Accelerometer-derived Metrics Research Articles

Practitioner Usage, Applications, and Understanding of Wearable GPS and Accelerometer Technology in Team Sports.

Dawson, L, McErlain-Naylor, SA, Devereux, G, and Beato, M. Practitioner usage, applications, and understanding of wearable GPS and accelerometer technology in team sports. J Strength Cond Res 38(7): e373-e382, 2024-Monitoring training load is essential for optimizing the performance of athletes, allowing practitioners to assess training programs, monitor athlete progress, and minimize the risk of injury and overtraining. However, there is no universal method for training load monitoring, and the adoption of wearable global positioning system (GPS) and accelerometer technology in team sports has increased the volume of data and, therefore, the number of possible approaches. This survey investigated the usage, applications, and understanding of this technology by team sports practitioners. Seventy-two practitioners involved in team and athlete performance monitoring using GPS and accelerometer technology completed the survey. All respondents reported supporting the use of GPS technology in their sport, with 70.8% feeling that GPS technology is important for success. Results showed that 87.5% of respondents use data from wearable technology to inform training prescription, although only 50% use the data to influence decisions in competition. In addition, results showed that GPS metrics are used more than accelerometer-derived metrics; however, both are used regularly. Discrepancies in accelerometer usage highlighted concerns about practitioners' understanding of accelerometer-derived metrics. This survey gained insight into usage, application, understanding, practitioner needs, and concerns and criticisms surrounding the use of GPS and accelerometer metrics for athlete load monitoring. Such information can be used to improve the implementation of this technology in team sport monitoring and highlight gaps in the literature that will help to design future studies to support practitioner needs.

Impaired 24-h activity patterns are associated with an increased risk of Alzheimer’s disease, Parkinson’s disease, and cognitive decline

BackgroundSleep-wake regulating circuits are affected during prodromal stages in the pathological progression of both Alzheimer’s disease (AD) and Parkinson’s disease (PD), and this disturbance can be measured passively using wearable devices. Our objective was to determine whether accelerometer-based measures of 24-h activity are associated with subsequent development of AD, PD, and cognitive decline.MethodsThis study obtained UK Biobank data from 82,829 individuals with wrist-worn accelerometer data aged 40 to 79 years with a mean (± SD) follow-up of 6.8 (± 0.9) years. Outcomes were accelerometer-derived measures of 24-h activity (derived by cosinor, nonparametric, and functional principal component methods), incident AD and PD diagnosis (obtained through hospitalization or primary care records), and prospective longitudinal cognitive testing.ResultsOne hundred eighty-seven individuals progressed to AD and 265 to PD. Interdaily stability (a measure of regularity, hazard ratio [HR] per SD increase 1.25, 95% confidence interval [CI] 1.05–1.48), diurnal amplitude (HR 0.79, CI 0.65–0.96), mesor (mean activity; HR 0.77, CI 0.59–0.998), and activity during most active 10 h (HR 0.75, CI 0.61–0.94), were associated with risk of AD. Diurnal amplitude (HR 0.28, CI 0.23–0.34), mesor (HR 0.13, CI 0.10–0.16), activity during least active 5 h (HR 0.24, CI 0.08–0.69), and activity during most active 10 h (HR 0.20, CI 0.16–0.25) were associated with risk of PD. Several measures were additionally predictive of longitudinal cognitive test performance.ConclusionsIn this community-based longitudinal study, accelerometer-derived metrics were associated with elevated risk of AD, PD, and accelerated cognitive decline. These findings suggest 24-h rhythm integrity, as measured by affordable, non-invasive wearable devices, may serve as a scalable early marker of neurodegenerative disease.

Physical Behavior Profiles Among Older Adults and Their Associations With Physical Capacity and Life-Space Mobility.

We identified data-driven multidimensional physical activity (PA) profiles using several novel accelerometer-derived metrics. Participants aged 75, 80, and 85 (n = 441) wore triaxial accelerometers for 3-7days. PA profiles were formed with k-means cluster analysis based on PA minutes, intensity, fragmentation, sit-to-stand transitions, and gait bouts for men and women. Associations with physical capacity and life-space mobility were examined using age-adjusted general linear models. Three profiles emerged: "Exercisers" and "actives" accumulated relatively high PA minutes, with actives engaging in lighter intensity PA. "Inactives" had the highest activity fragmentation and lowest PA volume, intensity, and gait bouts. Inactives showed lower scores in physical capacity and life-space mobility compared with exercisers and actives. Exercisers and actives had similar physical capacity and life-space mobility, except female exercisers had higher walking speed in the 6-min walk test. Our findings demonstrate the importance of assessing PA as multidimensional behavior rather than focusing on a single metric.

