Journal of Aging and Physical Activity, 2015, 23, 569  -579 http://dx.doi.org/10.1123/japa.2013-0262 © 2015 Human Kinetics, Inc.

Original Research

A Novel Device to Preserve Physical Activities of Daily Living in Healthy Older People Wolfram Haslinger, Lisa Müller, Nejc Sarabon, Christian Raschner, Helmut Kern, and Stefan Löfler Objective: To determine the effectiveness of exercise in improving sensorimotor function and functional performance, crucial parts of activities of daily living in healthy older adults. Design: RCT. Setting: Laboratory. Participants: 39 subjects (M = 71.8 years, range: 61–89 years). Intervention: Task-oriented visual feedback balance training. Primary outcome measure: Timed Up & Go (TUG). Secondary outcome measures: Chair stand test (CST), self-paced walk test, maximum isometric torque, quiet stand posturography, and dynamic balance (DB). Results: Postintervention comparison of the treatment group (TG) and control group (CG) showed better TUG (p < .01), CST (p < .001), and DB (p < .025) for the TG. Pre–post intervention comparison of the TG showed better clinically-relevant outcomes in TUG (p < .001), CST (p < .001), and DB (p < .001). Conclusion: Active driven visual feedback balance training is effective in improving functional performance and dynamic balance in older adults. Keywords: postural balance, exercise therapy, aging, functional performance

The world’s population is aging rapidly. In developed countries, the life expectancy of a 65-year-old is approximately 21 years for women and 17 years for men (Sherrington et al., 2008). Approximately 42% of the American population, or 15.6 million people, have one or more limitations performing daily tasks (e.g., walking two to three blocks, transferring from the chair) that are fundamental for maintaining independence in the community (Federal Interagency Forum on Aging-Related Statistics, 2008, p.32). The sensorimotor system plays an important role in the ability to manage activities of daily living (ADL). Age-related structural and functional changes lead to a general slowing down of neuromuscular performance. The process of aging results in a decline in muscle mass (sarcopenia; Rosenberg, 1997), reduced ability to develop maximal and explosive force (dynapenia; Clark & Manini, 2008; Manini & Clark, 2012), as well as reduced static and dynamic postural control and can therefore increase the risk of falling (Gillespie et al., 2009; Granacher, Zahner, & Gollhofer, 2008; Muir, Berg, Chesworth, Klar, & Speechley, 2010). As age-related health care costs are enormous (Janssen, Shepard, Katzmarzyk, & Roubenoff, 2004; Johansen & Stone, 2000), the development and implementation of effective and cost-efficient prevention strategies are an urgent health challenge (Sherrington et al., 2008) and could have a major impact on quality of life and health care costs for older adults (Nadon et al., 2008). In a Cochrane review, Gillespie et al. (2009) investigated the methods that are effective in reducing the incidence of falls in community-dwelling individuals. They included 111 trials, with 55,303 participants. Programs that contained two or more components of the aspects of strength, balance, flexibility, or endurance were able to reduce the rate of falls Haslinger, Müller, and Raschner are with the Department of Sport Science, University of Innsbruck, Innsbruck, Austria. Sarabon is with the Department of Health Study, Andrej Marusic Institute, University of Primorska, Koper, Slovenia; and with the S2P, Science to Practice, d.o.o., Laboratory for Motor Control and Motor Behaviour, Ljubljana, Slovenia. Kern and Löfler are with Ludwig Boltzmann Institute of Electrical Stimulation and Physical Rehabilitation, Wilhelminenspital, Austria. Kern is also with the Institute for Physical Medicine and Rehabilitation, Wilhelminenspital, Austria. Address author correspondence to Stefan Löfler at [email protected].

