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Review

Scientometric Research and Critical Analysis of Gait and Balance in Older Adults

by
Qian Mao
1,†,
Wei Zheng
2,†,
Menghan Shi
3 and
Fan Yang
4,*
1
School of Design, The Hong Kong Polytechnic University, Hong Kong
2
Department of Computer Science and Technology, Tsinghua University, Beijing 100190, China
3
Lancaster Imagination Lab, Lancashire, Lancaster LA1 4YD, UK
4
Electrical and Electronic Engineering Department, The Hong Kong Polytechnic University, Hong Kong
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sensors 2024, 24(10), 3199; https://doi.org/10.3390/s24103199
Submission received: 30 April 2024 / Revised: 15 May 2024 / Accepted: 16 May 2024 / Published: 17 May 2024
(This article belongs to the Section Wearables)
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Abstract

:
Gait and balance have emerged as a critical area of research in health technology. Gait and balance studies have been affected by the researchers’ slow follow-up of research advances due to the absence of visual inspection of the study literature across decades. This study uses advanced search methods to analyse the literature on gait and balance in older adults from 1993 to 2022 in the Web of Science (WoS) database to gain a better understanding of the current status and trends in the field for the first time. The study analysed 4484 academic publications including journal articles and conference proceedings on gait and balance in older adults. Bibliometric analysis methods were applied to examine the publication year, number of publications, discipline distribution, journal distribution, research institutions, application fields, test methods, analysis theories, and influencing factors in the field of gait and balance. The results indicate that the publication of relevant research documents has been steadily increasing from 1993 to 2022. The United States (US) exhibits the highest number of publications with 1742 articles. The keyword “elderly person” exhibits a strong citation burst strength of 18.04, indicating a significant focus on research related to the health of older adults. With a burst factor of 20.46, Harvard University has made impressive strides in the subject. The University of Pittsburgh displayed high research skills in the area of gait and balance with a burst factor of 7.7 and a publication count of 103. The research on gait and balance mainly focuses on physical performance evaluation approaches, and the primary study methods include experimental investigations, computational modelling, and observational studies. The field of gait and balance research is increasingly intertwined with computer science and artificial intelligence (AI), paving the way for intelligent monitoring of gait and balance in the elderly. Moving forward, the future of gait and balance research is anticipated to highlight the importance of multidisciplinary collaboration, intelligence-driven approaches, and advanced visualization techniques.

1. Introduction

With the intensification of ageing in many countries around the world, by 2050, the number of elderly people will reach 1.5 billion, accounting for 16% of the world’s total population [1]. Elderly health has progressively emerged as a crucial topic of study. The goal of research has been to find better techniques to keep track of older patients’ health and lower their risk of illness and falls. Balance and gait have been intensively investigated because they are considered key factors in predicting health status and fall risk [2,3,4,5,6].
Human gait is an essential component of daily life and is a significant predictor of general health [7,8,9]. People’s ability to walk and gait speed may gradually deteriorate as they get older [10,11,12]. Hence, the study of gait, which includes the posture and behavioural traits of the human body while walking, has grown in significance, especially in relation to the health and wellbeing of older persons [12,13,14]. Gait is now a factor in determining how well an aged person is doing [15,16]. Balance was described as a sophisticated bodily function used to carry out tasks requiring equilibrium [17,18]. While falls are the primary cause of morbidity in older adults, given their longer life expectancies and more active lifestyles, it is important to spot any changes in their gait patterns in order to decrease the frequency of falls as well as to make it simpler for older adults to access diagnostic tools for reliable fall predictors, and finally, to develop a strategy to prevent such falls [16,19,20,21].
The global aging population has raised concerns about gait and balance, underscoring their vital role in preserving seniors’ health [22,23,24,25]. Many different parameters have been used to characterise and examine gait and balance, such as energy metabolism, kinematics, kinetics, and gait cycle [26,27,28]. Approximately one-fifth of individuals aged 65 and above rely on walkers for mobility assistance, while around 30% encounter challenges with activities like climbing stairs or walking longer distances. The prevalence of gait impairments tends to rise with age, evident in both acute hospital environments and long-term care facilities [1,29]. Research indicates that over a quarter of individuals aged 70 to 74 exhibit abnormal gait patterns, with the percentage increasing to nearly 60 for those aged 80 to 84 [19]. It is crucial to comprehend the stability of gait and the distribution of gravity in order to successfully avoid falls using sensors and other devices because gait and balance deficiencies are thought to be the primary risk factors for falls in older persons [30,31,32]. Few articles, however, provide a thorough appraisal of the state of gait and balance research from 1993 through 2022.
Gait and balance are frequently investigated to assess the recovery of patients who have suffered from trauma or disease. For instance, the evaluation of gait analysis, balance, and vestibular testing can reveal subtle changes in gait and balance in patients with traumatic brain injury (TBI), facilitating the exploration of the interplay between gait and balance deficits after TBI [26]. Functional gait disorders are prevalent among patients referred to Movement Disorders Clinics, with studies indicating that 2–20% of these patients exhibit psychogenic movement disorders. Interestingly, approximately 40% of individuals diagnosed with psychogenic movement disorders also present with gait abnormalities. This underscores the complexity of gait disorders and highlights the need for comprehensive assessment and management strategies in clinical settings.
The scientific method is still required to examine the research development and the state of gait and balance in older individuals by counting all the literature and analysing the characteristics [33]. In this study, scientific statistics on the literature in the field of gait and balance are used for the first time. The development summary and trend prediction of gait and balance are then realised using the relevant information, such as literature keywords and subject distribution. The current paper offers a more thorough and objective evaluation of the most recent studies on gait and balance in older persons than earlier reviews have as well as reliable forecasts. This study aims to comprehensively explore and analyse the application areas of gait and balance of older adults through scientometric research and critical analysis, providing detailed descriptions and insights into these crucial aspects of human locomotion.
The following sections make up the review: The study’s methodology is described in Section 2 of this article. As established by a CiteSpace analysis, Section 3 analyses the significant publications and conferences on the subject of gait and balance from WoS as well as the major source nations and organisations, core authors, and keywords. In Section 4, an analysis of the experimental techniques utilised in gait and balance studies from 1993 to 2022 is presented. Gait and balance research participants are examined in Section 5. The final section provides a summary of the review.

2. Research Methodology

We selected the Web of Science (WoS) database for this investigation because it is renowned for its reputable and high-impact academic articles. A thorough search and analysis technique was necessary to ensure the calibre of the literature and enable speedy visualisation due to the abundance of publications on gait and balance. Thus, we restricted our search criteria to conferences and peer-reviewed journals only published in the English language. Figure 1 depicts the study’s overall structure. To analyse the literature retrieved from WoS, we used CiteSpace software (Version 6.2.2).
This study focuses on gait and balance-related journal and conference articles published between 1993 and 2020 in the scholarly database Web of Science (WoS). We conduct statistical analysis on academic journal trends, discipline distribution, journal distribution, research institution distribution, and research methodology using CiteSpace software. The keywords “older adults” and “gait and balance” were adopted as the selected criteria for the articles. In order to help researchers in the field, we aim to provide a thorough review of papers on gait and balance research, identifying research hotspots and development patterns. Our research provides a thorough grasp of the state of gait and balance studies today.

3. Visual Scientometric Analysis

3.1. Yearly Quantitative Analysis of Academic Publications

As shown in Figure 2, 4484 academic publications including journal articles and conference proceedings on gait and balance in older adults from the year 1993 to 2022 are analysed.
The findings show a consistent rise in scholarly publications on gait and balance in senior citizens from 1993 to 2022, which reflects a persistent demand from the ageing population. Eight years stood out, with increases of 133.33%, 50%, 175%, 41.17%, 40%, 45.24%, 43.64%, 39.76%, and 38.91%, respectively. These years were 1995, 1997, 2000, 2006, 2007, 2009, and 2017. According to the data, there has been a steady increase between 1993 and 2021 in scientific papers about senior citizens’ gait and balance, which is a reflection of the population’s ageing-related demand. With gains of 133.33%, 50%, 175%, 41.17%, 40%, 45.24%, 43.64%, 39.76%, and 38.91%, respectively, eight years stood out. The years 1995, 1997, 2000, 2006, 2007, 2009, and 2017 were among them.

3.2. Timeline

In 1970, the field of gait and balance research started to take shape. The biomechanics of gait and balance as they relate to ageing, rehabilitation, and disease are the main topics of early research. [34,35]. In 1982, a method for measuring gait and balance objectively was devised [36], which serves as a starting point for the investigation of gait and balance in elderly people. In 1985, the deterioration of motor and sensory control processes emerged as a significant factor in falls [31]. In 1992, a study examining the impact of strength training and aerobic exercise on balance and gait in elderly male nursing home residents was conducted [37].
In 1993, research endeavoured to tackle several issues inherent in existing studies on exercise outcomes in older adults by employing an experimental design. Key features of the study design include a population-based sample; eligibility criteria based on physiological and functional deficits; random assignment to exercise groups; high-intensity exercise regimen; blinded assessment of physiological and functional outcomes; post-exercise follow-up; and a large sample size [38]. In 1995, the theory of lower limb strength and its relationship to gait and balance was put forth and researched [12]. In 2000, researchers used gait and balance to examine structural changes in the brains of men who had previously abstained from alcohol but later relapsed. The results revealed that men with alcoholism who maintain sobriety can show significant functional improvement that is connected to a change in brain structure [39]. In 2001, a dynamic model of ageing and illness that takes into account gait and balance issues was created [7]. Consequently, modern imaging and neurophysiology provide a more precise diagnosis and shed light on the pathogenesis of movement disorders [7]. Higher MRI white matter hyperintensity was thought to correlate with altered gait and balance in 2003 [10]. KineAssist, a robotic ground gait and balance training apparatus, was created and unveiled in 2005 [6]. In 2007, the impact of training regimens on healthy older adults’ gait and balance function was examined, and the theory has significant implications for enhancing older individuals’ gait and balance [40]. In 2010, there was increasing focus on techniques to improve gait and balance in Parkinson’s patients thanks to a comparison of the effects of partnered and unpartnered dance exercises on these two symptoms [41]. In 2011, research was conducted on the ground-based gait and balance training system ZeroG, which is crucial for the systematic observation and restoration of gait and balance [19].
In 2014, a study sought to quantitatively evaluate the impact of vigorous and light physical activity (VPA, LPA) on static balance, gait, and sit-to-stand (STS) tasks among a group of healthy older adults. Findings indicate significant improvements in most gait parameters and STS time within the VPA group, contrasting with the LPA group where such improvements were not observed [42]. Parkinson’s disease subtypes’ gait and balance are also being objectively measured and classified, which has significant consequences for the rehabilitation of patients with Parkinson’s [28]. The thorough analysis of traditional Chinese exercise’s impact on gait and balance in stroke patients demonstrates the significance of traditional Chinese exercise in the field of gait and balance research in 2015 [43]. In 2016, researchers looked into a measurement instrument for evaluating balance, gait, and posture in persons with Parkinson’s disease [13]. In 2017, the effect of small vessel disease in the brain on gait and balance was hypothesized and examined. The results showed that a variety of factors can affect gait and balance [44]. In 2019, research delved into quantifying the impacts of diverse factors on gait stability among older adults. The aim was to formulate tailored intervention strategies aimed at enhancing gait stability [45]. The year 2020 saw the proposal and implementation of virtual reality technology for gait and balance research and rehabilitation in Parkinson’s disease, which is crucial for the recovery and training of gait and balance in patients with Parkinson’s [8]. In 2022, a new study focus will be formed by the effects of remote gait and balance evaluation on studies conducted during and after COVID-19 [46].
The historical summary of seminal research on gait and balance demonstrates that the evaluation approach has been improved over time by the development of new techniques and models. Since the chronological summary lacks accurate statistics of significant study content, an analysis of keywords will be conducted to provide further information in the next section.

