Accelerometry data in health research: challenges and opportunities

Validation of the SQUASH physical activity questionnaire using accelerometry: The NEO study

Objective

To investigate the construct validity of the SQUASH (Short QUestionnaire to ASsess Health-enhancing physical activity).

Design

This is a cross-sectional analysis using baseline measurements from middle-aged participants in the Netherlands Epidemiology of Obesity (NEO) study. The SQUASH consists of questions on eleven physical activities investigating days per week, average duration per day and intensity, leading to a summed score in Metabolic Equivalent of Task hours (MET h) per week. To assess convergent validity, a Spearman's rank correlation between SQUASH and ActiHeart was calculated. To assess extreme group validity, three groups expected to differ in SQUASH total physical activity outcome were compared. For discriminative validity, a Spearman's rank correlation between SQUASH physical activity and participant height was investigated.

Results

SQUASH data were available for 6550 participants (mean age 56 years, 44% men, mean BMI 26.3, 15% with knee OA, 13% with hand OA). Median physical activity (interquartile range) was 118 (76; 154) MET h/week according to SQUASH and 75 (58; 99) according to ActiHeart. Convergent validity was weak (rho ​= ​0.20). For all three extreme group comparisons, a statistically significant difference was present. Discriminative validity was present (rho ​= ​0.01). Compared with the reference quintile, those with a discrepancy SQUASH ​> ​ActiHeart and SQUASH ​< ​ActiHeart were relatively younger and more often male.

Conclusions

The construct validity of the SQUASH seems sub-optimal. Physical activity reported by the SQUASH was generally higher than reported by ActiHeart. Whether the differences between SQUASH and ActiHeart are e.g. due to different underlying domains, limitations to our study, or reflect true differences needs further investigation.

Tracking amyotrophic lateral sclerosis disease progression using passively collected smartphone sensor data

BACKGROUND

Passively collected smartphone sensor data provide an opportunity to study physical activity and mobility unobtrusively over long periods of time and may enable disease monitoring in people with amyotrophic lateral sclerosis (PALS).

METHODS

We enrolled 63 PALS who used Beiwe mobile application that collected their smartphone accelerometer and GPS data and administered the self-entry ALS Functional Rating Scale-Revised (ALSFRS-RSE) survey. We identified individual steps from accelerometer data and used the Activity Index to summarize activity at the minute level. Walking, Activity Index, and GPS outcomes were then aggregated into day-level measures. We used linear mixed effect models (LMMs) to estimate baseline and monthly change for ALSFRS-RSE scores (total score, subscores Q1-3, Q4-6, Q7-9, Q10-12) and smartphone sensor data measures, as well as the associations between them.

FINDINGS

The analytic sample (N = 45) was 64.4% male with a mean age of 60.1 years. The mean observation period was 292.3 days. The ALSFRS-RSE total score baseline mean was 35.8 and had a monthly rate of decline of -0.48 (p-value <0.001). We observed statistically significant change over time and association with ALSFRS-RSE total score for four smartphone sensor data-derived measures: walking cadence from top 1 min and log-transformed step count, step count from top 1 min, and Activity Index from top 1 min.

INTERPRETATION

Smartphone sensors can unobtrusively track physical changes in PALS, potentially aiding disease monitoring and future research.

Use of accelerometry to detect varus thrust of osteoarthritic knees before and one year after high tibial osteotomy

BACKGROUND

The purpose of this study was to determine the effects of high tibial osteotomy (HTO) on varus thrust during gait in patients with medial compartment knee osteoarthritis (KOA), and to identify factors that influence thrust before and one year after surgery.

METHODS

HTO was performed in 60 KOA patients (70 knees, including 56 knees by open wedge and 14 by closed wedge). The control group comprised 28 normal, control subjects. Several parameters were evaluated before surgery and one year thereafter. Varus thrust was defined as acceleration of the thigh relative to the lower leg in the coronal plane. Knee-injury-and-osteoarthritis-outcome scores (KOOSs), knee joint angles, radiography, and mediolateral knee acceleration during free speed gait were measured and analyzed.

RESULTS

One-year after HTO, KOOSs, knee extension angles, and range of knee motion were improved (p < 0.001). The hip-knee-ankle angle and joint-line-convergent angle (JLCA) had decreased (p < 0.001), and walking speed had increased (p < 0.001). Preoperatively, patient acceleration was significantly (p < 0.05) higher than that of controls, and it did not change after HTO. However, it was reduced significantly (p < 0.05) after adjusting for walking speed. Walking speed correlated significantly with acceleration preoperatively, postoperatively, and among controls. Surgical methods (open-wedge/closed-wedge HTO) and correction angle did not affect postoperative acceleration. There was a low correlation between acceleration and KOOSs (KOOSa, KOOSp), knee joint angles, or JLCA postoperatively, but no relationship was found between acceleration and these parameters in the preoperative or the control group.

CONCLUSIONS

Walking speed correlated significantly with acceleration preoperatively, postoperatively, and with those of controls. Mediolateral acceleration of the thigh relative to the lower leg in patients with KOA was significantly higher than that of normal controls before surgery, and it did not change after HTO. However, after surgery it was reduced significantly after adjusting for walking speed.

Wearable Sensors to Monitor Physical Activity in Heart Failure Clinical Trials: State-of-the-Art Review

BACKGROUND

Estimation of the effects that drugs or other interventions have on patients' symptoms and functions is crucial in heart failure trials. Traditional symptoms and functions clinical outcome assessments have important limitations. Actigraphy may help to overcome these limitations due to its objective nature and the potential for continuous recording of data. However, actigraphy is not currently accepted as clinically relevant by key stakeholders.

METHODS AND RESULTS

In this state-of-the-art study, the key aspects to consider when implementing actigraphy in heart failure trials are discussed. They include which actigraphy-derived measures should be considered, how to build endpoints using them, how to measure and analyze them, and how to handle the patients' and sites' logistics of integrating devices into trials. A comprehensive recommendation based on the current evidence is provided.

CONCLUSION

Actigraphy is technically feasible in clinical trials involving heart failure, but successful implementation and use to demonstrate clinically important differences in physical functioning with drug or other interventions require careful consideration of many design choices.

Classification of Free-Living Body Posture with ECG Patch Accelerometers: Application to the Multicenter AIDS Cohort Study

Purpose

As health studies increasingly monitor free-living heart performance via ECG patches with accelerometers, researchers will seek to investigate cardio-electrical responses to physical activity and sedentary behavior, increasing demand for fast, scalable methods to process accelerometer data. We extend a posture classification algorithm for accelerometers in ECG patches when researchers do not have ground-truth labels or other reference measurements (i.e., upright measurement).

Methods

Men living with and without HIV in the Multicenter AIDS Cohort study wore the Zio XT® for up to two weeks (n = 1,250). Our novel extensions for posture classification include (1) estimation of an upright posture for each individual without a reference upright measurement; (2) correction of the upright estimate for device removal and re-positioning using novel spherical change-point detection; and (3) classification of upright and recumbent periods using a clustering and voting process rather than a simple inclination threshold used in other algorithms. As no posture labels exist in the free-living environment, we perform numerous sensitivity analyses and evaluate the algorithm against labelled data from the Towson Accelerometer Study, where participants wore accelerometers at the waist.

Results

On average, 87.1% of participants were recumbent at 4am and 15.5% were recumbent at 1pm. Participants were recumbent 54 minutes longer on weekends compared to weekdays. Performance was good in comparison to labelled data in a separate, controlled setting (accuracy = 96.0%, sensitivity = 97.5%, specificity = 95.9%).

Conclusions

Posture may be classified in the free-living environment from accelerometers in ECG patches even without measuring a standard upright position. Furthermore, algorithms that fail to account for individuals who rotate and re-attach the accelerometer may fail in the free-living environment.

Determination of cut-off points for the Move4 accelerometer in children aged 8-13 years

BACKGROUND

To assess physical activity (PA) there is a need of objective, valid and reliable measurement methods like accelerometers. Before these devices can be used for research, they need to be calibrated and validated for specific age groups as the locomotion differs between children and adults, for instance. Therefore, the aim of the present study was the calibration and validation of the Move4 accelerometer for children aged 8-13 years.

METHODS

53 normal weighted children (52% boys, 48%girls) aged 8-13 years (mean age = 10.69 ± 1.46, mean BMI = 17.93 kg/m- 2, 60th percentile), wore the Move4 sensor at four different body positions (thigh, hip, wrist and the Move4ecg including heart rate measurement at the chest). They completed nine activities that considered the four activity levels (sedentary behavior (SB), light PA (LPA), moderate PA (MPA) and vigorous PA (VPA)) within a test-retest design. Intensity values were determined using the mean amplitude deviation (MAD) as well as the movement acceleration intensity (MAI) metrics. Determination of activities and energy expenditure was validated using heart rate. After that, cut-off points were determined in Matlab by using the Classification and Regression Trees (CART) method. The agreement for the cut-off points between T1 and T2 was analyzed.

