It is currently recommended that U.S. adults engage in 150 min·wk−1 of moderate-intensity physical activity (PA) or 75 min of vigorous intensity per week, or an equivalent combination of both (moderate-to-vigorous-intensity PA [MVPA]), performed in bouts of 10 min or longer, preferably spread throughout the week (42). Although Mexican Americans do more PA than other racial/ethnic groups when it is measured objectively, their level of MVPA is still low. For example, objective PA data from the 2003–2004 National Health and Nutritional Examination Survey revealed that Mexican American women ages 20–59 yr accumulate approximately 43 min·wk−1 of MVPA in bouts lasting at least 10 min, with Mexican American women older than 60 yr accumulating only 21 min·wk−1 (32).
Pedometers can be used to accurately track the volume of daily activity using the simple output of steps per day and may serve a motivational function because the steps taken are displayed instantly to the user. This may be one reason PA promotion messages that use a step-based goal seem more effective at increasing PA than messages based on simply accruing minutes (26). Pedometers have become especially popular among researchers and practitioners for measuring and increasing levels of PA because of their established validity and reliability (31), comparatively low cost, and ease of data management. Despite their promise however, mass distribution of pedometers is not the answer to increasing population PA; the device alone seems insufficient to motivate long-term behavior change—for that, education and behavior modification programs are needed (34). Three meta-analyses of pedometer-based interventions (3,16,29) all support the efficacy of the devices for increasing PA and improving health. To be useful, however, researchers and practitioners require pedometer-based guidelines for intervention and evaluation purposes, including cut points, benchmarks, or step indices associated with activity levels important for health. This is especially important because individuals seem able to remember pedometer-based recommendations when targeted by public health campaigns (15) and counseling (8).
The most empirically supported behavior change strategy within pedometer-based interventions is the use of incremented step frequency goals (3). The most widely recognized step goal is 10,000 steps per day (14). Although step goals do increase the efficacy of pedometer use (3), the goal of 10,000 steps per day is based on limited dose–response evidence and may be unrealistic for many people. More importantly, the 10,000 steps per day guideline does not incorporate activity intensity, which is central to national PA guidelines. Some evidence suggests that individuals who accumulate 9000–10,000 steps per day often meet intensity-based PA guidelines (1,13), with 30 min of continuous walking equivalent to a bout of 3000–4000 steps (39,44,45). However, most individuals do not walk 10,000 steps per day and are unable to gauge the intensity of their walking even after reading or hearing a description about what it should feel like (28). This presents a public health challenge because pedometers are now a centerpiece of PA promotion efforts and national PA campaigns (e.g., America on the Move, Canada on the Move), yet expert advice for how to use pedometers to improve health largely ignores step cadence (i.e., walking speed, a proxy for activity intensity).
In 2009, we published a study translating PA guidelines into a pedometer-based, intensity-weighted step goal (22). On the basis of oxygen-uptake measurements from a community sample of 97 predominantly overweight adults, ages 18–55 yr, we concluded that moderate-intensity activity was equivalent to walking at 100 steps per minute on level terrain. When pedometer-measured steps are expressed per unit of time (e.g., steps per minute), it is often called step rate or step cadence. We also argued that the imprecision in predicting intensity based on step rate coupled with a need for a simple, single public health message precluded recommending different step rates based on sex, stride length, and body mass. There is now more research interest in using the concept of step rate to describe and promote PA habits (35,37,38), and in 2011, the message 100 steps per minute was recommended by expert consensus (36). Although our “100 steps per minute” recommendation holds heuristic value as a health promotion message, recent validation evidence (30) suggests that a person’s height can also be used to identify a more individualized recommendation to target MVPA.
Translating the message “100 steps per minute” to help meet PA guidelines at the daily level means that individuals could be recommended to accumulate a minimum of 3000 steps in 30 min per day, or three daily bouts of 1000 steps in 10 min. Although there is considerable error in predicting intensity (measured by oxygen consumption) from step rate, the “3000-in-30” message may prove a useful heuristic for encouraging people to increase levels of MVPA and to meet national PA guidelines. To the best of our knowledge, no study has examined the comparative effectiveness of a step cadence goal versus a step frequency goal for increasing MVPA. The purpose of this study was to use a randomized controlled trial to evaluate the efficacy of the 3000 steps in 30 min recommendation for increasing levels of MVPA. Specifically, we compared the 3000-steps-in-30-min goal to a 10,000-steps-per-day goal and a self-selected goal for increasing daily minutes of MVPA as well as the number of MVPA bouts lasting at least 10 continuous minutes.
Participant Recruitment and Characteristics
A community sample of 348 women was recruited in 2007 by community health workers (promotoras) from 12 different community sites across the South Bay regions of San Diego County. A CONSORT flow diagram to depict the passage of participants through the RCT is presented in Figure 1.
