Spatiotemporal gait parameter fluctuations in older adults affected by mild cognitive impairment: comparisons among three cognitive dual-task tests

doi:10.21203/rs.3.rs-2489973/v1

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Research Article

Spatiotemporal gait parameter fluctuations in older adults affected by mild cognitive impairment: comparisons among three cognitive dual-task tests

https://doi.org/10.21203/rs.3.rs-2489973/v1

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published 27 Sep, 2023

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Backgrounds: Gait disorder is associated with cognitive functional impairment, and this disturbance is more pronouncedly when performing additional cognitive tasks. Our study aimed to characterize gait disorders in mild cognitive impairment (MCI) under three dual tasks and determine the association between gait performance and cognitive function.

Methods: A total of 260 participants were enrolled in this cross-sectional study and divided into MCI and cognitively normal control. Spatiotemporal and kinematic gait parameters (31 items) in single task and three dual tasks (serial 100-7, naming animals and words recall) were measured using a wearable sensor. Baseline characteristics of the two groups were balanced using propensity score matching. Important gait features were filtered using random forest method and LASSO regression and further described using logistic analysis.

Results: After matching, 106 participants with MCI and 106 normal controls were recruited. Top 5 gait features in random forest and 4~6 important features in LASSO regression were selected. Robust variables associating with cognitive function were temporal gait parameters. Participants with MCI exhibited decreased swing time and terminal swing, increased mid stance and variability of stride length compared with normal control. Subjects walked slower when performing an extra dual cognitive task. In the three dual tasks, words recall test exhibited more pronounced impact on gait regularity, velocity and dual task cost than the other two cognitive tests.

Conclusion:Gait assessment under dual task conditions, particularly in words recall test, using wearable sensors could be useful as a complementary strategy for early detection of MCI.

mild cognitive impairment

dual tasks

gait analysis

spatiotemporal gait parameters

aging adults

wearable sensors

Mild cognitive impairment (MCI) is an intermediate transitional stage between normal aging and dementia that is predominately manifested by the decline of cognitive functions involving memory, executive function, language, attention, and visuospatial roles, but not significantly affecting the ability of daily life or meeting the diagnostic criteria of dementia. Epidemiological evidence showed that MCI affected about 20% of the aging individuals worldwide and 10%~15% of them progressed into dementia annually, which in turn leading to decreased quality of life of the population and escalated social economic burden [1–3]. Therefore, early identification and prophylactic intervention of MCI will retard its progression and reduce the transitional risk to dementia in the scenario that there is a lack of effective therapeutic options for reversing dementia. Currently, neuropsychiatric scales depending screen of MCI is time-consuming and requires a professional clinician; the result scores are always influenced by educational level and other personal factors of the subjects [4].

Gait refers to the posture of a healthy person when walking and it varies with age, body condition, training and certain disease conditions [5]. Qualitative and quantitative gait assessment using observational scales and instrumental analysis are helpful for the judgement of a normal or pathological movement pattern; it has been used in the clinical diagnosis of orthopedics, rehabilitation, neurological and other diseases, especially in the neurological diseases [6–8]. As a compelling instrument for quantitative gait analysis, the recently applied wearable sensor enables the quantitative gait measurement even to monitor subtle changes during walking, showing great promise in replacing the human observational scale or optical motion capture system due to its low cost, real-time, portable, versatile and informative features [9].

Up to date, multiple lines of studies have confirmed that walking is a learned behavior rather than an automatic process that requires the coordination of cognitive function [10, 11]. Gait performance is associated with individual capacities of executive function and working memory, especially in the dual-task tests [12]. Dual-task gait test is a measuring modality to monitor changes of gait parameters when simultaneously performing an attention-demanding cognitive task and another motor task. Previous studies revealed that the gait patterns in performing dual task tests are disturbed more frequently and pronouncedly in subjects with MCI [13, 14]. Thus, gait analysis in dual task may represent a promising option for the diagnosis and outcome prediction in the target population. However, no consensus has been achieved with regarding to the gait characteristics in distinguishing MCI from normal aging populations due to the varieties in gait measuring tools, loaded cognitive tasks, reported gait parameters as well as individual cognitive capability [15]. Our study aimed to analyze gait parameters (spatiotemporal and kinematic characteristics) of MCI using a wearable sensor under single task and three different cognitive tasks, including counting backwards by 7s, naming animals and words recall. Then we selected important gait features contributing to MCI and compared the performance of gait in different tasks, which may provide objective evidence for the clinical screening of MCI.

