Corbett et al. BMC Res Notes (2018) 11:80 https://doi.org/10.1186/s13104-018-3151-x RESEARCH ARTICLE The eects ofÂ a 12 -week worksite physical activity intervention onÂ anthropometric indices, blood pressure indices, andÂ plasma biomarkers ofÂ cardiovascular disease risk amongÂ university employeesDuaneÂ B.Â Corbett1* n CurtisÂ Fennell2, KyleneÂ Peroutky3, J.Â DerekÂ Kingsley3 andÂ EllenÂ L.Â Glickman3Abstract Background: To determine the eectiveness of a low-cost 12-week worksite physical activity intervention targeting a goal of 10,000 steps per day on reducing anthropometric indices, blood pressure indices, and plasma biomarkers of cardiovascular disease (CVD) risk among the employees of a major university.Methods: Fifty university employees (nÂ Â 43 female, nÂ Â 7 male; mean ageÂ Â 48Â Â 10Â years) participated in the 12-week physical activity intervention (60Â min, 3Â day/week). Each session included both aerobic (cardiorespiratory endurance) and muscle-strengthening (resistance) physical activity using existing university facilities and equip-ment. Anthropometric indices, blood pressure indices, and plasma biomarkers of CVD risk assessed included those for obesity (body mass index), hypertension (systolic blood pressure, SBP; diastolic blood pressure, DBP), dyslipidemia (high-density lipoprotein, HDL; low-density lipoprotein, LDL; total serum cholesterol), and prediabetes (impaired fast-ing glucose, IFG). Steps per day were assessed using a wrist-worn activity monitor. Participants were given the goal of 10,000 steps per day and categorized as either compliers (Â 10,000 steps per day on average) or non-compliers (<Â 10,000 steps per day on average) based on their ability to achieve this goal.Results: Overall, 34% of participants at baseline were already at an elevated risk of CVD due to age. On average, 28% of participants adhered to the goal of 10,000 steps per day. After 12-weeks, participants in both groups (compliers and non-compliers) had lower BMI scores (pÂ <Â 0.001), lower HDL scores (pÂ <Â 0.034), and higher IFG scores (pÂ <Â 0.001). The non-compliers had a greater reduction of BMI scores than the compliers (pÂ Â 0.003). Participants at risk for CVD had greater reductions than those not at risk for several risk factors, including SBP (pÂ Â 0.020), DBP (pÂ Â 0.028), IFG (pÂ Â 0.002), LDL (pÂ Â 0.006), and total serum cholesterol (pÂ Â 0.009).Conclusion: While the physical activity intervention showed mixed results overall with both favorable changes in anthropometric indices yet unfavorable changes in plasma biomarkers, it was particularly benecial in regards to both blood pressure indices and plasma biomarkers among those already at risk of CVD.Trial registration ClinicalTrials.gov NCT03385447; retrospectively registeredKeywords: Sedentary behavior, Exercise, Physical inactivity, Workplace, Health promotion The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Open Access BMC Research Notes *Correspondence: firstname.lastname@example.org 1 Department ofÂ Aging andÂ Geriatric Research, College ofÂ Medicine, University ofÂ Florida, Gainesville, FL 32611, USAFull list of author information is available at the end of the article
Page 2 of 8 Corbett et al. BMC Res Notes (2018) 11:80 BackgroundPhysical inactivity is well established as a leading pre-ventable cause of cardiovascular disease (CVD)Â—the leading cause of death and a major contributor to rising healthcare costs in the United States [1, 2]. In fact, people who are physically inactive spend 38% more days hospi-talized compared to those who are physically active . While the paramount importance of increasing physical activity is to improve quality of life and longevity, it is also a critical factor in the eort to reduce the continually increasing national healthcare burden. With the declin-ing prevalence of adults with no known major CVD risk factors, the national healthcare burden of CVD is fore-casted to triple by the year 2030 . is information is a major concern to employers, who on average pay 72% of continually increasing annual health coverage premiums . Institutions of higher education, whose workforce is predominantly limited to sedentary labor, may have more concern for this information . Taking advantage of the recent surge in wearable technology that makes personal tness tracking an aordable reality , this study exam-ines the possibility of reducing the risk of CVD through a low-cost, goal-based worksite physical activity interven-tion in a university setting. Worksite physical activity interventions represent an attractive, cost-eective investment for employers through improved healthcare costs, rates of absenteeism, and worker productivity [8 9 ]. While eliminating many of the barriers that prevent adults from being physically active in the rst place (e.g., lack