entry. Lower is better — high values suggest onboarding friction or unclear value proposition.e.g. 0.5 days = users start immediately. 3+ days = they signed up but hesitated to engage-
↻Retention
?When users last tracked something. Healthy apps have most users in "Today" or "1-7 days." Heavy "30+ days" or "Never" segments signal churn risk.If 40% are in "Never tracked" — onboarding isn't converting signups to active users
When users last tracked something
💬App Feedback Activity
?How actively users are providing feedback. High participation rates mean engaged users. Track submission trends and response times to maintain feedback loop health.
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Measures the health of your feedback loop — are users engaged, and are you responding?
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Total Feedback
?All feedback submissions ever. Includes bug reports, feature requests, and general feedback across all time.
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Submitters
?Unique users who have submitted at least one piece of feedback. Compare to total users to gauge participation breadth.e.g. 5 of 12 users submitted feedback = 42% participation
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Participation Rate
?% of cohort users who have ever submitted feedback. Target 50%+ for healthy engagement. Below 30% means most users are silent.e.g. 42% — decent but could improve. Consider prompting inactive users
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Last 7 Days
?Feedback received in the past week. A recency signal — are testers still actively providing input, or has feedback dried up?e.g. 0 in last 7 days after initial burst = feedback fatigue, may need to re-engage
Feedback by Type
?Distribution of feedback types. Mostly bug reports = stability issues. Mostly feature requests = users want to invest in the product. Balance is healthy.
Response Pipeline
?Where feedback items sit in the workflow. Heavy "New" = falling behind on reviews. Growing "Resolved" = healthy feedback loop. Track response rate and time to maintain trust with testers.
Response Rate
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Avg Response Time
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OK to Contact
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Top Feedback Areas
?Which parts of the app generate the most feedback. High-feedback areas need the most attention — either they're broken or they're the most-used surfaces.
Weekly Submissions
?Feedback volume per week. An initial spike is normal. Watch for sustained trickle vs. complete drop-off. Bug reports trending down = stability improving.
Bugs
Features
General
👤Per-User Activity
?Individual user breakdown showing signup date, last activity, and per-feature usage counts. Identify power users, at-risk users (no recent activity), and users who never engaged.
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Sorted by total entries. Green dot = active in last 7 days. Red = dormant (7+ days). Gray = never tracked.
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Signed Up↕
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Error Log Details
Permanent Account Deletion
You are about to permanently delete the account for:
This action CANNOT be undone
The following will be permanently deleted:
User profile and account credentials
All health tracking data (bathroom events, wellness checks)