Designing interventions for blood pressure control in challenging settings: Active not passive intervention is needed
Bibliographic record
Abstract
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".