Physical activity levels in asthma: relationship with disease severity, body mass index and novel accelerometer-derived metrics

Objectives Patients with asthma may feel limited in physical activity (PA). Reduced PA has been demonstrated in asthmatics versus healthy controls, and increasing PA associated with improved asthma outcomes. Obesity is commonly found with difficult-to-control asthma and worsens outcomes. We compared PA levels in participants with difficult-to-control asthma and elevated body mass index (BMI) (DOW group) and two mild-moderate asthma groups: one with BMI <25 kg/m2 (MHW) and one with BMI ≥25 (MOW). Methods This cross-sectional study used 7-day recordings from wrist-worn accelerometers to compare PA between groups. Inactive time, light (LPA), moderate-vigorous PA (MVPA) were measured, along with two novel metrics: intensity gradient (IG) reflecting PA intensity, and average acceleration (AA) reflecting PA volume. PA parameters were compared using ANOVA or Kruskall-Wallis testing. Correlation and linear regression analyses explored associations between PA parameters and asthma outcomes. As AA was the PA parameter correlated most closely with asthma-related outcomes, an exploratory analysis compared outcomes in highest and lowest AA quartiles. Results 75 participants were recruited; 57 accelerometer readings were valid and included in analysis. Inactive time was significantly higher (p < 0.001), and LPA (p < 0.007), MVPA (p < 0.001), IG (p < 0.001) and AA (p < 0.001) all significantly lower in DOW versus MHW and MOW groups, even after adjusting for age and BMI. Quartiles based on AA had significantly different asthma profiles. Conclusions Overweight/obese participants with difficult-to-control asthma performed less PA, and activity of reduced intensity and volume. Increased AA is associated with improvement in several asthma-related outcomes. Increased PA should be recommended to relevant patients.

The Effect of Rifle Carriage on the Physiological and Accelerometer Responses During Biathlon Skiing.

PurposeInvestigate the effect of biathlon rifle carriage on physiological and accelerometer-derived responses during biathlon skiing.MethodsTwenty-eight biathletes (11F, 17M) completed two XC skiing time-trials (~2,300 m), once with and once without the biathlon rifle, with concurrent measurements of HR, skiing speed and accelerations recorded from three triaxial accelerometers attached at the Upper-spine, Lower-spine and Pelvis. Exercise intensity was quantified from HR, skiing speed as well from accelerometry-derived PlayerLoad™ per minute (PL·min−1) and average net force (AvFNet). All metrics were analyzed during Uphill, Flat and Downhill sections of the course. Relationships between accelerometry-derived metrics and skiing speed were examined.ResultsTime-trials were faster for males compared with females (mean difference: 97 ± 73 s) and No-Rifle compared to With-Rifle (mean difference: 16 ± 9 s). HR was greatest during Downhill (183 ± 5 bpm), followed by Uphill (181 ± 5 bpm) and was lowest in the Flat sections (177 ± 6 bpm, p <0.05). For PL·min−1 and AvFNet there were 3-way Rifle x Gradient x Sensor-Position interactions. Typically, these metrics were greatest during Uphill and Flat sections and were lowest during Downhill sections. Rifle carriage had no impact on the AvFNet at the Lower-Spine or Pelvis. Significant positive linear relationships were identified between skiing speed and accelerometer-derived metrics during Uphill, Flat and Downhill skiing (r = 0.12–0.61, p < 0.05).ConclusionsThe accelerometry-derived approach used in this study provides the potential of a novel method of monitoring the external demands during skiing. In particular, AvFNet with sensors located close to the center of mass displayed greatest utility because it followed the expected response of external intensity where responses were greatest during uphill sections, followed by flats and lowest during downhills. In addition, there were significant positive relationships between AvFNet and skiing speed ranging from small to large. Accelerometry-derived measures could provide useful estimates of the external demands in XC skiing and biathlon.

Evaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis

BackgroundOne in two people with multiple sclerosis (PwMS) will fall in a three-month period. Predicting which patients will fall remains a challenge for clinicians. Standardized functional assessments provide insight into balance deficits and fall risk but their use has been limited to supervised visits. Research questionThe study aim was to characterize unsupervised 30-second chair stand test (30CST) performance using accelerometer-derived metrics and assess its ability to classify fall status in PwMS compared to supervised 30CST. MethodsThirty-seven PwMS (21 fallers) performed instrumented supervised and unsupervised 30CSTs with a single wearable sensor on the thigh. In unsupervised conditions, participants performed bi-hourly 30CSTs and rated their balance confidence and fatigue over 48-hours. ROC analysis was used to classify fall status for 30CST performance. ResultsNon-fallers (p = 0.02) but not fallers (p = 0.23) differed in their average unsupervised 30CST performance (repetitions) compared to their supervised performance. The unsupervised maximum number of 30CST repetitions performed optimized ROC classification AUC (0.79), accuracy (78.4%) and specificity (90.0%) for fall status with an optimal cutoff of 17 repetitions. SignificanceBrief durations of instrumented unsupervised monitoring as an adjunct to routine clinical assessments could improve the ability for predicting fall risk and fluctuations in functional mobility in PwMS.

Association of Low Physical Activity Levels With Gait Patterns Considered at Risk for Clinical Knee Osteoarthritis Progression.

ObjectiveAlthough gait analysis provides an estimate of joint loading magnitude and patterns during a typical step, accelerometry provides information about loading frequency. Understanding the relationships between these components of loading and knee osteoarthritis (OA) progression may improve conservative management, as gait interventions may need to account for physical activity levels or vice versa. The primary objective was to examine relationships between gait patterns that have previously been associated with OA progression and accelerometer‐derived metrics of loading frequency. The secondary objective examined the association of accelerometer‐derived metrics and total knee arthroplasty (TKA) at a mean follow‐up of 3.5 years.MethodsFifty‐seven individuals with knee OA underwent gait analysis and 1 week of accelerometer wear. Spearman correlations were calculated between accelerometer‐derived metrics and gait patterns. Differences across quartiles of step count were examined with Jonckheere‐Terpstra tests. In a subsample, baseline differences between TKA and no TKA groups were examined with Mann‐Whitney U‐tests.ResultsGait variables previously related to progression were correlated to both step count and moderate‐ to vigorous‐intensity, but not lower‐intensity, physical activity. Individuals in the lowest quartile (~4000 steps/day) exhibited gait patterns previously related to progression. There were no differences in any baseline accelerometer‐derived metrics between those that did and did not undergo TKA at follow‐up.ConclusionComplex relationships exist between gait, physical activity, and OA progression. Accelerometer‐derived metrics may contribute unique information about overall loading for individuals above a certain activity threshold, but for those with lower activity levels, gait may be sufficient to predict clinical progression risk, at least over the short term.

Objective Measurement of 24-Hour Movement Behaviors in Preschool Children Using Wrist-Worn and Thigh-Worn Accelerometers.

In recent years, more attention has been paid towards the study of 24-h movement behaviors (including physical activity (PA), sedentary behavior (SB) and sleep) in preschoolers instead of studying these behaviors in isolation. This study aimed to evaluate the feasibility of using wrist- vs. thigh-worn accelerometers and to report accelerometer-derived metrics of 24-h movement behaviors in preschoolers. A convenience sample of 16 preschoolers (50.0% boys, 4.35 years) and one of their parents were recruited for this study. Preschoolers had to wear the ActivPAL accelerometer (attached to the upper thigh) and Axivity accelerometer (using a wrist band) simultaneously for 7 consecutive days and for 24 h a day. Parents completed an acceptability survey. In total, 16 preschoolers (100.0%) had a minimum of 6 days of valid wrist-worn data, while only 10 preschoolers (62.5%) had a minimum of 6 days of valid thigh-worn data (p = 0.002). When looking at the acceptability, 81.3% of parents indicated that it was easy for their child to wear the Axivity for 7 consecutive days, and 93.8% of parents indicated so for the ActivPAL (p = 0.88). However, some parents stated that the wristband of the Axivity accelerometer was big, which might have affected data collection. Significant differences were found for the measurement of total volume of PA, SB and sleep across 24 h. Total PA was 464.44 min/day (±64.00) with the ActivPAL compared with 354.94 min/day (±57.46) with the Axivity (p < 0.001). The volume of SB was 290.94 min/day (±55.05) with the ActivPAL compared with 440.50 min/day (±50.01) with the Axivity (p < 0.001). The total volume of sleep was also significantly different between both devices (p = 0.001; ActivPAL: 684.63 min/day ± 51.96; Axivity: 645.69 min/day ± 46.78). Overall, parents perceived both devices to be feasible to use for preschoolers. However, future studies are required to validate both devices for the measurement of preschoolers’ 24-h movement behaviors since significant differences in the classification of PA, SB and sleep were found in this small sample.

