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Enregistrement W2902504007 · doi:10.1111/jch.13431

Designing interventions for blood pressure control in challenging settings: Active not passive intervention is needed

2018· article· en· W2902504007 sur OpenAlexaffabout
Raj Padwal

Notice bibliographique

RevueJournal of Clinical Hypertension · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueBlood Pressure and Hypertension Studies
Établissements canadiensMontreal Heart InstituteUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésMedicinePsychological interventionDisadvantagedIndigenousSocioeconomic statusRandomized controlled trialPopulationIntervention (counseling)Family medicineGerontologyEnvironmental healthNursingInternal medicine

Résumé

récupéré en direct d'OpenAlex

In this issue of the Journal, Tobe and colleagues report the results of a 243-patient randomized controlled trial comparing the efficacy of “active” vs “passive” text messaging to achieve BP control in six rural and remote Canadian First Nations communities.1 Active messaging included advice and education on BP management plus health behavior management suggestions whereas passive messaging consisted of only the latter. The setting in which the trial was conducted is important because of the higher risk of cardiovascular disease and lower socioeconomic status that exists in First Nations communities relative to the rest of the Canadian population.2 The results of the trial demonstrate that active text messaging was not better than passive messaging in terms of reducing systolic BP (between-group difference of 0.8 mm Hg [95% CI: −4.2 to +5.8 mm Hg]), diastolic BP (−1.0 mm Hg [−3.7 to 1.8 mm Hg]) or achieving BP control (37.5% vs 32.8%; P = 0.6).1 Four aspects of this trial deserve comment. First, the trial investigators should be commended for their considerable efforts to engage the First Nations communities participating in the study. Members of the investigative team had specific expertise in Indigenous health research; they ensured that study interventions were culturally appropriate; community research readiness was assessed; and the research project was integrated into community health provision. Nevertheless, enrollment was slower than expected, which speaks to the challenge of enrolling participants who reside in lower income, remote, and socioeconomically disadvantaged communities. Notably, enrollment improved when study personnel took a more active role in conducting on-site periodic visits within communities. Second, mean baseline BP, although above thresholds considered normal, was relatively low—143/84 mm Hg in the active message arm and 145/86 mm Hg in the passive message arm. This may have limited the magnitude of the absolute BP reduction that could have been expected through intervention. Third, BP was reduced by about 5-6/2-3 mm Hg in both study arms, which indicates that some potential benefit from text messaging occurred. Unfortunately, in the absence of a “no text messaging” study arm, it is not possible to determine if this BP reduction occurred because of the text messaging or if it was simply a result of temporal trends or the Hawthorne effect. Fourth, even the “active” text messaging arm employed a relatively passive intervention that consisted of twice-weekly, Canadian guideline-concordant, hypertension-specific, management short message service (SMS) text messages. The problem with this type of intervention is that it does little to address the important barriers and challenges to optimal hypertension management that occur because of limited patient engagement, medication non-adherence, poor access to care, and socioeconomic constraints. What can be concluded from this trial? Relatively passive mHealth interventions are unlikely to be very effective in achieving BP control in challenging settings. It is likely that more dynamic and supportive interventions would be required to produce effective results. One example of more active care model is protocolized case management, which in the field of hypertension, is typically performed by pharmacists.3 Given their training in therapeutics, pharmacists are ideal case managers, especially when they possess medication-prescribing privileges, because they are empowered to actively titrate medications, which limits the “therapeutic inertia” that may result if additional steps or approvals are required to adjust therapy. It is important that the pharmacist function as part of a team that includes a physician and employs a protocol directed, collaborative care approach. A recent example of this type of care delivery structure was examined in a cluster-randomized trial conducted the United States to achieve BP control in black male barbershop patrons. In this partially analogous setting, in which many of the same barriers and challenges to achieving BP control apply, the prescribing pharmacist case manager intervention led to a 22 mm Hg greater BP reduction compared to a control intervention (barber-led health behavior advice and encouragement to seek follow-up care) and increased BP control substantially (64% vs 12%; P < 0.001).4 Although the black barbershop trial was not an mHealth intervention, it exemplifies the importance of personalized case management for successful BP control. Combining pharmacist case management with a broader electronic care provision option consisting of BP measurement telemonitoring is certainly feasible and is supported by prior studies. To perform telemonitoring, BP measurements are tele-transmitted to an electronic portal, where they are summarized for use by providers. In the trial by Tobe and colleagues, community health providers did use a Bluetooth transmission capable automated BP device to measure BP, but in a limited fashion, because office BP measurements were first performed and the results were subsequently faxed to trial participant's care providers. A potentially more effective design, if feasible, would have combined home BP self-measurement with pharmacist case management. Although the impact of home BP monitoring alone is limited, it does encourage adherence to promote patient self-activation.5 In a meta-analysis that included 22 randomized controlled trials comparing home BP monitoring to usual care, BP reductions were small when home BP was used alone (−1.3 mm Hg [95% CI: −0.3 to −2.2 mm Hg; 17 trials]), but greater when telemonitoring was used (−3.2 mm Hg [95% CI: −1.7 to −4.7 mm Hg; 17 trials])6 In a more contemporary meta-analysis that included 12 trials enrolling hypertensive subjects, BP reductions were 4.9 (95% CI: 3.0-6.8)/2.3 (95% CI: 1.3-3.3) mm Hg with BP telemonitoring compared to usual care.7 The aforementioned pharmacist case management and BP telemonitoring data, taken separately and together, illustrate how we should proceed when designing and implementing future interventions to achieve BP control. Unfortunately, the BP telemonitoring meta-analyses published to date lump together a heterogeneous group of trials that employed disparate study designs, including those that used home BP telemonitoring alone, home BP telemonitoring with self-titration of medication, and home BP monitoring with dedicated case management. Usual care also varied in terms of the degree of intervention provided. To truly isolate the effect of home BP telemonitoring plus pharmacist case management vs usual care alone in hypertensive individuals, one needs to examine specific studies. A relevant example is a cluster-randomized trial of 450 adults with uncontrolled BP, in which home BP telemonitoring plus pharmacist case management reduced systolic BP by 6.6 mm Hg (95% CI: 2.5-10.7 mm Hg) after 18 months.8 In subsequent analyses, self-monitoring and active medication titration were identified as the major factors contributing to BP reduction.9 Clearly more data are needed examining the effectiveness and cost-effectiveness of dedicated case management coupled with home BP telemonitoring to reduce BP in marginalized populations and the feasibility and method of implementation in this setting requires careful thought. However, in my opinion, notwithstanding the need for additional evidence, this type of “active” intervention is much preferred to the office-based measurements coupled with relatively passive SMS messaging employed in the trial by Tobe and colleagues Home BP telemonitoring and active case management may be particularly useful for delivering care to remote and rural populations, including Canadian First Nations, and emerging data indicate that BP control is not diminished by providing care virtually (by phone or video calls) instead of in-person.10 Indeed, virtual care is on the rise, technological advancements will make virtual connectivity progressively easier and cheaper, and these benefits must be made available to all individuals, not just the socioeconomically advantaged.11 Therefore, it is important to now move beyond passive mHealth interventions and, by leveraging technology, toward actively assisting individuals helping address all the barriers and challenges that prevent optimal BP control. RP is a co-founder of an early stage blood pressure measurement start-up company, mm Hg Inc

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)0,002
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,434
Score d'incertitude au seuil0,586

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,130
Tête enseignante GPT0,409
Écart entre enseignants0,279 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeAutre devis
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

Citations3
Publié2018
Routes d'admission2
Résumé présentoui

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