of social support, limited access to resources) [ 10Â– 12], a worksite physical activity intervention in the uni-versity settingÂ—a traditionally underrepresented population [ 13]Â—also provides the unique environmental opportunity to minimize start-up costs through use of existing facili-ties and equipment (e.g., gymnasiums, health and physical education equipment) . Furthermore, the ability to hold such an intervention in a location separate from younger adults (i.e., students), such as an annex gymnasium that is not part of student recreation, may improve group cohesive-ness and physical activity participation compared to other physical activity options with intermixed age groups . As mentioned previously, the recent advancement of wearable technology provides aordable options for the objective monitoring of physical activity [7 ]. While the ben-et of activity monitors has traditionally been limited to researchers due to their prohibitively high cost, advances in technology have now drastically increased their consumer accessibility. As such, it is now possible for virtually anyone to self-monitor their physical activity through wearables (e.g., pedometers). Since there is limited literature on the benet of wearables, a worksite physical activity interven-tion designed to reduce CVD risk that includes wearables is intriguing. In fact, a meta-analysis of 32 studies showed the use of wearables had a moderate and positive eect on increasing physical activity, although the direct measure of CVD risk was not included in the analysis . Interest-ingly, studies in this analysis that included a goal of 10,000 steps per day had the greatest eect. erefore, including a goal of 10,000 steps per day may be an important compo-nent when designing a worksite physical activity interven-tion aimed at reducing CVD risk. In view of these considerations, the purpose of the fol-lowing study was to examine the eects of a 12-week lowcost, goal-based worksite physical activity intervention on anthropometric indices, blood pressure indices, and plasma biomarkers for risk of CVDÂ—the leading cause of death and disability in the United States Â—among fac-ulty and sta members at a major university. In addition, we also examined the eects of providing participants with a low-cost physical activity monitor to self-monitor their eort throughout the duration of the intervention. As such, we hypothesized that a low-cost worksite physi-cal activity intervention consisting of both aerobic and anaerobic physical activity that targets the goal of 10,000 steps per day would improve indices and biomarkers of CVD risk among university employees. We also hypoth-esized that intervention participants who adhered to the goal of 10,000 steps per day would have greater improve-ments in indices and biomarkers of CVD risk than those who did not adhere. Finally, we hypothesized that indi-viduals at risk for CVD through specic indices and biomarkers, would have greater improvements in those indices and biomarkers than those not at risk.MethodsParticipantsFifty university employees (n 43 females; 28Â–65years) were recruited for the physical activity intervention and participation in the study. Enrollment was based on a convenience sample with recruitment achieved through university mass email in addition to university newspa-per and website advertising. Primary inclusion criteria limited participants to university faculty and sta mem-bers who were self-reported to be sedentary with no con-traindications to physical activity prior to participation in the intervention as reported via a questionnaire (Physical Activity Readiness Questionnaire) and physician release form, respectively. e study was approved by the Kent State University Institutional Review Board and all par-ticipants gave their informed consent in writing.Physical activity interventione physical activity intervention targeted the current federal physical activity guidelines recommendations for adults , consisting of 60-min sessions of both aero-bic (cardiorespiratory endurance) and anaerobic (muscle
Page 3 of 8 Corbett et al. BMC Res Notes (2018) 11:80 strengthening resistance) physical activity, 3days/week with a day of rest between each session, for a period of 12-weeks. e intervention was oered as both a morn-ing (6a.m.) and noon (12p.m.) session with participation restricted to one session per day. For each session, par-ticipants reported to the exercise physiology laboratory which included multiple gymnasiums and equipment for group-based activities. ese gymnasiums were inde-pendent of the student recreation center, which allows employee participants to engage in physical activity at a separate environment than the students. e 60-min ses-sions included 5-min warm-up and cool-down periods. A variety of instructor-led group-based