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Identifying clinical course patterns in SMS data using cluster analysis.

Peter Kent1, Alice Kongsted

  • 1Research Department, The Spine Centre of Southern Denmark, Lillibaelt Hospital, Institute of Regional Health Services Research, Member of the Clinical Locomotion Network, University of Southern Denmark, Middelfart, Denmark. peter.kent@slb.regionsyddanmark.dk.

Chiropractic & Manual Therapies
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PubMed
Summary
This summary is machine-generated.

Cluster analysis of short message service (SMS) data can identify distinct clinical course patterns in low back pain patients. This simple method offers clinically relevant insights without complex data transformation.

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Area of Science:

  • Health Informatics
  • Data Science in Healthcare
  • Clinical Research Methodology

Background:

  • Short message service (SMS) data offers potential for frequent patient monitoring.
  • Previous studies used visual analysis or spline-transformed data for low back pain clinical course patterns.
  • Untransformed SMS data cluster analysis may provide a simpler approach.

Purpose of the Study:

  • To determine if cluster analysis of untransformed SMS data can identify distinct clinical course patterns.
  • To explore the potential, clinical relevance, strengths, and weaknesses of this approach.
  • To identify clinically important subgroups within research study outcomes.

Main Methods:

  • Secondary analysis of longitudinal SMS data from 322 patients in two randomized controlled trials.
  • Fortnightly SMS data on problematic low back pain days and sick leave over one year.
  • Two-Step (probabilistic) Cluster Analysis applied to original, untransformed SMS time points.

Main Results:

  • Clinically interpretable clinical course patterns, distinct from the overall group, were identified.
  • Similar patterns emerged even when reducing SMS data points to monthly intervals.
  • The advantages and disadvantages of this method were compared to spline-transformed data analysis.

Conclusions:

  • Cluster analysis of all SMS time points effectively identifies clinical course patterns.
  • The method is simple, intuitive, and requires minimal statistical expertise.
  • Further research, including head-to-head comparisons, is needed to determine optimal SMS data analysis techniques.