Statistical Methods for Studies - Research Paper Example

METHODS Search strategy Key words and phrases such as heart rate inconsistency, exercise, athletes, activity, bradycardia, autonomic control among others were used as search terms to identify and establish the Ovid database and PubMed database. The references for articles used were sought out manually with the intent to gather additional researches that were not obtainable electronically. Criteria For the study, English language studies were the only ones used. The studies had to entail fit individuals who were more than eighteen years of age. An essential requirement was a not less than a month duration aerobic exercise program. Initially, an inclusion criterion for the study was to employ a control group, but owing to the small number of studies meeting the standards, the use of a control group as a criterion was retracted. Be it as it may, where a control group was included, a randomized group allocation was utilized as a decisive factor. It was imperative for all the studies to calculate the high frequency spectral power (HF). Conclusively, the studies that met the criteria mentioned above, alterations in RR interval or the heart rate were subsequently sought. Review process From the searches, eight studies were identified, which were prospective for inclusion in the analyses. Owing to the usual use of the search terms in combination, the searches generated numerous but immaterial studies. Using the inclusion criteria, the studies were evaluated and forty eight of which were chosen for further deliberation on. Studies that recorded high frequency spectral power (HF) were incorporated in the analysis only if they were generated from autoregressive modeling, from Fourier type transformation or noted as harmonic component from (CGSA) – the coarse graining spectral analysis. Primarily, when calculated in the supine position, autoregressive modeling and Fast Fourier transformation generate substantially comparable approximates of frequency spectral power (13). More often than not, lower values for frequency spectral power is noted when the coarse graining spectral analysis is weighed against other spectral measures (37). Important to note is that the reliability of spectral HRV techniques are comparable and under particular situations, the HRV resting measures are vastly can be replicated (31). In the analysis, it was necessary that all the frequency spectral power (HF) data be recorded in transformed or raw (ms2) units with ratios, normalized units and percentage of total power being omitted. Studies that did not offer assurance of accurate processing of RR interval data, were not included in the analysis. More distinctively, authors had to comply with the condition that they make clear statements validating that the data had gone through manual or automated screening for anomalous beats. The only data recordings included in the analysis were short term data recordings that were generated in the supine position. It was not important that the respiratory rates were managed while compiling the short term data. On usage of ambulatory measures, only the full twenty four hour data were integrated and the ambulatory recordings had to be specifically done during a day which the respondents did not exercise. It was mandatory that collection of data was done not more than twenty four hours after the last bout of exercise training for both methods used to gather data. Using the afore-mentioned criteria, thirty studies failed to meet it and were therefore eliminated. Of the eighteen remaining studies that were considered for inclusion in the analysis of change in HRV due to exercise, one study recorded but failed to present the frequency spectral power (HF) data, another study recorded but presented the HF data in inapt units while three more studies presented the HF data in a form that barred calculation of the standard deviation (d). As required, all the authors of the studies prepared accurate and clear statements guaranteeing they had made automated checks for anomalous beats, while the twenty four hour data were visually examined. In the Meta analysis, a total of thirteen studies and twenty trials were included, with data on change in RR interval from all the studies being compiled in a different Meta analysis with the exception of three studies. Owing to a substantially small sample sizes, two trials from one study were discarded while in two additional studies, RR interval data were not recorded in a form that allowed the calculation of the size of the effects. Statistical methods A calculation of the standardized effect size was done to assess the differences in frequency spectral power (HF) and RR. Using pre test and post test standard deviations of the experimental group, a calculation of the collective standard deviation was done. Preceding data imply that HRV data does not show regression toward the mean subsequent an exercise intervention, which permits the use of this standard deviation in the analysis (16). Since this method does not consider the link between two set of scores, it can faintly over estimate the standard difference. The main reason why the method was selected is because HRV is a dynamic calculation that often demonstrates some random deviation from test to retest, which has the impact of minimizing the estimates of the effect