RESULTS

MAD and MAI accelerometer values were lowest when children were lying on the floor and highest when running or doing jumping jacks. The mean correlation coefficient between acceleration values and heart rate was 0.595 (p = 0.01) for MAD metric and 0.611 (p = 0.01) for MAI metric, indicating strong correlations. Further, the MAD cut-off points for SB-LPA are 52.9 mg (hip), 62.4 mg (thigh), 86.4 mg (wrist) and 45.9 mg (chest), for LPA-MPA they are 173.3 mg (hip), 260.7 mg (thigh), 194.4 mg (wrist) and 155.7 mg (chest) and for MPA-VPA the cut-off points are 543.6 mg (hip), 674.5 mg (thigh), 623.4 mg (wrist) and 545.5 mg (chest). Test-retest comparison indicated good values (mean differences = 9.8%).

CONCLUSION

This is the first study investigating cut-off points for children for four different sensor positions using raw accelerometer metrics (MAD/MAI). Sensitivity and specificity revealed good values for all positions. Nevertheless, depending on the sensor position, metric values differ according to the different involvement of the body in various activities. Thus, the sensor position should be carefully chosen depending on the research question of the study.

Open-Source, Step-Counting Algorithm for Smartphone Data Collected in Clinical and Nonclinical Settings: Algorithm Development and Validation Study

BACKGROUND

Step counts are increasingly used in public health and clinical research to assess well-being, lifestyle, and health status. However, estimating step counts using commercial activity trackers has several limitations, including a lack of reproducibility, generalizability, and scalability. Smartphones are a potentially promising alternative, but their step-counting algorithms require robust validation that accounts for temporal sensor body location, individual gait characteristics, and heterogeneous health states.

OBJECTIVE

Our goal was to evaluate an open-source, step-counting method for smartphones under various measurement conditions against step counts estimated from data collected simultaneously from different body locations ("cross-body" validation), manually ascertained ground truth ("visually assessed" validation), and step counts from a commercial activity tracker (Fitbit Charge 2) in patients with advanced cancer ("commercial wearable" validation).

METHODS

We used 8 independent data sets collected in controlled, semicontrolled, and free-living environments with different devices (primarily Android smartphones and wearable accelerometers) carried at typical body locations. A total of 5 data sets (n=103) were used for cross-body validation, 2 data sets (n=107) for visually assessed validation, and 1 data set (n=45) was used for commercial wearable validation. In each scenario, step counts were estimated using a previously published step-counting method for smartphones that uses raw subsecond-level accelerometer data. We calculated the mean bias and limits of agreement (LoA) between step count estimates and validation criteria using Bland-Altman analysis.

RESULTS

In the cross-body validation data sets, participants performed 751.7 (SD 581.2) steps, and the mean bias was -7.2 (LoA -47.6, 33.3) steps, or -0.5%. In the visually assessed validation data sets, the ground truth step count was 367.4 (SD 359.4) steps, while the mean bias was -0.4 (LoA -75.2, 74.3) steps, or 0.1%. In the commercial wearable validation data set, Fitbit devices indicated mean step counts of 1931.2 (SD 2338.4), while the calculated bias was equal to -67.1 (LoA -603.8, 469.7) steps, or a difference of 3.4%.

CONCLUSIONS

This study demonstrates that our open-source, step-counting method for smartphone data provides reliable step counts across sensor locations, measurement scenarios, and populations, including healthy adults and patients with cancer.

Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers?

Wearable accelerometers allow for continuous monitoring of function and behaviors in the participant's naturalistic environment. Devices are typically worn in different body locations depending on the concept of interest and endpoint under investigation. The lumbar and wrist are commonly used locations: devices placed at the lumbar region enable the derivation of spatio-temporal characteristics of gait, while wrist-worn devices provide measurements of overall physical activity (PA). Deploying multiple devices in clinical trial settings leads to higher patient burden negatively impacting compliance and data quality and increases the operational complexity of the trial. In this work, we evaluated the joint information shared by features derived from the lumbar and wrist devices to assess whether gait characteristics can be adequately represented by PA measured with wrist-worn devices. Data collected at the Pfizer Innovation Research (PfIRe) Lab were used as a real data example, which had around 7 days of continuous at-home data from wrist- and lumbar-worn devices (GENEActiv) obtained from a group of healthy participants. The relationship between wrist- and lumbar-derived features was estimated using multiple statistical methods, including penalized regression, principal component regression, partial least square regression, and joint and individual variation explained (JIVE). By considering multilevel models, both between- and within-subject effects were taken into account. This work demonstrated that selected gait features, which are typically measured with lumbar-worn devices, can be represented by PA features measured with wrist-worn devices, which provides preliminary evidence to reduce the number of devices needed in clinical trials and to increase patients' comfort. Moreover, the statistical methods used in this work provided an analytic framework to compare repeated measures collected from multiple data modalities.

Compositional functional regression and isotemporal substitution analysis: Methods and application in time-use epidemiology

The distribution of time that people spend in physical activity of various intensities has important health implications. Physical activity (commonly categorised by the intensity into light, moderate and vigorous physical activity), sedentary behaviour and sleep, should not be analysed separately, because they are parts of a time-use composition with a natural constraint of 24 h/day. To find out how are relative reallocations of time between physical activity of various intensities associated with health, herewith we describe compositional scalar-on-function regression and a newly developed compositional functional isotemporal substitution analysis. Physical activity intensity data can be considered as probability density functions, which better reflects the continuous character of their measurement using accelerometers. These probability density functions are characterised by specific properties, such as scale invariance and relative scale, and they are geometrically represented using Bayes spaces with the Hilbert space structure. This makes possible to process them using standard methods of functional data analysis in the L 2 space, via centred logratio (clr) transformation. The scalar-on-function regression with clr transformation of the explanatory probability density functions and compositional functional isotemporal substitution analysis were applied to a dataset from a cross-sectional study on adiposity conducted among school-aged children in the Czech Republic. Theoretical reallocations of time to physical activity of higher intensities were found to be associated with larger and more progressive expected decreases in adiposity. We obtained a detailed insight into the dose-response relationship between physical activity intensity and adiposity, which was enabled by using the compositional functional approach.

Association between circadian physical activity patterns and mortality in the UK Biobank

BACKGROUND

The benefit of physical activity (PA) for increasing longevity is well-established, however, the impact of diurnal timing of PA on mortality remains poorly understood. We aimed to derive circadian PA patterns and investigate their associations with all-cause mortality.

METHODS

We used 24 h PA time series from 96,351 UK Biobank participants aged between 42 and 79 years at accelerometry in 2013-2015. Functional principal component analysis (fPCA) was applied to obtain circadian PA patterns. Using multivariable Cox proportional hazard models, we related the loading scores of these fPCs to estimate risk of mortality.

RESULTS

During 6.9 years of follow-up, 2,850 deaths occurred. Four distinct fPCs accounted for 96% of the variation of the accelerometry data. Using a loading score of zero (i.e., average overall PA during the day) as the reference, a fPC1 score of + 2 (high overall PA) was inversely associated with mortality (Hazard ratio, HR = 0.91; 95% CI: 0.84-0.99), whereas a score of -2 (low overall PA) was associated with higher mortality (1.69; 95% CI: 1.57-1.81; p for non-linearity < 0.001). Significant inverse linear associations with mortality were observed for engaging in midday PA instead of early and late PA (fPC3) (HR for a 1-unit increase 0.88; 95% CI: 0.83-0.93). In contrast, midday and nocturnal PA instead of early and evening PA (fPC4) were positively associated with mortality (HR for a 1-unit increase 1.16; 95% CI: 1.08-1.25).

CONCLUSION

Our results suggest that it is less important during which daytime hours one is active but rather, to engage in some level of elevated PA for longevity.

Bouts of rest and physical activity in C57BL/6J mice

The objective was to exploit the raw data output from a scalable home cage (type IIL IVC) monitoring (HCM) system (DVC®), to characterize pattern of undisrupted rest and physical activity (PA) of C57BL/6J mice. The system's tracking algorithm show that mice in isolation spend 67% of the time in bouts of long rest (≥40s). Sixteen percent is physical activity (PA), split between local movements (6%) and locomotion (10%). Decomposition revealed that a day contains ˜7100 discrete bouts of short and long rest, local and locomotor movements. Mice travel ˜330m per day, mainly during the dark hours, while travelling speed is similar through the light-dark cycle. Locomotor bouts are usually <0.2m and <1% are >1m. Tracking revealed also fits of abnormal behaviour. The starting positions of the bouts showed no preference for the rear over the front of the cage floor, while there was a strong bias for the peripheral (75%) over the central floor area. The composition of bouts has a characteristic circadian pattern, however, intrusive husbandry routines increased bout fragmentation by ˜40%. Extracting electrode activations density (EAD) from the raw data yielded results close to those obtained with the tracking algorithm, with 81% of time in rest (<1 EAD s-1) and 19% in PA. Periods ≥40 s of file when no movement occurs and there is no EAD may correspond to periods of sleep (˜59% of file time). We confirm that EAD correlates closely with movement distance (rs>0.95) and the data agreed in ˜97% of the file time. Thus, albeit EAD being less informative it may serve as a proxy for PA and rest, enabling monitoring group housed mice. The data show that increasing density from one female to two males, and further to three male or female mice had the same effect size on EAD (˜2). In contrast, the EAD deviated significantly from this stepwise increase with 4 mice per cage, suggesting a crowdedness stress inducing sex specific adaptations. We conclude that informative metrics on rest and PA can be automatically extracted from the raw data flow in near-real time (< 1 hrs). As discussed, these metrics relay useful longitudinal information to those that use or care for the animals.

