GRAPHICAL ABSTRACT

INTRODUCTION

The physical condition of older adults significantly affects their ability to perform daily activities and represents a fundamental component of overall bodily function. This is especially true for those experiencing limited mobility, which may include diminished muscle strength, impaired coordination, and a decline in neuromuscular control systems [1]. Impairments across various physiological systems in older adults increase their risk of falling. Falls are the most common type of accident in this population. In Thailand, more than 1000 older adults die each year from falls—an average of 3 deaths per day—with males exhibiting a mortality rate more than 3 times higher than that of females due to accidental falls [2,3]. The risk of falls escalates with advancing age, leading to a diminished ability to control bodily movements and resulting in a decreased overall quality of life [4].

The promotion of fall prevention among older adults has recently received significant attention. Multi-dimensional exercise regimens have been encouraged, as they train diverse aspects of physical function. Chittrakul et al. [2] found that 8 weeks of continuous exercise reduced falls in older adults. Dual-task training has become a widely used method for individuals with neurological conditions and for older adults at increased risk of falling [5]. Dual-tasking involves performing 2 activities simultaneously, requiring individuals to manage distinct tasks concurrently. This approach engages perception, processing, and mechanisms for skill execution, with each component functioning independently. Previous research distinguishes between mechanical and cognitive tasks, with motor-cognitive dual-tasking involving both types. Such training has been shown to enhance cognitive abilities, lower the risk of dementia, and mitigate issues related to balance and movement [1,5,6].

As individuals age, their ability to manage dual tasks declines, as the cognitive demands of dual-task paradigms exceed those required for performing single motor or cognitive tasks [7]. Similarly, the central nervous system’s capacity to process increased volumes of sensory input from multiple sources becomes compromised. As a result, these declines can delay functional motor responses and heighten the risk of accidental falls [8,9]. Therefore, dual-task training is necessary to prevent such outcomes. Additionally, dual-task training has been demonstrated to be more effective than single-task methods [5,9]. Dual-task demands generally involve mental tracking tasks (e.g., serial subtractions), verbal fluency tasks (e.g., naming animals aloud), and manual motor activities (e.g., carrying a glass of water on a tray with 1 hand) [5,9].

A study conducted by Yuzlu et al. [5] in 2022 examined the effects of 2 types of simultaneous dual-task training on improving balance and walking abilities in older adults over an 8-week period. Their results indicated that both simultaneous and sequential dual-task training were effective in enhancing balance and walking ability, contributing to increased walking speed in older adults. Moreover, such exercises may decrease the fear of falling, thereby providing effective fall prevention. However, previous studies have reported limited evidence regarding the effects of dual-task training in the context of group aerobic exercise.

The objective of this study was to evaluate the effectiveness of dual-task training for balance performance, with the aim of improving balance, walking ability, and quality of life in older adults. Based on the dual-task hypothesis, we anticipated that similar improvements in balance performance would result from dual-task training. This program could be used as an additional approach to enhance physical fitness among older adults, ultimately leading to improved health outcomes and a reduced risk of falls in the future.

METHODS

Experimental Design

A randomized controlled trial was conducted involving 72 participants. Participants were randomly assigned in a 1:1 ratio to either the intervention group (IG) or the control group (CG). The IG participants underwent dual-task protocol training for 8 weeks, while the CG participants performed general stretching exercises consistently over the same period. Both groups were assessed at allocation, 4 weeks post-allocation, and 8 weeks post-allocation.

The study adhered to the recommendations of the World Medical Association Declaration of Helsinki and the Consolidated Standards of Reporting Trials (CONSORT) guidelines. The flow diagram is shown in Figure 1.

Sample Size

The sample size calculation was based on the timing of the Timed Up and Go (TUG) test combined with a cognitive task, which served as the primary outcome [9]. A minimal clinically important difference of 0.05 m/s was established, with an effect size of 0.20, a power of 80% (1−β), an alpha level of 5%, and the use of F-statistics for repeated measures. Sixty participants were initially required. To account for a projected 20% sample loss, 72 participants were ultimately evaluated and assigned to each group in a 1:1 ratio. The sample size was calculated using G^*Power 3 [10].

Eligibility Criteria and Recruitment

Older adults who took more than 12 seconds on the TUG test, could walk unassisted for a distance of 10 meters at a self-selected speed of at least 1 m/s, and had mild cognitive impairment (MoCA score less than 26) were included in the study [9]. Exclusion criteria were as follows: (1) contraindications to postural balance and/or cognitive training; (2) a history of 2 or more falls within the preceding 6 months; (3) enrollment or participation in any regular, structured physical or cognitive exercise program 2 or more times per week within the preceding 6 months; (4) any chronic health condition contraindicating physical exercise, such as uncontrolled hypertension and/or uncontrolled diabetes; and (5) a history of upper or lower limb fractures, surgical operations on the knees, ankles, or hips, or recent muscular injuries within the past 6 months. No adjustments to the methodology were necessary after the study commenced.

