External validity of the reduced Morningness-Eveningness Questionnaire for Children and Adolescents: an actigraphic study

The aim of this study was to examine the external validity of the reduced Morningness-Eveningness Questionnaires for Children and Adolescents, using circadian motor activity, assessed through actigraphy, as an external criterion. Overall, 458 participants (269 females), with a mean (standard deviation) age of 15.75 (1.16) years, took part in this study. Each adolescent was requested to wear the actigraph Micro Motionlogger Watch actigraph (Ambulatory Monitoring, Inc., Ardlsey, NY, USA) around the non-dominant wrist for 1 week. At the end of the actigraphic recording, participants completed the reduced Morningness-Eveningness Questionnaires for Children and Adolescents. We extracted the motor activity counts, minute-by-minute over the 24 h, to depict the 24-h motor activity pattern, and we adopted the statistical framework of functional linear modelling to examine its changes according to chronotype. According to the reduced Morningness-Eveningness Questionnaires for Children and Adolescents cut-off scores, 13.97% of participants (n = 64) belonged to the evening-types category, 9.39% (n = 43) to morning-types, while the remaining (76.64%, n = 351) to the intermediate-types category. Evening types moved significantly more than the intermediate and morning types from around 10:00 p.m. to 2:00 a.m., while the opposite pattern of results was observed around 4:00 a.m. The results highlighted a significant difference in the 24-h motor activity pattern between chronotypes, in line with the expectations based on their well-known behaviour. Therefore, this study shows that the external validity of the reduced Morningness-Eveningness Questionnaire for Children and Adolescents, established by considering motor activity (recorded through actigraphy) as an external criterion, is satisfactory.

Comprehensive comparison of Apple Watch and Fitbit monitors in a free-living setting

Objectives

The aim of this study was to evaluate the accuracy of three consumer-based activity monitors, Fitbit Charge 2, Fitbit Alta, and the Apple Watch 2, all worn on the wrist, in estimating step counts, moderate-to-vigorous minutes (MVPA), and heart rate in a free-living setting.

Methods

Forty-eight participants (31 females, 17 males; ages 18–59) were asked to wear the three consumer-based monitors mentioned above on the wrist, concurrently with a Yamax pedometer as the criterion for step count, an ActiGraph GT3X+ (ActiGraph) for MVPA, and a Polar H7 chest strap for heart rate. Participants wore the monitors for a 24-hour free-living condition without changing their usual active routine. MVPA was calculated in bouts of ≥10 minutes. Pearson correlation, mean absolute percent error (MAPE), and equivalence testing were used to evaluate the measurement agreement.

Results

The average step counts recorded for each device were as follows: 11,734 (Charge2), 11,922 (Alta), 11,550 (Apple2), and 10,906 (Yamax). The correlations in steps for the above monitors ranged from 0.84 to 0.95 and MAPE ranged from 17.1% to 35.5%. For MVPA minutes, the average were 76.3 (Charge2), 63.3 (Alta), 49.5 (Apple2), and 47.8 (ActiGraph) minutes accumulated in bouts of 10 or greater minutes. The correlation from MVPA estimation for above monitors were 0.77, 0.91, and 0.66. MAPE from MVPA estimation ranged from 44.7% to 55.4% compared to ActiGraph. For heart rate, correlation for Charge2 and Apple2 was higher for sedentary behavior and lower for MVPA. The MAPE ranged from 4% to 16%.

Conclusion

All three consumer monitors estimated step counts fairly accurately, and both the Charge2 and Apple2 reported reasonable heart rate estimation. However, all monitors substantially underestimated MVPA in free-living settings.

Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography

PURPOSE

In this proof of principle study, we evaluated the diagnostic accuracy of the novel Nox BodySleep^TM 1.0 algorithm (Nox Medical, Iceland) for the estimation of disease severity and sleep stages based on features extracted from actigraphy and respiratory inductance plethysmography (RIP) belts. Validation was performed against in-lab polysomnography (PSG) in patients with sleep-disordered breathing (SDB).

METHODS

Patients received PSG according to AASM. Sleep stages were manually scored using the AASM criteria and the recording was evaluated by the novel algorithm. The results were analyzed by descriptive statistics methods (IBM SPSS Statistics 25.0).

RESULTS

We found a strong Pearson correlation (r=0.91) with a bias of 0.2/h for AHI estimation as well as a good correlation (r=0.81) and an overestimation of 14 min for total sleep time (TST). Sleep efficiency (SE) was also valued with a good Pearson correlation (r=0.73) and an overestimation of 2.1%. Wake epochs were estimated with a sensitivity of 0.65 and a specificity of 0.59 while REM and non-REM (NREM) phases were evaluated a sensitivity of 0.72 and 0.74, respectively. Specificity was 0.74 for NREM and 0.68 for REM. Additionally, a Cohen's kappa of 0.62 was found for this 3-class classification problem.

CONCLUSION

The algorithm shows a moderate diagnostic accuracy for the estimation of sleep. In addition, the algorithm determines the AHI with good agreement with the manual scoring and it shows good diagnostic accuracy in estimating wake-sleep transition. The presented algorithm seems to be an appropriate tool to increase the diagnostic accuracy of portable monitoring. The validated diagnostic algorithm promises a more appropriate and cost-effective method if integrated in out-of-center (OOC) testing of patients with suspicion for SDB.

The feasibility and reliability of actigraphy to monitor sleep in intensive care patients: an observational study

BACKGROUND

Sleep amongst intensive care patients is reduced and highly fragmented which may adversely impact on recovery. The current challenge for Intensive Care clinicians is identifying feasible and accurate assessments of sleep that can be widely implemented. The objective of this study was to investigate the feasibility and reliability of a minimally invasive sleep monitoring technique compared to the gold standard, polysomnography, for sleep monitoring.