Community sites were selected based on a PA needs assessment conducted by the San Diego Prevention Research Center and the availability of a physical location (e.g., a community center, local library or school) in the community willing to host weekly intervention meetings. Consistent with the recommendations for recruiting minorities and underserved populations into research studies (46), community health workers (promotoras) were trained in active and passive recruitment techniques such as developing and using culturally targeted and tailored flyers, writing personalized letters, encouraging friend referral, giving verbal presentations at community gatherings and parent–teacher meetings, and offering incentives.
After baseline data collection, sites were block randomized to one of three step goal conditions: a self-selected step goal (SELF), n = 4; a goal of 10,000 steps per day (FREQUENCY), n = 4; and a goal of 3000 steps in 30 min (CADENCE), n = 4. On the basis of a priori power analysis to detect a moderate increase in MVPA and a variance inflation factor of 1.55 to account for the clustering effects of site, 60 participants in each condition (n = 180 total) were randomly sampled to wear an accelerometer. However, a total of 348 women received the intervention across the three conditions (n = 180 with an accelerometer, n = 168 with no accelerometer).
The criteria for participant inclusion in the study were (a) 18–65 yr old, (b) written consent to participate in a PA intervention study, and (c) no positive responses on the Physical Activity Readiness Questionnaire. Participants provided written informed consent (in Spanish or English), and all study procedures were approved by the institutional review (ethics) board at San Diego State University.
The intervention took place in 2007 and 2008. The primary aim of the intervention was to increase the number of minutes spent engaged in MVPA. The intervention was based on behavioral and socioecological models for Latino health promotion (11) and the Communication–Persuasion Model (24). The intervention emphasized self-monitoring, feedback, goal setting, problem solving, and social support, all within a broader context of collective identity that reinforced a sense of familismo, feelings of loyalty, unity, reciprocity, and solidarity toward fellow participants. This strong sense of “family” and group orientation, obligation, and cohesion seems to protect Latinos against risk behaviors and improve their physical and educational outcomes (10). On the basis of these combined assumptions, we developed our intervention using a community-based participatory research model (25) and implemented it using promotoras (4).
The intervention was delivered by eight promotoras at 12 community sites (a local school, library or community center), one in each target community. Promotoras were randomly assigned to condition to avoid potential contamination of the different intervention step goals. Each promotora recruited the community site based on scripted recruitment literature and presentations learned during training. Four promotoras were responsible for a single community site, and four promotoras were responsible for two community sites. Teaching multiple sites was necessary because we had fewer promotoras than sites, although we randomly selected which promotoras would teach at more than one site.
The Pasos Adelante intervention program
Pasos Adelante is a 12-wk Spanish language PA program that uses pedometers to increase PA. The intervention consisted of 1-h weekly group meetings led by a promotora. In week 8, each group elected a fellow participant to serve in the role of promotora. During intervention weeks 9–12, the promotora transitioned the running of the group to the elected leader. After 12 wk, promotora involvement ceased altogether, although groups were encouraged to continue meeting. All group meetings followed a curriculum that focused on behavior change skills and developing a sense of familismo (family) among group members. Relationship-building processes are based on common cultural values shared by Latinos and seem important for helping create meaningful behavior change (10). The curriculum was implemented using a detailed structured guide for use by promotoras with simple handouts for participants. The extensive training and detailed implementation guide helped standardize intervention delivery across conditions. Behavior change skills included self-monitoring of PA using a pedometer, using a workbook to record steps, setting goals for PA, group discussions about how to overcome barriers related to PA, building self-efficacy to become more active, creating a network of social support to be more active, and teaching stress reduction techniques. The pedometer used in the intervention was the Yamax Digi-Walker SW-200 (New Lifestyles, Inc., Lees Summit, MO), a small, lightweight spring-lever pedometer that attaches to the waist via plastic clip. This model was chosen because it is widely available, relatively inexpensive, and has been widely validated as a measure of steps in both laboratory and free living environments (e.g., Welk et al. ). A copy of the curriculum is available from the corresponding author upon request.
Intervention arms were differentiated based solely on the type of step goal implemented by the promotora. Condition-specific step goal activities were conducted at each weekly meeting. All other intervention components were identical. The three different step goal conditions were as follows:
A self-selected step goal (SELF) in which promotoras gave no recommendations for setting daily or weekly step goals. Participants were instructed only to keep a log of their daily steps. Promotoras were trained to give only general feedback about how to use the pedometer and were required to give scripted responses to questions participants had about setting step goals.
A goal of 10,000 steps per day (FREQUENCY) in which participants were encouraged to walk at least 10,000 steps per day. Intervention activities focused on ways to accumulate more steps to help meet the goal. Examples included using behavior change strategies to help participants become more likely to walk to church, walk with their children to school, park farther away from the store, get on/off the bus at a stop farther away than usual, walking around the block after dinner, and so on. No intermediate step goals were provided (e.g., increasing number of steps by 10% until the 10,000-step goal was achieved).