physical activity choices were oered during each session. ese choices included, but were not limited to, group walking and run-ning, aerobic dancing, yoga, basketball, dodgeball, bad-minton, and various boot-camp style classes. In addition, participants were oered a more independent alterna-tive which included a room with cardiovascular equip-ment and xed-weight machines. Previous work shows that having a greater variety of activities may enhance adherence through increased enjoyment and decreased boredom [19, 20]. Participants were encouraged to pro-gress to more challenging activities as the intervention continued. All activities were instructed, supervised, and monitored by trained exercise specialists. All facilities used by the intervention were during periods of non-con-ict with university courses. All equipment used for the intervention were pre-owned by departments within the Kent State University College of Education, Health, and Human Services and were not being used otherwise.MeasurementsAssessments were conducted during laboratory visits at baseline and at the end of 12-weeks. All assessments were conducted by certied exercise physiologists or trained phlebotomists. Assessments included selfreported demographic and contact information, anthro-pometric indices for CVD risk, blood pressure indices for CVD risk, and plasma biomarkers for CVD risk. Anthro-pometric indices, blood pressure indices, and plasma bio-markers assessed were in accordance to current criteria outlined by the American College of Sports Medicine . Anthropometric indices assessed included body mass index (BMI)Â—a marker for obesityÂ—calculated as weight in kilograms divided by height in meters squared and scored as 30kg/m2 for both men and women. Blood pressure indices were non-gender specic and included systolic blood pressure (SBP) and diastolic blood pressure (DBP)Â—markers for hypertensionÂ—scored as 140 or 90mmHg, respectively. Plasma biomark-ers assessed were also non-gender specic and included high-density lipoprotein (HDL), low-density lipoprotein (LDL), and total serum cholesterolÂ—markers for dyslipi-demiaÂ—scored as<40, 130, and 200mg/dL, respec-tively; and impaired fasting glucose (IFG)Â—a marker for prediabetesÂ—scored as 100 and 125mg/dL. Partici-pants were required to fast to at least 8h prior to having blood drawn. In addition to the assessments done during laboratory visits to establish risk factors for CVD, each participant was given a Movband activity monitor (Movable, Cleve-land, OH). e Movband is a low-cost commercially available wrist-worn activity monitor that features a triaxial accelerometer to assess daily step count. e device also features a liquid crystal display screen that allows the user to monitor their step count in real-time. In-house preliminary data suggest the Movband to be a valid meas-ure of free-living physical activity . Participants were instructed to wear the activity monitors on their nondominant wrist each day during periods of wake through-out the duration of the study with a goal of 10,000 steps per day. Participants were categorized as either compliers (10,000 steps per day) or non-compliers (<10,000 steps per day) based on their ability to achieve the 10,000 steps per day goal on average over the duration of the study. e 10,000 steps per day cut-point was chosen based on previously proposed indices that suggest this level to be a reasonable estimate of recommended daily physical activity for apparently healthy adults . Non-wear days were dened as having fewer than 200 steps recorded and were not included in the analysis. Individuals with seven or more non-wear days were excluded from the steps per day compliance analysis. Attendance was recorded daily through login sheets upon arrival to the facility.Data analysisAll data analyses were conducted using Stata 13.1 (Stata Corp, College Station, TX). Baseline participant charac-teristics were compared across compliance groups using independent samples t-tests and Chi square tests for cat-egorical variables. Measures for CVD risk at both base-line and follow-up (12-weeks) were compared overall using paired samples t-tests and across compliance base-line risk groups using independent samples t-tests with pre-post values. Participant characteristics and measures for CVD risk at both baseline and follow-up (12-weeks) are summarized in table form (mean, SD; n, %). Aver-age daily step count was assessed for each participant as an average of the weekly average for all 12weeks of the study. Attendance was assessed as a total percentage for each participant, averaged as both the entire sample and by compliance group. For this study, enrollment was lim-ited to a small convenience sample and therefore a sam-ple size calculation was not necessary. Alpha was set a priori at p0.05.