METHODS

Prospective observational study employing a within subject design in adult patients admitted to an Intensive Care Unit. Sleep monitoring was undertaken amongst minimally sedated patients via concurrent polysomnography and actigraphy monitoring over a 24-h duration to assess agreement between the two methods; total sleep time and wake time.

RESULTS

We recruited 80 patients who were mechanically ventilated (24%) and non-ventilated (76%) within the intensive care unit. Sleep was found to be highly fragmented, composed of numerous sleep bouts and characterized by abnormal sleep architecture. Actigraphy was found to have a moderate level of overall agreement in identifying sleep and wake states with polysomnography (69.4%; K = 0.386, p < 0.05) in an epoch by epoch analysis, with a moderate level of sensitivity (65.5%) and specificity (76.1%). Monitoring accuracy via actigraphy was improved amongst non-ventilated patients (specificity 83.7%; sensitivity 56.7%). Actigraphy was found to have a moderate correlation with polysomnography reported total sleep time (r = 0.359, p < 0.05) and wakefulness (r = 0.371, p < 0.05). Bland-Altman plots indicated that sleep was underestimated by actigraphy, with wakeful states overestimated.

CONCLUSIONS

Actigraphy was easy and safe to use, provided moderate level of agreement with polysomnography in distinguishing between sleep and wakeful states, and may be a reasonable alternative to measure sleep in intensive care patients. Clinical Trial Registration number ACTRN12615000945527 (Registered 9/9/2015).

Reactivity to accelerometer measurement of youth with moderate and severe intellectual disabilities

BACKGROUND

Reactivity occurs when research participants alter their behaviours due to the awareness of being monitored, which is a concern with using wearable devices to measure physical activity. The purpose of this study was to examine reactivity to accelerometer measurement among youth with moderate and severe intellectual disabilities (ID).

METHODS

A sample of 175 youth with ID (108 with moderate and 67 with severe ID) was recruited from residential centres in China. Demographic data were measured using a parent-reported questionnaire, and light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) were measured using the ActiGraph GT3X accelerometers. Data were analysed using an analysis of covariances where Day 1 LPA/MVPA, Day 2-6 LPA/MVPA, and Day 7 LPA/MVPA were repeated measures.

RESULTS

Youth with moderate ID had significantly higher LPA (8.01%) and MVPA (10.30%) on Day 1 than Day 2-6. Similarly, youth with severe ID had significantly higher LPA (21.69%) and MVPA (19.48%) on Day 1 than Day 2-6. An inverse reactivity was also found on Day 7 among youth with severe ID for LPA (-10.65%) and MVPA (-14.82%).

CONCLUSIONS

Reactivity to accelerometer measurement was found for youth with moderate and severe ID. Findings support the utilisation of a 1-day familiarisation period, as well as discounting the final day of measurement, when examining physical activity behaviours among youth with moderate and severe ID.

Comparative validity of energy expenditure prediction algorithms using wearable devices for people with spinal cord injury

STUDY DESIGN

Cross-sectional validation study.

OBJECTIVES

To conduct a literature search for existing energy expenditure (EE) predictive algorithms using ActiGraph activity monitors for manual wheelchairs users (MWUs) with spinal cord injury (SCI), and evaluate their validity using an out-of-sample dataset.

SETTING

Research institution in Pittsburgh, USA.

METHODS

A literature search resulted in five articles containing five sets of predictive equations using an ActiGraph activity monitor for MWUs with SCI. Out-of-sample data were collected from 29 MWUs with chronic SCI who were asked to follow an activity protocol while wearing an ActiGraph GT9X Link on the dominant wrist. They also wore a portable metabolic cart which provided the criterion measure for EE. The out-of-sample dataset was used to evaluate the validity of the five sets of EE predictive equations.

RESULTS

None of the five sets of predictive equations demonstrated equivalence within 20% of the criterion measure based on an equivalence test. The mean absolute error for the five sets of predictive equations ranged from 0.87 to 6.41 kilocalories per minute (kcal min-1) when compared with the criterion measure, and the intraclass correlation estimates ranged from 0.06 to 0.59. The range between the Bland-Altman upper and lower limits of agreement was from 4.70 kcal min-1 to 25.09 kcal min-1.

CONCLUSIONS

The existing EE predictive equations based on ActiGraph monitors for MWUs with SCI showed varied performance when compared with the criterion measure. Their accuracies may not be sufficient to support future clinical and research use. More work is needed to develop more accurate EE predictive equations for this population.

Step-counting accuracy of activity monitors in persons with Down syndrome

BACKGROUND

Accelerometers and pedometers have been used to monitor the number of steps. However, the evidence on the step-counting accuracy of these devices - especially accelerometers - is limited in persons with Down syndrome (DS). This study therefore examined the accuracy of accelerometers placed on the hip or wrist and of a pedometer with a uni-axial accelerometer mechanism in measuring steps in persons with DS and whether device error is associated with walking speed, height, weight, body mass index, waist circumference, leg length, age or sex.

METHOD

Seventeen persons with DS (eight women and nine men; age 33 ± 15 years) walked over-ground for 6 min at their preferred speed. The steps were measured with a hip-worn and a wrist-worn ActiGraph accelerometer using the manufacturer's default (DF) and low-frequency extension (LFE) filters, and with the NL-1000 New Lifestyles pedometer on the hip. Steps were also measured with hand tally which served as the criterion.