and the number of fallers. In another review, Howe, Rochester, Neil, Skelton, and Ballinger (2011) examined the effects of exercise intervention on balance in people aged 60 and older living in the community or in institutional care, including 94 studies with 9,821 participants. They found weak evidence for moderate effectiveness of exercise programs involving (a) gait, balance, coordination, and functional tasks; (b) strengthening exercise, and; (c) 3D exercise and multiple exercise methods in improving measures of balance immediately after the intervention. Further reports suggest that interventions involving task-oriented sensorimotor exercise (e.g., visually-guided center of pressure [COP] target-matching or targettracking tasks; Zemkova, 2011), weight shifting from medial to lateral (Hilliard et al., 2008; Lord, Rogers, Howland, & Fitzpatrick, 1999) and anterior to posterior (Hatzitaki, Amiridis, Nikodelis, & Spiliopoulou, 2009), or active perturbations (Mansfield, Peters, Liu, & Maki, 2010; Piirainen, Avela, Sippola, & Linnamo, 2010; Santos, Kanekar, & Aruin, 2010) may have the potential to positively address sensorimotor and neuromuscular deficits of older adults. In addition, visual feedback (Lajoie, 2004; Sihvonen, Sipilä, & Era, 2004) and a type of activity-promoting video game (Fitzgerald, Trakarnratanakul, Smyth, & Caulfield, 2010; Heiden & Lajoie, 2010; Pluchino, Lee, Asfour, Roos, & Signorile, 2012) are known to improve balance control and motivation to participate in training programs. The sensorimotor training (SMT) intervention should meet the criteria of an individualized application of the training parameter and ensure sufficient gains. Furthermore, the exercise should always be challenging yet safe (Sturnieks, 2006). Several assessment tools have been developed, most of which focus only on one of these beneficial aspects. Many are passive by nature or have major shortcomings in regard to individualization, variability, and continuous gain of the training process, or they may require attendance at a formal class and sometimes safety can be an issue. To overcome these shortcomings, a new pneumatic driven, three-dimensional mechatronic motion platform that can be individually adjusted for all levels of performance (i.e., to support the subject, allow independent movements, or disturb the training individual with unpredicted perturbations) was developed (Haslinger et al., 2014). To our knowledge, no comparable intervention for the sensorimotor system exists that combines all of the potential positive properties mentioned above in one system. 569

570  Haslinger et al.

The specific aim of the current study was to examine the effects of a multidimensional SMT on a newly-developed device on the functional performance of community-dwelling individuals aged 60 and older. SMT on the system was hypothesized to lead to significant improvements in functional performance (Timed Up & Go [TUG], chair stand test [CST], self-paced walk test, maximum isometric torque) and balance scores (quiet stand posturography, dynamic balance [DB]) compared with a control group.

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Methods Older adults were recruited by a physician via personal solicitations at local senior citizen social clubs and the ambulance of a local hospital between August and October 2011. Inclusion criteria for all participants were 60 years of age and older and independence in performing ADLs. The interventions took place between September 2011 and January 2012. A total of 44 healthy subjects (18 men and 26 women; M = 72.7, SD = 6.9 years) agreed to participate in the study and were randomly assigned to the treatment group (TG, n = 22) or the control group (CG, n = 22). They were asked not to participate in any additional formal exercise programs for the duration of the study. All participants provided informed consent in accordance with the policies of the facility’s institutional review board and the national ethical committee. Testing was carried out according to

the Declaration of Helsinki. Testing and training took place in a laboratory setting. Participants, investigators, and assessors were not blinded. All statistical analysis was done by an independent person with masked data. Figure 1 presents the flow diagram of the study.

Measures One week before the beginning of the intervention and one week after the 18-session treatment, all participants (TG and CG) were tested for approximately 1 hr. All participants completed a health status questionnaire under medical supervision to confirm study eligibility. Their age, weight, and height were recorded. According to the recommendations of Cruz-Jentoft et al. (2010), a complete set of tests was applied to each subject belonging to the different groups. The battery included the following: primary outcome of TUG; secondary outcomes including 10-m self-paced walk test, five-times-CST, quiet stand posturography, dynamic body sway tests, and maximal isometric unilateral leg extension. The description, practical application, and psychometric information of the widely-used and investigated TUG (Podsiadlo & Richardson, 1991), five-times-CST (Guralnik et al., 2000), and self-paced walk test (Sarabon, Loefler, Fruhmann, Burggraf, & Kern, 2010) have been described previously in the literature by Bennell, Dobson, and Hinman (2011).

Figure 1 — Flow diagram of the study. TG = treatment group; CG = control group. JAPA Vol. 23, No. 4, 2015

