Enhance Access to Pulmonary Rehabilitation with a Structured and Personalized Home-Based Program-reabilitAR: Protocol for Real-World Setting.
Sarah BernardRui VilarinhoInês PintoRosa CantanteRicardo CoxoRosa FonsecaSagrario Mayoralas-AlisesSalvador Diaz-LobatoJoão CarvalhoCátia EstevesCatia CaneirasPublished in: International journal of environmental research and public health (2021)
Home-based models represent one of the solutions to respond to the poor accessibility of pulmonary rehabilitation (PR) services in patients with chronic respiratory disease (CRD). The main goal of this protocol is to present the implementation of the first nationwide home-based PR program-reabilitAR-in Portugal and the strategies to assess its benefits in patients with CRD. The program consists of 2 phases: a 12-week intensive phase and a 40-week maintenance phase (total: 52 weeks, 1 year). The intervention in both phases is composed of presential home visits and phone-call follow ups, including exercise training and the self-management educational program Living Well with COPD. Dyspnea, impact of the disease, emotional status, and level of dyspnea during activities of daily living are used as patient-reported outcomes measures. A one-minute sit-to-stand test is used as a functional outcome, and the number of steps as a measure of physical activity. To ensure safety, fall risk and the cognitive function are assessed. Data are collected at baseline, at 12 weeks, at 26 weeks and at 52 weeks. This is the first nationwide protocol on enhancing access to PR, providing appropriate responses to CRD patients' needs through a structured and personalized home-based program in Portugal.
Keyphrases
- quality improvement
- patient reported outcomes
- randomized controlled trial
- healthcare
- physical activity
- primary care
- pulmonary hypertension
- end stage renal disease
- chronic obstructive pulmonary disease
- gestational age
- newly diagnosed
- ejection fraction
- electronic health record
- peritoneal dialysis
- clinical trial
- prognostic factors
- machine learning
- artificial intelligence
- palliative care
- preterm birth
- lung function
- patient reported
- advanced cancer