1,887,438 research outputs found
The SF-36: a simple, effective measure of mobility disability for epidemiological studies
No full textBackgroundMobility disability is a major problem in older people. Numerous scales exist for the measurement of disability but often these do not permit comparisons between study groups. The physical functioning (PF) domain of the established and widely used Short Form-36 (SF-36) questionnaire asks about limitations on ten mobility activities.ObjectivesTo describe prevalence of mobility disability in an elderly population, investigate the validity of the SF-36 PF score as a measure of mobility disability, and to establish age and sex specific norms for the PF score.MethodsWe explored relationships between the SF-36 PF score and objectively measured physical performance variables among 349 men and 280 women, 59-72 years of age, who participated in the Hertfordshire Cohort Study (HCS). Normative data were derived from the Health Survey for England (HSE) 1996.Results32% of men and 46% of women had at least some limitation in PF scale items. Poor SF-36 PF scores (lowest fifth of the gender-specific distribution) were related to: lower grip strength; longer timed-up-and-go, 3m walk, and chair rises test times in men and women; and lower quadriceps peak torque in women but not men. HSE normative data showed that median PF scores declined with increasing age in men and women.ConclusionOur results are consistent with the SF-36 PF score being a valid measure of mobility disability in epidemiological studies. This approach might be a first step towards enabling simple comparisons of prevalence of mobility disability between different studies of older people. The SF-36 PF score could usefully complement existing detailed schemes for classification of disability and it now requires validation against them
Sample sizes for the SF-6D preference based measure of health from the SF-36: a practical guide
Get PDFBackground Health Related Quality of Life (HRQoL) measures are becoming more frequently used in clinical trials and health services research, both as primary and secondary endpoints. Investigators are now asking statisticians for advice on how to plan and analyse studies using HRQoL measures, which includes questions on sample size. Sample size requirements are critically dependent on the aims of the study, the outcome measure and its summary measure, the effect size and the method of calculating the test statistic. The SF-6D is a new single summary preference-based measure of health derived from the SF-36 suitable for use clinical trials and in the economic evaluation of health technologies. Objectives To describe and compare two methods of calculating sample sizes when using the SF-6D in comparative clinical trials and to give pragmatic guidance to researchers on what method to use. Methods We describe two main methods of sample size estimation. The parametric (t-test) method assumes the SF-6D data is continuous and normally distributed and that the effect size is the difference between two means. The non-parametric (Mann-Whitney MW) method assumes the data are continuous and not normally distributed and the effect size is defined in terms of the probability that an observation drawn at random from population Y would exceed an observation drawn at random from population X. We used bootstrap computer simulation to compare the power of the two methods for detecting a shift in location. Results This paper describes the SF-6D and retrospectively calculated parametric and nonparametric effect sizes for the SF-6D from a variety of studies that had previously used the SF-36. Computer simulation suggested that if the distribution of the SF-6D is reasonably symmetric then the t-test appears to be more powerful than the MW test at detecting differences in means. Therefore if the distribution of the SF-6D is symmetric or expected to be reasonably symmetric then parametric methods should be used for sample size calculations and analysis. If the distribution of the SF-6D is skewed then the MW test appears to be more powerful at detecting a location shift (difference in means) than the t-test. However, the differences in power (between the t and MW tests) are small and decrease as the sample size increases. Conclusions We have provided a clear description of the distribution of the SF-6D and believe that the mean is an appropriate summary measure for the SF-6D when it is to be used in clinical trials and the economic evaluation of new health technologies. Therefore pragmatically we would recommend that parametric methods be used for sample size calculation and analysis when using the SF-6D.sample size; health-related quality of life; SF-36; preference-based measures of health; bootstrap simulation
SF Gospel: Blog contents, 2006-2015
No full textSF Gospel (2006-2015) was a blog exploring religious and theological themes in science fiction and popular culture by Gabriel Mckee, author of The Gospel According to Science Fiction. The primary PDF contains the textual content of the blog, along with most images that accompanied the original posts. The appendix PDF contains guest posts written by Mckee for other blogs and websites (including SF Signal, Holy Heroes, Nerve.com, and Religion Dispatches) during the course of SF Gospel's existence
Estimating the burden of disease in chronic pain with and without neuropathic characteristics: does the choice between the EQ-5D and SF-6D matter?
Get PDFThe EQ-5D and Short Form (SF)12 are widely used generic health-related quality of life (HRQoL) questionnaires. They can be used to derive health utility index scores, on a scale where 0 is equivalent to death and 1 represents full health, with scores less than zero representing states "worse than death." We compared EQ-5D or SF-6D health utility index scores in patients with no chronic pain, and chronic pain with and without neuropathic characteristics (NC), and to explore their discriminant ability for pain severity. Self-reported health and chronic pain status was collected as part of a UK general population survey (n=4451). We found moderate agreement between individual dimensions of EQ-5D and SF-6D, with most highly correlated dimensions found for mental health and anxiety/depression, role limitations and usual activities, and pain and pain/discomfort. Overall 43% reported full health on the EQ-5D, compared with only 4.2% on the SF-6D. There were significant differences in mean utilities for chronic pain with NC (EQ-5D 0.47 vs SF-6D 0.62) and especially for severe pain (EQ-5D 0.33 vs SF-6D 0.58). On the EQ-5D, 17% of those with chronic pain with NC and 3% without NC scored "worse than death," a state which is not possible using the SF-6D. Health utilities derived from EQ-5D and SF-12/36 can discriminate between group differences for chronic pain with and without NC and greater pain severity. However, the instruments generate widely differing HRQoL scores for the same patient groups. The choice between using the EQ-5D or SF-6D matters greatly when estimating the burden of disease
SF-PC functions for REST.
No full textVisualizations of the first four SF-PC functions obtained by the proposed SF-PCA, computed on the entire population during REST, and plotted separately for CTRL and SCHZ groups. Top: median(sCTRL) ⋅ SF-PC. Bottom: median(sSCHZ)⋅ SF-PC. The figure highlights the median behaviour on each component, for subjects in the two populations.</p
GPS-IMU-SF-comparison
No full textdata set for 12 horses (column 1), 3 runs each (column 2: 1 inside lane, 2 middle lane, 3 outside lane)), at target speed of 16.1m/s (36 miles per hour). Data set containing 61 samples per run including:
-stride frequency extracted from GPS speed fluctuations collected with a 10 Hz GPS logger (SF GPS), Vbox sport, 2022 version
-stride frequency extracted from a IMU data logger (Xsens DOT 2nd generation) logging at 120 Hz and configured for 'dynamic' settings, (SF IMU). SF IMU data downsampled to match SF GPS data.
-synchronization fo GPS and IMU data via root mean square error calculation of GPS and IMU heading (yaw) values.
-average value of SF GPS and SF IMU
-difference between SF GPS and SF IMU
-average and difference for Bland and Altman analysis of method agreement.
in addition to the GPS and IMU SF data, a MATLAB script is provided (findSFfromGPSspeed.m) that can be used to extract SF values from GPS speed values (sampled at 10 Hz). AN example file containing GPS speed (Horse12-Speed.csv) is also given for that purpose. </p
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