RESULTS

Absolute percent error was considerable and differed statistically between devices (P = 0.001); however, error improved for accelerometers when LFE was applied (Hip-DF: 31.6 ± 18.8%; Hip-LFE: 9.7 ± 12.8%; Wrist-DF: 32.7 ± 14.2%; Wrist-LFE: 13.6 ± 10.2%; Pedometer: 23.2 ± 22.8%). Bland-Altman plots indicated underestimation of steps for accelerometers and the pedometer. Application of LFE, however, improved the prediction of the accelerometers. The number of steps measured by the hip accelerometer with LFE and by the pedometer did not differ statistically from actual steps. Steps by the remaining methods were significantly lower than hand tally (P ≤ 0.001). Correlations between percent error for each device and walking speed, anthropometry, age or sex ranged between -0.28 and +0.48, and were non-significant, except for age.

CONCLUSIONS

The results demonstrated that the pedometer and ActiGraph accelerometers have considerable error in measuring steps of persons with DS. Application of LFE, however, significantly improved the step-counting performance of the Actigraph accelerometers.

Agreement between self-reported and objectively measured sleep duration among white, black, Hispanic, and Chinese adults in the United States: Multi-Ethnic Study of Atherosclerosis

Study Objectives

To identify systematic biases across groups in objectively and subjectively measured sleep duration.

Methods

We investigated concordance of self-reported habitual sleep duration compared with actigraphy- and single-night in-home polysomnography (PSG) across white, black, Hispanic, and Chinese participants in the Multi-Ethnic Study of Atherosclerosis.

Results

Among 1910 adults, self-reported sleep duration, determined by differences between bed and wake times, was overestimated in all racial groups compared with PSG and actigraphy. Compared with whites (ρ = 0.45), correlations were significantly lower only in blacks (ρ = 0.28). Self-reporting bias for total sleep time compared with wrist actigraphy was 66 min (95% confidence interval [CI]: 61-71) for whites, 58 min (95% CI: 48-69) for blacks, 66 min (95% CI: 57-74) for Hispanics, and 60 min (95% CI: 49-70) for Chinese adults. Compared with PSG, self-reporting bias in whites at 73 min (95% CI: 67-79) was higher than in blacks (54 min [95% CI: 42-65]) and Chinese (49 min [95% CI: 37-61]) but not different from Hispanics (67 min [95% CI: 56-78]). Slight agreement/concordance was observed between self-reported and actigraphy-based total sleep time (kw = 0.14 for whites, 0.10 for blacks, 0.17 for Hispanics, and 0.11 for Chinese) and PSG (kw = 0.08 for whites, 0.04 for blacks, 0.05 for Hispanics, and 0.01 for Chinese) across race/ethnicity.

Conclusions

Self-reported sleep duration overestimated objectively measured sleep across all races, and compared with PSG, overestimation is significantly greater in whites compared with blacks. Larger reporting bias reduces the ability to identify significant associations between sleep duration and health among blacks compared with whites. Sleep measurement property differences should be considered when comparing sleep indices across racial/ethnic groups.

Initial evaluation of PetPace activity monitor

Mobility is considered a vital component of health and quality of life in humans and companion animals. Wearable devices for pets that can monitor activity and other aspects of health are increasingly being marketed to veterinarians and owners, with claims around their ability to monitor aspects of health. However, there is little scientific evidence to support the validity of these claims. To address this, the objective of this study was to assess the correlation of the activity measurement from the PetPace device compared to activity output from Actigraph and the validated Actical device. Ten client-owned, healthy dogs were used for the study. The three devices were mounted simultaneously on a dedicated collar and activity was recorded during a period of 7 days. There were moderate correlations between the Actical and the PetPace (r²=0.59, P=<0.001). There was high correlation between the PetPace and the Actigraph (r²=0.85, P=<0.001) and between the Actical and the Actigraph (r²=0.72, P=<0.001). If the Actical activity counts were limited under 50,000 per hour, there was strong correlation between the Actical and the PetPace (r²=0.71, P=<0.001) and between the Actical and the Actigraph (r²=0.86, P=<0.001). PetPace has a moderate correlation with the most validated activity monitor that has been used in veterinary medicine. Its real-time data acquisition, user friendly interface for owners and cost make this device an attractive tool for monitoring activity in dogs. Further studies maybe needed to evaluate its performance, validity and clinical utility in the field.

Convergent validity of actigraphy with polysomnography and parent reports when measuring sleep in children with Down syndrome

BACKGROUND

There is a need for rigorous measures of sleep in children with Down syndrome as sleep is a substantial problem in this population and there are barriers to obtaining the gold standard polysomnography (PSG). PSG is cost-prohibitive when measuring treatment effects in some clinical trials, and children with Down syndrome may not cooperate with undergoing a PSG. Minimal information is available on the validity of alternative methods of assessing sleep in children with Down syndrome, such as actigraphy and parent ratings. Our study examined the concurrent and convergent validity of different measures of sleep, including PSG, actigraphy and parent reports of sleep among children with Down syndrome.

METHOD

A clinic (n = 27) and a community (n = 47) sample of children with Down syndrome were examined. In clinic, children with Down syndrome wore an actigraph watch during a routine PSG. In the community, children with Down syndrome wore an actigraph watch for a week at home at night as part of a larger study on sleep and behaviour. Their parent completed ratings of the child's sleep during that same week.

RESULTS

Actigraph watches demonstrated convergent validity with PSG when measuring a child with Down syndrome's total amount of sleep time, total wake time after sleep onset and sleep period efficiency. In contrast, actigraph watches demonstrated poor correlations with parent reports of sleep, and with PSG when measuring the total time in bed and total wake episodes. Actigraphy, PSG and parent ratings of sleep demonstrated poor concurrent validity with clinical diagnosis of obstructive sleep apnoea.

CONCLUSION

Our current data suggest that actigraph watches demonstrate convergent validity and are sensitive to measuring certain sleep constructs (duration, efficiency) in children with Down syndrome. However, parent reports, such as the Children's Sleep Habits Questionnaire, may be measuring other sleep constructs. These findings highlight the importance of selecting measures of sleep related to target concerns.