3.3. Leading Journals and Conference Proceedings

A rapid overview of the research landscape on the topic can be obtained by identifying top journals and conference proceedings. The top publications and conference proceedings for gait and balance research from 1993 through 2022 are shown in Tables S1 and S2, respectively. The top three journals are Gait Posture, Journals of Gerontology Series A Biological Sciences and Medical Sciences, and Journal of Biomechanics, while the top three conference proceedings are IEEE Engineering in Medicine and Biology Society Conference Proceedings, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), and Gerontologist. The findings show that publications on gait and balance studies were primarily focused on medical and rehabilitation fields. To fill in any gaps in the study information in this subsection, additional keyword analysis will be conducted.

3.4. Keywords

A keyword analysis is shown in Figure 3 to acquire a more thorough grasp of the important ideas and developments in gait and balance research and development.
The analysis results indicate that “older adults”, “balance”, “gait”, and “fall” are the keywords that co-occur most frequently. The earliest occurrences were of the terms “balance”, “fall”, and “gait”. The keyword “older adults” appears most frequently. The radius of the red circle represents the frequency of occurrence. The study of balance among older adults encompasses the analysis of falls and their associated risks, as well as prediction models. Balance is affected by factors such as stability, strength, and exercise. It shows the research has explored the balance change with age or injury, and the approach to improving them can enhance the quality of life and reduce the risk of falls.
To further investigate the cluster analysis of the keywords, we identified a total of nine clusters. The clusters, based on keyword frequency, are displayed in different colours in Figure 4. The cluster analysis algorithm used was LLR (Log-Likelihood Rate).
Figure 4 demonstrates that clustering provides a more accurate classification of research directions and hotspots in the field. Two indicators commonly used to assess the effectiveness of cluster analysis are modularity and silhouette. An index exceeding 0.3 is often used to determine whether the structure of a cluster analysis is reasonable. Figure 4 achieved modularity and silhouette values of 0.3474 and 0.6668, respectively, indicating a high level of reliability in the cluster analysis. The categories are dynamic stability, physical performance, fear of falling, accidental falls, Parkinson’s disease, tai chi, cerebral palsy, gait variability, gait pattern, and disease. Numbers #0–#9 correspond to the frequencies of the keywords, ranked from highest (#0) to lowest (#9). The clusters are ranked from #0 to #9, with #0 focusing on dynamic stability as the core research component dating back to 2007, with topics including postural control, perturbation, risk factors, and parameter estimation. Cluster #1, physical performance, includes topics such as nursing homes, controlled trials, multicomponent exercise programmes, and physical activity, with an average year of 2008. Cluster #2, fear of falling, includes topics such as postural balance, exercise therapy, Parkinson’s disease, and neurological rehabilitation, with a new trend of combining artificial neural networks and dual extended Kalman filters. Cluster #3, accidental falls, focuses on postural control, risk factors, Alzheimer’s disease, and various methods and technologies for assessing and enhancing gait and balance, including wearable sensors, virtual reality training, and exercise programs. Cluster #4, Parkinson’s disease required physical education, including topics such as mobility performance, smartphones, clinical measures, and performance evaluation approaches focusing on motion-capturing systems and virtual reality, with the average year of 2012. Cluster #5, tai chi research, includes topics such as fall prevention, multiple sclerosis, and Xbox Kinect, with an average year of 2010. Cluster #6, cerebral palsy, includes topics such as diabetes insipidus, palpebral fissure, glucose tolerance, and dexamethasone suppression. Cluster #7, gait variability research, focuses on diabetes insipidus, palpebral fissure, glucose tolerance, and the common approach of parameter estimation by inertial sensors. Cluster #8, gait pattern, includes topics such as risk of falling and bone mineral density. Finally, Cluster #9 indicates diseases occurring in dwelling older adults, with topics including Parkinson’s disease, mild cognitive impairment, and neurological disorders. Table 1 demonstrates that older adults in the community have been the primary subjects of research in the field of gait and balance. The researchers also paid attention to motility and lower extremity strength.

3.5. Country

Figure 5 depicts the geographical distribution of the published literature on gait and balance, with 80 nodes representing 80 countries and the circular radius of each node representing the number of publications. The United States (US) exhibits the highest number of publications with 1742 articles, followed by Canada (432 articles), Australia (344 articles), and England (279 articles). These countries have demonstrated a significant contribution to the advancement of the field of gait and balance through their extensive research output. It is worth noting that other nations, although not explicitly highlighted in the graph, have also made noteworthy contributions in this area.
A valuable tool for analysing the frequency of publications in a certain nation over a given time period is the citation burst index, which offers information on the trends and advancements of nations, organisations, and keywords in the field of gait and balance. The top 6 nations with the most significant citation bursts are shown in Table 2, showing their significance and contributions to the discipline. Researchers and practitioners can use this study to stay current on the most recent research developments and to find possible areas for collaboration and knowledge exchange.
The citation burst index serves as a benchmark for trends and developments in the gait and balance research community by revealing information about the frequency of publications in a certain nation during a given period of time. By frequently published articles, the six nations with the highest citation burst index have shaped gait and balance research. Importantly, as seen by their impressive citation burst indices of 44.56 and 19.52, respectively, the USA and Canada have been leaders in gait and balance research for a considerable amount of time. Since 2019, Spain and the Czech Republic have made recent strides in this industry. With a citation burst index of 7.48, Switzerland comes in third place, demonstrating its research potential and competitiveness in the field of gait and balance. With information on chronology and burst strength paired with a country-by-country examination of gait and balance research, academic collaboration and communication among academics are encouraged.

3.6. Author

Stephen R. Lord is the author of 61 articles and is the author with the most publications on gait and balance. Indicating the importance and relevance of their most recent research advancement, the burst coefficients of Wang, Shuaijie, and Franzen, Erika, were discovered to be as high as 6.39 and 6.48, respectively, between 2016 and 2020. Also, Hausdorff, Jeffrey My has shown the ground-breaking relevance of their team’s research in the area of gait and balance with a total of 35 published publications and a burst coefficient of 3.67 between 2013 and 2016. These scholars’ contributions have increased our understanding of gait and balance, and their work offers valuable insights and recommendations for further study in the area. See Figure 6.
The analysis from Figure 7 shows that the University of Pittsburgh, which has a total of 103 papers with a burst factor of 7.7, is at the forefront of gait and balance research. With a burst factor of 20.46, Harvard University, on the other hand, has made impressive strides in the subject, demonstrating its dedication to enhancing gait and balance research. With a combined total of 50, 71, and 53 articles, respectively, Tel Aviv University, the University of British Columbia, and the Hong Kong Polytechnic University have made major contributions to this topic. When it comes to finding possible research partners and encouraging global collaboration to further scientific discovery and innovation, such information is helpful to the academic community.

4. Research Approaches for Gait and Balance

Several disciplines, including neuroscience, rehabilitation, sports medicine, and biomechanics, have shown an interest in the study of gait and balance [47,48,49]. Understanding how people move while remaining stable and reducing their risk of falling is the goal of the gait and balance field of study [3,50,51,52]. The mechanics of gait and balance have been studied using a variety of study approaches, including experimental studies, observational studies, and computational modelling.

4.1. Experimental Studies

To determine how particular parameters affect gait and balance, experimental research directly manipulates variables such as changes in body weight distribution, visual cues, and outside disturbances [37,43,53,54,55,56]. Instrumented treadmills, force plates, motion capture systems, and other pieces of equipment are frequently used in this research to monitor a variety of variables, including joint angles, ground response forces, and muscle activity [29,39,57,58,59,60,61,62,63]. Intervention studies that use exercise regimens, physical therapy, or other forms of care to enhance gait and balance in people with particular medical disorders are also considered experimental investigations [2,64,65,66,67,68,69].

4.2. Observational Studies

Conversely, observational studies concentrate on monitoring and evaluating gait and balance in various populations, such as children, the elderly, and those with neurological diseases, rather than directly manipulating parameters [12,70,71,72,73]. These studies can evaluate gait and balance features, such as stride length, gait speed, and postural sway, using a variety of observational techniques, including clinical scales, video recordings, and wearable sensors [74,75,76,77,78,79,80].

4.3. Computational Modelling

In contrast, observational studies concentrate on observing and analysing gait and balance in various populations, such as children, the elderly, and people with neurological disorders [81,82,83,84,85]. These studies do not directly manipulate parameters, instead focusing on gait and balance in different populations. To evaluate gait and balance features such as stride length, gait speed, and postural sway, these studies can use a variety of observational techniques, including clinical scales, video recordings, and wearable sensors [86,87,88,89,90,91,92,93,94].
Wearable devices, including accelerometers, gyroscopes, and pressure sensors, have been developed as a result of contemporary technical advancements, and they can be used to monitor balance and gait when outside [94,95,96,97]. These devices can be combined with machine-learning algorithms to assess enormous amounts of data and discover patterns that may be useful for recognising and treating gait and balance disorders [98,99,100]. Virtual reality (VR) technology has also been used to create immersive environments that may mimic a range of real-world situations as well as gait and balance problems [101,102,103]. VR can be used to study how various environmental factors affect gait and balance or as a training tool to help persons with mobility issues walk and balance better.
In conclusion, research on gait and balance involves various approaches, including experimental studies, observational studies, and computational modelling, as well as the use of wearable devices and VR technology. These approaches have significantly enhanced our understanding of the mechanisms underlying gait and balance control, resulting in new insights into the diagnosis and treatment of related disorders. Future research in this field is expected to delve deeper into emerging technologies and methodologies to advance our knowledge of gait and balance control.

