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Objective evaluation of refractive data and astigmatism: quantification and analysis.

ABSTRACT: The aim of this study was to present methods to improve the analysis of refractive data. A comparison of methods is used to analyse refractive powers using individual powers and aggregate data. Equations are also developed for the representation of the average power of a lens or refractive data as a univariate measure, which includes spherical, coma, and/or other aberrations. The equations provide a precise representation of refractive power, which is useful for comparing individual and aggregate data. Average lens power in the principal meridian can be adequately computed as can the average lens power through orthogonal and oblique meridians, providing a good univariate representation of astigmatism and refractive power. Although these formulae are perhaps not as easy to use as, for example, the spherical equivalent, they are more precise and superior in principle involving fewer approximations and are not subject to systematic bias. These effects are of significance when dealing with high-powered lenses such as intraocular lenses or the cornea. They need to be taken into account particularly for calculations of intraocular lens power, toric intraocular lenses, and cornea refractive surgery, especially if outcomes are to be improved. Such issues are of particular importance when dealing with aggregate data and determining statistical significance of treatment effects.

SUBMITTER: Kaye SB

PROVIDER: S-EPMC3930272 | biostudies-other | 2014 Feb

REPOSITORIES: biostudies-other

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Project description:PurposeThis 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.MethodsSeven-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.ResultsMost 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.ConclusionsWrist-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.

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Objective evaluation of refractive data and astigmatism: quantification and analysis.

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