Comparison of wrist-worn Fitbit Flex and waist-worn ActiGraph for measuring steps in free-living adults

Introduction

Accelerometers are commonly used to assess physical activity. Consumer activity trackers have become increasingly popular today, such as the Fitbit. This study aimed to compare the average number of steps per day using the wrist-worn Fitbit Flex and waist-worn ActiGraph (wGT3X-BT) in free-living conditions.

Methods

104 adult participants (n = 35 males; n = 69 females) were asked to wear a Fitbit Flex and an ActiGraph concurrently for 7 days. Daily step counts were used to classify inactive (<10,000 steps) and active (≥10,000 steps) days, which is one of the commonly used physical activity guidelines to maintain health. Proportion of agreement between physical activity categorizations from ActiGraph and Fitbit Flex was assessed. Statistical analyses included Spearman’s rho, intraclass correlation (ICC), median absolute percentage error (MAPE), Kappa statistics, and Bland-Altman plots. Analyses were performed among all participants, by each step-defined daily physical activity category and gender.

Results

The median average steps/day recorded by Fitbit Flex and ActiGraph were 10193 and 8812, respectively. Strong positive correlations and agreement were found for all participants, both genders, as well as daily physical activity categories (Spearman's rho: 0.76–0.91; ICC: 0.73–0.87). The MAPE was: 15.5% (95% confidence interval [CI]: 5.8–28.1%) for overall steps, 16.9% (6.8–30.3%) vs. 15.1% (4.5–27.3%) in males and females, and 20.4% (8.7–35.9%) vs. 9.6% (1.0–18.4%) during inactive days and active days. Bland-Altman plot indicated a median overestimation of 1300 steps/day by the Fitbit Flex in all participants. Fitbit Flex and ActiGraph respectively classified 51.5% and 37.5% of the days as active (Kappa: 0.66).

Conclusions

There were high correlations and agreement in steps between Fitbit Flex and ActiGraph. However, findings suggested discrepancies in steps between devices. This imposed a challenge that needs to be considered when using Fibit Flex in research and health promotion programs.

Calibration and Cross-Validation of the ActiGraph wGT3X+ Accelerometer for the Estimation of Physical Activity Intensity in Children with Intellectual Disabilities

Background

Valid objective measurement is integral to increasing our understanding of physical activity and sedentary behaviours. However, no population-specific cut points have been calibrated for children with intellectual disabilities. Therefore, this study aimed to calibrate and cross-validate the first population-specific accelerometer intensity cut points for children with intellectual disabilities.

Methods

Fifty children with intellectual disabilities were randomly assigned to the calibration (n = 36; boys = 28, 9.53±1.08yrs) or cross-validation (n = 14; boys = 9, 9.57±1.16yrs) group. Participants completed a semi-structured school-based activity session, which included various activities ranging from sedentary to vigorous intensity. Direct observation (SOFIT tool) was used to calibrate the ActiGraph wGT3X+, which participants wore on the right hip. Receiver Operating Characteristic curve analyses determined the optimal cut points for sedentary, moderate, and vigorous intensity activity for the vertical axis and vector magnitude. Classification agreement was investigated using sensitivity, specificity, total agreement, and Cohen’s kappa scores against the criterion measure of SOFIT.

Results

The optimal (AUC = .87−.94) vertical axis cut points (cpm) were ≤507 (sedentary), 1008−2300 (moderate), and ≥2301 (vigorous), which demonstrated high sensitivity (81−88%) and specificity (81−85%). The optimal (AUC = .86−.92) vector magnitude cut points (cpm) of ≤1863 (sedentary), 2610−4214 (moderate), and ≥4215 (vigorous) demonstrated comparable, albeit marginally lower, accuracy than the vertical axis cut points (sensitivity = 80−86%; specificity = 77−82%). Classification agreement ranged from moderate to almost perfect (κ = .51−.85) with high sensitivity and specificity, and confirmed the trend that accuracy increased with intensity, and vertical axis cut points provide higher classification agreement than vector magnitude cut points.

Conclusions

This study provides the first valid methods of interpreting accelerometer output in children with intellectual disabilities. The calibrated physical activity cut points are notably higher than existing cut points, thus raising questions on the validity of previous low physical activity estimates in children with intellectual disabilities that were based on typically developing cut points.

Intensity Thresholds on Raw Acceleration Data: Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) Approaches

Objectives

(1) To develop and internally-validate Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) thresholds for separating sedentary behaviours from common light-intensity physical activities using raw acceleration data collected from both hip- and wrist-worn tri-axial accelerometers; and (2) to compare and evaluate the performances between the ENMO and MAD metrics.

Methods

Thirty-three adults [mean age (standard deviation (SD)) = 27.4 (5.9) years; mean BMI (SD) = 23.9 (3.7) kg/m²; 20 females (60.6%)] wore four accelerometers; an ActiGraph GT3X+ and a GENEActiv on the right hip; and an ActiGraph GT3X+ and a GENEActiv on the non-dominant wrist. Under laboratory-conditions, participants performed 16 different activities (11 sedentary behaviours and 5 light-intensity physical activities) for 5 minutes each. ENMO and MAD were computed from the raw acceleration data, and logistic regression and receiver-operating-characteristic (ROC) analyses were implemented to derive thresholds for activity discrimination. Areas under ROC curves (AUROC) were calculated to summarise performances and thresholds were assessed via executing leave-one-out-cross-validations.