Participants

A total of 260 participants meeting the inclusion and exclusion criteria were enrolled in this cross-sectional study from December 2020 to December 2021 in memory clinics of our hospital. The inclusion criteria were: (1) aging over 50; (2) voluntary to participate in the gait test and provide the informed consent; (3) being able to complete the questionnaire and test independently or with the assistance of the examiners. The exclusion criteria were as follows: (1) patients diagnosed with dementia; (2) severe verbal, hearing, optical impairments affecting the gait performance; (3) walking disorder due to lower limbs musculoskeletal diseases, including trauma, arthritis, pain or orthopedic surgeries; (4) central or peripheral nervous diseases that cause gait disorders, such as cerebral stroke, Parkinson's disease, Lewy body dementia, spinal diseases, normal pressure hydrocephalus; (5) severe concomitant intracranial diseases such as encephalitis, meningitis, seizure, and brain tumors.

Clinical characteristics of the eligible participants were recorded, including age, gender, educational years, smoking or alcohol consuming and medical history of diseases as well as memory complaints. Participants were interviewed about whether they had a subjective memory decline for 3 months or not, and whether they were worried about this decline. Meanwhile, their family history of AD/dementia were asked by “Did your parents ever suffer from Alzheimer’s disease/dementia?”. Sleep disorder referred to reduced sleep duration compared to age and sex matched population, difficulty in falling asleep, frequent awakenings, early morning awakenings, and excessive daytime sleepiness, and was reported by the subjects themselves. Afterwards, the subjects were examined with cognitive scales, daily functioning as well gait performance. The diagnosis of MCI was based on the Petersen criteria [16], briefly as followings, (1) subjective complaints of memory decline or other cognitive decline on self- or informant report or doctors’ assessment; (2) one or more cognitive domain impairment based on neuropsychological testing standardized by the patient’s age and educational background; (3) intact daily functioning in ADL scales and being independent in daily living; (4) not meeting criteria of dementia with a clinical dementia rating (CDR) score less than 0.5. MCI was diagnosed by two independent neurological doctors after comprehensive consideration of clinical characteristics, medical history, daily life ability and neuropsychological testing performance. Subjects didn’t meet the diagnostic criteria were categorized to be cognitive normal control (NC). Finally, 134 patients with MCI and 126 NC were included. This study was approved by the ethnic committee of Xi’an No.3 Hospital and the informed consents were obtained from all participants.

Cognitive Functional Assessment

Cognitive function was assessed using the mini-mental status examination (MMSE), Montreal cognitive assessment (MoCA) and CDR scales. Daily functioning was evaluated using Activity of Daily Living (ADL) scale. MMSE scale assesses cognitive functions of 5 domains, including orientation, memory-recall, attention-calculation and language; while MoCA scale further measures visuospatial-executive and abstraction abilities. Both MMSE and MoCA have a total score of 30 points, and higher score indicates better cognitive ability. One more point will be added to the original total score of MoCA in case of the educational years one attains are less than 12, and the final score < 26 indicates cognitive impairment. MMSE ≤ 24 was used to exclude subjects with possible dementia. CDR scale was used to grade the severity of dementia by scoring from 0 to 3 with score ≥ 1 diagnosed as mild to severe dementia. ADL scale is used to assess daily life ability to assist the diagnosis of MCI. The total score of ADL is 100 and a higher score indicates a more independent status of the participant.

Gait Assessment

Intelligent device for energy expenditure and activity (IDEEA version 3.0, MiniSun, CA, USA) is an integrated portable system used to monitor energy consumption and physical activities. The device carries five miniaturized sensors projecting real-time anthropometric signals of angle, relative position, and acceleration to an advanced microprocessor for processing and saving the information on a memory card. Then, these data are downloaded and analyzed on the peripheral computer using an Actview™ software. IDEEA3.0 system was applied to monitor gait parameters of the participants when performing a single or three dual-tasks in our study. After checking the device, the recorder was fixed on the waist of the subjects and the five sensors were positioned one on the sternum, one on the anterior part of each thigh and one under the plantar arch of each foot, all fixed to the skin by an adhesive tape before the measurement. Then, the subject was asked to walk naturally to acclimate to the device. The gait test was performed in a quiet room, and the subject was requested to walk 12 m in a straight line on the ground marked with starting and ending points. The gait data of the middle 10 m were collected for final data analysis. Gait parameters encompass spatiotemporal (step and stride length, velocity, cadence, stance, swing and double support duration, etc) and kinematic characteristics (thigh twitch acceleration, thigh swing work, ground reaction forces and heel angle to the ground). Variability of stride time or stride length (stride time/length CV) were calculated by dividing the standard variability of stride time/length to their mean values. Dual task cost (DTC) of velocity was used to assess the influence of cognitive task challenge on gait performance; it was calculated as [(velocity of single task-velocity of dual task)/velocity of single task] × 100%.

