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Purpose: To investigate whether active standing can be identified separately from passive standing via accelerometry data and to develop and test the accuracy of a machine-learning model to classify active and passive standing. Methods: Ten participants wore a thigh-mounted activPAL monitor and stood for three 5-min periods in the following order: (a) PASSIVE: standing with no movement; (b) ACTIVE: five structured weight-shifting micromovements in the medial–lateral, superior–inferior, and anterior–poster planes while standing; and (c) FREE: participant’s choice of active standing. Averages of absolute resultant acceleration values in 15-s epochs were compared via analysis of variance (Bonferroni adjustment for pairwise comparisons) to confirm the dichotomization ability of the standing behaviors. Absolute resultant acceleration values and SDs in 2- and 5-s epochs were used to develop a machine-learning model using leave-one-subject-out cross validation. The final accuracy of the model was assessed using the area under the curve from a receiver operating characteristic curve. Results: Comparison of resultant accelerations across the three conditions (PASSIVE, ACTIVE, and FREE) resulted in a significant omnibus difference, F(2, 19) = [116], p < .001, η² = .86, and in all pairwise post hoc comparisons (all p < .001). The machine-learning model using 5-s epochs resulted in 94% accuracy for the classification of PASSIVE versus ACTIVE standing. Model application to the FREE data resulted in an absolute average difference of 4.8% versus direct observation and an area under the curve value of 0.71. Conclusions: Active standing in three planes of movement can be identified from thigh-worn accelerometry via a machine-learning model, yet model refinement is warranted.

Background: Self-report questionnaires are a fast and cost-efficient method to determine habitual sedentary time (sitting/lying time while awake), but their accuracy versus thigh-worn accelerometry (criterion), which can distinguish between sitting and standing postures, is unclear. While the validity of sedentary questionnaires has previously been evaluated, they have not been investigated simultaneously in the same sample population. We tested the hypothesis that common sedentary questionnaires underpredict habitual sedentary time compared with an objective, monitor-based assessment. Methods: Ninety-three participants (30 ± 18 years, 59 females) wore the activPAL inclinometer on the midthigh 24 hr per day for 6.9 ± 0.4 days and completed the SIT-Q, Sedentary Behavior Questionnaire (SBQ), International Physical Activity Questionnaire (IPAQ), and Physical Activity and Sedentary Behavior Questionnaire (PASB-Q). Results: In comparison to the activPAL (9.9 ± 1.9 hr/day), the SIT-Q measured more time (12.9 ± 5.4 hr/day), but the SBQ (7.5 ± 3.3 hr/day), IPAQ (7.4 ± 3.0 hr/day), and PASB-Q (6.6 ± 3.0 hr/day) measured less time (all p < .001). The SIT-Q was positively and weakly correlated (ρ = .230 [95% confidence interval: .020, .422], p = .028) with the activPAL, but the SBQ, IPAQ, and PASB-Q were not (all ps > .760). Equivalence testing demonstrated poor equivalence for the SIT-Q (±40%), SBQ (±31%), IPAQ (±36%), and PASB-Q (±29%). The SIT-Q (β = −1.36), SBQ (β = −0.97), and IPAQ (β = −0.78) exhibited a negative proportional bias (all ps < .002). Conclusions: In summary, the SIT-Q, SBQ, IPAQ, and PASB-Q demonstrated poor validity. Researchers and health promoters should be cautious when implementing these self-report sedentary time questionnaires, as they may not reflect the true sedentary activity and negatively impact study results.

Background: Physical activity (PA) is a crucial factor in maintaining good health and preventing chronic diseases. However, accurately measuring PA is challenging. Euclidean Norm Minus One (ENMO), ActiGraph Counts, and Monitor-Independent Movement Summary (MIMS) units are processing metrics used to classify PA through accelerometry, but they employ different methods to calculate activity levels. This study aimed to compare ENMO, ActiGraph Counts, and MIMS accelerometer metrics using machine learning algorithms. Methods: Data from a smartphone accelerometer were collected from 50 participants who held the smartphone in their right hand while completing six activities. The data were used to generate ENMO, ActiGraph Counts, and MIMS acceleration metrics. Random Forest, K-Nearest Neighbor, and Support Vector Machine algorithms were applied to the data to classify PA into different levels of activity intensity and types. The algorithms’ performance was evaluated using various metrics such as accuracy, precision, and recall. Results: The results showed that both the Random Forest and K-Nearest Neighbor algorithms performed well, achieving above 80% accuracy in classifying PA into different intensity levels and types. Both the ENMO and MIMS metrics proved more accurate than ActiGraph Counts in classifying moderate to vigorous PA. Conclusions: This study provides evidence that both ENMO and MIMS metrics can accurately measure PA with accelerometry, and machine learning algorithms can classify the activity into different intensity levels. These metrics and methods are valuable tools for monitoring PA and understanding the relationship between PA and health outcomes.

