Faster or longer steps: Maintaining fast walking in older adults at risk for mobility disability

Abstract

Background:

The ability to walk at various speeds is essential to independence for older adults. Maintaining fast walking requires changes in spatial-temporal measures, increasing step length and/or decreasing step time. It is unknown how mobility affects the parameters that change between preferred and fast walking.

Research Question:

How does preferred walking performance and measures of strength and mobility relate to the approach (decreasing step time or increasing step length) older adults at risk for mobility disability use to maintain fast walking speeds?.

Methods:

Peak isokinetic dynamometry of knee and ankle and several mobility evaluations, including the Timed Up-and-Go, Short Physical Performance Battery, and Dynamic Gait Index, assessed mobility and strength in 57 participants, aged 65–80. Biomechanical gait analysis was used to analyze step length, step time, gait speed at preferred and fast gait speeds and ground reaction force during preferred walking. A score combining the differences between step length and time at fast and preferred speeds (Length-Time Difference) separated participants into two groups: (1) Length, representing a predominant increase in step length to walk fast and (2) Time, a predominant decrease in step time.

Results:

Those who decreased step time to produce increased speed performed worse during repeated chair stands (p = .006) with no difference in isokinetic strength (p ≥ .15). During preferred walking, the Time group displayed increased propulsive impulse compared to the Length group (p = .007), despite no differences in preferred speed, step length, or time (p ≥ .50).

Significance:

While kinetics of preferred walking differed between groups separated by Length-Time Difference, basic spatial-temporals of preferred walking did not in this homogenous population. Length-Time Difference relates to a common mobility assessment and could be easily calculated by clinicians to provide a quantitative and more sensitive measure of ambulatory performance.

1. Introduction

Gait speed relates to morbidity, mortality, and long-term independence and comfortable gait speed is considered a vital sign of the health of older adults [1,2]. However, community settings require the ability to walk at various speeds; crowded environments may necessitate walking slower to avoid bumping into the crowd of people in front of them. Conversely, the ability to catch a bus or traverse a crosswalk within the time allocated may depend on fast walking speed. Both preferred and fast walking speeds are associated with falls in older adults [2]. From a mobility disability assessment and management perspective, fast walking may be challenging enough to reveal significant gait deficits [3].

Two factors determine walking speed: the length of each step (spatial) and each step’s timing (temporal). However, little research focuses on understanding how individually, or the interplay between, these two features govern the ability to walk faster [4]. For many older adults and populations with walking deficits, reduced step lengths are often observed but the underlying causes are unknown. While reduced step length is often associated with slower, more “cautious” gait as observed in the elderly [5,6], fallers [7], and those with neurological conditions such as Parkinson’s disease [8], changes in step time may also contribute to overall reduced speed. As a result, older adults who display more “cautious” gait may rely on increased step cadence to achieve fast walking [6]. While increasing cadence may be a common compensatory pattern for reduced step length [6,8], it may not be the safest approach for fast walking. Specifically, faster cadence increases the acceleration of the swinging limb up and away from the stance leg and decreases foot clearance during walking, which may influence the risk of trips and falls [10]. Unfortunately, the underlying features that contribute to the strategy to achieve or maintain fast walking remain largely unknown.

Strength, functional utilization of force capabilities (e.g., production of ground reaction force (GRF)), and composite mobility ability may influence an individual’s specific strategy to sustain increased walking speed. The associations between gait speed and measures of strength, including isokinetic dynamometry, suggest the strength of individual muscle groups may play a role. For example, knee extensor strength correlates with step length and gait speed [5], while other studies suggest hip extensor [10] and ankle plantarflexor strength [5] may better predict speed. General mobility performance, which incorporates multiple levels of function, is also related to gait speed. Validated tests such as the Dynamic Gait Index (DGI), the Short Physical Performance Battery (SPPB), and the Timed Up-and-Go (TUG) are performed regularly to determine those at risk of mobility disability, and are related to gait speed, falls, and independence [11-13]. Regardless of the assessment, the associations are positive; greater muscle strength, functional strength, and mobility performance are associated with increased speed [5,10,12,13]. Yet, the link between forces, clinical measures of mobility and strength, and strategies used to achieve a more demanding task, such as walking at a maximal speed, have not been bridged.

