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Enregistrement W2289695911 · doi:10.1155/2011/279201

Telehealth Technology: An Emerging Method of Delivering Pulmonary Rehabilitation to Patients with Chronic Obstructive Pulmonary Disease

2011· letter· en· W2289695911 sur OpenAlexaff
Dina Brooks

Notice bibliographique

RevueCanadian Respiratory Journal · 2011
Typeletter
Langueen
DomaineMedicine
ThématiqueChronic Obstructive Pulmonary Disease (COPD) Research
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésTelehealthMedicinePulmonary rehabilitationCOPDPsychological interventionPhysical therapyEmergency departmentRehabilitationPulmonary diseaseRandomized controlled trialTelemedicinePopulationHealth careQuality of life (healthcare)Medical emergencyNursingInternal medicine

Résumé

récupéré en direct d'OpenAlex

(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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,266
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0040,001
Études des sciences et des technologies0,0010,001
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0010,004
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,020
Tête enseignante GPT0,294
Écart entre enseignants0,274 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations9
Publié2011
Routes d'admission1
Résumé présentoui

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