Impact of Latin Dance on Physical Activity,Cardiorespiratory Fitness,and Sedentary Behavior Among Latinos Attending an Adult Day Center

Abstract

Objective: The aim of this study was to determine whether a Latin dance program with sedentary behavior information would have an impact on physical activity, cardiorespiratory fitness (CRF), and sedentary behavior among older Latinos attending an adult day center (ADC). Method: Participants (N = 21, 75.4 ± 6.3 years old, Mini-Mental State Examination [MMSE] score = 22.4 ± 2.8) were randomized into a dance or wait-list control group. Participants wore an accelerometer and inclinometer and completed a sedentary behavior questionnaire, and a nonexercise equation was used to calculate CRF. Results: Findings indicate small to medium effect sizes in the desired direction during midpoint of the intervention for physical activity, sedentary behavior–related outcomes, CRF, and self-reported sedentary behavior in the dance group; however; dance participants did not maintain that trajectory for the remaining 2 months of the intervention. Discussion: Future studies may consider implementing behavioral strategies during midpoint of the intervention to encourage participants attending an ADC to maintain physical activity and sedentary behavior changes.

Keywords

Hispanics sitting time accelerometer dance adult day services

Introduction

Older Latinos in the United States are projected to represent 22.5% of the total population over 65 years by 2030 (West, Cole, Goodkind, & He, 2014). The increased older Latino population has implications for a demand in long-term care services. However, many older Latinos prefer to age in place and expect to be taken care by family members than be admitted into a long-term care facility (Herrera, Angel, Venegas, & Angel, 2012). Adult day centers (ADCs) are community-based services that help older adults age in place by offering a variety of services during daytime hours that support the health, nutritional, social, and daily living needs of adults with functional limitations (Fields, Anderson, & Dabelko-Schoeny, 2014). As such, ADCs are well-positioned settings for preventing and ameliorating chronic diseases via physical activity and sedentary behavior interventions.

Low levels of physical activity and low cardiorespiratory fitness (CRF) are associated with an increased risk of heart disease, diabetes, and mortality (U.S. Department of Health and Human Services, 2008). More recent studies have also shown that sedentary behavior, defined as sitting or reclining postures during waking hours (Sedentary Behaviour Research Network, 2012), is also associated with obesity, cardiovascular disease, and cancer, independent of regular moderate-to-vigorous physical activity (MVPA; Sedentary Behaviour Research Network, 2012; Wilmot et al., 2012).

ADCs report offering physical activity programs (Anderson, Dabelko-Schoeny, & Johnson, 2013), which should be a critical element in these settings as more than 40% of ADC participants have some form of disability, and nearly half of all ADC participants have mild cognitive impairment (MCI; Fields et al., 2014). Physical activity has been shown to slow the decline in activities of daily living (Rolland et al., 2007), improve neuropsychiatric symptoms (Hoffmann et al., 2015), and decrease rate in cognitive decline (Farina, Tabet, & Rusted, 2016) among older adults. Unfortunately, there are limited data examining physical activity and sedentary behaviors among ADC participants.

Examining physical activity and sedentary behavior is especially pertinent among this population given that older Latinos aged 65 to 74 are 46% less likely to engage in leisure-time physical activity than older non-Latino Whites (Marquez, Neighbors, & Bustamante, 2010). Furthermore, a recent longitudinal cohort study found that in a representative sample of Latinos aged 65 to 74, older Latinos spent an average of 11.11 hr/day in sedentary behaviors as measured by accelerometer (Merchant et al., 2015). A recent meta-analysis found that clinically meaningful reductions in sedentary time can be produced within physical activity interventions that include some component of reducing sedentary behaviors (Prince, Saunders, Gresty, & Reid, 2014). Thus, interventions that concomitantly improve physical activity and reduce sedentary behavior among older Latinos are warranted. To the best of our knowledge, studies have not yet examined physical activity and sedentary behavior of older Latinos attending ADCs, nor have physical activity and sedentary behavior interventions been implemented with this specific population.