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The force testing to assess maximum isometric torque of knee extensors was carried out on a knee dynamometer (S2P Ltd., Ljubljana, Slovenia). The reliability of the dynamometer proved to be good (ICC = .97–.99; Sarabon et al., 2013). The participants were positioned with their hip at 90° flexion, their knee at 60° flexion (full knee extension = 0°), and their arms crossed at the chest. The shank brace was positioned on the distal one-third of the lower leg with two fingers above the malleolus lateralis, and the trunk was fixed with a seat belt-tightening system. To measure maximum isometric torque of knee extensors, each person was instructed to push alternating with one leg as fast and as hard as possible against the shank support. In each case, the maximum isometric torque should be sustained for 3 s or until the maximum is reached. All measurements were repeated three times each for the right and left leg. The best attempt for each leg was selected. Balance was assessed using a portable force platform (FITRO Sway Check, FITRONiC, Bratislava, Slovakia) with a sampling frequency of 100 Hz. For the quiet stand posturography sway measurements, the participant’s task was to maintain a quiet upright hip wide parallel stance with open eyes on a force plate. Their hands were placed on their hips with knees extended, but the subjects passively avoided locking the joint in hyperextension. Their gaze was directed at a certain point at eye level located 1.5 m in front of their bodies (Sarabon, Rosker, Loefler, & Kern, 2010). The common COP Euclidian distance was registered. For the dynamic sway measurements, the participants stood on a force plate (hands were placed on their hips with knees extended, but the subjects passively avoided locking the joint in hyperextension) with their gaze directed at a display in front of the body, which provided the participants with feedback on the COP displacement. Their task was to trace a curve flowing in the vertical direction by shifting their COP in the medial-lateral direction. The

deviation of the instant COP position from the curve was recorded as the means. For visually-guided COP tracking tasks, the analyses of repeated measurements showed test–retest correlation coefficients of .97 and .93 for the COP mean distance and the sum of the COP crossings, respectively (Sedliak et al., 2013). The participants performed each sway task alternating with three trials, each lasting 30 s, with a rest of 30 s in between each task (Sarabon, Kern, Loefler, & Rosker, 2010). The testing order was held constant across the sessions. The participants were familiarized with and were able to perform all of the tests. The primary outcome measure of the study was change in TUG (time in seconds) during an 18-session treatment between the TG and the CG. The changes in CST (time in seconds), self-paced walk test (mean velocity in m/s), maximum isometric torque of knee extensors (N∙m), quiet stand posturography (mean velocity in mm/s and mean distance in mm), and DB (mean distance in mm) were analyzed as secondary outcome measures.

Intervention The participants in the TG came to the training facility twice a week for 18 training sessions and used the novel device (Figure 2). Each session consisted of 5 min of warm-up and two periods of 6.3 min of training, with a 2-min rest in between, and was approximately 24 min long. Although most of the participants followed a two-day per week pattern, deviations occurred because of commitments and other personal reasons. Two persons did not finish the intervention due to time commitments and due to nonstudy knee problems. Data from a total of 20 participants were analyzed. During the training sessions, the participants began with a warmup phase, consisting of a game task in which the subject attempted

Figure 2 — The novel device used by the treatment group. Reprinted in accordance with the MDPI Open Access Information and Policy. Originally published by MDPI: Haslinger, W., Müller, L., Mildner, E., Löfler, S., Kern, H., & Raschner, C. (2014). Assessment of a newly developed, active pneumatic-driven, sensorimotor test and training device. Sensors (Basel), 14(12), 24174–24187. doi:10.3390/s141224174 JAPA Vol. 23, No. 4, 2015

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to acquire as many randomized displayed butterflies as possible in 1.5 min and defined stabilization, rotation, and lateral shifting tasks of 2.5 min. Participants also had to perform 11 different predefined 30-s exercises on the active moving platform (Table 1). Following the tasks, individuals were asked to maneuver a control circle (the small gray circle represents the body’s COP) into a target circle (black circle, 2.5 times bigger than the control circle) by shifting their weight. Once the mission was accomplished, the subjects received points calculated by an algorithm, which took into consideration the time needed for the control circle to overlap 75% of the target.

Participants started training in the easiest of the three modes provided by the system. The so-called “supported” mode allowed the platform to follow all of the movements and hence assisted the individual with the performance of the movement tasks. The platform attempted to actively maneuver the individuals COP into the system’s displayed target circle. As the intervention followed the principles of motor learning (Mansfield, Peters, Liu, & Maki, 2007), participants changed training modes by reaching a specific number of points in three sequenced sessions. Figure 3 shows the generic training progress of one participant. In the next most challenging

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Table 1  Exercises of the Active Balance System Name of Exercise

Description of Exercise

1. Pop up top/bottom

The control circle has to be moved as quickly as possible into the target circle, which appears alternately above and below.

2. Pop up left/right

The control circle has to be moved as quickly as possible into the target circle, which appears alternately left and right.

3. Vertically moved

The control circle has to follow the movement and vertically change the position of the target circle as accurately as possible.

4. Horizontally moved

The control circle has to follow the movement and horizontally change the position of the target circle as accurately as possible.