A total of four gait trials were administered to each patient. Single task trail was performed when walking as usual to gather baseline gait parameters in the absence of cognitive load. For the dual task tests, the subject was asked to finish the three additional cognitive tasks when walking, including serial 100-7, naming animals and specific word recalling, respectively. No priority was allowed to finish either motor or cognitive task. Only one trial was allowed for each task to balance and minimize the effects of learning and fatigue. In order to avoid the events of "walking without thinking" and "thinking without walking" during the gait test, the subject was immediately reminded of "keep thinking" when he/she discontinued the cognitive task, or "keep walking" when he/she stopped walking. In addition, if the subject made an error in the calculation/recall/naming process, the cognitive task proceeded regardless of the error.

Data analysis

All data were analyzed using R software version 4.1.2 (Vienna, Austria) and SPSS 26.0 (IBM Corp., Chicago, Illinois, USA). The MCI and NC groups were 1:1 matched using propensity scores with a caliper value of 0.4. The matched covariates included gender, age, height, weight, and educational years. Countable data were presented as frequency and percentage (%) and the difference between two groups was compared using Chi-square test or Fisher’s exact test. Quantitative data were expressed as means ± SD and statistical difference between two groups was analyzed using independent t-test or paired t-test. Difference among multiple groups were compared using one-way analysis of variance (ANOVA) followed by Bonferroni post-hoc test. Important features of gait parameters associating with MCI were selected using recursive feature elimination (RFE) based random forest method and LASSO regression to screen robust variables. Univariate and adjusted logistic regression analysis were applied to quantitatively describe the association between selected gait parameters with MCI. P < 0.05 was considered statistically significant.

1. Baseline characteristics of the participants

The primary sample size for this cross-sectional study was 260 containing 126 cognitive normal control and134 MCI. The baseline clinical characteristics was shown in Table 1. The data showed that in the overall population, the participants with MCI had higher percentage of female gender, higher body weight as well as BMI, and lower educational level with 6–9 years accounting for 42.5% of the group. The two groups had similar comorbid conditions such as cardiovascular diseases, subjective sleep disorder and family history of AD. The MoCA sores were 26.72 ± 1.98 in NC group and 21.52 ± 2.35 in MCI group, indicating a significant difference between the two groups (P < 0.05). Meantime, there was statistical difference in MMSE scores between them. To balance the significant confounding variables potentially affecting cognitive and gait functions, a matched population at a ratio of 1:1 in NC and MCI group was created using propensity score matching (PSM) method after adjusting gender, age, height, weight, and educational years. Eventually, a total of 212 subjects, 106 in each group, were matched and most of the significant imbalances were removed without yielding other imbalanced variables between the two groups. As expected, significant difference on MMSE and MoCA remained existing between the NC and MCI groups after PSM matching.