A Novel Accelerometry Method to Perioperatively Quantify Essential Tremor Based on Fahn-Tolosa-Marin Criteria

The disease status, progression, and treatment effect of essential tremor (ET) patients are currently assessed with clinical scores, such as the Fahn-Tolosa-Marin Clinical Rating Scale for Tremor (FTM). The use of objective and rater-independent monitoring of tremors may improve clinical care for patients with ET. Therefore, the focus of this study is to develop an objective accelerometry-based method to quantify ET, based on FTM criteria. Thirteen patients with ET and thirteen matched healthy participants underwent FTM tests to rate tremor severity, paired with tri-axial accelerometric measurements at the index fingers. Analogue FTM assessments were performed by four independent raters based on video recordings. Quantitative measures were derived from the accelerometric data, e.g., the area under the curve of power in the 4-8 Hz frequency band (AUCP) and maximal tremor amplitude. As such, accelerometric tremor scores were computed, using thresholds based on healthy measurements and FTM criteria. Agreement between accelerometric and clinical FTM scores was analyzed with Cohen's kappa coefficient. It was assessed whether there was a relationship between mean FTM scores and the natural logarithm (ln) of the accelerometric outcome measures using linear regression. The agreement between accelerometric and FTM scores was substantial for resting and intention tremor tests (≥72.7%). However, the agreement between accelerometric postural tremor data and clinical FTM ratings (κ = 0.459) was low, although their logarithmic (ln) relationship was substantial (R² ≥ 0.724). Accelerometric test-retest reliability was good to excellent (ICC ≥ 0.753). This pilot study shows that tremors can be quantified with accelerometry, using healthy thresholds and FTM criteria. The test-retest reliability of the accelerometric tremor scoring algorithm indicates that our low-cost accelerometry-based approach is a promising one. The proposed easy-to-use technology could diminish the rater dependency of FTM scores and enable physicians to monitor ET patients more objectively in clinical, intraoperative, and home settings.

Triaxial accelerometer-measured physical activity and functional behaviours among people with High Grade Glioma: The BrainWear Study

BACKGROUND

High-grade gliomas (HGG) account for 60-75% of all adult gliomas. The complexity of treatment, recovery and survivorship creates a need for novel monitoring approaches. Accurate assessment of physical function plays a vital role in clinical evaluation. Digital wearable tools could help us address unmet needs by offering unique advantages such as scale, cost and continuous real-world objective data. We present data from 42 patients enrolled into the BrainWear study.

METHODS

An AX3 accelerometer was worn by patients from diagnosis or at recurrence. Age-, sex-matched UK Biobank control groups were chosen for comparison.

RESULTS

80% of data were categorised as high-quality demonstrating acceptability. Remote, passive monitoring identifies moderate activity reduces both during a course of radiotherapy (69 to 16 minutes/day) and at the time of progressive disease assessed by MRI (72 to 52 minutes/day). Mean acceleration (mg) and time spent walking daily (h/day) correlated positively with the global health quality of life and physical functioning scores and inversely with the fatigue score. Healthy controls walked on average 2.91h/day compared to 1.32h/day for the HGG group on weekdays and 0.91h/day on the weekend. The HGG cohort slept for longer on weekends (11.6h/day) than weekdays (11.2h/day) compared to healthy controls (8.9h/day).

CONCLUSION

Wrist-worn accelerometers are acceptable and longitudinal studies feasible. HGG patients receiving a course of radiotherapy reduce their moderate activity by 4-fold and are at least half as active as healthy controls at baseline. Remote monitoring can provide a more informed and objective understanding of patient activity levels to help optimise health related quality of life (HRQoL) among a patient cohort with an extremely limited lifespan.

Decentralized clinical trials and rare diseases: a Drug Information Association Innovative Design Scientific Working Group (DIA-IDSWG) perspective

BACKGROUND

Traditional clinical trials require tests and procedures that are administered in centralized clinical research sites, which are beyond the standard of care that patients receive for their rare and chronic diseases. The limited number of rare disease patients scattered around the world makes it particularly challenging to recruit participants and conduct these traditional clinical trials.

MAIN BODY

Participating in clinical research can be burdensome, especially for children, the elderly, physically and cognitively impaired individuals who require transportation and caregiver assistance, or patients who live in remote locations or cannot afford transportation. In recent years, there is an increasing need to consider Decentralized Clinical Trials (DCT) as a participant-centric approach that uses new technologies and innovative procedures for interaction with participants in the comfort of their home.

CONCLUSION

This paper discusses the planning and conduct of DCTs, which can increase the quality of trials with a specific focus on rare diseases.

Validation of an open-source smartphone step counting algorithm in clinical and non-clinical settings

Background

Step counts are increasingly used in public health and clinical research to assess wellbeing, lifestyle, and health status. However, estimating step counts using commercial activity trackers has several limitations, including a lack of reproducibility, generalizability, and scalability. Smartphones are a potentially promising alternative, but their step-counting algorithms require robust validation that accounts for temporal sensor body location, individual gait characteristics, and heterogeneous health states.

Objective

Our goal was to evaluate an open-source step-counting method for smartphones under various measurement conditions against step counts estimated from data collected simultaneously from different body locations ("internal" validation), manually ascertained ground truth ("manual" validation), and step counts from a commercial activity tracker (Fitbit Charge 2) in patients with advanced cancer ("wearable" validation).

Methods

We used eight independent datasets collected in controlled, semi-controlled, and free-living environments with different devices (primarily Android smartphones and wearable accelerometers) carried at typical body locations. Five datasets (N=103) were used for internal validation, two datasets (N=107) for manual validation, and one dataset (N=45) used for wearable validation. In each scenario, step counts were estimated using a previously published step-counting method for smartphones that uses raw sub-second level accelerometer data. We calculated mean bias and limits of agreement (LoA) between step count estimates and validation criteria using Bland-Altman analysis.

Results

In the internal validation datasets, participants performed 751.7±581.2 (mean±SD) steps, and the mean bias was -7.2 steps (LoA -47.6, 33.3) or -0.5%. In the manual validation datasets, the ground truth step count was 367.4±359.4 steps while the mean bias was -0.4 steps (LoA -75.2, 74.3) or 0.1 %. In the wearable validation dataset, Fitbit devices indicated mean step counts of 1931.2±2338.4, while the calculated bias was equal to -67.1 steps (LoA -603.8, 469.7) or a difference of 0.3 %.

Conclusions

This study demonstrates that our open-source step counting method for smartphone data provides reliable step counts across sensor locations, measurement scenarios, and populations, including healthy adults and patients with cancer.

Wrist Accelerometer Estimates of Physical Activity Intensity During Walking in Older Adults and People Living With Complex Health Conditions: Retrospective Observational Data Analysis Study

BACKGROUND

Accurate measurement of daily physical activity (PA) is important as PA is linked to health outcomes in older adults and people living with complex health conditions. Wrist-worn accelerometers are widely used to estimate PA intensity, including walking, which composes much of daily PA. However, there is concern that wrist-derived PA data in these cohorts is unreliable due to slow gait speed, mobility aid use, disease-related symptoms that impact arm movement, and transient activities of daily living. Despite the potential for error in wrist-derived PA intensity estimates, their use has become ubiquitous in research and clinical application.

OBJECTIVE

The goals of this work were to (1) determine the accuracy of wrist-based estimates of PA intensity during known walking periods in older adults and people living with cerebrovascular disease (CVD) or neurodegenerative disease (NDD) and (2) explore factors that influence wrist-derived intensity estimates.

METHODS

A total of 35 older adults (n=23 with CVD or NDD) wore an accelerometer on the dominant wrist and ankle for 7 to 10 days of continuous monitoring. Stepping was detected using the ankle accelerometer. Analyses were restricted to gait bouts ≥60 seconds long with a cadence ≥80 steps per minute (LONG walks) to identify periods of purposeful, continuous walking likely to reflect moderate-intensity activity. Wrist accelerometer data were analyzed within LONG walks using 15-second epochs, and published intensity thresholds were applied to classify epochs as sedentary, light, or moderate-to-vigorous physical activity (MVPA). Participants were stratified into quartiles based on the percent of walking epochs classified as sedentary, and the data were examined for differences in behavioral or demographic traits between the top and bottom quartiles. A case series was performed to illustrate factors and behaviors that can affect wrist-derived intensity estimates during walking.

