RESUMO
INTRODUCTION: Accelerometer-derived physical activity (PA) from cardiac devices are available via remote monitoring platforms yet rarely reviewed in clinical practice. We aimed to investigate the association between PA and clinical measures of frailty and physical functioning. METHODS: The PATTErn study (A study of Physical Activity paTTerns and major health Events in older people with implantable cardiac devices) enrolled participants aged 60 + undergoing remote cardiac monitoring. Frailty was measured using the Fried criteria and gait speed (m/s), and physical functioning by NYHA class and SF-36 physical functioning score. Activity was reported as mean time active/day across 30-days prior to enrolment (30-day PA). Multivariable regression methods were utilised to estimate associations between PA and frailty/functioning (OR = odds ratio, ß = beta coefficient, CI = confidence intervals). RESULTS: Data were available for 140 participants (median age 73, 70.7% male). Median 30-day PA across the analysis cohort was 134.9 min/day (IQR 60.8-195.9). PA was not significantly associated with Fried frailty status on multivariate analysis, however was associated with gait speed (ß = 0.04, 95% CI 0.01-0.07, p = 0.01) and measures of physical functioning (NYHA class: OR 0.73, 95% CI 0.57-0.92, p = 0.01, SF-36 physical functioning: ß = 4.60, 95% CI 1.38-7.83, p = 0.005). CONCLUSIONS: PA from cardiac devices was associated with physical functioning and gait speed. This highlights the importance of reviewing remote monitoring PA data to identify patients who could benefit from existing interventions. Further research should investigate how to embed this into clinical pathways.
Assuntos
Acelerometria , Desfibriladores Implantáveis , Exercício Físico , Tecnologia de Sensoriamento Remoto , Humanos , Masculino , Idoso , Idoso de 80 Anos ou mais , Exercício Físico/fisiologia , Fragilidade , Idoso Fragilizado , Marca-Passo Artificial , Acelerometria/instrumentação , Acelerometria/métodos , Velocidade de Caminhada , Desempenho Físico Funcional , Tecnologia de Sensoriamento Remoto/instrumentação , Tecnologia de Sensoriamento Remoto/métodos , Pessoa de Meia-IdadeRESUMO
BACKGROUND: Foot collapse is primarily diagnosed and monitored using lateral weight-bearing foot x-ray images. There are several well-validated measurements which aid assessment. However, these are subject to inter- and intra-user variability. OBJECTIVE: To develop and validate a software system for the fully automatic assessment of radiographic changes associated with foot collapse; automatically generating measurements for calcaneal tilt, cuboid height and Meary's angle. METHODS: This retrospective study was approved by the Health Research Authority (IRAS 244852). The system was developed using lateral weight-bearing foot x-ray images, and evaluated against manual measurements from five clinical experts. The system has two main components: (i) a Random Forest-based point-finder to outline the bones of interest; and (ii) a geometry-calculator to generate the measurements based on the point positions from the point-finder. The performance of the point-finder was assessed using the point-to-point error (i.e. the mean absolute distance between each found point and the equivalent ground truth point, averaged over all points per image). For assessing the performance of the geometry-calculator, linear mixed models were fitted to estimate clinical inter-observer agreement and to compare the performance of the software system to that of the clinical experts. RESULTS: A total of 200 images were collected from 79 subjects (mean age: 56.4 years ±12.9 SD, 30/49 females/males). There was good agreement among all clinical experts with intraclass correlation estimates between 0.78 and 0.86. The point-finder achieved a median point-to-point error of 2.2 mm. There was no significant difference between the clinical and automatically generated measurements using the point-finder points, suggesting that the fully automatically obtained measurements are in agreement with the manually obtained measurements. CONCLUSIONS: The proposed system can be used to support and automate radiographic image assessment for diagnosing and managing foot collapse, saving clinician time, and improving patient outcomes.