A culturally appropriate dance program, BAILAMOS^©, for cognitively intact, community dwelling older Latinos has previously shown that participation in this program can increase physical activity (Marquez, Bustamante, Aguinaga, & Hernandez, 2015). It is unknown whether this program could have a similar impact on physical activity among older Latinos attending an ADC who may have cognitive impairment (Fields et al., 2014). Thus, the purpose of the present study was to determine whether the BAILAMOS^© program infused with discussion sessions about sedentary behavior information would have an impact on physical activity, CRF, and sedentary behavior among older Latinos attending an ADC. We hypothesized that participants in the dance group compared with the wait-list control group would have greater increases in physical activity and CRF and decreases in sedentary time.

Method

Sample

Older Latinos were recruited via study flyers and announcements at an Adult Wellness Center in Chicago to participate in a 16-week Latin dance program. The Adult Wellness Center (considered a 501c3 tax-exempt public charity) is an ADC which offers bilingual and bicultural services to the Spanish-speaking older adult population of Chicago and provides many supervised, structured activities that maintain, improve, and restore participants’ abilities. To attend the ADC, individuals must meet certain eligibility criteria, including >60 years old, U.S. citizen, Illinois resident, physician authorization, and a score of 29 or higher on the Determination of Need Assessment (DON). Inclusion criteria for the present study were the following: (a) age ≥60 years, (b) self-identification as Latino/Hispanic, (c) ability to speak or understand Spanish, (d) MCI with scores of 18 to 26 (Logsdon et al., 2010; McGough et al., 2011) as measured by the Mini-Mental State Examination (MMSE; Folstein, Folstein, & McHugh, 1975), and (e) no plans to leave the country for more than two consecutive weeks over the next 4 months (study duration). Exclusion criteria included (a) regular use of assistance to walk (e.g., cane), (b) stroke at any time, and (c) >150 min of self-reported exercise defined as structured, planned, and repetitive aerobic activity like walking or swimming over an extended period of time with a specific objective such as increasing fitness, physical performance, or health (Bouchard, Shephard, & Stephens, 1994). In addition, participants who passed initial screening were asked to get medical clearance to participate in the research. Study approval was obtained from the University of Illinois at Chicago Institutional Review Board.

Design

The study was a pilot randomized controlled trial with a wait-list control. Participants who met inclusion criteria and completed baseline testing were randomized to either the dance program or the wait-list control group. Randomization was done by computer-generated random numbers and was delivered by a research staff member. The wait-list control group was asked to maintain their usual activities during Weeks 1 through 16.

Testing

All testing took place at the ADC. This was preferred because participants already attended the ADC, and the space necessary for testing was available. At the testing, a bilingual research staff member, blinded to study condition, explained the study and read the informed consent to the participant. After participants agreed to participate, they signed the informed consent. Consent of participants on their own was deemed appropriate because they had MCI, not dementia. Questionnaires and assessments were then administered, and all measures were available in Spanish or English. Participants were also given an ActiGraph accelerometer and ActivPAL monitor to wear for 1 week. Testing occurred at baseline, 2 months, 4 months (post testing), 6 months, and 8 months (4-month follow-up). The 4-month follow-up occurred during the month of February. For all time points, questionnaires and tests were administered in the same order as baseline testing. Participants were compensated with US$15 dollars for completion of the questionnaires, and another US$15 dollars for returning their accelerometer and ActivPAL monitors.

Intervention

BAILAMOS^© includes a 4-month, twice-weekly program (Marquez et al., 2015). Every dance session is 1 hr in length. BAILAMOS^© includes four dance styles: Merengue, Cha Cha Cha, Bachata, and Salsa. The sequencing of dance styles was determined by level of difficulty, with the simplest style introduced first (i.e., Merengue) and the most difficult style last (i.e., Salsa). Monthly discussion sessions are also held in which a research assistant focuses on increasing knowledge, outcome expectations, social support, and self-efficacy to increase lifestyle physical activity among participants. For the current intervention, several changes were made to the BAILAMOS^© program. For instance, a research staff member was present at all dance sessions to set up the room and observe the class. Participants wore an orange Velcro bracelet on their right wrist and a green Velcro bracelet on their left wrist to help them distinguish between moves to the left and right. We followed the BAILAMOS^© manual but were cognizant of elements that seemed to confuse participants. We recorded such challenges and revised the program as needed. For example, if some of the dance turns that are part of BAILAMOS^© were too confusing, we revised the moves in ways that still challenged participants physically and cognitively but did not overwhelm them or put their safety at risk. The monthly discussion sessions were also modified from the original version. For example, the first discussion session included an overview on program expectations and specifics, and a brief description of physical activity recommendations and sedentary behavior. The participants also received visual sedentary behavior feedback in which they were shown an output page of their baseline ActivPAL data. We then identified portions of their day in which they were the most sedentary (i.e., after lunch), and participants were encouraged to reduce their sedentary time and increase sedentary breaks throughout those specific times (the times were also highlighted in red). The second discussion session included information about the consequences associated with prolonged sitting and the benefits of sedentary breaks. The third discussion session had participants brainstorm on strategies to reduce sedentary behavior throughout the day (i.e., standing during TV commercials). Finally, the fourth discussion session discussed the importance of habit formation and encouraged participants to share strategies that they had been incorporating.

