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Table 4 Characteristics and results of studies that examined validity and accuracy of ActiGraphs 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
Keadle et al. (2014) [28] n: 7650 Only females Mean age: 71.4 ± 5.8 y USA: Data collected for an ancillary study of participants from the Women’s Health Study, a randomized trial of aspirin and vitamin E to reduce risk of cardiovascular disease and cancer. Data collected after completion of the trial. ActiGraph GT3X+ Worn on hip 60-s epochs Free-living Activities: activities with VA < 100 cpm and VM < 200 cpm Criterion: paper logs Observation period: 7 consecutive days during waking hours Valid hours and days: ≥10 h, ≥1 day and ≥ 4 days Non-wear algorithm: Troiano et al. [62] and Choi et al. [61] Analysis: computed min/day of sedentary activities and used Wilcoxon signed rank sum test to compare output between VA and VM VA: min/day (95% CI) Log+Troiano et al. algorithm: 530.1 (480.1, 578.6) Log+Choi et al. algorithm: 581.6 (521.1, 639.8) VM: min/day (95% CI) Log+Troiano et al. algorithm: 474.6 (417.0, 529.6) Log+Choi et al. algorithm: 506.0 (439.2, 570.9) Differences between VA and VM were significant (p < 0.001) Using dates from logs combined with Choi algorithm minimalised missing data and researcher burden. Using algorithm only resulted in misclassification of days when accelerometers were being posted to participants as accelerometer wear days.
Evenson et al. (2015) [33] n: 200 Females only Mean age = 75.5 ± 7.7 y USA: Data collected for a calibration sub-study of participants from the Women’s Health Initiative Long Life Study. Data collected after completion of main study. ActiGraph GT3X+ Worn on hip 15-s epochs Laboratory-based Activities: sitting, watching DVD; sitting assembling a puzzle Criterion measure: portable calorimeter Observation period: 7 min per activity Analysis: computed Spearman correlation using normal and low frequency extension for ActiGraph; used ROC analysis to determine optimal cut-points; computed AUC, sensitivity and specificity for normal and low-frequency extension filter MAXIMISING SUM OF SENSITIVITY+SPECIFICITY VA: Normal filter Optimal: 0 counts/15 s AUC: 0.73; Sensitivity: 91%; Specificity: 62% VA: Low-frequency extension filter Optimal: 0 counts/15 s AUC: 0.79; Sensitivity: 79%; Specificity: 81% VM: Normal filter Optimal: ≤42 counts/15 s AUC: 0.88; Sensitivity: 87%; Specificity: 80% VM: Low-frequency extension filter Optimal: ≤65 counts/15 s AUC: 0.90; Sensitivity: 87%; Specificity: 81% BALANCING NUMBER OF FALSE POSITIVES AND FALSE NEGATIVES VA: Normal filter Optimal: 0 counts/15 s AUC: 0.73; Sensitivity: 91%; Specificity: 62% VA: Low-frequency extension filter Optimal: 0 counts/15 s AUC: 0.79; Sensitivity: 79%; Specificity: 81% VM: Normal filter Optimal: ≤12 counts/15 s AUC: 0.88; Sensitivity: 76%; Specificity: 88% VM: Low-frequency extension filter Optimal: ≤31 counts/15 s AUC: 0.90; Sensitivity: 71%; Specificity: 88%
