Risk‐of‐falling related outcomes improved in community‐dwelling older adults after a 6-week sideways walking intervention: a feasibility and pilot study

The highest proportion of falls in older adults occurs during level walking [14‐17]. Maintaining walking balance in older adults is a requirement for avoiding falls. However, with advancing aging, declines in sensorimotor function reduce balance control during walking resulting in increased fall risk [18‐20]. As a result, interventions able to improve walking balance and thus, decrease fall risk in older adults are necessary [13]. An approach to decrease fall risk in older adults is to strengthen their capability to execute self-stable walking patterns [21‐23]. The basic premise behind training walking patterns is to allow older adults to walk as planned in the presence of small instabilities. Based on this theoretical framework, walking corresponds to a behavioral attractor, where attractor dynamics are responsible for walking stability [24]. As such, stable walking is based on the passive dynamics of the musculoskeletal system to facilitate foot placement during gait cycles. In silico simulation and physical biped-legged models, corroborated with human walking experiments, showed that the mechanisms that underlie foot placement mechanics rely on the passive dynamics of the musculoskeletal system and on the active control from the central nervous system [25‐33].

During walking, passive dynamics arises from the biomechanical properties of the body and its mechanical interaction with the environment. Practically, passive dynamics governs walking stability in the fore-aft direction [24, 26, 34‐36]. Nevertheless, active control from the central nervous system governs walking stability in the lateral direction [24‐26]. Step width variability, expressed as the standard deviation of the mediolateral distance between sequential left and right heel-strikes at double support, reflects the amount of active control from the central nervous system in the frontal plane through lateral foot placement [26]. A recent systematic review and meta-analysis showed that older adults have higher step width variability than younger adults [37]. In older adults, active control is subject to subclinical declines in sensorimotor functions, resulting in increased step width variability [27, 38‐42]. Evidence has surfaced to support the link between increased step width variability and high fall risk in older adults [43]. Moreover, step width variability predicted fall incidence among older adults [44].

Therefore, an intervention to improve walking stability in older adults (and decrease fall risk, thereafter) would be more effective if it targeted to decrease the excessive amount of step width variability. Currently, external stabilization devices and body weight support systems can be used to offload the need of active control and decrease the amount of step width variability during walking [27, 45‐48]. This has implications for walking stability intervention in older adults, which could be directed to exploit the mechanical features of gait dynamics, such as motion-dependent torques [49]. Previous studies showed that passive dynamics are less sensitive to age-related deficiencies of active control or the lack thereof [26, 31, 32]. For example, it was postulated that the ability to gradually offload the need of active control in treadmill walking through external devices can be used in rehabilitation medicine for walking stability in older adults [24, 45]. However, step width variability can be decreased by increasing older adults’ ability to control foot placement as well [47].

Recently, it has been found that active control during walking in any direction is dependent on the direction of progression [50, 51]. Specifically, when performing sideways walking, where the mediolateral direction is the direction of progression instead of the anteroposterior, the participants experienced a reversal of what is found in typical forward walking; the mediolateral direction had a lower amount of variability than the anteroposterior direction [51]. Practically, when walking laterally, side stepping became the primary direction of progression, and step width variability was less than step length variability. This implies that all planes of motion can benefit from both active and passive control properties. Exercise-based interventions attempting to improve walking stability and reduce fall risk, would not need to always target the mediolateral direction during typical walking. Therefore, we suggest that adaptations from sideways walking training could transfer to improve gait stability during forward walking. This suggested transfer effect, could provide an alternative intervention approach to reduce fall risk in older adults.

The objectives of this study were to (i) determine the feasibility of implementing a novel 6-week sideways walking exercise intervention for older adults and (ii) to collect preliminary evidence of efficacy of such intervention on risk-of-falling related outcomes. The specific feasibility objectives of the study were (i) to determine the eligibility criteria, (ii) to evaluate the recruitment capability and the characteristics of the sample who expressed interest to participate in the study, (iii) to evaluate the fidelity with which the intervention was implemented in terms of compliance with the protocol and adherence to the procedure (i.e., participant and instructor fidelity), and (iv) to evaluate the feasibility and suitability of data collection procedures and the risk-of-falling related outcomes measures [52]. Furthermore, it was hypothesized that the intervention would improve risk-of-falling related outcomes. It was also expected that the intervention will result in a decrease in the amount of step width variability and an increase in walking speed during forward walking at post-intervention. Moreover, the effects would be retained for 6 weeks after the completion of the intervention.

