July 7, 2017

Measure of Association

Relative Risk
The relative risk is considered the risk among exposed subjects divided by the risk among unexposed subjects. If a risk ratio were equal to one, there is no association between exposure and disease. A risk ratio greater than none indicates that the exposure increases the incidence of the disease. However, if the risk ratio is less than one, the exposure provides protection against the disease or condition.

Odds Ratio
An odds ratio is the probability of an event occurring compared to the event not occurring in a particular group. If the odds ratio is greater than one, there is an increased likelihood of exposure among subjects who have the disease. An odds ratio less than one would indicate a decreased likelihood of exposure among diseased individuals.

July 3, 2017

Observational Studies

The purpose of cohort studies is to describe the incidence and natural history of a disease or condition. Incidence is the rate of new cases of a condition that develop over a period of time.

Advantages of cohort studies
  • Gather data regarding sequence of events
  • Can assess causality
  • Examine multiple outcomes for a given exposure
  • Good for investigating rare exposures
  • Can calculate rates of disease in exposed and unexposed individuals over time (e.g., incidence, relative risk)

Disadvantages of cohort studies

  • Large number of subjects are required to study rare exposures
  • Susceptible to selection bias

There are two types of cohort studies; prospective and retrospective. Prospective cohort studies monitor a group of individuals, who may or may not have been exposed to a disease, over a period of time. A number of relevant variables are monitored over the same period of time in order to determine if there is a relationship between exposure and disease that has already been collected through previous studies or through medical charts.

Disadvantages of prospective cohort studies
  • May be expensive to conduct
  • May require long durations for follow-up
  • Maintaining follow-up may be difficult
  • Susceptible to loss to follow-up or withdrawals

Disadvantages of retrospective cohort studies
  • Susceptible to recall bias or information bias
  • Less control over variables

Cross-sectional studies are used to determine the prevalence of a condition. Prevalence is the number of cases in the population at a given time. Therefore, a cross-sectional study can be considered a one-time snapshot of the sample or population.

  • Quick
  • Low cost
  • No follow-up required
  • Provides a rationale for conducting cohort studies

  • Unable to determine cause and effect
  • Rare conditions cannot be studied

Case-control studies are designed to investigate rare conditions. Subjects are identified by outcome status at the beginning of the study. They are then divided into two groups, cases and controls. Data with respect to risk factors are collected retrospectively.

  • Good for examining rare outcomes
  • Quick to conduct
  • Inexpensive
  • Existing records can be used
  • Few subjects
  • Multiple exposures or risk factors can be examined

  • Recall bias
  • Information bias
  • Difficult to validate information
  • Cannot account for confounding variables
  • Selection of appropriate comparison group may be difficult
  • Rates of disease in exposed and unexposed individuals cannot be determined

  1. Legangie P. Application and interpretation of simple odds ratios in physical therapy-related research. J Orthop Sports Phys There 2001;31:496-503.
  2. Mann C. Observational research methods. Research design II: cohort, cross sectional, and case-control studies. Emerg Med J 2003;20:54-60.
  3. Song J, Chung K. Observational studies: Cohort and case-control studies. Plast Reconstr Surg 2010;126:2234-2242.

January 6, 2017

Patellofemoral pain syndrome (PFPS)

Patellofemoral pain syndrome is a term to describe a group of pathologies associated with anterior knee pain. 

Decreased quadriceps range of motion (ROM) (3)
Decreased gastrocnemius ROM (3)
Dynamic valgus collapse (2)
Decrease quadriceps strength (1)
Decrease hamstring strength (1)

1. Boling M, Padua D, Marshall S, Guskiewicz K, Pyne S, Beutler A. A prospective investigation of biomechanical risk factors for patellofemoral pain syndrome: The joint undertaking to monitor and prevent ACL injury (JUMP-ACL) cohort. Am J Sports Med 2009;37(11):2108-2116.

2. Holden S, Boreham C, Doherty C, Delahunt E. Two-dimensional knee valgus displacement as a predictor of patellofemoral pain in adolescent females. Scand J Med Sci 2015;Epub ahead of print.

3. Witvrouw E, Lysens R, Bellemans J, Cambier D, Vanderstraeten G. Intrinsic risk factors for the development of anterior knee pain in an athletic population. A two year prospective study. Am J Sports Med 2000;28(4):480-489.

Search Date
November 14, 2016

Search String
(patellofemoral syndrome OR patellofemoral pain OR pfps) AND (risk factor*)

Search Results
CINAHL Complete = 93
Medline with Full-text = 110
SPORTDiscus = 66

Outcome measures

An outcome measure is the method used to assess the effect of an intervention. The purpose of an outcome measure is to:

1. Discriminate among subjects at one point in time
2. Predict a subsequent event or outcome
3. Assess change over time.

Standardized outcome measures
1. Have explicit instructions for administering, scoring, and interpreting results
2. They are supported to the extent that information concerning their measurement properties has been estimated, reported, and defended in the peer-reviewed literature.
3. The outcome measure is valid, reliable, sensitive and specific. 