Bai et al. (2016) [34] n: 194 Females only Mean age = 75.4 ± 7.7 y USA: Data collected for a calibration sub-study of participants from the Women’s Health Initiative Long Life Study. Data collected after completion of main study. ActiGraph GT3X+ Worn on hip 1-s epochs Laboratory-based Activities: sitting, watching DVD; sitting assembling a puzzle Criterion measure: portable calorimeter Observation period: 7 min per activity Analysis: used ROC analysis to compare an activity index created for this study with activity count using the normal filter and LFE, and another method of summarise raw data, the Euclidean Norm Minus One (ENMO); computed AUC also to compare these measures on predicting energy expenditure greater than SB Compare watching DVD vs washing dishes or doing laundry, respectively Activity index: AUC: 0.98, 0.98 Activity count (normal filter): AUC: 0.39, 0.74 Activity count (LTF): AUC: 0.27, 0.87 ENMO: AUC: 0.40, 0.69 Predicting whether MET is < or ≥ 1.5 MET Activity index: AUC: 0.96 Activity count (normal filter): AUC: 0.86 Activity count (LFE): AUC: 0.91 ENMO: AUC: 0.85
Chudyk et al., 2017 [26] n: 106 39 men and 76 women Mean age = 74.1 ± 6.4 y Canada: Data collected for Walk the Talk: Transforming the Built Environment to Enhance Mobility in Seniors, a cross-sectional study of older adults living on low incomes. A random stratified design based on neighbourhood walkability was used to recruit older adults who received a provincial government rental subsidy. ActiGraph GT3X+ Worn on right hip 60-s epochs Free-living Activities: VA < 100 cpm Criterion: paper log Observation period: 7 days during waking hours Valid hours and days: ≥8 h; ≥4 days Non-wear algorithms: 1. ≥60 min of continuous zeroes; allow for up to 2 min of counts ≤100 counts as non-wear time [62] 2. ≥90 min of consecutive zeroes; allow for up to 2 min of non-zero counts if the interruption was accompanied by 30 consecutive min of 0 counts upstream or downstream [61]. 3. ≥90 min of continuous zeroes; no allow for interruptions, as non-wear time 4. ≥90 min of continuous zeroes; allow for up to 2 min of counts≤50 counts as non-wear time 5. ≥90 min of continuous zeroes, while allowing for up to 2 min of counts ≤100 counts as non-wear time Analysis: used Bland Altman methods to compare logs to non-wear time algorithms COMPARISON OF EACH ALGORITHM TO LOGS Mean differences in SB min/day between log and ActiGraph (95% CI) 1. 37.5 (25.7, 49.3) 2. 5.8 (− 4.4, 16.0) 3. − 4.4 (− 14.6, 5.8) 4. 5.5 (− 4.9, 15.9) 5. 8.1 (− 2.3, 18.5) 95% limits of agreement between log and ActiGraph for wear-time 1. − 84.6, 159.6 2. − 100.2, 111.8 3. − 110.5, 101.8 4. − 103.0, 114.0 5. − 100.2, 116.4
Koster et al., 2016 [29] n: 62 26 males, 36 females (58.1% females) Mean age = 78.4 ± 5.7 y USA: Data collected for a methodological sub-study of the Developmental Epidemiologic Cohort Study ActiGraph GT3X+ Worn on hip, right wrist and left wrist concurrently 15-s and 60-s epochs Free-living Activities: sitting, lying Concurrent measure: ActivPAL worn on right thigh Observation period: 7 full days, with permission given for removal at night Valid hours and days: ≥10 h; ≥1 day Non-wear algorithm: Choi et al. [61] Analysis: for each monitor, used ROC to determine optimal cut-points; computed AUC, sensitivity, specificity, and kappa statistic STANDARD CUT-OFF POINTS FOR 60-SEC EPOCHS VA < 100 cpm: Sensitivity: 94%; Specificity: 58%; kappa: 0.55 Mean difference: − 114.3 min/day (95%CI -140.5, − 88.1) VM < 200 cpm: Sensitivity: 88%; Specificity: 79%; kappa: 0.68 Mean difference: − 9.9 min/day (95%CI -32.8, 13.0) OPTIMAL 