In addition to reduced strength and mobility, the forces that govern how we walk also change with age. Older adults display reduced propulsive forces [6] and altered joint specific forces during walking [14, 15]. Specifically, older adults redistribute forces, using proportionately greater hip and less ankle moment and power [15]. Yet, ankle power is arguably the most important kinetic factor to walking, suggesting force redistribution may be maladaptive [16]. Indeed, reduced ankle forces are related to falls in older adults [7]. For older adults with low mobility performance (SPPB < 9), this deficit grows when compared to higher functioning older adults walking at the same speed as they display reduced ankle power, increased hip power, and shorter step lengths [14]. When individuals with low mobility scores walk faster, ankle power remains reduced compared to their high functioning peers [14]. As increased cadence is associated with increased hip and knee work with no change in ankle work in healthy individuals [4], a time dominant strategy may be compensatory due to the lack of propulsive force capabilities. Indeed, when challenging the motor system with maximal speed walking, individuals who use temporal adjustments to increase speed alter single-step external joint moments very little, while those who use a spatial strategy display significantly increased moments at all lower extremity joints [4]. Different underlying gait mechanics, which suggest altered neuromuscular strategies, may influence the ability to alter step length or time to achieve faster walking.

Step length and time can be easily identified in a clinical setting. Thus, determining the strategy associated with increasing speed, and relating it to other clinical measures of strength and mobility, may help identify those at risk for mobility limitations and may lead to personalized interventions. Earlier identification of deficits in community ambulation can help clinicians better target individual’s needs. Therefore, the purpose of this study was to determine how measures of strength, mobility, and preferred walking kinetics relate to which strategy (step time versus step length) older adults at risk for mobility disability implement during fast walking. We hypothesized individuals that increase step length to walk fast will have better strength and mobility as compared to those who decrease step time. We also explored how individual fast walking strategy relates to clinical and mobility assessments.

2. Methods

2.1. Participants

Fifty-seven adults (18 men, 39 women) participated as part of a larger, interventional study for those at risk for mobility disability, as determined by physical inactivity, physical function, and preferred walking speed. Participants were sedentary older adults aged 65–80 who engaged in less than two hours of physical activity per week as identified using the Community Healthy Activities Model Program for Seniors [17]. The SPPB assessed physical function; participants who scored <6, or a perfect score of 12, were excluded from the study [12]. Participants with a preferred walking speed faster than 1.15 m/s were also excluded, as 1.2 m/s has been determined as a cut-off for successful community ambulation [1]. Participants were excluded if they had a cardiovascular, pulmonary, or neurologic disease, severe rheumatoid arthritis, or osteoarthritis that affected their walking, or insulin-treated diabetes. Finally, participants were excluded if they exhibited cognitive impairment based on the education-corrected Mini-Mental State Examination (<25 with 13+ years of education, <23 with 9–12 years, <21 with 5–8 years) [18].

2.2. Procedures

Procedures were explained and written consent was obtained as approved by the Institutional Review Board. Participants completed functional, strength, and gait testing over two days. Day one included consent, confirmation of inclusion status, and the SPPB. A follow-up appointment scheduled within a week consisted of the gait assessment, mobility assessments, and isokinetic strength testing, in this order. All data presented were collected prior to the beginning of the intervention of the parent study. While data from the parent study has been published previously [19,20], the data analyzed herein is unique to the aims of this investigation and have not been previously published.