Page 4 of 8 Corbett et al. BMC Res Notes (2018) 11:80 ResultsBaseline characteristicsParticipant characteristics at baseline, attendance, and average steps per day are presented in Table 1. Baseline characteristics were similar between the two groups; however, the compliers had signicantly higher HDL scores (p 0. 029) and average steps per day (p<0.001) compared to the non-compliers. Overall, 34% of partici-pants at baseline were already at an elevated risk of CVD due to age. Detailed statistics for anthropometric indices, blood pressure indices, and plasma biomarkers of CVD risk are presented in Tables 2 and 3. Based on the base-line data, 30% of participants were already at elevated risk of CVD due to BMI, 16% due to SBP, 6% due to DBP, 8% due to IFG, 36% due to LDL, 16% due to HDL, and 38% due to total serum cholesterol. Accordingly, 24% of all participants were at multiple risk, dened as being at risk of two or more risk factors associated with indices and biomarkers listed (e.g., SBP and DBP only repre-sents one risk) including negative risk for HDL (scored as130mg/dL), but not at risk due to age.Steps perÂ dayAverage steps per day by group by week are presented in Fig. 1 Across the duration of the intervention, only 24 participants (48%; n 14 compliers, n 10 non-compliers) fullled the inclusion criteria for analy-sis. Average steps per day were signicantly higher among the compliers compared to the non-compliers across all weeks of the physical activity intervention (p<0.05).Anthropometric indices, blood pressure indices, andÂ plasma biomarkers ofÂ CVD riskAfter 12-weeks, all participants had lower BMI scores (pre-post 0.59kg/m2, p<0.001), lower HDL scores (pre-post 2.58mg/dL, p<0.034), and higher IFG scores (pre-post 9.48mg/dL, p<0.001) after 12-weeks. e non-compliers had a greater reduction in BMI scores than the compliers (non-compliers: prepost 1.43kg/m2, compliers: pre-post 0.43kg/ m2, p 0.003). Participants at risk had greater reduc-tions than those not at risk for several risk factors, including SBP (risk: pre-post 12.29mmHg, norisk: pre-post 2.02mmHg, p 0.020), DBP (risk: prepost 12.00mmHg, no-risk: pre-post 1.04mmHg, p 0.028), IFG (risk: pre-post 18.00mg/dL, norisk: pre-post 11.39mg/dL, p 0.002), LDL (risk: pre-post 15.00mg/dL, no-risk: pre-post 6.85mg/ dL, p 0.006), and total serum cholesterol (risk: prepost 13.07mg/dL, no-risk: pre-post 8.71mg/dL, p 0.009). It should be noted that only three participants Table / 1 Characteristics ofÂ participantsData are means and SD unless otherwise noted. All measures are representative of baseline with the exception of steps per day and attendance percentage being the average over the duration of the study. CVD risk due to age, menÂ Â 45Â year, womenÂ Â 55Â year; attendance rating, average percentage of classes participated in. BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, IFG impaired fasting glucose, LDL low-density lipoprotein, HDL high-density lipoprotein *Â Signicant dierence from non-compliers at pÂ <Â 0.05 Total (nÂ Â 50) Participants withÂ activity monitor data Compliers (nÂ Â 14) Non-compliers (nÂ Â 10) Age (year) 48 (10) 48 (10) 44 (12) Female, % (n) 86 (43) 79 (11) 80.0 (8) Weight (kg) 93.8 (24.9) 88.6 (15.5) 109.1 (39.2) BMI (kg/m2) 32.7 (7.1) 31.5 (5.9) 37.9 (10.9) Ethnicity/race, % (n)Â White 84 (42) 93 (13) 70 (7)Â African American 14 (7) 7 (1) 20 (2)Â Other 2 (1) 0.0 (0) 10 (1) SBP (mmHg) 124.9 (12.6) 124.4 (16.6) 120.8 (8.1) DBP (mmHg) 75.2 (9.9) 74.9 (9.6) 73 (8.0) IFG (mg/dL) 89.4 (23.7) 93.4 (16.9) 87.4 (29.6) LDL (mg/dL) 115.0 (34.4) 107.4 (33.7) 102.2 (33.1) HDL (mg/dL) 51.2 (12.3) 50.3 (11.1)* 40.8 (7.6) Total serum cholesterol (mg/dL) 190.8 (39.3) 179.7 (39.9) 173.3 (40.9) CVD risk due to age, % (n) 34.0 (17) 42.9 (6) 30 (3) Steps per day 10,633.9 (2797.7) 12,418.2 (1863.5)* 8136.0 (1760.8) Attendance rating, mean % (SD) 65 (20) 75 (18) 63 (20)