5. Clockwise rotation

The target circle moves clockwise, and the control circle has to follow its changing position as accurately as possible.

6. Counterclockwise rotation

The target circle moves counterclockwise, and the control circle has to follow its changing position as accurately as possible.

7. Constant movement

The target circle moves diagonally without acceleration from the top left corner to the bottom right corner and back. The control circle has to follow its changing position as accurately as possible.

8. Pop up random location

The control circle has to be moved as quickly as possible into the target circle, which appears randomly in the operating space.

9. Accelerated movement

The target circle moves freely and with accelerated speed. The control circle has to follow its changing position as accurately as possible.

10. Centered stabilization

The control circle has to be stabilized in the center of a target circle.

11. Off-center stabilization

The control circle has to be stabilized in the center of an off-centered target circle.

Figure 3 — Generic training process of one participant. JAPA Vol. 23, No. 4, 2015

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mode (“independent” mode), only the elasticity of the now passiveacting springs determined the movement features of the platform. In the “independent” mode, the platform tried to distract the individual and interfered with the performance of the control movements. The platform attempts to actively maneuver the individual’s COP away from the system’s displayed target circle. Individuals in the CG received no training and were instructed to avoid any additional activities or training beside their regular ADL.

CG’s ICC 3.1 (Shrout & Fleiss, 1979) to ensure that the retest scores reflected real changes. To compare the effect of different measures and groups, the means of standardized change (Becker, 1988) was used. All data were processed using the Statistical Package for the Social Sciences (SPSS Version 18.0, IBM, Inc., Chicago, IL).

Statistical Analysis

The final study sample size of 39 community-dwelling older adults was analyzed for the primary outcome, with 20 subjects in the TG and 19 subjects in the CG. Baseline characteristics of the participants are presented in Table 2. All participants of the TG completed 18 trainings sessions. There were no significant differences between the two groups in terms of demographics or baseline geriatric assessment parameters (Table 2). The comparison of TG and CG postintervention through independent t tests showed significant better outcomes in the TUG (t[24.3] = 3.87, p < .01), CST (t[35] = 4.19, p < .001), and DB tests (z = –2.50, p < .025) for the participants who finished the visual feedback balance training (Table 3). No statistically significant differences between the TG and CG were found in walk, maximum isometric torque of knee extensors, or quiet stand posturography test after the intervention (Table 3). The comparison of the pre–post test scores of the TG showed significant improvements in TUG (–14.1%, t[19] = 4.51, p < .001), CST (–21.8%, t[16] = 4.29, p < .001), self-paced walk test at normal gait velocity (+5.5%, t[19] = –2.71, p < .025), self-paced walk test at maximal gait velocity (+3.7%, t[19] = –2.76, p < .025), and DB (–19%,

Based on a pilot study of 10 evenly sex-distributed participants who received 18 similar visual feedback balance interventions with baseline and postintervention TUG assessments, an a priori power analysis was conducted. With the calculated effect size of 0.86, a minimum group size of 13 participants was needed to achieve a power of 0.80. To reach a sufficient power to avoid a type II error at a .025 alpha level at the end of the study, we considered a minimum of 18 participants necessary. For this two-group study, we took a possible drop-out rate of 20% into account and recruited 44 participants. All power analyses were conducted with G*Power 3.1 (Faul, Erdfelder, Buchner, & Lang, 2009). Normal distribution of the data were verified using standard visual inspection and the Shapiro-Wilk test. The pre– post and intergroup mean differences were analyzed using the paired and unpaired two-sample Student’s t test, respectively. In absence of normal distribution, the Mann–Whitney U test or Wilcoxon-test was used. To avoid a type I error, a Bonferroni adjustment of the alpha level to .025 was conducted. In cases of significant differences, standard error of prediction (SEP; Weir, 2005) was calculated using the

Results

Table 2  Demographic Data of Participants at the Baseline Treatment Group (n = 20) Characteristic

Control Group (n = 19)

M

SD

M

SD

Between-Group

p$

Age (years)

70.0

5.8

73.8

6.0

Height (cm)

168.8

8.8

167.0

8.2

.51

Weight (kg)

74.4

12.1

71.5

12.5

.48

.06

Sex

.52^

Male (n)

10

7

Female (n)

10

12

Male/female ratio

1

0.6

Parameter

M

SD

M

SD

Diff

CI

p$

TUG (s)

6.6

1.9

5.9

1.2

0.7

–0.4 to 1.7

.202

CST (s)

