[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100657787":3},{"organization":4,"outcomesModule":7,"designInfo":76,"detailedDescription":83,"studyPopulation":80,"armGroups":84,"interventions":91,"overallOfficials":80,"centralContacts":99,"locations":105,"responsibleParty":120,"collaborators":122,"id":126,"slug":127,"hasResults":128,"nctId":129,"briefTitle":130,"officialTitle":131,"acronym":132,"eligibilityCriteria":133,"healthyVolunteers":128,"sex":134,"minAge":135,"maxAge":80,"enrollmentInfo":136,"targetDuration":80,"studyType":139,"phases":140,"briefSummary":141,"conditions":142,"keywords":144,"overallStatus":148,"whyStopped":80,"lastUpdateSubmitDate":149,"lastUpdatePostDateStruct":150,"startDateStruct":153,"completionDateStruct":155,"leadSponsor":157,"locationsCount":158},{"fullName":5,"class":6},"King's College London","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":47,"otherOutcomes":69},[9,13,17,21,24,27,30,34,37,40,43],{"measure":10,"description":11,"timeFrame":12},"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.","Up to 24 weeks",{"measure":14,"description":15,"timeFrame":16},"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.","Up to 12 months",{"measure":18,"description":19,"timeFrame":20},"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.","Up to 52 weeks",{"measure":22,"description":23,"timeFrame":20},"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.",{"measure":25,"description":26,"timeFrame":20},"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.",{"measure":28,"description":29,"timeFrame":20},"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.",{"measure":31,"description":32,"timeFrame":33},"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.","Baseline, 3 months, 6 months, 9 months and 12 months",{"measure":35,"description":36,"timeFrame":20},"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.",{"measure":38,"description":39,"timeFrame":20},"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.",{"measure":41,"description":42,"timeFrame":33},"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\u002Fnot applicable\" option. The outcome will be reported as the percentage of participants selecting \"easy\" or \"very easy.\"",{"measure":44,"description":45,"timeFrame":46},"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.","Up to 15 months",[48,51,54,57,60,63,66],{"measure":49,"description":50,"timeFrame":20},"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.",{"measure":52,"description":53,"timeFrame":20},"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.",{"measure":55,"description":56,"timeFrame":20},"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.",{"measure":58,"description":59,"timeFrame":20},"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.",{"measure":61,"description":62,"timeFrame":16},"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.",{"measure":64,"description":65,"timeFrame":16},"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.",{"measure":67,"description":68,"timeFrame":16},"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.",[70,73],{"measure":71,"description":72,"timeFrame":20},"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.",{"measure":74,"description":75,"timeFrame":20},"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.",{"allocation":77,"interventionModel":78,"interventionModelDescription":79,"primaryPurpose":6,"observationalModel":80,"timePerspective":80,"maskingInfo":81},"NA","SINGLE_GROUP","Single-arm feasibility study in which all participants receive the same AI-enhanced remote monitoring intervention for 12 months. There is no comparator or control group.",null,{"masking":82,"maskingDescription":80,"whoMasked":80},"NONE","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).\n\nThe 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.\n\nAIRM 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.\n\nAIRM 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.\n\nThe 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.\n\nA 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.\n\nFindings 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.",[85],{"label":86,"type":87,"description":88,"interventionNames":89},"AI-enhanced remote monitoring pathway","EXPERIMENTAL","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.",[90],"Device: AIRM v1.0 AI-Enhanced Remote Monitoring",[92],{"type":93,"name":94,"description":95,"armGroupLabels":96,"otherNames":97},"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.",[86],[98],"AIRM",[100],{"name":101,"role":102,"phone":103,"phoneExt":80,"email":104},"Marium Naqvi, MPharm MScPP","CONTACT","+4402071880599","marium.1.naqvi@kcl.ac.uk",[106],{"facility":5,"status":80,"city":107,"state":80,"zip":80,"country":108,"countryCode":109,"cosmosGeoPoint":110,"geoPoint":115,"contacts":116},"London","United Kingdom","UK",{"type":111,"coordinates":112},"Point",[113,114],-0.12574,51.50853,{"lat":114,"lon":113},[117],{"name":118,"role":102,"phone":119,"phoneExt":80,"email":104},"Marium Naqvi","4402071880599",{"type":121,"investigatorFullName":80,"investigatorTitle":80,"investigatorAffiliation":80,"oldNameTitle":80,"oldOrganization":80},"SPONSOR",[123],{"name":124,"class":125},"patientMpower Ltd.","INDUSTRY","100657787","artificial-intelligence-enhanced-remote-monitoring-in-interstitial-lung-disease-100657787",false,"NCT07844915","Artificial Intelligence Enhanced Remote Monitoring in Interstitial Lung Disease","A Mixed-Methods Feasibility Study Evaluating Artificial Intelligence Enhanced Remote Monitoring in Interstitial Lung Disease","AIRMILD","Inclusion Criteria:\n\n* Aged 18 years or older.\n* Diagnosis of fibrotic interstitial lung disease (fILD) confirmed through multidisciplinary team review at a tertiary ILD centre, based on high-resolution computed tomography (HRCT) and standard clinical assessment.\n* Forced vital capacity (FVC) \\>45% predicted.\n* Able to perform spirometry and use a home spirometer safely and reliably.\n* Availability of baseline pulmonary function tests (PFTs), preferably within the preceding 6 months as part of routine clinical care.\n* Access to a compatible smart device with internet connectivity.\n* Able to understand study procedures, communicate with the research team, and complete study questionnaires and remote monitoring tasks.\n* Has capacity to provide informed consent.\n\nExclusion Criteria:\n\n* Previous participation in the REMILD study.\n* Estimated life expectancy of less than 6 months, as judged by the treating clinician.\n* Contraindication to spirometry in accordance with Association for Respiratory Technology and Physiology (ARTP) guidelines.\n* Current or recent acute exacerbation of ILD, confirmed clinically and\u002For radiologically, within the previous 4 weeks.\n* Active or life-limiting malignancy that, in the opinion of the treating clinician, is likely to affect participation, adherence to study procedures, or interpretation of study outcomes.","ALL","18 Years",{"count":137,"type":138},120,"ESTIMATED","INTERVENTIONAL",[77],"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.\n\nThe main questions this study aims to answer are:\n\nIs 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?\n\nParticipants will take part for about 12 months. They will:\n\nMeasure their lung function and oxygen levels at home each week using connected monitoring devices.\n\nReport their breathlessness and cough symptoms each week using the patientMpower app.\n\nComplete questionnaires about their health and their experience of remote monitoring at the start of the study and every 3 months.\n\nContinue to receive their usual clinical care.\n\nSome 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.\n\nThe 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.",[143],"Fibrotic Interstitial Lung Disease",[145,146,147],"Artificial intelligence","Remote monitoring","Interstitial lung disease","NOT_YET_RECRUITING","2026-09-21",{"date":151,"type":152},"2026-09-28","ACTUAL",{"date":154,"type":138},"2026-12-01",{"date":156,"type":138},"2028-06-01",{"name":5,"class":6},1]