Page 5 of 8 Corbett et al. BMC Res Notes (2018) 11:80 IFG levels were at or above 125mg/dL at baseline (pre-post1.33mg/dL).Discussionis study examined the eects of a low-cost, goalbased 12-week worksite physical activity intervention on anthropometric indices, blood pressure indices, and plasma biomarkers of CVD risk among faculty and sta members at a major university. Variables were examined across time overall and by groups separated in two dier-ent ways: (1) based on compliance to the goal of 10,000 steps per day using a wrist-worn activity monitor, and (2) baseline risk for CVD for each individual index and biomarker. e use of the activity monitor allowed for objective measurement of daily physical activity for the duration of the study and also provided objective, realtime feedback on personal physical activity levels to the participants. As expected, overall improvements were found for anthropometric indices with a decrease in BMI scores. Unexpectedly, however, overall plasma biomarkers of CVD risk showed unfavorable reductions in both IFG and HDL levels. e intervention showed no eect on blood pressure indices. Surprisingly, there was no benet to compliance to the 10,000 steps per day goal due to the non-compliers actually reporting greater improvements in BMI scores than the compliers. For individuals at risk for CVD through baseline assessment according to each specic index and biomarker, the intervention was con-siderably benecial for blood pressure indices and plasma biomarkers in comparison to those with no baseline risk. Specically, participants with baseline risk found greater improvements in SBP, DBP, IFG, LDL, and total choles-terol levels compared to those with no baseline risk for those specic measures. Although our results may not be of clinical signi-cance, we do report that no participant increased their risk of CVD due to BMI. In addition, we also report Table / 2 Change inÂ anthropometric indices, blood pressure indices, andÂ plasma biomarkers ofÂ CVD risk overall andÂ by complianceData are presented as delta of means from baseline to 12-weeks BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, IFG impaired fasting glucose, LDL low-density lipoprotein, HDL high-density lipoprotein *Â Signicance at pÂ <Â 0.05a nÂ Â 49, b nÂ Â 48, c nÂ Â 38, d nÂ Â 13, e nÂ Â 10, f nÂ Â 8 Risk factor Total (nÂ Â 50) p value Participants withÂ activity monitor data p value Compliers (nÂ Â 14) Non-compliers (nÂ Â 10) ObesityÂ BMI (kg/m2) Â 0.59a<Â 0.001* Â 0.43 Â 1.43 0.003* Hypertension (mmHg)Â SBP Â 0.06b0.977 Â 2.31d1.50e0.549Â DBP 0.23b0.875 Â 1.08d3.70e0.249 PrediabetesÂ IFG (mg/dL) 9.84c<Â 0.001* 9.10 13.50f0.587 Dyslipidemia (mg/dL)Â LDL 0.53c0.887 3.70e0.75f0.650Â HDL Â 2.58c0.034* Â 1.20e1.13f0.331Â Total serum cholesterol 0.68c0.870 Â 2.80e1.75f0.896Table / 3 Change inÂ anthropometric indices, blood pressure indices, andÂ plasma biomarkers ofÂ CVD risk byÂ baseline riskData are presented as delta of means from baseline and 12-weeks follow-up BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, IFG impaired fasting glucose, LDL low-density lipoprotein, HDL high-density lipoprotein *Â Signicance at pÂ <Â 0.05 Risk factor n Risk n No-risk p value ObesityÂ BMI (kg/m2) 29 Â 0.73 20 Â 0.38 0.112 Hypertension (mmHg)Â SBP 7 Â 12.29 41 2.02 0.020*Â DBP 3 Â 12.00 45 1.04 0.028* PrediabetesÂ IFG (mg/dL) 2 Â 18.00 36 11.39 0.002* Dyslipidemia (mg/dL)Â LDL 11 Â 15.00 27 6.85 0.006*Â HDL 5 0.60 33 Â 3.06 0.296Â Total serum cholesterol 14 Â 13.07 24 8.71 0.009*
Page 6 of 8 Corbett et al. BMC Res Notes (2018) 11:80 that at 12-weeks follow-up, while one participant actu-ally dropped a level within the subcategories of obesity for BMI, three other participants were within the over-all average BMI reduction away from dropping BMI as a risk factor for CVD altogether. Regardless, the majority of changes were desirable with 80% of participants hav-ing lower BMI scores at 12-weeks follow-up compared to baseline. Similar concern could be raised in regards to our reported change in IFG and HDL levels. However, both IFG and HDL levels are historically prone to having a wide degree of variability, especially in response to exer-cise training for which an analysis may require control for a variety of potential confounders for which our current analysis could not account [24, 25]. However, our sample size severely limits the statistical power required for such an analysis so we will continue to report only the abso-lute pre-post values. at said, we would not necessarily interpret the unfavorable reductions in HDL and IFG lev-els as an increased risk of dyslipidemia or prediabetes. e ndings of this study may have important pub-lic health signicance. While the majority of worksite physical activity studies have focused on the industrial job sector, previous works shows a relatively equal level of health risk among white-collar workers [26, 27]. In our results, we found promising eects of such an interven-tion focused in the predominantly white-collar