Trajectories and Predictors of Steps in a Worksite Intervention: ASUKI-Step

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1 Trajectories and Predictors of Steps in a Worksite Intervention: ASUKI-Step Cheryl Der Ananian, PhD Ali Soroush, MD, PhD Barbara Ainsworth, PhD, MPH Michael Belyea, PhD Jenelle Walker, PhD Eric Poortvliet, MSc Pamela Swan, PhD Agneta Yngve, PhD, MPH, MSc Objectives: We evaluated the trajectory of steps over time, success in walking 10,000 steps/day for 100 days, and socio-demographic predictors of success in a pedometer-based intervention (ASUKI-Step). Methods A single-group, pre-post quasi-experimental design was used. Participants were university employees in Arizona (N = 712) and Sweden (N = 1390). Linear growth models and logistic regression were used to assess the trajectories of change in steps and the predictors of meeting the step standard, respectively. Results: Linear and curvilinear changes in steps occurred over time with individual variation in the trajectories of change (p <.01). Half of the participants (52.9%) accumulated 10,000 steps for 100 days. No changes were observed for accelerometer-derived minutes of activity. Conclusions: Individually tailoring pedometer-based interventions may enhance success. Key words: physical activity; worksite wellness; pedometer Health Behavior & Policy Review. 2015;2(1):46-61 DOI: There is clear, consistent evidence that regular participation in physical activity (PA) is beneficial for health. 1,2 National PA guidelines recommend that adults engage in at least 30 minutes of daily moderate to vigorous intensity PA or walk a minimum of 10,000 steps per day to improve health and well-being. 3-5 Despite the known benefits of PA and established guidelines, physical inactivity is a global health concern. 6 Rates of physical inactivity are slightly higher in countries with higher socioeconomic status and this may be due to sedentary vocations. The risks associated with a physical inactivity have been compared to those from cigarette smoking 7 and some have described it as the largest threat to public health, 8,9 suggesting a critical need to promote PA as a way to improve health outcomes. Walking 10,000 steps per day expends kcal per day, 10 and this amount of daily PA may improve health and prevent chronic diseases Iwane et al 14 showed that walking at least 10,000 steps per day resulted in reductions in elevated blood pressure and improved exercise capacity in hypertensive individuals. Other studies support walking at least 10,000 steps a day to promote health and prevent cardiovascular disease Accumulating 10,000 steps per day may also help people achieve the public health guidelines for PA. 18 Research suggests most adults are not achieving this goal. 11,19-21 Cheryl Der Ananian, School of Nutrition and Health Promotion, Arizona State University, Phoenix, AZ. Ali Soroush, Department of Sports Medicine, Imam Reza Hospital, Kermanshah University of Medical Sciences (KUMS), Kermanshah, Iran. Barbara Ainsworth, School of Nutrition and Health Promotion, Arizona State University, Phoenix, AZ. Michael Belyea, College of Nursing and Health Innovation, Arizona State University, Phoenix, AZ. Jenelle Walker, College of Nursing and Health Innovation, Arizona State University, Phoenix, AZ. Eric Poortvliet, Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden. Pamela Swan, School of Nutrition and Health Promotion, Arizona State University, Phoenix, AZ. Agneta Yngve, School of Hospitality, Culinary Arts and Meal Science, Örebro University, Sweden. Dr Der Ananian and Dr Soroush would like it to be known that they were co-first authors on this paper. Correspondence Dr Der Ananian; CherylD@asu.edu 46

2 Der Ananian et al Office-based employees accumulate between 4, steps per day, well below the recommended guidelines for health. 11 Individually adapted behavior change interventions and interventions that promote social support for PA in community settings, including worksites, are recommended by The Community Guide to Preventive Services to improve PA levels. 22 Promoting PA through worksite wellness programs may reach a large proportion of the population but findings on their effectiveness are mixed. A meta-analysis showed some worksite PA interventions can improve health and important workplace outcomes. 23 However, other systematic reviews on worksite PA intervention studies have reported small effects Evaluating the effectiveness of pedometer-based interventions in the worksite and identifying for whom they work will enhance our ability to tailor these programs to participants needs, identify which participants may need a more intensive intervention for success and improve adherence rates. Socio-demographic, worksite, and intervention characteristics may be potential moderators of the effectiveness of worksite interventions and these are not always systematically studied. 24 ASUKI-Step was a real world implementation and evaluation of a 6-month, pedometer-based intervention implemented at Arizona State University (ASU) and the Karolinska Instituet (KI). It was designed to assess the effects of a theory and evidence-based intervention on ambulatory activity and the predictors of changes in physical activity. 28 Specifically, this study examined (1) trajectories of pedometer-based step counts over the 6-month intervention period, (2) the proportion of individuals who accumulated 10,000 steps per day for at least 100 days during the intervention, and (3) trajectories of change in objectively measured PA during the intervention in a subset of individuals. Additionally, for all 3 aims, we examined the socio-demographic predictors of the respective PA outcome. METHODS Study Design ASUKI-Step used a single-group, pre-post, quasiexperimental design to evaluate PA trajectories, the proportion of individuals who met the study step goal and the socio-demographic predictors of these changes. 28 A comprehensive, web-based questionnaire was completed one week prior to initiation of the intervention and during months 3 and 6 of the intervention; only data from the baseline survey are presented. Participants recorded daily step-counts on the study website and a subset of individuals was asked to wear an accelerometer for one week at baseline (at the start of the study), 3 months and 6 months to assess PA trajectories. The researchers at ASU and KI have an established relationship which made it feasible to implement the intervention internationally and make cross-country comparisons. Participants Faculty, staff, and graduate students employed by ASU or KI were recruited via newsletters, posters, flyers, , electronic advertisements and kick off seminars. Eligibility criteria included: employee of ASU or KI, at least18 years of age, able to read, speak and understand English (ASU only), not currently pregnant or lactating, no known physical conditions that limit walking and no known contraindications to walking. The intervention occurred from mid-march to mid-september At the time of the study, the KI had 4000 full-time employees, 2000 doctoral students and an unknown number of temporary staff. The corresponding numbers at ASU were 9191 full and part-time faculty members and staff and 4420 doctoral students. Theoretical Framework ASUKI-Step was grounded in social support theory. 29 Participants enrolled as self-selected teams of 3-4 persons to enhance social support for PA and optimize the built in support structures of the worksite. The individual teams determined and utilized numerous social support mechanisms including self-initiated walking groups, verbal encouragement, reminders and within group competitions and rewards as reported elsewhere. 28 Incentive motivation was used to encourage walking via the dissemination of small prizes. Principles of self-regulation were integrated into the intervention. Participants were encouraged to set step goals to achieve the 10,000 steps per day and monitored goal attainment by recording their daily step counts on the website. Finally, the members of the team competed collectively for the grand prize of a paid trip to Arizona or Sweden, depending on base institution (ASU or KI). Health Behavior & Policy Review. 2015;2(1):46-61 DOI: 47

