Adaptive Personal Training Check-In
Combine a client check-in with deterministic training-load rules, stop progression on pain flags, and save a coach-ready next-session plan.
The full canvas, before you import it
Click any node to see its config.
Click a node to select it — same as the Heym editor; the panel shows its settings.
6 nodes · Free & source-available
Adaptive Personal Training Check-In
Give personal trainers a repeatable weekly check-in workflow without outsourcing safety decisions to the model. A deterministic Python tool compares planned and completed reps with perceived exertion, while the Agent turns that result into a coach-ready session plan and routes pain or symptom reports for professional review.
What this workflow does
- TrainingCheckIn captures the goal, experience, equipment, completed work, RPE, and symptoms
- PersonalTrainingCoach must call calculate_progression before drafting a plan
- NeedsProfessionalReview stops automatic progression when pain, symptoms, or unsafe context is present
- Review cases go to SafetyReview without updating the training log
- Normal cases append the decision and next-session plan to Google Sheets
- TrainingPlanReceipt returns the coach-facing plan
Use cases
- Weekly personal training check-ins
- Remote coaching program adjustments
- Consistent RPE-based progression decisions
- Separating coaching plans from medical or injury decisions
Setup
Connect an LLM credential to PersonalTrainingCoach and a Google Sheets credential to AppendTrainingSession. Map the sheet columns to your client log. This workflow does not diagnose injuries or replace qualified medical assessment; any pain or symptom flag stops progression and requires human review.
How to import this template
- 1Click Import → Copy JSON on this page.
- 2Open your Heym and navigate to a workflow canvas.
- 3PressCmd+V/Ctrl+V— nodes appear instantly.
- 4Add your API keys in the node config panels and click Run.
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