Associations of novel 24-h accelerometer-derived metrics with adiposity in children and adolescents

BackgroundFurther research is required to explore the associations between 24-h movement behaviours and health outcomes in the paediatric population. Therefore, this study aimed to examine the associations between novel data-driven 24-h activity metrics and adiposity among children and adolescents.MethodsThe sample included 382 children (8–13 years) and 338 adolescents (14–18 years). The average acceleration (AvAcc) of activity, intensity gradient (IG), and metrics representing the initial acceleration for the most active time periods of the 24-h cycle were calculated from raw acceleration data. Adiposity measures included body mass index z-score, fat mass percentage (FM%), and visceral adipose tissue (VAT). Data analysis was performed using multiple linear regression adjusted for wear time, sex, maternal education level, and maternal overweight and obesity.ResultsChildren demonstrated higher values in all 24-h activity metrics than did adolescents (p < 0.001 for all). For children, the initial acceleration for the most active 2, 5, 15, and 30 min of the 24-h cycle were negatively associated with FM% (p ≤ 0.043 for all) and VAT (p <0.001 for all), respectively. For adolescents, the IG was negatively associated with FM% (p = 0.002) and VAT (p = 0.007). Moreover, initial acceleration for the most active 2, 5, 15, 30, 60, and 120 min were associated with FM% (p ≤ 0.007 for all) and with VAT (p ≤ 0.023 for all).ConclusionsThe intensity distribution of activity and initial acceleration for the most active 2, 5, 15, 30, 60, and 120 min within the 24-h cycle are beneficial for the prevention of excess adiposity in the paediatric population.

Accelerometer-derived Physical Activity Estimates And Daily Wear Time In Children

Most of the studies applying the accelerometers to quantify physical activity (PA) and sedentary time (ST) require participants to wear the devices during waking hours only. There is no consensus on how many wearing hours are enough to reflect daily activity behavior in free-living conditions. Whether wear time has substantial impact on accelerometer-based estimates remains unclear. PURPOSE: To examine whether accelerometer-derived metrics were dependent on daily wear time. METHODS: Baseline data from 120 children (24.2% boys) aged 8 to 11 years who participated in the longitudinal study on active travel were analyzed. They were instructed to wear an ActiGraph wGT3x accelerometry on the waist for 7 to 10 consecutive days, only removing it while swimming and bathing. Accelerometer-derived metrics included daily wear time, total activity counts (TAC), ST, light-intensity PA (LPA), moderate-intensity PA (MPA), vigorous-intensity PA (VPA), moderate-to-vigorous PA (MVPA), moderate-to-vigorous PA (MVPA) bouts ≥ 10 minutes (MVPA-10), and proportion of VPA within MVPA (%VPA). Linear mixed models were performed to examine the relationships of these metrics with daily wear time which was categorized into quarters. RESULTS: Majority of the children (96%) provided at least 3 valid accelerometer wear days (defined as ≥ 480 minutes of valid wear time per day), with a total of 797 valid wear days included in analyses. After adjustment for age, sex, and body mass index, none of the PA metrics was completely independent on wear time. A linear relationship was found between wear time and four PA metrics including TAC, MPA, LPA, and ST. However, MVPA estimates were comparable between the first (8 to 11 hours per day) and second quarters (11 to 13 hours per day) of wear time (mean ± standard error: 31.4 ± 1.8 vs 35.7 ± 1.8 minutes, NS). Similar results were found for MVPA-10, VPA, and %VPA. CONCLUSIONS: Accelerometer-derived PA metrics were largely dependent on wear time. MVPA minutes seem to be comparable across wear time of 13 hours per day or less. Comparisons of accelerometer-derived PA estimates between studies need to be cautious and taken into account of differences in wear duration.

Monitoring Fatigue During Intermittent Exercise With Accelerometer-Derived Metrics.

