Keywords
smart watch, activity, abdominal surgery, perioperative care.
-
Views
-
Downloads
How to cite this article
Hunter A and Fabes J. Plymfit study: A study to investigate the feasibility of wrist-worn smartwatch use in perioperative care [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:325 (https://doi.org/10.12688/f1000research.161851.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
Export Citation
Sciwheel
EndNote
Ref. Manager
Bibtex
ProCite
Sente
▬
✚
Study Protocol
Plymfit study: A study to investigate the feasibility of wrist-worn smartwatch use in perioperative care
[version 1; peer review: awaiting peer review]
PUBLISHED 25 Mar 2025
OPEN PEER REVIEW
REVIEWER STATUS
Abstract
Corresponding author:
Alex Hunter
Competing interests:
No competing interests were disclosed.
Grant information:
Funded by the Association of Anaesthetists Great Britain and Northern Island. Grant Number: NIAA24R105.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Copyright:
© 2025 Hunter A and Fabes J.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite: Hunter A and Fabes J. Plymfit study: A study to investigate the feasibility of wrist-worn smartwatch use in perioperative care [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:325 (https://doi.org/10.12688/f1000research.161851.1)
First published: 25 Mar 2025, 14:325 (https://doi.org/10.12688/f1000research.161851.1)
Latest published: 25 Mar 2025, 14:325 (https://doi.org/10.12688/f1000research.161851.1)
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Introduction
The NHS carries out 2,400 major elective surgical procedures per 100,000 people each year.1 As demand for surgical procedures rises, a key priority is to minimise complications, which are costly for both patients and service providers.2,3 Risk stratification plays a crucial role in enabling perioperative care to be tailored to the specific needs of each patient.4 Over the past decade, routine cardiopulmonary exercise testing (CPET) before major surgery has been a fundamental aspect of perioperative risk stratification.5,6 This test is designed to simulate the physiological strain induced by major surgery in order to assess a patient’s physiological reserve. It is consistently shown that preoperative aerobic capacity predicts adverse perioperative outcomes across various surgical contexts.7–10 Consequently, improving aerobic capacity before surgery through ‘prehabilitation’11 should enhance a patient’s ability to withstand the surgical stress, thereby lowering perioperative risk.12,13
This system faces several challenges. CPET testing is both time-intensive and expensive, with limited centres able to accommodate all those who could benefit. While CPET assesses peak exercise response, it does not directly evaluate a patient’s habitual exercise patterns. Additionally, it offers a fixed-point assessment without dynamic feedback, which is crucial as patient fitness may fluctuate between evaluation and surgery. In the context of prehabilitation, implementing cost-effective strategies in clinical care remains difficult. Although many patients may be eager to change their behaviour,14,15 the perioperative setting lacks proactive, personalised approaches to facilitate this. CPET testing is typically used to identify patients requiring prehabilitation, yet there are often insufficient resources or capacity to monitor prehabilitation adherence or conduct follow-up assessments. Moreover, supervised prehabilitation programmes (such as structured gym-based classes) are expensive and not always scalable at speed.
Widely available wrist-worn activity monitors have the potential to provide solutions to many of these issues. These devices typically assess step count through 3-axis accelerometery and although validation of these devices in real life settings requires ongoing research attention16 small cohort studies have suggested a possible association between step count and perioperative outcomes.17–19 They have also been shown to approximate CPET data in the perioperative setting.20 Therefore, these devices may provide an alternative quantitative risk stratification strategy, bypassing the requirement to attend an in-person test, with patient, institutional and environmental benefit. By monitoring patients remotely over time, activity monitors will allow compliance with home-based exercise plans to be assessed. Furthermore, the closed loop feedback they provide (i.e. seeing an improvement in activity level) allows structured interventions, both pre- and post-operatively, to be assessed and monitored. As a result, a digital strategy is forming a key part of emerging prehabilitation interventions.21
However, in order to understand the potential of these devices in perioperative care, a baseline dataset is urgently required. Interventional studies to increase patients’ preoperative step count are frequently designed without baseline step count data of the study population and commonly accepted universal markers of activity such as 10,000 daily steps are unreliable.22,23
Hypothesis
Step count and activity may be able to;
1. Risk stratify patients preoperatively,
2. Determine the requirement for preoperative CPET,
3. Predict preoperative CPET results and thereby assist in shared decision-making,
4. Quantify and monitor the compliance and efficacy of prehabilitation programmes,
5. Track recovery after surgery,
6. Provide a potential early warning for patients developing post-operative complications,
7. Provide baseline data for future research and clinical care
Aims
1. To evaluate the use of smartwatches in a perioperative cohort in order to assess feasibility, test recruitment, compliance and data collection.