Table 1

Baseline characteristics of the overall and matched populations
Characteristics	Overall Population			Propensity Score-Matched (1:1) Population
Characteristics	NC (n = 126)	MCI (n = 134)	P	NC(n = 106)	MCI(n = 106)	P
Age (years)			0.122			0.319
50 ~ 59	55 (43.7)	42(31.3)		44(41.5)	33(31.4)
60 ~ 69	55 (43.7)	70(52.2)		47(44.3)	56(53.3)
70 ~ 79	16 (12.7)	20 (14.9)		15(14.2)	15(14.3)
≥ 80	0 (0.0)	2 (1.5)		0(0.0)	1(1.0)
Female n (%)	89 (70.6)	74 (55.2)	0.010	69(65.1)	65(61.3)	0.669
Height (cm)	163.08 ± 7.33	163.52 ± 8.11	0.647	163.56 ± 7.48	162.86 ± 7.51	0.499
Body weight (kg)	61.46 ± 9.84	65.52 ± 10.83	0.002	63.02 ± 9.95	63.96 ± 10.47	0.502
BMI (kg/m²)	23.16 ± 2.87	24.32 ± 2.97	0.002	23.54 ± 2.86	24.04 ± 3.06	0.210
Education n (%)			< 0.001^*			0.068
< 6 years	12 (9.5)	13 (9.7)		12(11.3)	10(9.4)
6–9 years	25 (19.8)	57 (42.5)		25(23.6)	43(40.6)
9–12 years	53 (42.1)	46(34.3)		50(47.2)	37(34.9)
> 12 years	36 (28.6)	18 (13.4)		19(17.9)	16(15.1)
Martial status (%)			0.442			0.318
Single	0(0.0)	1(0.8)		0(0.0)	1(0.9)
Married	121 (96.0)	122 (91.0)		102(96.2)	95(89.6)
Divorced	1(0.8)	2(1.5)		1(0.9)	2(1.9)
Widowed	4(3.2)	9(6.7)		3(2.8)	8(7.5)
Living alone n (%)	9 (7.1)	15(11.2)	0.259	7(6.6)	14(13.2)	0.167
Exercise n (%)			0.156			0.149
Never	8(6.3)	6(4.5)		6(5.7)	5(4.7)
< 3 times/week	49(38.9)	39(29.1)		43(40.6)	30(28.3)
> 3 times/week	69(54.8)	89(66.4)		57(53.8)	71(60.7)
Smoking n (%)	31 (24.6)	48 (35.8)	0.075	31(29.2)	32(30.2)	0.358
Alcohol use n (%)	29 (23)	40 (29.9)	0.326	28(26.4)	27(25.5)	0.809
Hypertension n (%)	46(53.6)	57(42.5)	0.321	44(41.5)	44(41.5)	1.000
Diabetes n (%)	11 (8.7)	20 (14.9)	0.123	9(8.5)	17(16.0)	0.094
Hyperlipemia n (%)	38 (30.2)	41(30.6)	0.939	33(31.3)	31(29.2)	0.881
Coronary arterial disease (%)	20 (15.9)	18(13.4)	0.578	17(16.0)	14(13.2)	0.698
Sleep disorder n (%)	67 (53.2)	65 (48.5)	0.452	59(55.7)	54(50.9)	0.582
Family history n (%)	14 (11.1)	10 (7.5)	0.310	10(9.4)	10(9.4)	1.000
MMSE	28.91 ± 1.17	27.35 ± 1.65	< 0.001	28.90 ± 1.05	27.30 ± 1.64	< 0.001
MoCA	26.72 ± 1.98	21.52 ± 2.35	< 0.001	26.22 ± 2.39	21.01 ± 2.52	< 0.001

2. Important Feature Selection Of Gait Parameters Associating With Cognitive Function

In this study, a panel of spatiotemporal and kinematic gait parameters as listed in Fig. 1 were measured using a portable sensor in all the enrolled participants. To analyze the association between gait performance and cognitive function in the matched population, all gait parameters (31 gait characteristics) were pooled into a random forest and a LASSO regression model to select important features contributing to MCI in single task and three dual-task modes. As shown in Table 2, in different task modes, top 5 important features based on random forest model were selected and 5 ~ 6 significant features at λ_min or λ_1SE in LASSO model were shown. For the single task, the common ranked features included variability of stride length and thigh twitch acceleration. While, in double task of serial 100-7 test and naming animals test, DTC and gait cycle of mid stance and terminal swing were common features associated with MCI. In words recall test, only DTC was selected as common feature by the two algorithms. Therefore, selection preference could be observed in different models, and the combined features were further analyzed in each task.

