Fall-prone older adults have multiple risk factors, particularly imbalance.1,2 Horak et al3,4 described a postural control model indicating how age- and disease-related changes in postural control system components lead to balance deficits and falls. One critical component is multisensory reweighting (MSR), an adaptive central nervous system process to estimate body position and motion in space.5–7 Multisensory reweighting of visual, vestibular, and somatosensory inputs allows us to maintain balance as environmental conditions change.8–11 Multisensory reweighting is impaired in healthy older adults and more so in the fall-prone elderly.3,12,13 Healthy older adults rapidly adapt to changing environments; fall-prone older adults do not.3,13–22 Impaired MSR may, therefore, be associated with increased fall risk.
Balance exercise interventions reduce fall risk.23–26 However, it is not known which types of balance exercises best remediate specific balance impairments, nor do we understand the mechanisms by which exercise might mediate changes in one or more components of the postural control system.27 This gap in our knowledge hampers clinical decision making.28
Sensory-challenge balance exercises contribute to improved balance and reduced fall risk.29,30 Whipple27 systematically reviewed 25 balance training studies performed with older adults. He found that successful exercise interventions incorporate sensory challenge activities. However, to our knowledge, no previous balance exercise intervention studies have restricted the intervention only to sensory-challenge balance exercises, excluding dynamic balance exercises, functional balance activities, or other forms of exercise.
Because prior studies used various types of balance exercises and other forms of exercise (eg, strengthening, stretching, aerobics) in addition to sensory-challenge exercises, it is not possible to separately assess the potential effects of the sensory challenge exercises. Furthermore, previous studies did not use discriminatory measures of MSR, nor examine whether changes in balance and fall risk occurred concurrently with changes in MSR. These studies used overall postural sway measures that do not discriminate changes in sensory reweighting from other postural control processes that affect sway.31
We explored MSR as a specific adaptive balance control mechanism using methods that permitted MSR changes to be explicitly determined.31,32 Exercises challenged sensory reweighting abilities by manipulating surface and visual conditions such that MSR would benefit postural stability. The purposes of this study were to (1) measure whether sensory-challenge exercises influenced MSR and (2) determine whether MSR changes were associated with improvements in clinical measures of balance and fall risk. Other influential postural control system components were also measured to isolate MSR contributions to changes in balance and fall risk.
We used a quasi-experimental, repeated-measures, within-subjects design in which participants served as their own controls. All tests were performed 3 times: pretest 1, pretest 2, and posttest. One 8-week no-training control period (between pretests 1 and 2) and one 8-week training period (between pretest 2 and posttest) were provided.
Adults 70 years of age and older with a history of falls in the prior year were recruited from a congregate retirement community. A fall was defined as an event resulting in a person coming to rest inadvertently on the ground, floor, or other lower level.33 Volunteers were screened via an eligibility questionnaire, telephone screening, and clinical screening by an experienced physical therapist (for exclusion criteria and screening tests, see Supplemental Digital Content 1, Appendix 1, http://links.lww.com/JNPT/A202) The participant recruitment, screening, testing, and attrition process is illustrated in Figure 1.
This study was approved by the Institutional Review Board at the University of Maryland, College Park. Written consent was obtained from all participants according to the guidelines proscribed by the Institutional Review Board at the University.
Laboratory MSR Assessment
The MSR experimental paradigm (Figure 2) has been previously described.31,34 Briefly, participants stood in front of a large rear-projection screen on a force plate (Kistler Instrument Corp, Amherst, New York) in a modified tandem stance. The narrowed stance induced mild instability to heighten the need for attention to sensory inputs. They wore goggles limiting their peripheral vision. Participants lightly touched a fingertip force plate, located at hip height on the right side. This force plate sounded an alarm if the touch force exceeded 1 N. Both visual and touch motion stimuli oscillated mediolaterally.
Medial-lateral center of mass (COM) motion was approximated using an ultrasound position tracking system (Logitech, Inc, Fremont, California). A posterior tracking sensor was attached to a waist belt. Participants wore a harness attached to the ceiling with slack straps to permit postural sway but prevent a fall. An assistant stood close behind each participant. Data were sampled at 50 Hz.