Measures

Device-assessed physical activity

Participants were given a triaxial GT3X+ accelerometer (Actigraph, Pensacola, Florida) over their nondominant wrist as compliance of accelerometers is higher when worn on the wrist (Freedson & John, 2013; Troiano, McClain, Brychta, & Chen, 2014). Participants were instructed to wear the accelerometer for seven consecutive days and were asked to remove it for showering or swimming. Participants wore the accelerometer at baseline, Month 2, Month 4, Month 6, and Month 8. Wear time is reported in days and hours. Data were included if the participant wore the accelerometer for ≥3 days and ≥10 hr/day. Data were processed with ActiLife software with data converted to 60-s epochs. Average counts per minute (CPM) are reported.

CRF

CRF was assessed using a validated regression equation that does not involve exercise testing (Jurca et al., 2005; Mailey et al., 2010). This equation estimates CRF based on sex, age, body mass index (BMI), resting heart rate, and level of physical activity on a scale from 1 to 5. CRF in metabolic equivalents (METs) was calculated using the following equation: gender × (2.77) – age × (0.10) – BMI × (0.17) − resting heart rate × (0.03) + physical activity score + 18.07 (Jurca et al., 2005).

Device-assessed sedentary behavior

Participants were given an ActivPAL 3TM® inclinometer monitor (PAL Technologies, Glasgow, Scotland, the United Kingdom). The research assistant placed the ActivPAL on their nondominant leg (same side as accelerometer), and participants were asked to keep it on for seven continuous days. Continuous wear was achieved by waterproofing the monitor with a nitrile sleeve to cover the monitor, wrapping it with a cushioned first aid tape (for comfort), and attaching it to the leg with an adhesive dressing (3M Tegaderm 9546HP). Participants were provided with written placement instructions and extra adhesive dressings in the event that they needed to remove the monitor and reattach it. Participants were requested to complete a sleep log in which they would record wake time and sleep time. Participants wore the ActivPAL at baseline, Month 2, Month 4, Month 6, and Month 8. The “Events” files were used to analyze data; these list all bouts of sitting/lying, standing, and steps, with the time each bout begins and bout duration (Edwardson et al., 2016). Waking time was isolated by using the sleep logs and visually identifying sleep times from the “Events” files; this occurred when participants did not return the sleep logs. An extremely long bout of sitting/lying would be identified around late evening or early morning, and that data would be removed from the file (Edwardson et al., 2016). Activity was classified as sedentary sum of time where activity code = 0, standing sum of time where activity code = 1, light sum of time where activity code = 1 or 2 and METs ≤3, and MVPA as sum of time where METs >3. Sedentary breaks were also calculated as the total number of sedentary periods (Hamilton, Hamilton, & Zderic, 2004).

Self-reported sedentary behavior

The Spanish version of the Sedentary Behavior Questionnaire (SBQ; Rosenberg et al., 2010) which has acceptable reliability (Munguia-Izquierdo et al., 2013) was used to assess sedentary behavior at baseline, Month 2, Month 4, Month 6, and Month 8. This questionnaire assesses the amount of time sitting while doing nine behaviors. As this questionnaire was translated using Spanish from Spain, several words were changed to Spanish from Latin America, specifically Mexico, to convey appropriate meaning of certain words. Cronbach’s alphas for the SBQ items were adequate (α = .84).

Data Analysis

Data analysis was conducted in SPSS (version 22; IBM, Chicago, Illinois). All participants were encouraged to complete testing at all five time points regardless of dance attendance rate. An intent-to-treat analysis was employed. Repeated-measures analyses of variance (ANOVAs) were used to determine the effects of the dance intervention compared with the wait-list control at midpoint (Month 0-Month 2) of the intervention and at Month 0-Month 4. We calculated Cohen’s d values as an estimate of the effect sizes (small, .20; medium, .50; and large, .80). We also conducted paired t tests to assess maintenance of physical activity from Month 4 to Month 8 in the dance group only.