10.5

3.0

9.0

2.3

1.5

–0.3 to 3.4

.094

(m/s)

1.30

0.18

1.40

0.13

–0.10

–0.2 to 0.04

.060

SPWTb (m/s)

1.76

0.33

1.93

0.27

–0.17

–0.4 to 0.02

.082

MIT tot. (Nm)

105

45

118

35

–13

–38.9 to 13.5

.196††

QSP velocity (mm/s)

11.1

3.2

11.7

3.6

–0.6

–2.8 to 1.6

.723††

SPWTa

QSP distance (mm)

4.5

2.2

4.2

1.1

0.3

–0.9 to 1.4

.628

DB distance (mm)

15.6

3.2

14.8

2.5

0.8

–1.0 to 2.7

.374

Abbreviations: TUG = Timed Up & Go; CST = chair stand test; SPWT = self-paced walk test; MIT = maximum isometric torque of knee extensors; tot. = M of left and right knee extensor value; QSP = quiet stand posturography; DB = dynamic balance; Diff = difference between M values; CI = 95% confidence interval. $ Independent-samples Student’s t test. ^ Chi-square test. †† Mann–Whitney test. a Normal gait velocity; b maximal gait velocity. JAPA Vol. 23, No. 4, 2015

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20

20

MIT tot. (Nm)

QSP velocity (mm/s)

QSP distance (mm)

14.8

4.2

11.7

118

1.93

1.40

9.0

5.9

M

2.5

1.1

3.6

35

0.27

0.13

2.3

1.2

SD

12.0

4.3

12.5

122

2.00

1.47

7.0

5.1

M

2.0

1.0

4.1

36

0.23

0.15

1.7

0.8

SD

2.8

–0.1

–0.8

–3

1.9 to 3.8

–0.7 to 0.5

–1.8 to 0.1

–10.5 to 3.6

19 19

< .001*

19

.678

.095†

18

< .025*

–0.1 to –0.2 .322

19 19

–0.08 –0.1 to –0.02 < .025* –0.07

1 to 2.9

0.4 to 1.2

19

2.0

0.8 19

n

p < .001*

CI

t

< .001*

Diff

Posttreatment test§

15.6

4.5

11.1

105

1.76

1.30

10.5

6.6

M

3.2

2.2

3.2

47

0.33

0.18

3.0

1.9

SD

Pretreatment

3.0

1.9

SD –0.2

–0.4

Diff –0.9 to 0.6

–0.9 to 0.2

CI

16.1

4.7

12.0

104

8.3

1.5

3.2

40

–0.4

–0.2

–0.9

2

–3.4 to 2.5

–0.9 to 0.6

–2.2 to 0.4

–6.1 to 9.1

1.82 0.32 –0.06 –0.2 to 0.03

1.34 0.22 –0.04 –0.1 to 0.02

10.7

6.9

M

Posttreatment

Control Group

.676

.189

.145

.681

.157

p

.072†

.627

.089†

t

test§

1.8 to 5.1

0.9 to 2.8

CI

4.1

0.3

–0.5

–18.1

0

0.2 to 8

–0.5 to 1.1

–2.8 to 1.9

–43.3 to 7.1

–0.35 to 0

–0.13 –0.3 to –0.01

3.44

1.9

Diff

Posttreatment

< .025*††

.415

.728††

.105††

.048

.031

< .001*

< .01*

p

t test$

Treatment/Control Group

Abbreviations: TUG = Timed Up & Go; CST = chair stand test; SPWT = self-paced walk test; MIT = maximum isometric torque of knee extensors; tot. = the M of left and right knee extensor value; QSP = quiet stand posturography; DB = dynamic balance; Diff = difference between M values; CI = 95% confidence interval. § Paired-samples Student’s t test. $ Independent samples Student’s t test. † Wilcoxon Test. †† Mann–Whitney U test. a Normal gait velocity; b maximal gait velocity. *Significant differences, p < .025, are in bold.

20

20

SPWTb (m/s)

DB distance (mm)

20

20

SPWTa (m/s)

20

17

TUG (s)

CST (s)

n

Parameter

Pretreatment

Treatment Group

Table 3  Means, SD, and t test Results for the Geriatric Assessment

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t[19] = 6.18, p < .001) after the intervention (Table 3). All other test improvements and the 2.9% decrease in the quiet stand posturography score were not statistically significant. In the CG, no statistically significant pre–post differences were found (Table 3). Means of standardized change for the geriatric assessments are presented in Figure 4. Note that not all significant differences in the retest scores of the TG reflect real changes. Only TUG, CST, and DB posttest results were found to fall within the limits of the SEP confidence interval. The results and the values of a posterior calculated power analysis at .025 alpha are shown in Table 4.