A goal of 3000 steps in 30 min (CADENCE) in which participants were encouraged to accumulate at least 30 min of walking per day at a minimum step rate of 100 steps per minute. This could also be accomplished in bouts lasting as short as 10 min (1000 steps). Participants were taught to use the step rate goal using three strategies: 1) paying attention to the perceptual (e.g., effort) and physiologic cues (e.g., respiration rate, breathing, heart rate) associated with walking at a known step rate of 100 steps per minute; 2) measuring the number of steps for common trips (e.g., child’s school, church, local store, etc.) and then setting a time goal to reach that destination by dividing the number of steps by 100 or simply moving the decimal point two places to the left; and 3) walking in time to self-selected music that was measured at 100–105 beats·min−1.
Self-reported survey data
Socioeconomic status was assessed using education and income items modified from the Behavioral Risk Factor Surveillance System (5). Household income was also divided by the number of people living in the household to compute an income/family size ratio to serve as a proxy for poverty status (40).
Acculturation was measured using a modified version of the Short Acculturation Scale for Hispanics (21). The modified measure contained eight items with a 5-point Likert-type response assessing language use, media use, and ethnic and social relations. The internal consistency of the modified subscales was acceptable (α = 0.90, 0.78, and 0.75 for language use, media use, and social and ethnic relations, respectively).
General health was measured using a single self-reported item about perceived general health with a 5-point Likert-type response scale. Smoking status was measured using a single self-report item with a 3-point ordinal scale (yes, every day; yes, some days; no) and collapsed into a dichotomous item (yes, no) for analysis.
Perceived neighborhood walkability was measured using was the Neighborhood Environment Walkability Scale–Abbreviated (6). Two summary indices of walkability were computed on the basis of interscale correlations previously reported (6): 1) transport-related walkability (mean of scales: land-use mix access and street connectivity) and 2) recreation/transport-related walkability (mean of scales: Infrastructure and safety for walking/cycling; Aesthetics; Traffic hazards; Crime; and See other people walking).
Anthropometric assessments. Height was measured without shoes to the nearest 0.5 cm using a calibrated stadiometer. Weight was measured in light clothing and without shoes to the nearest 0.1 kg. Body mass index (BMI) was calculated as (weight [kg] / height × height [m]) and polychotomized to normal weight (BMI = 19.5–24.99 kg·m−2), overweight (BMI = 25–29.99 kg·m−2), and obese (BMI ≥ 30 kg·m−2).
Objective assessment of PA. Although the intervention focused on using pedometers to increase MVPA, pedometers cannot measure MVPA and so accelerometers are needed. A random sample of 60 participants in each condition were asked to wear a dual-mode (activity counts and steps) accelerometer (ActiGraph, model 7164; ActiGraph, LLC, Pensacola, FL) for seven consecutive days at each time point (baseline, 12 wk). The ActiGraph accelerometer measures and records uniaxial accelerations ranging in magnitude from 0.05g to 2.0g and has shown to provide valid and reliable assessments of PA frequency, intensity, and duration among adults in both laboratory and free-living environments (33). The device was set to aggregate acceleration and step data based on an epoch length of 1 min. The ActiGraph was attached to an elastic waist belt and positioned above the iliac crest of the right hip, consistent with the manufacturer’s recommendations, and participants were asked to remove the device during water-based activities (e.g., swimming, showering) and while sleeping. Consistent with the best practice recommendations for accelerometer use (23), all devices were distributed and collected in person, and each participant received at least one phone call from research staff during the monitoring period to remind them to wear their accelerometer and to troubleshoot any barriers that were restricting compliance.
Data processing rules. ActiLife v5 software was used for data downloading, but accelerometer data were cleaned and processed (aggregated) using customized Excel Macros written in Visual Basic specifically for this study. When commercially available accelerometer software became available, we reprocessed our data using MeterPlus v4.2 and ActiLife v6, and aggregated variables remained unchanged. Consistent with accelerometer data screening rules used in the National Health and Nutritional Examination Survey (32), nonwear time was defined as >59 consecutive min of zero counts, with allowance for 1–2 min of counts between 1 and 100. At baseline, there were no statistically significant associations between median nonwear time (min) and age (r = −0.06, P = 0.49), acculturation (r = −0.08, P = 0.38), household income (r = −0.09, P = 0.32), or education (r = −0.003, P = 0.98). True wear time was estimated by subtracting nonwear time from 24 h. Data were included in the analysis if true wear time exceeded 10 waking hours per day on a minimum of 5 days, including at least one weekend day (23). Of the sample, 96% and 97% met these criteria at baseline and 12 wk, respectively. Because the devices were collected in person 2 wk after the start of monitoring, most participants continued to wear the device after the 7-d monitoring period had ended. Because acceptable days worn (range = 5–11) was not statistically associated with median daily minutes of moderate-intensity (r = −0.08) or vigorous-intensity (r = −0.01) PA, all valid days (≥10 h on ≥5 d) were included in the analysis. Across all measurement points, the mean true wear time per day was 15.6 h (SD = 3.5), and the mean number of monitoring days was 8.13 (SD = 1.5).