Results

Participant Characteristics

Twenty-one participants enrolled in the study. Ten participants were randomized to the dance intervention and 11 participants to the wait-list control group. Study details regarding flow of participants have been previously described (Aguiñaga & Marquez, 2017). The mean age of the participants was 75.4 ± 6.3 years, mean MMSE score was 22.4 ± 2.8, 76.2% were women, and mean years of education was 6.3 ± 4.3 years (Table 1). Dance participants had an attendance rate of 60%. Four participants dropped from the program due to back and lower extremity pain (n = 2), hip fracture (not associated with the dance program; n = 1), and perceiving the dance sessions as too difficult (n = 1). Dropouts were considered as those participants who stopped coming to the program and did not come back. Including dropouts, participants attended 19/32 dance sessions, and excluding dropouts, participants attended 27.17/32 dance sessions.

Table 1.

Participant Characteristics.

Demographics	All (N = 21)	Dance (n = 10)	Wait-list control (n = 11)
Demographics	M (±SD) or n (%)	M (±SD) or n (%)	M (±SD) or n (%)
Age	75.4 (±6.3)	76.0 (±6.0)	74.9 (±6.8)
Mini-Mental State Examination	22.4 (±2.8)	21.5 (±2.6)	23.2 (±2.8)
Body mass index	27.7 (±4.4)	26.3 (±2.5)	29.0 (±5.5)
Waist circumference (cm)	99.1 (±9.3)	95.6 (±6.8)	102 (±10.5)
Women	16 (76.2%)	8 (80.0%)	8 (72.7%)
Widow/widower	8 (38.1%)	6 (60.0%)	2 (18.2%)
Country of birth
Caribbean	9 (42.9%)	5 (55.6%)	4 (44.4%)
South America	6 (28.6%)	3 (50.0%)	3 (50.0%)
Other	6 (28.6%)	2 (33.3%)	4 (66.7%)
Years in the United States	29.5 (±20.1)	31.9 (±19.3)	27 (±21.5)
Annual income <US$10,000	11 (52.3%)	4 (40.0%)	7 (63.7%)
Years of education	6.3 (±4.3)	7.2 (±5.6)	5.6 (±2.9)

Device-Assessed Physical Activity

Mean accelerometer wear time was assessed at all five time points. Wear time ranged from 18.33 to 20.07 hr, and days worn ranged from 7.50 to 8.85 days. The repeated-measures ANOVA demonstrated no time or Time × Group effects for all of the time points. When assessing effect sizes, the midpoint analysis (Month 0-Month 2) revealed a medium effect (d = .68) which was driven by dance participants’ increase in activity counts (+251.81 CPM/day vs. –71.33 CPM/day). The Month 0-Month 4 analysis revealed a small effect (d = .40) that was driven by decreases in activity counts in the dance group, suggesting that changes made during the first 2 months were not maintained for the remaining duration of the intervention. During the maintenance period, dance participants experienced a small effect (d = .19), in which dance participants returned to baseline activity levels (Table 2).

Table 2.

Changes in Accelerometer, Fitness and Sedentary Behavior Outcomes at Baseline and Months 2, 4, 6, and 8.