Discussion

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Active driven visual feedback balance training leads to a significant improvement in functional tests and DB in healthy communitydwelling older adults (TG) compared with the CG. The significant

changes in functional tests in our study are not consistent with the findings of other visual feedback balance training studies. Pluchino et al. (2012) compared changes in postural control after training using a video game balance board and two standard activity-based balance intervention programs. They reported no significant changes in field tests for a single balance program on a Wii balance board. Szturm, Betker, Moussavi, Desai, and Goodman (2011) found no significant treatment effects for TUG after sixteen 45-min DB video game exercise sessions. The differences in our findings may be explained by the requirement of only relatively small movements (mainly weight shifting) on a static surface during the performance of the balance program. During balance control, the ankle muscles act first, leading to the activation of the knee and hip during more complex tasks (Kim & Robinson, 2005). During low amplitude tasks, a powerful and rapid force is not needed to control balance, which may explain the nonsignificant changes in functional tests

Figure 4 — Means of standardized change for the geriatric assessments. TUG = Timed Up & Go; CST = chair stand test; SPWTa = self-paced walk test at normal gait velocity; SPWTb = self-paced walk test at maximal gait velocity; MIT = maximum isometric torque of knee extensors; QSP = quiet stand posturography; DB =dynamic balance.

Table 4  SEP for Variables With Significant Differences Pre- and Posttreatment Pretreatment Group

Posttreatment

M

SD

CV%

M

SD

CV%

SEP (95% CI)

ICC Pre (95% CI)

TUG (s)

6.58

1.90

28.85

6.93

1.92

27.62

0.70 (5.20–7.96)

0.928 (0.72–0.982)

CST (s)

10.52

3.00

28.51

10.68

2.99

28.04

0.91 (8.75–12.30)

0.952 (0.806–0.988)

DB distance (mm)

15.64

3.24

20.68

16.09

8.31

51.63

1.78 (12.15–19.14)

0.958 (0.841–0.989)

5.92

1.19

20.17

5.08

0.84

16.43

0.41 (5.12–6.72)

.975

CST (s)

8.98

2.28

25.37

7.02

1.65

23.51

0.68 (7.65–10.31)

.955

DB distance (mm)

14.81

2.52

17.01

12.00

2.01

16.73

0.76 (13.32–16.31)

.982

Control

Treatment TUG (s)

Observed Power

Abbreviations: SEP = standard error of prediction; CV = coefficient of variation; CI = 95% confidence interval; ICC = (3,1) interclass correlation coefficient; TUG = Timed Up & Go; CST = chair stand test; DB = dynamic balance. JAPA Vol. 23, No. 4, 2015

