Methodologically, Pace Me uses real-time physiological data to set personalized thresholds and context-aware prompts, signaling a move toward precision digital therapeutics and remote care.

Researchers emphasize the importance of user feedback loops, refining algorithms to distinguish safe versus excessive activity, and integrating with multidisciplinary care teams for future efficacy.

Lead researchers included Dr. Lawrence Hayes, Dr. Nilihan Sanal-Hayes, and Professor Nicholas Sculthorpe from their respective institutions.

The study is published in Nature Communications in 2026, with DOI 10.1038/s41467-025-64831-y.

A randomized controlled trial tested Pace Me, a digital platform combining a Fitbit with a mobile app to help people with Long COVID manage energy and prevent overexertion, but the intervention did not beat standard care in reducing post-exertional malaise (PEM).

The study enrolled 250 participants, with 125 in the Pace Me group (full tracking and real-time alerts) and 125 in a control group using a non-tracking dummy version over six months.

Despite not achieving superior PEM reductions, the trial demonstrated that Pace Me is feasible, safe, acceptable, and well-received, laying a framework for future digital health tools in chronic fatigue-related conditions.

Findings are framed within health policy contexts, such as the NHS Long Term Plan and the Darzi report, highlighting digital transformation in chronic illness management.

Pace Me is described as a digital tool pairing wearable activity tracking with an intelligent app to help manage energy expenditure and prevent PEM in Long COVID.

The NIHR-funded trial compared Pace Me with full tracking and alerts to a data-input-only control, aiming to assess impact on PEM and energy management.

Over six months, both groups showed overall symptom and well-being improvements, with participants exceeding their energy allowance by roughly 50 days; about 13 participants in the Pace Me group moved from PEM-positive to PEM-negative, and PEM reports declined by about 10%.

Authors suggest adapting the platform for other PEM-like chronic illnesses (e.g., ME/CFS, lupus, MS) and emphasize tailoring digital interventions to recovery trajectories in line with broader NHS goals.