Outcome	Month 0M ± SD	Month 2M ± SD	M0-M2Time effect (p)Time × Group (p)Cohen’s d^a	Month 4M ± SD	M0-M4Time effect (p)Time × Group (p)Cohen’s d^a	Month 6M ± SD	Month 8M ± SD	M4-M8p valueCohen’s d^a
Accelerometer CPM
Dance (n = 9)	1,269.80 ± 566.27	1,521.61 ± 438.18	.55 .29 .68	1,173.55 ± 557.01	.77 .22 .40	1,394.14 ± 296.77	1,274.96 ± 510.75	.41 .19
Control	1,394.70 ± 380.34	1,323.37 ± 500.21	.55 .29 .68	1,486.16 ± 449.19	.77 .22 .40			.41 .19
CRF (estimated maxMet)
Dance	6.05 ± 1.68	6.49 ± 1.50	.04.70.07	7.82 ± 0.96	.001.49.25	6.83 ± 1.83	6.51 ± 1.56	.011.01
Control	5.84 ± 1.82	6.15 ± 2.04	.04.70.07	7.17 ± 2.19	.001.49.25			.011.01
ActivPAL-sitting (hr/day)
Dance	8.02 ± 2.30	8.11 ± 1.15	.68.51.25	7.86 ± 1.35	.26.47.27	7.86 ± 1.51	7.92 ± 1.45	.83.04
Control	9.47 ± 1.77	9.06 ± 1.60	.68.51.25	8.75 ± 1.10	.26.47.27			.83.04
ActivPAL-light (hr/day)
Dance	5.80 ± 2.08	5.51 ± 1.30	.91.47.25	5.48 ± 1.39	.49.80.09	6.01 ± 0.83	6.03 ± 1.28	.12.41
Control	5.02 ± 1.86	5.23 ± 1.14	.91.47.25	4.88 ± 1.31	.49.80.09			.12.41
ActivPAL-MVPA (min/day)
Dance	46.96 ± 30.85	51.85 ± 21.11	.91.37.26	44.63 ± 17.77	.27.57.15	45.74 ± 23.10	49.81 ± 26.34	.18.23
Control	43.51 ± 35.02	39.69 ± 12.82	.91.37.26	36.19 ± 17.32	.27.57.15			.18.23
ActivPAL-steps (steps/day)
Dance	6,182.99 ± 2,870.21	6,728.39 ± 2,341.82	.79.44.24	5,868.59 ± 2,075.52	.30.68.12	6,104.55 ± 2,642.65	6,490.57 ± 3,199.30	.15.23
Control	5,497.71 ± 3,756.66	5,231.35 ± 1,560.77	.79.44.24	4,778.39 ± 2,012.92	.30.68.12			.15.23
ActivPAL-breaks/day
Dance	43.62 ± 13.04	47.79 ± 8.93	.32.47.29	43.86 ± 12.21	.96.87.06	48.08 ± 10.01	44.55 ± 9.13	.74.06
Control	44.11 ± 10.79	44.77 ± 10.34	.32.47.29	43.68 ± 11.76	.96.87.06			.74.06
SBQ-Weekday sitting (hr/day)
Dance	5.95 ± 3.74	6.18 ± 3.49	.11.07.83	4.65 ± 2.96	.001.04.60	4.32 ± 2.10	3.90 ± 3.05	.19.25
Control	10.8 ± 5.10	7.3 ± 2.93	.11.07.83	6.80 ± 3.81	.001.04.60			.19.25
SBQ-Weekend sitting (hr/day)
Dance	4.68 ± 2.17	4.78 ± 5.42	.43.40.43	4.31 ± 2.64	.61.94.01	4.27 ± 1.79	2.98 ± 2.86	.29.48
Control	9.93 ± 9.36	7.07 ± 2.45	.43.40.43	9.66 ± 7.47	.61.94.01			.29.48

Note. CPM = counts per minute; CRF = cardiorespiratory fitness; maxMet = maximal metabolic equivalents; MVPA = moderate-to-vigorous physical activity; SBQ = Sedentary Behavior Questionnaire.

small, .20; medium, .50; and large, .80

CRF

The results of the repeated-measures ANOVA indicated a significant time effect at midpoint and at Month 0-Month 4 in which both groups improved their CRF, F(1, 19) = 24.06, p = .001, η² = .56. Interaction effects were not significant; however, a small effect size driven by improvements in CRF in the dance group at Month 0-Month 4 was observed (d = .25). During the maintenance period, dance participants experienced a large effect size, d = 1.01, in which dance participants significantly decreased their CRF levels, t(9) = 3.31, p = .01.

Device-Assessed Sedentary Behavior

Mean ActivPAL wear time was assessed at all five time points. Wear time ranged from 7.49 to 8.77 days. The repeated-measures ANOVA revealed no time or Time × Group effects for all of the time points. When assessing effect sizes, the midpoint analysis revealed small effects for sitting time and light activity which was driven by the control group’s decrease in sitting time and the dance group’s decrease in light activity (d = .25, .25). Small effects were also observed in MVPA, steps, and breaks, which were driven by improvements in these outcomes among the dance group. The Month 0-Month 4 analysis revealed a small effect for sitting time, driven by a reduction in sitting time in the wait-list control, and the effect sizes for light activity, MVPA, steps, and breaks were insignificant.