Results

For both hip and wrist monitor placements, in comparison to the ActiGraph GT3X+ monitors, the ENMO and MAD values derived from the GENEActiv devices were observed to be slightly higher, particularly for the lower-intensity activities. Monitor-specific hip and wrist ENMO and MAD thresholds showed excellent ability for separating sedentary behaviours from motion-based light-intensity physical activities (in general, AUROCs >0.95), with validation indicating robustness. However, poor classification was experienced when attempting to isolate standing still from sedentary behaviours (in general, AUROCs <0.65). The ENMO and MAD metrics tended to perform similarly across activities and accelerometer brands.

Conclusions

Researchers can utilise these robust monitor-specific hip and wrist ENMO and MAD thresholds, in order to accurately separate sedentary behaviours from common motion-based light-intensity physical activities. However, caution should be taken if isolating sedentary behaviours from standing is of particular interest.

Is missing geographic positioning system data in accelerometry studies a problem, and is imputation the solution?

The main purpose of the present study was to assess the impact of global positioning system (GPS) signal lapse on physical activity analyses, discover any existing associations between missing GPS data and environmental and demographics attributes, and to determine whether imputation is an accurate and viable method for correcting GPS data loss. Accelerometer and GPS data of 782 participants from 8 studies were pooled to represent a range of lifestyles and interactions with the built environment. Periods of GPS signal lapse were identified and extracted. Generalised linear mixed models were run with the number of lapses and the length of lapses as outcomes. The signal lapses were imputed using a simple ruleset, and imputation was validated against person-worn camera imagery. A final generalised linear mixed model was used to identify the difference between the amount of GPS minutes pre- and post-imputation for the activity categories of sedentary, light, and moderate-to-vigorous physical activity. Over 17% of the dataset was comprised of GPS data lapses. No strong associations were found between increasing lapse length and number of lapses and the demographic and built environment variables. A significant difference was found between the pre- and postimputation minutes for each activity category. No demographic or environmental bias was found for length or number of lapses, but imputation of GPS data may make a significant difference for inclusion of physical activity data that occurred during a lapse. Imputing GPS data lapses is a viable technique for returning spatial context to accelerometer data and improving the completeness of the dataset.

Validation of the Actical Accelerometer in Multiethnic Preschoolers: The Children's Healthy Living (CHL) Program

This study aimed to determine the validity and reliability of the Actical accelerometer for measuring physical activity (PA) in preschool children of mixed ethnicity, compared with direct observation via a modified System for Observing Fitness Instruction Time (SOFIT) protocol and proxy parental reports (PA Logs). Fifty children in Hawai'i wore wrist-mounted accelerometers for two 7-day periods with a washout period between each week. Thirty children were concurrently observed using SOFIT. Parents completed PA Logs for three days. Reliability and validity were measured by intra-class correlation coefficient and proportions of agreement concurrently. There was slight agreement (proportion of agreement: 82%; weighted Kappa=.17, P <.001) between the accelerometer and SOFIT as well as between the accelerometer and the PA Logs (proportions of agreement: 40%; weighted Kappa=0.15, P <.001). PA logs underestimated the PA levels of the children, while the Actical was found to be valid and reliable for estimating PA levels of multiethnic, mixed ethnicity preschoolers. These findings suggest that accelerometers can be objective, valid, and accurate physical activity assessment tools compared to conventional PA logs and subjective reports of activity for preschool children of mixed ethnicity.

Noninvasive dissection of mouse sleep using a piezoelectric motion sensor

BACKGROUND

Changes in autonomic control cause regular breathing during NREM sleep to fluctuate during REM. Piezoelectric cage-floor sensors have been used to successfully discriminate sleep and wake states in mice based on signal features related to respiration and other movements. This study presents a classifier for noninvasively classifying REM and NREM using a piezoelectric sensor.

NEW METHOD

Vigilance state was scored manually in 4-s epochs for 24-h EEG/EMG recordings in 20 mice. An unsupervised classifier clustered piezoelectric signal features quantifying movement and respiration into three states: one active; and two inactive with regular and irregular breathing, respectively. These states were hypothesized to correspond to Wake, NREM, and REM, respectively. States predicted by the classifier were compared against manual EEG/EMG scores to test this hypothesis.

RESULTS

Using only piezoelectric signal features, an unsupervised classifier distinguished Wake with high (89% sensitivity, 96% specificity) and REM with moderate (73% sensitivity, 75% specificity) accuracy, but NREM with poor sensitivity (51%) and high specificity (96%). The classifier sometimes confused light NREM sleep - characterized by irregular breathing and moderate delta EEG power - with REM. A supervised classifier improved sensitivities to 90, 81, and 67% and all specificities to over 90% for Wake, NREM, and REM, respectively.

COMPARISON WITH EXISTING METHODS

Unlike most actigraphic techniques, which only differentiate sleep from wake, the proposed piezoelectric method further dissects sleep based on breathing regularity into states strongly correlated with REM and NREM.

CONCLUSIONS

This approach could facilitate large-sample screening for genes influencing different sleep traits, besides drug studies or other manipulations.

A comparison of self-reported leisure-time physical activity and measured moderate-to-vigorous physical activity in adolescents and adults

BACKGROUND

Systematic reviews and results of Statistics Canada surveys have shown a discrepancy between self-reported and measured physical activity. This study compares these two methods and examines specific activities to explain the limitations of each method.

DATA AND METHODS

Data are from cycle 1 (2007 to 2009) and cycle 2 (2009 to 2011) of the Canadian Health Measures Survey. The survey involved an interview in the respondent's home and a visit to a mobile examination centre (MEC) for physical measurements. In a questionnaire, respondents were asked about 21 leisure-time physical activities. They were requested to wear an Actical accelerometer for seven days after the MEC visit. The analysis pertains to respondents aged 12 to 79 who wore the accelerometer for 10 or more hours on at least four days (n = 7,158).