RESULTS

Participants averaged 107.7 (SD 55.8) LONG walks with a median cadence of 107.3 (SD 10.8) steps per minute. Across participants, wrist-derived intensity classification was 22.9% (SD 15.8) sedentary, 27.7% (SD 14.6) light, and 49.3% (SD 25.5) MVPA during LONG walks. All participants measured a statistically lower proportion of wrist-derived activity during LONG walks than expected (all P<.001), and 80% (n=28) of participants had at least 20 minutes of LONG walking time misclassified as sedentary based on wrist-derived intensity estimates. Participants in the highest quartile of wrist-derived sedentary classification during LONG walks were significantly older (t₁₆=4.24, P<.001) and had more variable wrist movement (t₁₆=2.13, P=.049) compared to those in the lowest quartile.

CONCLUSIONS

The current best practice wrist accelerometer method is prone to misclassifying activity intensity during walking in older adults and people living with complex health conditions. A multidevice approach may be warranted to advance methods for accurately assessing PA in these groups.

Wearable device and smartphone data quantify ALS progression and may provide novel outcome measures

Amyotrophic lateral sclerosis (ALS) therapeutic development has largely relied on staff-administered functional rating scales to determine treatment efficacy. We sought to determine if mobile applications (apps) and wearable devices can be used to quantify ALS disease progression through active (surveys) and passive (sensors) data collection. Forty ambulatory adults with ALS were followed for 6-months. The Beiwe app was used to administer the self-entry ALS functional rating scale-revised (ALSFRS-RSE) and the Rasch Overall ALS Disability Scale (ROADS) surveys every 2-4 weeks. Each participant used a wrist-worn activity monitor (ActiGraph Insight Watch) or an ankle-worn activity monitor (Modus StepWatch) continuously. Wearable device wear and app survey compliance were adequate. ALSFRS-R highly correlated with ALSFRS-RSE. Several wearable data daily physical activity measures demonstrated statistically significant change over time and associations with ALSFRS-RSE and ROADS. Active and passive digital data collection hold promise for novel ALS trial outcome measure development.

A "one-size-fits-most" walking recognition method for smartphones, smartwatches, and wearable accelerometers

The ubiquity of personal digital devices offers unprecedented opportunities to study human behavior. Current state-of-the-art methods quantify physical activity using "activity counts," a measure which overlooks specific types of physical activities. We propose a walking recognition method for sub-second tri-axial accelerometer data, in which activity classification is based on the inherent features of walking: intensity, periodicity, and duration. We validate our method against 20 publicly available, annotated datasets on walking activity data collected at various body locations (thigh, waist, chest, arm, wrist). We demonstrate that our method can estimate walking periods with high sensitivity and specificity: average sensitivity ranged between 0.92 and 0.97 across various body locations, and average specificity for common daily activities was typically above 0.95. We also assess the method's algorithmic fairness to demographic and anthropometric variables and measurement contexts (body location, environment). Finally, we release our method as open-source software in Python and MATLAB.

Cardiac Effort to Compare Clinic and Remote 6-Minute Walk Testing in Pulmonary Arterial Hypertension

BACKGROUND

The SARS-CoV-2 pandemic has limited objective physiologic assessments. A standardized remote alternative is not currently available. "Cardiac effort" (CE), that is, the total number of heart beats divided by the 6-min walk test (6MWT) distance (beats/m), has improved reproducibility in the 6MWT and correlated with right ventricular function in pulmonary arterial hypertension.

RESEARCH QUESTION

Can a chest-based accelerometer estimate 6MWT distance remotely? Is remote cardiac effort more reproducible than 6MWT distance when compared with clinic assessment?

STUDY DESIGN AND METHODS

This was a single-center, prospective observational study, with institutional review board approval, completed between October 2020 and April 2021. Group 1 subjects with pulmonary arterial hypertension, receiving stable therapy for > 90 days, completed four to six total 6MWTs during a 2-week period to assess reproducibility. The first and last 6MWTs were performed in the clinic; two to four remote 6MWTs were completed at each participant's discretion. Masks were not worn. BioStamp nPoint sensors (MC10) were worn on the chest to measure heart rate and accelerometry. Two blinded readers counted laps, using accelerometry data obtained on the clinic or user-defined course. Averages of clinic variables and remote variables were used for Wilcoxon matched-pairs signed rank tests, Bland-Altman plots, or Spearman correlation coefficients.

RESULTS

Estimated 6MWT distance, using the MC10, correlated strongly with directly measured 6MWT distance (r = 0.99; P < .0001; in 20 subjects). Remote 6MWT distances were shorter than clinic 6MWT distances: 405 m (330-464 m) vs 389 m (312-430 m) (P = .002). There was no difference between in-clinic and remote CE: 1.75 beats/m (1.48-2.20 beats/m) vs 1.86 beats/m (1.57-2.14 beats/m) (P = .14).

INTERPRETATION

Remote 6MWT was feasible on a user-defined course; 6MWT distance was shorter than clinic distance. CE calculated by chest heart rate and accelerometer-estimated distance provides a reproducible remote assessment of exercise tolerance, comparable to the clinic-measured value.

Recommendations for Identifying Valid Wear for Consumer-Level Wrist-Worn Activity Trackers and Acceptability of Extended Device Deployment in Children

BACKGROUND

Self-reported physical activity is often inaccurate. Wearable devices utilizing multiple sensors are now widespread. The aim of this study was to determine acceptability of Fitbit Charge HR for children and their families, and to determine best practices for processing its objective data.

METHODS

Data were collected via Fitbit Charge HR continuously over the course of 3 weeks. Questionnaires were given to each child and their parent/guardian to determine the perceived usability of the device. Patterns of data were evaluated and best practice inclusion criteria recommended.

RESULTS

Best practices were established to extract, filter, and process data to evaluate device wear, r and establish minimum wear time to evaluate behavioral patterns. This resulted in usable data available from 137 (89%) of the sample.

CONCLUSIONS

Activity trackers are highly acceptable in the target population and can provide objective data over longer periods of wear. Best practice inclusion protocols that reflect physical activity in youth are provided.

Wearable Light-and-Motion Dataloggers for Sleep/Wake Research: A Review

Long-term recording of a person’s activity (actimetry or actigraphy) using devices typically worn on the wrist is increasingly applied in sleep/wake, chronobiological, and clinical research to estimate parameters of sleep and sleep-wake cycles. With the recognition of the importance of light in influencing these parameters and with the development of technological capabilities, light sensors have been introduced into devices to correlate physiological and environmental changes. Over the past two decades, many such new devices have appeared from different manufacturers. One of the aims of this review is to help researchers and clinicians choose the data logger that best fits their research goals. Seventeen currently available light-and-motion recorders entered the analysis. They were reviewed for appearance, dimensions, weight, mounting, battery, sensors, features, communication interface, and software. We found that all devices differed from each other in several features. In particular, six devices are equipped with a light sensor that can measure blue light. It is noteworthy that blue light most profoundly influences the physiology and behavior of mammals. As the wearables market is growing rapidly, this review helps guide future developments and needs to be updated every few years.

Development of a Novel Accelerometry-Based Performance Fatigability Measure for Older Adults

INTRODUCTION

Efforts to study performance fatigability have been limited because of measurement constrains. Accelerometry and advanced statistical methods may enable us to quantify performance fatigability more granularly via objective detection of performance decline. Thus, we developed the Pittsburgh Performance Fatigability Index (PPFI) using triaxial raw accelerations from wrist-worn accelerometer from two in-laboratory 400-m walks.

METHODS

Sixty-three older adults from our cross-sectional study (mean age, 78 yr; 56% women; 88% White) completed fast-paced ( n = 59) and/or usual-paced 400-m walks ( n = 56) with valid accelerometer data. Participants wore ActiGraph GT3X+ accelerometers (The ActiGraph LLC, Pensacola, FL) on nondominant wrist during the walking task. Triaxial raw accelerations from accelerometers were used to compute PPFI, which quantifies percentage of area under the observed gait cadence-versus-time trajectory during a 400-m walk to a hypothetical area that would be produced if the participant sustained maximal cadence throughout the entire walk.

RESULTS

Higher PPFI scores (higher score = greater fatigability) correlated with worse physical function, slower chair stands speed and gait speed, worse cardiorespiratory fitness and mobility, and lower leg peak power (| ρ | = 0.36-0.61 from fast-paced and | ρ | = 0.28-0.67 from usual-paced walks, all P < 0.05). PPFI scores from both walks remained associated with chair stands speed, gait speed, fitness, and mobility, after adjustment for sex, age, race, weight, height, and smoking status; PPFI scores from the fast-paced walk were associated with leg peak power.

CONCLUSIONS

Our findings revealed that the objective PPFI is a sensitive measure of performance fatigability for older adults and can serve as a risk assessment tool or outcome measure in future studies and clinical practice.

Calibration of an Accelerometer Activity Index among Older Women and Its Association with Cardiometabolic Risk Factors

Purpose

Traditional summary metrics provided by accelerometer device manufacturers, known as counts, are proprietary and manufacturer specific, making them difficult to compare studies using different devices. Alternative summary metrics based on raw accelerometry data have been introduced in recent years. However, they were often not calibrated on ground truth measures of activity-related energy expenditure for direct translation into continuous activity intensity levels. Our purpose is to calibrate, derive, and validate thresholds among women 60 years and older based on a recently proposed transparent raw data based accelerometer activity index (AAI), and to demonstrate its application in association with cardiometabolic risk factors.

