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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.