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% |