60-SEC EPOCHS Hip-worn ActiGraph VA < 22 cpm: AUC: 0.85; Sensitivity: 85%; Specificity: 74%; kappa: 0.59 Mean difference: − 5.0 min/day (95%CI -29.5, 19.5) VM < 174 cpm: AUC: 0.89; Sensitivity: 87%; Specificity: 80%; kappa: 0.67 Mean difference: 2.5 min/day (95%CI -20.4, 25.5) Wrist-worn ActiGraph VM < 2303 cpm (dominant wrist): AUC: 0.86; Sensitivity: 81%; Specificity: 78%; kappa: 0.58 Mean difference: 30.2 min/day (95% CI 10.7, 49.6) VM < 1853 cpm (non-dominant wrist): AUC: 0.86; Sensitivity: 82%; Specificity: 77%; kappa: 0.57 Mean difference: 22.6 min/day (95%CI 0.5, 44.6) OPTIMAL 15-S EPOCHS Hip-worn ActiGraph VA < 1 count/15 s: AUC: 0.75; Sensitivity: 87%; Specificity: 61%; kappa: 0.50 Mean difference: − 53.4 min/day (95%CI -76.4, − 30.4) VM < 20 counts/15 s: AUC: 0.83; Sensitivity: 83%; Specificity: 73%; kappa: 0.56 Mean difference: 12.8 min/day (95%CI -8.8, 34.4) Wrist-worn ActiGraph VM < 517 counts/15 s (dominant wrist): AUC: 0.81; Sensitivity: 75%; Specificity: 75%; kappa: 0.48 Mean difference: 64.7 min/day (95%CI 45.7, 83.7) VM < 376 counts/15 s (non-dominant wrist): AUC: 0.81; Sensitivity: 75%; Specificity: 74%; kappa: 0.47 Mean difference: 64.7 min/day (95%CI 44.3, 85.0)
Rosenberg et al. (2017) [30] n: 39 Only females Mean age = 69.4 (range: 56–94) USA: A convenience sample ActiGraph GT3X+ Worn on right hip 60-s epochs Free-living Activities: sitting and riding in a vehicle Criterion: DO (images produced via SenseCam camera worn on lanyard around neck) Observation period: 7 days during waking hours Valid hours and days: not reported Non-wear algorithms: Choi et al. [61] Analysis: used machine learning algorithm to classify activities (sitting and riding in vehicle analysed separately), computed sensitivity and specificity Sitting Sensitivity: 89%; Specificity: 91% Median counts = 0 (IQR: 0, 17), indicating that sitting occurred at a lower intensity than would be detected by existing threshold of < 100 cpm Riding in a vehicle Sensitivity: 84%; Specificity: 99% Median counts = 72 (IQR: 21, 177), indicating that riding occurred at a higher intensity than would be detected by threshold of < 100 cpm
Aguilar-Farias et al. (2014) [17] n: 37 13 males, 24 females (64.9% female) Mean age: 73.5 ± 7.3 y Australia: A convenience sample recruited mainly via flyers displayed at senior centres and exercise centres, and emails to university staff ActiGraph GT3X+ Worn on right hip 1-s, 15-s, 60-s epochs Free-living Activities: sedentary activities defined as VM and VT counts being below cut-points set separately for 1-s, 15-s and 1-m epochs as follows: 1-s (< 1 to < 10 in increments of 1 counts/s), 15-s (< 1 to < 100 in increments of 5 counts/15 s) and 60-s epochs (< 1 to < 400 in increments of 25 cpm) Concurrent measure: ActivPAL3™ worn on right thigh Observation period: 7 consecutive days during waking hours Valid hours and days: ≥10 h; ≥5 days Non-wear algorithms: 90-min of consecutive zeros with no interruptions allowed Analysis: used ROC to determine optimal cut-points, calculated AUC and computed sensitivity, specificity, percent correctly classified, mean bias (min/day) OPTIMAL CUT-POINTS FOR VA < 1 count/s AUC:0.67; Sensitivity:92%; Specificity:43% Correctly classified: 74% Mean bias:156.61 (95%LoA: − 34.5, 347.7) < 10 counts/15 s