5. Research Contents of Gait and Balance

5.1. Participants

Studies on gait and balance often focus on older individuals due to their increased susceptibility to age-related changes in these bodily systems [104,105,106]. Participants are often considered “older” if they are over the age of 65 [107,108,109]. The musculoskeletal, neurological, and sensory systems are more likely to shift in this age range, which might affect balance and gait [109]. Age-related changes in muscular strength, joint flexibility, and sensory perception, for instance, might alter gait patterns and increase the risk of falling in older persons [110,111,112,113,114].
Because older persons are more prone to experience age-related alterations in their physical systems, older adults are commonly utilised as study subjects in gait and balance research [115,116,117]. If a participant is over the age of 65, they are frequently seen as “older” [117,118,119]. At this age range, the musculoskeletal, neurological, and sensory systems are more likely to change, which could have an impact on balance and gait [119,120,121,122]. For example, age-related changes in sensory perception, joint flexibility, and muscle strength may modify gait patterns in older people and increase their risk of falling [119,123,124,125].
Nervous system disorders like Parkinson’s disease, multiple sclerosis, and cerebral palsy are among the neurological illnesses that frequently affect study participants in gait and balance [126,127,128]. Changes in gait and balance can occur as a result of these disorders having an impact on the neurological system [129,130,131]. For instance, stiffness, bradykinesia, and tremors are symptoms of Parkinson’s disease that can affect a person’s walk and balance. Knowing how these individuals’ gaits and balances vary can help guide therapies that could increase their mobility and lower their risk of falling [132,133,134,135].
Another participant group that can be researched in gait and balance studies is athletes, especially in sports that demand agility, coordination, and balance [136,137,138,139]. Compared to elderly persons and people with neurological disorders, athletes are often younger and have different physical traits [77,139,140]. They might, for instance, have more flexible and strong muscles, which can affect the way they walk and how they balance. Knowing an athlete’s gait and balance patterns might help develop training plans that could boost their performance and lower their risk of injury [140].
Children are a participant group in studies on gait and balance as well, particularly those who seek to understand how these abilities develop. Children’s musculoskeletal, neurological, and sensory systems alter as they grow and develop, which affects how they walk and balance [141,142]. Interventions that could enhance their motor abilities and lower their risk of falling can be informed by an understanding of these developments [143]. To comprehend how these disorders affect gait and balance, research may also be conducted on kids with developmental disabilities, such as cerebral palsy.
The research question, study design, and sample size are only a few of the variables that affect how participants are chosen in gait and balance studies [144]. For instance, older folks would be the ideal participant group if the study’s goal was to examine the efficiency of a gait training programme in this population. The choice of participants may also be influenced by the study’s design, such as a randomised controlled trial. The sample size should be adequate to identify significant changes in these functions, for instance, if the study’s goal is to examine how an intervention affects gait and balance.
To comprehend the elements that affect these processes, it is crucial to carefully choose volunteers for gait and balance studies. Gait and balance study frequently examines older adults, those with neurological disorders, athletes, and children, each of whom offers a different perspective on the influences of these processes [137,145]. A number of variables, such as the research topic, study design, and sample size, have an impact on participant selection [139]. Designing studies that offer useful insights into gait and balance requires an understanding of participant characteristics and the factors that affect participant selection.

5.2. Environments

The qualities of the surface on which gait and balancing exercises are carried out are referred to as surface characteristics [139,146]. Surface qualities, including roughness, hardness, and slipperiness, can have a big effect on how a participant walks and balances [147]. Walking on a damp or slippery surface, for instance, might lead to instability and alter gait patterns. When choosing surfaces for their studies, researchers should take into account the environment’s surface characteristics.
Gait and balance performance can also be affected by lighting conditions [148]. Inadequate lighting can make falls more likely and make gait and balance assessments less accurate. The testing environment’s illumination should be suitable for the tasks being carried out, and participants should be able to view the testing equipment properly. A participant’s ability to focus on the job at hand may be impacted by noise in the testing area, which can be distracting. In order to ensure that participants can concentrate on the gait and balance task without being distracted, researchers should keep testing environments as quiet as possible.
Another crucial aspect of gait and balance is the ambient temperature [140]. Excessive temperatures can make people tired and alter their walking patterns. For participants to complete the activity safely and accurately, the testing area should be at a comfortable temperature, according to researchers.
Spatial limitations have always been important when evaluating participants’ gait and balance [149]. A participant’s ability to complete some gait and balance activities may be restricted in small testing areas, but fatigue and longer walking distances may develop in larger testing regions. While choosing a suitable testing space that enables participants to complete the job safely and accurately, researchers should take into account the physical limits of the environment.
Researchers should carefully take into account the surface qualities, illumination, noise, temperature, and space limits as these environmental elements may have an impact on participant performance. By taking into account these variables, researchers can guarantee the validity and reliability of their study findings as well as the safety of test subjects.

6. Discussion and Limitations

Gait and balance are crucial aspects of daily life, and problems with these abilities can cause falls, injuries, and a decline in quality of life, particularly in senior people. By leveraging the WoS database, this study seeks to examine the body of knowledge, research horizons, and application trends of gait and balance. Correlation analysis and CiteSpace processing of the literature were used in the research technique. Gait and balance research hotspots were analysed using the co-citation theory and burst detection analysis. This study seeks to provide a clear and intuitive observation of the development path and research trend of gait and balance through visualisation of the research and analysis. This method makes a significant addition to the academic community by giving a thorough grasp of the gait and balance research environment.
(1)
Gait and balance research usually refers to “older individuals” and “balance”, which suggests that older persons have been the primary research subjects for studies of gait and balance as shown in Figure 3. The experimental sample was primarily composed of elderly residents of the community who were not receiving any care.
(2)
The analysis and characterisation of experimental results in the area of gait and balance depend heavily on physical performance evaluation. Significantly, as seen in Figure 4, research on particular populations and disorders, like Alzheimer’s disease, is centred in two main groups, namely Cluster #1 and Cluster #9.
(3)
A significant portion of the keyword analysis is devoted to the research of gait speed and walking speed, as illustrated in Figure 3, and speed in balance is a crucial evaluation criterion.
(4)
Figure 3 shows that the terms fall prevention, posture control, etc. are commonly used in keyword searches, thus it is important to prioritise posture control research using different tools like virtual reality, artificial intelligence algorithms, wearable sensors, etc.
Despite the increased interest in gait and balance research, the terms gait and balance are not consistently defined, which makes it challenging to compare study findings and prevents the area from developing a common vocabulary. To comprehend how gait and balance problems develop over time, further longitudinal research in the field is required. Also, more study is required to determine how exercises and physical therapy affect clinical groups’ gait and balance. An interdisciplinary strategy involving cooperation between researchers from several domains, including biomechanics, neuroscience, and rehabilitation, is necessary due to the intricacy of gait and balance. The compartmentalised structure of many research institutes, however, can restrict collaboration and impede advancement in the area.

7. Conclusions

A rapidly expanding subject, gait and balance research has significant implications for therapeutic practise. Gait and balance research has recently concentrated on a few trends. There is a growing trend towards employing technology to analyse and improve gait and balance. Recent studies have evaluated and trained gait and balance using wearable sensors, virtual reality, and artificial intelligence algorithms [147,148,149]. Furthermore, there has been a surge of interest among researchers to investigate the effects of exercise on gait and balance. This growing body of research aims to shed light on the mechanisms underlying the observed improvements in gait and balance among different populations, such as older adults or individuals with neurological disorders, and to optimize exercise interventions that promote functional mobility and reduce fall risk. Gait and balance can be improved by exercise regimens in both healthy and clinical populations. The impact of cognitive and sensory deficits on gait and balance has drawn more attention in recent years. It has been established through research that therapies aimed at improving cognitive and sensory deficits can also enhance gait and balance. Research on how environmental influences affect gait and balance is becoming more and more popular. According to research, gait and balance can be impacted by ambient elements like sunlight, surface properties, and impediments. In order to enhance the health and wellbeing of people with gait and balance impairments, clinicians and researchers should take these patterns into account when developing treatment plans and research projects.
The path of the research on gait and balance is becoming more entangled with computer science and artificial intelligence (AI). Intelligent gait balance monitoring for the elderly is poised to emerge as a prominent area of research in the foreseeable future, drawing significant attention from scholars and researchers alike. Future directions in gait and balance research are expected to emphasize multidisciplinary collaboration, intelligence, and visualization.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s24103199/s1, Table S1: Leading Journal Publications in gait and balance from 1993 to 2022; Table S2: Leading Conference Publications in gait and balance from 1993 to 2022.

Author Contributions

Q.M. and F.Y. participated in the design of the study and contributed to data collection and data reduction/analysis; Q.M. participated in the design of the study; F.Y. participated in the design of the study and contributed to data collection; Q.M. and W.Z. participated in the writing-review; W.Z. and M.S. participated in the formal analysis and contributed to software; Q.M. contributed to data reduction/analysis; F.Y. contributed to data analysis and interpretation of results. All authors have read and agreed to the published version of the manuscript.

Funding

It was funded by the Research Grants Council General Research Fund Hong Kong GRF 15225422.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available upon readers’ request.

Acknowledgments

This study was undertaken at the Hong Kong Polytechnic University.

Conflicts of Interest

The authors declare that they have no competing interests.

