Intraocular pressure and ocular pulse amplitude using dynamic contour tonometry and contact lens tonometry

BACKGROUND: The new Ocular Dynamic Contour Tonometer (DCT), investigational device supplied by SMT (Swiss Microtechnology AG, Switzerland) allows simultaneous recording of intraocular pressure (IOP) and ocular pulse amplitude (OPA). It was the aim of this study to compare the IOP results of this new device with Goldmann tonometry. Furthermore, IOP and OPA measured with the new slitlamp-mounted DCT were compared to the IOP and OPA measured with the hand-held SmartLens(®), a gonioscopic contact lens tonometer (ODC Ophthalmic Development Company AG, Switzerland). METHODS: Nineteen healthy subjects were included in this study. IOP was determined by three consecutive measurements with each of the DCT, SmartLens(®), and Goldmann tonometer. Furthermore, OPA was measured three times consecutively by DCT and SmartLens(®). RESULTS: No difference (P = 0.09) was found between the IOP values by means of DCT (mean: 16.6 mm Hg, median: 15.33 mm Hg, SD: +/- 4.04 mm Hg) and Goldmann tonometry (mean: 16.17 mm Hg, median: 15.33 mm Hg, SD: +/- 4.03 mm Hg). The IOP values of SmartLens(® )(mean: 20.25 mm Hg, median: 19.00 mm Hg, SD: +/- 4.96 mm Hg) were significantly higher (P = 0.0008) both from Goldmann tonometry and DCT. The OPA values of the DCT (mean: 3.08 mm Hg, SD: +/- 0.92 mm Hg) were significantly lower (P = 0.0003) than those obtained by SmartLens(® )(mean: 3.92 mm Hg, SD: +/- 0.83 mm Hg). CONCLUSIONS: DCT was equivalent to Goldmann applanation tonometry in measurement of IOP in a small group of normal subjects. In contrast, SmartLens(® )(contact lens tonometry) gave IOP readings that were significantly higher compared with Goldmann applanation tonometer readings. Both devices, DCT and SmartLens(® )provide the measurement of OPA which could be helpful e.g. for the management of glaucoma

Systematic comparative validation of self-report measures of sedentary time against an objective measure of postural sitting (activPAL)

Background: Sedentary behaviour is a public health concern that requires surveillance and epidemiological research. For such large scale studies, self-report tools are a pragmatic measurement solution. A large number of self-report tools are currently in use, but few have been validated against an objective measure of sedentary time and there is no comparative information between tools to guide choice or to enable comparison between studies. The aim of this study was to provide a systematic comparison, generalisable to all tools, of the validity of self-report measures of sedentary time against a gold standard sedentary time objective monitor. Methods: Cross sectional data from three cohorts (N = 700) were used in this validation study. Eighteen self-report measures of sedentary time, based on the TAxonomy of Self-report SB Tools (TASST) framework, were compared against an objective measure of postural sitting (activPAL) to provide information, generalizable to all existing tools, on agreement and precision using Bland-Altman statistics, on criterion validity using Pearson correlation, and on data loss. Results: All self-report measures showed poor accuracy compared with the objective measure of sedentary time, with very wide limits of agreement and poor precision (random error > 2.5 h). Most tools under-reported total sedentary time and demonstrated low correlations with objective data. The type of assessment used by the tool, whether direct, proxy, or a composite measure, influenced the measurement characteristics. Proxy measures (TV time) and single item direct measures using a visual analogue scale to assess the proportion of the day spent sitting, showed the best combination of precision and data loss. The recall period (e.g. previous week) had little influence on measurement characteristics. Conclusion: Self-report measures of sedentary time result in large bias, poor precision and low correlation with an objective measure of sedentary time. Choice of tool depends on the research context, design and question. Choice can be guided by this systematic comparative validation and, in the case of population surveillance, it recommends to use a visual analog scale and a 7 day recall period. Comparison between studies and improving population estimates of average sedentary time, is possible with the comparative correction factors provided

Validity of Resting Energy Expenditure Predictive Equations before and after an Energy-Restricted Diet Intervention in Obese Women

Background We investigated the validity of REE predictive equations before and after 12-week energy-restricted diet intervention in Spanish obese (30 kg/m2>BMI<40 kg/m2) women. Methods We measured REE (indirect calorimetry), body weight, height, and fat mass (FM) and fat free mass (FFM, dual X-ray absorptiometry) in 86 obese Caucasian premenopausal women aged 36.7±7.2 y, before and after (n = 78 women) the intervention. We investigated the accuracy of ten REE predictive equations using weight, height, age, FFM and FM. Results At baseline, the most accurate equation was the Mifflin et al. (Am J Clin Nutr 1990; 51: 241–247) when using weight (bias:−0.2%, P = 0.982), 74% of accurate predictions. This level of accuracy was not reached after the diet intervention (24% accurate prediction). After the intervention, the lowest bias was found with the Owen et al. (Am J Clin Nutr 1986; 44: 1–19) equation when using weight (bias:−1.7%, P = 0.044), 81% accurate prediction, yet it provided 53% accurate predictions at baseline. Conclusions There is a wide variation in the accuracy of REE predictive equations before and after weight loss in non-morbid obese women. The results acquire especial relevance in the context of the challenging weight regain phenomenon for the overweight/obese population.The present study was supported by the University of the Basque Country (UPV 05/80), Social Foundation of the Caja Vital- Kutxa and by the Department of Health of the Government of the Basque Country (2008/111062), and by the Spanish Ministry of Science and Innovation (RYC-2010-05957)

Criteria for the use of omics-based predictors in clinical trials: Explanation and elaboration

High-throughput 'omics' technologies that generate molecular profiles for biospecimens have been extensively used in preclinical studies to reveal molecular subtypes and elucidate the biological mechanisms of disease, and in retrospective studies on clinical specimens to develop mathematical models to predict clinical endpoints. Nevertheless, the translation of these technologies into clinical tests that are useful for guiding management decisions for patients has been relatively slow. It can be difficult to determine when the body of evidence for an omics-based test is sufficiently comprehensive and reliable to support claims that it is ready for clinical use, or even that it is ready for definitive evaluation in a clinical trial in which it may be used to direct patient therapy. Reasons for this difficulty include the exploratory and retrospective nature of many of these studies, the complexity of these assays and their application to clinical specimens, and the many potential pitfalls inherent in the development of mathematical predictor models from the very high-dimensional data generated by these omics technologies. Here we present a checklist of criteria to consider when evaluating the body of evidence supporting the clinical use of a predictor to guide patient therapy. Included are issues pertaining to specimen and assay requirements, the soundness of the process for developing predictor models, expectations regarding clinical study design and conduct, and attention to regulatory, ethical, and legal issues. The proposed checklist should serve as a useful guide to investigators preparing proposals for studies involving the use of omics-based tests. The US National Cancer Institute plans to refer to these guidelines for review of proposals for studies involving omics tests, and it is hoped that other sponsors will adopt the checklist as well. © 2013 McShane et al.; licensee BioMed Central Ltd