Participants were recruited from the Nang Lae Subdistrict Older Adults School in Chiang Rai Province, Thailand. Data collection took place from October 2024 to December 2024. Recruitment was conducted via broadcast radio advertisements.

Randomization and Allocation Concealment

An independent researcher who was unaware of the group assignments generated the randomization sequence using Microsoft Excel version 11.65 (Microsoft, Redmond, WA, USA). The numerical series was placed in opaque envelopes, sequentially numbered from 1 to 72 according to the generated sequence. The allocation information was kept confidential and was inaccessible to both participants and evaluators, who remained blinded to group assignments until the study concluded.

Blinding

All outcome measurements were performed by independent assessors who were blinded to group assignments to minimize assessment bias and uphold the integrity of the single-blind study design.

Intervention

Participants in each group (IG and CG) were required to attend at least 20 out of 24 total sessions (80% attendance), 3 times per week, for 8 weeks. The intervention program for the IG consisted of 40-minute exercise sessions and included: (1) a 10-minute warm-up phase with supervised walking on a flat surface, stretching, and breathing exercises; (2) 20 minutes of balance training with dual tasks (4 components, 5 minutes each); and (3) a 10-minute relaxation phase including breathing exercises and global muscle stretching [11]. After 4 weeks, the difficulty of the exercise program was increased (progression of balance exercises) based on participants’ abilities. For the CG, participants performed a global stretching exercise protocol for 30 minutes, 3 times per week, throughout the 8 weeks. The intervention protocol for each group is presented in Table 1.

Training was conducted in a group setting, with a maximum of 10 participants per group. A physiotherapist and a nursing professional, each with at least 5 years of experience in dual-task training, supervised all sessions. The protocol did not include individualized interventions, as participants demonstrated independent functional performance. However, minor adaptations were made as needed until each participant was able to complete the exercises without difficulty (Index of item objective congruence 0.92).

Primary Outcome

All measurements were performed by blinded assessors (single-blinded design).

TUG test

The TUG test evaluates balance, sit-to-stand, walking, and turning in vulnerable populations. The TUG test has several variations, including differences in pace (self-selected or fast), distance (2.44 to 3.05 m [8 to 10 ft]), turning mechanism (walking to a line and turning, walking around a cone, or walking to a cone and turning), chair type (with or without armrests, backrest, chair height [40 to 46 cm]), and number of trials. The TUG test has been validated in populations with stroke, Parkinson’s disease, dementia, spinal injuries, vestibular disorders, osteoarthritis, brain trauma, and older adults in various settings. Test–retest reliability is excellent across these populations. The TUG test has demonstrated validity and responsiveness in geriatric rehabilitation [12]. The TUG test is simple and time-efficient, and recent guidelines recommend its use in clinical settings for assessing fall risk. It has excellent inter-rater reliability (intraclass correlation coefficient [ICC], 0.99) and high intra-rater reliability (ICC, 0.99) [13].

Participants performed the TUG test under 3 conditions: (1) Conventional TUG; (2) TUG with a cognitive task (participants performed the TUG test while engaging in a cognitive task, such as mathematics, counting backwards from 100 in decrements of 7, or naming red fruits); and (3) TUG with a motor task (participants performed the TUG test while holding a full glass of water on a plate). The sequence of walking conditions was randomly assigned before starting the gait assessments.

Secondary outcomes

The MoCA

The Montreal Cognitive Assessment (MoCA) is an instrument designed to detect moderate cognitive impairment and early signs of dementia. It is valuable for identifying individuals at risk for Alzheimer’s disease and for screening conditions such as Parkinson’s disease, brain tumors, substance abuse, and head trauma. Introduced in 2005, the MoCA is considered an improvement over the Mini-Mental State Examination, which was first established in 1975 [14]. The assessment consists of 30 items and typically requires about 10 minutes to complete. Although the MoCA is effective at identifying dementia, it does not distinguish between different types of dementia [14]. The test uses a scoring system with a maximum score of 30 points. It evaluates 7 domains of cognitive function through 11 distinct tasks, assessing both executive and visuospatial abilities. In older adults, a MoCA score above 25 points is considered indicative of typical cognitive function [14].

The 10MWT

The 10-meter walk test (10MWT) evaluates an individual’s walking ability over a distance of 10 meters (32.8 feet) without assistance. Participants are asked to walk at their preferred, comfortable speed for the full distance. Timing is recorded for the middle 6 meters (19.7 feet) to minimize the effects of acceleration and deceleration. The timer starts when the toes of the leading foot cross the 2-meter mark and stops at the 8-meter mark. If assistive devices are used, their use should remain consistent and be documented for all trials. Each participant completes 3 trials, and the average time is calculated [15]. The minimally clinically important difference for the 10MWT is defined as a small meaningful change of 0.05 m/s and a substantial meaningful change of 0.13 m/s [15,16].

