[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100654256":3},{"organization":4,"outcomesModule":7,"designInfo":40,"detailedDescription":47,"studyPopulation":39,"armGroups":48,"interventions":60,"overallOfficials":39,"centralContacts":66,"locations":72,"responsibleParty":92,"collaborators":39,"id":94,"slug":95,"hasResults":96,"nctId":97,"briefTitle":98,"officialTitle":99,"acronym":100,"eligibilityCriteria":101,"healthyVolunteers":102,"sex":103,"minAge":104,"maxAge":39,"enrollmentInfo":105,"targetDuration":39,"studyType":108,"phases":109,"briefSummary":111,"conditions":112,"keywords":114,"overallStatus":75,"whyStopped":39,"lastUpdateSubmitDate":116,"lastUpdatePostDateStruct":117,"startDateStruct":120,"completionDateStruct":122,"leadSponsor":124,"locationsCount":125},{"fullName":5,"class":6},"University of Southampton","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":39},[9],{"measure":10,"description":11,"timeFrame":12},"To determine the safety, acceptability and efficacy of a digital treatment guidance algorithm for inhaled treatment in patients with COPD. The intention is to measure the impact of the algorithm in prompting patients to achieve optimal management, and t","Proportion (measured in %) of participants receiving guideline adherent treatment at Baseline and trial completion (Month 6). Range 0%-100%.","6 months",[14,17,20,23,26,30,33,36],{"measure":15,"description":16,"timeFrame":12},"To assess effect of intervention on exacerbation frequency and patient wellness scores","• Change in COPD Assessment Test (CAT) score between Baseline and trial completion (Month 6)\n\nCAT test results 0 minimal symptom impact - Best and 40 maximum symptom impact\n\nAll will be described in terms of:\n\n* Mean and SD\n* Median and range and will be reported as descriptive stats",{"measure":18,"description":19,"timeFrame":12},"To assess effect of the intervention on patients' adherence to guideline mandated treatment","\\- Rate of indication of algorithm to change treatment (0 times to 150 times)\n\n• Rate if change in response to treatment change indication (0 times to 150 times)\n\nAll measured via frequency and percentage and description of change.\n\nThe combination of measurements will be reported as descriptive stats",{"measure":21,"description":22,"timeFrame":12},"To understand the utility of the intervention pathway in the pilot phase and to identify additional patient support mechanisms for the efficacy phase in order to maximise healthcare equality","\\- % Patients receiving guideline adherent treatment at Baseline and trial completion (Month 6). 100% good. 0 % bad.\n\nAbove described qualitatively and in terms of % and frequency",{"measure":24,"description":25,"timeFrame":12},"To assess the levels of app algorithm usage and engagement","-Frequency of app engagement\n\n* Rate of completion of endpoint data\n* Resource requirement to allow patients to act on intervention information\n\nMeasured by frequency (numbers of participant who completed everything) % (0% of expected use bad and 100% of expected use good). This will be reported as descriptive stats.",{"measure":27,"description":28,"timeFrame":29},"To understand patients' experience of using the intervention","This section relates to the qualitative sub-study and will look at the following:\n\n\\- Patient experience and patient and patient advocate feedback on experience of using app\n\n• Patient satisfaction Rating using the Patient\n\nUsing feedback questionnaire at month 6 and qualitative interviews as part of the sub -study\n\n* Qualitative data will be thematically analysed and written up in a report. Tables will be produced outlining key themes and illustrative quotes\n* Frequency and percentage.\n* Means and standard deviations.\n* Statistical tests may be performed to explore any difference in experience of using the intervention relative to cohort groupings\n\nThis will be reported in a separate analysis document using qualitative research analysis and reporting techniques and norms","3-6 months - interviews. 6 months - feedback questionnaire",{"measure":31,"description":32,"timeFrame":12},"To assess the safety of the algorithm","• Rate of algorithm inappropriately firing notifications\n\nThis is data collected via MMHLAB apps and qualitatively via patient advocate.