RESULTS

Averages of self-reported leisure-time physical activity and moderate-to-vigorous physical activity measured by accelerometer were within a couple of minutes of each other. However, at the individual level, the difference between estimates could exceed 37.5 minutes per day in one direction or the other, and around 40% of the population met physical activity thresholds according to one measurement method, but not according to the other. The disagreement is supported by weak observed correlations.

INTERPRETATION

The lack of a systematic trend in the relationship between the two methods of measuring physical activity precludes the creation of correction factors or being confident in using one method instead of the other. Accelerometers and questionnaires measure different aspects of physical activity.

On the impact of arousals on the performance of sleep and wake classification using actigraphy

We evaluated the impact of arousals on the performance of actigraphy-based sleep/wake classification. Using a dataset of 15 healthy adults and a threshold optimized for this task we found that the percentage of sleep epochs with activity counts above that threshold was significantly larger in epochs with and following arousals. We also found that 41.1% of all false positive classifications occurred in these epochs. Finally, we determined that excluding these epochs from the evaluation led to a maximum precision increase of 17.2%. Considering wake detections in those epochs as correct led to a maximum precision increase of 31.3%. We concluded that unless arousals can be automatically identified or at least distinguished from wake, the performance of actigraphy-based sleep/wake classifiers is limited by their presence.

Validity of SC-StepRx pedometer-derived moderate and vigorous physical activity during treadmill walking and running in a heterogeneous sample of children and youth

BACKGROUND

The purpose of this study was to determine the validity of the SC-StepRx pedometer to assess moderate and vigorous physical activity during treadmill walking and running in a heterogeneous sample of children and youth aged 10-17 years.

METHODS

Physical activity intensity assessed via indirect calorimetry served as the criterion standard. A convenience sample of 40 participants (20 boys, 20 girls) wore 6 SC-StepRx pedometers, 2 ActiGraph GT3X accelerometers, 2 Actical accelerometers, 1 Walk4Life MVP pedometer and 1 NL-1000 pedometer while walking/running at speeds approximating 2, 3, 4, 6 and 7 METs. Associations between indirect calorimetry and each activity monitor were assessed using linear regression analyses in SAS 9.2.

RESULTS

Estimates of moderate and vigorous physical activity from all monitors were significantly associated with the criterion standard of indirect calorimetry. The strongest associations with the criterion measure were observed for the SC-StepRx with moderate/vigorous thresholds of 110/130 steps•minute(-1), and the NL-1000 (R2 = 0.82, p <0.05). The SC-StepRx with moderate/vigorous thresholds of 110/130 steps•minute(-1) also exhibited the highest combined sensitivity (92.9%) and specificity (96.5%) for correctly identifying a bout of moderate-to-vigorous physical activity.

CONCLUSIONS

This study demonstrates that the SC-StepRx pedometer is a valid tool for the measurement of moderate and vigorous physical activity in children and youth.

Recommendations for collecting and processing accelerometry data in elderly people

Due to the large variety of methods for collecting and analyzing accelerometer data to assess physical activity, it is difficult to compare results from different studies. Therefore, this work aims to provide recommendations for the collection and processing of such data in older people, focusing on the assessment of wearing time and intensity levels.

Activity classification using the GENEA: optimum sampling frequency and number of axes

INTRODUCTION

The GENEA shows high accuracy for classification of sedentary, household, walking, and running activities when sampling at 80 Hz on three axes. It is not known whether it is possible to decrease this sampling frequency and/or the number of axes without detriment to classification accuracy. The purpose of this study was to compare the classification rate of activities on the basis of data from a single axis, two axes, and three axes, with sampling rates ranging from 5 to 80 Hz.

METHODS

Sixty participants (age, 49.4 yr (6.5 yr); BMI, 24.6 kg·m (3.4 kg·m)) completed 10-12 semistructured activities in the laboratory and outdoor environment while wearing a GENEA accelerometer on the right wrist. We analyzed data from single axis, dual axes, and three axes at sampling rates of 5, 10, 20, 40, and 80 Hz. Mathematical models based on features extracted from mean, SD, fast Fourier transform, and wavelet decomposition were built, which combined one of the numbers of axes with one of the sampling rates to classify activities into sedentary, household, walking, and running.

RESULTS

Classification accuracy was high irrespective of the number of axes for data collected at 80 Hz (96.93% ± 0.97%), 40 Hz (97.4% ± 0.73%), 20 Hz (96.86% ± 1.12%), and 10 Hz (97.01% ± 1.01%) but dropped for data collected at 5 Hz (94.98% ± 1.36%).

CONCLUSION

Sampling frequencies >10 Hz and/or more than one axis of measurement were not associated with greater classification accuracy. Lower sampling rates and measurement of a single axis would result in a lower data load, longer battery life, and higher efficiency of data processing. Further research should investigate whether a lower sampling rate and a single axis affects classification accuracy when considering a wider range of activities.

Validity and comparability of a wrist-worn accelerometer in children

BACKGROUND

Wrist-worn accelerometers may provide an alternative to hip-worn monitors for assessing physical activity as they are easier to wear and may thus facilitate long-term recordings. The current study aimed at a) assessing the validity of the Actiwatch (wrist-worn) for estimating energy expenditure, b) determining cut-off values for light, moderate, and vigorous activities, c) studying the comparability between the Actiwatch and the Actigraph (hip-worn), and d) assessing reliability.

METHODS

For validity, indirect calorimetry was used as criterion measure. ROC-analyses were applied to identify cut-off values. Comparability was tested by simultaneously wearing of the 2 accelerometers during free-living condition. Reliability was tested in a mechanical shaker.

RESULTS

All-over correlation between accelerometer output and energy expenditure were found to be 0.80 (P < .001).Based on ROC-analysis, cut-off values for 1.5, 3, and 6 METs were found to be 80, 262, and 406 counts per 15 s, respectively. Energy expenditure estimates differed between the Actiwatch and the Actigraph (P < .05). The intra- and interinstrument coefficient of variation of the Actiwatch ranged between 0.72% and 8.4%.

