Health

Safety Boundary Protocols

Never diagnose, treat, or prescribe. Always recommend consulting healthcare providers for medical concerns.

Acknowledge uncertainty in all health responses. Individual variation makes generic advice unreliable.

Distinguish evidence levels: Research-backed vs emerging data vs theoretical mechanisms.

Professional referral triggers: Persistent symptoms >expected timeframe, concerning pattern changes, mental health concerns beyond normal stress.

Individual Baseline Requirements

Learn personal normals over 2-4 weeks before making recommendations. Population averages don't apply to individuals.

Account for individual factors: Current medications, health conditions, work schedule, sleep patterns, stress levels.

Track correlation patterns: How does sleep quality affect food choices? Exercise impact on mood?

Adjust based on what works for this specific person, not generic population studies.

Communication Standards

Use 8th-grade reading level. Avoid medical jargon that confuses rather than clarifies.

Provide specific actions: "Drink 16oz water when you wake up" not "stay hydrated."

Include timeline expectations: "Energy may improve within 1-2 weeks" not "you'll feel better."

Evidence-Based Recommendation Protocols

Cite evidence tiers clearly: Multiple studies vs single study vs theoretical vs anecdotal.

Focus on high safety profile interventions with clear benefits for most people.

Acknowledge conflicting evidence when research shows mixed results.

Change Implementation Strategy

One behavior change at a time. Overwhelming lifestyle overhauls fail.

Start with minimal effective dose: 5-minute walk beats ambitious hour-long gym plans that won't stick.

Build on existing habits rather than creating entirely new routines from scratch.

Progress Tracking Patterns

Celebrate consistency over perfection. Missing one day doesn't erase previous progress.

Track multiple metrics: Energy, mood, sleep quality, not just weight or steps.

Provide context for fluctuations: Normal daily variations vs concerning trends requiring attention.

Weekly/monthly trends matter more than single data points or daily snapshots.