Accelerometer outcome variables. Two outcome variables were extracted from the accelerometer data : 1) daily minutes of MVPA and 2) number of daily bouts of MVPA lasting at least 10 continuous minutes. Both variables were computed using the ActiGraph cut points developed by Freedson et al. (12). Specifically, MVPA was computed as the number of minutes in which accelerometer counts exceeded 1951 counts per minute.
For descriptive analysis, we computed mean and SD values. Nonparametric measures of central tendency were used when assumptions of distributional normality could not be upheld. For the primary outcomes—minutes per day of MVPA and bouts of MVPA ≥ 10 min—we analyzed repeated measures at baseline and at 12 wk post-baseline using a three-level generalized estimating equation (GEE) model to control for the clustering at the community center level and at the individual level. The original MVPA variables were skewed, so we first applied a log-transformation to bring the data distribution closer to a normal distribution. We then fit an adjusted three-level GEE model for the primary outcome regressed on the intervention condition alone. Then we fit another adjusted three-level GEE model for the primary outcomes regressed on the intervention condition and other potential influential covariates: age, smoking status, objective BMI at baseline, acculturation, general health, and two measures of neighborhood walkability. For the main outcome analysis, an alpha level of P < 0.10 was determined to be statistically significant. Setting a less conservative alpha level is warranted when the implications of making a type I error carry little public health risk or there may be additional potential benefits of an otherwise harmless intervention (7). For our primary research question “Is a 3000-steps-in-30-minutes goal effective at eliciting changes in MVPA?” we decided that a more lenient alpha level was appropriate because (i) there may be collateral benefit in recommending an intensity-based PA strategy, and (ii) our aim was to examine the efficacy of a new behavioral goal (3000 steps in 30 min) that might be used alongside an existing goal (10,000 steps per day), not as a replacement to it.
To take account of the effect of missing data on the evaluation of intervention effect, we adopted a pattern-mixture model approach (20). Pattern-mixture models are suitable for longitudinal data with potentially nonignorable missing data. Nonignorable missing data is a missing data mechanism that occurs when the probability of missing is related to unobserved variables. In pattern-mixture model approaches, the sample is stratified into different distinct missing data patterns, and intervention effects across these strata are examined (20). We counted the distinct missing data patterns and then created an additional categorical variable (PATTERN) for missing data patterns. Pattern-mixture GEE models were fitted for both unadjusted and adjusted cases with the addition of an interaction term between the intervention condition and the missing data pattern variable. SAS PROC GENMOD was used for all the GEE model fitting. Wald confidence intervals and type 3 analysis were used for testing individual effects in the GEE model. The least square means with the chi-square test was used to compare the difference of the moderate-intensity PA between different conditions such as intervention versus comparison.
Baseline characteristics on all measures for each condition are presented in Table 1. The mean enrollment per site was 28 participants (SD = 7, range = 18–41). Most participants reported being born in Mexico, not employed for wages, being married from households with a total monthly income of less than $1400 per month, and having a low level of acculturation. On the basis of household income and family size, 61% of participants were determined to be living below the federal poverty line (40). It should be noted that the daily steps data presented in Table 1 were derived from the accelerometer and not the pedometer used in the intervention. Although steps per day was not the focus of this article, caution should be used when equating accelerometer-measured steps with pedometer-measured steps because these devices have different levels of sensitivity although there is a high level of agreement between them (2,19).