Self-Reported Sedentary Behavior

The repeated-measures ANOVA revealed no time or Time × Group effect in the midpoint analysis. During Month 0-Month 4, there was a significant time effect for weekday sedentary time, F(1, 19) = 18.71, p = .001, η² = .50, in which both groups decreased their sitting time, and a significant Time × Group effect, F(1, 19) = 4.87, p = .04, η² = .20. There were large effect sizes during the midpoint analysis and the Month 0-Month 4 for weekday sitting, which were driven by greater decreases in sitting time in the wait-list control. A small effect was seen at midpoint for weekend sitting time that was driven by decreases in the wait-list control group’s sitting time. During the maintenance period, the dance group experienced small to medium effects in which participants continued to decrease weekday and weekend sedentary behavior.

Discussion

This study assessed the impact of a Latin dance intervention on physical activity, CRF, and sedentary behavior among older Latinos attending an ADC. We found small to medium effect sizes favoring the dance group for accelerometer CPM, MVPA, steps, and breaks in which these outcomes improved during the first 2 months of the intervention suggesting that changes may occur rather quickly. However, participants did not maintain that trajectory for the remaining 2 months of the intervention for some of the outcomes (accelerometer CPM, MVPA, steps, and breaks), and participants returned to baseline levels during Month 4 assessments. Additional behavioral strategies may be necessary during midpoint of the intervention to encourage participants attending an ADC to maintain physical activity and sedentary behavior changes. Notably, both groups did significantly improve in CRF during the Month 0-Month 4 time points.

The increase in CRF by 1.77 metabolic equivalents in the dance group represents a large effect size; however, the wait-list control group also increased their CRF by 1.33 metabolic equivalents during the Month 0-Month 4 time point. Changes in CRF among the control group may be due to participation in other activities at the ADC or possibly, but highly unlikely, contamination effects. Whether these improvements are attributed to the dance intervention or the ADC programming, increasing CRF among this population is important because increased CRF has been associated with better physical and cognitive functions in older adults (Barnes, Yaffe, Satariano, & Tager, 2003; Kaminsky et al., 2013). Furthermore, Martinez-Gomez and colleagues (2015) found that a 1-MET increase in nonexercise CRF is associated with a 20% lower mortality among older women. Interestingly, data comparing Month 4 with Month 8 indicate that dance participants decreased their CRF levels by 1.31 metabolic equivalents, suggesting that dance participants’ CRF was maintained above baseline levels once the dance intervention ended. Thus, participating in dance may be important for maintaining CRF in this group of older Latinos attending an ADC.

Self-reported weekday sedentary behavior data showed that dance participants self-reported a decrease in sedentary behavior by 1.3 hrs from Month 0 to Month 4 while the wait-list control group self-reported a decrease in sedentary behavior of 4 hrs. Thus, all participants were self-reporting less time spent sedentary; however, this did not correspond to the ActivPAL sitting data. According to the ActivPAL sitting data, dance participants had reduced sedentary behavior by 9.6 min, whereas the wait-list control group reduced their sedentary behavior by 43 min. Dance participants continued to self-report even less sitting time during the maintenance period; however, their Month 8 ActivPAL sitting data showed that they were sitting twice as much as what they were self-reporting. Our findings are contrary to the literature in which most self-reported data estimate daily sedentary time 2 hrs greater than what is measured by the accelerometer (Marshall et al., 2015; Rosenberg, Bellettiere, et al., 2015). However, Rosenberg and colleagues also found that older adults were underreporting their sitting time on the SBQ in comparison with both the accelerometer data and ActivPAL data (Rosenberg, Gell, et al., 2015). To the best of our knowledge, no studies have examined device-assessed and self-reported sedentary behavior among older Latinos. Thus, using device-assessed and self-reported measures to assess sedentary behavior among older Latinos needs further exploration to determine whether older Latinos overreport or underreport their sedentary behavior in comparison with their device-assessed sedentary behavior.

