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The 8th International Conference on Ambulatory Monitoring of Physical Activity and Movement

Open access

Integrity and Performance of Four Tape Solutions for Mounting Accelerometry Devices: Lolland-Falster Health Study

Therese Lockenwitz Petersen, Jan C. Brønd, Eva Benfeldt, and Randi Jepsen

Background: Tape-mounted Axivity AX3 accelerometers are increasingly being used to monitor physical activity of individuals, but studies on the integrity and performance of diffe1rent attachment protocols are missing. Purpose: The purpose of this paper was to evaluate four attachment protocols with respect to skin reactions, adhesion, and wear time in children and adults using tape-mounted Axivity AX3 accelerometers and to evaluate the associated ease of handling. Methods: We used data from the Danish household-based population study, the Lolland-Falster Health Study. Participants were instructed to wear accelerometers for seven consecutive days and to complete a questionnaire on skin reactions and issues relating to adhesion. A one-way analysis of variance was used to examine differences in skin reactions and adhesion between the protocols. A Tukey post hoc test compared group means. Ease of handling was assessed throughout the data collection. Results: In total, 5,389 individuals were included (1,289 children and 4,100 adults). For both children and adults, skin reactions were most frequent in Protocols 1 and 2. Adhesion problems were most frequent in Protocol 3. Wear time was longest in Protocol 4. Skin reactions and adhesion problems were more frequent in children compared to adults. Adults achieved longest wear time. Discussion: Covering the skin completely with adhesive tape seemed to cause skin reactions. Too short pieces of fixation tape caused accelerometers to fall off. Protocols necessitating removal of remains of glue on the accelerometers required a lot of work. Conclusion: The last of the four protocols was superior in respect to skin reactions, adhesion, wear time, and ease of handling.

Open access

Missing Step Count Data? Step Away From the Expectation–Maximization Algorithm

Mia S. Tackney, Daniel Stahl, Elizabeth Williamson, and James Carpenter

In studies that compare physical activity between groups of individuals, it is common for physical activity to be quantified by step count, which is measured by accelerometers or other wearable devices. Missing step count data often arise in these settings and can lead to bias or imprecision in the estimated effect if handled inappropriately. Replacing each missing value in accelerometer data with a single value using the Expectation–Maximization (EM) algorithm has been advocated in the literature, but it can lead to underestimation of variances and could seriously compromise study conclusions. We compare the performance in terms of bias and variance of two missing data methods, the EM algorithm and Multiple Imputation (MI), through a simulation study where data are generated from a parametric model to reflect characteristics of a trial on physical activity. We also conduct a reanalysis of the 2019 MOVE-IT trial. The EM algorithm leads to an underestimate of the variance of effects of interest, in both the simulation study and the reanalysis of the MOVE-IT trial. MI should be the preferred approach to handling missing data in accelerometer, which provides valid point and variance estimates.

Open access

Erratum. The 7th International Conference on Ambulatory Monitoring of Physical Activity and Movement

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Agreement of Step-Based Metrics From ActiGraph and ActivPAL Accelerometers Worn Concurrently Among Older Adults

Eric T. Hyde, Steve Nguyen, Fatima Tuz-Zahra, Christopher C. Moore, Mikael Anne Greenwood-Hickman, Rod L. Walker, Loki Natarajan, Dori Rosenberg, and John Bellettiere

Purpose : Our study evaluated the agreement of mean daily step counts, peak 1-min cadence, and peak 30-min cadence between the hip-worn ActiGraph GT3X+ accelerometer, using the normal filter (AGN) and the low frequency extension (AGLFE), and the thigh-worn activPAL3 micro (AP) accelerometer among older adults. Methods: Nine-hundred and fifty-three older adults (≥65 years) were recruited to wear the ActiGraph device concurrently with the AP for 4–7 days beginning in 2016. Using the AP as the reference measure, device agreement for each step-based metric was assessed using mean differences (AGN − AP and AGLFE − AP), mean absolute percentage error (MAPE), and Pearson and concordance correlation coefficients. Results: For AGN − AP, the mean differences and MAPE were: daily steps −1,851 steps/day and 27.2%, peak 1-min cadence −16.2 steps/min and 16.3%, and peak 30-min cadence −17.7 steps/min and 24.0%. Pearson coefficients were .94, .85, and .91 and concordance coefficients were .81, .65, and .73, respectively. For AGLFE − AP, the mean differences and MAPE were: daily steps 4,968 steps/day and 72.7%, peak 1-min cadence −1.4 steps/min and 4.7%, and peak 30-min cadence 1.4 steps/min and 7.0%. Pearson coefficients were .91, .91, and .95 and concordance coefficients were .49, .91, and .94, respectively. Conclusions: Compared with estimates from the AP, the AGN underestimated daily step counts by approximately 1,800 steps/day, while the AGLFE overestimated by approximately 5,000 steps/day. However, peak step cadence estimates generated from the AGLFE and AP had high agreement (MAPE ≤ 7.0%). Additional convergent validation studies of step-based metrics from concurrently worn accelerometers are needed for improved understanding of between-device agreement.