Methods

We first built calibration equations for estimating metabolic equivalents (METs) continuously using AAI and personal characteristics using internal calibration data (n=199). We then derived AAI cutpoints to classify epochs into sedentary behavior and intensity categories. The AAI cutpoints were applied to 4,655 data units in the main study. We then utilized linear models to investigate associations of AAI sedentary behavior and physical activity intensity with cardiometabolic risk factors.

Results

We found that AAI demonstrated great predictive accuracy for METs (R2=0.74). AAI-based physical activity measures were associated in the expected directions with body mass index (BMI), blood glucose, and high density lipoprotein (HDL) cholesterol.

Conclusion

The calibration framework for AAI and the cutpoints derived for women older than 60 years can be applied to ongoing epidemiologic studies to more accurately define sedentary behavior and physical activity intensity exposures which could improve accuracy of estimated associations with health outcomes.

Patterns of objectively measured physical activity differ between men living with and without HIV

OBJECTIVE

To use accelerometers to quantify differences in physical activity (PA) by HIV serostatus and HIV viral load (VL) in the Multicenter AIDS Cohort Study (MACS).

METHODS

MACS participants living with (PLWH, n = 631) and without (PWOH, n = 578) HIV wore an ambulatory electrocardiogram monitor containing an accelerometer for 1-14 days. PA was summarized as cumulative mean absolute deviation (MAD) during the 10 most active consecutive hours (M10), cumulative MAD during the six least active consecutive hours (L6), and daily time recumbent (DTR). PA summaries were compared by HIV serostatus and by detectability of VL (>20 vs. ≤20 copies/ml) using linear mixed models adjusted for sociodemographics, weight, height, substance use, physical function, and clinical factors.

RESULTS

In sociodemographic-adjusted models, PLWH with a detectable VL had higher L6 (β = 0.58 mg, P = 0.027) and spent more time recumbent (β = 53 min/day, P = 0.003) than PWOH. PLWH had lower M10 than PWOH (undetectable VL β = -1.62 mg, P = 0.027; detectable VL β = -1.93 mg, P = 0.12). A joint test indicated differences in average PA measurements by HIV serostatus and VL (P = 0.001). However, differences by HIV serostatus in M10 and DTR were attenuated and no longer significant after adjustment for renal function, serum lipids, and depressive symptoms.

CONCLUSIONS

Physical activity measures differed significantly by HIV serostatus and VL. Higher L6 among PLWH with detectable VL may indicate reduced amount or quality of sleep compared to PLWH without detectable VL and PWOH. Lower M10 among PLWH indicates lower amounts of physical activity compared to PWOH.

Activity tracker-based intervention to increase physical activity in patients with type 2 diabetes and healthy individuals: study protocol for a randomized controlled trial

BACKGROUND

One relevant strategy to prevent the onset and progression of type 2 diabetes mellitus (T2DM) focuses on increasing physical activity. The use of activity trackers by patients could enable objective measurement of their regular physical activity in daily life and promote physical activity through the use of a tracker-based intervention. This trial aims to answer three research questions: (1) Is the use of activity trackers suitable for longitudinal assessment of physical activity in everyday life? (2) Does the use of a tracker-based intervention lead to sustainable improvements in the physical activity of healthy individuals and in people with T2DM? (3) Does the accompanying digital motivational intervention lead to sustainable improvements in physical activity for participants using the tracker-based device?

METHODS

The planned study is a randomized controlled trial focused on 1642 participants with and without T2DM for 9 months with regard to their physical activity behavior. Subjects allocated to an intervention group will wear an activity tracker. Half of the subjects in the intervention group will also receive an additional digital motivational intervention. Subjects allocated to the control group will not receive any intervention. The primary outcome is the amount of moderate and vigorous physical activity in minutes and the number of steps per week measured continuously with the activity tracker and assessed by questionnaires at four time points. Secondary endpoints are medical parameters measured at the same four time points. The collected data will be analyzed using inferential statistics and explorative data-mining techniques.

DISCUSSION

The trial uses an interdisciplinary approach with a team including sports psychologists, sports scientists, health scientists, health care professionals, physicians, and computer scientists. It also involves the processing and analysis of large amounts of data collected with activity trackers. These factors represent particular strengths as well as challenges in the study.

TRIAL REGISTRATION

The trial is registered at the World Health Organization International Clinical Trials Registry Platform via the German Clinical Studies Trial Register (DRKS), DRKS00027064 . Registered on 11 November 2021.

Comparison of Accelerometry-Based Measures of Physical Activity: Retrospective Observational Data Analysis Study

Background

Given the evolution of processing and analysis methods for accelerometry data over the past decade, it is important to understand how newer summary measures of physical activity compare with established measures.

Objective

We aimed to compare objective measures of physical activity to increase the generalizability and translation of findings of studies that use accelerometry-based data.

Methods

High-resolution accelerometry data from the Baltimore Longitudinal Study on Aging were retrospectively analyzed. Data from 655 participants who used a wrist-worn ActiGraph GT9X device continuously for a week were summarized at the minute level as ActiGraph activity count, monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity. We calculated these measures using open-source packages in R. Pearson correlations between activity count and each measure were quantified both marginally and conditionally on age, sex, and BMI. Each measures pair was harmonized using nonparametric regression of minute-level data.

Results

Data were from a sample (N=655; male: n=298, 45.5%; female: n=357, 54.5%) with a mean age of 69.8 years (SD 14.2) and mean BMI of 27.3 kg/m2 (SD 5.0). The mean marginal participant-specific correlations between activity count and monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity were r=0.988 (SE 0.0002324), r=0.867 (SE 0.001841), r=0.913 (SE 0.00132), and r=0.970 (SE 0.0006868), respectively. After harmonization, mean absolute percentage errors of predicting total activity count from monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity were 2.5, 14.3, 11.3, and 6.3, respectively. The accuracies for predicting sedentary minutes for an activity count cut-off of 1853 using monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity were 0.981, 0.928, 0.904, and 0.960, respectively. An R software package called SummarizedActigraphy, with a unified interface for computation of the measures from raw accelerometry data, was developed and published.

Conclusions

The findings from this comparison of accelerometry-based measures of physical activity can be used by researchers and facilitate the extension of knowledge from existing literature by demonstrating the high correlation between activity count and monitor-independent movement summary (and other measures) and by providing harmonization mapping.

Criterion Validity of Linear Accelerations Measured with Low-Sampling-Frequency Accelerometers during Overground Walking in Elderly Patients with Knee Osteoarthritis

Sensors with a higher sampling rate produce higher-quality data. However, for more extended periods of data acquisition, as in the continuous monitoring of patients, the handling of the generated big data becomes increasingly complicated. This study aimed to determine the validity and reliability of low-sampling-frequency accelerometer (SENS) measurements in patients with knee osteoarthritis. Data were collected simultaneously using SENS and a previously validated sensor (Xsens) during two repetitions of overground walking. The processed acceleration signals were compared with respect to different coordinate axes to determine the test-retest reliability and the agreement between the two systems in the time and frequency domains. In total, 44 participants were included. With respect to different axes, the interclass correlation coefficient for the repeatability of SENS measurements was [0.93-0.96]. The concordance correlation coefficients for the two systems' agreement were [0.81-0.91] in the time domain and [0.43-0.99] in the frequency domain. The absolute biases estimated by the Bland-Altman method were [0.0005-0.008] in the time domain and [0-0.008] in the frequency domain. Low-sampling-frequency accelerometers can provide relatively valid data for measuring the gait accelerations in patients with knee osteoarthritis and can be used in the future for remote patient monitoring.

Smartphone-Based Gait Cadence to Identify Older Adults with Decreased Functional Capacity

<b><i>Background:</i></b> Functional capacity assessment is a critical step in the preoperative evaluation to identify patients at increased risk of cardiac complications and disability after major noncardiac surgery. Smartphones offer the potential to objectively measure functional capacity but are limited by inaccuracy in patients with poor functional capacity. Open-source methods exist to analyze accelerometer data to estimate gait cadence (steps/min), which is directly associated with activity intensity. Here, we used an updated Step Test smartphone application with an open-source method to analyze accelerometer data to estimate gait cadence and functional capacity in older adults. <b><i>Methods:</i></b> We performed a prospective observational cohort study within the Frailty, Activity, Body Composition and Energy Expenditure in Aging study at the University of Chicago. Participants completed the Duke Activity Status Index (DASI) and performed an in-clinic 6-min walk test (6MWT) while using the Step Test application on a study smartphone. Gait cadence was measured from the raw accelerometer data using an adaptive empirical pattern transformation method, which has been previously validated. A 6MWT distance of 370 m was used as an objective threshold to identify patients at high risk. We performed multivariable logistic regression to predict walking distance using a priori explanatory variables. <b><i>Results:</i></b> Sixty patients were enrolled in the study. Thirty-seven patients completed the protocol and were included in the final data analysis. The median (IQR) age of the overall cohort was 71 (69–74) years, with a body mass index of 31 (27–32). There were no differences in any clinical characteristics or functional measures between participants that were able to walk 370 m during the 6MWT and those that could not walk that distance. Median (IQR) gait cadence for the entire cohort was 110 (102–114) steps/min during the 6MWT. Median (IQR) gait cadence was higher in participants that walked more than 370 m during the 6MWT 112 (108–118) versus 106 (96–114) steps/min; <i>p</i> = 0.0157). The final multivariable model to identify participants that could not walk 370 m included only median gait cadence. The Youden’s index cut-point was 107 steps/min with a sensitivity of 0.81 (95% CI: 0.77, 0.85) and a specificity of 0.57 (95% CI: 0.55, 0.59) and an AUCROC of 0.69 (95% CI: 0.51, 0.87). <b><i>Conclusions:</i></b> Our pilot study demonstrates the feasibility of using gait cadence as a measure to estimate functional capacity. Our study was limited by a smaller than expected sample size due to COVID-19, and thus, a prospective study with preoperative patients that measures outcomes is necessary to validate our findings.