References

  1. Salzman, B. Gait and balance disorders in older adults. Am. Fam. Physician 2010, 82, 61–68. [Google Scholar] [PubMed]
  2. Osoba, M.Y.; Rao, A.K.; Agrawal, S.K.; Lalwani, A.K. Balance and gait in the elderly: A contemporary review. Laryngoscope Investig. Otolaryngol. 2019, 4, 143–153. [Google Scholar] [CrossRef] [PubMed]
  3. Reelick, M.F.; van Iersel, M.B.; Kessels, R.P.C.; Rikkert, M.G.M.O. The influence of fear of falling on gait and balance in older people. Age Ageing 2009, 38, 435–440. [Google Scholar] [CrossRef]
  4. Iersel, M.B.V.; Kessels, R.P.C.; Bloem, B.R.; Verbeek, A.L.M.; Rikkert, M.G.M.O. Executive functions are associated with gait and balance in community-living elderly people. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 2008, 63, 1344–1349. [Google Scholar] [CrossRef] [PubMed]
  5. Visser, H. Gait and balance in senile dementia of Alzheimer’s type. Age Ageing 1983, 12, 296–301. [Google Scholar] [CrossRef] [PubMed]
  6. Peshkin, M.; Brown, D.; Santos-Munne, J.; Makhlin, A.; Lewis, E.; Colgate, J.; Patton, J.; Schwandt, D. KineAssist: A robotic overground gait and balance training device. In Proceedings of the 9th International Conference on Rehabilitation Robotics, 2005, ICORR 2005, Chicago, IL, USA, 28 June–1 July 2005. [Google Scholar]
  7. Wolfson, L.J.T.N. Gait and balance dysfunction: A model of the interaction of age and disease. Neuroscientist 2001, 7, 178–183. [Google Scholar] [CrossRef] [PubMed]
  8. Canning, C.G.; Allen, N.E.; Nackaerts, E.; Paul, S.S.; Nieuwboer, A.; Gilat, M. Virtual reality in research and rehabilitation of gait and balance in Parkinson disease. Nat. Rev. Neurol. 2020, 16, 409–425. [Google Scholar] [CrossRef]
  9. Furnari, A.; Calabrò, R.S.; Gervasi, G.; La Fauci-Belponer, F.; Marzo, A.; Berbiglia, F.; Paladina, G.; De Cola, M.C.; Bramanti, P. Is hydrokinesitherapy effective on gait and balance in patients with stroke? A clinical and baropodometric investigation. Brain Inj. 2014, 28, 1109–1114. [Google Scholar] [CrossRef]
  10. Baloh, R.W.; Ying, S.H.; Jacobson, K.M. A longitudinal study of gait and balance dysfunction in normal older people. Arch. Neurol. 2003, 60, 835–839. [Google Scholar] [CrossRef]
  11. Bahureksa, L.; Najafi, B.; Saleh, A.; Sabbagh, M.; Coon, D.; Mohler, M.J.; Schwenk, M. The impact of mild cognitive impairment on gait and balance: A systematic review and meta-analysis of studies using instrumented assessment. Gerontology 2017, 63, 67–83. [Google Scholar] [CrossRef]
  12. Fukagawa, N.K.; Wolfson, L.; Judge, J.; Whipple, R.; King, M. Strength is a major factor in balance, gait, and the occurrence of falls. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 1995, 50, 64–67. [Google Scholar] [CrossRef]
  13. Bloem, B.R.; Marinus, J.; Almeida, Q.; Dibble, L.; Nieuwboer, A.; Post, B.; Ruzicka, E.; Goetz, C.; Stebbins, G.; Martinez-Martin, P.; et al. Measurement instruments to assess posture, gait, and balance in Parkinson’s disease: Critique and recommendations. Mov. Disord. 2016, 31, 1342–1355. [Google Scholar] [CrossRef]
  14. Shanahan, C.J.; Boonstra, F.M.C.; Lizama, L.E.C.; Strik, M.; Moffat, B.A.; Khan, F.; Kilpatrick, T.J.; Van Der Walt, A.; Galea, M.P.; Kolbe, S.C. Technologies for advanced gait and balance assessments in people with multiple sclerosis. Front. Neurol. 2018, 8, 708. [Google Scholar] [CrossRef]
  15. Allet, L.; Armand, S.; de Bie, R.A.; Golay, A.; Monnin, D.; Aminian, K.; Staal, J.B.; de Bruin, E.D. The gait and balance of patients with diabetes can be improved: A randomised controlled trial. Diabetologia 2010, 53, 458–466. [Google Scholar] [CrossRef]
  16. Lei, C.; Sunzi, K.; Dai, F.; Liu, X.; Wang, Y.; Zhang, B.; He, L.; Ju, M. Effects of virtual reality rehabilitation training on gait and balance in patients with Parkinson’s disease: A systematic review. PLoS ONE 2019, 14, e0224819. [Google Scholar] [CrossRef]
  17. Martin, C.L.; Phillips, B.A.; Kilpatrick, T.J.; Butzkueven, H.; Tubridy, N.; McDonald, E.; Galea, M.P. Gait and balance impairment in early multiple sclerosis in the absence of clinical disability. Mult. Scler. J. 2006, 12, 620–628. [Google Scholar] [CrossRef]
  18. Rasch, A.; Dalén, N.; Berg, H.E. Muscle strength, gait, and balance in 20 patients with hip osteoarthritis followed for 2 years after THA. Acta Orthop. 2010, 81, 183–188. [Google Scholar] [CrossRef]
  19. Hidler, J.; Brennan, D.; Black, I.; Nichols, D.; Brady, K.; Nef, T. ZeroG: Overground gait and balance training system. J. Rehabil. Res. Dev. 2011, 48, 287–298. [Google Scholar] [CrossRef]
  20. Welter, M.-L.; Demain, A.; Ewenczyk, C.; Czernecki, V.; Lau, B.; El Helou, A.; Belaid, H.; Yelnik, J.; François, C.; Bardinet, E.; et al. PPNa-DBS for gait and balance disorders in Parkinson’s disease: A double-blind, randomised study. J. Neurol. 2015, 262, 1515–1525. [Google Scholar] [CrossRef]
  21. Oates, A.R.; Arora, T.; Lanovaz, J.L.; Musselman, K.E. The effects of light touch on gait and dynamic balance during normal and tandem walking in individuals with an incomplete spinal cord injury. Spinal Cord 2021, 59, 159–166. [Google Scholar] [CrossRef] [PubMed]
  22. Thomas, M.; Jankovic, J.; Suteerawattananon, M.; Wankadia, S.; Caroline, K.S.; Vuong, K.D.; Protas, E. Clinical gait and balance scale (GABS): Validation and utilization. J. Neurol. Sci. 2004, 217, 89–99. [Google Scholar] [CrossRef]
  23. Robinson, J.; Dixon, J.; Macsween, A.; van Schaik, P.; Martin, D. The effects of exergaming on balance, gait, technology acceptance and flow experience in people with multiple sclerosis: A randomized controlled trial. BMC Sports Sci. Med. Rehabil. 2015, 7, 8. [Google Scholar] [CrossRef]
  24. Dewey, D.C.; Miocinovic, S.; Bernstein, I.; Khemani, P.; Dewey, R.B.; Querry, R.; Chitnis, S. Automated gait and balance parameters diagnose and correlate with severity in Parkinson disease. J. Neurol. Sci. 2014, 345, 131–138. [Google Scholar] [CrossRef]
  25. Muňoz, V.M.; van Kan, G.A.; Cantet, C.R.; Cortes, F.; Ousset, P.-J.; Rolland, Y.; Vellas, B. Gait and balance impairments in Alzheimer disease patients. Alzheimer Dis. Assoc. Disord. 2010, 24, 79–84. [Google Scholar] [CrossRef]
  26. Basford, J.R.; Chou, L.-S.; Kaufman, K.R.; Brey, R.H.; Walker, A.; Malec, J.F.; Moessner, A.M.; Brown, A.W. An assessment of gait and balance deficits after traumatic brain injury. Arch. Phys. Med. Rehabil. 2003, 84, 343–349. [Google Scholar] [CrossRef]
  27. Newell, D.; Shead, V.; Sloane, L. Changes in gait and balance parameters in elderly subjects attending an 8-week supervised Pilates programme. J. Bodyw. Mov. Ther. 2012, 16, 549–554. [Google Scholar] [CrossRef]
  28. Herman, T.; Weiss, A.; Brozgol, M.; Giladi, N.; Hausdorff, J.M. Gait and balance in Parkinson’s disease subtypes: Objective measures and classification considerations. J. Neurol. 2014, 261, 2401–2410. [Google Scholar] [CrossRef]
  29. Chastan, N.; Do, M.C.; Bonneville, F.; Torny, F.; Bloch, F.; Westby, G.W.M.; Dormont, D.; Agid, Y.; Welter, M.-L. Gait and balance disorders in Parkinson’s disease: Impaired active braking of the fall of centre of gravity. Mov. Disord. 2009, 24, 188–195. [Google Scholar] [CrossRef]
  30. Patton, J.; Brown, D.A.; Peshkin, M.; Santos-Munné, J.J.; Makhlin, A.; Lewis, E.; Colgate, E.J.; Schwandt, D. KineAssist: Design and development of a robotic overground gait and balance therapy device. Top. Stroke Rehabil. 2008, 15, 131–139. [Google Scholar] [CrossRef]
  31. Wolfson, L.I.; Whipple, R.; Amerman, P.; Kaplan, J.; Kleinberg, A. Gait and balance in the elderly: Two functional capacities that link sensory and motor ability to falls. Clin. Geriatr. Med. 1985, 1, 649–659. [Google Scholar] [CrossRef]
  32. Al-Momani, M.; Al-Momani, F.; Alghadir, A.H.; Alharethy, S.; Gabr, S. Factors related to gait and balance deficits in older adults. Clin. Interv. Aging 2016, 11, 1043–1049. [Google Scholar]
  33. Baezner, H.; Blahak, C.; Poggesi, A.; Pantoni, L.; Inzitari, D.; Chabriat, H.; Erkinjuntti, T.; Fazekas, F.; Ferro, J.M.; Langhorne, P.; et al. Association of gait and balance disorders with age-related white matter changes: The LADIS study. Neurology 2008, 70, 935–942. [Google Scholar] [CrossRef]
  34. Meyerowitz, S.; Engel, G.L.; Mei-Tal, V. The role of psychological process in a somatic disorder: Multiple sclerosis: 1. The emotional setting of illness onset and exacerbation. Psychosom. Med. 1970, 32, 67–86. [Google Scholar] [CrossRef] [PubMed]
  35. Prensky, A.L.; Davis, D.O. Obstruction of major cerebral vessels in early childhood without neurological signs. Neurology 1970, 20, 945. [Google Scholar] [CrossRef]
  36. Nayak, U.S.L.; Gabell, A.; Simons, M.A.; Isaacs, B. Measurement of gait and balance in the elderly. J. Am. Geriatr. Soc. 1982, 30, 516–520. [Google Scholar] [CrossRef] [PubMed]
