Detecting accelerometer non-wear periods using change in acceleration combined with rate-of-change in temperature.

BACKGROUND: Accelerometery is commonly used to estimate physical activity, sleep, and sedentary behavior. In free-living conditions, periods of device removal (non-wear) can lead to misclassification of behavior with consequences for research outcomes and clinical decision making. Common methods for non-wear detection are limited by data transformations (e.g., activity counts) or algorithm parameters such as minimum durations or absolute temperature thresholds that risk over- or under-estimating non-wear time. This study aimed to advance non-wear detection methods by integrating a 'rate-of-change' criterion for temperature into a combined temperature-acceleration algorithm. METHODS: Data were from 39 participants with neurodegenerative disease (36% female; age: 45-83 years) who wore a tri-axial accelerometer (GENEActiv) on their wrist 24-h per day for 7-days as part of a multi-sensor protocol. The reference dataset was derived from visual inspection conducted by two expert analysts. Linear regression was used to establish temperature rate-of-change as a criterion for non-wear detection. A classification and regression tree (CART) decision tree classifier determined optimal parameters separately for non-wear start and end detection. Classifiers were trained using data from 15 participants (38.5%). Outputs from the CART analysis were supplemented based on edge cases and published parameters. RESULTS: The dataset included 186 non-wear periods (85.5% < 60 min). Temperature rate-of-change over the first five minutes of non-wear was - 0.40 ± 0.17 °C/minute and 0.36 ± 0.21 °C/minute for the first five minutes following device donning. Performance of the DETACH (DEvice Temperature and Accelerometer CHange) algorithm was improved compared to existing algorithms with recall of 0.942 (95% CI 0.883 to 1.0), precision of 0.942 (95% CI 0.844 to 1.0), F1-Score of 0.942 (95% CI 0.880 to 1.0) and accuracy of 0.996 (0.994-1.000). CONCLUSION: The DETACH algorithm accurately detected non-wear intervals as short as five minutes; improving non-wear classification relative to current interval-based methods. Using temperature rate-of-change combined with acceleration results in a robust algorithm appropriate for use across different temperature ranges and settings. The ability to detect short non-wear periods is particularly relevant to free-living scenarios where brief but frequent removals occur, and for clinical application where misclassification of behavior may have important implications for healthcare decision-making.

Measuring Foot Abduction Brace Wear Time Using a Single 3-Axis Accelerometer.

The recommended treatment for idiopathic congenital clubfoot deformity involves a series of weekly castings, surgery, and a period of bracing using a foot abduction brace (FAB). Depending on the age of the child, the orthotic should be worn for periods that reduce in duration as the child develops. Compliance is vital to achieve optimal functional outcomes and reduce the likelihood of reoccurrence, deformity, or the need for future surgery. However, compliance is typically monitored by self-reporting, which is time-consuming to implement and lacks accuracy. This study presents a novel method for objectively monitoring FAB wear using a single 3-axis accelerometer. Eleven families mounted an accelerometer on their infant's FAB for up to seven days. Parents were also given a physical diary that was used to record the daily application and removal of the orthotic in line with their treatment. Both methods produced very similar measurements of wear that visually aligned with the movement measured by the accelerometer. Bland Altman plots showed a -0.55-h bias in the diary measurements and the limits of agreement ranging from -2.96 h to 1.96 h. Furthermore, the Cohens Kappa coefficient for the entire dataset was 0.88, showing a very high level of agreement. The method provides an advantage over existing objective monitoring solutions as it can be easily applied to existing FABs, preventing the need for bespoke monitoring devices. The novel method can facilitate increased research into FAB compliance and help enable FAB monitoring in clinical practice.

Development and testing of methods for detecting off-wrist in actimetry recordings.

STUDY OBJECTIVES: In field studies using wrist-actimetry, not identifying/handling off-wrist intervals may result in their misclassification as immobility/sleep and biased estimations of rhythmic patterns. By comparing different solutions for detecting off-wrist, our goal was to ascertain how accurately they detect nonwear in different contexts and identify variables that are useful in the process. METHODS: We developed algorithms using heuristic (HA) and machine learning (ML) approaches. Both were tested using data from a protocol followed by 10 subjects, which was devised to mimic contexts of actimeter wear/nonwear in real-life. Self-reported data on usage according to the protocol were considered the gold standard. Additionally, the performance of our algorithms was compared to that of visual inspection (by 2 experienced investigators) and Choi algorithm. Data previously collected in field studies were used for proof-of-concept analyses. RESULTS: All methods showed similarly good performances. Accuracy was marginally higher for one of the raters (visual inspection) than for heuristically developed algorithms (HA, Choi). Short intervals (especially < 2 h) were either not or only poorly identified. Consecutive stretches of zeros in activity were considered important indicators of off-wrist (for both HA and ML). It took hours for raters to complete the task as opposed to the seconds or few minutes taken by the automated methods. CONCLUSIONS: Automated strategies of off-wrist detection are similarly effective to visual inspection, but have the important advantage of being faster, less costly, and independent of raters' attention/experience. In our study, detecting short intervals was a limitation across methods.