Scalar on time-by-distribution regression and its application for modelling associations between daily-living physical activity and cognitive functions in Alzheimer's Disease

Wearable data is a rich source of information that can provide a deeper understanding of links between human behaviors and human health. Existing modelling approaches use wearable data summarized at subject level via scalar summaries in regression, temporal (time-of-day) curves in functional data analysis (FDA), and distributions in distributional data analysis (DDA). We propose to capture temporally local distributional information in wearable data using subject-specific time-by-distribution (TD) data objects. Specifically, we develop scalar on time-by-distribution regression (SOTDR) to model associations between scalar response of interest such as health outcomes or disease status and TD predictors. Additionally, we show that TD data objects can be parsimoniously represented via a collection of time-varying L-moments that capture distributional changes over the time-of-day. The proposed method is applied to the accelerometry study of mild Alzheimer's disease (AD). We found that mild AD is significantly associated with reduced upper quantile levels of physical activity, particularly during morning hours. In-sample cross validation demonstrated that TD predictors attain much stronger associations with clinical cognitive scales of attention, verbal memory, and executive function when compared to predictors summarized via scalar total activity counts, temporal functional curves, and quantile functions. Taken together, the present results suggest that SOTDR analysis provides novel insights into cognitive function and AD.

Physical activity in early childhood: a five-year longitudinal analysis of patterns and correlates

Background

Knowledge on longitudinal patterns and related factors of young children’s physical activity (PA) is still scarce. Therefore, the aim of this study was to examine patterns and changes of accelerometer-measured PA over time in two to six-year-old children. Furthermore, the aim was to investigate if parental PA, socioeconomic status, sex, weight status, and motor skills are related to child PA over time, using prospective cohort data from a clustered randomized controlled trial.

Methods

One hundred and six children (52% girls) and their parents had PA measured yearly from age two to six with an Actigraph GT3X. The actigraph was worn on the non-dominant wrist for one week; anthropometric data and motor skills, as well as background information, was collected simultaneously. The outcome was counts per minute from the vector magnitude, and linear mixed-effect models were used to answer the research questions.

Results

Among the children, accelerometer-measured PA increased on average by 11% per year from two years of age (mean 3170 cpm (3007-3334 95% CI)) onwards to six years of age (mean 4369 cpm (4207-4533 95% CI)). From three years of age, children were more active on weekdays than on weekend days. The rate of difference varied across low, medium, and highly active children (based on tertiles). No significant differences in weekdays/weekend PA among the lowest active children was found. Despite this, they were still significantly less active on weekend days than the most active children. Maternal, but not paternal PA was found to be significantly positively related to child PA over time, with a medium to large effect size. But no significant relationships were found between child PA and sex, weight status, or socioeconomic status.

Conclusions

PA increased on average with 11% per year, similarly for boys and girls. From three years of age children were more active during weekdays than weekend days. These results indicate that child PA benefits from active stimulation by parents and care takers already from early ages. It is important to identify attributes of possible intervention designs for weekend days for families with young children as well as characterize the least active children.

Trial registration

Early STOPP was prospectively registered in the clinical trials registry: clinicaltrials.gov, ID NCT01198847

Supplementary Information

The online version contains supplementary material available at 10.1186/s12966-022-01289-x.

Considerations to address missing data when deriving clinical trial endpoints from digital health technologies

Digital health technologies (DHTs) enable us to measure human physiology and behavior remotely, objectively and continuously. With the accelerated adoption of DHTs in clinical trials, there is an unmet need to identify statistical approaches to address missing data to ensure that the derived endpoints are valid, accurate, and reliable. It is not obvious how commonly used statistical methods to handle missing data in clinical trials can be directly applied to the complex data collected by DHTs. Meanwhile, current approaches used to address missing data from DHTs are of limited sophistication and focus on the exclusion of data where the quantity of missing data exceeds a given threshold. High-frequency time series data collected by DHTs are often summarized to derive epoch-level data, which are then processed to compute daily summary measures. In this article, we discuss characteristics of missing data collected by DHT, review emerging statistical approaches for addressing missingness in epoch-level data including within-patient imputations across common time periods, functional data analysis, and deep learning methods, as well as imputation approaches and robust modeling appropriate for handling missing data in daily summary measures. We discuss strategies for minimizing missing data by optimizing DHT deployment and by including the patients' perspective in the study design. We believe that these approaches provide more insight into preventing missing data when deriving digital endpoints. We hope this article can serve as a starting point for further discussion among clinical trial stakeholders.

Modified Application of Cardiac Rehabilitation in Older Adults (MACRO) Trial: Protocol changes in a pragmatic multi-site randomized controlled trial in response to the COVID-19 pandemic

BACKGROUND

Older adults are at higher risk for cardiovascular disease and functional decline, often leading to deterioration and dependency. Cardiac rehabilitation (CR) provides opportunity to improve clinical and functional recovery, yet participation in CR decreases with age. Modified Application of CR in Older Adults (MACRO) is a National Institute on Aging (NIA)-funded pragmatic trial that responds to this gap by aiming to increase enrollment of older adults into CR and improving functional outcomes. This article describes the methodology and novel features of the MACRO trial.

METHODS

Randomized, controlled trial of a coaching intervention (MACRO-I) vs. usual care for older adults (age ≥ 70 years) eligible for CR after an incident cardiac hospitalization. MACRO-I incorporates innovations including holistic risk assessments, flexible CR format (i.e., helping patients to select a CR design that aligns with their personal risks and preferences), motivational prompts, nutritional emphasis, facilitated deprescription, enhanced education, and home visits. Key modifications were necessitated by the COVID-19 pandemic, including switching from a performance-based primary endpoint (Short Physical Performance Battery) to a patient-reported measure (Activity Measure for Post-Acute Care Computerized Adaptive Testing). Changes prompted by COVID-19 maintain the original intent of the trial and provide key methodologic advantages.

CONCLUSIONS

MACRO is exploring a novel individualized coaching intervention to better enable older patients to participate in CR. Due to COVID-19 many aspects of the MACRO protocol required modification, but the primary objective of the trial is maintained and the updated protocol will more effectively achieve the original goals of the study.

Physical Activity and Its Association with Traditional Outcome Measures in Pulmonary Arterial Hypertension

Rationale Limitation of physical activity is a common presenting complaint for patients with pulmonary arterial hypertension (PAH). Physical activity is thought to be determined by cardiopulmonary function, yet there are limited data that investigate this relationship. Objective We aimed to study the relationship between right ventricular function and daily activity and its impact on health-related quality of life (HRQoL) in PAH. Methods Baseline data for 55 patients enrolled in PHANTOM, an ongoing multicenter randomized controlled trial of anastrozole in PAH were used. Post-menopausal women and men were eligible and underwent six-minute walk testing, echocardiography and completed HRQoL questionnaires. Each patient wore an accelerometer for 7-days. Multivariable linear regression models were used to study the association between tricuspid annular plane systolic excursion (TAPSE) and vector magnitude counts, and between daily activity and HRQoL. Principal component analysis and K-means clustering were used to identify activity-based phenotypes. K-nearest neighbors' classification was applied to an independent cross-sectional cohort from the University of Pennsylvania. Results The mean age of patients in PHANTOM was 61 years. 67% were women with idiopathic PAH as the most common etiology. A 0.4 cm increase in TAPSE was associated with an increase in daily vector magnitude counts (ß:34000, 95%CI:900-67000, p=0.004) after adjustment for age, sex, body mass index, etiology of PAH and wear time. A 1-standard deviation increase in vector magnitude counts was associated with higher six-minute walk distance (ß: 56.1 meters, 95%CI:28.6-83.7, p<0.001) and lower emPHasis-10 scores (ß:-3.3, 95%CI:0.3-6.4, p=0.03). Three activity phenotypes, low, medium, and high, were identified. The most active phenotype had greater six-minute walk distances (p=0.001) and lower emPHasis-10 scores (p=0.009) after adjustment for age, sex, body mass index, WHO functional class and parenteral prostacyclin use. Phenotypes of physical activity were reproduced in the second cohort and were independently associated with six-minute walk distance. Conclusion Better right ventricular systolic function was associated with increased levels of activity in PAH. Increased daily activity was associated with greater six-minute walk distance and better HRQoL. Distinct activity-based phenotypes may be helpful in risk stratification of PAH patients or provide novel endpoints for clinical trials.

