Many people notice shifts in their mood but struggle to pinpoint what influences them. While major triggers like stressful events are easy to spot, subtler factors—such as sleep patterns, hydration, or social interactions—often go unnoticed. Existing tools tend to either passively collect data (like fitness trackers) or require manual journaling, leaving a gap for systems that provide clear, actionable insights.
One approach could involve creating a mobile app where users could:
For instance, after a few weeks of tracking, the app might surface patterns like "Your mood scores are 20% higher on days when you sleep more than 7 hours." The system could pull in data from wearables or calendars to reduce manual input while maintaining user privacy through local-first data storage.
Current mood-tracking apps typically focus either on manual logging (like Daylio) or clinical screening (like Moodpath). There may be an opportunity for a tool that bridges these approaches by:
A basic version could begin with manual mood tracking and simple pattern detection, then expand to include features like:
The key would be maintaining ease of use while demonstrating clear value from the earliest stages of use.
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Digital Product