Skip to main content

Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Table 5 Characteristics and results of studies that examined validity and accuracy of other accelerometers and inclinometers for measuring sedentary behaviour in older community-dwelling, healthy adults (mean age ≥ 60 years), ordered from largest to smallest sample size

From: Validity of objective methods for measuring sedentary behaviour in older adults: a systematic review

Study Participants Monitor and epochs analysed Methods Results for Sedentary Behaviour
Hutto, et al., 2013 [37] n: 200
85 males, 115 females (58% female)
Mean age = 63.5 ± 8.3
USA: Data collected as subs-tudy of The Reasons for Geographic and Racial Differences in Stroke Study, a national cohort study of racial and regional disparities in stroke risk and mortality
Actical
Worn on waist
60-s epochs
Free-living
Activities: VA ≤100 cpm
Concurrent measure: wear-time logs
Observation period: 7 consecutive days during waking hours
Valid hours and days: ≥10 h; ≥4 days
Non-wear algorithms: 60-, 90-, 120-, 150-, and 180-min of consecutive zeros, no allowance for interruptions
Analysis: compared algorithms for estimating wear and non-wear time by computing min/day and % of total wear time in SB for each algorithm
ALGORITHMS USING THE FOLLOWING MINUTES OF CONSECUTIVE ZEROES TO MEASURE NON-WEAR TIME
Min/day were classified as SB
60-min: 618 ± 81
90-min: 649 ± 88
120-min: 667 ± 97
150-min: 675 ± 98
180-min: 679 ± 101
% of total wear time was SB
60-min: 75 ± 10
90-min: 77 ± 10
120-min: 77 ± 10
150-min: 77 ± 10
180-min: 77 ± 10
Klenk et al. (2016) [38] n: 53
31 males and 22 females (41.5% female)
Mean age: 75.3 ± 4.6 y
Germany: Data collected as sub-study of ActiFE-Ulm study, a national cohort study of physical activity and health outcomes
ActivPAL3
Worn on left thigh
Laboratory-based
Activities: 2 bouts of sitting; 2 bouts of lying
Concurrent measure: ActivPAL worn on left thigh concurrently
Observation period: 10 s per bout of activity with a total observation period of mean 156.5 min ± 16.5
Analysis: computed agreement between the 2 monitors by (1) using Bland Altman methods and (2) computing for the activPAL sitting/lying category the degree to which activPAL3™ identified sitting/lying or different activities
Mean difference: − 2.00 s (±2 SD: 3.52)
Median agreement: 98.0% (IQR 95.9–99.0)
Expected difference in SB duration (95% CI) for 24-h measurement: − 44.5 min (− 69.9, − 20.0)
Wullems et al. (2017) [39] n: 40
20 males, 20 females (50.0% female)
Mean age = 73.5 ± 6.3 y
UK: Convenience sample
2 GENEActiv
Worn on thigh
Laboratory-based
Activities: sitting in a chair; lying down
Observation period: 4 min per activity
Criterion: indirect calorimeter
Analysis: compared machine learning algorithm to three cut-point algorithms for classifying intensities of activities (if MET value ≤1.5 and position was not upright), computed sensitivity and specificity
3 methods using cut-point algorithms
Sensitivity: 99.3–99.9%
Specificity: 99.7%
Accuracy: 99.5–99.8%
Random Forest machine learning
Sensitivity: 99.9%
Specificity: 99.2%
Accuracy: 99.6%
Other findings: Participant-specific accuracies resulted in perfect score (100%) for all algorithms for SB.
Landry et al., 2015 [40] n: 23
7 males and 16 females (69.6% female)
Mean age: 70.0 ± 6.6 y
Canada: Convenience sample recruited through newspaper advertisements, brochures distributed at community centres, and word of mouth
MotionWatch 8
Two worn on non-dominant wrist
60-s epochs
Laboratory-based
Activities: sitting in a chair; lying down
Criterion: portable calorimeter
Observation period: 5-min per activity
Analysis: used ROC to determine optimal cut-points with SB defined as < 1.5 MET, computed AUC, sensitivity, specificity, PPV, negative predicted value
AUC: 0.81 (95%CI: 0.78, 0.85)
Optimal cut-point for SB: ≤178.5 (d2 = 0.14)
Sensitivity: 78%
Specificity: 70%
Accuracy: 70%
PPV: 30%
Negative predictive value: 94%
  1. Abbreviations: AUC Area under the ROC curve that is used to evaluate classification accuracy, cpm Counts per minute, IQR Inter-quartile range, ICC Intraclass correlation coefficient, LoA Low-frequency extension filter, LTE Limit of Agreement, PPV Positive predictive value, ROC Receiver operator characteristic analysis, SD Standard deviation; valid hours and days: for free-living studies lasting at least 7 days, number of hours per day and days during observation period that were required for data to be included in analysis; VA Vertical axis, VM Vector magnitude, m Minutes, s Seconds, h Hours, y Years