Assessment of Physical Activity in Adults using Wrist Accelerometers

The health benefits of physical activity have been widely recognized, yet traditional measures of physical activity including questionnaires and category-based assessments of volume and intensity provide only broad estimates of daily activities. Accelerometers have advanced epidemiologic research on physical activity by providing objective and continuous measurement of physical activity in free-living conditions. Wrist-worn accelerometers have become especially popular due to low participant burden. However, the validity and reliability of wrist-worn devices for adults have yet to be summarized. Moreover, accelerometer data provide rich information on how physical activity is accumulated throughout the day, but only a small portion of these rich data have been utilized by researchers. Lastly, new methodological developments that aim to overcome some of the limitations of accelerometers are emerging. The purpose of this review is to provide an overview of accelerometry research, with a special focus on wrist-worn accelerometers. We describe briefly how accelerometers work, summarize the validity and reliability of wrist-worn accelerometers, discuss the benefits of accelerometers including measuring light-intensity physical activity, and discuss pattern metrics of daily physical activity recently introduced in the literature. A summary of large-scale cohort studies and randomized trials that implemented wrist-worn accelerometry is provided. We conclude the review by discussing new developments and future directions of research using accelerometers, with a focus on wrist-worn accelerometers.

Estimation of free-living walking cadence from wrist-worn sensor accelerometry data and its association with SF-36 quality of life scores

Objective. We evaluate the stride segmentation performance of the Adaptive Empirical Pattern Transformation (ADEPT) for subsecond-level accelerometry data collected in the free-living environment using a wrist-worn sensor.Approach. We substantially expand the scope of the existing ADEPT pattern-matching algorithm. Methods are applied to subsecond-level accelerometry data collected continuously for 4 weeks in 45 participants, including 30 arthritis and 15 control patients. We estimate the daily walking cadence for each participant and quantify its association with SF-36 quality of life measures.Main results. We provide free, open-source software to segment individual walking strides in subsecond-level accelerometry data. Walking cadence is significantly associated with the role physical score reported via SF-36 after adjusting for age, gender, weight and height.Significance. Methods provide automatic, precise walking stride segmentation, which allows estimation of walking cadence from free-living wrist-worn accelerometry data. Results provide new evidence of associations between free-living walking parameters and health outcomes.

Circadian Phase Prediction From Non-Intrusive and Ambulatory Physiological Data

Chronotherapy aims to treat patients according to their endogenous biological rhythms and requires, therefore, knowing their circadian phase. Circadian phase is partially determined by genetics and, under natural conditions, is normally entrained by environmental signals (zeitgebers), predominantly by light. Physiological data such as melatonin concentration and core body temperature (CBT) have been used to estimate circadian phase. However, due to their expensive and intrusive obtention, other physiological variables that also present circadian rhythmicity, such as heart rate variability, skin temperature, activity, and body position, have recently been proposed in several studies to estimate circadian phase. This study aims to predict circadian phase using minimally intrusive ambulatory physiological data modeled with machine learning techniques. Two approaches were considered; first, time-series were used to train artificial neural networks (ANNs) that predict CBT and melatonin dynamics and, second, a novel approach that uses scalar variables to build regression models that predict the time of the minimum CBT and the dim light melatonin onset (DLMO). ANNs require less than 48 hours of minimally intrusive data collection to predict circadian phase with an accuracy of less than one hour. On the other hand, regression models that use only three variables (body mass index, activity, and heart rate) are simpler and show higher accuracy with less than one minute of error, although they require longer times of data collection. This is a promising approach that should be validated in further studies considering a broader population and a wider range of conditions, including circadian misalignment.

Validity of Hip and Ankle Worn Actigraph Accelerometers for Measuring Steps as a Function of Gait Speed during Steady State Walking and Continuous Turning

This study aimed to investigate the accuracy and reliability of hip and ankle worn Actigraph GT3X+ (AG) accelerometers to measure steps as a function of gait speed. Additionally, the effect of the low frequency extension filter (LFEF) on the step accuracy was determined. Thirty healthy individuals walked straight and walked with continuous turns in different gait speeds. Number of steps were recorded with a hip and ankle worn AG, and with a Stepwatch (SW) activity monitor positioned around the right ankle, which was used as a reference for step count. The percentage agreement, interclass correlation coefficients and Bland–Altmann plots were determined between the AG and the reference SW across gait speeds for the two walking conditions. The ankle worn AG with the default filter was the most sensitive for step detection at >0.6 m/s, whilst accurate step detection for gait speeds < 0.6 m/s were only observed when applying the LFEF. The hip worn AG with the default filter showed poor accuracy (12–78%) at gait speeds < 1.0 m/s whereas the accuracy increased to >87% for gait speeds < 1.0 m/s when applying the LFEF. Ankle worn AG was the most sensitive to measure steps at a vast range of gait speeds. Our results suggest that sensor placement and filter settings need to be taken into account to provide accurate estimates of step counts.

Prevalence of global physical activity among young people: an updated systematic review for the Brazil’s Report Card

abstract This study aimed to update the review of Brazil’s Report Card on the prevalence of global physical activity (PA) among Brazilian children and adolescents. This systematic review included an electronic search of eight databases (PubMed, Scopus, Web of Science, LILACS, SPORTDiscus, BIREME, Scielo, and Google Scholar) and a manual search of the references of retrieved studies. Studies published in 2018 and 2019 that assessed global PA among Brazilian youth were included. A narrative approach to the results was adopted. The initial search retrieved 1,892 potentially relevant titles (1,244 titles after duplicate analysis), of which 62 (47 different studies) met all the inclusion criteria. Most updated studies were carried out in Southern (40.4%) and Southeastern (25.5%) Brazil. Six studies provided data from national surveys (12.8%), and one study included preschool children (< 5 years old). Ten studies objectively measured PA (accelerometer or pedometer devices). In the updated studies, the overall proportion of young people who were physically active ranged from 9.8% to 79.6%. Three national surveys reported the prevalence of physically active students, ranging from 18.4% to 78.8%. There was an increase of surveys that objectively measured PA and with children under 12 years of age in the 2018 and 2019 studies. However, important research gaps (e.g., variations in the measurement of global PA), even in the same study, should be considered to improve the monitoring and evaluation of global PA in Brazil.

Wearable Devices: Current Status and Opportunities in Pain Assessment and Management

INTRODUCTION

We investigated the possibilities and opportunities for using wearable devices that measure physical activity and physiometric signals in conjunction with ecological momentary assessment (EMA) data to improve the assessment and treatment of pain.

METHODS

We considered studies with cross-sectional and longitudinal designs as well as interventional or observational studies correlating pain scores with measures derived from wearable devices. A search was also performed on studies that investigated physical activity and physiometric signals among patients with pain.

RESULTS

Few studies have assessed the possibility of incorporating wearable devices as objective tools for contextualizing pain and physical function in free-living environments. Of the studies that have been conducted, most focus solely on physical activity and functional outcomes as measured by a wearable accelerometer. Several studies report promising correlations between pain scores and signals derived from wearable devices, objectively measured physical activity, and physical function. In addition, there is a known association between physiologic signals that can be measured by wearable devices and pain, though studies using wearable devices to measure these signals and associate them with pain in free-living environments are limited.

CONCLUSION

There exists a great opportunity to study the complex interplay between physiometric signals, physical function, and pain in a real-time fashion in free-living environments. The literature supports the hypothesis that wearable devices can be used to develop reproducible biosignals that correlate with pain. The combination of wearable devices and EMA will likely lead to the development of clinically meaningful endpoints that will transform how we understand and treat pain patients.

Quantifying the Varying Predictive Value of Physical Activity Measures Obtained from Wearable Accelerometers on All-Cause Mortality over Short to Medium Time Horizons in NHANES 2003-2006

Physical activity measures derived from wearable accelerometers have been shown to be highly predictive of all-cause mortality. Prediction models based on traditional risk factors and accelerometry-derived physical activity measures are developed for five time horizons. The data set contains 2978 study participants between 50 and 85 years old with an average of 13.08 years of follow-up in the NHANES 2003–2004 and 2005–2006. Univariate and multivariate logistic regression models were fit separately for five datasets for one- to five-year all-cause mortality as outcome (number of events 46, 94, 155, 218, and 297, respectively). In univariate models the total activity count (TAC) was ranked first in all five horizons (AUC between 0.831 and 0.774) while the active to sedentary transition probability (ASTP) was ranked second for one- to four-year mortality models and fourth for the five-year all-cause mortality model (AUC between 0.825 and 0.735). In multivariate models age and ASTP were significant in all one- to five-year all-cause mortality prediction models. Physical activity measures are consistently among the top predictors, even after adjusting for demographic and lifestyle variables. Physical activity measures are strong stand-alone predictors and substantially improve the prediction performance of models based on traditional risk factors.

