An interventional study of AIRM v1.0 AI-Enhanced Remote Monitoring in Fibrotic Interstitial Lung Disease, sponsored by King's College London. Not yet recruiting at 1 site in United Kingdom. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-09-28.
Sponsored by King's College London · Not applicable, Interventional, and Other
The goal of this clinical trial is to learn whether artificial intelligence AI-enhanced remote monitoring (AIRM) is feasible, safe and acceptable for adults with fibrotic interstitial lung disease (fILD). The artificial intelligence (AI) system uses information collected through home monitoring to identify patterns that may suggest a person's lung condition is getting worse. The AI does not make treatment decisions. Its outputs are reviewed by healthcare professionals.
The main questions this study aims to answer are:
Is AIRM feasible and acceptable for people with fILD and their healthcare professionals? Can it be used safely to help identify possible worsening of fILD? How well does the AI system identify clinical deterioration?
Participants will take part for about 12 months. They will:
Measure their lung function and oxygen levels at home each week using connected monitoring devices.
Report their breathlessness and cough symptoms each week using the patientMpower app.
Complete questionnaires about their health and their experience of remote monitoring at the start of the study and every 3 months.
Continue to receive their usual clinical care.
Some participants will also be invited to an interview or focus group to discuss their experience of AI-enhanced remote monitoring. Healthcare professionals involved in the study may also be invited to discuss their experience of using the system.
The AI system is investigational and is being evaluated as part of this study. Healthcare professionals will review relevant AI-generated alerts before taking any clinical action. The AI will support, rather than replace, clinical judgement.
AIRMILD is a prospective, single-arm, mixed-methods feasibility study evaluating artificial intelligence-enhanced remote monitoring (AIRM) in people with fibrotic interstitial lung disease (fILD). Approximately 120 participants will be recruited from the interstitial lung disease (ILD) service at Guy's and St Thomas' NHS Foundation Trust (GSTT).
The study will evaluate AIRM version 1.0, an investigational artificial intelligence (AI) algorithm integrated within the patientMpower remote monitoring platform. AIRM analyses longitudinal multimodal remote monitoring data, including home spirometry, pulse oximetry, symptom scores and patient-reported measures, to identify patterns associated with potential clinical deterioration.
AIRM is intended to support clinical review rather than replace clinical assessment or clinical decision-making. It will not make autonomous diagnoses or treatment decisions. AIRM-generated outputs that may contribute to clinical management will be reviewed by an appropriately qualified clinician before clinical action is taken. Clinicians will retain independent clinical oversight and responsibility for clinical decisions. Participants will continue to receive usual clinical care throughout the study.
AIRM version 1.0 is investigational and is not UKCA or CE marked. The prediction algorithm will be fixed before prospective evaluation and will not be retrained or modified during participant follow-up. This will allow the performance of the predefined model to be evaluated prospectively against clinically identified deterioration.
The study will assess the feasibility of delivering an AIRM pathway in clinical practice, including participant engagement with remote monitoring and the practical integration of AI-generated information into clinical review. Safety evaluation will include assessment of situations in which AIRM generates an alert without subsequent clinical deterioration and situations in which clinical deterioration occurs without a preceding AIRM alert. The study will also evaluate the performance of AIRM in identifying potential clinical deterioration.
A mixed-methods evaluation will explore the acceptability, usability and perceived clinical value of AIRM. A purposive sample of participants will be invited to take part in qualitative interviews or focus groups following their period of remote monitoring. Healthcare professionals involved in the pathway will also be invited to participate. Qualitative data will be analysed thematically to explore experiences of remote monitoring, interaction with the AI-enhanced pathway, perceived benefits and concerns, and factors that may affect future implementation.
Findings from the quantitative and qualitative components will be considered together to assess the feasibility, safety and acceptability of the pathway and to inform the design of future evaluation of AIRM in fILD.