Validity of Two Awake Wear-Time Classification Algorithms for activPAL in Youth, Adults, and Older Adults.

BACKGROUND: The authors assessed agreement between participant diaries and two automated algorithms applied to activPAL (PAL Technologies Ltd, Glasgow, United Kingdom) data for classifying awake wear time in three age groups. METHODS: Study 1 involved 20 youth and 23 adults who, by protocol, removed the activPAL occasionally to create nonwear periods. Study 2 involved 744 older adults who wore the activPAL continuously. Both studies involved multiple assessment days. In-bed, out-of-bed, and nonwear times were recorded in the participant diaries. The CREA (in PAL processing suite) and ProcessingPAL (secondary application) algorithms estimated out-of-bed wear time. Second- and day-level agreement between the algorithms and diary was investigated, as were associations of sedentary variables with self-rated health. RESULTS: The overall accuracy for classifying out-of-bed wear time as compared with the diary was 89.7% (Study 1) to 95% (Study 2) for CREA and 89.4% (Study 1) to 93% (Study 2) for ProcessingPAL. Over 90% of the nonwear time occurring in nonwear periods >165 min was detected by both algorithms, while <11% occurring in periods ≤165 min was detected. For the daily variables, the mean absolute errors for each algorithm were generally within 0-15% of the diary mean. Most Spearman correlations were very large (≥.81). The mean absolute errors and correlations were less favorable for days on which any nonwear time had occurred. The associations between sedentary variables and self-rated health were similar across processing methods. CONCLUSION: The automated awake wear-time classification algorithms performed similarly to the diary information on days without short (≤2.5-2.75 hr) nonwear periods. Because both diary and algorithm data can have inaccuracies, best practices likely involve integrating diary and algorithm output.

Ambulatory sleep scoring using accelerometers-distinguishing between nonwear and sleep/wake states.

BACKGROUND: Differentiating nonwear time from sleep and wake times is essential for the estimation of sleep duration based on actigraphy data. To efficiently analyze large-scale data sets, an automatic method of identifying these three different states is required. Therefore, we developed a classification algorithm to determine nonwear, sleep and wake periods from accelerometer data. Our work aimed to (I) develop a new pattern recognition algorithm for identifying nonwear periods from actigraphy data based on the influence of respiration rate on the power spectrum of the acceleration signal and implement it in an automatic classification algorithm for nonwear/sleep/wake states; (II) address motion artifacts that occur during nonwear periods and are known to cause misclassification of these periods; (III) adjust the algorithm depending on the sensor position (wrist, chest); and (IV) validate the algorithm on both healthy individuals and patients with sleep disorders. METHODS: The study involved 98 participants who wore wrist and chest acceleration sensors for one day of measurements. They spent one night in the sleep laboratory and continued to wear the sensors outside of the laboratory for the remainder of the day. The results of the classification algorithm were compared to those of the reference source: polysomnography for wake/sleep and manual annotations for nonwear/wear classification. RESULTS: The median kappa values for the two locations were 0.83 (wrist) and 0.84 (chest). The level of agreement did not vary significantly by sleep health (good sleepers vs. subjects with sleep disorders) (p = 0.348, p = 0.118) or by sex (p = 0.442, p = 0.456). The intraclass correlation coefficients of nonwear total time between the reference and the algorithm were 0.92 and 0.97 with the outliers and 0.95 and 0.98 after the outliers were removed for the wrist and chest, respectively. There was no evidence of an association between the mean difference (and 95% limits of agreement) and the mean of the two methods for either sensor position (wrist p = 0.110, chest p = 0.164), and the mean differences (algorithm minus reference) were 5.11 [95% LoA -15.4-25.7] and 1.32 [95% LoA -9.59-12.24] min/day, respectively, after the outliers were removed. DISCUSSION: We studied the influence of the respiration wave on the power spectrum of the acceleration signal for the differentiation of nonwear periods from sleep and wake periods. The algorithm combined both spectral analysis of the acceleration signal and rescoring. Based on the Bland-Altman analysis, the chest-worn accelerometer showed better results than the wrist-worn accelerometer.