2.2.1. Isokinetic strength

Strength testing was performed after walking using an isokinetic dynamometer (KinCom, Chattecx Corp, Hixon, TN, USA). Participants performed five maximum voluntary contractions for knee extension and flexion, and ankle plantarflexion at an angular velocity of 90°/s. The knee is most commonly associated with activities of daily living including rising from a chair and ascending stairs [21], while the plantarflexors contribute the power and work to forward propulsion [15,16]. The velocity of 90°/s was chosen as a median value for the most common velocities used for strength that do not require more than one familiarization session (i.e., 60–120°/s) [22].

2.2.2. Functional mobility

Participants completed functional tests (SPPB, DGI, and TUG), chosen for their common use in clinical settings. Combined, these three assessments characterize most parts of community ambulation. The SPPB combines three domains: gait, balance, and strength/endurance to produce a summary score [12]. Time to complete repeated chair stands was also recorded. While the SPPB measures forward progression of gait and rising and sitting in a chair separately, the 6 m TUG combines rising and sitting in a chair, forward walking and turning into one measure [11]. Finally, the DGI consists of multiple complex walking tasks (e.g., obstacle crossing and avoidance) to test stability during locomotion [13]. During the stair portion of the DGI, 23 steps were used with a landing at the 14th stair. Total DGI score and time to complete the stairs were analyzed.

2.2.3. Gait

Participants walked barefoot across an 8-meter walkway wearing reflective markers per the Plug-In Gait full-body model. Data were collected using a ten-camera, three-dimensional motion analysis system (Vicon Motion Systems, Oxford, UK) with three embedded force plates (Bertec Corporation, Columbus, OH, USA) at a frequency of 120 Hz and 360 Hz, respectively. Gait trials were performed at two different speeds: preferred and fast. For the preferred speed trials, participants walked at their typical, comfortable speed. For fast speed trials, participants walked as “quickly and safely as possible, without running.” Since GRF during the preferred speed trials were of particular interest, participants’ starting positions were adjusted to ensure foot strikes on the force plates without bringing attention to foot placement or targeting. Five trials were analyzed for each speed.

2.3. Data analysis

2.3.1. Strength

Strength performance data from the isokinetic dynamometer test were low-pass, fourth-order Butterworth filtered at 5 Hz. Peak knee extension, knee flexion, and ankle plantarflexion torque were extracted using custom MATLAB software (MathWorks, Natick, MA, USA).

2.3.2. Gait

Power spectrum analyses were conducted to ensure appropriate filtering cut-offs for trajectory and force data. Marker trajectories were filtered with a low-pass, fourth-order Butterworth filter, cutoff frequency of 8 Hz. Gait speed, step length, and step time were calculated for both preferred and fast walking trials. Step length was defined as the anterior-posterior distance between the heel markers at heel-strike, and step time was defined as the time between consecutive heel strikes. Measures for limbs were combined (averaged) as asymmetry was not expected [23]. Length-Time Difference (LTD) determined the strategy used to maintain increased walking speed between the preferred and fast walking trials [4]. LTD represents the percentage difference between the time or length domains when comparing the preferred and fast pace walking parameters (Eq.1).

$$LTD(\%)=\frac{\left(lengt{h}_{fast}-lengt{h}_{preferred}\right)}{\left(lengt{h}_{preferred}\right)}+\frac{\left(tim{e}_{fast}-tim{e}_{preferred}\right)}{\left(tim{e}_{preferred}\right)}$$ (1)

A negative LTD indicates a time-dominant strategy (i.e., decreased step time to achieve increased speed), while a positive LTD indicates a length-dominant strategy (i.e., increased step length). LTD of 0 represents an equal percent change in step time and length. Participants were divided into two groups based on their LTD: individuals who utilized a Time versus a Length strategy (Fig. 1).

Fig. 1.

Conceptual figure demonstrating the dichotomization of strategies used to maintain an increased speed.

Force data were filtered using a low-pass fourth-order Butterworth filter with a cutoff frequency of 15 Hz. Anterior-posterior GRF (GRF_ap) were normalized by body weight. Propulsive phases for each leg were defined as the positive portion of the GRF_ap during each gait cycle, while the negative portion represented braking. Each were identified using custom MATLAB software. Impulse for each cycle was the integration of each phase.