The aim of this study was to assess the sensitivity of accelerometer-derived metrics for monitoring fatigue during an intermittent exercise protocol. Fifteen university students were enrolled in the study (age 20 ± 1 years). A submaximal intermitted recovery test (Sub-IRT) with a duration of 6 min and 30 s (drill 1) was performed. In order to increase the participants’ fatigue, after that, a repeated sprint protocol (1×6 maximal 20 m sprints) was performed. Following that, participants repeated the Sub-IRT (drill 2) to evaluate the external and internal training load (TL) variations related to fatigue. Apex 10 Hz global navigation satellite system (GNSS) units were used to collect the variables total distance (TD), high metabolic distance (HMD), relative velocity (RV), average metabolic power (MP), heart rate maximal (HRmax) and mean (HRmean), muscular (RPEmus) and respiratory rating of perceived exertion (RPEres), dynamic stress load (DSL), and fatigue index (FI). A Bayesian statistical approach was used. A likelihood difference (between drill 1 and drill 2) was found for the following parameters: TD (BF10 = 0.33, moderate per H0), HMD (BF10 = 1.3, anecdotal), RV (BF10 = 0.29, moderate per H0), MP (BF10 = 1.3, anecdotal), accelerations (BF10 = 1.6, anecdotal ), FI (BF10 = 4.7, moderate), HRmax (BF10 = 2.2, anecdotal), HRmean (BF10 = 4.3, moderate), RPEmus (BF10 = 11.6, strong), RPEres (BF10 = 3.1, moderate), DSL (BF10 = 5.7, moderate), and DSL•m−1 (BF10 = 4.3, moderate). In conclusion, this study reports that DSL, DSL•m−1, and FI can be valid metrics to monitor fatigue related to movement strategy during a standardized submaximal intermittent exercise protocol.

Low physical activity levels in knee osteoarthritis associated with "at risk" gait patterns

Purpose: While knee joint biomechanics and muscle activation patterns collected during gait analysis provide information about joint loading magnitude and duration during a single step, physical activity data collected with accelerometers provides an objective, surrogate metric of joint loading frequency (step count) during daily life. Together with data from gait analysis, frequency data might provide further insight into the relationships between in vivo joint loading exposure and clinical knee osteoarthritis (OA) progression. To investigate whether joint loading frequency data adds to our understanding of clinical knee OA progression, the following hypotheses were tested on individuals with medial knee OA: 1) joint loading magnitude and pattern metrics (from gait analysis) and joint loading frequency (from accelerometer data) will not be correlated, and 2) individuals who progress clinically - total knee arthroplasty (TKA) endpoint - at 3.5-year follow-up will exhibit baseline differences in gait metrics (matching those previously reported) and lower joint loading frequency compared to those who do not progress. Methods: Fifty-seven individuals with knee OA and primarily medial tibiofemoral compartment involvement underwent standardized three-dimensional instrumented baseline gait analysis during over-ground walking at self-selected speed. Surface electromyography of the quadriceps and hamstrings was collected concurrently. For one week following gait analysis, participants wore a tri-axial accelerometer during waking hours. Principal component analysis extracted major modes of variation (PCs) from knee joint moment waveforms during gait, including PCs describing time varying magnitude and patterns over the gait cycle. Step count was calculated from the accelerometer data using proprietary formulas within ActiLife software.Spearman correlation coefficients were calculated between step count and gait metrics that have been associated with OA progression, including features describing less dynamic patterns in the knee adduction (KAM), flexion (KFM), and rotation (KRM) moments and prolonged quadriceps and hamstrings activation in mid-stance. Jonckheere-Terpstra tests for ordered alternatives examined gait patterns across quartiles of step count with post-hoc comparisons made using Bonferroni corrections. For a subsample of 33 participants, Mann Whitney U-tests examined baseline differences in gait and accelerometer-derived metrics between individuals who progressed (TKA) at 3.5-year follow-up and those who did not. Results: Contrary to our hypothesis, significant correlations were found between step count and gait variables that have been associated with osteoarthritis progression. Specifically, lower step count was associated with a lack of KAM unloading, prolonged activation of the quadriceps and hamstrings in mid-stance, and a smaller range of KFM and KRM. Inspection of gait variables across quartiles of step count revealed that individuals in the lowest quartile (averaging just under 4000 steps/day) exhibited gait patterns that were significantly different than the other quartiles, including smaller KFM range and prolonged quadriceps and hamstrings activity through mid-stance (Table 1, Figure 1). The group that progressed clinically at 3.5-year follow-up had a higher overall KAM magnitude and prolonged vastus medialis activation through mid-stance at baseline compared to those who did not progress, but there were no significant differences in step count at baseline between those who did or did not progress at follow-up. Conclusions: The moment and EMG features that were correlated with low step count and were observed in the lowest quartile of step count have previously been associated with clinical knee OA progression (TKA endpoint) at 8-year follow-up. This may indicate these individuals are modulating both their gait and physical activity levels in response to their OA. Interestingly, there were few differences in gait patterns among those in the highest three quartiles of step count, suggesting there may be a threshold below which individuals exhibit this combination of “at risk” gait patterns and low physical activity levels. It is also of note that many of the gait features associated with step count were describing patterns rather than magnitude of joint loading. While the results of the longitudinal analysis support prior work showing baseline gait differences related to future clinical OA progression, a longer follow-up period may be needed to understand whether this combination of “at risk” gait patterns and low joint loading frequency puts individuals at higher or lower risk for longer-term OA progression.

