Wellness Habit Builder
PRODesign personalized habit stacks across sleep, exercise, nutrition, and mental health. AI-driven system that integrates four wellness domains for sustainable behavior change.
Example Usage
“I sleep 5-6 hours nightly but need to be sharp for 10-hour workdays. I work from home, exercise zero, eat inconsistent meals. My doctor said my cortisol is elevated. Build me a 6-week habit stack that prioritizes sleep first, then energy, without requiring gym membership or meal prep. Start tiny—I’m skeptical of habit apps but willing to try 5 minutes max per day.”
Skill Prompt
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How to Use This Skill
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Suggested Customization
| Description | Default | Your Value |
|---|---|---|
| The main health outcome the user wants to achieve | improve sleep quality | |
| Existing daily routines that can serve as triggers | morning coffee, evening dinner, brushing teeth | |
| Minutes available for new habits | 15 | |
| Health tracking device if any | Apple Watch | |
| Relevant health considerations | none | |
| Preferred feedback modality | visual |
Research Sources
This skill was built using research from these authoritative sources:
- Atomic Habits: An Easy & Proven Way to Build Good Habits and Break Bad Ones James Clear's foundational framework on identity-based habits, habit stacking, and the 3 Rs of habit change
- Tiny Habits: The Small Changes That Change Everything (BJ Fogg) Fogg Behavior Model (B = M + A + T) showing how motivation + ability + trigger enable behavior
- Digital Behavior Change Intervention Designs for Habit Formation: Systematic Review (JMIR, 2024) Meta-analysis of 41 DBCIs showing most effective techniques: self-monitoring, goal setting, prompts/cues
- The Neuroscience of Habit Formation (ScienceExcel, 2024) Explores basal ganglia circuits, neuroplasticity, and how meditation, sleep, sunlight, and exercise shape neural landscape
- Context Stability in Habit Building Increases Automaticity and Goal Attainment (PLoS ONE, 2022) Demonstrates context (time, location, preceding action) has ongoing effect on habit execution
- What can machine learning teach us about habit formation? Evidence from exercise and hygiene (PNAS, 2023) ML models reveal which context variables predict behavior; shows interventions should target individuals' specific context sensitivities
- Effects of habit formation interventions on physical activity habit strength: meta-analysis (IJBNPA, 2023) Meta-analysis of 10 studies on PA habit interventions; identifies key BCTs: self-monitoring, cue planning, habit reversal
- Self-Efficacy in Habit Building: How General and Habit-Specific Self-Efficacy Influence Behavioral Automatization (Front. Psychol., 2021) Shows lagged habit-specific self-efficacy predicts automaticity; creates positive feedback loop
- The Shape of Mobile Health: A Systematic Review of Health Visualization on Mobile Devices (2024) Reviews 56 mHealth studies; shows bar/line charts most popular for health data; highlights personalization critical
- Evaluating the Acceptability and Utility of a Personalized Wellness App (Aspire2B) Using AI-Enabled Digital Biomarkers (JMIR Formative Research, 2025) Recent study on AI-powered personalized wellness app showing behavior-change-strategy integration boosts adherence