Analyze Fitness Data and Generate Insights

Analyzes exercise data to identify patterns, track fitness progress, and generate personalized training recommendations. Its five functions: trend analysis (volume, frequency, and intensity-distribution changes, with direction, magnitude, and significance); progress tracking by activity type - running (pace, distance, heart rate), strength (weight lifted, volume, RPE), endurance (duration and distance), flexibility (range of motion) - reporting start-versus-current values, percentage improvement, and milestones; habit analysis (preferred workout time, weekly frequency pattern, activity-type preference, rest-day distribution, a 0-100 consistency score); correlation analysis between exercise and other health metrics - weight, blood pressure, blood glucose, mood and sleep - reporting a correlation coefficient, strength, statistical significance, and practical advice; and personalized recommendations (frequency, intensity, activity type, timing, recovery) grounded in WHO, ACSM, and AHA exercise guidelines plus the user's own history and goals.

配速 = 运动时长 / 距离

Its algorithms: linear-regression trend analysis (slope sign indicates rising, falling, or stable), the Pearson correlation coefficient (absolute value under 0.3 is weak, 0.3-0.7 moderate, 0.7 or above strong), pace calculation (duration divided by distance), and MET-based calorie estimation (MET times body weight in kilograms times hours, with specific MET values - brisk walking 3.5-5, jogging 8, running 10, swimming 6-10, casual cycling 4, strength training 5, yoga 3). It explicitly flags danger signals during analysis: exercise heart rate above 95% of max, resting heart rate above 100 bpm, systolic blood pressure at or above 180 mmHg or diastolic at or above 110, seven consecutive days of high-intensity training or RPE above 17, and weekly weight loss exceeding 1 kg.

Use it when analyzing exercise logs, training intensity, workout habits, or fitness progress, covering running, strength, endurance, or flexibility, or when correlating exercise data with other health modules.

Its medical safety boundary is explicit: it can do exercise statistics, trend visualization, correlation calculation, and general exercise advice; it cannot diagnose disease, assess exercise-related injury risk, prescribe a specific exercise regimen, or diagnose or treat an exercise injury. Recommendations are graded in three levels - general guideline-based advice, personalized-but-reference-only advice, and medical-management advice requiring physician confirmation.

Input: exercise log data (data/fitness-logs/YYYY-MM/YYYY-MM-DD.json), a user fitness profile (data/fitness-tracker.json), and correlated health data (hypertension, diabetes, weight/BMI trackers). Output: a trend report, a progress-tracking report, or a correlation report - each with specific numbers, direction or significance, and actionable advice - triggered via commands like /fitness trend 3months, /fitness analysis progress running, or /fitness analysis correlation blood_pressure.

Reads from and correlates with existing hypertension-tracker, diabetes-tracker, and profile modules within the same health-tracking system.

Users wanting a statistics-grounded view of their exercise trends, progress, and health correlations, not a substitute for medical exercise prescription or injury diagnosis.