Table 2

Important gait characteristics selection associated with cognitive function in matched populations
Task	Random forest		LASSO regression		The combined features
Task	Features	Importance	Features	Importance	The combined features
Single task	Stride length CV	0.577	Thigh swing work	-0.120	Stride length CV Stride time CV Load response Initial swing Mid swing Thigh twitch acceleration Thigh swing work
	Mid swing	0.555	Load response	-0.012
	Thigh twitch acceleration	0.531	Stride length CV	0.010
	Stride time CV	0.524	Thigh twitch acceleration	0.001
	Initial swing	0.511
Subsequent 100-7	Mid stance	0.592	Swing time	-0.484	Stance time CV Swing time Terminal swing Mid stance Thigh swing work DTC
	Thigh swing work	0.547	Terminal swing	-0.032
	Stance time CV	0.540	Mid stance	0.002
	DTC	0.534	DTC	0.002
	Terminal swing	0.528
Naming Animals	Terminal swing	0.565	Stride length	-0.271	Stride length Stride length CV Terminal swing Mid stance DTC Stride time CV Swing time Swing time CV
	Stride time CV	0.533	Swing time	-0.225
	Swing time CV	0.528	Terminal swing	-0.039
	DTC	0.526	Mid stance	0.013
	Mid stance	0.526	Stride length CV	0.008
			DTC	0.003
Words recall	Stride time CV	0.568	Swing time	-0.117	Velocity Stride length Stride time CV Swing time DTC Mid stance Terminal swing Thigh swing work Thigh twitch acceleration
	Thigh swing work	0.543	Terminal swing	-0.015
	Velocity	0.540	Thigh twitch acceleration	-0.021
	DTC	0.532	DTC	0.002
	Stride length	0.522	Mid stance	0.001

3. Logistic Regression Analysis Of The Selected Gait Parameters Associating With Mci

To further delineate the association between the above selected important gait features and MCI, an univariate logistic model and a multivariate logistic model adjusting gender, age, height, body weight were created, and the odds ratio (OR) with 95% confidence interval (CI) and testing significance were shown in a forest plot in Fig. 2. In the single task, only stride length CV was identified as a risk factor for MCI in the adjusted logistic model (OR = 1.083, 95% CI: 1.012–1.060, P = 0.021). In serial 100-7 dual task, swing time and terminal swing phase were screened as two independent risk factors for MCI, with shorter swing period and lower terminal swing cycle indicating higher risk of MCI (OR = 0.001, 95% CI: 0-0.484 and OR = 0.792, 95% CI: 0.665–0.942). In dual task of naming animals, in addition to the above variables, mid stance phase, stride length CV and swing time were also contributing factors indicating MCI. When performing a word recall test, DTC, swing time mid stance, and terminal swing independently predicted cognitive impairment. Collectively, the logistic regression analysis suggested that mid stance, terminal swing, and swing time were filtered as common hallmarks of gait predicting cognitive functional decline.

4. Comparison Of Gait Parameters Between Normal Aging People And Cognitive Impaired Population

Based on the above results, we further traced and compared the changes of these filtered gait parameters in the two groups. As shown in the scatter-box plots in Fig. 3, stride length CV obviously increased in MCI subjects in the condition of single task and naming animals dual task test compared with NC group (P = 0.024 for each comparison). Although elevated stride length CV was also found in MCI under the dual tasks of serial 100-7 and words recall, it didn’t reach statistical significance. Mid stance and terminal swing phase are two temporal gait parameters accounting for approximately 18% and 11% of the whole gait cycle for normal adults, respectively. In our study, the mean mid stance of NC group was 19.59% in performing single task, which was not differed from MCI group of 19.73 ± 2.51% (P = 0.066). In the three dual tasks, the mean mid stance (%) of MCI group increased to 20.46% in serial100-7 test, 20.39% in naming animals test and 20.42% in words recall test, which were all significantly higher than that in NC group. For the terminal swing, the two groups had comparable percentage under single task condition; reduced terminal swing was found in MCI group than NC group when performing dual tasks. Likewise, there was no significant difference in the mean swing time between NC and MCI in single task; however, MCI group exhibited significantly decreased swing time than NC when performing the three cognitive tasks.

5. Effects Of Different Dual Cognitive Tasks On The Performance Of Gait Test

To elucidate the effect of different cognitive tasks on the performance of gait test, we compared the the above screened gait parameters, velocity and DTC between single task and three dual tasks as illustrated in Fig. 4. The ANOVA analysis revealed that there was statistical difference in the stride length CV among the different tasks both in NC group and MCI group (F = 8.819, P < 0.001 and F = 3.225, P = 0.026), and subjects tended to have higher stride length CV when performing the dual task gait tests, particularly in finishing the words recall test. The percentage of mid stance didn’t differ significantly under the four modes of tasks either in NC or MCI group. Statistical difference on percentage of terminal swing phase was shown among the four task modes in NC group (F = 3.96, P = 0.008) with significantly higher values in counting backward by 7 test and enumerating different animals. Whereas, this difference was not shown in MCI group. Besides, participants had higher expenditure in swing time when performing the dual tasks either in NC or MCI groups (F = 44.11, P < 0.001 and F = 18.32, P < 0.001, respectively), with a stepwise increase between serial 100-7 and naming animals, especially higher in words recall test than the other two dual tasks (both P < 0.05). Because velocity was an important hallmark parameter of gait, we then evaluated the changes of velocity under different tasks. Notably, slower speed was monitored in dual tasks in both NC and MCI groups, and the mutual comparison following ANOVA suggested much lower velocity in words recall test comparing with counting backward by 7 test and enumerating different animals test (both P < 0.05). Likewise, significantly higher DTC on velocity was required when finishing the words recall test than the others in both NC and MCI groups.

