Effectiveness of Pollution Monitoring in Clinical Exercise Rehabilitation

Air pollution is a global public health crisis, contributing to approximately 4.2 million premature deaths annually from stroke, heart disease, lung cancer, and chronic respiratory diseases \[1\]. In the United Kingdom, ambient air pollution is estimated to contribute to 30,000-40,000 premature deaths per year \[2\]. For individuals with pre-existing cardiac and pulmonary conditions, exposure to polluted air can exacerbate symptoms, trigger acute events, and diminish the effectiveness of rehabilitation programmes \[3,4\]. Physical activity confers well-established cardiovascular and respiratory benefits, and exercise-based rehabilitation is a cornerstone of management for patients with chronic cardiac and pulmonary conditions. However, exercising in polluted environments may offset these benefits, particularly in vulnerable populations. Short-term exposure to traffic-related pollution has been shown to attenuate the beneficial cardiopulmonary effects of walking in both healthy individuals and those with ischaemic heart disease and chronic obstructive pulmonary disease (COPD) \[3,4\]. Airborne particulate matter (PM2.5, PM10) and nitrogen dioxide (NO2) induce oxidative stress and systemic inflammation through elevated proinflammatory cytokines including interleukin (IL)-1β, IL-6, IL-23, and tumour necrosis factor alpha (TNF-α) \[5,6\]. This chronic low-grade inflammation increases the risk of cardiovascular disease \[7\], insulin resistance \[8\], and other systemic disorders \[9\]. Despite these well-documented risks, the integration of air quality data into clinical practice remains limited, particularly in the context of cardiac rehabilitation (CR) and pulmonary rehabilitation (PR) programmes. Meta-analytic evidence indicates that a 1.0 μg/m3 reduction in PM2.5 exposure is associated with an approximately 0.5 mmHg reduction in mean blood pressure, and active commuting modes yield a 50% reduction in NO2 and PM2.5 exposures compared with driving \[10\]. These findings suggest that relatively modest behavioural modifications such as adjusting exercise timing, location, or route could meaningfully reduce pollution exposure during rehabilitation. Mobile health (mHealth) technologies integrated with real-time air pollution data with structured physical activity guidance offer a promising vehicle for delivering personalised pollution exposure information. Therefore this study will evaluate the feasibility and potential effectiveness of this method in a clinical rehabilitation setting. EPIC-AIR is a stratified, blinded, randomised controlled feasibility trial with two parallel arms conducted at a single site (University Hospitals of Leicester NHS Trust). The study comprises a 1-week enrolment and baseline data collection period followed by a 12-week intervention period. A follow-up assessment is planned at 3 months post-allocation. Setting Participants will be recruited from cardiac and pulmonary rehabilitation services at University Hospitals of Leicester NHS Trust and from the local community surrounding Leicester, UK. Eligibility criteria Participants will be recruited in two cohorts. Cohort A comprises patients with long-term conditions; Cohort B comprises healthy volunteers. Cohort A: Patients with long-term conditions Inclusion criteria for Cohort A are: male or female adults aged ≥18 years; clinical diagnosis of one or more of asthma, COPD, interstitial lung disease (ILD), coronary heart disease (CHD), or heart failure (HF), and signed off for exercise rehabilitation by a clinician; ownership of a GPS-enabled smartphone with internet access; ability to walk outdoors for a minimum of 5 minutes without feeling uneasy or unsteady; availability to complete the 13-week intervention within the recruitment window (February-May 2026); willingness and ability to give informed consent; and willingness to wear a Fitbit device for \>70% of the study duration. Cohort B: Healthy volunteers Inclusion criteria for Cohort B are identical to Cohort A, with the exception that participants must have no diagnosis of asthma, COPD, ILD, CHD, or HF. Exclusion criteria (both cohorts) Exclusion criteria for both cohorts are: diagnosis of dementia, learning disability, severe mental health disorders (excluding depression or anxiety), or epilepsy; receiving palliative care; insufficient English language ability to understand study documentation and use the platform; having been advised not to exercise by a healthcare professional within the past 12 months; currently pregnant; presence of chest pain at rest; and