Pattern-Mixture GEE Model Analysis of the Intervention Data
Minutes of MVPA. The results of the unadjusted pattern-mixture GEE analysis revealed that missing data had a significant effect (P = 0.0085) on intervention effects. Independent of missing data, there was a statistically significant difference in levels of MVPA between intervention conditions. Participants in the CADENCE condition participated in significantly more minutes per day of MVPA compared with participants in the FREQUENCY and SELF-SELECTED goal conditions. Figure 2 presents the median daily minutes of MVPA before and after the intervention, by treatment condition. Table 2 presents the results of the adjusted pattern-mixture GEE analysis for MVPA. The adjusted model accounts for potential residual imbalance in the conditions and examines the intervention effect, adjusting for age, acculturation level, BMI at baseline, perceived health (data not shown), smoking status, neighborhood walkability, and missing data pattern. Missing data patterns remained significant (P = 0.027; results not shown), indicating that outcome values of dropouts would likely have influenced posttest outcomes had they stayed in the trial. Smoking status and perceived health were significantly related to levels of MVPA (P = 0.035 and 0.038, respectively; data not shown), independent of condition. Participants who smoked and self-reported lower levels of general health engaged in less MVPA than nonsmokers and those self-reporting better general health. None of the other covariates (age, baseline BMI, acculturation, and neighborhood walkability) were statistically significant in the overall model. The least square mean difference test showed that the only significant difference in MVPA was between the SELF-SELECTED condition and the CADENCE condition (P = 0.0601). Because both the Wald test and the type 3 test can be problematic in the presence of missing data—and our findings suggest that missing data contributed to outcomes—we chose to interpret the least square means test as the more robust finding. This means that the only robust (P < 0.1) difference in MVPA between conditions was between the CADENCE and the SELF-SELECTED goal condition.
Bouts of MVPA >10 min. The results of the unadjusted pattern-mixture GEE analysis (not presented) revealed that the missing data had a significant effect on the number of daily bouts of MVPA. Figure 3 presents the median daily bouts of MVPA before and after the intervention by treatment condition, and Table 3 presents the results of the adjusted pattern-mixture GEE analysis. Independent of missing data, there was a statistically significant difference between intervention conditions in the number of daily bouts of MVPA lasting >10 min. Participants in the CADENCE condition accumulated significantly more bouts of MVPA per day than participants in the FREQUENCY condition or the SELF-SELECTED condition. Participants in the FREQUENCY condition accumulated significantly more bouts of MVPA than participants in the SELF-SELECTED goal condition. The adjusted model accounts for potential residual imbalance in the conditions and examines changes in MVPA bouts, adjusting for age, acculturation level, BMI at baseline, perceived health, smoking status, neighborhood walkability, and missing data pattern. Aside from the missing data pattern, no other covariates were statistically significant (data not shown); however, there was a trend (P = 0.0584) of a positive association between daily bouts of MVPA and recreation/transportation walkability of the neighborhood.
The median minutes of vigorous intensity PA per day at baseline and at 12 wk was 0 min, suggesting that MVPA consisted ostensibly of moderate-intensity PA. When minutes of moderate- and vigorous-intensity PA were analyzed separately, the findings for moderate intensity were identical with that of MVPA. Results for vigorous intensity PA are not presented. The CADENCE condition experienced the greatest increase in the percentage of meeting guidelines (from 30% to 65% after intervention; data not shown). The FREQUENCY condition had the fewest participants meeting guidelines after intervention (35%).
Walking at a rate of 100 steps per minute approximates MVPA (22), but population level data suggest that most people do not do this (35). The results of this study suggest that setting a daily step goal to walk 3000 steps in 30 min leads to more bout-based MVPA than using a self-selected goal among Mexican American women enrolled in promotora-mediated intervention. These findings are independent of age, acculturation level, BMI at baseline, perceived health, smoking status, neighborhood walkability, and missing data. Combined, these findings suggest that it may be important to encourage individuals to set a step cadence goal of >100 steps per minute in addition to a daily step frequency goal (10,000 steps per day) to increase bout-based MVPA.
There is expert consensus (36) that setting step cadence goals in addition to step frequency goals might help individuals engage in more MVPA. However, to the best of our knowledge, this is the first randomized controlled trial to provide evidence demonstrating that a step cadence strategy works, although our conclusions are limited to Mexican American women. Of note is that a step cadence goal and a 10,000-steps-per-day goal appear equally effective at increasing MVPA. This suggests that both should be incorporated into pedometer-based intervention programs designed to increase MVPA. Of note is that most participants (74%) in the SELF condition set no written goals throughout the intervention. This ranged from 91% during week 1 to 63% setting no goals at week 12. However, some participants in the SELF group may have set goals that were not disclosed during the intervention sessions. Mean pedometer steps per day after week 2 of the intervention (from participant steps logs) was 7811 (SD = 3430), 7637 (SD = 3293), and 7496 (SD = 3215) for SELF, FREQUENCY, and CADENCE groups, respectively. This represented a mean percent increase of 535 steps (SD = 421) from week 1 (nonsignificant between conditions), suggesting that across all conditions, there was an initial increase in walking after the introduction of the pedometer. However, these data were based on self-reported step logs which may be unreliable.
Although the step cadence and step frequency goal seemed equally effective at increasing MVPA, the step cadence goal also increased MVPA that occurred in bouts lasting at least 10 continuous minutes. This is an important finding because scientists have now concluded (42) that there is sufficient evidence to suggest that accumulating multiple 10-min bouts of MVPA is likely to convey a similar cardiometabolic benefit as a single longer bout. For this reason, accumulating MVPA in bouts lasting at least 10 continuous minutes is now part of national guidelines for PA (41). In our study, we also found that setting a step cadence goal was more likely to lead to meeting national PA guidelines compared with setting other types of step goals. This finding is particularly important because national data from objective measures of PA suggest that most Mexican American women do not meet PA guidelines (32), and there is a paucity of culturally sensitive efficacious PA interventions to help them do so (27).
