Accelerometer

Number of steps taken

Detects walking motion

Calories burned represents total daily energy expenditure (TDEE), comprising basal metabolic rate (BMR), thermic effect of food, and activity energy expenditure. Wearables estimate calories using heart rate, motion, and user demographics (age, weight, height, sex). Accuracy is typically ±15-25% compared to doubly-labeled water or indirect calorimetry. Heart rate-based estimation improves accuracy over accelerometer-only methods. Significant individual variation exists in metabolic efficiency. Calorie data is useful for relative comparisons (yesterday vs. today) rather than absolute values. For weight management, tracking trends over weeks is more meaningful than daily figures. Exercise calories are often overestimated, leading to compensatory overeating. Energy expenditure decreases with weight loss through metabolic adaptation.

Movement-based estimation

Total time asleep

Detects movement during sleep

Sleep stages classify sleep into light sleep (N1/N2), deep sleep (N3/slow-wave), and REM (rapid eye movement) sleep. A typical night cycles through stages 4-6 times, with more deep sleep early and more REM later. Deep sleep is critical for physical restoration, immune function, and memory consolidation. REM sleep supports emotional processing, learning, and creativity. Adults should get 1-2 hours of deep sleep and 1.5-2 hours of REM per night. Wearables estimate sleep stages using heart rate variability patterns and movement, but accuracy is limited compared to polysomnography (the clinical gold standard using EEG). Consumer devices may misclassify stages by 20-30%. Trends are more reliable than individual night data. Alcohol, medications, and sleep disorders significantly alter stage distribution.

Combined with HR for staging