Postural sway measures in the time domain (Figure 3) were converted to the frequency domain (Figure 4) to allow the discrimination of postural response adaptations to separate visual and touch stimuli. Each sensory motion input stimulus was provided at a different frequency. The visual display and fingertip touch plate were simultaneously oscillated mediolaterally at 0.20 and 0.28Hz, respectively. The peak amplitudes of the 2 stimuli were varied such that across 5 conditions, touch motion amplitude declined while visual motion amplitude increased (see Table 1).
Participants performed 3 trials in each of the 5 conditions (15 total). Trial duration was 120 s, with the order of trials pseudorandomized within 5-trial blocks. Seated rests were taken between trials.
To determine whether improvements in MSR were associated with improvements in balance measures associated with fall risk, the Berg Balance Scale (BBS) was used as a global balance and fall-risk measure.35–39 Multiple impairment outcome measures associated with fall risk were also used, including the Activities-specific Balance Confidence Scale,38,40 composite bilateral lower extremity range of motion (goniometry) and strength (hand-held dynamometer; Chatillon CSD100, AMETEK, Inc, Largo, Florida),41–44 the Sensory Organization Test (NeuroCom International, Inc, Clackamas, Oregon, now Natus Medical, Inc, Pleasanton, California),10–13,32,45–47 and Limits of Stability Test (NeuroCom International, Inc, Clackamas, Oregon, now Natus Medical, Inc, Pleasanton, California).30,48 Each of these tests has been shown to be reliable with older adults. Appendix 2 describes these tests (see Supplemental Digital Content 2, Appendix 2, http://links.lww.com/JNPT/A202). All clinical testing was performed by an experienced PT prior to laboratory testing.
Sensory Challenge Balance Exercise Program
Participants attended two, 45-minute exercise sessions each week for 8 weeks. All sessions occurred in an outpatient physical therapy clinic in the congregate retirement community. One-on-one sessions were provided by trainers (1 physical therapist and 2 physical therapist assistants) who were blinded to test results and trained in the exercise protocol by 1 author who is a physical therapist.
The exercise program was designed (by author LKA) to improve (1) estimation of body position and motion in space and (2) adaptation to changing sensory environments (see Supplemental Digital Content 1, Appendix 1, http://links.lww.com/JNPT/A202). All exercises were performed on a SMART Balance Master (NeuroCom International, Inc, Clackamas, Oregon; now Natus Medical, Inc, Pleasanton, California), a computerized balance testing and training device. We chose to use this equipment because it provides operator-controlled surface and/or visual environment motion that can be finely graded up or down in small, quantifiable increments that are precisely repeatable between sessions and participants and closely matched to participant ability levels. If desired, the equipment also provides visual feedback about center-of-gravity position and motion to promote early motor learning. Participants stood quietly, with instructions to avoid “standing too stiffly, like a soldier”, to achieve maximum stability in each exercise. Stability was challenged by progressively reducing base-of-support size, reducing target size to increase the control demand, and making the motion of the support surface and visual surround larger and less predictable.
All participants followed the same standardized balance exercise progression, with the initial exercise difficulty level adjusted for each participant. Balance exercises became progressively more difficult over the 16 sessions. Visual center of gravity feedback was provided during and weaned over the first 8 training sessions to facilitate the development of correct spatial orientation. Supplemental Digital Content 1, Appendix 3, http://links.lww.com/JNPT/A202, describes the progression of balance exercise tasks and environmental motion conditions and the reduction in visual center of gravity feedback.
Conversion of Data From Time Series to Frequency Domain
A sample time series of the medial-lateral COM postural sway data and the visual and touch motion stimuli is shown in Figure 3. To represent the same data in the frequency domain, the amplitude spectrum was computed from the time series by taking the absolute value of the Fourier transform (Figure 4A-E).