A Case for Promoting Movement Medicine: Preventing Disability in the LIFE Randomized Controlled Trial.

The movement profile of older adults with compromised function is unknown, as is the relationship between these profiles and the development of major mobility disability (MMD)-a critical clinical outcome. We first describe the dimensions of movement in older adults with compromised function and then examine whether these dimensions predict the onset of MMD. Older adults at risk for MMD (N = 1,022, mean age = 78.7 years) were randomized to receive a structured physical activity intervention or health education control. We assessed MMD in 6-month intervals (average follow-up of 2.2 years until incident MMD), with activity assessed at baseline, 6-, 12- and 24-month follow-up via accelerometry. A principal components analysis of 11 accelerometer-derived metrics yielded three components representing lifestyle movement (LM), extended bouts of moderate-to-vigorous physical activity (MVPA), and stationary body posture. LM accounted for the greatest proportion of variance in movement (53%). Within health education, both baseline LM (HR = 0.74; 95% CI 0.62 to 0.88) and moderate-to-vigorous physical activity (HR = 0.69; 95% CI 0.54 to 0.87) were associated with MMD, whereas only LM was associated with MMD within physical activity (HR = 0.74; 95% CI 0.61 to 0.89). There were similar nonlinear relationships present for LM in both physical activity and health education (p < .04), whereby risk for MMD was lower among individuals with higher levels of LM. Both LM and moderate-to-vigorous physical activity should be central in treatment regimens for older adults at risk for MMD. clinicaltrials.gov Identifier NCT01072500.

Wrist-worn Accelerometry for Runners: Objective Quantification of Training Load.

ABSTRACTPurposeThis study aimed to apply open-source analysis code to raw habitual physical activity data from wrist-worn monitors to: 1) objectively, unobtrusively, and accurately discriminate between “running” and “nonrunning” days; and 2) develop and compare simple accelerometer-derived metrics of external training load with existing self-report measures.MethodsSeven-day wrist-worn accelerometer (GENEActiv; Activinsights Ltd, Kimbolton, UK) data obtained from 35 experienced runners (age, 41.9 ± 11.4 yr; height, 1.72 ± 0.08 m; mass, 68.5 ± 9.7 kg; body mass index, 23.2 ± 2.2 kg·m−2; 19 [54%] women) every other week over 9 to 18 wk were date-matched with self-reported training log data. Receiver operating characteristic analyses were applied to accelerometer metrics (“Average Acceleration,” “Most Active-30mins,” “Mins≥400 mg”) to discriminate between “running” and “nonrunning” days and cross-validated (leave one out cross-validation). Variance explained in training log criterion metrics (miles, duration, training load) by accelerometer metrics (Mins≥400 mg, “workload (WL) 400-4000 mg”) was examined using linear regression with leave one out cross-validation.ResultsMost Active-30mins and Mins≥400 mg had >94% accuracy for correctly classifying “running” and “nonrunning” days, with validation indicating robustness. Variance explained in miles, duration, and training load by Mins≥400 mg (67%–76%) and WL400–4000 mg (55%–69%) was high, with validation indicating robustness.ConclusionsWrist-worn accelerometer metrics can be used to objectively, unobtrusively, and accurately identify running training days in runners, reducing the need for training logs or user input in future prospective research or commercial activity tracking. The high percentage of variance explained in existing self-reported measures of training load by simple, accelerometer-derived metrics of external training load supports the future use of accelerometry for prospective, preventative, and prescriptive monitoring purposes in runners.

Effects of a Long-Term Physical Activity Program on Activity Patterns in Older Adults.