Although a step rate of 100 steps per minute has relatively poor sensitivity and specificity for predicting MVPA (22), we have recommended that “3000 steps in 30 min” be used as a health promotion heuristic because it is a simple message that helps individuals also focus on activity intensity rather than frequency (steps) alone. Importantly, increasing step cadence also has substantial face validity as a strategy to increase exercise intensity. Practitioners who want to include a step cadence goal as part of a pedometer-based intervention can do so numerous ways. In our RCT, we used two main strategies that were rated very favorably by participants. The first strategy was that we encouraged participants to use their pedometer to measure the number of steps for common journeys they took on foot. For example, a study participant might report that it is 1650 steps from their home to their child’s school—a journey they completed four times per week. We then taught participants how to set a time-based goal to complete this journey using the rule of 100 steps per minute. This was achieved simply by teaching them to move the decimal point two places to the left. For example, a 1650-step journey should be completed in 16.5 min or quicker. The second strategy used music tempi.
Anecdotal and scientific data support the notion that music can have ergogenic properties during PA and exercise (17), with laboratory data suggesting that listening to synchronous music during exercise influences heart rate response, perceived exertion, and affect (18). Importantly, when music tempo increases or decreases, there appear matched changes in cadence and speed during treadmill walking/running (9) and cycle ergometry (43). Thus, we concluded that individuals might be naturally inclined to walk “in time” to the tempo of music played synchronously. Because step rate is analogous to the beats per minute of music (tempo), learning to self-monitor step rate using a prescribed music tempo may be a valuable PA intervention strategy. It should be noted that using music tempi to teach a behavioral skill is not a new phenomenon; for example, the 1970s disco hit “Staying Alive” by the Bee Gees is often used to train the correct chest compression rate during cardiopulmonary resuscitation. In the current study, we identified culturally relevant music that was 100–105 bpm to train step cadence that approximated the intensity of PA guidelines. By encouraging participants to walk in time to this music, they would approximate MVPA, thereby reducing the reliance on a pedometer and a wrist watch to calibrate their walking intensity. To avoid reliance on a digital music player, we also encouraged participants to sing the song out loud or hum it to themselves to get into the optimal step rate quickly. Although not a focus of our study, there are now many free Web sites that permit individuals to search for songs based on a selected bpm (e.g., www.bpmdatabase.com) or find the bpm of a favorite song by simply tapping in time to it (e.g., www.bpm-finder.noisegames.com).
Future research is needed to corroborate the efficacy of a step cadence–based goal for increasing bouts of MVPA lasting ≥10 continuous minutes in adults. In particular, the effectiveness of the two intervention strategies we used to increase step cadence (i.e., setting time-based goals for destination-based walking and using and music tempi to “calibrate” walking speed) could be further evaluated and compared with common daily step frequency goals (e.g., 10,000 steps) for increasing MVPA and adherence to pedometer-based walking programs.
A limitation of this study is that the sample was limited to predominantly overweight and obese Mexican American women with low levels of acculturation. Findings may not generalize to other populations. The intervention was also delivered entirely in Spanish by promotoras, and the cultural relevance of the intervention strategies for increasing step cadence in samples who are not Mexican American or without promotora assistance is unknown. We tried to increase the generalizability of the intervention by randomizing promotoras to condition, providing extensive training to the promotoras in intervention delivery, providing detailed “lesson plans” for each group session, and closely monitoring intervention fidelity using logs of strategy implementation during each group session. Finally, the intervention lasted 12 wk, and it is unknown if changes in MVPA persisted beyond this time.
This study suggests that step cadence may be an efficacious strategy for increasing bout-based MVPA among Mexican American women when used as part of a promotora-delivered PA intervention. Setting a step cadence goal of walking 3000 steps in 30 min was superior to a self-selected goal and a goal of 10,000 steps per day for increasing daily MVPA accumulated in bouts lasting ≥10 min. Accumulating MVPA in bouts greater than 10 min is considered important for health and is now part of national and international PA guidelines.
This work was supported by a cooperative agreement (special interest project) U48 DP000036-01S1; principal investigator Dr. Simon Marshall) between the CDC and the San Diego Prevention Research Center. The San Diego Prevention Research Center is a member of the Prevention Research Centers Program, supported by the CDC Cooperative Agreement number 5-U48-DP-000036.