marked unsteadiness when standing or walking. Recruitment Recruitment will commence following receipt of all necessary approvals. Clinical participants (cardiac and pulmonary rehabilitation patients) will be identified through review of rehabilitation programme records at University Hospitals of Leicester NHS Trust by members of the patients' existing clinical care team. Healthy volunteers will be recruited from the local community via email, social media advertisement, and university networks. The study aims to recruit up to 80 participants to achieve a target of 60 completers (15 cardiac rehabilitation patients, 15 pulmonary rehabilitation patients, and 30 healthy controls), accounting for an anticipated 25% dropout rate. Informed consent Potential participants will receive a Participant Information Sheet and will be given at least 24 hours to consider participation. Written informed consent will be obtained by suitably qualified and authorised members of the research team prior to any study activities. Participants will be informed of their right to withdraw at any time without prejudice. Randomisation and blinding Participants will be stratified by long-term condition (cardiac versus pulmonary) and randomised 1:1 using a computer-generated allocation sequence via a secure web-based system (Sealed Envelope Ltd). Randomisation will occur after eligibility confirmation, consent, baseline data collection, and successful app setup. The study is blinded: participants are unaware of their group allocation, outcome assessors are blinded, and the statistician performing the analysis will use coded group identifiers. Blinding is maintained by ensuring the platform interface appears identical in both arms, with the air quality feature either enabled or disabled according to allocation. The allocation code will be broken at the end of the study following data collection and preliminary screening. Interventions All participants will receive a Fitbit Charge 6 wearable activity monitor and sign into the platform. Following a 1-week baseline monitoring period, participants will receive progressive walking guidance through the platform for 12 weeks, tailored to individual baseline activity levels, along with motivational messages and reminders via the app. In addition to these common elements, participants randomised to the intervention arm will receive real-time air quality information (PM2.5, PM10, NO2 levels) through the platform, delivered using GPS-based location data. The app will provide adaptive guidance based on current pollution levels, including suggestions to alter walking routes, adjust the timing of outdoor activity, or reduce exercise intensity during high-pollution periods. Participants in the control arm will use the platform with identical exercise guidance and tracking features but will not receive air quality data or pollution-related guidance. Outcomes The primary outcome is personal pollution exposure (PM2.5, PM10, and NO2) during outdoor physical activity, inferred from GPS-tracked walking routes and modelled air quality data via the platform. Secondary outcomes include physical activity levels, measured by the Recent Physical Activity Questionnaire (RPAQ) and Fitbit-recorded step counts, heart rate, and sleep patterns; health-related quality of life, measured by the SF-12 version 2 (SF-12v2) physical and mental component summary scores; cardiovascular biomarkers, including lipid profile (total cholesterol, HDL, LDL, triglycerides) and inflammatory markers (C-reactive protein and exploratory markers of systemic inflammation), measured from venous blood samples at baseline and 12 weeks; aerobic fitness, measured by the Incremental Shuttle Walk Test (ISWT) at baseline, 12 weeks, and 3 months; exercise self-efficacy, measured by the Jenkins Self-Efficacy for Exercise Scale at baseline, 6 weeks, 12 weeks, and 3 months; clinical measures (blood pressure, height, and weight); and wearable-derived physiological data including heart rate, heart rate variability (RMSSD, LF/HF ratio), SpO2, breathing rate, skin temperature, and on-demand ECG and electrodermal activity (EDA) via Fitbit Charge 6. Feasibility outcomes include recruitment rate (number screened, eligible, consented, and randomised), retention rate (proportion completing the 13-week study), adherence (Fitbit wear time, app engagement metrics), usability and acceptability of the platform, completeness of outcome data collection, and rates of adverse events and healthcare utilisation (non-routine GP visits, unexpected hospitalisations). Data collection schedule Data will be collected at five timepoints: enrolment/baseline (-2 weeks), allocation (week 0), and post-allocation at 6 weeks, 12 weeks, and 3 months, with continuous passive data collection via the platform and Fitbit throughout the study period. Sample handling Venous blood samples (EDTA, 6-8 mL) will be collected at baseline and 12 weeks by trained clinical research nurses at Glenfield Hospital, University Hospitals of Leicester. Samples will be labelled with pseudonymised participant codes and processed within 2 hours of collection. Whole blood will be centrifuged at 1,800 × g for 10 minutes at 4°C. Plasma will be aliquoted (≥2 × 0.5 mL) and stored at -80°C until batch analysis at the University of Leicester Cardiovascular Research Centre (CVRC). Samples with visible haemolysis or inadequate volume will be re-collected where feasible. Analyses will be performed using validated assays in accordance with CVRC standard operating procedures. Sample size The sample size was informed by a two-sample t-test power calculation based on data from a previous study by our group \[10\], which demonstrated that switching from driving to active travel modes yields approximately 20-25% reductions in pollutant exposure. To achieve \>80% power at a 5% significance level requires ≥22 participants per group reaching the study endpoint. We therefore aim for 30 participants per group (60 total) to complete the study. Accounting for an anticipated 25% dropout rate, the target recruitment is up to 80 participants (20 cardiac rehabilitation, 20 pulmonary rehabilitation, 40 healthy controls). Statistical analysis All analyses will be conducted in accordance with a pre-specified statistical analysis plan. Baseline characteristics will be summarised using means and standard deviations (or medians and interquartile ranges) for continuous variables and counts with percentages for categorical variables. Primary analysis: Mean pollution exposure (PM2.5, PM10, NO2) during physical activity will be compared between intervention and control arms using independent-samples t-tests or Mann-Whitney U tests, as appropriate. Secondary analyses: SF-12v2 physical and mental component summary scores and RPAQ data will be analysed for between-group differences at each timepoint. Changes in cardiovascular biomarkers from baseline to 12 weeks will be compared between groups. Activity trends will be reported graphically over the 13-week period. Healthcare utilisation (GP visits, hospitalisations) will be summarised descriptively. Feasibility outcomes: Recruitment, eligibility, enrolment, and retention rates will be reported as counts and proportions. App engagement data will be summarised using descriptive statistics. Missing data: Scoring guidelines specific to each questionnaire will be applied. Analysis will be based on available data up to each participant's last completed follow-up. No interim analyses are planned. Data management Participant data will be managed using REDCap (Research Electronic Data Capture), a secure web-based application for survey administration and database management. Each participant will be assigned a unique pseudonymised identifier; a separate, password-protected contacts list will be held securely at UHL. Recorded data will be streamed to a secure University of Leicester server. Access is restricted to authorised research staff. Fitbit data will be collected via pre-registered accounts controlled by the research team. All data will be stored on the University of Leicester Research Filestore and analysed on university-owned, BitLocker-encrypted devices. Data will be archived for a minimum of 6 years following study completion in accordance with University of Leicester standard operating procedures. Ethics and dissemination Ethical approval The study will be conducted in conformity with the Declaration of Helsinki (2013 amendment), the UK Policy Framework for Health and Social Care Research (2017), ICH-GCP guidelines, and the sponsor's standard operating procedures. Ethical approval has been obtained from the South Central - Oxford C Research Ethics Committee (reference: 26/SC/0048). HRA and Health and Care Research Wales (HCRW) approval was granted on 3 March 2026 (IRAS project ID: 348160; protocol number: 1058). The study sponsor is the University of Leicester Research \& Enterprise Division, Research Governance Office. Safety considerations The platform provides guidance only and is not classified as a medical device according to MHRA guidance. No serious adverse effects are anticipated. Participants will be advised to stop using the app if they feel unwell or unsteady. Data on non-routine GP visits and unexpected