Anaesthesia
July 20, 2026 · via Europe PMC
Addressing inequities in the design and delivery of prehabilitation in the UK: case studies of challenges, complexities and good practice.
Hadley C, Zhang X, Stewart H, +8 more, Shelton C.
AI summary — for doctors, generated from the abstract only
Prehabilitation can widen health inequalities unless services are designed around patients' circumstances and resources.
What was studied
Qualitative multiple case study of six UK prehabilitation services for cancer surgery, with 47 professional interviews, 47 patient/carer interviews and 28 observations, analysed thematically.
Key findings
- Patients' circumstances shape their ability to engage.
- How prehabilitation is introduced affects the value patients see in it.
- Services can both reduce and worsen inequalities while working with an incomplete evidence base and limited resources.
Clinical relevance
Prehabilitation is a complex intervention that must fit around other treatments, and equitable access takes deliberate design.
Who should care
Limitations
Qualitative findings from six services in England and Wales may not generalize and do not measure clinical outcomes.
When referring or running prehabilitation, actively check for barriers such as transport, time and understanding, and adapt delivery.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Introduction
Prehabilitation has become increasingly prominent in the context of cancer surgery. While prehabilitation offers significant benefits, it can also inadvertently broaden inequalities because it is often more accessible to some than others. To explore the connection between health inequalities and prehabilitation before cancer surgery, we designed a qualitative study based on case study methodology.
Methods
Diverse prehabilitation services were purposively sampled. We conducted orientation interviews with service leads; semi-structured interviews with professionals, patients and carers; and observations of prehabilitation practice. These were conducted following topic guides informed by implementation science theory. We employed an inductive thematic approach to allow themes to emerge, highlighting connections between prehabilitation and health inequalities.
Results
Six prehabilitation services across England and Wales were included, comprising a combination of established and new services, urban and semi-rural settings and varied healthcare delivery models. We conducted 47 interviews with professionals, 47 interviews with patients (including eight with carers) and 28 observations of practice. We developed six themes, which illuminated how: patients' circumstances influence their ability to engage; the value of prehabilitation to patients depends on how it is introduced and understood; services can address, but also exacerbate, health inequalities; services may offer more and/or less than the prototypical prehabilitation model; services use and develop an incomplete evidence base; and services work with limited resources to provide the most benefit.
Discussion
Our findings highlight that while prehabilitation is often presented as a simple concept in the literature, in practice it is a complex intervention that must integrate with other ongoing treatments (e.g. surgery, chemotherapy, radiotherapy) during an already challenging period in patients' lives. Our qualitative findings from real-world settings illustrate how and why disparities arise in practice and offer insights for more equitable prehabilitation.
DOI
10.1111/anae.70286
PMID
42473333
PMCID
PMC13659788
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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