Exclusion Criteria:
Participants will receive AI-enhanced remote monitoring (AIRM) for 12 months using the patientMpower platform. Participants will undertake home spirometry and pulse oximetry, report symptoms, and complete patient-reported measures. AIRM version 1.0, an investigational artificial intelligence (AI) algorithm, will analyse longitudinal remote monitoring data to identify patterns associated with potential clinical deterioration. AIRM-generated outputs that may contribute to clinical management will be reviewed by an appropriately qualified clinician before any clinical action is taken. AIRM will support, rather than replace, clinical judgement, and participants will continue to receive usual clinical care.
Device: AIRM v1.0 AI-Enhanced Remote Monitoring
AI-enhanced remote monitoring (AIRM) delivered through the patientMpower platform for 12 months. Participants will use connected home spirometry and pulse oximetry devices and report symptoms and patient-reported measures. AIRM version 1.0 is an investigational artificial intelligence (AI) algorithm that analyses longitudinal multimodal remote monitoring data to identify patterns associated with potential clinical deterioration in fibrotic interstitial lung disease (fILD). AIRM-generated outputs that may contribute to clinical management will be reviewed by an appropriately qualified clinician before any clinical action is taken. The algorithm will remain fixed during the prospective study and will not be retrained or modified during participant follow-up. AIRM supports, rather than replaces, clinical judgement and usual clinical care.
Also known as: AIRM
Recruitment Rate of Eligible Participants
Recruitment rate will be calculated as the number of eligible individuals who provide informed consent and enrol in the study divided by the total number of eligible individuals approached for participation, multiplied by 100. The outcome will be reported as a percentage.
Time frame: Up to 24 weeks
Percentage of Enrolled Participants Retained at 12 Months
Retention rate will be calculated as the number of enrolled participants who remain enrolled in the study through to the end of the 12-month active remote monitoring period divided by the total number of participants enrolled, multiplied by 100. The outcome will be reported as a percentage.
Time frame: Up to 12 months
Percentage of Weekly Home Spirometry Measurements Completed
Calculated as the number of scheduled weekly home spirometry measurements completed by participants divided by the total number of scheduled home spirometry measurements, multiplied by 100.
Time frame: Up to 52 weeks
Percentage of Scheduled Pulse Oximetry Measurements Completed
Calculated as the number of scheduled weekly pulse oximetry measurements completed by participants divided by the total number of scheduled pulse oximetry measurements, multiplied by 100.
Time frame: Up to 52 weeks
Percentage of Scheduled Weekly Cough Assessments Completed (Cough Visual Analogue Scale)
Calculated as the number of scheduled weekly Cough Visual Analogue Scale assessments completed by participants divided by the total number of scheduled Cough Visual Analogue Scale assessments, multiplied by 100. The Cough Visual Analogue Scale assesses cough severity during the past week on a scale from 0 to 100, with higher scores indicating greater cough severity.
Time frame: Up to 52 weeks
Percentage of Scheduled Weekly Breathlessness Assessments Completed (Breathlessness Visual Analogue Scale)
Calculated as the number of scheduled weekly Breathlessness Visual Analogue Scale assessments completed by participants divided by the total number of scheduled Breathlessness Visual Analogue Scale assessments, multiplied by 100. The Breathlessness Visual Analogue Scale assesses breathlessness severity during the past week on a scale from 0 to 100, with higher scores indicating greater breathlessness severity.
Time frame: Up to 52 weeks
Percentage of Scheduled Patient-Reported Measures Completed
Completion of scheduled patient-reported measures will be assessed across the Idiopathic Pulmonary Fibrosis Patient Reported Outcome Measure (IPF-PROM), Idiopathic Pulmonary Fibrosis Patient Reported Experience Measure (IPF-PREM), EQ-5D-5L, SpiroQ Home Spirometry Satisfaction Questionnaire, Patient Health Questionnaire-9 (PHQ-9), Generalised Anxiety Disorder-7 (GAD-7), and Porter-Novelli 10-item Patient Engagement Scale. The outcome will be calculated as the number of scheduled patient-reported measures completed divided by the total number of patient-reported measures scheduled for completion, multiplied by 100, and reported as a percentage.
