REF-244 · McIlroy et al., 2021 · Frontiers in Sports and Active Living, Vol. 3, · Paper · PubMed

Virtual Training of Endurance Cycling – A Summary of Strengths, Weaknesses, Opportunities and Threats

C+thin evidenceMeasured on a mixed groupWritten before October 2026, being re-checked

Key Findings (content)

A SWOT discussion of virtual cycling training platforms, written because "critical evaluation of virtual training platforms is limited" while virtual online training sits among the top 20 worldwide fitness trends for 2021.

Strengths: immersive worlds, innovative drafting mechanics, versatility.

Weaknesses: questionable data accuracy; inadequate strength and reliability of power-speed algorithms.

Opportunities: strategic partnerships with major cycling races, brands and sponsors; improved user experience through video capture and "e-coaching".

Threats: cheating during competition; lack of uptake and acceptance by a broader community.

The abstract does not mention ERG mode, specific platforms, trainer-brand accuracy or indoor-outdoor transfer; anything on those topics below is inference or from the full text.

Practical Application

A 2021 SWOT commentary by McIlroy, Passfield, Holmberg and Sperlich: virtual cycling's strengths are immersion, drafting and versatility; its weaknesses are questionable data accuracy and weak power-speed algorithms; its threats are cheating and limited uptake — a framing paper, not evidence about training outcomes.
Inference, not tested: the paper offers no comparison of training adaptations, so it cannot be cited for or against ERG mode producing better fitness than free riding; it only frames what indoor platforms do well and badly.
Inference, not tested: "questionable data accuracy" is the practical warning — if the trainer or platform mis-reads power, every zone built on it is off. Check the trainer against a known power meter rather than trusting the platform.
Inference, not tested: virtual performance (speed, race results) and outdoor performance are computed differently (power-speed algorithms are named as a weakness), so treat virtual results as their own domain.

Limitations

SWOT format — qualitative, not experimental; no systematic search or data. No direct comparison of training outcomes. Rapidly evolving technology may make some observations outdated. The abstract does not discuss ERG mode at all; the base's ERG-related reading of this paper is inference.

How this entry was checked and changed
Why this level

regraded 2026-09-27 from B: narrative SWOT discussion without systematic method or data

Content correction 2026-09-27
Level B -> C+ (narrative SWOT commentary, no systematic method). Tier "mixed (review/meta)" -> N/A.
Removed claims not in the abstract: ERG mode "described as key feature", "TrainerRoad", "power measurement accuracy varies between trainer brands", "discrepancies between virtual and real-world performance", "limited uptake in some demographics, lack of standardization"; Key Content now mirrors the abstract's SWOT lists.
Lead Practical bullet said the review "confirms ERG mode's primary value" -> the abstract never mentions ERG; lead now states what the commentary actually says, ERG and accuracy bullets labelled as inference.