Revolutionizing Care: Unleashing the Potential of Digital Health Technology in Physiotherapy Management for People With Cystic Fibrosis
Bibliographic record
Abstract
Unlabelled: This viewpoint paper explores the dynamic intersection of physiotherapy and digital health technologies (DHTs) in enhancing the care of people with cystic fibrosis (CF), in the context of advancements such as highly effective modulator therapies that are enhancing life expectancy and altering physiotherapy needs. The role of DHTs, including telehealth, surveillance, home monitoring, and activity promotion, has expanded, becoming crucial in overcoming geographical barriers and accelerated by the recent pandemic. Physiotherapy, integral to CF care since 1946, has shifted toward patient-centered approaches, emphasizing exercise training and a physically active lifestyle. The reduction in inpatient admissions due to highly effective modulator therapies has led to increased home care and online or electronic consultations, and DHTs have revolutionized service delivery, offering flexibility, self-management, and personalized care options; however, there is a need to comprehensively understand user experiences from both people with CF and physiotherapists. This paper highlights the essential exploration of user experiences to facilitate clinician adaptation to the digital requirements of modern clinical management, ensuring equitable care in the "future hospitals" arena. Identifying research gaps, this paper emphasizes the need for a thorough evaluation of DHT use in CF physiotherapy education, training, and self-monitoring, as well as the experiences of people with CF with online or electronic consultations, self-monitoring, and remote interventions. Online group exercise platforms address historical challenges relating to infection control but necessitate comprehensive evaluations of user experiences and preferences. Future-proofing DHTs within the physiotherapy management of CF demands a shift toward full integration, considering stakeholder opinions and addressing barriers. While DHTs have the potential to extend physiotherapy beyond the hospital, this paper stresses the importance of understanding user experiences, addressing digital poverty, and working toward more equitable health care access. A flexible approach in the "future hospital" is advocated, emphasizing the need for a nuanced understanding of user preferences and experiences to optimize the integration of DHTs in CF care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".