| Title |
Self-report measures of medication adherence behavior: recommendations on optimal use
|
|---|---|
| Published in |
Translational Behavioral Medicine, July 2015
|
| DOI | 10.1007/s13142-015-0315-2 |
| Pubmed ID | |
| Authors |
Michael J. Stirratt, Jacqueline Dunbar-Jacob, Heidi M. Crane, Jane M. Simoni, Susan Czajkowski, Marisa E. Hilliard, James E. Aikens, Christine M. Hunter, Dawn I. Velligan, Kristen Huntley, Gbenga Ogedegbe, Cynthia S. Rand, Eleanor Schron, Wendy J. Nilsen |
| Abstract |
Medication adherence plays an important role in optimizing the outcomes of many treatment and preventive regimens in chronic illness. Self-report is the most common method for assessing adherence behavior in research and clinical care, but there are questions about its validity and precision. The NIH Adherence Network assembled a panel of adherence research experts working across various chronic illnesses to review self-report medication adherence measures and research on their validity. Self-report medication adherence measures vary substantially in their question phrasing, recall periods, and response items. Self-reports tend to overestimate adherence behavior compared with other assessment methods and generally have high specificity but low sensitivity. Most evidence indicates that self-report adherence measures show moderate correspondence to other adherence measures and can significantly predict clinical outcomes. The quality of self-report adherence measures may be enhanced through efforts to use validated scales, assess the proper construct, improve estimation, facilitate recall, reduce social desirability bias, and employ technologic delivery. Self-report medication adherence measures can provide actionable information despite their limitations. They are preferred when speed, efficiency, and low-cost measures are required, as is often the case in clinical care. |
X Demographics
The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 33% |
| United States | 2 | 22% |
| Canada | 1 | 11% |
| Unknown | 3 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 33% |
| Practitioners (doctors, other healthcare professionals) | 3 | 33% |
| Scientists | 2 | 22% |
| Science communicators (journalists, bloggers, editors) | 1 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 617 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | <1% |
| Switzerland | 1 | <1% |
| Unknown | 615 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 97 | 16% |
| Student > Ph. D. Student | 96 | 16% |
| Researcher | 64 | 10% |
| Student > Bachelor | 50 | 8% |
| Student > Doctoral Student | 37 | 6% |
| Other | 117 | 19% |
| Unknown | 156 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 154 | 25% |
| Pharmacology, Toxicology and Pharmaceutical Science | 66 | 11% |
| Nursing and Health Professions | 59 | 10% |
| Psychology | 50 | 8% |
| Social Sciences | 23 | 4% |
| Other | 80 | 13% |
| Unknown | 185 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 31. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 January 2023.
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#1,236,580
of 25,081,419 outputs
Outputs from Translational Behavioral Medicine
#58
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#14,961
of 267,706 outputs
Outputs of similar age from Translational Behavioral Medicine
#1
of 17 outputs
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