Measures of validity
1. The outcome measure assess what it is intended to measure (face validity)
2. The outcome measure is appropriate for the population of interest (content validity)
3. The outcome measure provides results that are consistent with the gold standard (criterion validity)

Measures of reliability
1. Multiple assessments of one individual will provide consistent results (test-retest/absolute reliability)
2. The outcome measure can determine the degree to which the condition exists (relative reliability)

The ability of a test to reliability detect the presence of a condition. Therefore, if the test is negative, the subject will not have the condition (true positives / total positive results; SNOUT rules out).

The ability of a test to reliably detect the absence of a condition. Therefore, if the test is positive, the subject will have the condition (true negatives / total negative results; SPIN rules in)

Study methodology


Internal validity
Internal validity reflects the quality of the study design, implementation and data analysis in order to minimize the level of bias and determine a true 'cause and effect'

External validity
External validity describes the circumstances under which the results of the research can be generalized


Sample size
This is the number of subjects included in the study from a given population. Larger sample sizes tend to be more representative of the population

Participants are randomly assigned to one of two or more interventions. Randomization minimizes the risk of confounding variables, reduces the risk of bias, and allows for examination of direct relationships. A major limitation of using an RCT is that it is impossible to obtain a true random sample of the population (2). This can make it difficult to generalize the results.

Concealed allocation
The person responsible for determining whether a subject was eligible for inclusion in the trial should be unaware of the group the subject is allocated (6). If allocation is not concealed, the decision about whether or not to include a person in a trial could be influenced by knowledge of whether the subject was to receive treatment or not. This could produce systematic biases in an otherwise random allocation (6).

This is a method to prevent study participants, as well as those collecting and analyzing data from knowing who is in the intervention group and who is in the control group (1). When subjects are blinded, it is less likely that the results of the treatment are due to a placebo effect (6). Blinding assessors prevents their personal bias from affecting the results (6).

Baseline comparability
Baseline comparability involves a comparison of the baseline values of the groups (intervention and control). There should be statistically significant difference between groups. An appropriate randomization should ensure that groups are similar at baseline. This may provide an indication of potential bias arising by chance with random allocation (6). A significant difference between groups may indicate an issue with randomization procedures (6). 

The intervention should be described in enough detail for reproducibility. An inadequate description decreases internal validity as it is unclear of the exact mechanism that led to the change in outcomes.

Adequate follow-up
The number of subjects who completed the trial to provide follow-up data for statistical analysis must be sufficient. The PEDro group states that data collected from a minimum of 85% of subjects increases internal validity (6). It is important that measurements of outcomes are made on all subjects who are randomized to groups. Subjects who are not followed up may differ systematically from those who are, and this potentially introduces bias. The magnitude of the potential bias increases with the proportion of subjects not followed up (6).

Intention-to-treat analysis
This is a strategy that ensures that all subjects allocated to either the treatment or control groups are analyzed together as representing that treatment arm, whether or not they received the prescribed treatment or completed the study (1). When patients are excluded from the analysis, the main rationale for randomization is defeated, leading to potential bias (6).

Between-group comparisons
This comparison is a statistical comparison of one group with another. It is performed to determine if the difference between groups is greater than can plausibly be attributed to chance (6).

Point estimates (effect size) and variability
A point estimate or effect size is a value that represents the most likely estimate of the true population (4). Some examples include the mean difference, regression coefficient, Cohen's d, correlation coefficient.

It is important to consider the variability of the effect size (point estimate). A few examples of variability include: the standard deviation, standard error of the mean, and a range of value. The standard deviation is an estimate of the degree of scatter (variability) of individual sample data points about the mean of a sample (3).

Study limitations
A description of the limitations of the study design and methodology allows for transparency as it should describe potential biases. This includes an explanation of possible errors in the internal and external validity.


1. Akobeng A. Understanding randomized controlled trials. Arch Dis Child 2005;90:840-844.

2. Carter R, Lubinsky J, Domholdt E. Rehabiliation Research. 4th ed. 2010. Elsvier; St. Louis, Missouri.

3. Gaddis G, Gaddis M. Introduction to biostatistics: part 3, sensitivity, specificity, predictive value, and hypothesis testing. Ann Emerg Med 1990;19:145-151.

4. Nakagawa S, Cuthill I. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol Rev 2007;82(4):591-605.

5. Pannucci C, Wilkins E. Identifying and avoiding bias in research. Plast Reconstr Surg 2010;126(2):619-625.

6. Physiotherapy Evidence Database. PEDro Scale (1999). http://www.pedro.org.au/english/downloads/pedro-scale/. Accessed on January 27, 2015.

January 4, 2017

Effect size

The effect size is a statistic which estimates the magnitude of an effect (e.g. mean difference, regression coefficient, Cohen's d, correlation coefficient). It can be used as a relevant interpretation of an estimated magnitude (weak, moderate, or strong effect). An effect size can be displayed as both an unstandardized and a standardized value.

