1. Introduction

Health-related quality of life (HRQOL) is a health outcome that has seen a growing interest in physical activity research. The United States Department of Health and Human Services (USDHHS), in its publication entitled Healthy People 2020, includes the enhancement of quality of life as a major public health service goal ^[1]. HRQOL is a broad concept that includes both subjective and objective indicators of a people’s lives that affect their physical and/or mental health status ^[2]. A more comprehensive description of HRQOL includes several dimensions including but not limited to physical functioning, psychological well-being, social functioning, role functioning, and health perceptions ^[3].

HRQOL has become a standard outcome measure in both intervention and observational studies ^[2]. A search on Pubmed.gov showed only 1,410 publication hits from the years 1980 to 1999 using the key word term “health-related quality of life.” The same search resulted in 15,180 hits from the years 2000 to 2011. HRQOL is also being included in research studies alongside the more conventional and objective measures of health status ^[4]. Given the overwhelming interest in HRQOL as an outcome measure in physical activity research, there is a strong need for a better understanding of the measurement properties of HRQOL assessments commonly used in physical activity research.

The Short-Form Health Survey (SF-36) is the most widely used HRQOL instruments in physical activity research. The SF-36 was developed from the Medical Outcomes Study (MOS) conducted by RAND ^[5]. The SF-36 is a multi-dimensional scale consisting of 36 items, 8 health-related dimensions, and two domains. The dimensions include: 1) vitality, 2) physical functioning, 3) bodily pain, 4) general health, 5) physical role functioning, 6) emotional role functioning, 7) social role functioning, and 8) mental health. The physical domain consists of the physical functioning, bodily pain, general health, and physical role functioning dimensions and the mental domain consists of the vitality, emotional role functioning, social role functioning, and mental health dimensions ^[6]. The SF-36 is intended to measure HRQOL in adults and is easily self-administered in physical activity research. The SF-12 and SF-8 are both shorter versions of the original form with 12 and 8 items, respectively. Both shorter versions maintain the measurement of the 8 dimensions as well as the two domain-specific summary scores ^[7].

A Reliability index can be thought of as a percentage of variance in a set of scores that is true or from non-error factors ^[8]. Reliability also refers to the consistency or stability of test scores. This can be consistency of scores over time, consistency of scores across items (internal consistency) or consistency of scores across raters ^[9]. Of specific interest to this study are the internal consistency and test-retest reliability coefficients. Internal consistency refers to the extent to which items in an assessment tool are inter-correlated ^[10]. If such an inter-correlation exists, the items of the scale are said to measure a unidimensional construct. Test-retest reliability measures the stability of measurements over repeated trials ^[8]. Since reliability is not a property of an assessment tool itself but rather a property of the assessment scores, it should be common practice for researchers to report reliability of their scores at hand ^[11]. Despite the inability to infer an assessment’s reliability from a single study to all future studies, it is still possible to strengthen the reliability generalization using meta-analysis ^[12].

To date, there are no studies which assess the reliability generalization of the SF-36 HRQOL assessment in physical activity research using meta-analysis. The purpose of this study was to perform a systematic review and meta-analysis of reliability coefficients on the SF-36 HRQOL assessment applied in physical activity research so as to assess the generalizability of HRQOL measurement reliability. A secondary purpose of the study was to examine potential moderators which may account for extra variance associated with the HRQOL measurements, such as age, gender, and even HRQOL assessment tool. This approach will provide valuable evidence as to the strengths and weaknesses of HRQOL scales in terms of measurement reliability.

2. Materials and Methods

Using PubMed.gov, a systematic search was conducted to identify all physical activity studies using the SF-36 assessment or one of its variants (i.e., SF-12 or SF-8). The following search terms were used: ("physical activity" OR exercise) AND ("short-form 36" OR "sf-36" OR "MOS 36" OR "rand 36"). The same search strategy was used for identifying studies using the SF-12 and SF-8 forms. After all potential studies were identified; inspection of titles and abstracts ensued to identify exclusion criteria.

Studies were excluded if 1) they were published in a non-English language, 2) they were non-research based, 3) they were from a non-peer-reviewed journal, 4) HRQOL was not measured in the study using the SF-36 or one of its variants, 5) the study was a validation study where reliability measures were repeatedly computed due to changes made to the scale, 6) the HRQOL assessment tool was modified from its original form prior to its administration, 7) the study was not physical activity oriented, or 8) the study population involved youth under the age of 18 years.

By searching the remaining articles surviving exclusion criteria, reported measures of reliability were retrieved. Reliability measures were defined as Cronbach’s alpha, Kuder-Richardson coefficient, Split-half reliability coefficient, or test-retest coefficients. When possible, reliability measures were extracted for each of the SF-36 domains (physical and mental). If a situation occurred where an article published reliability in the form of a range (i.e., .78 to .88), the midpoint of that range was used as the effect size. If a study reported a lower-bound value (i.e., > .80), the lower-bound was used as the effect size. Precaution was taken to not include redundant coefficients from multiple publications on the same study population, by matching author names and study characteristics.