3 Trajectories and Predictors of Steps in a Worksite Intervention: ASUKI-Step Measures Socio-demographic and PA data were obtained from the baseline administration of an online, comprehensive survey. All participants were required to complete the baseline survey to gain access to the study website and materials. To enhance integrity of the data, the participant could not exit the baseline survey unless all questions were completed. The survey contained 108 items and took approximately 60 minutes to complete. Socio-demographics. Participants self-reported age, sex, marital status, education level, employment type (managerial vs non-managerial), height, and weight. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Physical activity level. Self-reported PA level was assessed using the short version of the International PA Questionnaire (IPAQ). 30 The IPAQ has 7 items regarding the number of days/week and hours and minutes/session spent in moderate-intensity (4.0 METs (metabolic equivalent)) and vigorous-intensity (8 METs) PA and walking PA (3.5 METs) during the past week. Using established IPAQ scoring protocols, individuals were classified in one of 3 PA categories: low, moderate, or high active (www. ipaq.ki.se). Pedometer-determined steps per day. Daily step counts were obtained from a Yamax SW-200 pedometer, a valid and reliable tool for counting steps in healthy adults Participants wore the pedometers over the anterior aspect of the right hip, every day and recorded their daily step count on the website throughout the 6-month study period. To capture participation in other types of activities not captured by a pedometer, such as bicycling or swimming, the participants were instructed to add 2700 steps per half hour of activity. 34 To assess changes in step counts over time, the average reported step counts/day were calculated for months 1, 3 and 6. To categorize initial pedometer step levels, participants were classified using step counts/ day from Tudor-Locke et al: 12 sedentary (< 5000), low active/inactive ( ), somewhat active ( ), active (10,000-12,499), and highly active (> 12,500). Accelerometer-based PA level. Every third person from each team was selected to wear an ActiGraph GT1M accelerometer (ActiGraph, Pensacola, FL). Individuals were instructed to wear the accelerometer during all waking hours, except while in the water, for one week during each measurement period (baseline and months 3 and 6). Accelerometers were worn over the right anterior hipbone using a provided belt and pouch. PA levels were assessed using the following cut-points: inactivity or sedentary, ct. min -1, 35 light intensity, ct. min -1, moderate lifestyle intensity, 760 1,951 ct. min -1, 36 moderate exercise/walking intensity, ct. min -1, and vigorous intensity, 5,725 ct. min Minutes spent in the respective movement intensities were summed across the days of wear time. A minimum of 3 days during the week with 10 h. d -1 of wear time was required for data to be included in the analysis. 38 Data Analysis Descriptive statistics including frequency, means, and standard deviations (SD) were used to depict participant characteristics. Prior to analyses, all continuous variables were checked for normality. A multi-level growth curve modeling approach was used to examine predictors of initial pedometerdetermined step counts and changes in pedometer step counts over time. Using this modeling approach, the following questions were examined: (1) at the group level, what was the trajectory of change (eg, linear or curvilinear), (2) was there an individual level variation in the trajectory of change as assessed by the random effects in the model, and (3) what variables predicted the trajectories of change? Pedometer step counts were analyzed using 3 hierarchical models: (1) an unconditional model which included trajectories of change over time; (2) a conditional model which included site and (3) a conditional model that included potential predictors of steps over time including socio-demographic variables, body mass index, and initial levels of PA as reported on the IPAQ. Consistent with the recommendations of Cohen and Cohen, 39 to explore significant continuous predictors, such as age, estimates were calculated as 1 SD above and below the mean. Average pedometer counts for months 1, 3 and 6 were the dependent variable in the model. Step counts recorded as zeros on the website were included in these averages as there was no way to differentiate whether a zero indicated no steps recorded or no steps taken. 48

