Addressing the growing burden of atrial fibrillation: evidence, sustainability and accessibility more important than territory
Notice bibliographique
Résumé
The systematic review by Lowres and colleagues1 describes best current evidence on lifestyle interventions in atrial fibrillation (AF) patients. The findings are noteworthy, encouraging and tantalizing. The promise of lifestyle interventions is evident in the review. Although the review only included five trials of moderate quality, the findings are broadly positive. Lowres et al.1 found the interventions, which focused predominantly on exercise, reduced heart rate at rest (9–15%) and during moderate exercise (6–9%), and improved exercise capacity and health-related quality of life in AF patients. The review also drew attention to important trends. The comparatively low volume and quality of research into the interventions is in inverse proportion to the current and future AF burden. In high-income countries, the prevalence of AF will double over the next 40 years.2 As with heart failure (HF), the increasing burden of AF has its roots in rising life expectancy.2 Risk of AF increases 25-fold from ages 55–85 years3 and almost 1 in 5 people aged 85 or over have AF.3 Rising obesity levels compound these patterns. A recent high-quality meta-analysis of 16 epidemiological studies identified that obesity increases risk of AF by 49% (RR: 1.49, 95% CI:1.36 to 1.64).4 Effective non-pharmacological interventions for people with AF are needed to address this large and growing burden. Most of the extensive and rising costs of providing treatment to the growing population with AF arise from hospitalization.5 Though a range of effective medicines exists for patients with AF, there is consistent evidence of under-prescription of anticoagulation drugs to patients at high risk of stroke.6 Medication non-compliance also occurs in around 30% of patients and is associated with adverse psychosocial health.7 Non-pharmacological interventions aimed at reducing the AF burden should not only promote physical activity but also include other components including weight, psychosocial health, medication reviews and management, help seeking, smoking and self-care skills. Family members who may assist with ongoing self-care should also be included in these interventions. Well-designed randomized trials of lifestyle interventions in AF populations are needed to establish with more precision the size of any benefits. Key outcomes of future trials should include: hospitalization, quality of life and return to work. Interventions should incorporate cost-benefit analyses and not exclude patients based on age. Where possible, blinding of assessors of outcomes should be incorporated to reduce the likelihood of bias. Trials are needed to test interventions using different methods and in different venues. Interventions using technology such as the internet, telecommunications and telehealth offer economical and potentially more accessible alternatives to complement face-to-face provision. General practice, community and home-based programs offer viable alternatives to centralized provision of programs in hospitals.8 In this research, the components of interventions must be developed well and described comprehensively.9,10 Prior to evaluation in full-scale trials, interventions should be designed carefully to ensure patient needs and preferences are adequately addressed.10 Qualitative research and surveys can be used to ascertain the nature and prevalence of particular patient needs that programs need to focus on. As potentially important determinants of outcomes, the effects of exercise timing, type and duration should be examined.11 In the present economic climate, the pressures to integrate health services for patients with different forms of cardiovascular disease are likely to increase. To ensure sustainability, cardiac rehabilitation and HF disease management services should be amalgamated into generic but responsive ‘Cardiac Self-care and Prevention’ programs, including those programs that have so far been developed, evaluated and provided discretely as secondary prevention. There is considerable potential for lifestyle programs for patients with AF to be integrated into such programs. While the nature and needs of patients with acute coronary syndrome (ACS), HF and AF remain different, this integration is advisable because of the shared dimensions of self-care across ACS, HF and AF concerning weight, physical activity, medication management and psychosocial health. Depression, sedentary behaviors, social isolation and low compliance with medicines are also common but potentially avoidable barriers to better health outcomes across these conditions. Future programs for AF should be accessible to all patient groups, irrespective of age, sex, wealth or location. Comparable with HF and ACS,12 the fastest growing population with AF is older women with heart disease. Yet, this population is consistently one of the least likely to participate in lifestyle interventions.13 Other frequently excluded groups also tend to have higher needs, including patients from rural settings, ethnic minorities and those on low incomes. While proponents of different lifestyle interventions should compare and contrast the effects of various program types, patient accessibility must trump clinical or research territory. Interventions should not be compared to each other with the overriding aim of identifying the definitive single best type of program for all patients. Rather, different kinds of programs should be seen as offering alternative modes of provision from which patients may select based on their location, needs and preferences. In conclusion, Lowres et al.1 demonstrated that lifestyle interventions offer considerable promise for addressing the growing health needs of AF patients. Despite this, the authors also identified a current shortage of effective intervention programs. Future interventions should prioritize both accessibility and sustainability while promoting optimal self-care, psychosocial wellbeing, quality of life and return to work either discretely or as part of larger programs for patients with other forms of heart disease.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,051 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,026 | 0,027 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,002 |
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 source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».