For each sensory input (vision or touch) the mathematical transfer function at the stimulus frequency was calculated by dividing the transformed COM postural sway by the transformed sensory stimulus motion. From each transfer function, we obtained 2 COM motion variables, gain and phase, that together represent how the participant responded to the sensory stimuli motions. Changes in the sensitivity and timing of the postural sway response to the unique vision/touch motion frequencies (0.2 Hz/0.28 Hz) were indicated by gain and phase values, respectively. The postural sway response at all frequencies other than the 2 stimulus frequencies is the residual COM displacement and was used to calculate 2 additional variables, position variability and velocity variability. Hence, the 6 dependent variables in the MSR analysis were vision and touch gain, vision and touch phase, position variability, and velocity variability. Supplemental Digital Content 1, Appendix 4, http://links.lww.com/JNPT/A202, describes these measures and the rationale for their inclusion in this study. Supplemental Digital Content 1, Appendix 5, http://links.lww.com/JNPT/A202, contains a list of definitions for many terms used in this article that may or may not be familiar to the reader.
All statistical analyses were performed using SPSS Version 12. Multisensory reweighting gain and phase data were analyzed with a Test by Condition (3 × 5) repeated measures (RM) multivariate analysis of variance (MANOVA). Position variability and velocity variability were examined separately with a Test by Condition (3 × 5) RM-MANOVA. Each RM MANOVA was followed by planned multiple pairwise comparisons with Bonferroni correction to P ≤ 0.05.
Differences in scores from the ABC Scale and BBS were analyzed using Freidman test, with planned multiple pairwise comparisons performed using related samples paired t tests with Bonferroni correction to P ≤ 0.016. Four composite scores were compiled from the (1) bilateral lower extremity range of motion, (2) bilateral lower extremity strength measures, (3) the SOT Equilibrium scores, and (4) the LOS Maximum Excursion scores. These composite scores were examined using a 3 × 3 (Test by Trainer) RM MANOVA. Unless otherwise noted, significance was set at 0.05 or less for all analyses. Nonsignificant results are not reported.
Of the 33 participants who were enrolled in the study, 20 completed all training and testing sessions. Participants had a history of 1 or more unexplained falls within the past year (range: 1-6, mean = 3). The first stage of screening was a questionnaire; 95/104 were returned. Thirty-three volunteers were excluded at this stage due to medical conditions (N = 23) and major polypharmacy (N = 10). Telephone calls were made to the remaining 62 volunteers; at this stage, 16 volunteers declined to participate further. Forty-six volunteers underwent clinical screening, and 13 of these were excluded due to sensory loss (N = 11) and frailty (N = 2). Thirty-three fall-prone older adults were accepted into the study. Twenty-eight participants (22 female; mean age of 83 ± 5 years) began the training program and 20 finished all 16 sessions. The primary reasons reported for discontinuing the study were problems related to age: health status changes (N = 6) and caregiver burden (N = 4). The exercise program was well tolerated, with participants reporting temporary fatigue after exercise but no adverse effects.
Multisensory Reweighting Outcome Measures
At all 3 test periods, we saw decreasing vision gains with increasing visual motion amplitudes and increasing touch gains with decreasing touch motion amplitudes (Figure 5A and B). Significant differences between Conditions were found for touch phase, but not for vision phase (Figure 5C and D). Following training, both vision and touch gain values decreased overall.
The RM-MANOVA multivariate analysis for vision and touch gain and phase revealed significant main effects for Test (Wilks' Lambda: 0.735, F = 2.119, P = 0.041) and Condition (Wilks' Lambda: 0.073, F = 30.814, P ≤ 0.001) but no Test by Condition interaction. For the main effect of condition, univariate tests revealed statistically significant changes for touch gain (F = 69.005, P ≤ 0.001), touch phase (F = 7.478, P ≤ 0.001), and vision gain (F = 85.838, P ≤ 0.001) but not for vision phase. Significant differences between Conditions were found for vision and touch gains and touch phase (Figure 5A, B and D respectively). Planned pairwise comparisons revealed significant differences between numerous pairs of conditions for vision gain, touch gain, and touch phase (see Table 2).
The second RM-MANOVA for position and velocity variability revealed a significant main effect only for Condition (Wilks' Lambda: 0.699, F = 10.527, P ≤ 0.001). Univariate tests revealed statistically significant changes for position variability (F = 4.728, P = 0.002) and velocity variability (F = 22.878, P ≤ 0.001). Significant differences in position and velocity variability between Conditions were found (Figure 6 A and B). Planned pairwise comparisons revealed significant differences between numerous pairs of conditions for position and velocity variability (see Table 2).
