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Mendeley readers
Attention Score in Context
| Title |
Glatiramer acetate treatment persistence - but not adherence - in multiple sclerosis patients is predicted by health-related quality of life and self-efficacy: a prospective web-based patient-centred study (CAIR study)
|
|---|---|
| Published in |
Health and Quality of Life Outcomes, March 2017
|
| DOI | 10.1186/s12955-017-0622-z |
| Pubmed ID | |
| Authors |
Peter Joseph Jongen, Wim A. Lemmens, Erwin L. Hoogervorst, Rogier Donders |
| Abstract |
In patients with relapsing remitting multiple sclerosis (RRMS) the persistence of and adherence to disease modifying drug (DMD) treatment is inadequate. To take individualised measures there is a need to identify patients with a high risk of non-persistence or non-adherence. As patient-related factors have a major influence on persistence and adherence, we investigated whether health-related quality of life (HRQoL) and self-efficacy could predict persistence or adherence. In a prospective web-based patient-centred study in 203 RRMS patients, starting treatment with glatiramer acatete (GA) 20 mg subcutaneously daily, we measured physical and mental HRQoL (Multiple Sclerosis Quality of Life-54 questionnaire), functional and control self-efficacy (Multiple Sclerosis Self-Efficacy Scale), the 12-month persistence rate and, in persistent patients, the percentage of missed doses. HRQoL and self-efficacy were compared between persistent and non-persistent patients, and between adherent and non-adherent patients. Logistic regression analysis was used to assess whether persistence and adherence were explained by HRQoL and self-efficacy. Persistent patients had higher baseline physical (mean 58.1 [standard deviation, SD] 16.9) and mental HRQoL (63.8 [16.8]) than non-persistent patients (49.5 [17.6]; 55.9 [20.4]) (P = 0.001; P = 0.003) with no differences between adherent and non-adherent patients (P = 0.46; P = 0.54). Likewise, in persistent patients function (752 [156]) and control self-efficacy (568 [178]) were higher than in non-persistent patients (689 [173]; 491 [192]) (P = 0.009; P = 0.004), but not in adherent vs. non-adherent patients (P = 0.26; P = 0.82). Logistic regression modelling identified physical HRQoL and control self-efficacy as factors that explained persistence. Based on predicted scores from the model, patients were classified into quartiles and the percentage of non-persistent patients per quartile was calculated: non-persistence in the highest quartile was 23.4 vs. 53.2% in the lowest quartile. Risk differentiation with respect to adherence was not possible. Based on these findings we propose a practical work-up scheme to identify patients with a high risk of non-persistence and to identify persistence-related factors. Findings suggest that pre-treatment physical HRQoL and control self-efficacy may identify RRMS patients with a high risk of early discontinuation of injectable DMD treatment. Targeting of high-risk patients may enable the efficient use of persistence-promoting measures. Nederlands Trial Register code: NTR2432 . |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 88 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 10% |
| Student > Bachelor | 9 | 10% |
| Student > Doctoral Student | 7 | 8% |
| Student > Master | 7 | 8% |
| Other | 5 | 6% |
| Other | 12 | 14% |
| Unknown | 39 | 44% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 13 | 15% |
| Nursing and Health Professions | 11 | 13% |
| Neuroscience | 6 | 7% |
| Agricultural and Biological Sciences | 3 | 3% |
| Social Sciences | 3 | 3% |
| Other | 9 | 10% |
| Unknown | 43 | 49% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 15 March 2017.
All research outputs
#17,883,247
of 22,959,818 outputs
Outputs from Health and Quality of Life Outcomes
#1,509
of 2,183 outputs
Outputs of similar age
#221,120
of 307,966 outputs
Outputs of similar age from Health and Quality of Life Outcomes
#38
of 62 outputs
Altmetric has tracked 22,959,818 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,183 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 307,966 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 62 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.