2.4. Statistical analysis

As LTD score is an inventive metric, outliers, three or more standard deviations (sd) from the mean, were eliminated to reduce the potential to skew results. One participant was eliminated from all analyses due to their LTD value being 3.6sd outside the mean (LTD= −27.87 %). Two outliers were identified within the variables of interest: one participant (LTD= −4.34 %) with a SPPB chair stand time (37.98 s), another participant (LTD= +3.15 %) with a plantarflexion moment (174.40 N m. The following variables had missing data across 16 participants: SPPB chair stand (2), DGI stair (1), DGI total score (1), TUG time (5), knee strength (6), ankle strength (7), and kinetics (2). Outliers and missing data were handled using listwise deletion for the individual analyses. All data were confirmed normal after removing outliers and presented in text as (mean ± sd).

Demographics and cognitive performance were compared between the two strategy groups using independent samples t-tests. Fast walking speed and corresponding fast step time and length were further compared to characterize group differences. Independent samples t-tests compared measures of functional and isokinetic strength, preferred speed spatiotemporal measures and GRF_ap and False Discovery Rate (FDR) corrections were applied (α = .05, number of comparisons = 14) [24].Cohen’s d was calculated to determine effect size between groups and considered 0.2–0.5 as small, 0.5–0.8 as medium, and >0.8 as large. Pearson correlations pooling the groups were performed to assess the relationship between measures and LTD in an exploratory analysis, with FDR corrections [24]. SPSS was used for all statistical analyses (p < .05, SPSS Inc., Chicago, IL, USA).

3. Results

3.1. Participants

No significant differences were observed between the Time and Length groups in demographics or gait speed characteristics except LTD score, confirming the polarity of the groups (Table 1; Fig. 2; t(541) = 8.69). Step time during fast walking was not significantly different (Time: 0.46 ± 0.05 s vs. Length: 0.47 ± 0.04 s; t(54) = 1.04, p = .30, d = 0.28). During fast walking, the Length group (0.60 ± 0.06 m), had significantly longer steps than the Time group (0.56 ± 0.06 m, t(54) = −2.48, p = .02, d = 0.67).

Table 1.

Mean and standard deviations of participant demographics and gait speed characteristics across groups. Significant differences denoted by bolded values.

	Time Strategy (n = 32)	Length Strategy (n = 24)	p	d
Age (years)	72 ± 4	73 ± 4	.82	0.06
Height (m)	1.6 ± 0.1	1.7 ± 0.1	.07	0.49
Mass (kg)	86.2 ± 22.0	86.8 ± 17.5	.92	0.03
Leg Length (m)	0.9 ± 0.1	0.9 ± 0.1	.77	0.08
MMSE	28 ± 2	27 ± 2	.14	0.41
Preferred Gait Speed (m/s)	0.95 ± 0.14	0.98 ± 0.12	.50	0.19
Fast Gait Speed (m/s)	1.30 ± 0.23	1.34 ± 0.23	.48	0.19
LTD (%)	−5.58 ± 4.62	4.25 ± 3.52	<.001	2.39

Fig. 2.

Confirmation that the average between Length group and Time groups are significantly different.

3.2. Strength

None of the isokinetic measures differed between groups (p > .15). After correcting for multiple comparisons, only SPPB chair stand time (p = .01) was significantly different between groups. The Length strategy group performed faster than the Time group (Table 2).

Table 2.

Mean and standard deviations of measures by group. Significant differences after applying the false discovery rate denoted by bolded values. When Levene’s Test for Equality of Variances was significant, adjusted statistics were reported. Confidence Interval (CI) reported at 99 %.