\n\n* Frequency and percentage. (0 times good, not maximum time - But anything over 0 is bad)\n* Means and standard deviations.",{"measure":34,"description":35,"timeFrame":12},"To assess the change in frequency of exacerbations","Frequency of exacerbation between Baseline and trial completion (Month 6) • Frequency and percentages (for number of exacerbations) Frequency will be measured from 0-150 with 0 being optimal. This will be reported as descriptive stats.",{"measure":37,"description":38,"timeFrame":12},"To assess the change in Quality of Life between baseline and 6 months","Change in Quality of Life. Ratings measured using EQ-5D questionnaire between Baseline and trial completion (Month 6). All will be described in terms of: • Mean and SD • Median and range. Score 11111 will be classed as full health and 55555 will be classed worst health. This will be reported as descriptive stats",null,{"allocation":41,"interventionModel":42,"interventionModelDescription":43,"primaryPurpose":44,"observationalModel":39,"timePerspective":39,"maskingInfo":45},"RANDOMIZED","PARALLEL","Randomised Control Trial of an interventional device","SUPPORTIVE_CARE",{"masking":46,"maskingDescription":39,"whoMasked":39},"NONE","This study is an open-label randomised control trial with inbuilt pilot phase and qualitative sub-study:\n\nPhase 1 (pilot - 50 participants): Interim futility analysis on adherence to intervention advice Phase 2 (efficacy - 746 participants (including pilot phase participants): Safety acceptability and efficacy of intervention. This includes the qualitative sub study 15-20 participants (from the 746 Phase 2 participants).\n\nQualitative Sub-study: Acceptability of the intervention (15-20 intervention participants)\n\nThis trial will explore whether the introduction of the treatment realignment algorithm can be used as a safe, effective and acceptable tool to assist patients to achieve optimal, guideline based inhaled therapy and therefore provide greater symptom control and lowered exacerbation risk for COPD patients. To assess effect of the intervention on patients' symptom control, including exacerbation frequency and patient wellness scores.\n\nMain objectives\n\n* To understand the intervention pathway in the pilot phase and to identify additional patient support mechanisms for the efficacy phase in order to maximise healthcare equality\n* To assess the levels of app usage and engagement\n* To assess patients' experience of using the intervention\n\nStudy Design:\n\nOpen-Label Randomised Control Trial with inbuilt pilot phase Qualitative Sub study:\n\nPhase 1 (Pilot): Interim futility analysis on adherence to intervention advice Phase 2 (Efficacy): Safety, acceptability and efficacy of intervention Qualitative Sub-study: Acceptability of the intervention.\n\nThe research question is to explore the safety and efficacy of the treatment re-alignment algorithm as part of a digital self-management application (app) in patients with COPD.",[49,55],{"label":50,"type":51,"description":52,"interventionNames":53},"MMH-LAB1 Control This arm is based on the original COPD app the participants already use","PLACEBO_COMPARATOR","Participants for this study are already users of a COPD self management app. This arm is a replica of this app, with additional questionnaires built in",[54],"Device: MMH-LAB1 app",{"label":56,"type":57,"description":58,"interventionNames":59},"MMH-LAB1 Test This arm is based on the original COPD app the participants use plus an algorithm","EXPERIMENTAL","Participants for this study are already users of a COPD self management app. This arm is a replica of this app, with additional questionnaires built in plus an algorithm which identifies, based on the symptoms and medication reported onto the app by the participant, whether they are on the correct medication or not. If not, the app will generate a notification and inform the participant that they need to approach their healthcare provider and inform them of this and that they need a medication review.",[54],[61],{"type":62,"name":63,"description":64,"armGroupLabels":65,"otherNames":39},"DEVICE","MMH-LAB1 app","This app is based on a COPD self management app, which they are already using and includes an algorithm which will notify participants if there is a discrepancy between the symptoms they report onto the app and their medication regime, and informs them to approach their healthcare provider for a medication review",[50,56],[67],{"name":68,"role":69,"phone":70,"phoneExt":39,"email":71},"Trial Manager","CONTACT","+44 23 8059 6920","mysmartcopd@soton.ac.uk",[73],{"facility":74,"status":75,"city":76,"state":77,"zip":78,"country":79,"countryCode":80,"cosmosGeoPoint":81,"geoPoint":86,"contacts":87},"University