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Table 1 One-dimensional Convolutional Neural Network (1D CNN) SWAN follow-up visit 15 (2015–2017) participant characteristics, overall and by dataset

From: Method for Activity Sleep Harmonization (MASH): a novel method for harmonizing data from two wearable devices to estimate 24-h sleep–wake cycles

Characteristic

Sample

(N = 1112)

Training set

(n = 625)

Test set

(n = 278)

Validation set

(n = 209)

% (n)

% (n)

% (n)

% (n)

Age (M ± SD)

65.5 ± 2

65.4 ± 2

65.2 ± 2

65.8 ± 2

Race/ethnicity

    

 Black

25.8 (287)

27.8 (174)

23.7 (66)

22.5 (47)

 Chinese

12.9 (143)

13.0 (81)

15.1 (42)

9.6 (20)

 Hispanic

3.0 (33)

3.0 (19)

2.5 (7)

3.3 (7)

 Japanese

12.1 (134)

11.2 (70)

13.3 (37)

12.9 (27)

 White

46.3 (515)

45.0 (281)

45.3 (126)

51.7 (108)

Education

    

  < High school

4.0 (45)

5.0 (31)

2.5 (7)

3.3 (7)

 High school

14.9 (166)

13.9 (87)

16.2 (45)

16.3 (34)

 Some college

31.3 (348)

28.8 (180)

33.1 (92)

36.4 (76)

 College

22.8 (253)

23.5 (147)

21.6 (60)

22.0 (46)

 Post-college

26.3 (292)

28.5 (178)

25.2 (70)

21.1 (44)

 Missing

0.7 (8)

0.3 (2)

1.4 (4)

1.0 (2)

Obesity (BMI ≥ 30 kg/m2)

36.1 (401)

38.2 (239)

32.7 (91)

34.0 (71)

 Missing

1.0 (11)

1.0 (6)

1.1 (3)

1.0 (2)

Self-rated health

    

 Poor, fair, or good

47.3 (526)

47.8 (299)

49.3 (137)

43.1 (90)

 Missing

0.8 (9)

1.1 (7)

0.7 (2)

 

Difficulty walking one mile

36.5 (406)

39.4 (246)

34.2 (95)

31.1 (65)

  1. BMI body mass index
  2. There were no statistically significant differences between the training, test, and validation datasets at the P = 0.05 level using t-tests for continuous variables or chi-square tests for categorical variables