To examine the effect of a long-term structured physical activity (PA) intervention on accelerometer-derived metrics of activity pattern changes in mobility-impaired older adults. Participants were randomized to either a PA or health education (HE) program. The PA intervention included a walking regimen with strength, flexibility, and balance training. The HE program featured health-related discussions and a brief upper body stretching routine. Participants (n = 1341) wore a hip-worn accelerometer for ≥10 h·d for ≥3 d at baseline and again at 6, 12, and 24 months postrandomization. Total PA (TPA)-defined as movements registering 100+ counts per minute-was segmented into the following intensities: low-light PA (LLPA; 100-759 counts per minute), high light PA (HLPA; 760-1040 counts per minute), low moderate PA (LMPA; 1041-2019 counts per minute), and high moderate and greater PA (HMPA; 2020+ counts per minute). Patterns of activity were characterized as bouts (defined as the consecutive minutes within an intensity). Across groups, TPA decreased an average of 74 min·wk annually. The PA intervention attenuated this effect (PA = -68 vs HE: -112 min·wk, P = 0.002). This attenuation shifted TPA composition by increasing time in LLPA (10+ bouts increased 6 min·wk), HLPA (1+, 2+, 5+, and 10+ bouts increased 6, 3, 2, and 1 min·wk, respectively), LMPA (1+, 2+, 5+, and 10+ bouts increased: 19, 17,16, and 8 min·wk, respectively), and HMPA (1+, 2+, 5+, and 10+ bouts increased 23, 21, 17, and 14 min·wk, respectively). The PA intervention increased PA by shifting the composition of activity toward higher-intensity activity in longer-duration bouts. However, a long-term structured PA intervention did not completely eliminate overall declines in total daily activity experienced by mobility-impaired older adults.

Effects of a Long-Term Physical Activity Program on Activity Patterns in Mobility Impaired Older Adults

PURPOSE: To examine the effect of a long-term structured physical activity intervention on accelerometer-derived metrics of activity composition changes in older adults at high risk for mobility disability. METHODS: Participants were randomized to either a physical activity (PA) or health education (HE) program. The PA intervention included a walking regimen with strength, flexibility, and balance training. The HE program featured health-related discussions and a brief upper body stretching routine. Participants (n = 1,341) wore a hip-worn accelerometer for ≥10 h/day for ≥3 days at baseline and again at 6, 12 and 24 months post-randomization. Total physical activity (TPA)—defined as movements registering 100+ counts/min—was segmented into the following intensities: low light (LLPA; 100-759 counts/min), high light (HLPA; 760-1,040 counts/min), low moderate (LMPA; 1,041-2,019 counts/min), and high moderate and greater (HMPA; 2,020+ counts/min) physical activity. Patterns of activity were characterized as bouts (defined as the consecutive minutes within an intensity). RESULTS: Both groups decreased TPA (-10.5±1.0 minutes/day annually), but the PA intervention attenuated this effect (PA vs HE: +6.4±2.1 minutes/day, p<0.001). This attenuation shifted TPA composition by increasing daily time in HLPA (1+ bouts: 0.8±0.3; 5+ bouts: 0.2±0.04; 10+ bouts: 0.1±0.02 minutes), LMPA (1+ bouts: 2.7±0.4; 2+ bouts: 2.4±0.3; 5+ bouts: 2.0±0.2; 10+ bouts: 1.1±0.1 minutes), and HMPA (1+ bouts: 2.8±0.4; 2+ bouts: 2.5±0.3; 5+ bouts: 2.1±0.3; 10+ bouts: 1.7±0.2 minutes). All findings were statistically significant at p<0.01. DISCUSSION: The PA intervention increased physical activity by shifting the composition of activity toward higher intensity activity in longer duration bouts. However, a long-term structured physical activity intervention did not completely eliminate overall declines in total daily activity experienced by mobility impaired older adults.

An Evaluation of Accelerometer-derived Metrics to Assess Daily Behavioral Patterns.