Data were extracted from each identified study using the following strategy. Reliability estimates along with the number of items for each assessment tool were recorded. If reliability estimates were reported separately for different populations (i.e., gender) then each population was considered a separate sample. Common demographic variables were included such as gender (male, female, both) and age (mean). Sample size was recorded for each study for its impact on reliability estimates. Other possible moderator data collected was disease status (diseased, non-diseased), reliability type (internal consistency, test-retest) and study design (randomized controlled trial [RCT], other). Also, the SF-36 form used for the HRQOL measure was recorded (SF-36, SF-12, SF-8).

A separate meta-analysis was performed for each HRQOL domain (physical and mental). Since internal consistency reliability coefficients represent a proportion of variance not accounted for by error, the square root of each coefficient was first taken. Fisher’s r to Z transformation was then performed as recommended for correlation coefficients ^[13]. Meta-analytic mean effect sizes and 95% confidence intervals were computed for each reliability study using a random effects model. A mean effect size of .70 or greater was considered acceptable evidence of reliability ^[14]. A moderator analysis was performed to account for the extra variance by examining different study characteristics. The Q test of homogeneity was used to support the moderator analysis in determining if the variance in reliability coefficients was significantly different from zero. The Comprehensive Meta-Analysis Version 2.0 software was used for all meta-analyses ^[15].

3. Results

A total of 1,358 articles were retrieved using the search strategy. After reviewing titles and/or abstracts, 1,080 were eliminated using exclusion criteria. After full review of the remaining 278 articles, 20 physical domain and 21 mental domain reliability coefficients were retrieved. After contacting study authors from the remaining articles, an additional 24 and 22 coefficients were retrieved for the physical and mental scales, respectively. Table 1 contains 87 effect sizes used in this study, 44 for the physical health domain and 43 for the mental health domain [16-62]^[16].

Figure 1 shows the effect sizes and 95% confidence intervals for the physical health domain across each study. The weighted mean effect size from the physical health domain was computed using a random effects model. The effect size was strong and significantly different from zero, ES = .90 (95% CI: .88, .92), p < .001. Therefore, the physical health scales of the SF-36 assessment are reliable across a wide variety of physical activity studies. The test of homogeneity was significant, Q = 2057.21, df = 43, p < .001, I2 = 97.91. This indicates that some effect sizes may come from different populations and a moderator analysis should be performed. Table 2 shows results of the moderator analysis for the physical health studies. Results showed that studies of various gender (female or both sexes) did not account for significant variance in effect sizes, Q = 1.77, df = 1, p = .184. Studies using different designs (RCT or other types) did not account for significant variance in effect sizes, Q = 0.02, df = 1, p = .884. Also, studies using participants of different health status (diseased or non-diseased) did not account for significant variance in effect sizes, Q = 0.77, df = 1, p = .379. However, studies using different forms of the SF-36 (SF-36, SF-12 or SF-8) did account for significant variance in effect sizes, Q = 12.82, df = 2, p = .002. As well, studies publishing different reliability coefficients (alpha or retest) accounted for significant variance in effect sizes, Q = 8.00, df = 1, p = .005. Finally, mean age of study participants was a significant predictor (b = -.003) of effect size, Q = 9.59, df = 1, p = .002. Figure 3 displays the linear relationship between age and the transformed effect sizes.

Table 1. Characteristics of the physical activity and SF-36 studies

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Table 2. Effect size by moderator for the SF-36 physical health studies

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Figure 1. Forest plot of physical domain reliability effect sizes and 95% confidence intervals

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Figure 2. Forest plot of mental domain reliability effect sizes and 95% confidence intervals

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Figure 3. Weighted least squares regression of physical domain effect size regretted on age

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Figure 2 shows the effect sizes and 95% confidence intervals for the mental health domain across each study. The weighted mean effect size from the random effects model was strong and significantly different from zero, ES = .90 (95% CI: .89, .91), p < .001. Therefore, the mental health scales of the SF-36 assessment are also reliable across a various physical activity studies. The test of homogeneity was also significant for the mental health domain, Q = 529.90, df = 42, p < .001, I2 = 92.07. This also shows that a moderator analysis should be performed. Table 3 shows results of the moderator analysis for the mental health studies. Results showed that studies of various gender (female or both sexes) did not account for significant variance in effect sizes, Q = 0.03, df = 1, p = .864. Studies using different designs (RCT or other types) did not account for significant variance in effect sizes, Q = 1.03, df = 1, p = .310. Also, studies using participants of different health status (diseased or non-diseased) did not account for significant variance in effect sizes, Q = 0.34, df = 1, p = .557. However, studies using different forms of the SF-36 (SF-36, SF-12 or SF-8) did account for significant variance in effect sizes, Q = 46.89, df = 2, p < .001. As well, studies publishing different reliability coefficients (alpha or retest) accounted for significant variance in effect sizes, Q = 77.39, df = 1, p < .001. Finally, mean age of study participants was also a significant predictor (b = -.006) of effect size, Q = 58.04, df = 1, p < .001. Figure 4 displays the relationship between age and the transformed effect sizes.