This study explored the gait performance in normal aging people and subjects with cognitive decline in finishing single or three different dual task paradigms. In the significant confounders balanced population, important variables associating with MCI were filtered using random forest and LASSO models in each task. The commonly differed variables between NC and MCI groups were temporal gait characteristics including swing time, mid stance, and terminal swing as well as spatial characteristic DTC in the dual task modes as further evaluated by logistic regression analysis. Compared with single task, all subjects walked slower when executing an additional cognitive task as reflected by the substantially decreased velocity, and this decrease was particularly obvious in finishing the words recall task requiring the memory capability. Therefore, disturbed temporal gait parameters under dual tasks may provide objective evidence for the clinical screening of cognitive decline in aging population.

Accumulating studies supported that walking is a complicated activity involving both motor and cognitive functions and their interplay or coordination rather than an automatic process [10, 11]. Based on this rational, gait characteristics of patients with cognitive decline have been discussed in several previous studies [13, 17, 18]. A meta-analysis summarizing the effect of MCI on gait involving 11 studies concluded that MCI affected specific gait parameters, and these changes were particularly pronounced when subjects were challenged cognitively [15]. In these retrieved studies, four criteria were used for the diagnosis of MCI and three modalities of instruments were used to measure the gait function, including electronic walkways, force plates and body-worn sensors. Items of gait parameter varied substantially across the included studies even when the same instrument was used, and only routine spatial gait parameters were reported. Therefore, more studies are needed due to insufficient evidence of these heterogeneous studies. In the past decades, wearable sensors were prevalent in gait analysis and proved to be useful as they permitted a simple, objective assessment of human gait [19]. In our study, a wearable sensor was used and about 30 items of gait characteristics were captured. This portable device could collect spatial-temporal and kinetic gait features. These measured features were pooled into algorithms and spatial-temporal features were selected for their association with MCI, indicating their sensitivity in detecting cognitive decline.

We evaluated the gait performance of normal aging people and patients with MCI in single task and three different dual tasks challenging cognitive capabilities. It was found that participants with MCI exhibited significantly higher variability of stride length in single task than the normal controls, suggesting the disturbed gait regularity in these subjects. This gait disturbance also can be reflected under dual task condition when performing naming animals test. The greater variability of stride length in MCI were also reported in other studies [20, 21]. Moreover, our study identified that in dual task mode, swing time, in paralleled with the percent of terminal swing phase in a gait cycle greatly reduced in cognitively impaired individuals compared to normal aging people. Swing time refers to the duration between the Toe-Off and the Heel-Strike of one leg inside a gait cycle, which takes 0.36 ~ 0.40 s and accounts for approximate 40% of this cycle. During this phase, the leg first pushes backwards and then swings forwards, transforming the potential energy into kinetic energy, and resulting in the highest values in the acceleration and angular velocity signal to propel the forward motion of the whole body [9]. The terminal swing phase, the ending of swing phase and the entire gait cycle, is responsible for decelerating forward motion of the lower limbs and preparing for foot landing for the next gait cycle. The decreased swing time and terminal swing in MCI may indicate an impaired capacity for moving forwards when conducting a cognitive task. Meanwhile, we found that MCI patients showed elevated mid stance than the control. The mid stance is the only phase of a single support for whole gravity, which functions to maintain the stability of the knee joint and control the forward inertial motion of the limb. Therefore, the participants with MCI may experience insufficient walking stability and posture control when performing a dual task that needed being compensated by prolonged mid stance. Conventionally, gait velocity, cadence and stride length were frequently focused in MCI patients in previous studies, while the detailed temporal characteristics were rarely portrayed [22]. Thus, a comprehensive understanding of specific gait pattern in MCI population is needed since gait is a complex integrated activity and slow speed is a nonspecific variable linking with many subjective and objective factors [23]. We did not observe substantial difference in gait speed between MCI and control under single and dual tasks in spite adjusting the covariates of age, sex and height or weight. The previous study tended to believe that patients with MCI exhibited slower speed under single or dual task conditions although controversy existed in other studies showing no difference between them [12, 24, 25]. It can be explained that our study recruited subjects aging over 50 years with 85% of them ranging from 50 and 70, which may differ from other studies in age distribution. Meanwhile, no priority was permitted to perform motor or cognitive tasks, thus, the attention capacity and preference in allocating these resources between the two tasks may also affect the performance in gait speed.

