Yes. The records fall into a few natural groupings, but they differ a lot in how useful they are. These breakdowns all come from the three queries above, which compare average sleep, sleep latency, fatigue and bedtime phone minutes per segment. **1. Sleep-debt category is the clearest segmentation, but it is an outcome.** - It has four tiers: Optimal Recovery (1,387 users), Mild Deficit (2,004), Moderate Debt (4,462) and Severe Sleep Debt (647). - It is computed from `total_sleep_hours`, so it describes outcomes rather than independent behaviour. - Every other measure moves steadily across the tiers. Average sleep falls from 8.35h to 3.76h and sleep latency rises from 28.9 to 72.7 minutes. Fatigue climbs from 1.08 to 9.58 and bedtime phone minutes from 33.5 to 123.6. - Severe Sleep Debt is a distinct tail: phone use of about 124 minutes is roughly double the Moderate group's 64. **2. Chronotype and occupation are the strongest input-side segments.** - Night Owls (2,455 users) average 5.01h of sleep and fatigue of 5.4. Morning Larks (2,165) average 7.38h and fatigue of 2.53, with Intermediate in between at 6.44h. - Healthcare / Shift Workers (997) stand out in the same way, at 5.02h and fatigue of 5.54. The other four occupations all sit at about 6.4h and fatigue of 3.5–3.7. - These two dimensions split sleep duration and fatigue far more than anything else in the data. **3. Bedtime app mainly separates sleep latency.** - Average latency runs from 33.2 minutes for News / Reading and 36.4 for Streaming up to 42.5 for Instagram / Reddit and 46.1 for TikTok / Reels. - Sleep duration and phone minutes barely differ across apps. The split is a moderate "stimulating vs. passive" one. **4. Several dimensions are not useful for segmenting.** - Gender gives almost identical groups: 6.26–6.31h of sleep and 3.74–3.80 fatigue. - Blue light filter use shows only a small gap: 6.33h vs 6.21h of sleep and fatigue of 3.58 vs 3.99. **Recommended grouping:** use chronotype × occupation (especially Night Owl and shift-worker groups) as the main profile segments. Use primary bedtime app as a secondary lens for latency, and sleep-debt category as the outcome label, as in the chart below. These are observational averages, so they show association rather than cause. I did not run a clustering analysis or cross the dimensions together, so any combined segments would still need to be checked. {"action":"final","content":"Yes. The records fall into a few natural groupings, but they differ a lot in how useful they are. These breakdowns all come from the three queries above, which compare average sleep, sleep latency, fatigue and bedtime phone minutes per segment.\n\n**1. Sleep-debt category is the clearest segmentation, but it is an outcome.**\n- It has four tiers: Optimal Recovery (1,387 users), Mild Deficit (2,004), Moderate Debt (4,462) and Severe Sleep Debt (647).\n- It is computed from `total_sleep_hours`, so it describes outcomes rather than independent behaviour.\n- Every other measure moves steadily across the tiers. Average sleep falls from 8.35h to 3.76h and sleep latency rises from 28.9 to 72.7 minutes. Fatigue climbs from 1.08 to 9.58 and bedtime phone minutes from 33.5 to 123.6.\n- Severe Sleep Debt is a distinct tail: phone use of about 124 minutes is roughly double the Moderate group's 64.\n\n**2. Chronotype and occupation are the strongest input-side segments.**\n- Night Owls (2,455 users) average 5.01h of sleep and fatigue of 5.4. Morning Larks (2,165) average 7.38h and fatigue of 2.53, with Intermediate in between at 6.44h.\n- Healthcare / Shift Workers (997) stand out in the same way, at 5.02h and fatigue of 5.54. The other four occupations all sit at about 6.4h and fatigue of 3.5–3.7.\n- These two dimensions split sleep duration and fatigue far more than anything else in the data.\n\n**3. Bedtime app mainly separates sleep latency.**\n- Average latency runs from 33.2 minutes for News / Reading and 36.4 for Streaming up to 42.5 for Instagram / Reddit and 46.1 for TikTok / Reels.\n- Sleep duration and phone minutes barely differ across apps. The split is a moderate \"stimulating vs. passive\" one.\n\n**4. Several dimensions are not useful for segmenting.**\n- Gender gives almost identical groups: 6.26–6.31h of sleep and 3.74–3.80 fatigue.\n- Blue light filter use shows only a small gap: 6.33h vs 6.21h of sleep and fatigue of 3.58 vs 3.99.\n\n**Recommended grouping:** use chronotype × occupation (especially Night Owl and shift-worker groups) as the main profile segments. Use primary bedtime app as a secondary lens for latency, and sleep-debt category as the outcome label, as in the chart below. These are observational averages, so they show association rather than cause.\n\nI did not run a clustering analysis or cross the dimensions together, so any combined segments would still need to be checked.","charts":[{"kind":"bar","x":"sleep_debt_category","series":["avg_fatigue","avg_latency_min"],"title":"Fatigue and sleep latency by sleep-debt category"}]}