HLTH 511 Week 8 Research Project: Final Submission Example

Active Adults Rate Their Health Higher: Final Report of a Survey Analysis of Aerobic Activity and Self-Rated Health

Abstract

Purpose: To learn whether adults who reach the federal aerobic activity target describe their own health more favorably. Methods: Cross-sectional analysis of a de-identified statewide survey extract of 1,200 adults, using a chi-square test and a Welch t-test in SPSS. Results: Adults meeting the guideline were about two and a half times as likely, in odds terms, to call their health good or better (odds ratio 2.48), and adults in better health averaged 93.7 more weekly activity minutes (d = 0.60). Conclusion: Active adults rated their health more favorably, supporting local promotion of the guideline while leaving the direction of the relationship unresolved.

Introduction

Physical activity lowers the risk of chronic disease and early death, yet most Americans fall short of the federal recommendations, which call for 150 or more minutes of moderate aerobic activity a week plus muscle strengthening (Piercy et al., 2018). National examination data put adherence to the aerobic part of the guideline at 65.2% in 2015-2016, unchanged over a decade (Du et al., 2019). Self-rated health, a single five-point question, is a practical outcome for local agencies because it predicts later mortality (Idler & Benyamini, 1997). This project asked two questions: whether guideline adherence is associated with good or better self-rated health, and whether activity minutes differ by self-rated health. For each, the null hypothesis stated no association or no difference. Answering these questions with data a state already collects shows how routine surveillance can inform program planning without the cost of a new study, and it gives local planners evidence drawn from their own residents rather than from national averages that may not fit their communities.

Methods

The study analyzed existing survey responses at a single time point (Jacobsen, 2017). The extract contained 1,200 adults with complete activity and health data and no identifying information. Adherence was coded from reported weekly moderate-equivalent minutes, counting vigorous minutes double, with 150 or more minutes defined as meeting the guideline. Self-rated health was split into good, very good or excellent versus fair or poor. SPSS was used for descriptive statistics, a chi-square test of independence (reported with an odds ratio and Cramér's V), and an independent samples t-test with Levene's check and Cohen's d, at alpha .05, and the skewed minutes variable was rechecked with the rank-based Mann-Whitney U procedure. Human subjects training was completed before analysis, and because the data were de-identified and analyzed only in aggregate, the project posed minimal risk to respondents. Age group, sex and education were summarized to describe the sample but were not entered as controls, since the course project calls for bivariate tests; the discussion returns to this choice.

Results

Just over half of the sample (52.0%) met the guideline, and 79.0% reported good or better health. Women made up 54.0% of respondents, and about a quarter were 65 or older.

Guideline status and self-rated health were significantly related (Cramér's V = .18; χ² with 1 df = 39.03; p below .001), and the odds of good or better health were 2.48 times higher among guideline-meeters, with an interval running from 1.85 to 3.31. Mean weekly activity was 212.4 minutes (SD 158.6) for adults in good or better health and 118.7 minutes (SD 141.2) for those in fair or poor health. Levene's test pointed to unequal variances, so the Welch statistic is given: the 93.7-minute gap (interval 73.5 to 113.9 minutes) was significant, t = 9.12 on 434.6 df, p below .001, and Cohen's d was 0.60. The nonparametric check agreed. Both null hypotheses were rejected. Put plainly, roughly one in seven guideline-meeters rated their health fair or poor, against more than one in four of those below the guideline.

Discussion

Adults who met the guideline were notably more likely to describe their health positively, and the standardized difference in activity between health groups was of medium size by the benchmarks in Cohen (1992). These results fit a broad body of evidence linking activity to health. In a national accelerometer study, adults taking 8,000 steps a day had roughly half the all-cause mortality hazard of those taking 4,000 (Saint-Maurice et al., 2020), suggesting that the better self-ratings observed here may reflect real differences in health. For a local health department, the practical message is simple: residents who move more also feel better, and that is a message people tend to hear. The adherence rate in this extract was lower than the national figure reported by Du et al. (2019), a gap that could come from survey wording about activity as much as from what people actually do. The size of the association is also worth noting: a Cramér's V of .18 is modest, which means guideline status explains only part of why people rate their health as they do. Income, chronic disease, mental health and social support all shape self-rated health, and activity is one contributor among several rather than the whole story.