Table 3. Effect size by moderator for the SF-36 mental health studies

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Figure 4. Weighted least squares regression of mental domain effect size regressed on age

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4. Discussion

The primary purpose of this study was to perform a systematic review and meta-analysis of reliability coefficients from the physical and mental health scales of the SF-36 HRQOL assessment applied to physical activity research. The results indicated that both SF-36 HRQOL domains are highly reliable in physical activity research. The secondary purpose of this study was to examine potential moderators which may account for extra variance associated with the HRQOL measurements. Results of the moderator analysis showed that the different forms, the different types of reliability, and mean age significantly contributed to effect size variability.

The SF-36 was more reliable when used in its compact SF-8 form compared to its SF-12 or SF-36 form. This result is surprising since, generally, scales longer in length provide greater reliability coefficients ^[8]. It is suggested that future research investigate the reliability comparison between the SF-8 and SF-12/SF-36 forms in physical activity research. The implications for finding the shorter form more reliable than the two longer forms are great when many physical activity studies are already burdened with many stages of intervention procedures. Caution should be taken; however, when interpreting the moderator analysis by form, since only one study was included using the SF-8 form.

More reliable scales were also seen when internal consistency reliability was the coefficient as compared to a test-retest coefficient. A lower reliability coefficient for test-retest situations compared to internal reliability seems appropriate since measures of stability are heavily affected by participant’s carry-over effects such as memory, mood, or actual changes in the traits as well as length of time between administrations ^[8]. Finally, study mean age was indirectly related to reliability for both scales, with the relationship stronger for the mental scales than for the physical scales. This observation could be explained by the fact that older research participants have more problems interpreting the SF-36 items as well as more problems responding and completing all items of the assessment ^[63]. The reasoning behind the stronger age effect in the mental health scales; however, is not as clear. It is possible that perceptions of mental health is not as clear to older individuals as is their perceptions of physical health.

Results of the moderator analyses showed that studies with females only did not have significantly different reliability of SF-36 scales from studies using both males and females. Results showed that studies utilizing RCTs did not have significantly different reliability from studies using other designs. Also, studies using the SF-36 to assess subjects with chronic disease did not have significantly different reliability from studies using non-diseased subjects. The non-significant moderators provide evidence of the robustness of the SF-36 reliability across gender, design, and disease.

This study has some limitations. First, the results of this study may have been affected by publication bias. The fact that the reliability coefficients were published in peer-reviewed journals makes it possible that studies reporting lower reliabilities for the SF-36 were not accepted by reviewers and therefore biased the results toward higher reliability estimates. It is also possible that researchers using the SF-36 in physical activity research, who computed low reliability measures, removed the SF-36 as an outcome variable from their study. Another limitation in this study is the fact that there were no male only studies used in the moderator analysis. Therefore, we are uncertain whether the reliability of the SF-36 is different for males compared to females or different from both male and female studies. The reason reliability coefficients from male only studies were not analyzed in this study was not because there were no physical activity articles using the SF-36 assessment on males only; but rather, there were no studies of this kind reporting the reliability. This bias should be recognized and future research is needed to assess the specific reliability generalization to the male population in physical activity research.

A strength of this study is that it was limited to the collection of reliability coefficients coming from publications on physical activity research. This type of focus allows for a more precise generalization of reliability. To know that the SF-36 assessment was on average reliable across a broad area of research topics would indeed be valuable; it is perhaps more valuable for the physical activity researchers to know that it performs reliably across a large span of physical activity research areas. Another strength of this study was its ability to separately analyze reliability in the physical and mental health domains. Many physical activity researchers use both physical and mental health components in their research ^[17], while others use only the physical component ^[25] or the mental component ^[36]. From a measurement perspective, it is more valuable to research the reliability of a set of scores in the way in which they are used in research. Finding that the SF-36 on average produces scores that are reliable for both domains in physical activity research provides more psychometric information than if reliability was only generalized to the SF-36 as a whole.

5. Conclusion

In conclusion, the evidence from this study overwhelmingly supports the use of the SF-36 HRQOL assessment in physical activity research. Both physical and mental health domains maintain very strong reliability across studies of different gender, different research design, and different diseases states. The SF-8 may provide slightly greater reliability compared to its longer counterparts. Measures of internal consistency provide greater reliability coefficients as compared to measures of stability. Finally, reliability may slightly decrease among the aging population.

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