Do Sensory-Challenge Exercises Influence MSR?
From the RM-MANOVA for gain and phase, for the main effect of test, univariate tests revealed statistically significant decreases between test periods for touch gain (F = 5.946, P = 0.005) and touch phase (F = 3.312, P = 0.044) but not for vision gain or phase. However, in absolute terms, vision gain decreased more than touch gain (mean change posttraining: 0.282 vs 0.189) (see Figure 5 A and B and Table 3). The power to detect significant differences in vision gain and phase was lower than in touch gain and phase (0.389 and 0.197 vs 0.861 and 0.604, respectively). The standard error for vision gain was over twice that for touch gain (vision SE = 0.16; touch SE = 0.07). Planned pairwise comparisons found significant decreases in touch gain between pretest 1 versus posttest (mean change: 0.239, SE = 0.070, P = 0.001) and pretest 2 versus posttest (mean change: 0.138, SE = 0.070, P = 0.05), and in touch phase between pretest 1 versus posttest (mean change: −0.229, SE = 0.099, P = 0.024) and pretest 2 versus posttest (mean change: −0.210, SE = 0.099, P = 0.038).
Are Posttraining Changes in MSR Accompanied by Improvements in Clinical Measures of Balance and Fall Risk?
Clinical outcome results are reported in Table 4. Friedman test indicated significant between-test differences for the BBS (P ≤ 0.001). Planned related samples paired t tests (Bonferroni-corrected significance level, P < 0.016) demonstrated significant differences between pretest 1 versus pretest 2 (P = 0.006), pretest 1 versus posttest (P ≤ 0.001), and pretest 2 versus posttest (P = 0.002).
A Test by Trainer (3 × 3) MANOVA for the SOT, LOS, and lower extremity strength scores found a significant main effect for Test only (Wilks' Lambda: 0.154, F = 8.000, P ≤ 0.001). Univariate tests revealed significant differences between tests for the composite SOT Equilibrium scores (F = 25.979, P ≤ 0.001), LOS Maximum Excursion scores (F = 21.882, P ≤ 0.001), and lower extremity strength scores (F = 5.846, P = 0.006) but not for the ABC or lower extremity composite range of motion scores.
Significant differences were found for the composite SOT Equilibrium score between pretest 1 versus pretest 2 (P = 0.011), pretest 1 versus posttest (P ≤ 0.001), and pretest 2 versus posttest (P ≤ 0.001). For the composite LOS Maximum Excursion scores, significant differences were found between pretest 1 versus posttest (P ≤ 0.001) and pretest 2 versus posttest (P ≤ 0.001). For the composite lower extremity Strength scores, significant differences were found between pretest 1 versus posttest (P ≤ 0.028) and pretest 2 versus posttest (P = 0.003).
To determine whether the increase in strength scores explained the improved SOT, LOS, and BBS scores, 2 unplanned repeated measures multivariate analysis of covariance analyses were run with strength as the covariate. In the first, these 3 outcome measures were the dependent variables. In the second, the differences in scores between tests were the dependent variables. For both analyses, all previously reported significant effects were maintained. Significant differences in scores following training (pretest 2 vs posttest) were observed for the SOT (P = 0.001), LOS (P = 0.003), and BBS (P = 0.007), indicating that strength was not a factor.
Sensory-Challenge Exercises Influence MSR
Prior to training, vision and touch gain values for fall-prone older adults were markedly higher than for healthy young and older adults.11,32 After training, the range of gain values in the fall-prone older adults was decreased such that it was similar to that of healthy young adults. This finding may reflect improved discrimination and dissociation of self-motion from environmental motion. Posttraining declines in vision and touch gain values cannot be attributed to an overall reduction in residual postural sway, since there was no posttraining difference in position or velocity variability. These results may represent a generalized ability to suppress or decouple from environmental motion cues, since no training under laboratory testing conditions occurred.49 The sensory-challenge exercise program did improve MSR in these fall-prone older adults.