Several limitations temper these conclusions. Because activity and health were measured together, the association may run in both directions; people with chronic illness or pain are likely to be less active and to rate their health lower, so some of the difference may reflect health limiting activity rather than activity improving health. Self-reported activity is often overstated, which could shrink or distort the true relationship. The extract lacked survey weights, so the percentages describe these respondents rather than the state's adult population, and the bivariate tests did not adjust for age, sex or education, which are related to both variables. Older adults, for example, are both less active and more likely to report fair or poor health, so part of the observed association may reflect age differences. Finally, dichotomizing both variables simplified the analysis but discarded information about degrees of activity and health.

Implications and Future Research

Local programs could use self-rated health as a simple, meaningful outcome when evaluating efforts to increase activity, and could emphasize how activity makes people feel, not only its long-term benefits. Future analyses should apply survey weights, use logistic regression to adjust for demographic factors and, ideally, use longitudinal data to see whether becoming more active is followed by better self-rated health. Including device-measured activity in a subsample would test how much self-report bias affects the results, and examining the association within age groups would show whether it holds for older adults, who stand to gain the most from staying active.

Revisions Made From Feedback

Several changes were made in response to instructor comments on the earlier parts, and each one made the report more accurate or more concise. The introduction was shortened to its essential background so the full report would fit the word limit. The methods now state that vigorous minutes were counted twice and explain why covariates were described rather than adjusted. In the results, the Welch-corrected t-test replaced the equal-variance version after Levene's test was found significant, and confidence intervals were added for both the odds ratio and the mean difference. Finally, and most importantly for honest reporting, causal wording such as "activity improves health" was replaced throughout with language describing association.

Conclusion

In this sample, meeting the aerobic guideline and higher activity were both associated with better self-rated health, with effects of practical size. The cross-sectional design cannot show cause, and the effects are modest rather than dramatic, but the findings support continued local promotion of the guideline and point to clear next steps for stronger analysis. Completing this project also showed how much careful planning, from writing testable hypotheses to choosing tests that match the data, shapes what a study can honestly claim.

References

Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159. https://doi.org/10.1037/0033-2909.112.1.155

Du, Y., Liu, B., Sun, Y., Snetselaar, L. G., Wallace, R. B., & Bao, W. (2019). Trends in adherence to the Physical Activity Guidelines for Americans for aerobic activity and time spent on sedentary behavior among US adults, 2007 to 2016. JAMA Network Open, 2(7), e197597. https://doi.org/10.1001/jamanetworkopen.2019.7597

Idler, E. L., & Benyamini, Y. (1997). Self-rated health and mortality: A review of twenty-seven community studies. Journal of Health and Social Behavior, 38(1), 21-37. https://doi.org/10.2307/2955359

Jacobsen, K. H. (2017). Introduction to health research methods: A practical guide (2nd ed.). Jones & Bartlett Learning.

Piercy, K. L., Troiano, R. P., Ballard, R. M., Carlson, S. A., Fulton, J. E., Galuska, D. A., George, S. M., & Olson, R. D. (2018). The Physical Activity Guidelines for Americans. JAMA, 320(19), 2020-2028. https://doi.org/10.1001/jama.2018.14854

Saint-Maurice, P. F., Troiano, R. P., Bassett, D. R., Graubard, B. I., Carlson, S. A., Shiroma, E. J., Fulton, J. E., & Matthews, C. E. (2020). Association of daily step count and step intensity with mortality among US adults. JAMA, 323(12), 1151-1160. https://doi.org/10.1001/jama.2020.1382