4 Der Ananian et al Table 1 Baseline Socio-demographic and Physical Activity Characteristics of ASUKI-Step Participants Variable Sex Men Women All (N = 2102) Mean + SD or N (%) 433 (20.74) 1655 (79.26) ASU (N = 712) Mean + SD or N (%) 124 (17.42) 588 (82.58) KI (N = 1390) Mean + SD or N (%) 309 (22.46) 1067 (77.54) χ 2 or t- value p-value Age (years) BMI (kg/m 2 ) Underweight Normal weight Overweight Obese Education < High School (<12 yrs) College ( 13 years) Marital Status Single Married or cohabiting Employment Managerial Non-managerial IPAQ Category Low Moderate High 61 ( 2.95) 1211 (58.5) 540 (26.09) 258 (12.46) 255 (12.22) 1832 (87.78) 673 (32.25) 1414 (67.75) 487 (23.33) 1600 (76.67) 318 (15.23) 903 (43.25) 867 (41.52) 20 ( 2.87) 299 (42.84) 202 (28.94) 177 (25.36) 47 (6.61) 664 (93.39) 267 (37.55) 444 (62.45) 231 (32.49) 480 (67.51) 132 (18.54) 261 (36.66) 319 (44.80) 41 ( 2.99) 912 (66.47) 338 (24.64) 81 ( 5.90) 208 (15.12) 1168 (84.88) 406 (29.51) 970 (70.49) 256 (18.60) 1120 (81.40) 186 (13.52) 642 (46.66) 548 (39.83) < < < <.0001 Week 1 Step Count 11, , , <.0001 Step Category Sedentary Low active Somewhat active Active Highly Active 181 ( 8.61) 129 ( 6.14) 301 (14.32) 705 (33.54) 786 (37.39) 68 ( 9.55) 67 ( 9.41) 138 (19.38) 264 (37.08) 175 (24.48) 113 ( 8.13) 62 ( 4.46) 163 (11.73) 441 (31.73) 611 (43.96) 90.5 <.0001 Note. a BMI Categories: Underweight (<18.50), Normal ( ), Overweight ( ), Obese ( 30.00) b IPAQ Categories: Low (some activities is reported but not enough to meet moderate or high categories), Moderate (5 or more days of any combination of walking, moderate- or vigorous-intensity activities achieving a minimum of at least 600 MET-min/week), High (7 or more days of any combination of walking, moderate- or vigorous-intensity activitie accumulating at least 3000 MET-minutes/week) c Step category is based on the average steps per day obtained from a pedometer and reported during the first week of the intervention. Step categories were defined utilizing the cut points suggested by Tudor-Locke and colleagues 16 To evaluate the proportion of individuals meeting the standard of 10,000 steps per day for 100 days, frequencies were calculated. Simple and multiple logistic regression analyses were used to evaluate associations between socio-demographic characteristics and meeting the step standard. To examine predictors of and changes in accelerometer-determined PA levels over time, the same multi-level growth modeling approach that was described above was used. Each accelerometerderived PA intensity outcome was modeled separately. Statistical intent-to-treat analyses using all available data were performed using SAS software version 9.2. The level of significance was set at p <.05 for all tests. RESULTS Participant Characteristics A total of 2102 participants from ASU (N = 712) Health Behavior & Policy Review. 2015;2(1):46-61 DOI: 49

5 Trajectories and Predictors of Steps in a Worksite Intervention: ASUKI-Step Table 2 Descriptive Statistics for Physical Activity Outcomes Time Point N Mean Std Dev Range Pedometer Step Counts 1 Month , ,422 3 Month ,446 6 Month ,106 Accelerometer Outcomes Minutes of inactivity (<100 cts/min) 1 Month Month Month Minutes of light activity ( ct. min -1 ) 1 Month Month Month Minutes of moderate lifestyle activity ( ct.min -1 ) 1 Month Month Month Minutes of moderate activity (1, ct. min -1 ) 1 Month Month Month and KI (N = 1390) enrolled in the ASUKI Step intervention. Table 1 provides an overview of the demographic characteristics for the full sample and by site. The mean age was 42.4 ± 12 years (ASU 41.4 ± 11.9 years; KI 42.9 ± 12.0 years, T 2085 =2.81, p =.005). A greater proportion of participants from KI were male, married, employed in non-managerial positions and had a high school level education or less (p <.01). At baseline, a greater proportion of ASU participants (54.3%) were overweight or obese than KI participants (30.54%; χ 2 = 188.0, p <.0001) and more ASU participants (18.5%) than KI participants (13.5%; χ 2 = 21.4, p <.0001) were classified as low active/inactive based on their responses to the IPAQ survey. Trajectory of Pedometer-Determined Step Counts The effects of site and participant characteristics on steps were examined using individual growth models for the 3 measurement points, including step counts that were reported as zeros. Mean step counts for months 1, 3 and 6 are provided in Table 2. The intra class correlation, calculated from an empty (ie, random intercept only) model, was for steps; such that approximately 60% of the variance in steps was within-persons, over time. Preliminary analyses suggested that a random intercept and slope model for the variances of steps over time had acceptable fit, and thus, all conditional models were examined using that structure as a baseline. The unconditional model, model 1 in Table 3, indicates that on average, the starting or initial step counts for participants was 11,063. There was a significant linear (t = , p =.001) and curvilinear change in steps over time (t = 7.65, p =.001) for the group as a whole. Tests of the random effects indicated significant individual differences in the intercepts or starting values for steps (z = 23.98, p <.0001) and slopes or linear change over time (z = 17.09, p <.005). Steps decreased over time but this decrease slowed over time (Figure 1). The 50

