Journal of Aging Science

The Association of Coordination with Physical Activity Levels of Older Adults

Eric G. James^1*, Murat Karabulut², Phillip Conatser², Suzanne G. Leveille³, Soham Al Snih⁴, Kyriakos S. Markides⁵ and Jonathan F. Bean^{6 1}Department of Physical Medicine and Rehabilitation, University of Texas Southwestern Medical Center, Dallas, USA
²Department of Health and Human Performance, University of Texas Rio Grande Valley, Brownsville, USA
³College of Nursing and Health Sciences, University of Massachusetts Lowell, Boston, USA
⁴Division of Rehabilitation Sciences, University of Texas Medical Branch, Galveston, USA
⁵Department of Preventive Medicine and Community Health, University of Texas Medical Branch, Texas, USA
⁶Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, USA
^*Correspondence: Eric G. James, Department of Physical Medicine and Rehabilitation, University of Texas Southwestern Medical Center, Dallas, USA, Tel: (214) 648-4898, Fax: (214) 648-9207, Email:

Received: 18-Sep-2019 Published: 09-Oct-2019

Introduction

Participation in regular physical activity is associated with health promotion and the prevention of chronic disease among older adults [1]. However, levels of physical activity decrease with age [2], leading many older adults to be underactive [3,4]. Even small increases in physical activity can produce health benefits in older adults. Determinants of physical activity in older adults are not well understood [5] and adherence to different types of exercise are predicted by different determinants [5,6]. Male gender and younger age have been associated with greater physical activity [5,7,8]. Adherence to exercise is also predicted by factors such as fitness, non-smoking, having an active lifestyle, high self-efficacy [9], muscle strength, short reaction time [10,11], and having fewer chronic conditions [12,13]. Rhythmic interlimb coordination [14-17] and gait coordination [18] decline with aging and has shown to be independent of other measures of mobility. Interlimb coordination is the ability to rhythmically synchronize movements of two body segments while sitting or lying supine, whereas gait coordination is the ability to coordinate the right-left stepping pattern during walking. It is possible to suggest that coordination decline in older adults contributes to decreasing physical activity levels, or vice versa. For example, poor coordination may lead older adults to feel uncomfortable or expend greater effort during movement, thereby leading them to be physically inactive [19]. Evidence regarding the relationship between coordination decline and physical activity levels in older adults is lacking. Therefore, we evaluated the association of rhythmic interlimb and gait coordination with self-reported physical activity levels among community-dwelling older adults. We hypothesized that good coordination would be significantly associated with higher levels of physical activity. Further, we explored whether this association would be independent of other known predictors (e.g., mobility performance, health status, gender, body-mass index).

Materials and Methods

Study design and participants

We recruited community-dwelling older adults (N=77) aged 67-99 years. Inclusion criteria were age 65 years or older, able to walk 20 feet without personal assistance, hearing sufficient to synchronize movements with auditory metronome tones, and ability to communicate, read and write in English. Exclusion criteria were a Mini-Mental State Exam (MMSE) score of less than 18 or having a neurological disorder or terminal disease. Participants gave written informed consent for methods approved by the University of Massachusetts Lowell Institutional Review Board.

Measurements

Interlimb coordination assessment: Participants were positioned supine on a physical therapy table. Orthoses with reflective markers were placed on each lower leg to confine ankle movements to dorsi-plantarflexion. A standard inter-limb synchronization protocol was used to assess ankle and shoulder coordination [20]. To assess shoulder coordination the participants were instructed to extend their arms vertically in front of them and to maintain their elbows in an extended position. Participants were instructed to rhythmically move their right and left ankles or arms at the shoulder (glenohumeral joint) simultaneously in opposite directions (anti-phase coordination) in synchronization with 0.5 Hz auditory metronome tones, which were played 10 seconds before and throughout movement trials. Data collection began 10 seconds after participants began moving their ankles or shoulders and continued until they had completed 25 movement cycles. A Qualisys (Gothenburg, Sweden) motion capture system collected kinematic data on the movement of the reflective markers at a sampling rate of 100 Hz. Data for angular position of the markers were used to estimate ankle angular position. The point estimate of relative phase between the ankles was calculated as previously described [21]. The variability of interlimb ankle and shoulder coordination were measured using the standard deviation of the first 40 point estimates of relative phase [21] of plantar and dorsiflexion. Lower values indicate less variable, and better, coordination.

Gait coordination assessment: Participants walked at their usual pace across a 22 foot long course that included a 16 foot Zeno Electronic Walkway, (Protokinetics; Havertown, PA), making 6 passes or 20 right strides across the walkway, whichever came last. The gait phase for individual strides was determined by dividing step times by stride times and multiplying by 360°. Phase Coordination Index (PCI) was calculated from gait phase values as previously described (18). Lower PCI values indicate better gait coordination. The standard deviation of ankle and shoulder coordination, and PCI, were calculated using custom written MATLAB software.

Short physical performance battery assessment: Mobility performance was assessed using the SPPB. The SPPB is a wellestablished, reliable, and valid measure of mobility performance [22,23] that is also applicable in clinical settings [24]. SPPB scores are positively associated with physical activity levels among older adults [25]. The SPPB includes measures of usualpaced walking speed, time to rise from a chair five times and standing balance. Scores from each component are scored between 0-4 which when summed creates a total score ranging from 0 to 12, with higher scores indicating better performance. SPPB score was calculated in the standard manner [23].