Hospital Southampton","RECRUITING","Southampton","England","SO16 6YD","United Kingdom","UK",{"type":82,"coordinates":83},"Point",[84,85],-1.40428,50.90395,{"lat":85,"lon":84},[88],{"name":89,"role":69,"phone":90,"phoneExt":39,"email":91},"Tom Wilkinson, MA Cantab MBBS PhD FRCP FERS","02381 205341","T.Wilkinson@soton.ac.uk",{"type":93,"investigatorFullName":39,"investigatorTitle":39,"investigatorAffiliation":39,"oldNameTitle":39,"oldOrganization":39},"SPONSOR","100654256","phase-1-to-identify-if-a-copd-self-management-app-can-safety-and-appropriately-identify-when-patients-are-not-on-the-best-medication-plan-for-their-copd-symptoms-and-support-their-copd-management-100654256",false,"NCT07797985","To Identify if a COPD Self-management App Can Safety and Appropriately Identify When Patients Are Not on the Best Medication Plan for Their COPD Symptoms and Support Their COPD Management.","To Explore the Safety and Clinical Efficacy of a Treatment Realignment Algorithm Using Data From a Digital Self-management Application for Patients With COPD","mySmartCOPD","Inclusion Criteria:\n\n1. Adult patients over 18 years of age and able to give informed consent\n2. A clinical diagnosis of COPD (all patients registered on the app have a confirmed diagnosis of COPD confirmed by their clinician before being prescribed the app).\n3. A registered and activated user of the myCOPD app\n4. One or more CAT questionnaire completed on the myCOPD app in the last 12 months\n5. One or more exacerbation form completed on the myCOPD app in the last 12 months CTU\u002FFORM\u002F5321 - CIP template for Clinical Investigations of Medical Devices, Version 1 14-DEC-2023 mySmartCOPD Clinical Investigation Plan v1 25-FEB-2025 Page 25 of 53\n6. One or more COPD medication diary completed on the myCOPD app in the last 12 months\n7. Classified appropriately into 3 groups:\n\n   (A) Few symptoms (CAT \\\u003C10, 0 or 1 moderate exacerbations) (B) Many symptoms (CAT ≥10, ≥0 or 1 moderate exacerbations) (C)Exacerbators (≥2 moderate or ≥1 severe exacerbation independent of CAT)\n\n   Exclusion Criteria:\n8. Cannot read or write in English as required to understand the trial materials and provide the trial data.\n9. Has not opted in to the research agreement via MyCOPD",true,"ALL","18 Years",{"count":106,"type":107},746,"ESTIMATED","INTERVENTIONAL",[110],"PHASE1","Although COPD cannot be cured, managing COPD can help reduce symptoms control, slows progression of the disease, and may improve the quality of life. Management of COPD becomes less effective when healthcare professionals fail to prescribe appropriate inhaled therapies, due to poor adherence to evidence-based guidelines and underdiagnosis, or when patients fail to adhere to prescribed treatment regimens.\n\nmy mhealth has developed an app called myCOPD to allow patients to record their COPD symptoms and medications, which in term supports them with their COPD. This app is now used by COPD patients across the UK, via a prescription from their healthcare professionals. my mHealth has now developed a research app MMH-LAB1 which, in the intervention arm, includes an algorithm to assist patients with understanding their level of symptom burden according to the severity of their daily symptoms and exacerbation risk. The algorithm then analyses current patient prescribed inhaled medication against those recommended by the GOLD 2023 report and informs the patient via the MMH-LAB1 app as to whether their medication needs to be changed by their healthcare provider. This trial will explore whether the introduction of the algorithm within the MMH-LAB1 app can be used as a safe, effective and acceptable tool to assist patients to achieve optimal guideline based inhaled therapy and therefore provide greater symptom control and lowered exacerbation risk for COPD patients.\n\nParticipants will be randomised to the MMH-LAB1 app - control arm (MMH-LAB\\_Control\\_1 where participants just record COPD symptoms and medications), or to the MMH\\_LAB1 app - intervention arm (MMH-LAB1\\_Test\\_1 where participants record their COPD symptoms and medication plus receiving information from the algorithm, as to whether their medications are in guidance or not, and if not, be notified to visit their healthcare professional.",[113],"Respiration Disorders",[115],"Chronic Obstructive Pulmonary Disorder","2026-08-28",{"date":118,"type":119},"2026-09-01","ACTUAL",{"date":121,"type":119},"2025-10-08",{"date":123,"type":107},"2026-09",{"name":5,"class":6},1]