Validation of the GENEA accelerometer

Purpose: The study aims were: 1) to assess the technical reliability and validity of the GENEA using a mechanical shaker; 2) to perform a GENEA value calibration to develop thresholds for sedentary and light-, moderate-, and vigorous-intensity physical activity; and 3) to compare the intensity classification of the GENEA with two widely used accelerometers. Methods: A total of 47 GENEA accelerometers were attached to a shaker and vertically accelerated, generating 15 conditions of varying acceleration and/or frequency. Reliability was calculated using SD and intrainstrument and interinstrument coefficients of variation, whereas validity was assessed using Pearson correlation with the shaker acceleration as the criterion. Next, 60 adults wore a GENEA on each wrist and on the waist (alongside an ActiGraph and RT3 accelerometer) while completing 10-12 activity tasks. A portable metabolic gas analyzer provided the criterion measure of physical activity. Analyses involved the use of Pearson correlations to establish criterion and concurrent validity and receiver operating characteristic curves to establish intensity cut points. Results: The GENEA demonstrated excellent technical reliability (CVintra = 1.4%, CVinter = 2.1%) and validity (r = 0.98, P < 0.001) using the mechanical shaker. The GENEA demonstrated excellent criterion validity using V̇O as the criterion (left wrist, r = 0.86; right wrist, r = 0.83; waist, r = 0.87), on par with the waist-worn ActiGraph and RT3. The GENEA demonstrated excellent concurrent validity compared with the ActiGraph (r = 0.92) and the RT3 (r = 0.97). The waist-worn GENEA had the greatest classification accuracy (area under the receiver operating characteristic curve (AUC) = 0.95), followed by the left (AUC = 0.93) and then the right wrist (AUC = 0.90). The accuracy of the waist-worn GENEA was virtually identical with that of the ActiGraph (AUC = 0.94) and RT3 (AUC = 0.95). CONCLUSION:: The GENEA is a reliable and valid measurement tool capable of classifying the intensity of physical activity in adults. © 2011 by the American College of Sports Medicine

Additional file 1: of Low physical activity, high television viewing and poor sleep duration cluster in overweight and obese adults; a cross-sectional study of 398,984 participants from the UK Biobank

The socio-demographics of the missing cases and logistic regression analysis with waist circumference. (DOCX 27 kb

Electrochemical Detection of Plasma Immunoglobulin as a Biomarker for Alzheimer's Disease

The clinical diagnosis and treatment of Alzheimer's disease (AD) represent a challenge to clinicians due to the variability of clinical symptomatology as well as the unavailability of reliable diagnostic tests. In this study, the development of a novel electrochemical assay and its potential to detect peripheral blood biomarkers to diagnose AD using plasma immunoglobulins is investigated. The immunosensor employs a gold electrode as the immobilizing substrate, albumin depleted plasma immunoglobulin as the biomarker, and polyclonal rabbit Anti-human immunoglobulin (against IgA, IgG, IgM) as the receptor for plasma conjugation. The assay showed good response, sensitivity and reproducibility in differentiating plasma immunoglobulin from AD and control subjects down to 10-9 dilutions of plasma immunoglobulin representing plasma content concentrations in the pg mL-1 range. The newly developed assay is highly sensitive, less time consuming, easy to handle, can be easily modified to detect other dementia-related biomarkers in blood samples, and can be easily integrated into portable devices

Ten commandments for the future of ageing research in the UK: a vision for action-0

<p><b>Copyright information:</b></p><p>Taken from "Ten commandments for the future of ageing research in the UK: a vision for action"</p><p>http://www.biomedcentral.com/1471-2318/7/10</p><p>BMC Geriatrics 2007;7():10-10.</p><p>Published online 3 May 2007</p><p>PMCID:PMC1868025.</p><p></p

Ten commandments for the future of ageing research in the UK: a vision for action-1

<p><b>Copyright information:</b></p><p>Taken from "Ten commandments for the future of ageing research in the UK: a vision for action"</p><p>http://www.biomedcentral.com/1471-2318/7/10</p><p>BMC Geriatrics 2007;7():10-10.</p><p>Published online 3 May 2007</p><p>PMCID:PMC1868025.</p><p></p>ilable from

Inter-relationships of factors associated with perceived facial age.

<p>Factors were identified as statistically significant at the p<0.05 level from bivariate analyses and classified into interacting domains.</p

Multiple linear regression analysis of lifestyle variables.

<p>Lifestyle variables included in the final model are given together with the number of responses (n) and the mean difference between perceived age and chronological age are given (Age difference LSMean). Responses are given in order of those with smallest difference first. The statistical confidence for each variable is also given (*F-test p-value). Those individual responses joined by the same letter were not found to be significantly different at p<0.05. The maximum difference between any two categories is given together with the statistical confidence that the mean perceived ages for these categories differs (^$F-test p-value).</p

Bivariate analysis of lifestyle variables.

<p>For each variable the number of responses (n) and the mean difference between perceived age and chronological age are given (Age difference LSMean). Chronological age was included as a covariate in all tests. The statistical confidence for each variable is given (*F-test p-value). Those responses joined by the same letter were not found to be significantly different at p<0.05. The maximum difference between any two categories is given together with the statistical confidence that the mean perceived ages for these categories differs (^$F-test p-value). ^‡For the question on ‘How much activity during the day’ the option of ‘not very active all day’ was an option but was not selected by any subjects. ^§For daily teeth cleaning options of ‘less than once a day’ and ‘more than twice a day’ were also given but not selected by any subjects.</p

A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer

<div><p>Wrist-worn accelerometers are increasingly being used for the assessment of physical activity in population studies, but little is known about their value for sleep assessment. We developed a novel method of assessing sleep duration using data from 4,094 Whitehall II Study (United Kingdom, 2012–2013) participants aged 60–83 who wore the accelerometer for 9 consecutive days, filled in a sleep log and reported sleep duration via questionnaire. Our sleep detection algorithm defined (nocturnal) sleep as a period of sustained inactivity, itself detected as the absence of change in arm angle greater than 5 degrees for 5 minutes or more, during a period recorded as sleep by the participant in their sleep log. The resulting estimate of sleep duration had a moderate (but similar to previous findings) agreement with questionnaire based measures for time in bed, defined as the difference between sleep onset and waking time (kappa = 0.32, 95%CI:0.29,0.34) and total sleep duration (kappa = 0.39, 0.36,0.42). This estimate was lower for time in bed for women, depressed participants, those reporting more insomnia symptoms, and on weekend days. No such group differences were found for total sleep duration. Our algorithm was validated against data from a polysomnography study on 28 persons which found a longer time window and lower angle threshold to have better sensitivity to wakefulness, while the reverse was true for sensitivity to sleep. The novelty of our method is the use of a generic algorithm that will allow comparison between studies rather than a “count” based, device specific method.</p></div

Example of arm angle, detected sustained inactivity bouts, reported sleep and detected darkness from light sensor from the wrist monitor in four participants with distinct individual characteristics (A-D), all on a Thursday evening.

<p>Example of arm angle, detected sustained inactivity bouts, reported sleep and detected darkness from light sensor from the wrist monitor in four participants with distinct individual characteristics (A-D), all on a Thursday evening.</p