hospitalisations will be collected throughout the study period. All serious adverse events will be reported to the sponsor within 24 hours in accordance with sponsor standard operating procedures. Data protection Personal data will be processed in compliance with the Data Protection Act 2018, the EU General Data Protection Regulation, and ICH-GCP. The University of Leicester is the data controller. Participants who have opted out of data sharing via the national data opt-out will be excluded from any identifiable datasets, in collaboration with the UHL Data Protection Officer. Patient and public involvement Patient and public involvement (PPI) groups reviewed study documents and the participant pathway during protocol development. PPI feedback informed several design decisions, including the provision of supported app download options, an onboarding guide, and a dedicated study support helpline. Dissemination Results will be disseminated through peer-reviewed publication, conference presentations, the EXCEED cohort website, and stakeholder dissemination events, irrespective of outcome. The study will be reported in accordance with the CONSORT statement, adapted for feasibility studies. Authorship will follow ICMJE guidelines. Participants will have the opportunity to receive a summary of findings. Data collected on app usability and acceptability will be used to populate the Digital Technology Assessment Criteria (DTAC) to support potential NHS adoption. Trial registration The trial is registered at \[TODO: ISRCTN/ClinicalTrials.gov\] (registration number: \[TODO: number\]). IRAS project ID: 348160. Discussion EPIC-AIR addresses a critical gap at the intersection of environmental health, digital technology, and clinical exercise rehabilitation. While the adverse effects of air pollution on cardiovascular and respiratory health are well established, actionable integration of real-time pollution data into patient care pathways remains largely unexplored. Notably, there is very limited evidence on the impact of air pollution on individuals exercising outdoors, particularly those with pre-existing cardiovascular or respiratory disease. The Oxford Street studies \[3,4\] represent one of the very few investigations examining the influence of air pollution on exercise in individuals with pre-existing cardiorespiratory conditions, and the majority of chamber-based exposure studies have been conducted in healthy volunteers or those with mild illness. The Committee on the Medical Effects of Air Pollutants (COMEAP) has highlighted this evidence gap and recommended that further research be undertaken as part of the Department for Environment, Food and Rural Affairs (Defra) Air Quality Indicative Standards (AQIS) review \[12\]. This study is, to our knowledge, the first to evaluate whether providing personalised air quality information through a smartphone application can reduce pollution exposure during structured rehabilitation programmes for cardiac and pulmonary patients. The proposed platform offers a scalable, low-cost intervention that leverages existing mobile technology and satellite-derived air quality data. By providing real-time, location-specific pollution information and adaptive exercise guidance, the app empowers patients to make informed decisions about when, where, and how to exercise outdoors. This approach aligns with the growing emphasis on personalised medicine and patient self-management in chronic disease care. Several design features strengthen this protocol. The blinded, randomised design with stratification by long-term condition minimises allocation bias while ensuring balanced representation of cardiac and pulmonary patients. The inclusion of healthy volunteers enables comparison of app engagement and behavioural responses across health states. The use of GPS-tracked walking routes provides objective, spatially resolved pollution exposure estimates, moving beyond reliance on fixed monitoring station data. As a feasibility study, EPIC-AIR is designed to generate the evidence necessary to inform a future definitive trial. Key uncertainties include patient willingness to engage with pollution data alongside exercise guidance, the technical reliability of real-time air quality delivery in diverse urban and suburban environments, and whether 12 weeks is sufficient to observe meaningful behavioural change. The feasibility outcomes, including recruitment, retention, adherence, and app usability, will directly inform the design, sample size, and delivery of a subsequent fully powered randomised controlled trial. Potential limitations include the