The Analytical and Clinical Validity of the pfSTEP Digital Biomarker of the Susceptibility/Risk of Declining Physical Function in Community-Dwelling Older Adults.

Measures of stepping volume and rate are common outputs from wearable devices, such as accelerometers. It has been proposed that biomedical technologies, including accelerometers and their algorithms, should undergo rigorous verification as well as analytical and clinical validation to demonstrate that they are fit for purpose. The aim of this study was to use the V3 framework to assess the analytical and clinical validity of a wrist-worn measurement system of stepping volume and rate, formed by the GENEActiv accelerometer and GENEAcount step counting algorithm. The analytical validity was assessed by measuring the level of agreement between the wrist-worn system and a thigh-worn system (activPAL), the reference measure. The clinical validity was assessed by establishing the prospective association between the changes in stepping volume and rate with changes in physical function (SPPB score). The agreement of the thigh-worn reference system and the wrist-worn system was excellent for total daily steps (CCC = 0.88, 95% CI 0.83-0.91) and moderate for walking steps and faster-paced walking steps (CCC = 0.61, 95% CI 0.53-0.68 and 0.55, 95% CI 0.46-0.64, respectively). A higher number of total steps and faster paced-walking steps was consistently associated with better physical function. After 24 months, an increase of 1000 daily faster-paced walking steps was associated with a clinically meaningful increase in physical function (0.53 SPPB score, 95% CI 0.32-0.74). We have validated a digital susceptibility/risk biomarker-pfSTEP-that identifies an associated risk of low physical function in community-dwelling older adults using a wrist-worn accelerometer and its accompanying open-source step counting algorithm.

Social behaviour and transmission of lameness in a flock of ewes and lambs.

Introduction: Sheep have heterogenous social connections that influence transmission of some infectious diseases. Footrot is one of the top five globally important diseases of sheep, it is caused by Dichelobacter nodosus and transmits between sheep when infectious feet contaminate surfaces, e.g., pasture. Surfaces remain infectious for a few minutes to a few days, depending on surface moisture levels. Susceptible sheep in close social contact with infectious sheep might be at risk of becoming infected because they are likely to step onto infectious footprints, particularly dams and lambs, as they cluster together. Methods: High resolution proximity sensors were deployed on 40 ewes and their 54 lambs aged 5-27 days, in a flock with endemic footrot in Devon, UK for 13 days. Sheep locomotion was scored daily by using a 0-6 integer scale. Sheep were defined lame when their locomotion score (LS) was ≥2, and a case of lameness was defined as LS ≥2 for ≥2 days. Results: Thirty-two sheep (19 ewes, 9 single, and 4 twin lambs) became lame during the study, while 14 (5 ewes, 5 single, and 4 twin lambs) were lame initially. These 46 sheep were from 29 family groups, 14 families had >1 lame sheep, and transmission from ewes to lambs was bidirectional. At least 15% of new cases of footrot were from within family transmission; the occurrence of lameness was higher in single than twin lambs. At least 4% of transmission was due to close contact across the flock. Most close contact occurred within families. Single and twin lambs spent 1.5 and 0.9 hours/day with their dams, respectively, and twin lambs spent 3.7 hours/day together. Non-family sheep spent only 0.03 hours/day in contact. Lame single lambs and ewes spent less time with non-family sheep, and lame twin lambs spent less time with family sheep. Discussion: We conclude that most transmission of lameness is not attributable to close contact. However, in ewes with young lambs, some transmission occurs within families and is likely due to time spent in close contact, since single lambs spent more time with their dam than twin lambs and were more likely to become lame.

Accelerometer-assessed physical behavior and the association with clinical outcomes in implantable cardioverter-defibrillator recipients: A systematic review.