Falls assessment: Falls were defined for participants as ‘unintentionally coming to a rest on the ground, floor, or other lower level, whether or not you were injured.” [26] A history of falling was assessed by self-report of having fallen 0, 1, 2, or 3 or more times within the previous year. Falls have been associated with low levels of physical activity [27-29]. Number of falls was included in the statistical analysis as a potential confounder of the association between coordination and physical activity.

Physical activity levels: Participants completed the Physical Activity Scale for the Elderly (PASE) to quantify their selfreported level of physical activity. The PASE is a validated assessment that provides a summary performance of physical activities across a spectrum of physical exertion levels over the previous 7 days [30]. The amount of time spent in a range of activities was multiplied by a weighted ranking for exertion level (light, moderate, strenuous). The weighted sum of all reported physical activity categories is summed for a total PASE score. Higher PASE scores indicate higher levels of physical activity.

Demographics and comorbidities

Demographic characteristics included age, gender, and race. Additional potential confounding variables measured included Body Mass Index (BMI) determined from the measured weight in kilograms divided by height in squared meters and the Mini- Mental State Exam (MMSE) [31]. BMI [32] and cognitive function [33] have been associated with physical activity levels. Participants were asked if a physician had ever told them that they had heart disease, high blood pressure, high cholesterol, diabetes, gastric ulcer, kidney disease, liver disease, anemia, cancer, depression, osteoarthritis, spinal stenosis, rheumatoid arthritis, gout, lung disease, stroke, Parkinson’s disease, multiple sclerosis, an eye disease, fibromyalgia, or Alzheimer disease.

Statistical analyses

Descriptive statistics were calculated for participant characteristics using means and standard deviations for normally distributed data. The covariance of the corresponding predictor variables was evaluated using Pearson intercorrelation analysis. Next, to evaluate the potential association of coordination with physical activity level we performed multivariable linear regression modeling using backward elimination with an elimination criterion value of p>0.10. Age, gender, BMI, MMSE score, number of chronic conditions, number of falls in the previous year, SPPB score, ankle coordination, shoulder coordination, and gait coordination were included in the initial regression model as predictor variables and PASE score as the outcome. Evaluation of the assumption of normality of residuals was confirmed by inspection. In the final model, we examined the change in the variance (R) accounted for by the coordination variables after adjustment for the potential confounding variables that remained in the final model. Inferential statistical analysis was conducted using IBM SPSS software (version 22) using a Type I error rate of 0.05.

Results and Discussion

Descriptive results

Descriptive statistics for each variable examined are presented in Table 1. Participants had a mean (standard deviation) age of 81.51 (5.46) years and 64.9% (n=50) were female. Participants had a median of 5 chronic conditions (range: 0-15). All predictor variables had Pearson intercorrelation coefficients<0.40 and were included in the initial linear regression model (Table 1).

Table 1: Demographic and health characteristics of study participants (N=77).

Table 2 presents unadjusted Pearson correlation coefficients between predictor variables and PASE score.

Table 2: Pearson correlation coefficients of predictor variables with Physical Activity Scale for the Elderly score.

Table 3 presents the final multivariable linear regression model predicting PASE score. Only gender, SPPB, ankle coordination, shoulder coordination, and PCI remained in the final model. This model accounted for 29.4% of the variance in PASE score (R² =0.294; p<0.001). Ankle (p=0.092), shoulder (p=0.096), and gait (p=0.082) coordination were marginally significant (p=0.067) and fell within our criterion value of p<0.10 for inclusion in the final model. Gender and SPPB score accounted for 19.4% of the variance in PASE score (R² =0.194; p<0.001). After adjustment for gender and SPPB score, the three coordination measures (ankle, shoulder, and gait) accounted for an additional 10.0% of the variance in PASE score (R² =0.100; p=0.024) (Table 3).

Table 3: The final multivariable linear regression model evaluating the association between rhythmic interlimb ankle and shoulder coordination, and gait coordination with PASE score*. N=77.

Summary of findings

Our major finding was that better in ankle, shoulder, and gait coordination were associated with higher levels of physical activity. Coordination ability (ankle, shoulder, and gait) accounted for a substantial amount of the variance (10%) in physical activity levels, in addition to that accounted for by SPPB score and gender.

Significance and implications

Our findings support the hypothesis that coordination ability is associated with physical activity levels in community-dwelling older adults. The mechanism linking poor coordination with low physical activity in older adults, and the directionality of causation between these variables, are currently unknown. Greater energy expenditure is associated with poorer lower extremity function (34). It could be that poor coordination leads to increased effort or decreased comfort during movement, which in turn may discourage older adults from being physical active. Another possibility is that lower levels of physical activity may lead to peripheral or central changes [34-36] that contribute to a decline in coordination ability.

It is known that the coordination of older adults can be improved with training [17,37-39]. Improved coordination may be one of the mechanisms by which successful interventions increase physical activity levels in older adults. Future research is needed to assess possible changes in coordination created by physical activity interventions.

Strengths and limitations

Strength of this study is that, to our knowledge, it was the first to examine the association between coordination and selfreported physical activity levels. A limitation is that the sample size may have limited the statistical power needed determine possible significant associations between falls, age, body composition, or cognitive function with physical activity levels. Also, as this is a cross-sectional study, the direction of causality cannot be confirmed from these results.

Conclusion

The results support our hypothesis that coordination ability is associated with self-reported physical activity levels in community-dwelling older adults. This finding indicates possible causal relationships between coordination ability and physical activity levels in older adults, though the direction of this causality is unknown. Future research is needed to examine the association of coordination with objectively measured physical activity levels.

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