6 Der Ananian et al Figure 1 Changes in Pedometer-Determined Step Counts over Time Stratified by Site changes in pedometer-determined step counts over time stratified by age groups and sex are shown in Figures 2 and 3. The conditional model which included participant characteristics, model 2 in Table 3, indicated that variation in intercepts, or baseline values, was related to site (t = 9.26, p =.001), with KI starting with more steps on average than ASU. Site was also related to the slowing of the decline in steps over time (t = 3.22, p =.001). The rate of the decline over time was slower for KI than for ASU. In model 3 (Table 3), the participant characteristics of sex, age, education, BMI and baseline activity levels as determined by the IPAQ were entered into the model. The site differences did not change. Sex, age, and education were not related to initial starting levels of steps. However, sex was related to the linear rate of change (t = 3.41, p =.001), with men having a greater rate of decline in reported steps in comparison to women. Age was also related to the linear change in steps (t = 5.10, p =.001). As age increased the rate of decline in steps was less. BMI was related to initial step level but was not related to changes in step counts over time. Specifically, individuals who were classified as obese (BMI > 30 kg/m 2 ) had significantly lower reported step counts compared to individuals in the normal weight category (p =.000). Likewise, baseline PA level was associated with initial step counts (p =.000) but not with changes in steps over time. Individuals categorized as low active or moderately active had lower step counts initially compared to those who were high active. Step Goal of 10,000 Steps per Day for 100 Days Overall, 52.9% (N = 1105) of the study participants met the study target goal of accumulating at least 10,000 steps per day for at least 100 Health Behavior & Policy Review. 2015;2(1):46-61 DOI: 51

7 Trajectories and Predictors of Steps in a Worksite Intervention: ASUKI-Step Figure 2 Changes in Pedometer-Determined Mean Step Counts over Time by Age Groups days. A greater proportion of participants from KI (59.1%) than ASU (39.8%) met the step requirements (χ 2 = 70.99, p <.0001). Results from simple and multiple logistic regression analyses examining the relationship between socio-demographic variables and meeting the study step goal are presented in Table 4. In both simple and multiple logistic regression analyses, individuals who were employed at KI, older (age > 42), married, and who worked in a non-managerial position had an increased likelihood of meeting the step goal of 10,000 steps per day for at least 100 days (p <.05). In multiple logistic regression analyses, individuals who were obese or low-to-moderately active at baseline had a decreased likelihood of meeting the step goal (p <.05). Multiple logistic regression analyses examining predictors of meeting the step standards stratified by site yielded similar results to those for the full sample (data not shown; available upon request) At both ASU and KI, individuals 55 and older were significantly more likely (ASU: OR =1.78; KI: OR=2.8) to meet the step standards relative to younger adults (age < 30). Similarly, obese individuals and those who were low active or moderately active at baseline at either site were less likely to meet the step goals (p <.05). Unique predictors of meeting the step goal at the KI site included female sex (OR = 1.47; 95% CI = ) and a nonmanagerial occupational status (OR = 1.76; 95% CI: ). Objectively Measured Physical Activity Outcomes The effects of site and participant characteristics on minutes of inactivity, light, moderate lifestyle, and moderate-to-vigorous PA assessed by use of accelerometers were examined in 226 people (107 at 52

8 Der Ananian et al Figure 3 Changes in Pedometer-Determined Mean Step Counts over Time by Sex KI, 119 at ASU) using individual growth models for the 3 measurement points. There were no differences by sex, age, education, BMI category or IPAQ category between those selected to wear the accelerometer and those who did not. Mean minutes per day for each level of activity for months 1, 3 and 6 are provided in Table 2. Preliminary analyses suggested that a random intercept and slope model for the variances of minutes of PA over time had acceptable fit, and thus, all conditional (predictor) models were examined using that structure as a baseline. Results of the conditional models for all four accelerometer determined levels of PA are shown in Table 5. Minutes of inactivity. The conditional model indicated that the random effects for the model were not significant for individual differences in the starting value of minutes of inactivity or linear change over time. Variation in intercepts, or baseline values, was related to site (t = -8.53, p <.0001), with KI starting with fewer minutes of inactivity on average than ASU. Site was also related to changes in minutes of inactivity over time (t = 2.32, p =.03). Minutes of physical inactivity tended to increase at KI and to slightly decrease at ASU. Light activity. The conditional model indicated that the random effects for the model were significant for individual differences in the starting value of minutes of light activity (z = 4.27, p <.0001). Variation in intercepts, or baseline values, was related to site (t = 5.26, p <.0001), sex (t = 2.85, p =.005) and baseline levels of PA as measured on the IPAQ (t = -2.21, p =.03). KI participants started with more minutes of light activity on average than ASU participants and women had more minutes of light PA than men. Those who were classified as moderately active using the IPAQ at baseline had fewer initial minutes of light PA relative to those who were classified as high active according to the IPAQ. There was no group level change over time Health Behavior & Policy Review. 2015;2(1):46-61 DOI: 53