  37. Sauvage, L.R., Jr.; Myklebust, B.M.; Crow-Pan, J.; Novak, S.; Millington, P.; Hoffman, M.D.; Hartz, A.J.; Rudman, D. A clinical trial of strengthening and aerobic exercise to improve gait and balance in elderly male nursing home residents. Am. J. Phys. Med. Rehabil. 1992, 71, 333–342. [Google Scholar] [CrossRef]
  38. Buchner, D.M.; Cress, M.E.; Wagner, E.H.; de Lateur, B.J.; Price, R.; Abrass, I.B. The Seattle FICSIT/Movelt study: The effect of exercise on gait and balance in older adults. J. Am. Geriatr. Soc. 1993, 41, 321–325. [Google Scholar] [CrossRef]
  39. Sullivan, E.V.; Rosenbloom, M.J.; Lim, K.O.; Pfefferbaum, A. Longitudinal changes in cognition, gait, and balance in abstinent and relapsed alcoholic men: Relationships to changes in brain structure. Neuropsychology 2000, 14, 178–188. [Google Scholar] [CrossRef]
  40. Oddsson, L.I.E.; Boissy, P.; Melzer, I. How to improve gait and balance function in elderly individuals—Compliance with principles of training. Eur. Rev. Aging Phys. Act. 2007, 4, 15–23. [Google Scholar] [CrossRef]
  41. Hackney, M.E.; Earhart, G.M. Effects of dance on gait and balance in Parkinson’s disease: A comparison of partnered and nonpartnered dance movement. Neurorehabilit. Neural Repair. 2010, 24, 384–392. [Google Scholar] [CrossRef] [PubMed]
  42. Pau, M.; Leban, B.; Collu, G.; Migliaccio, G.M. Effect of light and vigorous physical activity on balance and gait of older adults. Arch. Gerontol. Geriatr. 2014, 59, 568–573. [Google Scholar] [CrossRef]
  43. Chen, B.-L.; Guo, J.-B.; Liu, M.-S.; Li, X.; Zou, J.; Chen, X.; Zhang, L.-L.; Yue, Y.-S.; Wang, X.-Q. Effect of traditional Chinese exercise on gait and balance for stroke: A systematic review and meta-analysis. PLoS ONE 2015, 10, e0135932. [Google Scholar]
  44. Pinter, D.; Ritchie, S.J.; Doubal, F.; Gattringer, T.; Morris, Z.; Bastin, M.E.; Hernández, M.d.C.V.; Royle, N.A.; Corley, J.; Maniega, S.M.; et al. Impact of small vessel disease in the brain on gait and balance. Sci. Rep. 2017, 7, 41637. [Google Scholar] [CrossRef]
  45. Hamacher, D.; Liebl, D.; Hödl, C.; Heßler, V.; Kniewasser, C.K.; Thönnessen, T.; Zech, A. Gait stability and its influencing factors in older adults. Front. Physiol. 2019, 9, 1955. [Google Scholar] [CrossRef] [PubMed]
  46. Madhavan, S.; Sivaramakrishnan, A.; Bowden, M.G.; Chumbler, N.R.; Field-Fote, E.C.; Kesar, T.M. Commentary: Remote assessments of gait and balance-Implications for research during and beyond COVID-19. Top. Stroke Rehabil. 2021, 29, 74–81. [Google Scholar] [CrossRef]
  47. Bland, D.C.; Zampieri, C.; Damiano, D.L. Effectiveness of physical therapy for improving gait and balance in individuals with traumatic brain injury: A systematic review. Brain Inj. 2011, 25, 664–679. [Google Scholar] [CrossRef] [PubMed]
  48. Mak, M.; Lau, K. Speed-dependent treadmill training is effective to improve gait and balance performance in patients with sub-acute stroke. J. Rehabil. Med. 2011, 43, 709–713. [Google Scholar] [CrossRef]
  49. Speedtsberg, M.B.; Kastoft, R.; Barfod, K.W.; Penny, J.; Bencke, J. Gait function and postural control 4.5 years after nonoperative dynamic treatment of acute Achilles tendon ruptures. Orthop. J. Sports Med. 2019, 7, 2325967119854324. [Google Scholar] [CrossRef]
  50. Bryant, M.S.; Rintala, D.H.; Hou, J.-G.; Protas, E.J. Influence of fear of falling on gait and balance in Parkinson’s disease. Disabil. Rehabil. 2014, 36, 744–748. [Google Scholar] [CrossRef]
  51. Kaufman, K.; Levine, J.; Brey, R.; Iverson, B.; McCrady, S.; Padgett, D.; Joyner, M. Gait and balance of transfemoral amputees using passive mechanical and microprocessor-controlled prosthetic knees. Gait Posture 2007, 26, 489–493. [Google Scholar] [CrossRef]
  52. Ramsey, V.K.; Blasch, B.B.; Kita, A. Effects of mobility training on gait and balance. J. Vis. Impair. Blind. 2003, 97, 720–726. [Google Scholar] [CrossRef]
  53. Okubo, Y.; Schoene, D.; Lord, S.R. Step training improves reaction time, gait and balance and reduces falls in older people: A systematic review and meta-analysis. Br. J. Sports Med. 2017, 51, 586–593. [Google Scholar] [CrossRef] [PubMed]
  54. Paillard, T.; Noé, F.; Bru, N.; Couderc, M.; Debove, L. The impact of time of day on the gait and balance control of Alzheimer’s patients. Chronobiol. Int. 2016, 33, 161–168. [Google Scholar] [CrossRef] [PubMed]
  55. Petrofsky, J.S.; Cuneo, M.; Lee, S.; Johnson, E.; Lohman, E. Correlation between gait and balance in people with and without Type 2 diabetes in normal and subdued light. Med. Sci. Monit. Int. Med. J. Exp. Clin. Res. 2006, 12, CR273-81. [Google Scholar]
  56. van Iersel, M.B.; Rikkert, M.G.O.; Borm, G.F. A method to standardize gait and balance variables for gait velocity. Gait Posture 2007, 26, 226–230. [Google Scholar] [CrossRef] [PubMed]
  57. Choi, W. Effects of robot-assisted gait training with body weight support on gait and balance in stroke patients. Int. J. Environ. Res. Public Health 2022, 19, 5814. [Google Scholar] [CrossRef] [PubMed]
  58. Kreisel, S.H.; Blahak, C.; Bäzner, H.; Inzitari, D.; Pantoni, L.; Poggesi, A.; Chabriat, H.; Erkinjuntti, T.; Fazekas, F.; Ferro, J.M.; et al. Deterioration of gait and balance over time: The effects of age-related white matter change-the LADIS study. Cerebrovasc. Dis. 2013, 35, 544–553. [Google Scholar] [CrossRef] [PubMed]
  59. Corrà, M.F.; Vila-Chã, N.; Sardoeira, A.; Hansen, C.; Sousa, A.P.; Reis, I.; Sambayeta, F.; Damásio, J.; Calejo, M.; Schicketmueller, A.; et al. Peripheral neuropathy in Parkinson’s disease: Prevalence and functional impact on gait and balance. Brain 2023, 146, 225–236. [Google Scholar] [CrossRef] [PubMed]
  60. Lee, C.-W.; Gil Kim, S.; Yong, M.S. Effects of hippotherapy on recovery of gait and balance ability in patients with stroke. J. Phys. Ther. Sci. 2014, 26, 309–311. [Google Scholar] [CrossRef]
  61. Kaski, D.; Dominguez, R.O.; Allum, J.H.; Bronstein, A.M. Improving gait and balance in patients with leukoaraiosis using transcranial direct current stimulation and physical training: An exploratory study. Neurorehabilit. Neural Repair. 2013, 27, 864–871. [Google Scholar] [CrossRef]
  62. Moraud, E.M.; Capogrosso, M.; Formento, E.; Wenger, N.; DiGiovanna, J.; Courtine, G.; Micera, S. Mechanisms underlying the neuromodulation of spinal circuits for correcting gait and balance deficits after spinal cord injury. Neuron 2016, 89, 814–828. [Google Scholar] [CrossRef] [PubMed]
  63. Borowicz, A.; Zasadzka, E.; Gaczkowska, A.; Gawłowska, O.; Pawlaczyk, M. Assessing gait and balance impairment in elderly residents of nursing homes. J. Phys. Ther. Sci. 2016, 28, 2486–2490. [Google Scholar] [CrossRef] [PubMed]
  64. Wang, X.; Pi, Y.; Chen, B.; Chen, P.; Liu, Y.; Wang, R.; Li, X.; Waddington, G. Cognitive motor interference for gait and balance in stroke: A systematic review and meta-analysis. Eur. J. Neurol. 2015, 22, 555-e37. [Google Scholar] [CrossRef] [PubMed]
  65. Balasubramanian, C.K. The community balance and mobility scale alleviates the ceiling effects observed in the currently used gait and balance assessments for the community-dwelling older adults. J. Geriatr. Phys. Ther. 2015, 38, 78–89. [Google Scholar] [CrossRef] [PubMed]
  66. Dalton, A.; Khalil, H.; Busse, M.; Rosser, A.; van Deursen, R.; ÓLaighin, G. Analysis of gait and balance through a single triaxial accelerometer in presymptomatic and symptomatic Huntington’s disease. Gait Posture 2013, 37, 49–54. [Google Scholar] [CrossRef] [PubMed]
  67. Waters, D.L.; Hale, L.; Grant, A.M.; Herbison, P.; Goulding, A. Osteoporosis and gait and balance disturbances in older sarcopenic obese New Zealanders. Osteoporos. Int. 2010, 21, 351–357. [Google Scholar] [CrossRef] [PubMed]
  68. Morton, S.M.; Tseng, Y.; Zackowski, K.M.; Daline, J.R.; Bastian, A.J. Longitudinal tracking of gait and balance impairments in cerebellar disease. Mov. Disord. 2010, 25, 1944–1952. [Google Scholar] [CrossRef]
  69. Patterson, K.K.; Wong, J.S.; Nguyen, T.-U.T.-U.; Brooks, D. A dance program to improve gait and balance in individuals with chronic stroke: A feasibility study. Top. Stroke Rehabil. 2018, 25, 410–416. [Google Scholar] [CrossRef] [PubMed]
  70. Suh, J.H.; Han, S.J.; Jeon, S.Y.; Kim, H.J.; Lee, J.E.; Yoon, T.S.; Chong, H.J. Effect of rhythmic auditory stimulation on gait and balance in hemiplegic stroke patients. NeuroRehabilitation 2014, 34, 193–199. [Google Scholar] [CrossRef]
  71. Koch, G.; Bonnì, S.; Casula, E.P.; Iosa, M.; Paolucci, S.; Pellicciari, M.C.; Cinnera, A.M.; Ponzo, V.; Maiella, M.; Picazio, S.; et al. Effect of cerebellar stimulation on gait and balance recovery in patients with hemiparetic stroke: A randomized clinical trial. JAMA Neurol. 2019, 76, 170–178. [Google Scholar] [CrossRef]
  72. Bernhard, F.P.; Sartor, J.; Bettecken, K.; Hobert, M.A.; Arnold, C.; Weber, Y.G.; Poli, S.; Margraf, N.G.; Schlenstedt, C.; Hansen, C.; et al. Wearables for gait and balance assessment in the neurological ward-study design and first results of a prospective cross-sectional feasibility study with 384 inpatients. BMC Neurol. 2018, 18, 1–8. [Google Scholar] [CrossRef] [PubMed]