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The Assessment of 24-Hr Physical Behavior in Children and Adolescents via Wearables: A Systematic Review of Laboratory Validation Studies

Marco Giurgiu, Carina Nigg, Janis Fiedler, Irina Timm, Ellen Rulf, Johannes B.J. Bussmann, Claudio R. Nigg, Alexander Woll, and Ulrich W. Ebner-Priemer

Purpose: To raise attention to the quality of published validation protocols while comparing (in)consistencies and providing an overview on wearables, and whether they show promise or not. Methods: Searches from five electronic databases were included concerning the following eligibility criteria: (a) laboratory conditions with humans (<18 years), (b) device outcome must belong to one dimension of the 24-hr physical behavior construct (i.e., intensity, posture/activity type outcomes, biological state), (c) must include a criterion measure, and (d) published in a peer-reviewed English language journal between 1980 and 2021. Results: Out of 13,285 unique search results, 123 articles were included. In 86 studies, children <13 years were recruited, whereas in 26 studies adolescents (13–18 years) were recruited. Most studies (73.2%) validated an intensity outcome such as energy expenditure; only 20.3% and 13.8% of studies validated biological state or posture/activity type outcomes, respectively. We identified 14 wearables that had been used to validate outcomes from two or three different dimensions. Most (n = 72) of the identified 88 wearables were only validated once. Risk of bias assessment resulted in 7.3% of studies being classified as “low risk,” 28.5% as “some concerns,” and 71.5% as “high risk.” Conclusion: Overall, laboratory validation studies of wearables are characterized by low methodological quality, large variability in design, and a focus on intensity. No identified wearable provides valid results across all three dimensions of the 24-hr physical behavior construct. Future research should more strongly aim at biological state and posture/activity type outcomes, and strive for standardized protocols embedded in a validation framework.

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Evaluation of Two Thigh-Worn Accelerometer Brands in Laboratory and Free-Living Settings

Alexander H.K. Montoye, Olivia Coolman, Amberly Keyes, Megan Ready, Jaedyn Shelton, Ethan Willett, and Brian C. Rider

Background: Given the popularity of thigh-worn accelerometers, it is important to understand their reliability and validity. Purpose: Our study evaluated laboratory validity and free-living intermonitor reliability of the Fibion monitor and free-living intermonitor reliability of the activPAL monitor. Free-living comparability of the Fibion and activPAL monitors was also assessed. Methods: Nineteen adult participants wore Fibion monitors on both thighs while performing 11 activities in a laboratory setting. Then, participants wore Fibion and activPAL monitors on both thighs for 3 days during waking hours. Accuracy of the Fibion monitor was determined for recognizing lying/sitting, standing, slow walking, fast walking, jogging, and cycling. For the 3-day free-living wear, outputs from the Fibion monitors were compared, with similar analyses conducted for the activPAL monitors. Finally, free-living comparability of the Fibion and activPAL monitors was determined for nonwear, sitting, standing, stepping, and cycling. Results: The Fibion monitor had an overall accuracy of 85%–89%, with high accuracy (94%–100%) for detecting prone and supine lying, sitting, and standing but some misclassification among ambulatory activities and for left-/right-side lying with standing. Intermonitor reliability was similar for the Fibion and activPAL monitors, with best reliability for sitting but poorer reliability for activities performed least often (e.g., cycling). The Fibion and activPAL monitors were not equivalent for most tested metrics. Conclusion: The Fibion monitor appears suitable for assessment of sedentary and nonsedentary waking postures, and the Fibion and activPAL monitors have comparable intermonitor reliability. However, studies using thigh-worn monitors should use the same monitor brand worn on the same leg to optimize reliability.

