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Table 5 Categories used to classify individual pain patterns by visual analysis

From: Analyzing repeated data collected by mobile phones and frequent text messages. An example of Low back pain measured weekly for 18 weeks

Possible categories describing the entire course by visual analysis Individuals in each subgroup, n, (%) Total number of pain days in each subgroup, mean (sd) Percentage with short duration the previous year
Improved-Mainly recovered 25 (12) 8.92 (6.01) 76
Improved-Stays in the category 64 (30) 26.83 (14.65) 33
Improved-Fluctuating 23 (11) 37.43 (21.17) 30
Improved-Moves towards mainly worsened 2 (1) 95.00 (11.31) 0
Unchanged-Mainly recovered 13 (6) 5.54 (5.68) 54
Unchanged-Moves towards mainly improved 18 (8) 38.11 (21.81) 50
Unchanged-Stays in the category 10 (5) 45.60 (51.21) 33
Unchanged-Fluctuating 40 (19) 51.28 (30.95) 38
Unchanged-Moves towards mainly worsened 2 (1) 84.00 (29.70) 0
Worsened-Mainly recovered 1 (0.5) 12 0
Worsened-Moves towards mainly improved 2 (1) 37.5 (17.68) 50
Worsened-Fluctuating 13 (6) 55.00 (20.29) 0
Worsened-Stays in the category 2 (1) 111.5 (17.68) 50
  1. The first step is to categorize the pattern in relation to the early course (improved, unchanged or worsened). Afterwards that category is combined with the relevant late course.