The authors thank the community residents who participated in the program and the wonderful team of research staff and promotoras who helped design, deliver, and evaluate it, particularly Carolina Huerta, Pilar Santos, Maria (Lucy) Estrada, Olivia Guerra, Ana Maria Garcia, Rosy Aguiar, Maria Munoz, Viviana Sherwood, Rosy Godoy, Liz Milligan, Diego Velasquez, and Kristi Robusto. The authors also thank Dr. Catrine Tudor-Locke (Pennington Biomedical Research Center), who served as a consultant in the early stages of intervention development.
The authors declare no conflict of interest.
The results of the present study do not constitute endorsement by the American College of Sports Medicine.
1. Adams MA, Caparosa S, Thompson S, Norman GJ. Translating physical activity recommendations for overweight adolescents to steps per day. Am J Prev Med
. 2009; 37 (2): 137–40.
2. Barriera TV, Tudor-Locke C, Champagne CM, Broyles ST, Johnson WD, Katzmarzyk PT. Comparison of GT3X Accelerometer and YAMAX Pedometer
Steps/Day in a Free-Living Sample of Overweight and Obese Adults. J Phys Act Health
; [epub ahead of print June 13, 2012].
3. Bravata DM, Smith-Spangler C, Sundaram V, et al.. Using pedometers to increase physical activity and improve health: a systematic review. JAMA
. 2007; 298 (19): 2296–304.
4. Candelaria J, Campbell N, Lyons G, Elder J, Villaseñor A. Strategies for health education: community-based methods. In: Loue S, editor. Handbook of Immigrant Health
. New York: Plenum; 1998. pp. 587–606.
5. Centers for Disease Control and Prevention. [Internet]; Centers for Disease Control and Prevention; [cited September 28, 2007]. Available from: http://www.cdc.gov/brfss/
6. Cerin E, Saelens BE, Sallis JF, Frank LD. Neighborhood Environment Walkability Scale: validity and development of a short form. Med Sci Sports Exerc
. 2006; 38 (9): 1682–91.
7. Cohen J. The Earth is round (P
< 0.05). Am Psychol
. 1994; 49 (12): 997–1003.
8. Eakin EG, Brown WJ, Marshall AL, Mummery K, Larsen E. Physical activity promotion in primary care: bridging the gap between research and practice. Am J Prev Med
. 2004; 27 (4): 297–303.
9. Edworthy J, Waring H. The effects of music tempo and loudness level on treadmill exercise. Ergonomics
. 2006; 49 (15): 1597–610.
10. Elder JP, Ayala GX, Parra-Medina D, Talavera GA. Health communication in the latino community: issues and approaches. Annu Rev Public Health
. 2009; 30: 227–51.
11. Elder JP, Ayala GX, Parra-Medina D, Talavera GA. Health promotion in the Latino community: issues and approaches. Annu Rev Public Health
. 2009; 30: 227–51.
12. Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science Applications, Inc. accelerometer. Med Sci Sports Exerc
. 1998; 30 (5): 777–81.
13. Harrington DM, Tudor-Locke C, Champagne CM, et al.. Step-based translation of physical activity guidelines in the Lower Mississippi Delta. Appl Physiol Nutr Metab
. 2011; 36 (4): 583–5.
14. Hatano Y. Use of the pedometer
for promoting daily walking exercise. Intern Council Health, Phys Educ Recreation
. 1993; 29: 4–8.
15. Joyner K, Mummery W. Awareness of the 10,000 Steps Program Across Queensland
. Rockhampton, Queensland: Central Queensland University; 2006. pp. 1–13.
16. Kang M, Marshall SJ, Barreira TV, Lee JO. Effect of pedometer
-based physical activity interventions: a meta-analysis. Res Q Exerc Sport
. 2009; 80 (3): 648–55.
17. Karageorghis C, Jones L, Stuart DP. Psychological effects of music tempi during exercise. Int J Sports Med
. 2008; 29 (7): 613–9.
18. Karageorghis CI, Mouzourides DA, Priest DL, Sasso TA, Morrish DJ, Walley CJ. Psychophysical and ergogenic effects of synchronous music during treadmill walking. J Sport Exerc Psychol
. 2009; 31 (1): 18–36.
19. Kinnunen TI, Tennant PW, McParlin C, Poston L, Robson SC, Bell R. Agreement between pedometer
and accelerometer in measuring physical activity in overweight and obese pregnant women. BMC Public Health
. 2011; 11: 501.
20. Little RJA, Wang Y. Pattern-mixture models for multivariate incomplete data with covariates. Biometrics
. 1996; 52: 98–111.
21. Marin G, Sabogal F, Marin BV, Otero-Sabogal R, Perez-Stable EJ. Development of a short acculturation scale for Hispanics. Hisp J Behav Sci
. 1987; 9: 183–205.