Time frame: Baseline, 3 months, 6 months, 9 months and 12 months
Percentage of Clinician-Confirmed Clinical Deterioration Events Without a Preceding AIRM Alert
Calculated as the number of clinician-confirmed clinical deterioration events occurring without a preceding AIRM alert within the previous 4 weeks divided by the total number of clinician-confirmed clinical deterioration events, multiplied by 100. The outcome will be reported as a percentage.
Time frame: Up to 52 weeks
Number of Device-Related Adverse Events
Number of adverse events related to the remote monitoring devices or monitoring platform during the 12-month active remote monitoring period.
Time frame: Up to 52 weeks
Percentage of Participants Reporting Remote Monitoring as Easy or Very Easy
Participant acceptability will be assessed using the SpiroQ Satisfaction Questionnaire. Participants rate the ease of remote monitoring using response categories ranging from "very easy" to "very difficult", with an additional "I don't know/not applicable" option. The outcome will be reported as the percentage of participants selecting "easy" or "very easy."
Time frame: Baseline, 3 months, 6 months, 9 months and 12 months
Clinician Perspectives on the Acceptability of AIRM
Clinician acceptability will be explored through focus groups with clinicians involved in the AIRM remote monitoring pathway. Qualitative data will be analysed thematically to identify themes relating to the acceptability of using AIRM within clinical practice.
Time frame: Up to 15 months
Percentage of AIRM Alerts That Are False-Positive Alerts
Calculated as the number of AIRM alerts not associated with a clinician-confirmed clinical deterioration event divided by the total number of AIRM alerts, multiplied by 100.
Time frame: Up to 52 weeks
Sensitivity of AIRM for Identifying Clinically Significant Deterioration Events
Calculated as the number of clinician-confirmed clinical deterioration events identified by AIRM divided by the total number of clinician-confirmed clinical deterioration events, multiplied by 100.
Time frame: Up to 52 weeks
Percentage of AIRM Alerts Leading to Clinical Review
Calculated as the number of AIRM-generated alerts that result in clinical review by a clinician divided by the total number of AIRM-generated alerts, multiplied by 100. The outcome will be reported as a percentage.
Time frame: Up to 52 weeks
Percentage of AIRM Alerts Leading to Clinical Intervention
Calculated as the number of AIRM-generated alerts that result in clinical intervention divided by the total number of AIRM-generated alerts, multiplied by 100. The outcome will be reported as a percentage.
Time frame: Up to 52 weeks
Number of Emergency Hospital Admissions
The total number of emergency hospital admissions occurring during the 12-month study period will be recorded for each participant and summarised across the study population.
Time frame: Up to 12 months
Number of Outpatient Attendances
The total number of outpatient attendances occurring during the 12-month study period will be recorded for each participant and summarised across the study population.
Time frame: Up to 12 months
Number of Other Clinically Relevant Healthcare Contacts
The total number of other clinically relevant healthcare contacts, e.g. helpline, occurring during the 12-month study period will be recorded for each participant and summarised across the study population.
Time frame: Up to 12 months
Specificity of AIRM for Identifying Clinically Significant Deterioration Events
The specificity of AIRM for identifying clinically significant deterioration will be assessed by comparing AIRM predictions with clinician-confirmed clinical deterioration. Specificity will be calculated as the proportion of events without clinically significant deterioration that are correctly identified by AIRM as not indicating deterioration and will be reported as a percentage.
Time frame: Up to 52 weeks
Area Under the Receiver Operating Characteristic Curve (AUROC) for Artificial Intelligence enhanced Remote Monitoring Prediction of Clinical Deterioration
The discriminative performance of Artificial Intelligence enhanced Remote Monitoring for predicting clinician-confirmed clinical deterioration will be assessed using the area under the receiver operating characteristic curve (AUROC). AUROC values range from 0.5 to 1.0, with higher values indicating greater ability to discriminate between deterioration and non-deterioration.
Time frame: Up to 52 weeks
Plan to share: No
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