setting of a major university, with desired changes seen in anthro-pometric indices, blood pressure indices, and plasma biomarkers for CVD risk at 3-months of follow-up. For perspective purposes, we compared our results with a comprehensive meta-analysis of 138 workplace physi-cal activity interventions ranging in duration from 4 to 2028 supervised physical activity sessions, with a simi-lar median duration of 36 sessions (Q1, 28 sessions; Q3, 60 sessions) . In this meta-analysis, diabetes risk was signicantly reduced (average IFG pre-post 12.6mg/ dL), and changes in lipids and anthropometrics were modest, yet desirable (average total cholesterol/HDL ratio pre-post 0.2; average BMI pre-post 0.3) . Blood pressure indices were not an included meas-ures of the analysis, however, several studies included in the analysis did account for blood pressure indices with signicantly positive results observed . In contrast of these results, our study showed that 3months of par-ticipation in a worksite physical activity intervention is ample duration to elicit similar eects (with the excep-tion of overall HDL and IFG levels). In fact, our results showed greater improvements than those reported in the meta-analysis for BMI scores overall and IFG levels among those already at risk. Important to note, only one study included in the meta-analysis was conducted in a university setting with the results never reaching publi-cation, leading further credence to the importance of the current study. Our nding that compliance to the goal of 10,000 steps per day had no benet on CVD risk factors was surpris-ing. While we hypothesized that the addition of a wear-able device to self-monitor compliance to the goal of 10,000 steps per day would be benecial improving CVD risk, our results showed no benet to compliance with non-compliers even having a greater reduction of CVD risk due to BMI. ese ndings contradict those of a pre-vious meta-analysis that showed the use of wearables to have positive benet on increasing physical activity and a goal of 10,000 steps per day to have the greatest ben-et . Without having the benet of knowing the true change in daily physical activity from baseline prior to beginning the intervention, the interpretation of these results should not be limited to face value. In fact, upon closer inspection, an argument could be made that the non-compliers group may have been more physically inactive than the compliers group at the start of the intervention, with evidence of their higher baseline BMI scores and lower HDL levels. As such, perhaps the noncompliers change in daily steps was smaller in compari-son to the compliers group in absolute value, yet larger in relative value. Again, we are limited to speculation since steps per day were only monitored once individu-als began the intervention. In contrast, it may be so that wearables provide no added benet to reducing CVD risk. In a randomized clinical trial, it was shown that wearables provided no enhanced benet to a standard behavioral intervention for weight loss, with partici-pants assigned wearables reporting less weight loss over a 24month period than those not assigned a wearable . It should be noted, however, that this trial was limited to non-supervised independent physical activity. Still, other work suggests that the successful use of wearables to facilitate health behavior changes may be dependent upon more complex engagement strategies that combine Fig. / 1 Average steps per day by week by compliance group
Page 7 of 8 Corbett et al. BMC Res Notes (2018) 11:80 elements of individual encouragement, social competi-tion and collaboration, and eective feedback loops . Strengths of the current study include its relatively heterogeneous sample with respect to age, gender, and ethnicity/race, and the baseline similarity between com-pliance groups among these variables. In addition, the occupational homogeneity of the sample representative of the non-manual labor work force, the inclusion of the activity monitor allowed for objective measure of daily physical activity over the duration of the study, and the university setting allowed for novelty in our approach to examine a highly underrepresented cohort in work-site physical activity research. e results of the current study should also be interpreted in the context of several limitations. One of the obvious limitations of our study was that it was essentially a pilot study with a