CONCLUSION

The wrist-worn Actiwatch appears to be valid and reliable for estimating energy expenditure and physical activity intensity in children aged 8 to 10 years.

Pilot study to evaluate ecological momentary assessment of tinnitus

OBJECTIVES

Because audiometric evaluation, symptom histories, questionnaires, and similar standard assessment tools may not adequately sample the effects of chronic tinnitus on day-to-day activities, there is a need for alternative methodological approaches to study the impact of tinnitus on day-to-day life. An innovative methodological approach that has shown great promise in the study of chronic health problems characterized by reported temporal and/or situational variability in symptoms and distress is known as ecological momentary assessment (EMA). EMA involves the real-time measurement of states, situational factors, and symptoms by individuals as they go about their day-to-day activities. The objective of this pilot investigation was to explore the feasibility of using EMA methods to examine within- and between-day effects of tinnitus.

DESIGN

This study was conducted in three phases: (1) design and development of an EMA methodology that could be used to assess effects of tinnitus; (2) refinement of the methodology through the use of two focus groups; and (3) field-test the methodology with individuals who experienced bothersome tinnitus. For Phase 3, each of the 24 participants wore, throughout their waking hours for 2 weeks, a personal digital assistant that produced alerts four times a day. The alerts prompted participants to respond to 19 questions, including 9 relating to situational and mood factors and 10 comprising the Tinnitus Handicap Inventory-Screening version (THI-S). To evaluate for potential reactive effects of performing the EMA protocol, each participant completed the paper-and-pencil version of the full 25-item THI before and after the 2-week EMA period.

RESULTS

Participants responded to the alerts with a 90% compliance rate, providing a total of 1210 completed surveys. At the time of their response, participants indicated that they were in their house or apartment (67.7%), alone (50.2%), happy (50%), and calm (54.5%). Across most responses, participants could hear their tinnitus (97%), and the loudness of their tinnitus averaged 4.7 on a 7-point increasing-loudness scale. The mean THI-S index score (out of a possible maximum 40 points for greatest tinnitus severity) was 17.0 (moderate self-perceived tinnitus handicap). Repeated THI-S index scores varied considerably both within and between participants. Mean 25-item THI scores were not significantly different before and after the EMA period, suggesting little reactivity of the EMA.

CONCLUSIONS

The high compliance rate, positive feedback from participants, lack of reactivity as a result of performing the EMA protocol, and data collected indicate that EMA methodology is feasible with patients who have tinnitus. Outcome data obtained with this methodology cannot be obtained any other way because retrospective questionnaires cannot capture the day-to-day reactions. This methodology has the potential to provide more in-depth and accurate assessments of patients receiving therapy for tinnitus.

Direct comparison of two new actigraphs and polysomnography in children and adolescents

STUDY OBJECTIVES

To evaluate the validity and reliability of 2 new models of commercially available actigraphs compared to polysomnography for children and adolescents.

DESIGN AND SETTING

Subjects concurrently wore the Ambulatory Monitoring Inc. Motionlogger Sleep Watch (AMI) and the Phillips Respironics Mini-Mitter Actiwatch-2 (PRMM) while undergoing overnight polysomnography (PSG) in a pediatric sleep laboratory housed in a tertiary care children's hospital.

PARTICIPANTS

115 youth (59 girls, 56 boys), ages 3-18 years (mean 8.8 years, SD 4.4 years).

MEASUREMENTS

Outcome variables were total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE). Epoch-by-epoch comparisons were made between the 2 devices and PSG to determine sensitivity, specificity, and accuracy. Agreement between the 2 devices was determined with t-tests and the Bland-Altman concordance technique. Different algorithms/sensitivities, developmental age groups, and sleep disordered breathing (SDB) status were also examined.

RESULTS

For both device brands, sensitivity (0.89-0.97), specificity (0.54-0.77), and accuracy (0.87-0.90) were similar to previous reports. Notably, compared to PSG, both device brands significantly overestimated WASO, while the AMI device also significantly underestimated TST. Inter-device comparison of the 2 brands found poor agreement for TST, WASO, and SE. Agreement with PSG differed depending on the scoring algorithm (AMI) or sensitivity setting (PRMM), as well as across developmental age group and sleep disordered breathing (SDB) status.

CONCLUSIONS

Similar to previous reports, both new actigraph brands were found to have good sensitivity (to detect sleep), but poorer specificity (to detect wake). Study results also suggest that researchers should adjust the scoring algorithm/sensitivity depending on a study's design (e.g., young children vs. adolescents, healthy children vs. youth with SDB). Further, inter-device reliability was poor, suggesting the need for caution when comparing results across studies that use different brands of actigraphic devices.

Agreement between pedometer and accelerometer in measuring physical activity in overweight and obese pregnant women

BACKGROUND

Inexpensive, reliable objective methods are needed to measure physical activity (PA) in large scale trials. This study compared the number of pedometer step counts with accelerometer data in pregnant women in free-living conditions to assess agreement between these measures.

METHODS

Pregnant women (n = 58) with body mass index ≥25 kg/m(2) at median 13 weeks' gestation wore a GT1M Actigraph accelerometer and a Yamax Digi-Walker CW-701 pedometer for four consecutive days. The Spearman rank correlation coefficients were determined between pedometer step counts and various accelerometer measures of PA. Total agreement between accelerometer and pedometer step counts was evaluated by determining the 95% limits of agreement estimated using a regression-based method. Agreement between the monitors in categorising participants as active or inactive was assessed by determining Kappa.

