HRV-Guided Pacing Randomized Controlled Trial

1 Background and Rationale

Energy envelope management (pacing) is the cornerstone of ME/CFS symptom management, but standard pacing relies on subjective symptom monitoring and retrospective crash analysis. Patients often discover they have exceeded their envelope only after PEM occurs. Heart rate variability (HRV) offers a potential objective, prospective measure of autonomic recovery that could guide daily activity decisions before crashes occur. HRV-guided training is well-established in sports science, where athletes adjust training intensity based on morning HRV readings. Translating this approach to ME/CFS pacing could improve crash prevention and patient confidence in activity decisions.

2 Hypothesis

3 Study Design

3.1 Design Overview

Two-arm parallel-group randomized controlled trial comparing HRV-guided pacing to standard symptom-based pacing.

3.2 Participants

  • n=60 adults with ME/CFS (ages 18–60)
  • Mild to moderate severity (Bell scale 40–70)
  • Experiencing \(\geq\) 2 PEM crashes per month on current pacing approach
  • Willing to use HRV monitoring device and follow assigned protocol
  • Smartphone ownership (for HRV app and data collection)

3.3 Randomization

1:1 allocation to HRV-guided or standard pacing, stratified by:

  • Baseline severity (Bell 40–55 vs. 56–70)
  • Baseline HRV (above vs. below median RMSSD for ME/CFS patients)

3.4 Intervention Arms

HRV-Guided Pacing (n=30)

  • Provided with validated HRV sensor (chest strap) and app

  • 2-week baseline HRV assessment to establish individual norms

  • Daily morning HRV measurement protocol

  • Activity calibration based on HRV (Protocol HRV-Guided Activity Management)

  • Weekly coaching calls for first month to support implementation

  • App-based activity recommendations throughout study Standard Symptom-Based Pacing (n=30)

  • Standardized pacing education session

  • Activity diary for self-monitoring

  • Symptom-based envelope identification

  • Weekly coaching calls for first month (attention control)

  • Usual pacing approach throughout study

3.5 Blinding

Open-label (blinding not feasible for behavioral intervention). Outcome assessors blinded to allocation for primary endpoint assessment.

4 Outcomes

4.1 Primary Outcomes

  • PEM crash frequency over 6 months (electronic diary)
  • Functional capacity at 6 months (Bell Disability Scale)

4.2 Secondary Outcomes

  • Crash severity when crashes occur
  • Patient-reported pacing confidence (validated scale)
  • Quality of life (SF-36)
  • Activity levels (actigraphy)
  • Protocol adherence (HRV measurement frequency, activity adjustment compliance)

4.3 Exploratory Outcomes

  • Baseline HRV as moderator of intervention effect
  • Learning curve: Does HRV-guided pacing improve over time as patients learn their patterns?
  • Interoceptive awareness: Do patients develop better symptom recognition with HRV feedback?

5 Analysis Plan

  • Primary analysis: Intention-to-treat comparison of crash frequency (negative binomial regression) and functional capacity (ANCOVA) between arms
  • Per-protocol sensitivity analysis for patients with \(\geq\) 80% HRV measurement adherence
  • Pre-specified subgroup analyses by baseline severity and HRV

6 Sample Size Justification

Based on preliminary data:

  • Assumed control arm crash rate: 3 per month (36 over 6 months)
  • Clinically meaningful reduction: 40% (to 1.8 per month)
  • With n=30 per arm: 80% power at \(\alpha\)=0.05
  • Accounts for 15% dropout

7 Expected Outcomes and Implications

If HRV-guided pacing shows benefit:

  • Establishes HRV monitoring as standard of care adjunct

  • Provides objective tool for patients and clinicians

  • Informs development of HRV-based pacing apps and devices

  • Opens research direction for personalized pacing algorithms If no benefit observed:

  • May indicate HRV is insufficiently predictive in ME/CFS

  • May suggest need for different HRV protocols or metrics

  • Would redirect research toward other objective pacing tools