small sam-ple size of 50 faculty and sta members, and as such, we did not conduct a power analysis. It was also a limitation that our analysis did not account for the varying inten-sity of the available activities performed. However, since the focus our analysis was on compliance to the goal of 10,000 steps per day and not adherence to intensity based guidelines, we feel the valid objective measure of physical activity used for this study was reasonable. In addition, the study design did not allow for objective assessment of physical activity prior to starting the intervention, and thus we were unable to examine physical activity at base-line. A similar limitation of our study design was the lack of an appropriate control or comparison group. However, we feel that our categorization of compliers and noncompliers satised this consideration to some degree. Lastly, while we make the argument that an attractive fea-ture of a worksite physical activity intervention would be reducing employee healthcare costs, we did not include any healthcare data in our analysis. However, proving an intervention such as the one here can exist in a university setting with minimal start-up and expenses, does make it more attractive through the minimal cost-associated risk involved.ConclusionsIn summary, the physical activity intervention showed somewhat mixed results for participants overall, with favorable changes in anthropometric indices yet unfa-vorable changes in plasma indices and essentially no benet for compliance to the goal of 10,000 steps per day. However, participation was particularly benecial for those who were already at risk of CVD through base-line assessment according to each specic index and bio-marker. In fact, those at risk showed favorable changes both blood pressure indices and plasma biomarkers rela-tive to those not at risk, while also reporting a favorable, although not signicant, trend in anthropometric indices. ese results suggest that a low-cost worksite physical activity intervention in a university setting is feasible and may be an eective method to reduce employee risk of CVD. As such, these results have implication that such an intervention may be an eective method for reduc-ing employee CVD risk in a university setting without the cost associated-risk of start-up. Further longitudinal study is needed, including employee healthcare expendi-ture data, to examine the long-term eects of such an intervention.Abbreviations CVD: cardiovascular disease; BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; IFG: impaired fasting glucose; LDL: lowdensity lipoprotein; HDL: high-density lipoprotein. AuthorsÂ’ contributions DC, CF, KP, and EG designed the research; DC, CF, KP, and EG performed the research; DC, CF, KP, JK, and EG analyzed the data; and DC and CF wrote the paper. All authors read and approved the nal manuscript. Author details1Â Department ofÂ Aging andÂ Geriatric Research, College ofÂ Medicine, University ofÂ Florida, Gainesville, FL 32611, USA. 2Â Department ofÂ Kinesiology, College ofÂ Education, University ofÂ Montevallo, Montevallo, AL 35115, USA. 3Â Depart ment ofÂ Exercise Physiology, School ofÂ Health Sciences, Kent State University, Kent, OH 44240, USA. Acknowledgements The authors would like to thank all the faculty and sta members who participated in the physical activity intervention for their hard work and dedication. Competing interests The authors declare that they have no competing interests. Availability of data and materials Data from this study will not be shared. Consent from participants was not sought to share the data more widely than for the purposes of this study. Consent for publication Not applicable. Ethics approval and consent to participate This study was approved by the Kent State University Institutional Review Board. Informed written consent was obtained from all participants. Funding This study was self-funded.PublisherÂ’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in pub lished maps and institutional aliations. Received: 16 January 2017 Accepted: 9 January 2018 Referencest 1.t Mokdad AH, Giles WH, Bowman BA, Mensah GA, Ford ES, Smith SM, Marks JS. Changes in health behaviors among older Americans, 1990 to 2000. Public Health Rep. 2004;119(3):356Â–61.t 2.t Booth FW, Roberts CK, Laye MJ. Lack of exercise is a major cause of chronic diseases. Compr Physiol. 2012;2(2):1143Â–211.
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