RESULTS

Pedometer step counts correlated moderately (r = 0.36 to 0.54) with most accelerometer measures of PA. Overall step counts recorded by the pedometer and the accelerometer were not significantly different (medians 5961 vs. 5687 steps/day, p = 0.37). However, the 95% limits of agreement ranged from -2690 to 2656 steps/day for the mean step count value (6026 steps/day) and changed substantially over the range of values. Agreement between the monitors in categorising participants to active and inactive varied from moderate to good depending on the criteria adopted.

CONCLUSIONS

Despite statistically significant correlations and similar median step counts, the overall agreement between pedometer and accelerometer step counts was poor and varied with activity level. Pedometer and accelerometer steps cannot be used interchangeably in overweight and obese pregnant women.

A computational framework for the standardization of motion analysis exploiting wearable inertial sensors

Movement analysis is a powerful tool for the diagnosis of neurological conditions, as well as patient assessment and follow-up during rehabilitation programs. In spite of the available systems allowing a quantitative analysis of a subject's movement control performances, the clinical assessment and diagnostic approach still relies mostly on non-quantitative exams, such as clinical scales. Further, studying balance control, gait and activities of daily living poses relevant technical challenges, which greatly limit the availability of testing facilities. The goal of our project was therefore to develop a new system based on wearable sensors for movement analysis and scoring of performances. A prototype 3-sensors system was tested on a group of 4 normal subjects while carrying out a set of full body movement exercises drawn by clinical scales for the assessment of movement and balance control.

In vivo laboratory validation of the physiometer: a measurement system for long-term recording of posture and movements in the workplace

Posture and movement are thought to be important risk factors for the development of work-related musculoskeletal disorders. Whole day occupational exposure assessment has typically used self-report or observation techniques, but the need for more accurate measurement is now recognised. The aim of this study was to compare the kinematic recordings of a frequently used field system (physiometer) with two laboratory-based systems (Fastrak and Peak) in vivo. Head, thorax and right arm kinematics were recorded simultaneously by the three systems whilst a subject performed 27 single and multiple plane physiological and simulated daily living task movement trials. Errors observed in the Fastrak and Peak data included gimbal lock and quadrant errors. Physiometer data errors included undervalues, overvalues and temporal errors of slow response and resonance. All three systems showed some cross-talk. Agreement between the physiometer and the other systems was generally high for physiological movements (R(2) > 0.8) and less for functional movements (R(2) > 0.5). STATEMENT OF RELEVANCE: The physiometer recording device can provide an indication of posture across time in the workplace; however, its accuracy is limited, particularly during functional movements. Further technology should be developed to unobtrusively capture accurate all day 3-D kinematics.

Refined two-regression model for the ActiGraph accelerometer

PURPOSE

The purpose of this study was to refine the 2006 Crouter two-regression model to eliminate the misclassification of walking or running when starting an activity in the middle of a minute on the ActiGraph clock.

METHODS

Forty-eight participants (mean [SD] age = 35 [11.4] yr) performed 10-min bouts of various activities ranging from sedentary behaviors to vigorous physical activity. Eighteen activities were divided into three routines, and 20 participants performed each routine. Participants wore an ActiGraph accelerometer on the hip, and a portable indirect calorimeter was used to measure energy expenditure. Forty-five routines were used to develop the refined two-regression model, and 15 routines were used to cross validate the model. Coefficient of variation (CV) was used to classify each activity as continuous walking or running (CV < or = 10) or intermittent lifestyle activity (CV > 10).

RESULTS

An exponential regression equation and a cubic equation using the natural log of the 10-s counts were developed to predict METs every 10 s for walking or running and intermittent lifestyle activities, respectively. The refined method examines each 10-s epoch and all combinations of the surrounding five 10-s epochs to find the lowest CV. In the cross-validation group, the refined method was not significantly different from measured METs for any activity (P > 0.05), except cycling (P < 0.05). In addition, the 2006 and the refined two-regression models had similar accuracy and precision for estimating energy expenditure during structured activities.

CONCLUSION

The refined two-regression model should eliminate the misclassification of transitional minutes when changing activities that start and stop in the middle of a minute on the ActiGraph clock, thus improving the estimate of free-living energy expenditure.

Invalidity of one actigraphy brand for identifying sleep and wake among infants

STUDY OBJECTIVES

Few commercially available brands of actigraphs (ACT) have been subjected to rigorous validation with infant participants. The purpose of this study was to examine the agreement between concurrent polysomnography (PSG) and one brand of ACT (AW-64, Mitter Co. Inc.) using appropriate statistical techniques among a sample of healthy infants.

METHODS

Twenty-two healthy infants (14.1+/-0.6 months) had one night of ankle ACT recording during research PSG at Kosair Children's Hospital Sleep Research Center in Louisville, Kentucky. Macroanalyses were conducted using the Bland-Altman concordance technique to assess agreement between total sleep time (TST) and wake after sleep onset (WASO) simultaneously measured by PSG and ACT, using two ACT algorithm settings. Microanalyses were also calculated to examine sensitivity, specificity, and accuracy of ACT within each PSG-identified sleep state. Correlations were calculated between PSG-identified arousals and the discrepancies between ACT and PSG.

RESULTS

The Bland-Altman concordance technique revealed that ACT underestimated TST by 72.25 (SD=61.48) minutes and by > or = 60 min among 54.55% of infants. Furthermore, ACT overestimated WASO by 13.85 (SD=30.94) minutes and by > or = 30 min among 40.91% of infants. Sensitivity, specificity, and accuracy analyses revealed that ACT adequately identified sleep, but poorly identified wake. PSG and ACT discrepancies were positively associated with PSG-identified arousals (r=.45).

CONCLUSIONS

Improved device and/or software development is needed before the AW-64 can be considered a valid method for identifying infant sleep and wake.