Posttraining Changes in MSR Are Accompanied by Improvements in Clinical Measures of Balance Associated With Fall Risk
Our pretraining findings of low scores on the clinical outcome measures were expected and consistent with results from previous studies.29,30,50–52 After training, we observed increased scores on the BBS, SOT, LOS, and lower extremity (LE) strength tests. We cannot say that these changes were caused by improvements in MSR, only that they occurred concurrently.
How Do the Current Findings Fit Within the Context of Prior Research?
Our current finding that sensory challenge balance exercises are associated with subsequently lower vision and touch gain values is consistent with prior intervention studies that used the SOT or “Clinical Test of Sensory Interaction on Balance” to reflect MSR capabilities.19,29,30,53 Thus, sensory-challenge practice may improve perception of environmental conditions, estimation of body position and motion, or both.
Prior studies of balance exercise programs that included sensory challenge exercises have reported positive SOT outcomes.27,29,30 Our posttraining improvements in SOT composite scores were consistent with these earlier findings. Similarly, our posttraining findings of increased LOS Maximum Excursion scores are consistent with other balance exercise intervention studies that used the LOS test as a measure of center of gravity excursion and control.30,52 In contrast, these studies included dynamic balance exercises, while ours did not. The significant improvements seen in dynamic balance following our static balance training program may thus seem unexpected from a specificity of training viewpoint. However, improved estimation of body position and motion in space (perception) could support and facilitate improved dynamic balance (action).
After training, we found significant increases in BBS scores. Other balance exercise intervention studies that have used the BBS to measure balance and fall risk have also reported improvements in BBS scores.30,54,55 We recognize that, as our intervention was intentionally limited to sensory challenge balance exercises, other studies with more comprehensive exercise interventions were likely to achieve larger increases in BBS scores.
Increases in BBS scores may seem even more surprising than increases in LOS scores from a specificity of training viewpoint. The BBS includes 14 activities, a majority of which are dynamic (eg, turning 360°) or mimic functional activities (eg sit-to-stand, retrieving an object from the floor). Our participants practiced only static standing balance exercises and no functional activities whatsoever. However, the BBS has 4 static balance test items that were practiced by our participants; this may have contributed to improved posttraining BBS scores.
The posttraining increase in lower extremity strength scores was unexpected as we intentionally did not include resistance training in our exercise program. Furthermore, prior research has demonstrated that balance exercises alone do not increase strength.51 It is possible that the observed lower extremity strength increase may have been associated with actual muscle hypertrophy due to increased physical activity in our sample of older individuals. A second explanation may be related to pretraining failure of central activation of muscle in older adults.56,57 A third possibility is that the recorded increase in lower extremity strength was due to measurement error. The 2 MANCOVA analyses revealed that improved lower extremity strength was not the primary reason for improvements in the BBS, LOS, or SOT clinical balance measures.
Posttraining ABC scores did not change despite improved SOT, LOS, and BBS scores. It is possible that because no functional training occurred, participants did not consider improved ability to perform the sensory-challenge exercises as related to the functional tasks in the ABC Scale. It is also possible that because the exercises were progressed aggressively, participants always felt the exercises to be effortful and never achieved a sense of ease and mastery that might heighten their confidence levels.58,59
We observed control-phase increases in the SOT and BBS scores that hamper a straightforward interpretation of the positive effects of the training program on SOT and BBS scores. Variability of performance in frailer older adults is well recognized and, in fall-prone older adults, more than 2 baseline measures may be advisable to establish a control-phase performance range.
Additional limitations include the small sample size and high dropout rate common to longitudinal studies with older adults. These may have contributed to the lack of statistical power for the analysis of the vision gain and phase variables. However, these data can be used for future power analyses.
Abnormally elevated sensitivity to dynamic environmental stimuli in the fall-prone elderly was reduced following participation in a sensory-challenge balance exercise program. This change may reflect an improved ability to discriminate and dissociate self-motion from environmental motion. These results support the hypotheses that MSR is one of the mechanisms through which sensory challenge balance exercises may effect improvements in postural control.
The authors recognize The Erickson Foundation for their generous support of this research and thank Linda House, PT, Julie Howe, PTA, and Gloria Wilmer, PTA, for their invaluable assistance with the clinical evaluations and exercise intervention.
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