Activity Energy Expenditure Predicts Clinical Average Levels of Physical Activity in Older Population: Results from Salus in Apulia Study

Abstract

Self-report questionnaires are a valuable method of physical activity measurement in public health research; however, accuracy is often lacking. Resolving the differences between self-reported and objectively measured physical activity is an important surveillance challenge currently facing population health experts. The present work aims at providing the relationship between activity energy expenditure estimated from wrist-worn accelerometers and intensity of self-reported physical activity (InCHIANTI structured interview questionnaire) in a sub-cohort of a population-based study on aging in Southern Italy. Linear regression was used to test the association between measured and reported physical activity. We found that activity energy expenditure predicted clinical average levels of PA assessed through InCHIANTI classification.

Physical and cognitive profiles in motoric cognitive risk syndrome in an older population from Southern Italy

“…Physical activity was assessed through an interviewer‐administered questionnaire [40]. Participants were asked to indicate their average level of physical activity during the past year, with the variable further dichotomized using the cut‐off value <2 [26].…”

“…The “Salus in Apulia Study” is an ongoing population‐based prospective cohort comprising 2472 individuals aged 65 years old or older and resident in Castellana Grotte, a town near Bari, Puglia, in the Southeast of Italy. The study focuses on lifestyle factors including physical activity [26], diet [27] and other age‐related sensory impairments [28,29] or frailty phenotypes [30]. Salus is a public health initiative funded by the Italian Ministry of Health and Apulia Regional Government, and conducted at our institution, that combines data from two previous populations: a baseline (MICOL3, M3: 2003–2005) and a follow‐up cohort (GreatAGE Study ‐ MICOL4, M4: 2013–2015).…”

Visualization-Driven Time-Series Extraction from Wearable Systems Can Facilitate Differentiation of Passive ADL Characteristics among Stroke and Healthy Older Adults

“…For example, at a data sampling rate of 100 Hz, one accelerometer sensor can provide over 8.6 million samples over a single day. Recently, Borton and coworkers reported that activity energy expenditure derived from wrist-worn wearables can serve as a good predictor of clinical average levels of physical activity in older adults (n = 2472) and thus objectively measure physical activity in a population-based study [20]. Although these vast datasets have rich mobility information, signal processing algorithms to analyze such enormous datasets for extracting essential features relevant to one's mobility are lacking.…”

Role of biomechanical assessment in rotator cuff tear repair: Arthroscopic vs mini-open approach

“…Motion analysis techniques can provide a more thorough description of 3-dimensional kinematics and offer a noninvasive, dynamic, quantitative alternative to radiographic methods. Motion analysis has been widely used to assess the motor abilities of people with neurological and musculoskeletal impairments[ 11 ]. To our knowledge, there is limited study regarding what biomechanical effect RTC tears have on different motion tasks and muscle activities after surgical treatment of RC tears.…”