9 Trajectories and Predictors of Steps in a Worksite Intervention: ASUKI-Step Table 3 Results from Conditional and Unconditional Growth Modeling of Step Outcomes Model 1 Standard Model 2 Standard Model 3 Standard Estimate Error p-value Estimate Error p-value Estimate Error p-value Intercept 11, , , Time a -3, , Time2 b Site 2, , Time*Site Time 2 *Site Sex Age Education BMI c : Underweight Normal weight Overweight Obese IPAQ d : Low Moderate High , , , time*sex 1, time*age time*eduation time*bmi: time*underweight time*normal time*overweight time*obese time*ipaq: time*ipaq Low time*ipaq Moderate time*ipaq High time 2 *Sex time 2 *Age , , time 2 *Education time 2 *BMI: time 2 *Underweight time 2 *Normal time 2 *Overweight time 2 *Obese time 2 *IPAQ: time 2 *IPAQ Low time 2 *IPAQ Moderate time 2 *IPAQ High Note. a Time evaluates the linear changes in steps over time b Time 2 evaluates the curvilinear changes over time c BMI Categories: Underweight (<18.50 kg/m 2 ), Normal ( kg/m 2 ), Overweight ( kg/m 2 ), Obese ( kg/m 2 ); d IPAQ Categories: Low (some activities is reported but not enough to meet moderate or high categories), Moderate (5 or more days of any combination of walking, moderate- or vigorous-intensity activities achieving a minimum of at least 600 MET-min/week), High (7 or more days of any combination of walking, moderate- or vigorous-intensity activities accumulating at least 3000 MET-minutes/week) 54

10 Der Ananian et al Table 4 Bivariate and Multivariate Associations with Meeting 10,000 Steps per Day for at Least 100 Days (ASU and KI Combined) Variable Sample size Bivariate Associations Met Standard N (%) Odds Ratio Simple Logistic Regression [95% Confidence Intervals] Beta Estimate Multiple Logistic Regression Analyses Wald Chi- Square p-value All Participants (52.9) Site ASU KI Sex Men Women Age (years) < >55 BMI a Underweight Normal weight Overweight Obese Education <12th grade College ( 13 years) Marital Status Single Married Employment Managerial Non- managerial IPAQ Category b Low Moderate High Week 1 Step c Category Sedentary Low active Somewhat active Active Highly active (39.8) 821 (59.1) 215 (49.65) 890 (53.78) 175 (44.4) 318 (48.6) 346 (55.10) 266 (62.44) 20 (32.8) 699 (57.7) 280 (51.9) 90 (34.9) 139 (54.51) 965 (52.67) 335 (49.8) 769 (54.4) 228 (46.82) 876 (54.75) 127 (39.9) 456 (50.5) 522 (60.21) 12 (6.63) 17 (13.2) 66 (21.9) 403 (57.2) 607 (77.2) Odds Ratio Multiple Logistic Regression [95% Confidence Interval] < [ ] 1.18 [ ] [ ] 1.54 [ ] 2.08 [ ] 0.36 [ ] 0.79 [ ] 0.39 [ ] 1.08 [ ] <.0001 < < [ ] 1.21 [ ] 1.74 [ ] 2.39 [ ] 0.63 [ ] 0.85 [ ] 0.54 [ ] [ ] 1.20 [ ] [ ] 0.44 [ ] 0.67 [ ] 0.02 [ ] 0.05 [ ] 0.08 [ ] 0.39 [ ] <.0001 < [ ] 1.39 [ ] 0.44 [ ] 0.60 [ ] Note. a BMI Categories: Underweight (<18.50 kg/m 2 ), Normal ( kg/m 2 ), Overweight ( kg/m 2 ), Obese ( kg/m 2 ) b IPAQ Categories: IPAQ Categories: Low (some activities is reported but not enough to meet moderate or high categories), Moderate (5 or more days of any combination of walking, moderate- or vigorous-intensity activities achieving a minimum of at least 600 MET-min/week), High (7 or more days of any combination of walking, moderate- or vigorous-intensity activities accumulating at least 3000 MET-minutes/week) c Step category is based on the average steps per day obtained from a pedometer and reported during the first week of the intervention. Step categories were defined utilizing the cut points suggested by Tudor-Locke and colleagues. 16 Only IPAQ Category was included in multiple logistic regression analysis due to collinearity with step category in minutes of light PA. However, site (t = -2.06, p=.04) was related to linear change over time and sex was related to linear (t = -2.33, p =.02) and curvilinear changes (t = 2.40, p =.02) in minutes of Health Behavior & Policy Review. 2015;2(1):46-61 DOI: 55

11 Trajectories and Predictors of Steps in a Worksite Intervention: ASUKI-Step Table 5 Results of Conditional Growth Models for Accelerometer-Determined Physical Activity Levels over Time (N = 226) Minutes of Inactivity Minutes of light activity Minutes Moderate Lifestyle Activity Minutes of Moderate Activity Estimate Error p-value Estimate Error p-value Estimate Error p-value Estimate Error p-value Intercept < < < <.0001 Time a Site < < < <.0001 Sex < Age (centered) Education BMI c : Underweight Normal weight Overweight Obese IPAQ d : Low Moderate High Time*site Time*Sex Time*Age Time*Education Time*BMI: Time*Underweight Time*Normal Time*Overweight Time*Obese Time*IPAQ: Time*IPAQ Low Time*IPAQ Moderate Time*IPAQ High b Time Time 2 *Site Time 2 *Sex Time 2 *Age Time 2 *Education Time 2 *BMI: Time 2 *Underweight Time 2 *Normal Time 2 *Overweight Time 2 *Obese Time 2 *IPAQ: Time 2 *IPAQ Low Time 2 * IPAQ Mod Time 2 *IPAQ High Note. a Time evaluates the linear changes in steps over time; b Time 2 evaluates the curvilinear changes over time; c BMI Categories: Underweight (<18.50), Normal ( ), Overweight ( ), Obese ( 30.00); d IPAQ Categories: Low (some activities is reported but not enough to meet moderate or high categories), Moderate (5 or more days of any combination of walking, moderate- or vigorous-intensity activities achieving a minimum of at least 600 METmin/week), High (7 or more days of any combination of walking, moderate- or vigorous-intensity activities accumulating at least 3000 MET-minutes/week) 56