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(Piirainen et al., 2010). There is evidence for improvement in the functional test (TUG) when performing more physical challenging activities (yoga, balance, aerobic, and strength options) on a Wii balance board, although no statistically significant changes were found by Nitz, Kuys, Isles, and Fu (2010). As the functional test assesses basic motor skills as well as strength or power, balance, and agility (Bennell et al., 2011), improvement in balance alone may not lead to significant changes (Schilling et al., 2009). Only programs that challenge balance to a high extent and include a higher dose of exercise may achieve a sufficient effect (Sherrington et al., 2008). However, Mansfield el. al. (2010) found significant improvements in perturbation for specific outcome measures but no significant changes in TUG after a six-week highly-dosed perturbation-based balance training program compared with a control program involving flexibility and relaxation training. This finding reflects the specificity-of-training principle and the need to tailor the dose of balance interventions and the content of training programs to the specific needs of daily life of older adults. As muscle power (alongside balance training) is strongly linked to the performance of daily tasks, its inclusion should also be considered in the context of a sufficient functional training program for older adults (Puthoff & Nielsen, 2007). The second functional test, the CST, assessed the ability to rise from a chair and sit down five times consecutively. For older adults, this ability is related to independence (Corrigan & Bohannon, 2001) and is considered an index of muscle strength (Bohannon, 1997). Because the test is specific to lower body strength and power, the significant pre–post change of the TG indicates the sufficient training effect of our intervention. According to our results, Manini and Clark (2012) indicated balance training to be effective for gain in muscular strength of the flexors and extensors of the knee. Although the mean posttreatment results of the TG showed better results, we nevertheless found no significant changes in maximum isometric torque of knee extensors or strength. These findings were confirmed by several studies (Ferrucci et al., 1997; Gruber & Gollhofer, 2004; Takai et al., 2009). Ferrucci et al. (1997) found a departure from linearity in the relation between sit-to-stand performance and muscular strength in older women over the value of 98 N. As most of the individuals in our study were moderately to highly active, no knee extension force value of any participant was below this value and, therefore, according to their study and to the results of Buchner, Larson, Wagner, Koepsell, and de Lateur (1996), our findings may also be ranked in a “plateau region” of the relationship between sit-to-stand time and muscular strength performance. Takai et al. (2009) observed no significant correlation between the time of sitto-stand and the maximal voluntary isometric knee extension force; but, on the other hand, the power index was highly correlated with the maximal voluntary isometric knee extension force. Gruber and Gollhofer (2004) showed that SMT over the period of four weeks increased only the rate of force development of seventeen 30-yearold healthy volunteers, without enhancing maximum strength. Moreover, the significant adaptations could only be found in the maximum rate of force development in the early time intervals. Rapid force production and maximum isometric torque of knee extensors, however, are reduced during aging, especially at the onset of torque production (Piirainen et al., 2010). Particularly, the initial phase of rising from a chair requires the greatest percentage of maximum muscular activity (Landers, Hunter, Wetzstein, Bamman, & Weinsier, 2001). The potential training-induced adaptations in the maximum rate of force development may reflect our significant improvement in functional performance of the CST in comparison with the nonsignificant change in the isometric strength test. Hence, muscle power has been found to be more relevant than muscle

strength for many ADL (Barbat-Artigas, Rolland, Zamboni, & Aubertin-Leheudre, 2012). Furthermore, the literature suggests that muscle power is strongly associated with balance (Orr et al., 2006), gait speed (Cuoco et al., 2004), and functional status (Foldvari et al., 2000). Because of the mismatch of muscle mass, muscle strength, and muscle power, Barbat-Artigas et al. (2012) suggested that functional incapacities are due to deterioration in muscle “quality”. Because this study did not investigate such power issues, further studies are needed to investigate the possible effects of SMT in the early time intervals (e.g., 0–50 ms) for an older population. Physical function is a multifactorial and complex process that is affected by biological, psychological, environmental, and sociological factors (Manini & Clark, 2012; Verbrugge & Jette, 1994). In addition to multiple sensorimotor and balance processes, foot abnormalities, mood, pain, and low motivation or lack of willpower may influence CST performance (Lord, Murray, Chapman, Munro, & Tiedemann, 2002). All of these parameters may have influenced our findings but were not part of our investigation and therefore further investigations are needed. Gait speed is relevant to the functioning of older adults in the community and is an important predictor for the onset of disability, commonly used by physical therapists and clinicians (Bohannon & Williams, 2011; Guralnik et al., 2000). Howe et al. (2011) reported in their review significant improvements immediately postintervention for (a) gait, (b) balance coordination and functional tasks, and (c) strengthening compared with a control group. No significant differences were observed for 3D exercises (Thai Chi, qi gong, dance, yoga) and no data were available for computerized balance training programs. Our data showed no clinically-relevant postintervention changes in gait speed for the TG and in comparison with the CG. This result may also be explained by the findings of Buchner et al. (1996). The authors suggested that small changes in physiological capacity might have substantial effects on individuals with poor performance, whereas large changes in capacity have little or no effects in healthy active individuals with high performance levels. Actually, the four reported studies that have considered gait speed in the review of Howe et al. (2011) investigated individuals with functional impairments and a history of falls (Vrantsidis et al., 2009), older individuals with a mean age of 80 years (Beling & Roller, 2009), or interventions with gait-specific training (Johansson & Jarnlo, 1991). Only Wolfson et al. (1996) investigated a cohort of participants with a physical performance level corresponding to our subjects. They found small but significant changes in usual gait velocity in the balance and strength training group, but no changes in the single balance or strength training group compared with a CG. Compared with the normative speed reference values (normal gait speed 1.15 m/s, age group 60–99 years) for healthy individuals reported in a meta-analysis (Bohannon & Williams, 2011), the mean pretest values of our 39 participants (1.40 m/s) were better. Therefore, the point of Buchner et al. (1996) could be a possible explanation of our results. In addition to an improvement in functional performance, the main finding of our study was that 18 sessions of active driven visual feedback balance training led to a significant change in the DB outcome measure. In contrast, we found no significant changes in quiet stand posturography. Our results are consistent with the findings of Piirainen et al. (2010). They investigated whether quiet stand balance and DB control are related to neuromuscular function and aging. DB control was more impaired with aging than was quiet stand balance control and the effects of aging seemed to be muscle-group-specific. This finding was explained by a decline in rapid torque production with aging and a significant age-dependent reduction in quadriceps maximal isometric torque and activation