2. To provide pilot data on the feasibility and signal of efficacy of smartwatches in subsequent substantive studies and to inform sample size calculations for candidate outcomes.
Methods
Study design (Study outline diagram – Figure 1)
Figure 1. Study outline diagram.
A prospective observational cohort study of step-count and activity in fifty adult patients awaiting elective major abdominal surgery at a large UK teaching hospital.
All patients will be issued with a smartwatch without randomisation or blinding. There is no intervention; this study seeks to determine the feasibility of the device and baseline patient physical activity, without influencing patient or clinician behaviour.
Study participants
We will recruit 50 patients between March 2025 and December 2025 in a single tertiary referral centre.
Eligibility criteria
Inclusion criteria;
• Adult patient (age ≥ 18 years),
• Listed for elective major abdominopelvic surgery, defined as;
• Pre-operative CPET completed within 3 months of anticipated date of surgery,
• Surgery date > 2 weeks from surgical consent date.
Exclusion criteria
Primary outcome
The feasibility of smartwatches as a perioperative tool to assess patient fitness and activity will be assessed. Feasibility is defined as stop (not feasible in current format), modify (feasible with modification) and go (feasible in current format) criteria;
Secondary outcomes
Patient identification and consent
Patients will be identified through screening of the pre-assessment CPET list. A member of the patient’s clinical team will introduce the trial and ensure consent for research contact. The research team will discuss the study and provide a written patient information sheet; consent will be taken at the clinic or over the phone on the subsequent day(s). Those consented by phone will have written confirmation recorded by a member of the research team according to local practice. Patients will then confirm consent and sign the consent form on the day of surgery.
Older patients may feel digitally excluded, be less inclined to exercise or have lower activity levels, and be less likely to consent. Expected event rates for post-operative complications will vary with regard to both co-morbidity and age. Hence, intentional sampling ( Table 1) will reduce potential bias when assessing association with perioperative outcomes and facilitate a representative sample of the higher-risk CPET population.
Table 1. Sampling stratification.
| Stratification domain I | Stratification domain II | Target sample (n) | Target sample (%) |
|---|---|---|---|
| ASA 1-2 | Age 18-64 | 5 | 10 |
| Age ≥65 | 15 | 30 | |
| ASA 3-4 | Age 18-64 | 10 | 20 |
| Age ≥65 | 20 | 40 | |
| Total | 50 | 100 |
The trial was authorised by South Central - Berkshire B Research Ethics Committee (REC Reference: 24/SC/0364) on 12/11/2024 and is sponsored by the University Hospitals Plymouth NHS Trust. The study will be conducted at Derriford Hospital, University Hospitals Plymouth. Written consent is obtained from participants using the REC approved Informed Consent Form. Participants were consented using the Research Ethics committee approved informed consent form either in person or via the telephone. This process was managed according the the Sponsor’s local policy.
Sample size
As a feasibility study, a formal sample size calculation has not been performed. Fifty patients will be recruited, providing sufficient data to answer our feasibility questions. A sample size of 50 participants would allow a recruitment rate of 30% to be estimated with an 90% confidence interval of ±10%.24
Local data shows that 80 patients per month have a preoperative CPET of which 90% meet the inclusion criteria. With a 30% recruitment rate this provides an ample patient pool to meet our recruitment target.