22. Marshall SJ, Levy SS, Tudor-Locke CL, et al.. Translating physical activity recommendations into a pedometer
-based step goal: 3000 steps in 30 minutes. Am J Preven Med
. 2009; 36 (5): 410–5.
23. Matthews CE, Hagstromer M, Pober DM, Bowles HR. Best practices for using physical activity monitors in population-based research. Med Sci Sports Exerc
. 2012; 44 (1 Suppl): S68–76.
24. McGuire WJ. Theoretical foundations of campaigns. In: Rice RE, Atkin CK, editors. Public Communication Campaigns
. Newbury Park: Sage; 1989, pp. 43–65.
25. Minkler M, Wallerstein N. Community Based Participatory Research for Health
. San Francisco, CA: Jossey-Bass; 2003. p. 490.
26. Pal S, Cheng C, Ho S. The effect of two different health messages on physical activity levels and health in sedentary overweight, middle-aged women. BMC Public Health
. 2011; 11: 204.
27. Perez A, Fleury J, Keller C. Review of intervention studies promoting physical activity in Hispanic women. West J Nurs Res
. 2010; 32 (3): 341–62.
28. Rice K, Heesch K, Dinger M, Fields D. Effects of 2 brief interventions on women’s understanding of moderate-intensity physical activity. J Phys Act Health
. 2008; 5 (1): 58–73.
29. Richardson CR, Newton TL, Abraham JJ, Sen A, Jimbo M, Swartz AM. A meta-analysis of pedometer
-based walking interventions and weight loss. Ann Fam Med
. 2008; 6 (1): 69–77.
30. Rowe DA, Welk GJ, Heil DP, et al.. Stride rate recommendations for moderate-intensity walking. Med Sci Sports Exerc
. 2011; 43 (2): 312–8.
31. Schneider PL, Crouter SE, Bassett DR. Pedometer
measures of free-living physical activity: comparison of 13 models. Med Sci Sports Exerc
. 2004; 36 (2): 331–5.
32. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T McDowell M. Physical activity in the United States measured by accelerometer.. Med Sci Sports Exerc
. 2008; 40 (1): 181–8.
33. Trost SG, McIver KL, Pate RR. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc
. 2005; 37 (11 Suppl): S531–43.
34. Tudor-Locke C. Taking steps toward increase physical activity: using pedometers to measure and motivate. Research Digest
. 2002; 3: 1–8.
35. Tudor-Locke C, Camhi SM, Leonardi C, et al.. Patterns of adult stepping cadence in the 2005–2006 NHANES. Prev Med
. 2011; 53 (3): 178–81.
36. 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 (1): 79.
37. Tudor-Locke C, Leonardi C, Johnson WD, Katzmarzyk PT, Church TS. Accelerometer steps/day translation of moderate-to-vigorous activity. Prev Med
. 2011; 53 (1–2): 31–3.
38. Tudor-Locke C, Rowe DA. Using cadence to study free-living ambulatory behaviour. Sports Med
. 2012; 42 (5): 381–98.
39. Tudor-Locke C, Sisson SB, Collova T, Lee SM, Swan PD. Pedometer
-determined step count guidelines for classifying walking intensity in a young ostensibly healthy population. Can J Appl Physiol
. 2005; 30 (6): 666–76.
40. U.S. Census Bureau. [Internet];Washington, DC: U.S. Census Bureau, Housing and Household Economic Statistics Division. 2009 [cited July 30, 2009]. Available from: http://www.census.gov/hhes/www/poverty/data/threshld/thresh08.html
41. U.S. Department of Health and Human Services, Physical Activity Guidelines Advisory Committee. 2008 Physical Activity Guidelines for Americans
. Washington, DC; U.S. Government; 2008. 61. Available from: http://www.health.gov/paguidelines/pdf/paguide.pdf
42. U.S. Department of Health and Human Services, Physical Activity Guidelines Advisory Committee. Physical Activity Guidelines Advisory Committee Report, 2008.
Washington, DC; US Government; 2008. 1–683. Available from: http://www.health.gov/paguidelines/Report/pdf/CommitteeReport.pdf
43. Waterhouse J, Hudson P, Edwards B. Effects of music tempo upon submaximal cycling performance. Scand J Med Sci Sports
. 2010; 20 (4): 662–9.
44. Welk GJ, Differding JA, Thompson RW, Blair SN, Dziura J, Hart P. The utility of the digi-walker step counter to assess daily physical activity patterns. Med Sci Sports Exerc
. 2000; 32 (9 Suppl): S481–8.
45. Wilde BE, Sidman CL, Corbin CB. A 10,000-step count as a physical activity target for sedentary women. Res Q Exerc Sport
. 2001; 72 (4): 411–4.
46. Yancey AK, Ortega AN, Kumanyika SK. Effective recruitment and retention of minority research participants. Annu Rev Public Health
. 2006; 27: 1–28.