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Self-Management Support Using Advertising Principles for Older Adults With Low Income at High Cardiovascular Risk: A Randomized Controlled Trial

2023· article· en· W4323255494 on OpenAlexafffundabout
David J.T. Campbell, Marcello Tonelli, Brenda R. Hemmelgarn, Peter Faris, Jianguo Zhang, Flora Au, Ross T. Tsuyuki, Chad Mitchell, Raj Pannu, Tavis S. Campbell, Noah Ivers, Jane Fletcher, Derek V. Exner, Braden Manns

Bibliographic record

VenueCirculation · 2023
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsUniversity of TorontoCampbell Scientific (Canada)Women's College HospitalGovernment of AlbertaAlberta Health ServicesLibin Cardiovascular Institute of AlbertaAlberta HealthUniversity of AlbertaUniversity of Calgary
FundersCanadian Institutes of Health ResearchAlberta Innovates
KeywordsMedicineRandomized controlled trialPsychological interventionPopulationPhysical therapyMyocardial infarctionRate ratioInternal medicineEnvironmental healthNursing

Abstract

fetched live from OpenAlex

Background: Self-management education and support (SMES) interventions have modest effects on intermediate outcomes for those at risk of cardiovascular disease, but few studies have measured or demonstrated an effect on clinical end points. Advertising for commercial products is known to influence behavior, but advertising principles are not typically incorporated into SMES design. Methods: This randomized trial studied the effect of a novel tailored SMES program designed by an advertising firm among a population of older adults with low income at high cardiovascular risk in Alberta, Canada. The intervention included health promotion messaging from a fictitious “peer” and facilitated relay of clinical information to patients’ primary care provider and pharmacist. The primary outcome was the composite of death, myocardial infarction, stroke, coronary revascularization, and hospitalizations for cardiovascular-related ambulatory care–sensitive conditions. Rates of the primary outcome and its components were compared using negative binomial regression. Secondary outcomes included quality of life (EQ-5D [EuroQoL 5-dimension] index score), medication adherence, and overall health care costs. Results: We randomized 4761 individuals, with a mean age of 74.4 years, of whom 46.8% were female. There was no evidence of statistical interaction ( P =0.99) or of a synergistic effect between the 2 interventions in the factorial trial with respect to the primary outcome, which allowed us to evaluate the effect of each intervention separately. Over a median follow-up time of 36 months, the rate of the primary outcome was lower in the group that received SMES compared with the control group (incidence rate ratio, 0.78 [95% CI, 0.61 to 1.00]; P =0.047). No significant between-group changes in quality of life over time were observed (mean difference, 0.0001 [95% CI, −0.018 to 0.018]; P =0.99). The proportion of participants who were adherent to medications was not different between the 2 groups ( P =0.199 for statins and P =0.754 for angiotensin-converting enzyme inhibitors/angiotensin receptor blockers). Overall adjusted health care costs did not differ between those receiving SMES and the control group ($2015 [95% CI, −$1953 to $5985]; P =0.320). Conclusions: For older adults with low income, a tailored SMES program using advertising principles reduced the rate of clinical outcomes compared with usual care. The mechanisms of improvement are unclear and further studies are required. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02579655.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.011
GPT teacher head0.246
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2023
Admission routes3
Has abstractyes

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