  73. Thiede, R.; Toosizadeh, N.; Mills, J.L.; Zaky, M.; Mohler, J.; Najafi, B. Gait and balance assessments as early indicators of frailty in patients with known peripheral artery disease. Clin. Biomech. 2015, 32, 1–7. [Google Scholar] [CrossRef] [PubMed]
  74. Demain, A.; Westby, G.W.M.; Fernandez-Vidal, S.; Karachi, C.; Bonneville, F.; Do, M.C.; Delmaire, C.; Dormont, D.; Bardinet, E.; Agid, Y.; et al. High-level gait and balance disorders in the elderly: A midbrain disease? J. Neurol. 2014, 261, 196–206. [Google Scholar] [CrossRef] [PubMed]
  75. Barthélemy, D.; Willerslev-Olsen, M.; Lundell, H.; Biering-Sørensen, F.; Nielsen, J.B. Assessment of transmission in specific descending pathways in relation to gait and balance following spinal cord injury. Prog. Brain Res. 2015, 218, 79–101. [Google Scholar] [PubMed]
  76. Curtze, C.; Nutt, J.G.; Carlson-Kuhta, P.; Mancini, M.; Horak, F.B. Objective gait and balance impairments relate to balance confidence and perceived mobility in people with Parkinson disease. Phys. Ther. 2016, 96, 1734–1743. [Google Scholar] [CrossRef] [PubMed]
  77. Corwin, D.J.; Mcdonald, C.C.; Arbogast, K.B.; Mohammed, F.N.; Metzger, K.B.; Pfeiffer, M.R.; Patton, D.A.; Huber, C.M.; Margulies, S.S.; Grady, M.F.; et al. Clinical and device-based metrics of gait and balance in diagnosing youth concussion. Med. Sci. Sports Exerc. 2020, 52, 542–548. [Google Scholar] [CrossRef] [PubMed]
  78. Montero-Odasso, M.; Pieruccini-Faria, F.; Bartha, R.; Black, S.E.; Finger, E.; Freedman, M.; Greenberg, B.; Grimes, D.A.; Hegele, R.A.; Hudson, C.; et al. Motor phenotype in neurodegenerative disorders: Gait and balance platform study design protocol for the Ontario neurodegenerative research initiative (ONDRI). J. Alzheimer’s Dis. 2017, 59, 707–721. [Google Scholar] [CrossRef] [PubMed]
  79. Mihailovic, A.; De Luna, R.M.; West, S.K.; Friedman, D.S.; Gitlin, L.N.; Ramulu, P.Y. Gait and balance as predictors and/or mediators of falls in glaucoma. Investig. Opthalmol. Vis. Sci. 2020, 61, 30. [Google Scholar] [CrossRef]
  80. Olson, M.; Lockhart, T.E.; Lieberman, A. Motor learning deficits in Parkinson’s disease (PD) and their effect on training response in gait and balance: A narrative review. Front. Neurol. 2019, 10, 62. [Google Scholar] [CrossRef]
  81. Earhart, G.M.; Clark, B.R.; Tabbal, S.D.; Perlmutter, J.S. Gait and balance in essential tremor: Variable effects of bilateral thalamic stimulation. Mov. Disord. 2009, 24, 386–391. [Google Scholar] [CrossRef]
  82. Allen, J.L.; McKay, J.L.; Sawers, A.; Hackney, M.E.; Ting, L.H. Increased neuromuscular consistency in gait and balance after partnered, dance-based rehabilitation in Parkinson’s disease. J. Neurophysiol. 2017, 118, 363–373. [Google Scholar] [CrossRef] [PubMed]
  83. Moore, J.L.; Virva, R.; Henderson, C.; Lenca, L.; Butzer, J.F.; Lovell, L.; Roth, E.; Graham, I.D.; Hornby, T.G. Applying the knowledge-to-action framework to implement gait and balance assessments in inpatient stroke rehabilitation. Arch. Phys. Med. Rehabil. 2022, 103, S230–S245. [Google Scholar] [CrossRef]
  84. Beauchet, O.; Annweiler, C.; Assal, F.; Bridenbaugh, S.; Herrmann, F.R.; Kressig, R.W.; Allali, G. Imagined Timed Up & Go test: A new tool to assess higher-level gait and balance disorders in older adults? J. Neurol. Sci. 2010, 294, 102–106. [Google Scholar] [PubMed]
  85. Norbye, A.D.; Midgard, R.; Thrane, G. Spasticity, gait, and balance in patients with multiple sclerosis: A cross-sectional study. Physiother. Res. Int. 2020, 25, e1799. [Google Scholar] [CrossRef] [PubMed]
  86. Gandolfi, M.; Geroin, C.; Picelli, A.; Munari, D.; Waldner, A.; Tamburin, S.; Marchioretto, F.; Smania, N. Robot-assisted vs. sensory integration training in treating gait and balance dysfunctions in patients with multiple sclerosis: A randomized controlled trial. Front. Hum. Neurosci. 2014, 8, 318. [Google Scholar] [CrossRef] [PubMed]
  87. Okawara, H.; Sawada, T.; Matsubayashi, K.; Sugai, K.; Tsuji, O.; Nagoshi, N.; Matsumoto, M.; Nakamura, M. Gait ability required to achieve therapeutic effect in gait and balance function with the voluntary driven exoskeleton in patients with chronic spinal cord injury: A clinical study. Spinal Cord 2020, 58, 520–527. [Google Scholar] [CrossRef] [PubMed]
  88. Duckrow, R.B.; Abu-Hasaballah, K.; Whipple, R.; Wolfson, L. Stance perturbation-evoked potentials in old people with poor gait and balance. Clin. Neurophysiol. 1999, 110, 2026–2032. [Google Scholar] [CrossRef]
  89. Galea, M.P.; Lizama, L.E.C.; Butzkueven, H.; Kilpatrick, T.J. Gait and balance deterioration over a 12-month period in multiple sclerosis patients with EDSS scores≤ 3.0. NeuroRehabilitation 2017, 40, 277–284. [Google Scholar] [CrossRef]
  90. Nutt, J.G.; Horak, F.B.; Bloem, B.R. Milestones in gait, balance, and falling. Mov. Disord. 2011, 26, 1166–1174. [Google Scholar] [CrossRef]
  91. Virmani, T.; Gupta, H.; Shah, J.; Larson-Prior, L. Objective measures of gait and balance in healthy non-falling adults as a function of age. Gait Posture 2018, 65, 100–105. [Google Scholar] [CrossRef]
  92. Wang, R.-Y.; Lin, P.-Y.; Lee, C.-C.; Yang, Y.-R. Gait and balance performance improvements attributable to ankle–foot orthosis in subjects with hemiparesis. Am. J. Phys. Med. Rehabil. 2007, 86, 556–562. [Google Scholar] [CrossRef] [PubMed]
  93. Suteerawattananon, M.; MacNeill, B.; Protas, E.J. Supported treadmill training for gait and balance in a patient with progressive supranuclear palsy. Phys. Ther. 2002, 82, 485–495. [Google Scholar] [CrossRef] [PubMed]
  94. Han, J.Y.; Kim, J.M.; Kim, S.K.; Chung, J.S.; Lee, H.-C.; Lim, J.K.; Lee, J.; Park, K.Y. Therapeutic effects of mechanical horseback riding on gait and balance ability in stroke patients. Ann. Rehabil. Med. 2012, 36, 762–769. [Google Scholar] [CrossRef] [PubMed]
  95. De Freitas, T.B.; Leite, P.H.W.; Doná, F.; Pompeu, J.E.; Swarowsky, A.; Torriani-Pasin, C. The effects of dual task gait and balance training in Parkinson’s disease: A systematic review. Physiother. Theory Pract. 2018, 36, 1088–1096. [Google Scholar] [CrossRef] [PubMed]
  96. Takahiko, Y.; Issei, S.; Yasuhiro, H.; Masahiro, K.; Daichi, S.; Makoto, N. Feasibility and efficacy of high-speed gait training with a voluntary driven exoskeleton robot for gait and balance dysfunction in patients with chronic stroke: Nonrandomized pilot study with concurrent control. Int. J. Rehabil. Res. 2015, 38, 338–343. [Google Scholar]
  97. Maetzler, W.; Nieuwhof, F.; Hasmann, S.E.; Bloem, B.R. Emerging therapies for gait disability and balance impairment: Promises and pitfalls. Mov. Disord. 2013, 28, 1576–1586. [Google Scholar] [CrossRef] [PubMed]
  98. Kahya, M.; Moon, S.; Ranchet, M.; Vukas, R.R.; Lyons, K.E.; Pahwa, R.; Akinwuntan, A.; Devos, H. Brain activity during dual task gait and balance in aging and age-related neurodegenerative conditions: A systematic review. Exp. Gerontol. 2019, 128, 110756. [Google Scholar] [CrossRef] [PubMed]
  99. Spain, R.I.; Mancini, M.; Horak, F.B.; Bourdette, D. Body-worn sensors capture variability, but not decline, of gait and balance measures in multiple sclerosis over 18 months. Gait Posture 2014, 39, 958–964. [Google Scholar] [CrossRef] [PubMed]
  100. Gaßner, H.; Steib, S.; Klamroth, S.; Pasluosta, C.F.; Adler, W.; Eskofier, B.M.; Pfeifer, K.; Winkler, J.; Klucken, J. Perturbation treadmill training improves clinical characteristics of gait and balance in Parkinson’s disease. J. Park. Dis. 2019, 9, 413–426. [Google Scholar] [CrossRef]
  101. Stephenson, J.; Zesiewicz, T.; Gooch, C.; Wecker, L.; Sullivan, K.; Jahan, I.; Kim, S.H. Gait and balance in adults with Friedreich’s ataxia. Gait Posture 2015, 41, 603–607. [Google Scholar] [CrossRef]
  102. Meldrum, D.; Herdman, S.; Moloney, R.; Murray, D.; Duffy, D.; Malone, K.; French, H.; Hone, S.; Conroy, R.; McConn-Walsh, R. Effectiveness of conventional versus virtual reality based vestibular rehabilitation in the treatment of dizziness, gait and balance impairment in adults with unilateral peripheral vestibular loss: A randomised controlled trial. BMC Ear Nose Throat Disord. 2012, 12, 3. [Google Scholar] [CrossRef] [PubMed]
  103. Zhou, H.; Al-Ali, F.; Rahemi, H.; Kulkarni, N.; Hamad, A.; Ibrahim, R.; Talal, T.K.; Najafi, B. Hemodialysis impact on motor function beyond aging and diabetes—Objectively assessing gait and balance by wearable technology. Sensors 2018, 18, 3939. [Google Scholar] [CrossRef] [PubMed]
  104. Mihara, M.; Fujimoto, H.; Hattori, N.; Otomune, H.; Kajiyama, Y.; Konaka, K.; Watanabe, Y.; Hiramatsu, Y.; Sunada, Y.; Miyai, I.; et al. Effect of neurofeedback facilitation on poststroke gait and balance recovery: A randomized controlled trial. Neurology 2021, 96, E2587–E2598. [Google Scholar] [CrossRef] [PubMed]
