Table 2 Summary of non-wear in wellness study according to different methods of detection.

From: A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study

 

Median

Mean ± SD

Range

Median Excluding Missing Data)

Mean/SD (Excluding Missing Data)

Range (Excluding Missing Data)

Participant-level statistics (Aggregated per Participant) (n = 95 participants)

% Non-wear: GT9X-Link Wear Sensor

13.04

16.32 ± 11.40

0–55.44

17.90

22.39 ± 16.16

3.83–86.19

% Non-wear: Choi

9.66

13.78 ± 12.71

0–56.60

12.78

20.28 ± 19.21

0.94–85.54

% Non-wear: Troiano

12.34

16.62 ± 12.79

0–58.64

18.63

23.67 ± 18.94

1.58–86.39

% Non-wear: van Hees

7.11

12.43 ± 12.62

0–55.35

9.04

18.56 ± 19.29

0.52–85.53

% Non-wear: Majority algorithm

7.79

12.50 ± 12.46

0–55.75

9.71

18.63 ± 19.06

0.80–85.66

% Non-wear: Majority algorithm (3)

8.41

12.73 ± 12.54

0 – 55.97

9.82

18.95 ± 19.20

0.80–85.74

Day-level statistics (Aggregated Across Entire Study) (n = 31,175)

% Non-wear: : GT9X-Link Wear Sensor

0.90

16.29 ± 28.85

0–100

3.19

20.08 ± 31.00

0–100

% Non-wear: Choi

0.00

13.86 ± 26.65

0–100

0.00

17.03 ± 28.78

0–100

% Non-wear: Troiano

4.86

16.74 ± 26.65

0–100

8.13

20.53 ± 28.34

0–100

% Non-wear: van Hees

0.00

12.55 ± 26.98

0–100

0.00

15.42 ± 29.32

0–100

% Non-wear: Majority algorithm

0.00

12.58 ± 26.24

0–100

0.00

15.48 ± 28.47

0–100

% Non-wear: Majority algorithm (3)

0.00

12.82 ± 26.60

0–100

0.00

15.77 ± 28.85

0–100