12 Der Ananian et al light PA over time. Minutes of light PA tended to increase at ASU and to slightly decrease at KI. Minutes of light PA in men increased from 0-3 months and this increase slowed over time. Whereas there was no effect of age on linear changes in minutes of light PA (p =.09), age was associated with curvilinear change in light PA over time (t = -2.11, p =.04). With age, the increase in minutes of light PA slowed over time. Minutes of lifestyle moderate activity. The conditional model indicated that the random effects for the model were significant for individual differences in the starting value of minutes of lifestyle moderate activity (z = 4.06, p <.0001). Variation in intercepts, or baseline values, was related to site (t = 7.16, p <.0001), with KI starting with more minutes of lifestyle moderate PA on average than ASU. Sex was a predictor of linear change over time (t = -2.06, p =.04). Women had a greater linear decline in minutes of lifestyle moderate PA compared to men. There was a trend (t = 1.9, p =.07) for this decline to decrease over time. Minutes of moderate activity. The conditional model indicated that the random effects for the model were significant for individual differences in the starting value of minutes of moderate PA (z = 4.03, p <.0001) but not for linear change over time. Variation in intercepts, or baseline values, was related to site (t = 7.16, p <.0001), with KI starting with more minutes of moderate PA on average than ASU. Group level minutes of moderate PA did not change over time (t = 0.28, p =.78). There were no individual level predictors of linear change over time (p >.05) or curvilinear change over time (p >.05) (Table 5). DISCUSSION This study evaluated trajectories of changes in step counts and objectively measured PA over time and the socio-demographic factors associated with these changes. Participants in ASUKIstep decreased their step counts over time and there was significant individual level variation in the observed trajectories of change. However, in the smaller subset of individuals for whom we had accelerometer-derived PA values, there were no changes over time in minutes of inactivity, light-, moderate lifestyle- or moderate PA suggesting that intervention participants maintained intensityspecific inactivity and PA levels over the 6 months. Our findings, while not ideal, are consistent with other research 40 examining the effect of a long-term pedometer-based intervention and provide insight regarding for whom a minimally intensive intervention might be most appropriate. The decline in steps in our study was observed at both ASU and KI. However, participants at the KI had a higher initial step count and a slower rate of decline over time. This site-level difference may be attributable to seasonality. The intervention occurred during the summer months at both sites. The summer weather in Stockholm is quite pleasant (70 o F) whereas daytime temperatures in Phoenix frequently exceed 100 o F, likely contributing to a greater decline in steps at the ASU site. Studies in the UK have found summer time step counts to be higher than winter step counts, providing additional support for the role seasonality played in the slower decline of steps taken at KI. 41,42 There were notable individual level differences in changes in step counts over time. Men had a greater rate of decline in step counts than women and as age increased the rate of decline in steps was slower. Both of these were unanticipated findings. Baseline PA levels and BMI were only associated with initial step counts. Individuals who were classified as low active or moderately active based on IPAQ responses had lower initial step counts but their activity levels were not associated with the trajectory of change. Similarly, individuals who were obese (based on self-report data on height and weight) had significantly lower initial step counts relative to normal weight individuals but this was not associated with trajectories of steps. Collectively, these findings suggest that a minimally intensive, pedometer-based intervention may not work across all persons and more intensive strategies may be needed for more sedentary and overweight individuals. It is also plausible that this type of intervention was not appealing to men, a notoriously challenging group to reach with interventions, or younger-age individuals. More research needs to be done to identify which types of interventions work best for which individuals. To explore the possibility that the observed decline in steps was due to zeros (no recorded steps) in the data, we recoded 4 sequential weeks of zeros to missing and the analysis for steps was redone Health Behavior & Policy Review. 2015;2(1):46-61 DOI: 57