Study procedure and justification
Participants will wear a Garmin vivofit 4 smartwatch during waking hours for 2 weeks (minimum 7 days, see below) prior to surgery and 4 weeks (minimum 7 days) post-operatively. Device data will be collected continuously during watch use. Participants will be set up with a device either at the time of consent, or the device will be sent to the patient remotely with usage instructions and a follow up phone call to aid device set up. Cross-sectional study data25 suggests that six days’ monitoring are needed to reliably capture habitual activity in all activity intensities. As such, a minimum of 7 days’ of data is set as our feasibility metric. Commercially available watches have specifically been chosen as, although research specific devices exist, these are likely to represent an easily applicable prehabilitation tool.
Data collection and follow up
(See extended data for data collection summary)
Patients will be followed up to day 30 post-operatively. Data will be collected through inpatient review of notes, telephone call following hospital discharge and digital questionnaire completion via smartphone. All data will be collected online either within a data safe haven (REDCap) (https://project-redcap.org/ ) or via the fully GDPR-compliant online Fitrockr (https://www.fitrockr.com/) platform, targeting a paperless study. The Fitrockr platform provides an efficient approach to collecting questionnaire data with easy tracking to maximise completion. Smartwatch data is tracked in real time to facilitate rapid identification and correction of issues such as participants not uploading data.
Electronic data captured in REDCap will be stored on the Sponsor’s centralised virtual storage infrastructure, split between two local data centres, and subject to the Sponsor’s Information Security and Network Security Policies. All electronic data are regularly backed up and retained for 30 days. The smartwatch data will be uploaded via the participant’s smartphone, pseudonymised, collected and centralised by the Fitrockr platform ( Figure 2). A number of participant questionnaire responses will be entered by participants themselves via their smartphone directly into the software platform. A relative’s smartphone can be used if required, to avoid excluding those without a smartphone, as long as proximity to the phone can be maintained at frequent intervals to allow syncing. Participants will be supported with data input via a telephone call from the research team, if required.
Figure 2. Data flow diagram.
A locally-designed questionnaire co-developed with patients will assess user interaction with, and experience of, the smartwatch devices.26
Discussion
Wearable technology has the potential to provide a unique opportunity to improve the care of the surgical patient. It is a potential vehicle for behavioural change both prior to, and after, surgery. While the surgical period may represent a ‘teachable moment’, whereby patients are more willing to engage with significant risk modifications to their health, methods to incorporate accountability into existing adherence model frameworks is lacking. Although, pre- and post-surgical optimisation provide interventions that promote physical and psychological health to reduce the incidence and/or severity of future impairments, current infrastructure allows very little assessment of, or adherence to, these changes. The magnitude of the surgical population demands large scale cost-effective, sustainable and up-scalable methods to establish health behaviour change. Smartwatches allow real time monitoring and an observer-coach effect whereby adherence to pre- and post-operative regimes may be improved by continuous longitudinal monitoring. However, it is essential that current and future research interventions using wearable technologies are designed using a robust evidence base for observed norms from which to determine appropriate interventional strategies.
This trial will lay the feasibility work for a definitive large observational study to use smartwatch devices to track the perioperative population which will in turn provide the evidence base for the design of effective interventions.
Reporting guidelines
The trial will be reported in accordance with the CONSORT statement extension for pilot and feasibility trials.26 Population-level descriptive statistics will be reported for feasibility and secondary outcomes. No statistical comparisons will be undertaken.
Software availability
REDCap software and consortium support are available at no charge to non-profit organizations that join the REDCap consortium.
Fitrockr is a proprietary software that can be replaced by the free alternative Garmin Express (https://www.garmin.com/en-GB/software/express).
Download
Export To
metrics
| Views | Downloads | |
|---|---|---|
| F1000Research | - | - |
|
PubMed Central
Data from PMC are received and updated monthly.
|
- | - |
Citations
CITE
how to cite this article
Hunter A and Fabes J. Plymfit study: A study to investigate the feasibility of wrist-worn smartwatch use in perioperative care [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:325 (https://doi.org/10.12688/f1000research.161851.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
track
receive updates on this article
Track an article to receive email alerts on any updates to this article.
Open Peer Review
Current Reviewer Status:
AWAITING PEER REVIEW
AWAITING PEER REVIEW
Key to Reviewer Statuses
VIEW
HIDE
ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations
A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions
Comments on this article Comments (0)