We completed a 6-week, single-arm pilot study of a sideways walking intervention with baseline, post-intervention, and retention measurements of risk-of-falling related outcomes. Reporting followed the Consolidated Standards of Reporting Trials (CONSORT) statements for randomized pilot and feasibility trials [53]. To ensure completeness of reporting, and replication of the intervention we followed the Template for Intervention Description and Replication (TiDieR) guidelines [54], which is recommended as extension on the CONSORT guidelines [53].

Fifteen older adults enrolled in the study. Inclusion criteria were (i) ≥ 65 years, (ii) be independently residing in the community, and (iii) ability to walk independently without walking aid and without the help of another person. Participants were not eligible if they (i) had a neurological disorder or progressive neurologic condition, (ii) had a musculoskeletal disorder or injury that could affect gait, (iii) had a surgery within the past 6 months, (iv) had a history of a cardiovascular event, and (v) were participating in any other studies that involves walking, balance, or exercise intervention.

Participants were recruited from 3 sources: (i) local retirement community, (ii) employees of the University of Nebraska at Omaha, and (iii) a sample of 190 older adults from the Mind & Brain Health Labs (MBHL) registry of the University of Nebraska Medical Center (UNMC). The MBHL registry provided records of older adults who met our inclusion/exclusion criteria. Between September 2017 and March 2018, one of the authors (AS) went to local retirement homes, fitness classes, and libraries to talk about the research and to distribute approved flyers. Moreover, an invitation to participate to the study was sent by email to all of the 190 members of the MBHL registry. A notice seeking volunteers was also announced to the university’s employees through campus-wide email posts linked to text on university’s website news page. Interested older adults contacted us by email or telephone and a screening visit at the Biomechanics Research Building was scheduled. Eligible older adults were identified by one of the authors (AS), and they asked whether they would like to participate in the study. All participants were asked to read, understand, and sign an informed consent form approved by the Institutional Review Board of the UNMC prior to participating in the study.

Before the first session, the participants were given a visual demonstration of sideways walking. Precise instructions were as such: (i) ‘keep the head up while stepping laterally’, (ii) ‘do not cross feet at any point’, (iii) ‘feet and legs are to be pointed in the same direction as the body’, and (iv) ‘at no point can both feet be off the ground’. Every session began with ‘warm-up’ exercises, which included 200–300 m forward walking at self-selected speed and stretching exercises at the comfort level of the participants. After the ‘warm-up,’ the participants started the sideways walking training. The 6-week intervention was performed at the indoor walking track (circumference of about 200 m) of the Recreation Building of the University of Nebraska at Omaha. The 10 m walkway was located at the straight part of the track. We used masking tape of distinctive color to create starting and finish lines on the track. All training sessions had a single participant and were supervised by one of the authors (AS).

The load of the training was based on the American College of Sports Medicine guidelines for older adults [55], which recommend 20–30 min on 2–3 days∙week^− 1 for neuromotor exercises (balance, agility, coordination and gait). Thus, each participant was trained 3 days∙week^− 1 for 6 weeks, resulting in a total of 18 sessions. Each participant performed 6 trials∙session^− 1 that were alternated with a rest period of 1–3 min [55, 56]. The participants were informed that they could choose their rest time but should be at least 1 min but no more than 3 min. Each trial consisted of 3 min sideways walking across a 10 m walkway changing body direction at the ends, thus alternating lead and lag limbs. Each training session lasted 30–45 min. The first session was at the participant’s self-selected sideways walking pace. The participants were given instructions that they should strive to increase their pace if they can, as they progress through the 18 sessions. The participants were informed that they could increase their pace at the start of each trial but may not decrease it at the next session. The time to cover the 10 m sections in each trial was manually recorded. Time stopped when the leading foot crossed the finish line. The averaged recorded time at each session was used to check participants’ adherence to the intervention protocol – i.e., whether recorded time was decreased at each consecutive training session. The session was rescheduled when a participant reported a level of muscle soreness or joint pain that prevented them for maintaining the previous walking speed. When this became evident after a session had already commenced, the session was curtailed but not rescheduled. For safety reasons, sideways walking was performed next to a horizontal handrail to grasp if required. A staff member helping AS was standing next to the participant to record walking time, monitor rest time, and to help as an added safety measure.

To evaluate the eligibility criteria, we used the ratio of included participants to those who did not meet the eligibility criteria. Participant and instructor fidelity at the intervention protocol was assessed by monitoring the walking pace at all trials per session. The feasibility and suitability of intervention outcomes was assessed by measuring the extent of the missing data, and ceiling or floor effects. Feasibility was measured by the ability to recruit and retain older adults until complete the follow-up (i.e., 6 weeks after the completion of the intervention). The study was considered feasible if we were able to recruit 3 participants∙week^− 1, and if ≥ 80 % of the sample was able to complete the follow-up.