13 Trajectories and Predictors of Steps in a Worksite Intervention: ASUKI-Step with the missing values (Table available with data from the 3 models upon request). Model 1 was similar to the original analysis showing a general curvilinear decline in steps over time. However, the initial starting level of steps with steps coded as missing was higher than missing steps coded as zero (11,678 versus 11,063), the linear decline was slower ( versus -3,065.34) and quadratic change was less ( versus ) for the group as a whole. Individual change over time was still significant despite the coding of missing steps. Model 2 was also similar with missing steps coded as zero but with a difference in steps between the sites. In Model 3 when sociodemographics and baseline PA were added to the model, the overall linear group change (time) went from significant to non-significant with the missing steps coded as missing. However, the individual linear change was still significant. The sex difference over time was no longer significant. However, there were still significant differences for site, age, BMI, and IPAQ similar to the original analysis. It is hard to know what accounts for these differences in results between coding the missing steps as zero or as missing. The overall linear group change could be due to which participants dropped out. More participants in the lower IPAQ categories dropped out and the loss of the linear trend for the overall group could be due to individuals maintaining their initial physical activity level. The loss of the sex difference also could be due to the fact that more men than women dropped out. Alternatively, the differences could be due to a loss of power because of the fewer participants contributing data at 3 months and 6 months. Despite of these differences, the basic trend of a curvilinear decline exhibited in the first two models remains consistent, and the differences for site, age, BMI, and IPAQ remain. One of the primary objectives of the ASUKI- Step intervention was for participants to accumulate at least 10,000 steps on a minimum of 100 days. This threshold equates to achieving 10,000 steps per day on an average of 4 days per week over the course of the intervention. Despite the observed decline in step counts, slightly more than half (52.9%) of the study participants achieved this goal. Participants at the KI (60%) were more likely than participants at ASU (40%) to meet this step count goal and, similar to other studies, 20,43-45 socio-demographic characteristics were associated with likelihood of meeting the step count objective. In previous studies, it has been shown that men, younger individuals, those with a higher education level and those employed in non-managerial positions accumulate more steps, and married individuals have fewer steps. 20,43,44,46 Our findings only partially agreed with the literature. In the present study, older adults were more likely to meet the step recommendations and had a slower decline in steps over time. Similarly, married individuals were more likely to meet the step recommendations and women, at the KI site only, were more likely to meet the recommendations. It is not clear why our findings were different but could suggest that the intervention was not appealing to younger adults. Neither self-reported PA level nor BMI at the beginning of the study were associated with changes in step counts reported over time but both were predictive of meeting the study step goal. Individuals who were moderately or low active were 55% and 60%, respectively, less likely to meet the step standard. Likewise, obese individuals were nearly 46% less likely to meet the study step goal. One potential reason for this finding is that individuals who were less active and/or obese were more likely to drop out of the intervention. Our finding is in line with other studies of worksite PA programs. De Cocker et al 40 found that individuals who are already active were more successful in worksite PA interventions. Specifically, individuals who were active at baseline had a smaller drop in reported steps over the course of the intervention and were less likely to drop out. Strengths and Limitations of the Study This evaluation of a real-world application of a theory-based and pedometer-based intervention was designed to look at changes in PA over time and predictors of these changes. The study was unique because it was an international worksite PA study that intervened across several campuses within 2 universities. The sample enrolled in the study was representative of employees in higher education worksites but may not be generalizable to other worksite settings. PA outcome measures were collected via valid and reliable instruments. However, data from the pedometer and question- 58

14 Der Ananian et al naire were self-reported which may result in overor under-reporting of data. The research team did not cross-check pedometer data reported on the website against actual recorded pedometer data. Because this study evaluated a real-world application of a worksite intervention, a single-group pre-post design was used for multiple reasons. This was the first implementation of the intervention; as such it was a pilot or proof of concept study. Moreover, because of concerns over the willingness of participants to be randomized and the possible contamination of the comparison group within the worksite, especially given the team-based approach, it was decided to use a single-group design. Furthermore, the use of pedometers is an intervention in itself, which makes use of a control group rather difficult, since the reporting of steps is subjective and recommendations regarding steps per day are readily available. Although we cannot know the effectiveness of the intervention in comparison to a control group, analyzing the single group over time informs us about the maintenance and sustainability of the intervention and potential predictors of change. IMPLICATIONS FOR HEALTH BEHAVIOR OR POLICY Our findings suggest that a minimally-intensive, pedometer-based intervention is not likely to be successful for all office-based employees. Consistent with other research, 47 our results indicate the need to use intervention approaches that are individually tailored to the needs of the participants and to set appropriate, realistic and individualized daily step goals. In the present study, all participants were encouraged to obtain 10,000 steps per day. Whereas slightly more than half of the participants achieved this goal on at least 100 days, certain segments of the target population were significantly less likely to achieve this goal. Our findings suggest sedentary individuals and those who are obese may need additional support to be successful. Provision of social support through a selfselected team, goal-setting and monitoring was not a sufficient intervention for these individuals and other more intensive strategies may be necessary. A uniform goal of 10,000 steps per day also may be counterproductive if people have to substantially increase their activity level to obtain this goal and perceive they are unable to do so. Interventions may be more successful if they use adaptive goal setting in which the target step goal is individualized and based on the participants current level of PA. Adams et al 48 demonstrated adaptive goal setting results in less variation in daily step counts and a larger proportion of individuals meeting their goals. Minimally intensive-pedometer based interventions in the worksite appear to be most appropriate for those who are already active and motivated to engage in PA. Acknowledgements This study was funded by New Lifestyles Pedometers Inc. and through in-kind support from Select Wellness, Arizona State University College of Nursing and Health Innovation and staff from the Karolinska Institutet Health Promotion Unit. We thank the leadership at the Karolinska Institutet and Arizona State University for their support of this study. The authors would like to thank the participants for their time and efforts in this study. We d also like to acknowledge the numerous students and staff who worked on this project. We could not have completed it without their tireless efforts. Human Subjects Approval Statement This study was approved by the institutional review boards of the Arizona State University and the Karolinska Institutet. All participants provided informed consent to participate in the study. Conflict of Interest Declaration The authors report no conflict of interest. References 1. US Department of Health and Human Services. Physical Activity and Health: A Report of the Surgeon General. Atlanta, GA: Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion; Haskell WL, Blair SN, Hill JO. Physical activity: health outcomes and importance for public health policy. Prev Med. 2009;49(4): Becker W. New Nordic nutrition recommendations Physical activity as important as good nourishing food. Lakartidningen. 2005;102(39): , Physical Activity Guidelines Advisory Committee. Physical Activity Guidelines Advisory Committee Report, Washington, DC: US Department of Health and Human Health Behavior & Policy Review. 2015;2(1):46-61 DOI: 59

