Improving Physical Activity Needs Assessment Results
Physical activity metrics are often used in conjunction with other key health behavior data to assess the health needs within specific communities. It is a foundational health indicator, with valid data essential for monitoring and planning public health programs to improve community health. According to the Department of Health and Human Services, more than 80% of Americans do not meet the physical activity standards as set by the 2008 Physical Activities Guidelines for Americans which include 150 minutes of moderate intensity physical activity such as brisk walking (U. S. Department of Health and Human Services [HHS], 2013). In St. Louis County, Missouri, a bi-annual Community Health Needs Assessment (CHNA) is used to identify population health issues within the four sub-counties. The 2011 health data associated with physical inactivity includes similar results as national findings (Saint Louis County, 2013). The adult obesity rate is approximately 27%, with more than 30% of adults having cardiovascular medical conditions, including high cholesterol and high blood pressure. Surveys of county residents indicate 22% of the adult population leads a sedentary lifestyle, with nearly 20% having been diagnosed with depression.
Assessment Preparation and Implementation
Assessing the population of nearly one million residents in St. Louis County to identify health status and service needs, requires a series of assessment tools to collect the comprehensive epidemiological-based health information and the associated behaviors of each sub-population. resulting in a list of priority health questions for community leaders, agencies, and the public (Saint Louis County, 2013). For the initial assessment, two groups were identified by a steering committee, including recognized community health leaders followed by a group of stakeholders knowledgeable about the specific health issues identified. The second interview process identified additional public health care issues not profiled in the first interview. A health status profile was developed based on the results of secondary data and the stakeholder interviews (Saint Louis County, 2013).
Additional indicators were derived from a random sample household telephone survey developed by the St. Louis County Health Department, the University of New England Center for Community and Public Health (CCPH), and Market Decisions, a custom marketing research firm. The preliminary survey was drafted and refined by committee members, and a pretest survey was tested with a sample group of St. Louis County residents. A total of 2,149 interviews were conducted, averaging 21.6 minutes, due to the wide range of health topics. The self-reported data included questions regarding behavior risk factors such as smoking, insufficient physical activity, and obesity, as well as medical risk patterns, treatment services, and availability. This paper will discuss the methodology of gathering physical activity data through self-reporting, and the associated limitations.
Self-reported methods of data collection, including questionnaires, activity diaries, and recall interviews have significant limitations when measuring physical activity as evident in several studies comparing self-reported results and activity monitoring with technology-based equipment, including accelerometers, pedometers, and heart rate monitors. For example, in a study of Vietnamese adolescents, a physical activity questionnaire showed a significantly lower amount of activity time than the accelerometer (Hong, Trang, van der Ploeg, Hardy, & Dibley, 2012). In another comparative study of validity, strong positive relationships were observed in vigorous activity, but weaker results occurred when monitoring moderate activity levels, suggestion individuals recall more precisely when activity engaged in higher intensity physical activity (Hagstromer, Oja, & Sjostrom, 2006).
These results suggest more accurate data can be collected using one of the technology-based tools for assessing physical activity (Tudor-Locke & Myers, 2001). While not feasible in large community settings, health planners may consider triangulation "for the purpose of confirming, disconfirming, or modifying information gained through one of the methods” (Issel, 2014, P. 497). In St. Louis County, where chronic illness and depression rank as priority health issues, validation of health risk behaviors associated with physical inactivity is critical to improving the population health status. Selecting a small group of participants within each sub-county to wear an accelerometer for a specified period of time, with electronic journaling, would allow planners to assess actual physical activity data. In one recent study, the ActiGraph GT3X accelerometer was used to measure sedentary work hours, with software that identifies time spent in activities categorized as sedentary, light, and moderate activity (Brown, Ryde, Gilson, Burton, & Brown, 2013). This data allows a more complete picture of actual physical activity duration, frequency, and intensity. Another option would include the use of pedometers, as they are inexpensive and readily available. While they are unable to assess intensity and several forms of activity such as cycling, for community assessment purposes, they can be used to measure walking.
Analyzing and interpreting the quantitative data from the software would allow planners to identify movement patterns, during working and non-working hours, with data for the four sub-counties available to create educational programming based on the results along with the demographics and psychographics unique to each sub-county. Additionally, with the increased accuracy of measurement, planners may use the results to validate the random sample household telephone survey results. This validation, or lack of, will contribute to decisions regarding future assessment methodologies.
The development of technology-based instruments for measuring physical activity should result in health professionals incorporating the devices into planning models to improve program validity and outcomes. With increased use, instrument and equipment costs will continue to decline, allowing greater accessibility for community needs assessments to reliably and accurately measure physical activity as a foundational health indicator.
Brown, H. E., Ryde, G. C., Gilson, N. D., Burton, N. W., & Brown, W. J. (2013). Objectively measured sedentary behavior and physical activity in office employees: relationships with presenteeism. Journal of Occupational and Environmental Medicine, 55(8), 945-953.
Centers for Disease Control and Prevention. (2013). Prevalence and trends date. Retrieved from http://apps.nccd.cdc.gov/brfssdisplay.aspyr=2010&cat=EX&qkey=4347&state=MO
Hagströmer, M., Oja, P., & Sjöström, M. (2006). The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public health nutrition, 9(06), 755-762.
Hong, T. K., Trang, N. H., van der Ploeg, H. P., Hardy, L. L., & Dibley, M. J. (2012). Validity and reliability of a physical activity questionnaire for Vietnamese adolescents. Int J Behav Nutr Phys Act, 9, 93.
Issel, L. M. (2014). Health program planning and evaluation: A practical, systematic approach for community health. Burlington, MA: Jones & Bartlett Learning.
Saint Louis County, Missouri. (2013). 2011 Community Health Needs Assessment. [PDF]. Retrieved from http://www.stlouisco.com/HealthandWellness/ HealthEducationandInformation/2011CommunityHealthNeedsAssessment
Tudor-Locke, C. E., & Myers, A. M. (2001). Challenges and opportunities for measuring physical activity in sedentary adults. Sports Medicine, 31(2), 91-100.
U. S. Department of Health and Human Services. (2013). Physical Activity Guidelines for Americans. Retrieved from http://www.health.gov/PAGuidelines/guidelines/default.aspx