	Time Strategy	Length Strategy	t(df)	p	d	99 % CI
Functional Tests
SPPB Total	9 ± 1	9 ± 1	0.99(54)	.326	0.27	[−1.27, 0.58]
SPPB Chair stand (s)	16.7 ± 4.4	14.0 ± 2.1	2.91(44.15)	.006	0.77	[0.20, 5.11]
DGI Total	20 ± 3	21 ± 2	1.92(54)	.849	0.45	[−2.64, 2.29]
DGI Stair (s)	20.8 ± 6.0	17.5 ± 3.1	2.39(53)	.020	0.68	[−0.39, 6.96]
6 m TUG (s)	11.6 ± 2.7	11.2 ± 1.5	0.58(47.05)	.565	0.16	[−1.39, 2.07]
Isokinetic Dynamometry
Knee Extensor Torque (N m	48.9 ± 27.2	41.5 ± 23.4	1.01(48)	.320	0.29	[−12.33, 27.12]
Knee Flexor Torque (N m	−27.0 ± 11.5	−32.8 ± 16.6	1.47(48)	.148	0.41	[−4.81, 16.50]
Ankle Plantarflexor Torque (N m	31.4 ± 14.6	35.9 ± 36.7	0.186(46)	.853	0.16	[−15.46, 13.46]
Preferred Walking
Step Time (s)	0.56 ± 0.06	0.55 ± 0.04	0.69(54)	.496	0.19	[−0.03, 0.04]
Step Length (m)	0.51 ± 0.06	0.51 ± 0.05	0.55(54)	.583	0.15	[−0.05, 0.03]
Braking Peak (% BW)	−11.81 ± 2.33	−10.84 ± 1.72	1.69(52)	.097	0.48	[−0.25, 0.06]
Propulsive Peak (% BW)	13.96 ± 3.17	13.23 ± 2.71	0.88(52)	.381	0.24	[−0.14, 0.28]
Braking Impulse (BW·s)	−1.14 ± 0.30	−0.98 ± 0.16	2.53(47.75)	.015	0.67	[−3.20, 0.01]
Propulsive Impulse (BW·s)	2.09 ± 0.52	1.77 ± 0.30	2.83(49.59)	.007	0.75	[0.17, 0.62]

3.3. Preferred walking

The only difference observed between during preferred walking groups after correcting for multiple comparisons was propulsive impulse (p = .01; Table 2). The Length group displayed reduced propulsive impulsive compared to the Time group. There were no differences in step time (p = .50) or step length (p = .58) between groups. Collectively, these results indicate differences in LTD are potentially driven by differences in propulsion.

3.4. Exploratory aim: correlations of LTD

When evaluating the LTD as a continuous measure, rather than a dichotomous variable, the correlations support group differences but do not explain a significant amount of the variability in LTD after correcting for multiple comparisons using False Discovery Rate (Table 3).

Table 3.

Pooled group Pearson correlations. After applying False Discovery Rate correction for multiple comparisons, no values were significant.

	r	p
Functional Tests
SPPB Total	.09	.53
SPPB Chair stand (s)	−.25	.07
DGI Total	.15	.29
DGI Stair (s)	−.22	.11
6 m TUG (s)	−.03	.83
Isokinetic Dynamometry
Knee Extensor Torque (N m	.04	.80
Knee Flexor Torque (N m	−.24	.09
Ankle Plantarflexor Torque (N m	.09	.56
Preferred Walking
Walking Speed	−.10	.46
Step Time	−.03	.84
Step Length	−.11	.41
Braking Peak (% BW)	.31	.02
Propulsive Peak (% BW)	−.30	.03
Braking Impulse (BW·s)	.34	.01
Propulsive Impulse (BW·s)	−.38	.01

