Insights and Analytics

The Circadian Rhythm feature in Bhaav helps you discover your body's natural daily mood patterns by identifying your typical "Peak Mood Time" and "Low Mood Time." To do this, the app analyzes all your logged moods, grouping them by the hour they were recorded. For each hour of the day, it calculates an average mood score based on the types of emotions you've logged (positive or negative) and their respective intensities. The hour with the highest average mood score is flagged as your general peak time, while the hour with the lowest average is identified as your typical low point. Understanding these daily variations is useful because it can help you become more aware of when you naturally have more emotional energy or when you might feel more subdued, allowing you to potentially schedule demanding tasks or self-care activities more effectively. For this analysis to be insightful, Bhaav requires at least 30 mood entries logged at various times spread across different days to build a comprehensive 24-hour mood profile. Consistent logging of your mood, its intensity, and the time of day is key. 

The Weekly Mood Profile in Bhaav offers a detailed view of your typical emotional patterns across different days of the week and specific times within those days. For each day (e.g., Monday, Tuesday), it calculates an average "mood score" for four distinct periods: Morning (6 AM - 11 AM), Afternoon (12 PM - 4 PM), Evening (5 PM - 9 PM), and Night (10 PM - 5 AM). This score is determined by the moods you've logged and their intensities, where positive moods contribute to a higher score and negative moods to a lower one. This profile is useful for identifying recurring trends, such as whether you consistently feel better on certain days or at particular times, like Sunday mornings versus Wednesday afternoons. Recognizing these patterns can help you better plan your activities and understand how your weekly rhythm might influence your emotional state. To generate this detailed profile, Bhaav requires at least 4 weeks of consistent mood entries (approximately 28 logs) spread across various days and times, with each entry including the mood, its intensity, and the date/time. 

The Positive Shift Patterns insight focuses on understanding how you successfully transition from a negative to a positive emotional state. This feature analyzes your mood history to find the most common instance of a negative mood entry being followed by a positive one, such as moving from 'Stressed' to 'Calm'. For this recurring transition, it then identifies the contextual tags most frequently associated with the positive outcome, suggesting which activities or situations may have contributed to the improvement. The result displays your most common positive shift, how many times it has occurred, and a list of the top contexts that were present, offering data-driven insight into what helps you recover. 

The Mood Stability Analysis provides a quantitative measure of your emotional consistency, which can be viewed overall or filtered to a specific context using your tags. This insight processes your entries chronologically to calculate two key metrics. The Stability Score is based on the magnitude of change between different mood types, using a pre-defined "distance" to distinguish between small and large emotional shifts. The Variability Score offers a more detailed measure by considering both this change in mood type and the change in its logged intensity from one entry to the next. The feature presents you with both a percentage-based Stability Score and a numerical Variability Score, offering a clear, data-driven look at the consistency of your moods.