15 Trajectories and Predictors of Steps in a Worksite Intervention: ASUKI-Step Services; Haskell WL, Lee IM, Pate RR, et al. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc. 2007;39(8): Hallal PC, Andersen LB, Bull FC, et al. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380(9838): King A. Public health: health risks of physical inactivity similar to smoking. Nat Rev Cardiol. 2012;9(9): Trost SG, Blair SN, Khan KM. Physical inactivity remains the greatest public health problem of the 21st century: evidence, improved methods and solutions using the 7 investments that work as a framework. Br J Sports Med. 2014;48(3): Blair SN. Physical inactivity: the biggest public health problem of the 21st century. Br J Sports Med. 2009;43(1): Hatano Y. Use of the pedometer for promoting daily walking exercise. ICPHER. 1993;29: Tudor-Locke C, Bassett DR, Jr. How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med. 2004;34(1): Tudor-Locke C, Hatano Y, Pangrazi RP, et al. Revisiting how many steps are enough? Med Sci Sports Exerc. 2008;40(7 Suppl):S537-S Tudor-Locke C, Craig CL, Brown WJ, et al. How many steps/day are enough? For adults. Int J Behav Nutr Phys Act. 2011;8: Iwane M, Arita M, Tomimoto S, et al. Walking 10,000 steps/day or more reduces blood pressure and sympathetic nerve activity in mild essential hypertension. Hypertens Res. 2000;23(6): Freak-Poli R, Wolfe R, Backholer K, et al. Impact of a pedometer-based workplace health program on cardiovascular and diabetes risk profile. Prev Med. 2011;53(3): Freak-Poli R, Wolfe R, Brand M, et al. Eight-month postprogram completion: change in risk factors for chronic disease amongst participants in a 4-month pedometerbased workplace health program. Obesity (Silver Spring). 2013;21(9):E360-E Murphy MH, Murtagh EM, Boreham CA, et al. The effect of a worksite based walking programme on cardiovascular risk in previously sedentary civil servants [NCT ]. BMC Public Health. 2006;6: Welk GJ, Differding JA, Thompson RW, et al. The utility of the Digi-walker step counter to assess daily physical activity patterns. Med Sci Sports Exerc. 2000;32(9 Suppl):S481-S Bassett DR, Wyatt HR, Thompson H, et al. Pedometermeasured physical activity and health behaviors in U.S. adults. Med Sci Sports Exerc. 2010;42(10): Tudor-Locke C, Ham SA, Macera CA, et al. Descriptive epidemiology of pedometer-determined physical activity. Med Sci Sports Exerc. 2004;36(9): Wyatt HR, Peters JC, Reed GW, et al. A Colorado statewide survey of walking and its relation to excessive weight. Med Sci Sports Exerc. 2005;37(5): Task Force on Community Preventive Services. Recommendations to increase physical activity in communities. Am J Prev Med. 2002;22(4 Suppl): Conn VS, Hafdahl AR, Cooper PS, et al. Meta-analysis of workplace physical activity interventions. Am J Prev Med. 2009;37(4): Rongen A, Robroek SJ, van Lenthe FJ, et al. Workplace health promotion: a meta-analysis of effectiveness. Am J Prev Med. 2013;44(4): Freak-Poli RL, Cumpston M, Peeters A, et al. Workplace pedometer interventions for increasing physical activity. Cochrane Database Syst Rev. 2013;4:CD Malik SH, Blake H, Suggs LS. A systematic review of workplace health promotion interventions for increasing physical activity. Br J Health Psychol. 2014;19(1): To QG, Chen TT, Magnussen CG, et al. Workplace physical activity interventions: a systematic review. Am J Health Promot. 2013;27(6):e Ainsworth BE, Ananian CD, Soroush A, et al. ASUKI Step pedometer intervention in university staff: rationale and design. BMC Public Health. 2012; Cohen S, Wills TA. Stress, social support, and the buffering hypothesis. Psychol Bull. 1985;98(2): Craig CL, Marshall AL, Sjostrom M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8): Crouter SE, Schneider PL, Karabulut M, et al. Validity of 10 electronic pedometers for measuring steps, distance, and energy cost. Med Sci Sports Exerc. 2003;35(8): Schneider P, Crouter S, Lukajic O, et al. Accuracy and reliability of ten pedometers for measuring steps over a 400-walk. Med Sci Sport Exerc. 2003;35: Schneider PL, Crouter SE, Bassett DR. Pedometer measures of free-living physical activity: comparison of 13 models. Med Sci Sports Exerc. 2004;36(2): Miller R, Brown W, Tudor-Locke C. But what about swimming and cycling? How to count non-ambulatory activity when using pedometers to assess physical activity. J Phys Act Health. 2006;3: Matthews CE, Chen KY, Freedson PS, et al. Amount of time spent in sedentary behaviors in the United States, Am J Epidemiol. 2008;167(7): Matthew CE. Calibration of accelerometer output for adults. Med Sci Sports Exerc. 2005;37(11 Suppl):S512- S Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc. 1998;30(5): Trost SG, McIver KL, Pate RR. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc. 2005;37(11 Suppl):S531-S Cohen J, Cohen P. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Hillsdale, NJ: Lawrence Erlbaum Associates; De Cocker KA, De Bourdeaudhuij IM, Cardon GM. The effect of a multi-strategy workplace physical activity intervention promoting pedometer use and step count increase. Health Educ Res. 2010;25(4): Clemes SA, Hamilton SL, Griffiths PL. Summer to winter variability in the step counts of normal weight and overweight adults living in the UK. J Phys Act Health. 2011;8(1):