4. Discussion

The present study compared strategies older adults at risk for mobility disability use to walk fast and related the results to clinical/functional measures of strength and mobility. Generally, those who decreased step time to walk fast (Time group) exhibited decreased functional mobility, but increased propulsion and braking during preferred pace walking without differences in step time, length, or gait speed. GRF_ap during preferred pace walking and time to complete the objective tasks (i.e., chair stand time and stair climb time) from the SPPB and DGI significantly predicted LTD before corrections were applied. Extrapolating from the differences observed in functional mobility and GRF_ap, those who utilize the time strategy potentially do so based on mobility limitations. Time-dominant individuals may use more of their underlying strength to achieve similar gait kinematics at comfortable gait speeds, and thus are not able or willing to tap into additional strength reserves, to increase step length as a means to speed up. Based on the present findings, individuals using the time strategy increased step frequency to walk fast, which reduces individual joint work needed per step [4] compared to increasing length, but may increase tripping risk [9]. We postulate the time-dominant strategy may signal early maladaptive walking patterns during both preferred walking and fast walking. However, we cannot know if this strategy is an intrinsic functional limitation and/or a volitional choice.

Older adults can perform locomotor activities that require substantial propulsive force such as walking uphill [25] or increasing speed [9]. Franz et al. has suggested that a “propulsive reserve” is key for older adult locomotion, in which individuals can access lower limb propulsive strength to execute locomotor tasks [26]. It is not completely understood why older adults walk at slower speeds and with altered neuromuscular patterns during comfortable walking if they are able to access additional propulsion capabilities when needed [6,14,15]. With aging and pathology, alterations of motor patterns, such as redistribution of force to more proximal joints [15], appear necessary to achieve normal, comfortable walking. The distal-to-proximal joint redistribution becomes more evident as demands increase to produce faster speeds [14]. Our data expands this evidence, suggesting those who display higher impulse during preferred walking may lack the capacity to apply additional force per step. While a strategy change may be a response to the demands of independent living, it may also be an early indicator of future reduced community ambulation, and signal a need for intervention. Specifically, the use of decreased step timing to maintain fast walking speed may be unsafe [9]. When developing interventions, differences in strategies should be evaluated and considered during gait analysis and rehabilitation, as seen with the inter-individual variability within this homogenous cohort [27].

Clinicians evaluate mobility with global assessments (e.g., DGI and SPPB) and/or with basic gait measures. Participants in this study were deemed “at risk for mobility disability” based on preferred gait speed, SPPB score, and sedentary behavior. As mobility disability is a dynamic process that can be improved, those at risk for mobility disability were investigated during this critical transitional stage in the disablement process [28] that is ideal for interventions. We suggest the composite scores from the included clinical tests lack the sensitivity required to differentiate gait strategies within this cohort. We agree with past studies [29,30] that evaluate the individual components as a simple way to expand the sensitivity of these tests and aid in identifying specific deficits to improve with individualized rehabilitation plans [3]. Additionally, measuring kinetics allows for increased discrimination and determination of the underlying causes of mobility deficits [6]. While the variance explained among the correlations was relatively small, we intentionally considered a homogenous group with similar gait speeds and SPPB scores. Expanding the participant pool to include physically active, healthy individuals and those whose capacity is below this group may clarify the nuances of gait strategies used during fast walking.

In this sample, a lack of difference in kinematics but significant changes in GRF_ap during preferred speed walking, suggests the extra time and cost of instrumented gait analysis is justified. Testing complex tasks such as maximal fast walking and evaluating the spatiotemporal difference between preferred pace and maximal speed walking may be more achievable in a clinical setting where instrumented gait analysis is not available. Those at risk for future mobility disability require sensitive assessments to accurately reveal compensation strategies that may not be apparent in preferred speed gait, mobility, and strength measures. LTD score can be easily calculated by clinicians already performing simple gait assessments to identify strategies that may be maladaptive. For example, calculation of a simplified LTD requires only walking distance, ambulation time, and the number of steps, all of which can be readily collected in a clinical setting (See Supplementary Spreadsheet). Thus, we recommend objective measures of mobility, including timed portions of mobility assessments, should be included and evaluated in clinical settings as they provide relevant information without additive tests. These additions can help shape specific rehabilitation interventions to mitigate the mobility decline seen with aging.