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level, but no significant reduction was found in the plantar flexors. Muehlbauer, Gollhofer, and Granacher (2012) examined the relationship between quiet stand and dynamic postural control in middle-aged healthy adults. The authors found no significant correlations between quiet stand balance and DB or between balance and strength variables, which is consistent with our findings. Quiet stand balance and DB appeared to be independent of each other and might have to be trained complementarily. The authors speculated that different neuromuscular mechanisms may be responsible for the regulation of stance perturbation impulses of different magnitudes. Severe stance perturbations have been suggested to demand significant reflex activations (Muehlbauer et al., 2012) and rapid torque production (Piirainen et al., 2010) of the ankle, knee, and hip muscles (Kim & Robinson, 2005), whereas mild stance perturbations may implicate only minor magnitudes and lesser muscles, which may not be significantly influenced by aging. The multiple sensory inputs involved in postural control may explain the lower sensitivity of static posturography. Smaller impairments of balance can be compensated in such a way that, under normal conditions (i.e., bipedal stance), no deficits in postural stability are apparent (Zemkova, 2011). Lindemann, Rupp, Muche, Nikolaus, and Becker (2004) mentioned that when standing on a static force plate in a position that is not challenging (feet side-to-side, eyes open—as in our study), sensitivity to change was not found. In contrast, under dynamic conditions, the control mechanisms were stressed to a higher extent so that individual differences could be detected (Zemkova, 2011). Because the level of physical performance influences balance control in both static and dynamic conditions (Paterson, Jones, & Rice, 2007), the high physical performance of our participants might be one reason why we identified no significant differences in static conditions. As practice with some type of reinforcement (e.g., visual or auditory feedback) is known to lead to better results based on perceptual improvements (Gibson, 1953; Hurkmans, Bussmann, Benda, Verhaar, & Stam, 2012), our significantly-better results in DB (19% postintervention improvement of the TG) could be an outcome of the task-oriented training applied in our intervention. This finding is in line with Zemkova and Hamar (2010), who postulated that visual feedback of the COP provided on a computer screen during training was associated with more precise perception of the COP position and better regulation of its movement. According to Zemkova (2011), the effect of our intervention may not only provide information on physiological adaptations (e.g., improvement of proprioceptive function) but also on mechanical changes in the technique (e.g., COP regulation with more precision but less effort). The hypothesis of Henry (1968) indicated that the transfer between abilities should be rather low because motor skills are specific to a particular task, which highlights the importance of implementing the principles of specificity in balance testing in rehabilitation settings (Zemkova, 2011).

Conclusions In summary, age-related structural and functional changes lead to a general slowing of neuromuscular performance and an impaired ability to manage ADL. The results of our study suggest that active driven visual feedback balance training is effective in improving functional performance and DB in healthy community-dwelling people between 61–89 years of age immediately posttreatment. The novel active balance system can offer an effective and varied training for all levels of performance in a safe way. The system is able to detect each individual’s personal training threshold so she or he can be optimally guided through the different training levels.

The training can always be designed in a challenging and motivating way. Additional studies are needed to investigate the possible power effects of SMT, especially in the early time intervals in this demographic group. Further studies are also needed to more precisely assess the possible effects on quiet stand posturography in more challenging standing positions. In dynamic conditions, additional anterior-posterior stability effects should be analyzed. The intervention-related improvements in functional performance and DB appear fundamental for performing daily tasks and are associated with a decreased risk of falling and therefore have a major impact on quality of life and health care costs for older adults. Improved mobility facilitates a more independent and socially active lifestyle. A limitation of this study was that, during assessment, the evaluators were not adequately blinded to the participant groups and thus tester bias may have affected the geriatric measurements. Other limitations were the simple study design, the short duration of the study, and the missing check of lasting effects. Future studies should be sufficiently blinded and have an improved study design. They should also have a longer training period, a check for lasting effects, and employ an intend-to-treat design. Acknowledgments This study was funded in the context of a “benefit” (Grant no. 825877), a program of the Austrian Ministry for Transport, Innovation and Technology (BMVIT).

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