Concurrent Validity Between Electronically Administered Physical Activity Questionnaires and Objectively Measured Physical Activity in Danish Community-Dwelling Older Adults

AIM

To investigate the concurrent validity of the International Physical Activity Questionnaire-short form (IPAQ-SF) and the Nordic Physical Activity Questionnaire-short (NPAQ-short) when compared with objectively measured daily steps among older adults.

METHODS

Spearman's ρ between IPAQ-SF and NPAQ-short and objectively measured steps using Garmin Vivofit 3 physical activity monitors.

RESULTS

A total of 54 participants were included. The IPAQ-SF subscales' moderate physical activity (PA), moderate to vigorous PA (MVPA), and sedentary time showed little or no correlation with daily steps. The NPAQ-short subscales' vigorous PA, moderate PA, and MVPA showed little or no correlation. The IPAQ-SF subscales' vigorous PA and walking showed fair correlation. Only the IPAQ-SF metabolic equivalent of task minutes showed moderate to good correlation with daily steps. The IPAQ-SF categories and NPAQ-short categorization of World Health Organization compliance were significantly different, but the magnitudes were small and distributions indicated problems with the categorization.

CONCLUSION

The concurrent validity is low, as the scores did not reflect objectively measured daily steps.

Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method

Background

In many countries, depressed individuals often first visit primary care settings for consultation, but a considerable number of clinically depressed patients remain unidentified. Introducing additional screening tools may facilitate the diagnostic process.

Objective

This study aimed to examine whether experience sampling method (ESM)-based measures of depressive affect and behaviors can discriminate depressed from nondepressed individuals. In addition, the added value of actigraphy-based measures was examined.

Methods

We used data from 2 samples to develop and validate prediction models. The development data set included 14 days of ESM and continuous actigraphy of currently depressed (n=43) and nondepressed individuals (n=82). The validation data set included 30 days of ESM and continuous actigraphy of currently depressed (n=27) and nondepressed individuals (n=27). Backward stepwise logistic regression analysis was applied to build the prediction models. Performance of the models was assessed with goodness-of-fit indices, calibration curves, and discriminative ability (area under the receiver operating characteristic curve [AUC]).

Results

In the development data set, the discriminative ability was good for the actigraphy model (AUC=0.790) and excellent for both the ESM (AUC=0.991) and the combined-domains model (AUC=0.993). In the validation data set, the discriminative ability was reasonable for the actigraphy model (AUC=0.648) and excellent for both the ESM (AUC=0.891) and the combined-domains model (AUC=0.892).

Conclusions

ESM is a good diagnostic predictor and is easy to calculate, and it therefore holds promise for implementation in clinical practice. Actigraphy shows no added value to ESM as a diagnostic predictor but might still be useful when ESM use is restricted.

Fundamental Movement Skills and Accelerometer-Measured Physical Activity Levels during Early Childhood: A Systematic Review

Early childhood is a key period for children to begin developing and practicing fundamental movement skills (FMS), while aiming to perform sufficient physical activity (PA). This study reviews the current evidence for the levels of achievement in FMS and PA measured using accelerometers among 4-5-year-old children and examines differences by gender. This review was conducted using the PRISMA framework. Keyword searches were conducted in Pubmed, Medline, Google Scholar and SPORTDiscus. Inclusion criteria included age: 4-5 years old; FMS measurement: Test of Gross Motor Development 2 and 3; PA measurement: objective methods; balance measurement: static single limb; study design: cross-sectional observational/descriptive, randomised control trials, intervention studies; language: English. Twenty-eight articles from twenty-one countries met the inclusion criteria and were split into either FMS and PA articles (n = 10) or balance articles (n = 18). Three articles showed children achieving 60 min of moderate to vigorous PA per day, two articles demonstrated significant differences between girls' and boys' performance of locomotor skills and five reported locomotor skills to be more proficient than object control skills at this age for both genders. Balance was measured in time (n = 12), points score (n = 3) or biomechanical variables (n = 3), displaying heterogeneity of not only measurement but also outcomes within these data, with static single limb balance held between 6.67 to 87.6 s within the articles. Four articles reported girls to have better balance than boys. There is little conclusive evidence of the current levels for FMS, PA and balance achievement in young children 4-5 years of age. The academic literature consistently reports low levels of FMS competence and mixed evidence for PA levels. Inconsistencies lie in balance measurement methodology, with broad-ranging outcomes of both low and high achievement at 4-5 years old. Further research is required to focus on increasing practice opportunities for children to improve their FMS, increase PA levels and establish sufficient balance ability. Consistent and comparable outcomes during early childhood through more homogenous methodologies are warranted.

Measuring circadian function in bipolar disorders: Empirical and conceptual review of physiological, actigraphic, and self-report approaches

BACKGROUND

Interest in biological clock pathways in bipolar disorders (BD) continues to grow, but there has yet to be an audit of circadian measurement tools for use in BD research and practice.

PROCEDURE

The International Society for Bipolar Disorders Chronobiology Task Force conducted a critical integrative review of circadian methods that have real-world applicability. Consensus discussion led to the selection of three domains to review-melatonin assessment, actigraphy, and self-report.

RESULTS

Measurement approaches used to quantify circadian function in BD are described in sufficient detail for researchers and clinicians to make pragmatic decisions about their use. A novel integration of the measurement literature is offered in the form of a provisional taxonomy distinguishing between circadian measures (the instruments and methods used to quantify circadian function, such as dim light melatonin onset) and circadian constructs (the biobehavioral processes to be measured, such as circadian phase).

CONCLUSIONS

Circadian variables are an important target of measurement in clinical practice and biomarker research. To improve reproducibility and clinical application of circadian constructs, an informed systematic approach to measurement is required. We trust that this review will decrease ambiguity in the literature and support theory-based consideration of measurement options.

Fit-for-Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience

Biometric monitoring technologies (BioMeTs) are becoming increasingly common to aid data collection in clinical trials and practice. The state of BioMeTs, and associated digitally measured biomarkers, is highly reminiscent of the field of laboratory biomarkers 2 decades ago. In this review, we have summarized and leveraged historical perspectives, and lessons learned from laboratory biomarkers as they apply to BioMeTs. Both categories share common features, including goals and roles in biomedical research, definitions, and many elements of the biomarker qualification framework. They can also be classified based on the underlying technology, each with distinct features and performance characteristics, which require bench and human experimentation testing phases. In contrast to laboratory biomarkers, digitally measured biomarkers require prospective data collection for purposes of analytical validation in human subjects, lack well‐established and widely accepted performance characteristics, require human factor testing, and, for many applications, access to raw (sample‐level) data. Novel methods to handle large volumes of data, as well as security and data rights requirements add to the complexity of this emerging field. Our review highlights the need for a common framework with appropriate vocabulary and standardized approaches to evaluate digitally measured biomarkers, including defining performance characteristics and acceptance criteria. Additionally, the need for human factor testing drives early patient engagement during technology development. Finally, use of BioMeTs requires a relatively high degree of technology literacy among both study participants and healthcare professionals. Transparency of data generation and the need for novel analytical and statistical tools creates opportunities for precompetitive collaborations.

An Optimal Self-Report Physical Activity Measure for Older Adults: Does Physical Function Matter?

The authors compared two self-report measures of physical activity, the Physical Activity Scale for the Elderly (PASE) and the Community Healthy Activities Model Program for Seniors (CHAMPS), against the device-derived SenseWear Armband (SWA), to identify a recommended self-report tool to measure physical activity in older adults across physical function levels. A total of 65 community-dwelling older adults completed the PASE, CHAMPS, and seven full days of SWA wear. The authors measured physical function using the modified short physical performance battery (SPPB) and a usual-paced 6-m walk. Age- and sex-adjusted Spearman correlations showed that CHAMPS and SWA were correlated in higher functioning participants (SPPB: ρ = .33, p = .03; gait speed: ρ = .40, p = .006) and also correlated in lower functioning participants for SPPB (ρ = .70, p = .003) only. PASE and SWA were not significantly correlated across physical function. When an objective measure of physical activity is not practical, the CHAMPS questionnaire appears to capture physical activity for older adults across physical function levels.

Augmented Movelet Method for Activity Classification Using Smartphone Gyroscope and Accelerometer Data

Physical activity, such as walking and ascending stairs, is commonly used in biomedical settings as an outcome or covariate. Researchers have traditionally relied on surveys to quantify activity levels of subjects in both research and clinical settings, but surveys are subjective in nature and have known limitations, such as recall bias. Smartphones provide an opportunity for unobtrusive objective measurement of physical activity in naturalistic settings, but their data tends to be noisy and needs to be analyzed with care. We explored the potential of smartphone accelerometer and gyroscope data to distinguish between walking, sitting, standing, ascending stairs, and descending stairs. We conducted a study in which four participants followed a study protocol and performed a sequence of activities with one phone in their front pocket and another phone in their back pocket. The subjects were filmed throughout, and the obtained footage was annotated to establish moment-by-moment ground truth activity. We introduce a modified version of the so-called movelet method to classify activity type and to quantify the uncertainty present in that classification. Our results demonstrate the promise of smartphones for activity recognition in naturalistic settings, but they also highlight challenges in this field of research.