single-site design, which may limit generalisability, and the reliance on modelled rather than directly measured personal pollution exposure. The 13-week study duration, while appropriate for assessing feasibility, may be insufficient to detect lasting health outcomes. Additionally, participants' awareness that they are in a study involving an app may influence behaviour regardless of group allocation (Hawthorne effect), although blinding to allocation status mitigates this concern. If the intervention proves feasible and shows promising signals of effectiveness, the findings could inform the development of personalised environmental health interventions integrated into NHS rehabilitation pathways, potentially establishing new standards for incorporating air quality considerations into clinical exercise prescription. References 1. World Health Organization. Ambient (outdoor) air pollution. 2022. Available from: https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health 2. Royal College of Physicians. Every breath we take: the lifelong impact of air pollution. London: Royal College of Physicians; 2016. 3. Sinharay R, Gong J, Barratt B, et al. Respiratory and cardiovascular responses to walking down a traffic-polluted road compared with walking in a traffic-free area in participants aged 60 years and older with chronic lung or heart disease and age-matched healthy controls: a randomised, crossover study. Lancet 2018;391(10118):339-349. 4. McCreanor J, Cullinan P, Nieuwenhuijsen MJ, et al. Respiratory effects of exposure to diesel traffic in persons with asthma. N Engl J Med 2007;357(23):2348-2358. 5. Cheng H, Saffari A, Sioutas C, et al. Nanoscale particulate matter from urban traffic rapidly induces oxidative stress and inflammation in olfactory epithelium with concomitant effects on brain. Environ Health Perspect 2016;124(10):1537-1546. 6. Chuang GC, Yang Z, Westbrook DG, et al. Pulmonary ozone exposure induces vascular dysfunction, mitochondrial damage, and atherogenesis. Am J Physiol Lung Cell Mol Physiol 2009;297(2):L209-L216. 7. Chen S-Y, Chan C-C, Su T-C. Particulate and gaseous pollutants on inflammation, thrombosis, and autonomic imbalance in subjects at risk for cardiovascular disease. Environ Pollut 2017;223:403-408. 8. Kim JS, Chen Z, Alderete TL, et al. Associations of air pollution, obesity and cardiometabolic health in young adults: the Meta-AIR study. Environ Int 2019;133(Pt A):105180. 9. Buoli M, Grassi S, Caldiroli A, et al. Is there a link between air pollution and mental disorders? Environ Int 2018;118:154-168. 10. Panchal R, et al. Exposure to air pollution during active travel. J Transp Health 2022;26:101365. 11. Chan A-W, Tetzlaff JM, Altman DG, et al. SPIRIT 2013 statement: defining standard protocol items for clinical trials. Ann Intern Med 2013;158(3):200-207. 12. Committee on the Medical Effects of Air Pollutants (COMEAP). Statement: overview of advice provided to the Air Quality Information System (AQIS) review steering group. London: COMEAP; 2024. Available from: https://www.gov.uk/government/publications/advice-given-to-the-air-quality-information-system-aqis-review-steering-group/comeap-statement-overview-of-advice-provided-to-the-air-quality-information-system-aqis-review-steering-group

Inclusion Criteria: * Cohort A: Patients with long-term conditions Inclusion criteria for Cohort A are: male or female adults aged ≥18 years; clinical diagnosis of one or more of asthma, COPD, interstitial lung disease (ILD), coronary heart disease (CHD), or heart failure (HF), and signed off for exercise rehabilitation by a clinician; ownership of a GPS-enabled smartphone with internet access; ability to walk outdoors for a minimum of 5 minutes without feeling uneasy or unsteady; availability to complete the 13-week intervention within the recruitment window (February-May 2026); willingness and ability to give informed consent; and willingness to wear a Fitbit device for \>70% of the study duration. * Inclusion criteria for Cohort B are identical to Cohort A, with the exception that participants must have no diagnosis of asthma, COPD, ILD, CHD, or HF. Exclusion Criteria: * Exclusion criteria for both cohorts are: diagnosis of dementia, learning disability, severe mental health disorders (excluding depression or anxiety), or epilepsy; receiving palliative care; insufficient English language ability to understand study documentation and use the platform; having been advised not to exercise by a healthcare professional within the past 12 months; currently pregnant; presence of chest pain at rest; and marked unsteadiness when standing or walking.