1. Define focus proxies before analysis
Most people ask for “focus” but do not define what they mean. Sarenica can help, but you’ll get stronger results if you choose a proxy set first: session continuity, active minutes, break patterns, and optionally fatigue.
Treat focus as a pattern across signals, not a single number. That is how you avoid overfitting to one metric.
2. Use comparable windows
Comparing today to last month is usually noisy. Better comparisons are:
- this week vs last week
- weekday vs weekend patterns over 30 days
- morning vs afternoon sessions over a consistent window
If coverage is low, start with a descriptive summary before asking for relationship or statistical conclusions.
3. Separate interruptions from low activity
Low active minutes can mean many things: breaks, meetings, context switching, or actual low-focus work. Use labels and session context where possible so the AI can explain patterns instead of guessing.
Good focus questions
- "Compare my session consistency on weekdays vs weekends over the last 30 days."
- "What time block shows the strongest repeatable activity pattern in the last 2 weeks?"
- "Summarize focus proxies and flag where coverage is too low for stronger claims."
4. Upgrade to deeper analysis only after patterns stabilize
If you ask for correlations or methods too early, you may get technically correct but operationally weak results. Start with pattern summaries, then move to comparisons, and only then ask for technical detail.