If the original units of measurements are meaningful, the presentation of unstandardized effect statistics is preferable over that of standardized effect statistics. Meta-analysis benefit from knowing the original units, as difference in measured quantities regarding the same subject could result in differences in standardized effect size estimations, which in turn bias the outcome of a meta-analysis.

Mean difference (MD)

Standardized effect statistics are always calculable if sample size and standard deviation are given along with unstandardized effect statistics.

Cohen's d

1. Nakagawa S, Cuthill I. Effect size, confidence interval statistical significance: a practical guide for biologists. Biol Rev 2007;82:591-605.

Confidence intervals

A confidence interval (CI) is usually interpreted as the range of values that encompass the actual population or 'true' value, with a given probability. The width of the interval indicates the precision of the estimate (effect size). The wider the interval, the less precise the estimated effected size. A study with a small sample size will have greater random error, leading to a wider interval. 

Confidence interval equation
CI = effect size +/- (appropriate multiplier x standard error of the difference)


Appropriate multiplier = (alpha level / tails) x critical value

Alpha level
The alpha level is the probability, the researcher is willing to accept, that the findings are a result of sampling error. It is determined by the level of confidence the researcher decides to use, and is typically set to either 90%, 95% or 99%. The greater the confidence level, the wider the interval. Sample size can also affect the width of the interval, with smaller sample sizes leading to wider intervals. A confidence of 95% will provide an alpha level of 0.05.

The number of tails depends on the question the researcher is asking. Two tails are used if the researcher would like to know if the results of an intervention differ from a control or alternate group. It is common to use 2 tails in intervention based research.

Critical value

The z-distribution is used when the variance of the population is known. In some cases, an author may choose to use the z-distribution if the sample size is greater than 30.

The t-distribution should be used when the true variance is not known and has been estimated from the sample. With larger sample sizes, a t-distribution value will become similar to that of a z-distribution.

Link: t-distribution table


Standard deviation (SD) versus standard error of the mean (SEM)
The SD is always greater than the SEM, therefore a CI expressed using the SD would be wider than a CI expressed using the SEM. The inappropriate use of SEM to describe sample data variability may be presented by authors in an attempt to imply that a significant difference exists between groups, when in fact no difference exists. Authors who present data as the mean +/- SEM instead of the mean +/- SD may be trying to actively impair the reader's ability to accurately identify the variability in the study data.

Pooled standard deviation
The pooled SD is the weighted average of each group's standard deviation. It should be used when comparing the mean difference between two different (independent) groups.

1. Gaddis G, Gaddis M. Introduction to biostatistics: part 2, descriptive statistics. Ann Emerg Med 1990;19:309-315.

Measurement variability

The range is the interval between the lowest and highest values within a data group.

Variance of a sample (s^2)
The variance represents the deviation from the mean, expressed as the square of the units used.

Standard deviation of a sample (s, SD)
The standard deviation provides an estimate of the degree of scatter of individual sample data points about the sample mean.

Standard error of the mean (SEM)
The SEM is an abstract concept. It is simply a quantification of the variability of the sample means. The SEM is properly used to estimate the precision or reliability of a sample, as it relates to the population from which the sample was drawn. The SEM does not provide an estimate of the scatter of sample data about the sample mean and should not be used as such.

1. Gaddis G, Gaddis M. Introduction to biostatistics: part 2, descriptive statistics. Ann Emerg Med 1990;19:309-315.

Measures of central tendency


Mean of a sample
The mean is the arithmetic average of data. It is affected by outliers, which are extreme values of data distribution.

The median is the "mid-most" value of a data distribution. It is the value above which or below which or below which have of the data points lie. The median is the 50th percentile of a distribution.

The mode is the most commonly obtained value or values on a data scale. It is also the highest point of a peak on a frequency distribution.

1. Gaddis G, Gaddis M. Introduction to biostatistics: part 2, descriptive statistics. Ann Emerg Med 1990;19:309-315.

February 10, 2016

Swimming-Related Injuries: A literature review with injury risk screening

Michael Rosenblat


There are a number of assessment screening tools that are used to assess the risk of developing an injury in competitive and recreational sports. The Functional Movement Screen (FMS) was one of the first tools created for this purpose. However, a recent systematic review with meta-analysis has shown that there is little correlation with FMS scores and injury risk (4). This may be due to the fact that the FMS was not created to assess sport specific risk factors. It was created as a general tool to assess common movement patterns.

In order to develop a valid outcome measure that can be used to assess the risk of developing swimming-related injuries, it is necessary to determine which injuries have the highest prevalence and incidence in swimming. It is also beneficial to have a greater understanding of how certain characteristics, including stroke specialty, mechanism of injury, etc. are associated with the described swimming-related injuries.

Once there is a clear understanding of the common swimming-related injuries, it is possible to determine the risk factors that are directly related to each injury. The appropriate variables/risk factors were collected and incorporated into a structured orthopaedic assessment tool.

The tools provided below have not been assessed for validity or reliability. Additional research would be beneficial to determine the psychometric properties of the assessment tools.