Current data suggest that older Latinos do not participate in the recommended levels of MVPA and participate in high levels of sedentary behavior (Marquez et al., 2010; Merchant et al., 2015; Sisson, Camhi, Tudor-Locke, Johnson, & Katzmarzyk, 2012; Vásquez et al., 2017). However, our data indicate that participants were already engaging in more than 30 min of MVPA at baseline even though participants were screened about their physical activity participation prior to enrolling in the study. The intervention also began in June, and the maintenance period was from October to February. Despite the maintenance period taking place during the Chicago fall/winter months, dance participants maintained their high MVPA levels throughout this time period; thus, weather did not seem to impact physical activity levels. These high activity levels may be attributed to attending a culturally tailored ADC; therefore, we may have not seen the full impact that Latin dance has on older Latinos due to ceiling effects. The data, however, provide evidence on how attending an ADC may influence physical activity, sedentary behavior, and CRF. It appears that, for this sample at least, the ADC is providing appropriate programs and activities for older Latinos to participate in more than the recommended amount of MVPA. The activities that the ADC offered were dance parties, folkloric dancing, walking after lunch, and other aerobic activities. Further research should be conducted among other ADCs to determine whether most older adults attending ADCs meet physical activity recommendations or whether these findings were unique to this particular ADC.

The present study has several notable strengths. First, this study assessed physical activity, sedentary behavior, and CRF using a mixture of device-assessed and self-reported measures among older Latinos attending an ADC. As such, we have demonstrated that older Latinos who attend an ADC have high compliance to wearing accelerometers on their wrist, wearing an ActivPAL continuously for ~7 days, and are able to answer questions about their sedentary behavior and level of physical activity. Furthermore, we were able to assess maintenance of physical activity among the dance participants during Months 4 to 8. To the best of our knowledge, no studies have assessed these outcomes among this population, and studies examining ADC participants have mostly examined non-Latino Whites (Anderson et al., 2013). These outcomes could be important not only for older Latinos themselves but also for ADCs and stakeholders who deliver services to older adults. These data could serve as an indicator of how well their programming is affecting their participants and could provide information as to whether they need to modify their programming so that their participants are more active and less sedentary. Another strength of the study was that it took place at the ADC as opposed to a university setting, thus eliminating transportation and access barriers that participants may face when enrolling in a study.

Limitations of this study include the small sample size and competing activities in the ADC. Because all participants were exposed to the enriched ADC environment, the control group also had many opportunities to participate in other physical activities while the dance intervention was going on. Thus, we were comparing our dance intervention with a rich array of other programming that was offered by the ADC. Given that the dance and control groups attended the same ADC and had similar changes in CRF, it cannot be ruled out that contamination occurred. Although the researchers did ask participants to not share information with other participants, participants may have still shared information. Future studies may consider randomizing groups to different ADCs and randomizing ADCs to different conditions (Keogh-Brown et al., 2007). Another limitation of the study was that the participants were already meeting physical activity guidelines at baseline even though the exclusion criteria indicated that participants engage in less than 150 min of self-reported exercise. Participants may have not perceived some of their activities (i.e., folkloric dancing) as exercise, thus underreporting their minutes of exercise. Such high levels of physical activity may explain ceiling or floor effects observed in physical activity and sedentary behavior outcomes. Future studies may consider using mixed methods (i.e., interviews and activity monitors) when assessing the impact of physical activity interventions. Such information could provide insight on how participants’ perceptions about physical activity compare with device-assessed physical activity.

In conclusion, these results warrant further research in the study of the impact of a Latin dance program and its impact on physical activity and sedentary behavior–related outcomes among older Latinos attending an ADC. However, this study demonstrates that ADCs may play a critical role in promoting MVPA as evidenced by the high levels of physical activity between both groups throughout the course of the intervention. Future studies may consider testing the BAILAMOS^© program in an ADC that is not currently offering dance as part of their programming. Furthermore, researchers may also consider implementing strategies during midpoint of the program to encourage participants to continue increasing physical activity and reducing sedentary behavior. Researchers interested in achieving significant reductions in sedentary behavior may create interventions that place a greater focus on sedentary behavior while still emphasizing the importance of MVPA.

Footnotes

Acknowledgements

The authors acknowledge the contributions and assistance of the Adult Wellness Program at Casa Central, Antonia Laurel, Tatiana Sanjines, study participants, Miguel Mendez, Edward Wang, Melissa Lamar, Susan Hughes, Angela Odoms-Young, Priscilla Vásquez, Jennifer Sinchi, Yuliana Soto, and Gabriela Hernandez.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The University of Illinois at Chicago and the Rush Alzheimer’s Disease Center supported this work.

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