Evidence map›Paper›PMID 22536057›Full record

ArticlePatient preference and adherence2012

Secondary analysis of electronically monitored medication adherence data for a cohort of hypertensive African-Americans.

George J Knafl, Antoinette Schoenthaler, Gbenga Ogedegbe

Open access · goldAbstract read
In one paragraph

Article in Patient preference and adherence, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Interventions to increase medication adherence in African-American and Latino populations: a literature review.Hawai'i journal of medicine & public health : a journal of Asia Pacific Medicine & Public Health · 2014
    Pooled it
  2. Trial
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  5. Medication Routines and Adherence Among Hypertensive African Americans.Journal of clinical hypertension (Greenwich, Conn.) · 2015
    Trial
  6. Article
  7. Is Glycemic Control the Secret to Tuberculosis Control?American journal of respiratory and critical care medicine · 2021
    Article
  8. A comparison of medication adherence between subsidized and self-paying patients in Malaysia.Malaysian family physician : the official journal of the Academy of Family Physicians of Malaysia · 2018
    Article
  9. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

George J KnaflSchool of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Antoinette Schoenthaler
Gbenga Ogedegbe
New York University · USUniversity of North Carolina at Chapel Hill · US

Funding

Trial Using Motivational Interviewing Positive Affect and Self-Affirmation in HyP60MD003421 · NIMHD · WEILL MEDICAL COLL OF CORNELL UNIV · PI BOUTIN-FOSTER, CARLA, CHARLSON, MARY E · 2009 to 2013
$8.9M
Motivational Interviewing in Hypertensive African Ameri*R01HL069408 · NHLBI · WEILL MEDICAL COLLEGE OF CORNELL UNIV · PI OGEDEGBE, GBENGA GODWIN · 2001 to 2004
$1.4M
Racial Differences in Patient-Provider Communication and Medication Adherence.K23HL098564 · NHLBI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI SCHOENTHALER, ANTOINETTE M · 2010 to 2014
$661k
Midcareer Investigator Award in Patient-Oriented Research for Dr. Gbenga OgedegbeK24HL111315 · NHLBI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI OGEDEGBE, OLUGBENGA G. · 2012 to 2016
$621k
NHLBI NIH HHS K23 HL098564NHLBI NIH HHS K24 HL111315NHLBI NIH HHS R01 HL069408NIMHD NIH HHS P60 MD003421
6 · The paper itself

Abstract

backgroundElectronic monitoring devices (EMDs) are regarded as the "gold standard" for assessing medication adherence in research. Although EMD data provide rich longitudinal information, they are typically not used to their maximum potential. Instead, EMD data are usually combined into summary measures, which lack sufficient detail for describing complex medication-taking patterns. This paper uses recently developed methods for analyzing EMD data that capitalize more fully on their richness.

methodsRecently developed adaptive statistical modeling methods were used to analyze EMD data collected with medication event monitoring system (MEMS™) caps in a clinical trial testing the effects of motivational interviewing on adherence to antihypertensive medications in a cohort of hypertensive African-Americans followed for 12 months in primary care practices. This was a secondary analysis of EMD data for 141 of the 190 patients from this study for whom MEMS data were available.

resultsNonlinear adherence patterns for 141 patients were generated, clustered into seven adherence types, categorized into acceptable (for example, high or improving) versus unacceptable (for example, low or deteriorating) adherence, and related to adherence self-efficacy and blood pressure. Mean adherence self-efficacy was higher across all time points for patients with acceptable adherence in the intervention group than for other patients. By 12 months, there was a greater drop in mean post-baseline blood pressure for patients in the intervention group, with higher baseline blood pressure values than those in the usual care group.

conclusionAdaptive statistical modeling methods can provide novel insights into patients' medication-taking behavior, which can inform development of innovative approaches for tailored interventions to improve medication adherence.

Indexed as

adaptive statistical modelinghypertensionmedication adherenceMedication Event Monitoring System

Identifiers

PMID22536057
PMCPMC3333812
OpenAlexW2066794577

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.