Background: Current implantable cardioverter-defibrillator (ICD) devices are equipped with a device-embedded accelerometer capable of capturing physical activity (PA). In contrast, wearable accelerometer-based methods enable the measurement of physical behavior (PB) that encompasses not only PA but also sleep behavior, sedentary time, and rest-activity patterns. Objective: This systematic review evaluates accelerometer-based methods used in patients carrying an ICD or at high risk of sudden cardiac death. Methods: Papers were identified via the OVID MEDLINE and OVID EMBASE databases. PB could be assessed using a wearable accelerometer or an embedded accelerometer in the ICD. Results: A total of 52 papers were deemed appropriate for this review. Out of these studies, 30 examined device-embedded accelerometry (189,811 patients), 19 examined wearable accelerometry (1601 patients), and 3 validated wearable accelerometry against device-embedded accelerometry (106 patients). The main findings were that a low level of PA after implantation of the ICD and a decline in PA were both associated with an increased risk of mortality, heart failure hospitalization, and appropriate ICD shock. Second, PA was affected by cardiac factors (eg, onset of atrial fibrillation, ICD shocks) and noncardiac factors (eg, seasonal differences, societal factors). Conclusion: This review demonstrated the potential of accelerometer-measured PA as a marker of clinical deterioration and ventricular arrhythmias. Notwithstanding that the evidence of PB assessed using wearable accelerometry was limited, there seems to be potential for accelerometers to improve early warning systems and facilitate preventative and proactive strategies.

Rationale and design of the SafeHeart study: Development and testing of a mHealth tool for the prediction of arrhythmic events and implantable cardioverter-defibrillator therapy.

Background: Patients with an implantable cardioverter-defibrillator (ICD) are at a high risk of malignant ventricular arrhythmias. The use of remote ICD monitoring, wearable devices, and patient-reported outcomes generate large volumes of potential valuable data. Artificial intelligence-based methods can be used to develop personalized prediction models and improve early-warning systems. Objective: The purpose of this study was to develop an integrated web-based personalized prediction engine for ICD therapy. Methods: This international, multicenter, prospective, observational study consists of 2 phases: (1) a development study and (2) a feasibility study. We plan to enroll 400 participants with an ICD (with or without cardiac resynchronization therapy) on remote monitoring: 300 participants in the development study and 100 in the feasibility study. During 12-month follow-up, electronic health record data, remote monitoring data, accelerometry-assessed physical behavior data, and patient-reported data are collected. By using machine- and deep-learning approaches, a prediction engine is developed to assess the risk probability of ICD therapy (shock and antitachycardia pacing). The feasibility of the prediction engine as a clinical tool, the SafeHeart Platform, is assessed during the feasibility study. Results: Development study recruitment commenced in 2021. The feasibility study starts in 2022. Conclusion: SafeHeart is the first study to prospectively collect a multimodal data set to construct a personalized prediction engine for ICD therapy. Moreover, SafeHeart explores the integration and added value of detailed objective accelerometer data in the prediction of clinical events. The translation of the SafeHeart Platform to clinical practice is examined during the feasibility study.

Measuring the Exercise Component of Energy Availability during Arduous Training in Women.

INTRODUCTION: Low energy availability (EA) may impede adaptation to exercise, suppressing reproductive function and bone turnover. Exercise energy expenditure (EEE) measurements lack definition and consistency. This study aimed to compare EA measured from moderate and vigorous physical activity from accelerometry (EEEmpva) with EA from total physical activity (EEEtpa) from doubly labeled water in women. The secondary aim was to determine the relationship of EA with physical fitness, body composition by dual-energy x-ray absorptiometry, heart rate variability (HRV), and eating behavior (Brief Eating Disorder in Athletes Questionnaire [BEDA-Q]). METHODS: This was a prospective, repeated-measures study, assessing EA measures and training adaptation during 11-month basic military training. Forty-seven women (23.9 ± 2.6 yr) completed three consecutive 10-d assessments of EEEmvpa, EEEtpa, and energy intake (EI). EA measures were compared using linear regression and Bland-Altman analyses; relationships of EA with fat mass, HRV, 1.5-mile run times, and BEDA-Q were evaluated using partial correlations. RESULTS: EA from EEEmvpa demonstrated strong agreement with EA from EEEtpa across the measurement range (R2 = 0.76, r = 0.87, P < 0.001) and was higher by 10 kcal·kg-1 FFM·d-1. However, EA was low in absolute terms because of underreported EI. Higher EA was associated with improved 1.5-mile run time (r = 0.28, P < 0.001), fat mass loss (r = 0.38, P < 0.001), and lower BEDA-Q score (r = -0.37, P < 0.001) but not HRV (all P > 0.10). CONCLUSION: Accelerometry-based EEE demonstrated validity against doubly labeled water during multistressor training, the difference representing 10 kcal·kg-1 FFM·d-1 EEE from nonexercise activity. Beneficial physical but not autonomic adaptations were associated with higher EA. EAmvpa and BEDA-Q warrant consideration for low EA assessment and screening.

