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Report on Myopia and Physical Activity in Adolescence

   

Added on  2020-05-04

16 Pages2630 Words59 Views
Myopia and Physical ActivityNameCourse NumberDateFaculty Name

Myopia1QUESTION 1 – APPRAISING MYOPIAAND LATER PHYSICAL ACTIVITYIN ADOLESCENCE1.1SAMPLESIZEThe sample size determination has been comprehensively explained in the publication;stating the criteria used in including the pregnant women. It has been clearly mentioned thatwomen who were expected to deliver between 1st April 1991 and 31 December 1992 in thecounty of Avon. This time length explains how 7159 children were selected into the study. Basedon children consenting for participating and cases of incomplete data, it has been indicated thatthe sample size reduced to 4880. 1.2STATISTICAL ANALYSISMultivariable regression analysis was conducted to predict physical activity based onwhether a child was myopic or not. Two model were developed; one having minimal control ofconfounding effect and the second with maximum control. Therefore, regression was used tocontrol for confounding factors in the analysis. After the data was downloaded from theActigraphs, only valid data was used for the analysis. Therefore, the researchers decided to dropcases of incomplete and invalid data from the study. Cases of loss of following were notaddressed. However, the sample size was large enough to accommodate issues of loss of follow-up, non-response, incomplete and missing data. The modelling process was conducted on basisof the ‘best eye’ and ‘worst eye’ recorded for the purpose of result and significance comparison. 1.3PARTICIPANTIt has documented with adequate reasons as to how the sample numbers transformedfrom one stage to another, hence defining how the final size was achieved.

1.4DESCRIPTIVE & OUTCOMEDATARelevant descriptive statistics were provided to the participant. Median statistics for theskewed continuous variables were reported and average variables for a variable withapproximate normal distributions. Other potential confounders and exposures such as age andethnicity were recorded, described and controlled for in the analysis. The number and percentageof myopes were reported in each on the two groups: best and worst eye. 1.5RESULTSMinimal and maximal results of the model were reported in each myopic categories at95% confidence level. Their results were used to determine the level of effects contributed by thepotential confounders. In the report, it was clearly defined which variables were controlled for inthe minimal and maximal models. Associations were developed between myopia and physicalactivities for the 12-year-olds. It was also reported categorically on which groups performedbetter based on the measured indicators of physical activity. 95% confidence level p-values ofthe models’ coefficients were reported on the results indicating the levels of significance for eachpredictor. 1.6OTHERANALYSISNo reports of other analysis conducted on the dataset for the study were documented.More data exploratory analysis methods could have been used to identify further distributions.

2QUESTION 2 – SEDENTARYHOURSPREDICTIONBYMYOPIA2.1DESCRIPTIVE STATISTICSTable 1: Descriptive statistics table of age and sed variablesVariableMeanS.DMedianMinMaxSkew KurtosisAge26.951.712723330.250.05sed11.182.5510.86.321.70.830.96The average age of the individuals included in the study was 26.95 with a standarddeviation of 1.71. The youngest respondent was 23 years and the oldest 33 years old. Theaverage and median values of sedentary time spent per week were 11.18 and 10.8 hoursrespectively. Table 2: Proportion of myopic and non-myopicMyopiaNormalCount227122Percent65.04%34.96%65.04% of the sample individuals were myopic and 34.96% had normal eyesight. Figure 1: Myopia Pie Chart

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