Telehealth interventions versus center-based cardiac rehabilitation: It’s time to strengthen the evidence
Notice bibliographique
Résumé
Dear Sir, Despite evidence from trials and meta-analysis that cardiac rehabilitation ‘works’, only 15–30% of eligible patients participate.1 This causes many to lament: why don't more patients get referred to and use cardiac rehabilitation programs? Using telehealth to deliver cardiac rehabilitation has been proposed as an innovative way of improving patient uptake, choice and access.2,3 The systematic review of telehealth cardiac rehabilitation programs by Huang et al.2 ostensibly provides more justification for the utilization of telehealth cardiac rehabilitation. However, the review actually draws attention to significant limitations about the credibility of current evidence supporting telehealth cardiac rehabilitation. We suggest that the included trials use of out-dated technology, short follow-up points and trial heterogeneity make it difficult to draw conclusions regarding the effectiveness of telehealth versus center-based cardiac rehabilitation programs. Only two of the nine included trials utilized the internet or email and no trials examined text messaging interventions. Telephone support was used in seven of the trials reviewed. Remarkably, none of the trials included dated from after 2007 – the year the iPhone was first introduced. It is difficult to make credible comparisons with the modern day from such dated trials in an area subject to rapid technological change and advances. In regard to follow-up, four of the included trials did not collect follow-up data beyond 12 weeks – only two included trials collected data beyond 12 months. While the goal of cardiac rehabilitation is to support patients to recover from their cardiac event, an equally important goal is to prevent future cardiac events. Studies with such short-term follow-up periods are unlikely to detect the long-term effects of cardiac rehabilitation – which meta-analysis indicates are likely to accrue only after two to five years.4 The heterogeneity in this review is high; there is evidence of substantial clinical heterogeneity (e.g. different study populations and phases of rehabilitation) and methodological heterogeneity (e.g. different technologies used and exercise patterns). Haung et al.2 could have better managed these variations and the statistical heterogeneity they contribute to by using a random effects statistical model to pool all of the study data (random effects model was used for total cholesterol data only). The random effects model, unlike the fixed effects model, does not assume that these diverse interventions have a single shared and identical underlying effect size despite their many differences.5 As these models can produce different results, this reliance on the more naïve fixed effects model is problematic. Is telehealth cardiac rehabilitation a compelling alternative to center-based cardiac rehabilitation? We suggest that strengthening our evidence should be urgently prioritized. Instead of reiterating the message that ‘telehealth cardiac rehabilitation interventions works’ and/or is comparable to center-based cardiac rehabilitation,2,3 the review by Huang et al.2 indicates the need for a landmark trial evaluating latest telehealth technology, including email, web and text messaging. This trial should utilize contemporary yet affordable technology platforms (like smart phones), incorporate best principles of trial description and design, and be powered to detect differences in outcomes at or beyond 12 months. Yours sincerely, Lianne D McLean Alexander M Clark The authors received no financial support for the research, authorship, and/or publication of this article. The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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