AUC:0.70; Sensitivity:84%; Specificity:65% Correctly classified: 79% Mean bias: − 4.29 (95%LoA: − 141.3, 132.8) < 25 cpm AUC:0.79; Sensitivity:83%; Specificity:75% Correctly classified: 80% Mean bias:4.81 (95%LoA: − 157.2, 166.8) OPTIMAL CUT-POINTS FOR VM < 1 count/s AUC:0.73; Sensitivity:85%; Specificity:62% Correctly classified: 76% Mean bias: 0.98 (95%LoA: − 113.4, 15.4) < 70 counts/15 s AUC:0.79; Sensitivity:87%; Specificity:70% Correctly classified: 82% Mean bias:0.80 (95%LoA: − 188.5, 120.1) < 200 cpm AUC:0.84; Sensitivity:89%; Specificity:79% Correctly classified: 85% Mean bias:18.05 (95%LoA: − 107.2, 143.3)
Sasaki, 2016 [31] n = 35 14 males; 21 females Mean age: 70.8 ± 4.9 y USA: A convenience sample ActiGraph GT3X+ Worn on dominant hip, wrist and ankle 20-s epochs LABORATORY-BASED Activities and observation period: performed sitting and lying down postures (30 s each); sat doing crossword puzzles or playing cards (5 min) Criterion measure: DO FREE-LIVING (N = 15) Activity: SB Criterion measure: DO (trained observers coded activities with continuous focal sampling software in a personal digital assistant) Observation period: 2–3 h Analysis: output used to train random forest (RF) and support vector machine (SVM) algorithms to classify activities; different algorithms developed for different body placement of monitor; computed percent correct classification % CORRECT CLASSIFICATION AS SB Lab-based algorithms applied to lab and free-living conditions: using 20-s epochs SVM hip: Lab: 92%; Free: 68% SVM wrist: Lab: 97%; Free: 73% SVM ankle: Lab: 92%; Free: 79% RF hip: Lab: 92%; Free: 62% RF wrist: Lab: 93%; Free: 71% RF ankle: Lab: 89%; Free: 76% Free-living-based algorithms applied to free-living conditions: using 20-s epochs SVM hip: Free: 82% SVM wrist: Free: 75% SVM ankle: Free: 87% RF hip: Free: 81% RF wrist: Free: 81% RF ankle: Free: 84% Free-living-based algorithms applied to free-living conditions: using other epochs 5-s epochs: hip: 79%; wrist: 70%; ankle: 78% 10-s epochs: hip: 82%; wrist: 74%; ankle: 82% 15-s epochs: hip: 82%; wrist: 75%; ankle: 87% 30-s epochs: hip: 82%; wrist: 78%; ankle: 87% RF ALGORITHMS 5-s epochs: hip: 72%; wrist:73%; ankle: 75% 10-s epochs: hip: 77%; wrist: 77%; ankle: 81% 15-s epochs: hip: 81%; wrist: 81%; ankle: 84% 30-s epochs: hip: 83%; wrist: 84%; ankle: 86% FREE-LIVING FOR HIGHEST OVERALL CLASSIFICATION RATES ACROSS ANKLE, HIP and WRIST ALGORITHMS, AT 30-S EPOCHS Sensitivity and specificity: Ankle (SVM): 82%; 94% Hip (RF): 79%; 93% Wrist (RF): 69%; 92%
Bourke et al., 2016 [25] n: 20% females not provided Mean age = 76.4 ± 5.6 y Norway: A convenience sample ActiGraph GT3X+ Worn on right hip 5-s epochs LABORATORY-BASED Activities: semi-structured protocol that included sitting and lying Criterion measure: DO (video camera) Observation period: in one session FREE-LIVING Activities: sitting and lying as part of tasks requested by researchers + normal routine Criterion measure: DO (camera on head) Observation period: partial day Analysis: used laboratory-based and free-living data together to assess % correctly classified using researcher-developed algorithm Across both conditions % correct classification: Sitting: 75% Lying: 51%
  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; 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