Identifying ActiGraph non-wear time in pregnant women with overweight or obesity.

OBJECTIVES: Non-wear time algorithms have not been validated in pregnant women with overweight/obesity (PW-OW/OB), potentially leading to misclassification of sedentary/activity data, and inaccurate estimates of how physical activity is associated with pregnancy outcomes. We examined: (1) validity/reliability of non-wear time algorithms in PW-OW/OB by comparing wear time from five algorithms to a self-report criterion and (2) whether these algorithms over- or underestimated sedentary behaviors. DESIGN: PW-OW/OB (N = 19) from the Healthy Mom Zone randomized controlled trial wore an ActiGraph GT3x + for 7 consecutive days between 8-12 weeks gestation. METHODS: Non-wear algorithms (i.e., consecutive strings of zero acceleration in 60-second epochs) were tested at 60, 90, 120, 150, and 180-min. The monitor registered sedentary minutes as activity counts 0-99. Women completed daily self-report logs to report wear time. RESULTS: Intraclass correlation coefficients for each algorithm were 0.96-0.97; Bland-Altman plots revealed no bias; mean absolute percent errors were <10%. Compared to self-report (M = 829.5, SD = 62.1), equivalency testing revealed algorithm wear times (min/day) were equivalent: 60- (M = 816.4, SD = 58.4), 90- (M = 827.5, SD = 61.4), 120- (M = 830.8, SD = 65.2), 150- (M = 833.8, SD = 64.6) and 180-min (M = 837.4, SD = 65.4). Repeated measures ANOVA showed 60- and 90-min algorithms may underestimate sedentary minutes compared to 150- and 180-min algorithms. CONCLUSIONS: The 60, 90, 120, 150, and 180-min algorithms are valid and reliable for estimating wear time in PW-OW/OB. However, implementing algorithms with a higher threshold for consecutive zero counts (i.e., ≥150-min) can avoid the risk of misclassifying sedentary data.

Is it on? An algorithm for discerning wrist-accelerometer non-wear times from sleep/wake activity.

The accuracy of sleep/wake estimates derived with actigraphy is often dependent on researchers being able to discern non-wear times from sleep or quiescent wakefulness when confronted by discrepancies in a sleep log. Without knowing when an accelerometer is being worn, non-wear could be inferred from periods of inactivity unlikely to occur while in bed. Data collected in our laboratory suggest that more than 50% of inactive periods during time in bed are <8 min in duration. This duration may be an appropriate minimum threshold for routine non-wear classification during self-reported wake. Higher thresholds could be chosen to derive non-wear definitions for self-reported bedtimes depending on the desired level of certainty. To determine non-wear at thresholds of 75%, 95% and 99%, for example, would require periods of inactivity lasting ≥18 min, ≥53 min and ≥85 min, respectively.

Classification of accelerometer wear and non-wear events in seconds for monitoring free-living physical activity.

OBJECTIVE: To classify wear and non-wear time of accelerometer data for accurately quantifying physical activity in public health or population level research. DESIGN: A bi-moving-window-based approach was used to combine acceleration and skin temperature data to identify wear and non-wear time events in triaxial accelerometer data that monitor physical activity. SETTING: Local residents in Swansea, Wales, UK. PARTICIPANTS: 50 participants aged under 16 years (n=23) and over 17 years (n=27) were recruited in two phases: phase 1: design of the wear/non-wear algorithm (n=20) and phase 2: validation of the algorithm (n=30). METHODS: Participants wore a triaxial accelerometer (GeneActiv) against the skin surface on the wrist (adults) or ankle (children). Participants kept a diary to record the timings of wear and non-wear and were asked to ensure that events of wear/non-wear last for a minimum of 15 min. RESULTS: The overall sensitivity of the proposed method was 0.94 (95% CI 0.90 to 0.98) and specificity 0.91 (95% CI 0.88 to 0.94). It performed equally well for children compared with adults, and females compared with males. Using surface skin temperature data in combination with acceleration data significantly improved the classification of wear/non-wear time when compared with methods that used acceleration data only (p<0.01). CONCLUSIONS: Using either accelerometer seismic information or temperature information alone is prone to considerable error. Combining both sources of data can give accurate estimates of non-wear periods thus giving better classification of sedentary behaviour. This method can be used in population studies of physical activity in free-living environments.