Protocol for Project Fizzyo, an analytic longitudinal observational cohort study of physiotherapy for children and young people with cystic fibrosis, with interrupted time-series design
Open accessAbstract
Introduction: Daily physiotherapy is believed to mitigate the progression of cystic fibrosis (CF) lung disease. However, physiotherapy airway clearance techniques (ACTs) are burdensome and the evidence guiding practice remains weak. This paper describes the protocol for Project Fizzyo, which uses innovative technology and analysis methods to remotely capture longitudinal daily data from physiotherapy treatments to measure adherence and prospectively evaluate associations with clinical outcomes. Methods and analysis: A cohort of 145 children and young people with CF aged 6–16 years were recruited. Each participant will record their usual physiotherapy sessions daily for 16 months, using remote monitoring sensors: (1) a bespoke ACT sensor, inserted into their usual ACT device and (2) a Fitbit Alta HR activity tracker. Real-time breath pressure during ACTs, and heart rate and daily step counts (Fitbit) are synced using specific software applications. An interrupted time-series design will facilitate evaluation of ACT interventions (feedback and ACT-driven gaming). Baseline, mid and endpoint assessments of spirometry, exercise capacity and quality of life and longitudinal clinical record data will also be collected. This large dataset will be analysed in R using big data analytics approaches. Distinct ACT and physical activity adherence profiles will be identified, using cluster analysis to define groups of individuals based on measured characteristics and any relationships to clinical profiles assessed. Changes in adherence to physiotherapy over time or in relation to ACT interventions will be quantified and evaluated in relation to clinical outcomes. Ethics and dissemination: Ethical approval for this study (IRAS: 228625) was granted by the London-Brighton and Sussex NREC (18/LO/1038). Findings will be disseminated via peer-reviewed publications, at conferences and via CF clinical networks. The statistical code will be published in the Fizzyo GitHub repository and the dataset stored in the Great Ormond Street Hospital Digital Research Environment. Trial registration number: ISRCTN51624752 ; Pre-results.
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Further data analyses were conducted in a data-driven manner with artificial intelligence, such as k-means or unsupervised cluster analysis, recursive feature elimination, rotation random forest classifier, and supervised machine learning algorithms.
“Further data analyses were conducted in a data-driven manner [223] with artificial intelligence, such as k-means [176] or unsupervised cluster analysis [172], recursive feature elimination technique [170], rotation random forest classifier [130], and supervised machine learning algorithms using logistic regression, decision tree, and random forest [215].”
From The impact of wearable technologies in health research: scoping review (2022) · cites this paper as [172]
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“There are currently three large multicentre studies investigating different physical activity monitoring with CF children including portable devices (project Fizzyo) (6), telehealth (project CyFit telehealth) (7), and web-based activity trackers (project ActionPACT) (8), but findings are yet to be published.”
Background
- Authors
- Raywood, E., Douglas, H., Kapoor, K., Filipow, N., Murray, N., O'Connor, R., Stott, L., Saul, G., Kuzhagaliyev, T., Davies, G., Liakhovich, O., Van Schaik, T., Furtuna, B., Booth, J., Shannon, H., Bryon, M., & Main, E.
- Affiliations
- University College London (GB)
- Royal Brompton & Harefield NHS Foundation Trust (GB)
- Paediatric Cystic Fibrosis Centre, Royal London Hospital, Barts Health NHS Trust, London, UK
- Microsoft (United Kingdom) (GB)
- Microsoft Research (United Kingdom) (GB)
- Commercial Software Engineering, Microsoft UK Ltd - Reading, Reading, UK
- Great Ormond Street Hospital (GB)
- Corresponding author
- Raywood, Emma
- ORCID
- Venue
- BMJ Open
- Publisher
- BMJ
- Published
- 2020-10
- Volume
- 10
- Issue
- 10
- Pages
- e039587
- ISSN
- 2044-6055 (ISSN-L)
- PMC
- PMC7542954
- OpenAlex
- W3092192562
- Cited by
- 19
- References
- 16
- Citations by year
- 2025: 1 · 2024: 5 · 2023: 4 · 2022: 7 · 2021: 2
- FWCI
- 1.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMJ Open (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (acceptedVersion)
- HighWire Press Open Archive (acceptedVersion)
- Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Rosetrees Trust (UCL Rosetrees Stoneygate prize; M712)
- Higher Education Funding Council for England (KEI2017-01-04)
- Cystic Fibrosis Trust (Clinical Excellence and Innovation Award, CEA010)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Cystic Fibrosis Research Advances
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Chronic Obstructive Pulmonary Disease (COPD) Research
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- cystic fibrosis, physiotherapy, airway clearance techniques, medication adherence, remote monitoring, wearable sensors, children and young people, lung function, exercise capacity, quality of life, gamification, feedback interventions, physical activity, heart rate, step count
- MeSH terms
- Adolescent, Age Factors, Child, Cystic Fibrosis, Humans, Longitudinal Studies, Quality of Life, Cohort Studies, Physical Therapy Modalities, Observational Studies as Topic
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