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

PURPOSE: This 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. METHODS: Seven-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; 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. RESULTS: Most 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. CONCLUSIONS: Wrist-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.

Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents.

Wearable acceleration sensors are increasingly used for the assessment of free-living physical activity. Acceleration sensor calibration is a potential source of error. This study aims to describe and evaluate an autocalibration method to minimize calibration error using segments within the free-living records (no extra experiments needed). The autocalibration method entailed the extraction of nonmovement periods in the data, for which the measured vector magnitude should ideally be the gravitational acceleration (1 g); this property was used to derive calibration correction factors using an iterative closest-point fitting process. The reduction in calibration error was evaluated in data from four cohorts: UK (n = 921), Kuwait (n = 120), Cameroon (n = 311), and Brazil (n = 200). Our method significantly reduced calibration error in all cohorts (P < 0.01), ranging from 16.6 to 3.0 mg in the Kuwaiti cohort to 76.7 to 8.0 mg error in the Brazil cohort. Utilizing temperature sensor data resulted in a small nonsignificant additional improvement (P > 0.05). Temperature correction coefficients were highest for the z-axis, e.g., 19.6-mg offset per 5°C. Further, application of the autocalibration method had a significant impact on typical metrics used for describing human physical activity, e.g., in Brazil average wrist acceleration was 0.2 to 51% lower than uncalibrated values depending on metric selection (P < 0.01). The autocalibration method as presented helps reduce the calibration error in wearable acceleration sensor data and improves comparability of physical activity measures across study locations. Temperature ultization seems essential when temperature deviates substantially from the average temperature in the record but not for multiday summary measures.

Assessing sedentary behavior with the GENEActiv: introducing the sedentary sphere.

BACKGROUND: The Sedentary Sphere is a method for the analysis, identification, and visual presentation of sedentary behaviors from a wrist-worn triaxial accelerometer. PURPOSE: This study aimed to introduce the concept of the Sedentary Sphere and to determine the accuracy of posture classification from wrist accelerometer data. METHODS: Three samples were used: 1) free living (n = 13, ages 20-60 yr); 2) laboratory based (n = 25, ages 30-65 yr); and 3) hospital inpatients (n = 10, ages 60-90 yr). All participants wore a GENEActiv on their wrist and activPAL on their thigh. The free-living sample wore an additional GENEActiv on the thigh and completed the Multimedia Activity Recall for Children and Adults. The laboratory-based sample wore the monitors while seated at a desk for 7 h, punctuated by 2 min of walking every 20 min. The free-living and inpatient samples wore the monitors for 24 h. Posture was classified from wrist-worn accelerometry using the Sedentary Sphere concept. RESULTS: Sitting time did not differ between the wrist GENEActiv and the activPAL in the free-living sample and was correlated in the three samples combined (rho = 0.9, P < 0.001), free-living and inpatient samples (r ≃ 0.8, P < 0.01). Mean intraindividual agreement was 85% ± 7%. In the laboratory-based and inpatient samples, sitting time was underestimated by the wrist GENEActiv by 30 min and 2 h relative to the activPAL, respectively (P < 0.05). Posture classification disagreed during reading while standing, cooking while standing, and brief periods during driving. Posture allocation validity was excellent when the GENEActiv was worn on the thigh, evidenced by the near-perfect agreement with the activPAL (96% ± 3%). CONCLUSIONS: The Sedentary Sphere enables determination of the most likely posture from the wrist-worn GENEActiv. Visualizing behaviors on the sphere displays the pattern of wrist movement and positions within that behavior.