Intervention outcomes were walking speed, gait variability (variability of step width, step length, stride time, and stance time), the Timed Up and Go test (TUG), the Berg Balance Scale (BBS), and the Falls Efficacy Scale-International (FES-I). Walking speed is a predictor of fall risk (when is less than 1 m·sec^− 1), and of disability, mortality, and adverse events in older adults [57‐61]. Change in walking speed near 0.05 m·sec^− 1 is small but meaningful and change near 0.10 m·sec^− 1 is substantial [62]. Increased variability of spatial (step length and width) and temporal (stride and stance time) gait characteristics compromises gait performance and increase the tendency of older adults to fall [63, 64]. Stance time variability is an indicator of preclinical disability mobility (when stance time variability ≥ 0.034 sec) [65], while step width variability ≥ 2.5 cm is considered excessive [37]. Meaningful changes are 0.25 cm for step length variability, and 0.01 sec for stance time variability [66]. The TUG test was designed for assessing mobility in older adults [67], and has been used for predicting fall risk (when > 12 sec) [57], as well as for screening for frailty in older adults [68]. The BBS (14 items, max score: 56) is a valid and reliable test to measure the functional balance in older adults and predicts fall risk (when < 50 points) [57, 69, 70]. The FES-I is a valid and reliable questionnaire (16 items, max score: 64) to assess confidence in the performance of activities relevant to daily life and can be used to enhance confidence in level of fall risk (when > 24 points) [57, 71, 72]. Additional measurements included: the Mini-Mental State Examination score (MMSE) (12 items, max score: 30) to measure cognitive impairment [73]; the short form of Geriatric Depression Scale (GDS) (15 items, max score: 15) to assess older adults for depression [74]; the short form of Brief Pain Inventory score (BPI) (4 Pain severity items, max score: 40; 7 Pain interference items, max score: 70) to measure the impact of pain on daily functions [75]. Furthermore, participants have been asked if they had sustained 2 or more falls in the past year. A fall was defined as an event that caused participants to rest on the floor.

Data were collected at the Biomechanics Research Building at baseline, post-intervention, and retention period (6 weeks following completion of the intervention). The building featured a 3D motion capture system with 17 high-speed Raptor cameras (Motion Analysis Corporation, Santa Rosa, CA, US) synchronized with an instrumented treadmill (AMTI, Watertown, MA, US). Upon arrival at the Biomechanics Research Building the participants changed into a tight-fitting suit. Then the participants walked for 3 min on a treadmill at a self-chosen pace to ‘warm-up’.

A convenience sample size of 15 participants was included. The sample size was based on feasibility. However, with 80 % power and a two-tailed alpha error of 5 % and with an estimate walking speed change of 0.1 ± 0.125 m·sec^− 1 (i.e., meaningful change [62]) this sample size could detect an effect size of ES = 0.80 (Cohen’s d statistic). The sample size was calculated by using the statistical program G*Power [77].

The outcomes measures of walking speed, TUG, and gait variability (step width variability, step length variability, stance variability, and stride time variability) were analyzed separately using a repeated measures ANOVA with Time (baseline, post-intervention, and retention assessments) as within factor. When sphericity was violated, a Greenhouse-Geisser correction was applied. The average values at each Time level were computed for the statistical analysis. If the ANOVA revealed effects (p < 0.05), further univariate comparisons were performed using a planned (simple) contrast in which all conditions were compared with the baseline. For the ANOVA comparisons, the Cohen’s f effect size was reported (f < 0.10 negligible, f < 0.25 small, f < 0.40 moderate, otherwise large effect). Responsiveness of outcomes was reported using the ES statistic, this is the mean change between baseline and post-intervention divided by the standard deviation of the measurement at baseline [78, 79] (| ES | < 0.20 negligible, | ES | < 0.50 small and | ES | < 0.80 moderate, otherwise large effect). The ordinal outcomes of the clinical tests (BBS, FES-I) were analyzed separately using Friedman test, followed if needed by Wilcoxon singed-rank test. Kendall’s W coefficient of concordance was used to report effect size (W < 0.10 negligible, W < 0.25 small, W < 0.40 moderate, otherwise large effect). Significance level was set at α = 0.05. Statistical analyses were performed in R software Version 3.6.3 (R Foundation for statistical computing, Vienna, Austria) [80] using the afex [81], emmeans [82], sjstats [83], and rstatix [84] packages.