Telehealth Technology: An Emerging Method of Delivering Pulmonary Rehabilitation to Patients with Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
(pages 216-220), Stickland et al (2) examined the efficacy of an outpatient pulmonary rehabilitation (PR) program delivered via Telehealth technology (Telehealth-PR) and compared it with PR delivered in person through a standard out-patient, hospital-based program. Using a parallel group, noninferior-ity experimental design, the authors demonstrated that the two programs resulted in similar improvements in quality of life and func-tional capacity.Telehealth is an option particularly for those living in isolated areas or who are unable to access transportation to hospital or out-patient programs (3). Although there is some evidence to support Telehealth in chronic obstructive pulmonary disease (COPD), there is little information regarding the delivery of rehabilitation in this population. With respect to Telehealth, Polisena et al (4) completed a systematic review of 10 articles describing 858 patients with COPD. Four studies compared home telemonitoring with usual care, while six randomized controlled trials compared telephone support with usual care. There was considerable variability among studies in terms of interventions and approach. The results showed that home Telehealth (home telemonitoring and telephone support) decreased the rates of hospitalization and emergency department visits, while findings for hospital bed days of care varied among studies. Another systematic review of this topic by Bartoli et al (5) reported that telemonitoring, consisting of routine data transmission between the patient’s home and a healthcare professional located in the hospital, was the most common service provided by Telehealth in individuals with COPD. Within the realm of rehabilitation, Lewis et al (6) examined whether telemonitoring after PR impacted health care use. They ran-domly assigned patients who had completed at least 12 sessions of outpatient PR who were also stable with moderate to severe COPD to receive standard care or telemonitoring. Health care professionals could access the data and receive alerts if there were concerns. There were fewer primary care contacts for respiratory issues in the Telehealth group, but no differences between the groups in emergency room visits, hospital admissions, days in hospital or contacts to the specialist COPD community nurse team. The trial by Stickland et al (2) is unique in that it addressed the delivery of PR rather than simply monitoring it. The individuals in the Telehealth-PR program were assessed by teleconferencing for their suitability for PR. They subsequently attended PR twice a week for eight weeks within their local community where they performed group exercises for 2 h and received education (via teleconferencing) for 1 h. The patients exercised in groups of two to six, and were supervised by a local health care professional. This model has the potential to increase accessibility to PR, especially for those in isolated areas with no access to large centres, but still allowing social interaction provided by a group setting and supervision from a health care professional. In summary, the results of the study by Stickland et al provide evi-dence in support of one Telehealth model for the delivery of PR. Future studies are needed to examine the long-term efficacy of this intervention after one year and beyond, and to explore other Telehealth models, pos-sibly within the home, for delivery of rehabilitation. ©2011 Pulsus Group Inc. All rights reserved
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".