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Quality-of-life assessment in dementia: the use of DEMQOL and DEMQOL-Proxy total scores

Overview of attention for article published in Quality of Life Research, June 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

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6 tweeters

Citations

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13 Dimensions

Readers on

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49 Mendeley
Title
Quality-of-life assessment in dementia: the use of DEMQOL and DEMQOL-Proxy total scores
Published in
Quality of Life Research, June 2016
DOI 10.1007/s11136-016-1343-1
Pubmed ID
Authors

Kia-Chong Chua, Anna Brown, Ryan Little, David Matthews, Liam Morton, Vanessa Loftus, Caroline Watchurst, Rhian Tait, Renee Romeo, Sube Banerjee

Abstract

There is a need to determine whether health-related quality-of-life (HRQL) assessments in dementia capture what is important, to form a coherent basis for guiding research and clinical and policy decisions. This study investigated structural validity of HRQL assessments made using the DEMQOL system, with particular interest in studying domains that might be central to HRQL, and the external validity of these HRQL measurements. HRQL of people with dementia was evaluated by 868 self-reports (DEMQOL) and 909 proxy reports (DEMQOL-Proxy) at a community memory service. Exploratory and confirmatory factor analyses (EFA and CFA) were conducted using bifactor models to investigate domains that might be central to general HRQL. Reliability of the general and specific factors measured by the bifactor models was examined using omega (ω) and omega hierarchical (ω h) coefficients. Multiple-indicators multiple-causes models were used to explore the external validity of these HRQL measurements in terms of their associations with other clinical assessments. Bifactor models showed adequate goodness of fit, supporting HRQL in dementia as a general construct that underlies a diverse range of health indicators. At the same time, additional factors were necessary to explain residual covariation of items within specific health domains identified from the literature. Based on these models, DEMQOL and DEMQOL-Proxy overall total scores showed excellent reliability (ω h > 0.8). After accounting for common variance due to a general factor, subscale scores were less reliable (ω h < 0.7) for informing on individual differences in specific HRQL domains. Depression was more strongly associated with general HRQL based on DEMQOL than on DEMQOL-Proxy (-0.55 vs -0.22). Cognitive impairment had no reliable association with general HRQL based on DEMQOL or DEMQOL-Proxy. The tenability of a bifactor model of HRQL in dementia suggests that it is possible to retain theoretical focus on the assessment of a general phenomenon, while exploring variation in specific HRQL domains for insights on what may lie at the 'heart' of HRQL for people with dementia. These data suggest that DEMQOL and DEMQOL-Proxy total scores are likely to be accurate measures of individual differences in HRQL, but that subscale scores should not be used. No specific domain was solely responsible for general HRQL at dementia diagnosis. Better HRQL was moderately associated with less depressive symptoms, but this was less apparent based on informant reports. HRQL was not associated with severity of cognitive impairment.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 24%
Unspecified 10 20%
Student > Ph. D. Student 8 16%
Student > Bachelor 5 10%
Researcher 5 10%
Other 9 18%
Readers by discipline Count As %
Psychology 13 27%
Unspecified 12 24%
Nursing and Health Professions 8 16%
Medicine and Dentistry 7 14%
Social Sciences 3 6%
Other 6 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 07 May 2019.
All research outputs
#3,150,906
of 13,331,643 outputs
Outputs from Quality of Life Research
#301
of 1,946 outputs
Outputs of similar age
#66,828
of 263,136 outputs
Outputs of similar age from Quality of Life Research
#8
of 63 outputs
Altmetric has tracked 13,331,643 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,946 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done well, scoring higher than 84% of its peers.
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 263,136 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 63 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.