In our study, three different dual cognitive tasks compassing subsequent 100-7, naming animals and words recall were loaded onto the gait assessment, and their impacts on gait performance of the participants were evaluated. Overall, all subjects exhibited significantly slower speed when conducting an additional cognitive task comparing with performing a single task, which was well acknowledged in other studies [22]. Meantime, we noticed that subjects had poorer gait performance in words recall test, as reflected by the more obvious decrease in velocity and higher velocity cost comparing with the other two tests. The arithmetic tasks (subsequent subtraction by 7s from 100) and verbal fluency tasks (enumerating animal names) seemed to have comparable effects on gait function. The three dual tasks challenged different cognitive resources. Serial 100-7 is a mental tracing task engaging numerical processing skills; verbal fluency tasks involving semantic knowledge and retrieval processes, while words recall activities engaging episodic memory encoding and retrieval processes. Activation of neural circuit in bilateral prefrontal cortices are implicated in all these tasks [26]. Related studies showed that verbal fluency tasks resulted in similar magnitude of interference as mental tracking tasks when walking [27]. In our study, words recall trial caused higher burden and disturbance on gait, indicating that this task is more complex and high-demanding, which is consistent with the high frequency of memory impairment in MCI. As depicted by Schwenk et al., more complex cognitive tasks seem to be required to elicit the gait speed differences between healthy from cognitively declined subjects [23].

Collectively, our study demonstrated dual task gait measurement is superior than the single task in discriminating cognitively decline people from normal aging population. In dual task condition, all subjects walked slower than that in single task mode, and the decrease of velocity were particularly obvious in completing the words recall task that competes the memory capability when walking. Commonly, participants with MCI exhibited decreased swing time and terminal swing phase, as well as increased mid stance and variability of stride length comparing with normal aging participants when performing the dual tasks, indicating the insufficient walking stability and posture control in these patients. This study is limited for its cross-sectional nature and a dynamic follow-up is advocated. Besides, multicenter study involving more participants should be performed to validate the conclusion.

Acknowledgements

We would like to thank all our participants for their generous contribution to this study.

Author contributions

Tian Y, Zhang GJ and Shi WZ have concepted and revised the manuscript. Ma XJ and Du S have written the manuscript. Du S, Mi Y, Du CX, Li XB, Tan Hh, Liang Chen and Yang Tian performed the experiment and data collection. Ma XJ, Du Shan, Jiachen Wang and Zhang J participated in the statistical analysis and interpretation of the data. All authors have read and approved the final manuscript.

Funding

This work was supported grants from Natural Science Foundation of China [No. 82104155], Key Research and Development Program of Shaanxi [2020ZDLSF04-03 and 2021SF-096], and Xi'an Science and Technology Planning Project [21YXYY0038 and 21YXYJ0004].

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the corresponding authors, upon reasonable request.

Ethics approval and consent to participate

The studies involving human participants were reviewed and approved by the Ethics Committee of the Affiliated Hospital of Northwest University (Xi'an No.3 Hospital) and in line with the principles of the Declaration of Helsinki. The patients/participants provided their written informed consents.

Consent for publication

Not applicable.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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No competing interests reported.

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Spatiotemporal gait parameter fluctuations in older adults affected by mild cognitive impairment: comparisons among three cognitive dual-task tests

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Journal Publication

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Abstract

Figures

Introduction

Methods

Participants

Cognitive Functional Assessment

Gait Assessment

Data analysis

Results

1. Baseline characteristics of the participants

2. Important Feature Selection Of Gait Parameters Associating With Cognitive Function

3. Logistic Regression Analysis Of The Selected Gait Parameters Associating With Mci

4. Comparison Of Gait Parameters Between Normal Aging People And Cognitive Impaired Population

5. Effects Of Different Dual Cognitive Tasks On The Performance Of Gait Test

Discussion

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1