Objective: The primary purpose was to examine the agreement in sedentary, light, and moderate to vigorous minutes and step counts between the activPAL micro4 and ActiGraph GT9X in adults following total knee replacement. A secondary purpose was to examine the agreement between the activPAL micro4 and ActiGraph GT9X accelerometers at two different time points after surgery (∼1 and 3 months). Methods: Participants in a randomized trial wore ActiGraph GT9X and activPAL micro4 monitors simultaneously for 7 days at ∼1 and 3 months after total knee replacement. The intraclass correlations for time spent in sedentary behavior, light, and moderate to vigorous activity in addition to step counts were estimated to determine consistency between the two monitors. Bland–Altman plot demonstrated the 95% limits of agreement between the monitors at both time points. Results: A total of 480 observations (days) were used from 41 participants (64.9 ± 7 years, 32.4 ± 6.5 kg/m², 75.6% White, 61% female). The intraclass correlations between the ActiGraph GT9X and activPAL micro4 accelerometers was .839 for sedentary behavior, .853 for light activity, .806 for moderate to vigorous activity, and .937 for steps. The 95% confidence intervals of intraclass correlations between time points indicate a significantly higher agreement between the monitors at 3 months as compared with 1 month. Conclusion: The results suggest that either the ActiGraph GT9X or the activPAL micro4 accelerometers may be used for measuring sedentary, light, and moderate to vigorous minutes and step counts in adults after knee replacement.

Measurement reactivity has implications for behavioral science, as it is crucial to determine whether changes in constructs of interest represent true change or are an artifact of assessment. This study investigated whether measurement reactivity occurs for movement-related behaviors, motivational antecedents of behavior, and associations between them. Data from ecological momentary assessment studies of older adults (n = 195) and women in midlife (n = 75) lasting 8–10 days with 5–6 prompts/day and ambulatory monitoring of movement were used for this secondary data analysis. To examine potential drop-off patterns indicative of measurement reactivity, multilevel models tested whether behavior, antecedents, and associations changed after the first or first 2 prompts compared with remaining prompts and the first, first 2, or first 3 days compared with remaining days. Older adults’ sedentary behavior was lower, and time spent upright and intentions and self-efficacy to stand/move were higher on the first 2 and first 3 days compared with remaining days. Associations between intentions and self-efficacy and subsequent sedentary behavior were weaker earlier in the study compared to later. For women in midlife, light physical activity was higher at the first and first 2 prompts compared with remaining prompts, and physical activity motivation was higher across all prompts and days tested. There was a stronger association between intended and observed minutes of moderate to vigorous physical activity on the first 2 days compared with remaining days. Measurement reactivity appeared as expected for movement-related behaviors and motivational antecedents, though changes in associations between these constructs are likely do not reflect measurement reactivity.

ActiGraph’s idle sleep mode (ISM) is an optional battery- and memory-conserving feature believed to engage during periods of nonwear, inactivity, and sleep, but it has not been well studied in free-living environments. Thus, we investigated ISM during a 7-day assessment in a nationally representative sample of 13,649 participants (6–80 years) in the United States and found it engaged 43.6% ± 0.2% (mean ± SE) of the 24 hr per day. ISM engagement was highest (78.4% ± 0.2%) during early morning (00:00–05:59) and lowest (20.4% ± 0.3%) during afternoon (12:00–17:59), corresponding to quadrants of lowest and highest of movement, respectively. ISM engagement was also inversely correlated with daily activity across all participants (R = −.72, p < .001). When restricted to participants averaging ≥21 hr per day of wear (N = 10,482), ISM still engaged 39.5% ± 0.2% of the day and inversely correlated to daily activity (R = −.58, p < .001). These results suggest ISM engages in activity level-dependent temporal patterns. Additional research is needed to better inform analyses and interpretation of ISM-enabled data including whether it is appropriate to process them with existing methods that were developed and validated using data without ISM enabled. This issue may be particularly relevant for methods used to detect and score sleep, as ISM engaged during a substantial portion of the typical overnight sleep period in the 8-hr window between ≥22:00 and <06:00 (74.0% ± 12.6%, mean ± SD).