  105. Abdallat, R.; Sharouf, F.; Button, K.; Al-Amri, M. Dual-task effects on performance of gait and balance in people with knee pain: A systematic scoping review. J. Clin. Med. 2020, 9, 1554. [Google Scholar] [CrossRef] [PubMed]
  106. Alak, Z.Y.S.; Bulut, E.A.; Dokuzlar, O.; Yavuz, I.; Soysal, P.; Isik, A.T. Long-term effects of vitamin D deficiency on gait and balance in the older adults. Clin. Nutr. 2020, 39, 3756–3762. [Google Scholar] [CrossRef] [PubMed]
  107. Cheng, W.-Y.; Bourke, A.K.; Lipsmeier, F.; Bernasconi, C.; Belachew, S.; Gossens, C.; Graves, J.S.; Montalban, X.; Lindemann, M. U-turn speed is a valid and reliable smartphone-based measure of multiple sclerosis-related gait and balance impairment. Gait Posture 2021, 84, 120–126. [Google Scholar] [CrossRef] [PubMed]
  108. Rentz, C.; Far, M.S.; Boltes, M.; Schnitzler, A.; Amunts, K.; Dukart, J.; Minnerop, M. System Comparison for Gait and Balance Monitoring Used for the Evaluation of a Home-Based Training. Sensors 2022, 22, 4975. [Google Scholar] [CrossRef] [PubMed]
  109. Loy, B.D.; Fling, B.W.; Horak, F.B.; Bourdette, D.N.; Spain, R.I. Effects of lipoic acid on walking performance, gait, and balance in secondary progressive multiple sclerosis. Complement. Ther. Med. 2018, 41, 169–174. [Google Scholar] [CrossRef] [PubMed]
  110. Hsieh, K.L.; Wood, T.A.; An, R.; Trinh, L.; Sosnoff, J.J. Gait and balance impairments in breast cancer survivors: A systematic review and meta-analysis of observational studies. Arch. Rehabil. Res. Clin. Transl. 2019, 1, 100001. [Google Scholar] [CrossRef]
  111. Grabli, D.; Karachi, C.; Welter, M.L.; Lau, B.; Hirsch, E.C.; Vidailhet, M.; François, C. Normal and pathological gait: What we learn from Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 2012, 83, 979–985. [Google Scholar] [CrossRef]
  112. Rudroff, T.; Proessl, F. Effects of muscle function and limb loading asymmetries on gait and balance in people with multiple sclerosis. Front. Physiol. 2018, 9, 531. [Google Scholar] [CrossRef] [PubMed]
  113. Dalmazane, M.; Gallou-Guyot, M.; Compagnat, M.; Magy, L.; Montcuquet, A.; Billot, M.; Daviet, J.-C.; Perrochon, A. Effects on gait and balance of home-based active video game interventions in persons with multiple sclerosis: A systematic review. Mult. Scler. Relat. Disord. 2021, 51, 102928. [Google Scholar] [CrossRef] [PubMed]
  114. Stack, B.; Sims, A. The relationship between posture and equilibrium and the auriculotemporal nerve in patients with disturbed gait and balance. Cranio® 2009, 27, 248–260. [Google Scholar] [CrossRef] [PubMed]
  115. Gallagher, R.; Marquez, J.; Osmotherly, P. Gait and balance measures can identify change from a cerebrospinal fluid tap test in idiopathic normal pressure hydrocephalus. Arch. Phys. Med. Rehabil. 2018, 99, 2244–2250. [Google Scholar] [CrossRef] [PubMed]
  116. Rubenstein, L.Z.; Josephson, K.R.; Trueblood, P.R.; Yeung, K.; Harker, J.O.; Robbins, A.S. The reliability and validity of an obstacle course as a measure of gait and balance in older adults. Aging Clin. Exp. Res. 1997, 9, 127–135. [Google Scholar] [CrossRef] [PubMed]
  117. Martens, K.A.E.; Matar, E.; Hall, J.M.; Phillips, J.; Szeto, J.Y.Y.; Gouelle, A.; Grunstein, R.R.; Halliday, G.M.; Lewis, S.J.G. Subtle gait and balance impairments occur in idiopathic rapid eye movement sleep behavior disorder. Mov. Disord. 2019, 34, 1374–1380. [Google Scholar] [CrossRef] [PubMed]
  118. Booth, C.E. Water exercise and its effect on balance and gait to reduce the risk of falling in older adults. Act. Adapt. Aging 2004, 28, 45–57. [Google Scholar] [CrossRef]
  119. Hill, K.K.; Campbell, M.C.; McNeely, M.E.; Karimi, M.; Ushe, M.; Tabbal, S.D.; Hershey, T.; Flores, H.P.; Hartlein, J.M.; Lugar, H.M.; et al. Cerebral blood flow responses to dorsal and ventral STN DBS correlate with gait and balance responses in Parkinson’s disease. Exp. Neurol. 2013, 241, 105–112. [Google Scholar] [CrossRef] [PubMed]
  120. Calderon-Garciduenas, L.; Torres-Solorio, A.K.; Kulesza, R.J.; Torres-Jardon, R.; Gonzalez-Gonzalez, L.O.; Garcia-Arreola, B.; Chavez-Franco, D.A.; Luevano-Castro, S.C.; Hernandez-Castillo, A.; Carlos-Hernandez, E.; et al. Gait and balance disturbances are common in young urbanites and associated with cognitive impairment. Air pollution and the historical development of Alzheimer’s disease in the young. Environ. Res. 2020, 191, 110087. [Google Scholar] [CrossRef]
  121. Teixeira-Leite, H.; Manhães, A.C. Association between functional alterations of senescence and senility and disorders of gait and balance. Clinics 2012, 67, 719–729. [Google Scholar] [CrossRef]
  122. Bohnen, N.I.; Albin, R.L.; Müller, M.L.; Chou, K.L. Advances in therapeutic options for gait and balance in Parkinson’s disease. Eur. Neurol. Rev. 2011, 7, 100. [Google Scholar] [CrossRef] [PubMed]
  123. Zhang, X.; Xu, F.; Shi, H.; Liu, R.; Wan, X. Effects of dual-task training on gait and balance in stroke patients: A meta-analysis. Clin. Rehabil. 2022, 36, 1186–1198. [Google Scholar] [CrossRef]
  124. Abou, L.; Malala, V.D.; Yarnot, R.; Alluri, A.; Rice, L.A. Effects of virtual reality therapy on gait and balance among individuals with spinal cord injury: A systematic review and meta-analysis. Neurorehabilit. Neural Repair 2020, 34, 375–388. [Google Scholar] [CrossRef]
  125. Louis, E.D.; Rao, A.K.; Gerbin, M. Functional correlates of gait and balance difficulty in essential tremor: Balance confidence, near misses and falls. Gait Posture 2012, 35, 43–47. [Google Scholar] [CrossRef]
  126. Lee, C.-W.; Cho, G.-H. Effect of stationary cycle exercise on gait and balance of elderly women. J. Phys. Ther. Sci. 2014, 26, 431–433. [Google Scholar] [CrossRef]
  127. Zhou, H.; Nguyen, H.; Enriquez, A.; Morsy, L.; Curtis, M.; Piser, T.; Kenney, C.; Stephen, C.D.; Gupta, A.S.; Schmahmann, J.D.; et al. Assessment of gait and balance impairment in people with spinocerebellar ataxia using wearable sensors. Neurol. Sci. 2021, 43, 2589–2599. [Google Scholar] [CrossRef] [PubMed]
  128. Sandrini, G.; Homberg, V.; Saltuari, L.; Smania, N.; Pedrocchi, A. Advanced Technologies for the Rehabilitation of Gait and Balance Disorders; Springer: Berlin/Heidelberg, Germany, 2018; Volume 19. [Google Scholar]
  129. Lewek, M.D.; Bradley, C.E.; Wutzke, C.J.; Zinder, S.M. The relationship between spatiotemporal gait asymmetry and balance in individuals with chronic stroke. J. Appl. Biomech. 2014, 30, 31–36. [Google Scholar] [CrossRef]
  130. Cantoral-Ceballos, J.A.; Nurgiyatna, N.; Wright, P.; Vaughan, J.; Brown-Wilson, C.; Scully, P.J.; Ozanyan, K.B. Intelligent carpet system, based on photonic guided-path tomography, for gait and balance monitoring in home environments. IEEE Sens. J. 2014, 15, 279–289. [Google Scholar] [CrossRef]
  131. Morelli, N.; Morelli, H. Dual task training effects on gait and balance outcomes in multiple sclerosis: A systematic review. Mult. Scler. Relat. Disord. 2021, 49, 102794. [Google Scholar] [CrossRef]
  132. Martínez-Amat, A.; Hita-Contreras, F.; Lomas-Vega, R.; Caballero-Martínez, I.; Alvarez, P.J.; Martínez-López, E. Effects of 12-week proprioception training program on postural stability, gait, and balance in older adults: A controlled clinical trial. J. Strength Cond. Res. 2013, 27, 2180–2188. [Google Scholar] [CrossRef]
  133. Williams, K.L.; Choy, N.L.L.; Brauer, S.G. Center-Based Group and Home-Based Individual Exercise Programs Have Similar Impacts on Gait and Balance in People With Multiple Sclerosis: A Randomized Trial. PM&R 2021, 13, 9–18. [Google Scholar]
  134. Howell, D.R.; Mayer, A.R.; Master, C.L.; Leddy, J.; Zemek, R.; Meier, T.B.; Yeates, K.O.; Arbogast, K.B.; Mannix, R.; Meehan, W.P. Prognosis for persistent post concussion symptoms using a multifaceted objective gait and balance assessment approach. Gait Posture 2020, 79, 53–59. [Google Scholar] [CrossRef]
  135. Tramontano, M.; Grasso, M.G.; Soldi, S.; Casula, E.P.; Bonni, S.; Mastrogiacomo, S.; D’Acunto, A.; Porrazzini, F.; Caltagirone, C.; Koch, G. Cerebellar intermittent theta-burst stimulation combined with vestibular rehabilitation improves gait and balance in patients with multiple sclerosis: A preliminary double-blind randomized controlled trial. Cerebellum 2020, 19, 897–901. [Google Scholar] [CrossRef]
  136. Peters, J.; Abou, L.; Wong, E.; Dossou, M.S.; Sosnoff, J.J.; Rice, L.A. Smartphone-based gait and balance assessment in survivors of stroke: A systematic review. Disabil. Rehabil. Assist. Technol. 2022, 19, 177–187. [Google Scholar] [CrossRef] [PubMed]
  137. Koehler-McNicholas, S.R.; Danzl, L.; Cataldo, A.Y.; Oddsson, L.I.E. Neuromodulation to improve gait and balance function using a sensory neuroprosthesis in people who report insensate feet—A randomized control cross-over study. PLoS ONE 2019, 14, e0216212. [Google Scholar] [CrossRef]