size. This attribute made the method beneficial for the analysis. In addition, it permitted incorporation of an extensive number of studies especially when the control group was not used. Using random effects model for frequency spectral power and RR interval, a calculation of the overall effect for the collective standardized variations were done, with all values being presented as means – 95% C.I to illustrate the confidence intervals and a value of P being used to illustrate the statistical significance. Tests for heterogeneity Through a calculation of the Q statistic for all data used in the analysis, the heterogeneity of the within group was examined. A calculation of the squared distance of every study used in the analysis from the pooled effect is made momentarily. Every value is evaluated, giving more weight to more accurate studies. Important to note is that the log values are used as basis for all calculations and the inverse variance method is used as a basis to calculate the standard error (SE). During the analysis, the Q/(k _ 1) statistic is determined where Q is the chi-squared value used to test for group heterogeneity and the k representing the total sum of studies in the meta analysis. The statistic has a unique aspect in which a value greater than one can be utilized to initiate further research even when the statistic is trivial. This is essential because the test is not exclusively reliant on the size of the sample. To analyze prospective sources of heterogeneity, sub group assessment of some probable moderator variables were carried out. Full documentation of the prospects to create false results from sub group assessment were indicated (17), which necessitates the need to assess only a small number of subgroup assessments. The subgroups were established a priori and were founded on causal mechanisms, statistical significance and the size of effects. Aligned with current proposals (17), all sub group assessments when suitable were physiologically warranted. Using the group value of the Q static, the group heterogeneity is determined, which is similar to the use of a one direction ANOVA. The analysis was conducted as per 434 Official Journal of the American College of Sports Medicinehttp://www.acsm-msse.org, using values from a static effects model and log values for all studies. Owing to the low disparity power of the Q statistic, the value of (P_0.1) was taken to demonstrate significant heterogeneity in between –group. Selection of moderator variables The prospective moderator variables established in this analysis were categorized as subject features, HRV methodological features or training intervention characteristics. The first subgroup assessment centered on subject features related to subject sex while trials encompassing mixed groups, male and females are included in the study. The known variations in HRV calculations among the sexes are the basis for the physiological justification for this subgroup (22). The second subgroup analysis related to subject age where the participants were classified into three groups namely the young with a mean age of 30 years, the middle aged with a mean age of 30 to 60 years and the old with a mean age of 65 years. The physiological justification for this analysis was established since the variations in HRV are apparent within these age groups (22). Generally, varied physiological calculations such as cardiopulmonary adjustment to exercises are impacted by age (5). Lastly, the subject feature utilized to identify the subgroups was the preceding training status. More often than not, subjects with high initial values in one physiological feature indicate less adjustment to training compared to subjects with low initial values. This trend is referred to as the law of initial values. Nevertheless, some data indicates an opposite course of effect for specific HRV features (15) while others indicate that high HRV levels may inhibit additional enhancement in very athletic individuals as a result of physiological elements including acetylcholine saturation of the SA node (11) & (14). The interval of intervention was the only analysis carried out on the training intervention subgroup. This subgroup encompassed a _12 week duration studies and _12 wk. The 12 week point of division was used since the 12wk was perceived as an upper limit to the short exercise interventions utilized. In regards to the point above the 12wk point, varied literatures and researches differed from 14wk to two yrs interval, which was vindicated by the existence of an array of intervention intervals in the studies. Numerous physiological variables indicate divergence in the size of adaptation reliant on the interval of the stimulus. It was perceived that studies with an eight week interval could not be evaluated fairly with studies with a one year interval. Further sub group assessment founded on the intensity of the exercise and the interval was not conducted owing to the homogeneity of variables among studies. For example, exercise intensity only varied between 60-80% of a given upper limit and interval of about 40-60 minutes. In addition, these calculations often differed among studies as a result of exercise progression. The final subgroup category was HRV methodology which included subgroup assessment between twenty four hour ECG recordings and the short term recordings. Read More