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Impact of COVID-19 Pandemic on Physical Activity, Pain, Mood, and Sleep in Adults With Knee Osteoarthritis

Michael J. Rose, Michael P. LaValley, S. Reza Jafarzadeh, Kerry E. Costello, Nirali Shah, Soyoung Lee, Belinda Borrelli, Stephen P. Messier, Tuhina Neogi, and Deepak Kumar

Objective: To examine changes in physical activity, sleep, pain, and mood in people with knee osteoarthritis during the ongoing COVID-19 pandemic by leveraging an ongoing randomized clinical trial. Methods: Participants enrolled in a 12-month parallel two-arm randomized clinical trial (NCT03064139) interrupted by the COVID-19 pandemic wore an activity monitor (Fitbit Charge 3) and filled out custom weekly surveys rating knee pain, mood, and sleep as part of the study. Data from 30 weeks of the parent study were used for this analysis. Daily step count and sleep duration were extracted from activity monitor data, and participants self-reported knee pain, positive mood, and negative mood via surveys. Metrics were averaged within each participant and then across all participants for prepandemic, stay-at-home, and reopening periods, reflecting the phased reopening in the state of Massachusetts. Results: Data from 28 participants showed small changes with inconclusive clinical significance during the stay-at-home and reopening periods compared with prepandemic for all outcomes. Summary statistics suggested substantial variability across participants with some participants showing persistent declines in physical activity during the observation period. Conclusion: Effects of the COVID-19 pandemic on physical activity, sleep, pain, and mood were variable across individuals with osteoarthritis. Specific reasons for this variability could not be determined. Identifying factors that could affect individuals with knee osteoarthritis who may exhibit reduced physical activity and/or worse symptoms during major lifestyle changes (such as the ongoing pandemic) is important for providing targeted health-care services and management advice toward those that could benefit from it the most.

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Investigating the Effects of Applying Different Actigraphy Processing Approaches to Examine the Sleep Data of Patients With Neuropathic Pain

Hannah J. Coyle-Asbil, Anuj Bhatia, Andrew Lim, and Mandeep Singh

Individuals suffering from neuropathic pain commonly report issues associated with sleep. To measure sleep in this population, researchers have used actigraphy. Historically, actigraphy data have been analyzed in the form of counts; however, due to the proprietary nature, many opt to quantify data in its raw form. Various processing techniques exist to accomplish this; however, it remains unclear how they compare to one another. This study sought to compare sleep measures derived using the GGIR R package versus the GENEActiv (GA) R Markdown tool in a neuropathic pain population. It was hypothesized that the processing techniques would yield significantly different sleep outcomes. One hundred and twelve individuals (mean age = 52.72 ± 13.01 years; 60 M) with neuropathic pain in their back and/or lower limbs were included. While simultaneously undergoing spinal cord stimulation, actigraphy devices were worn on the wrist for a minimum of 7 days (GA; 50 Hz). Upon completing the protocol, sleep outcome measures were calculated using (a) the GGIR R package and (b) the GA R Markdown tool. To compare these algorithms, paired-samples t tests and Bland–Altman plots were used to compare the total sleep time, sleep efficiency, wake after sleep onset, sleep onset time, and rise times. According to the paired-samples t test, the GA R Markdown yielded lower total sleep time and sleep efficiency and a greater wake after sleep onset, compared with the GGIR package. Furthermore, later sleep onset times and earlier rise times were reported by the GGIR package compared with the GA R Markdown.

Open access

CRIB: A Novel Method for Device-Based Physical Behavior Analysis

Paul R. Hibbing, Seth A. Creasy, and Jordan A. Carlson

Physical behaviors (e.g., sleep, sedentary behavior, and physical activity) often occur in sustained bouts that are punctuated with brief interruptions. To detect and classify these interrupted bouts, researchers commonly use wearable devices and specialized algorithms. Most algorithms examine the data in chronological order, initiating and terminating bouts whenever specific criteria are met. Consequently, the bouts may encapsulate or overlap with later periods that also meet the activation and termination criteria (i.e., alternative bout solutions). In some cases, it is desirable to compare these alternative bout solutions before making a final classification. Thus, comparison-focused algorithms are needed, which can be used in isolation or in concert with their chronology-focused counterparts. In this technical note, we present a comparison-focused algorithm called CRIB (Clustered Recognition of Interrupted Bouts). It uses agglomerative hierarchical clustering to facilitate the comparison of different bout solutions, with the final classification being made in favor of the smallest number of bouts that comply with user-specified criteria (i.e., limits on the number, individual duration, and cumulative duration of interruptions). For demonstration, we use CRIB to assess bouts of moderate to vigorous physical activity in accelerometer data from the National Health and Nutrition Examination Survey, and we include a comparison against results from two established chronology-focused algorithms. Our discussion explores strengths and limitations of CRIB, as well as potential considerations and applications for using it in future studies. An online vignette ( is available to assist users with implementing CRIB in R.