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Searching for the optimal measuring frequency in longitudinal studies -- an example utilizing short message service (SMS) to collect repeated measures among patients with low back pain

Overview of attention for article published in BMC Medical Research Methodology, September 2016
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Title
Searching for the optimal measuring frequency in longitudinal studies -- an example utilizing short message service (SMS) to collect repeated measures among patients with low back pain
Published in
BMC Medical Research Methodology, September 2016
DOI 10.1186/s12874-016-0221-4
Pubmed ID
Authors

Iben Axén, Lennart Bodin

Abstract

Mobile technology has opened opportunities within health care and research to allow for frequent monitoring of patients. This has given rise to detailed longitudinal information and new insights concerning behaviour and development of conditions over time. Responding to frequent questionnaires delivered through mobile technology has also shown good compliance, far exceeding that of traditional paper questionnaires. However, to optimize compliance, the burden on the subjects should be kept at a minimum. In this study, the effect of using fewer data points compared to the full data set was examined, assuming that fewer measurements would lead to better compliance. Weekly text-message responses for 6 months from subjects recovering from an episode of low back pain (LBP) were available for this secondary analysis. Most subjects showed a trajectory with an initial improvement and a steady state thereafter. The data were originally used to subgroup (cluster) patients according to their pain trajectory. The resulting 4-cluster solution was compared with clusters obtained from five datasets with fewer data-points using Kappa agreement as well as inspection of estimated pain trajectories. Further, the relative risk of experiencing a day with bothersome pain was compared week by week to show the effects of discarding some weekly data. One hundred twenty-nine subjects were included in this analysis. Using data from every other weekly measure had the highest agreement with the clusters from the full dataset, weighted Kappa = 0.823. However, the visual description of pain trajectories favoured using the first 18 weekly measurements to fully capture the phases of improvement and steady-state. The weekly relative risks were influenced by the pain trajectories and 18 weeks or every other weekly measure were the optimal designs, next to the full data set. A population recovering from an episode of LBP could be described using every other weekly measurement, an option which requires fewer weekly measures than measuring weekly for 18 weeks. However a higher measuring frequency might be needed in the beginning of a clinical course to fully map the pain trajectories.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 2%
Denmark 1 2%
Unknown 45 96%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 8 17%
Student > Ph. D. Student 6 13%
Other 5 11%
Student > Master 5 11%
Researcher 4 9%
Other 10 21%
Unknown 9 19%
Readers by discipline Count As %
Medicine and Dentistry 9 19%
Psychology 7 15%
Nursing and Health Professions 6 13%
Social Sciences 4 9%
Agricultural and Biological Sciences 2 4%
Other 6 13%
Unknown 13 28%

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 13 September 2016.
All research outputs
#15,384,302
of 22,888,307 outputs
Outputs from BMC Medical Research Methodology
#1,514
of 2,024 outputs
Outputs of similar age
#203,481
of 322,146 outputs
Outputs of similar age from BMC Medical Research Methodology
#33
of 46 outputs
Altmetric has tracked 22,888,307 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,024 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.1. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.