The way physical activity (PA) and sedentary behavior (SB) are accumulated throughout the day (i.e., patterns) may be important for health, but identifying measurable and meaningful metrics of behavioral patterns is challenging. This study evaluated accelerometer-derived metrics to determine whether they predicted PA and SB patterns and were reliably measured. We defined and measured 55 metrics that describe daily PA and SB using data collected by using the activPAL monitor in four studies. The first two studies were randomized crossover designs that included recreationally active participants. Study 1 experimentally manipulated time spent in moderate-to-vigorous-intensity PA and sedentary time, and study 2 held time in exercise constant and manipulated SB. Study 3 included inactive participants who increased exercise, decreased sedentary time, or both. The study conditions induced distinct behavioral patterns; thus, we tested whether the new metrics could improve the prediction of an individual's study condition after adjusting for the overall volume of PA or SB using conditional logistic regression. In study 4, we measured the 3-month reliability for the pattern metrics by calculating intraclass correlation coefficients in a community-dwelling sample who wore the activPAL monitor twice for 7 d. In each of the experimental studies, we identified new metrics that could improve the accuracy for predicting condition beyond SB and moderate-to-vigorous-intensity PA volume. In study 1, 23 metrics were predictive of a highly active condition, and in study 2, 24 metrics were predictive of a highly sedentary condition. In study 4, the median intraclass correlation coefficients (25-75th percentiles) of the metrics were 0.59 (0.46-0.65). Several new metrics were predictive of patterns of SB, exercise, and nonexercise behavior and are moderately reliable for a 3-month period. Applying these metrics to determine whether daily behavioral patterns are associated with health-outcomes is an important area of future research.

Reliability of accelerometer-determined physical activity and sedentary behavior in school-aged children: a 12-country study.

Focused on the accelerometer-determined physical activity and sedentary time metrics in 9-11-year-old children, we sought to determine the following: (i) number of days that are necessary to achieve reliable estimates (G⩾0.8); (ii) proportion of variance attributed to different facets (participants and days) of reliability estimates; and (iii) actual reliability of data as collected in The International Study of Childhood Obesity, Lifestyle and Environment (ISCOLE). The analytical sample consisted of 6025 children (55% girls) from sites in 12 countries. Physical activity and sedentary time metrics measures were assessed for up to 7 consecutive days for 24 h per day with a waist-worn ActiGraph GT3X+. Generalizability theory using R software was used to investigate the objectives i and ii. Intra-class correlation coefficients (ICC) were computed using SAS PROC GLM to inform objective iii. The estimated minimum number of days required to achieve a reliability estimate of G⩾0.8 ranged from 5 to 9 for boys and 3 to 11 for girls for light physical activity (LPA); 5 to 9 and 3 to 10, for moderate-to-vigorous physical activity (MVPA); 5 to 10 and 4 to 10 for total activity counts; and 7 to 11 and 6 to 11 for sedentary time, respectively. For all variables investigated, the 'participant' facet accounted for 30-50% of the variability, whereas the 'days' facet accounted for ⩽5%, and the interaction (P × D) accounted for 50-70% of the variability. The actual reliability for boys in ISCOLE ranged from ICCs of 0.78 to 0.86, 0.73 to 0.85 and 0.72 to 0.86 for LPA, MVPA and total activity counts, respectively, and 0.67 to 0.79 for sedentary time. The corresponding values for girls were 0.80-0.88, 0.70-0.89, 0.74-0.86 and 0.64-0.80. It was rare that only 4 days from all participants would be enough to achieve desirable reliability estimates. However, asking participants to wear the device for 7 days and requiring ⩾4 days of data to include the participant in the analysis might be an appropriate approach to achieve reliable estimates for most accelerometer-derived metrics.

Accelerometer-derived activity correlates with volitional swimming speed in lake sturgeon (Acipenserfulvescens)

Quantifying fine-scale locomotor behaviours associated with different activities is challenging for free-swimming fish. Biologging and biotelemetry tools can help address this problem. An open channel flume was used to generate volitional swimming speed (Us) estimates of cultured lake sturgeon (Acipenser fulvescens Rafinesque, 1817) and these were paired with simultaneously recorded accelerometer-derived metrics of activity obtained from three types of data-storage tags. This study examined whether a predictive relationship could be established between four different activity metrics (tail-beat frequency (TBF), tail-beat acceleration amplitude (TBAA), overall dynamic body acceleration (ODBA), and vectorial dynamic body acceleration (VeDBA)) and the swimming speed of A. fulvescens. Volitional Usof sturgeon ranged from 0.48 to 2.70 m·s−1(0.51–3.18 body lengths (BL)·s−1). Swimming speed increased linearly with all accelerometer-derived metrics, and when all tag types were combined, Usincreased 0.46 BL·s−1for every 1 Hz increase in TBF, and 0.94, 0.61, and 0.94 BL·s−1for every 1g increase in TBAA, ODBA, and VeDBA, respectively. Predictive relationships varied among tag types and tag-specific parameter estimates of Usare presented for all metrics. This use of acceleration data-storage tags demonstrated their applicability for the field quantification of sturgeon swimming speed.