  138. Kao, C.-C.; Chiu, H.-L.; Liu, D.; Chan, P.-T.; Tseng, I.-J.; Chen, R.; Niu, S.-F.; Chou, K.-R. Effect of interactive cognitive motor training on gait and balance among older adults: A randomized controlled trial. Int. J. Nurs. Stud. 2018, 82, 121–128. [Google Scholar] [CrossRef] [PubMed]
  139. Fein, G.; Smith, S.; Greenstein, D. Gait and balance in treatment-naïve active alcoholics with and without a lifetime drug codependence. Alcohol. Clin. Exp. Res. 2012, 36, 1550–1562. [Google Scholar] [CrossRef]
  140. Alon, G.; Yungher, D.A.; Shulman, L.M.; Rogers, M.W. Safety and immediate effect of noninvasive transcranial pulsed current stimulation on gait and balance in Parkinson disease. Neurorehabilit. Neural Repair 2012, 26, 1089–1095. [Google Scholar] [CrossRef] [PubMed]
  141. Sarasso, E.; Filippi, M.; Agosta, F. Clinical and MRI features of gait and balance disorders in neurodegenerative diseases. J. Neurol. 2023, 270, 1798–1807. [Google Scholar] [CrossRef]
  142. Mahoney, G.; Martin, J.; Martin, R.; Yager, C.; Smith, M.L.; Grin, Z.; Vogel-Rosbrook, C.; Bradley, D.; Appiah-Kubi, K.O.; Boolani, A. Evidence that feelings of energy and fatigue are associated differently with gait characteristics and balance: An exploratory study. Fatigue Biomed. Health Behav. 2021, 9, 125–138. [Google Scholar] [CrossRef]
  143. Abreu, S.; Caldas, C. Gait speed, balance and age: A correlational study among elderly women with and without participation in a therapeutic exercise program. Braz. J. Phys. Ther. 2008, 12, 324–330. [Google Scholar] [CrossRef]
  144. McNeely, M.E.; Duncan, R.P.; Earhart, G.M. Medication improves balance and complex gait performance in Parkinson disease. Gait Posture 2012, 36, 144–148. [Google Scholar] [CrossRef] [PubMed]
  145. Morris, R.; Martini, D.N.; Madhyastha, T.; Kelly, V.E.; Grabowski, T.J.; Nutt, J.; Horak, F. Overview of the cholinergic contribution to gait, balance and falls in Parkinson’s disease. Park. Relat. Disord. 2019, 63, 20–30. [Google Scholar] [CrossRef] [PubMed]
  146. Wecker, L.; Engberg, M.; Philpot, R.; Lambert, C.; Kang, C.; Antilla, J.; Bickford, P.; Hudson, C.; Zesiewicz, T.; Rowell, P.P. Neuronal nicotinic receptor agonists improve gait and balance in olivocerebellar ataxia. Neuropharmacology 2013, 73, 75–86. [Google Scholar] [CrossRef]
  147. Jiang, X.; Deng, F.; Rui, S.; Ma, Y.; Wang, M.; Deng, B.; Wang, H.; Du, C.; Chen, B.; Yang, X.; et al. The evaluation of gait and balance for patients with early diabetic peripheral neuropathy: A cross-sectional study. Risk Manag. Health Policy 2022, 15, 543–552. [Google Scholar] [CrossRef] [PubMed]
  148. Ülger, Ö.; Yağlı, N.V. Effects of yoga on balance and gait properties in women with musculoskeletal problems: A pilot study. Complement. Ther. Clin. Pract. 2011, 17, 13–15. [Google Scholar] [CrossRef] [PubMed]
  149. Yiou, E.; Caderby, T.; Delafontaine, A.; Fourcade, P.; Honeine, J.-L. Balance control during gait initiation: State-of-the-art and research perspectives. World J. Orthop. 2017, 8, 815–828. [Google Scholar] [CrossRef]
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Figure 1. The flowchart of the review methods.
Figure 1. The flowchart of the review methods.
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Figure 2. The change in academic publication numbers in gait and balance from 1993 to 2022.
Figure 2. The change in academic publication numbers in gait and balance from 1993 to 2022.
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Figure 3. Keyword distribution analysis.
Figure 3. Keyword distribution analysis.
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Figure 4. Clustering for the keywords.
Figure 4. Clustering for the keywords.
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Figure 5. The distribution map of countries.
Figure 5. The distribution map of countries.
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Figure 6. The distribution map of the authors.
Figure 6. The distribution map of the authors.
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Figure 7. The distribution map of universities.
Figure 7. The distribution map of universities.
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Table 1. The top 30 keywords with the biggest bursts of citations (Blue boxes show the period without a burst, and red boxes represent the burst period from beginning to end).
Table 1. The top 30 keywords with the biggest bursts of citations (Blue boxes show the period without a burst, and red boxes represent the burst period from beginning to end).
Keywords Year Strength Begin End 1993–2023
Elderly person 1995 18.04 1995 2014 ▂▂▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂
Community 1997 16.12 1997 2013 ▂▂▂▂▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂
Women 1996 12.85 2003 2011 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂
Age 1993 9.96 1996 2009 ▂▂▂▃▃▃▃▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂
Locomotion 1997 9.66 1997 2014 ▂▂▂▂▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂
Men 1993 9.04 1993 2010 ▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂
Posturography 2000 9.03 2000 2011 ▂▂▂▂▂▂▂▃▃▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂
Strength 1998 8.31 1998 2006 ▂▂▂▂▂▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂
Tai chi 2004 7.29 2004 2008 ▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂
Rating scale 2010 6.96 2010 2016 ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂
Human 1996 6.88 1996 2007 ▂▂▂▃▃▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂
Exercise 1995 6.88 1995 2004 ▂▂▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂
Elderly patient 1996 6.76 1996 2005 ▂▂▂▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂
Randomized controlled trial 1999 6.66 2014 2018 ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂
Machine learning 2021 6.61 2021 2023 ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃
Balance control 1996 6.31 2006 2008 ▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂
Hip fracture 1995 6.26 1995 2009 ▂▂▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂
Accelerometer 2015 6.22 2015 2018 ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂
Dwelling older adult 2002 6.11 2012 2017 ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂
Postural response 2003 6.07 2003 2009 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂
Quality 2004 5.98 2018 2023 ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃
Cognitive impairment 2010 5.93 2019 2023 ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃
Sway 2002 5.87 2002 2011 ▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂
Gait initiation 2007 5.79 2007 2013 ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂
Motor 2005 5.68 2020 2023 ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃
Mini BEStest 2017 5.58 2017 2021 ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂
Meta-analysis 2005 5.57 2017 2018 ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂
Pattern 1997 5.52 2005 2011 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂
Rehabilitation 2004 5.5 2009 2014 ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂
Adult 1993 5.47 2000 2009 ▂▂▂▂▂▂▂▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂
Table 2. Top six countries with the strongest citation bursts (Blue boxes show the period without a burst, and red boxes represent the burst period from beginning to end).
Table 2. Top six countries with the strongest citation bursts (Blue boxes show the period without a burst, and red boxes represent the burst period from beginning to end).
Countries Year Strength Begin End 1993–2022
USA 1993 44.56 1993 2007 ▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂
Spain 2010 10.26 2019 2022 ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃
Switzerland 2001 7.48 2010 2013 ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂
Canada 1994 4.92 2002 2007 ▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂
Saudi Arabia 2013 3.82 2014 2015 ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂
Czech Republic 2008 3.71 2020 2022 ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃
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Mao, Q.; Zheng, W.; Shi, M.; Yang, F. Scientometric Research and Critical Analysis of Gait and Balance in Older Adults. Sensors 2024, 24, 3199. https://doi.org/10.3390/s24103199

AMA Style

Mao Q, Zheng W, Shi M, Yang F. Scientometric Research and Critical Analysis of Gait and Balance in Older Adults. Sensors. 2024; 24(10):3199. https://doi.org/10.3390/s24103199

Chicago/Turabian Style

Mao, Qian, Wei Zheng, Menghan Shi, and Fan Yang. 2024. "Scientometric Research and Critical Analysis of Gait and Balance in Older Adults" Sensors 24, no. 10: 3199. https://doi.org/10.3390/s24103199

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