Quality of life

Quality of life was assessed using the World Health Organization Quality of Life Brief (WHOQOL-BREF) scale, which comprises 4 domains: physical health, psychological well-being, social relationships, and environment, for a total of 26 questions [17,18]. Each domain is rated on a 5-point Likert scale, with higher scores indicating better quality of life. Raw scores for each domain are transformed to a 0-100 scale, where higher values denote improved quality of life. The mean and median are calculated for each domain as well as the total score. There is no definitive threshold for distinguishing between “good” and “poor” quality of life or for assessing health satisfaction. In this study, participants scoring below the median were classified as having poor quality of life and unsatisfactory health perception, while those scoring at or above the median were classified as having good quality of life and satisfactory health perception [17]. A cutoff of<60 for overall quality of life yielded a sensitivity of 95.0% and a specificity of 54.4% [18].

Statistical Analysis

Statistical analyses were conducted using SPSS version 22.0 (IBM Corp., Armonk, NY, USA), with significance set at 5%. The Shapiro–Wilk test was used to assess normality. Continuous data are presented as mean±standard deviation (SD), while categorical variables are expressed as frequencies. Baseline characteristics and outcomes between the 2 groups were compared using the independent sample t-test and the chi-square test. Outcomes at baseline, week 4, and week 8 were analyzed using 2-way repeated measures analysis of variance. An intention-to-treat approach was applied, with missing data addressed using the last observation carried forward method. A 2-tailed p-value of less than 0.05 was considered statistically significant.

Ethics Statement

The study was approved by the Research Ethics Committee of the Chiang Rai Provincial Public Health Office (CRPHO 01/2567) and registered prospectively with the Thai Clinical Trials Registry (TCTR20240905004). After being informed about the purpose of the study and pro-viding informed consent, each participant signed a consent form prior to study commencement.

RESULTS

A total of 124 individuals were screened, of whom 72 participants were recruited and randomized to either the (n=36) or the CG (n=36). The mean age was 68.43 years for the IG and 67.20 years for the CG. The number of participants with comorbidities was 24 (66.7%) in the IG and 22 (61.1%) in the CG. The mean body mass index was 24.93 kg/m² in the IG and 23.85 kg/m² in the CG. The number of participants with a history of falls in the past 12 months was 16 (53.3%) in the IG and 14 (46.7%) in the CG. There were no statistically significant differences in baseline characteristics between the intervention and control groups (Table 2).

In the Table 3, the TUG test showed a significant interaction effect between group and time, F(2,116)=27.198, p<0.001, partial η²=0.319. Comparisons at each time point revealed no significant difference at baseline (F(1,58)=0.024, p=0.877, partial η²=0.001). However, at week 4, the TUG time in the IG was significantly lower than in the CG (F(1,58)=13.188, p=0.001, partial η²=0.185), with this difference further increasing at week 8 (F(1,58)=60.045, p<0.001, partial η²=0.509).

The TUG with cognitive task also demonstrated a significant interaction between group and time, F(2,116)=23.07, p<0.001, partial η²=0.285. At baseline, there was no significant difference between groups (F(1,58)=0.44, p=0.51, partial η²=0.008). At week 4, the IG showed significantly better performance than the CG (F(1,58)=41.08, p<0.001, partial η²=0.457), and this advantage persisted at week 8 (F(1,58)=27.198, p<0.001, partial η²=0.319).

The TUG with motor task also had a significant interaction between group and time, F(2,116)=12.309, p<0.001, partial η²=0.175. Baseline values did not differ significantly (F(1,58)=0.682, p=0.41, partial η²=0.012). At week 4, the IG performed significantly better than the CG (F(1,58)=20.368, p<0.01, partial η²=0.260), with the difference widening at week 8 (F(1,58)=65.769, p<0.01, partial η²=0.531).

The MoCA score revealed a significant interaction between group and time, F(2,116)=8.408, p=0.001, partial η²=0.127. There were no significant differences at baseline (F(1,58)=3.314, p=0.07, partial η²=0.054) or at week 4 (F(1,58)=1.23, p=0.27, partial η²=0.021). At week 8, however, the IG showed a significantly higher MoCA score than the CG (F(1,58)=8.03, p=0.006, partial η²=0.122).

The 10MWT did not show a significant interaction between group and time, F(2,116)=0.64, p=0.54, partial η²=0.01. There were no statistically significant differences at baseline (F(1,58)=0.103, p=0.750, partial η²=0.002), week 4 (F(1,58)=0.234, p=0.630, partial η²=0.019), or week 8 (F(1,58)=1.149, p=0.288, partial η²=0.019).

The WHOQOL-BREF score showed a significant interaction between group and time, F(2,116)=4.92, p=0.001, partial η²=0.07. At week 4, the IG reported significantly higher quality of life scores than the CG (F(1,58)=37.459, p<0.01, partial η²=0.392), and this difference was maintained at week 8 (F(1,58)=12.309, p<0.01, partial η²=0.175).

A comparison of balance ability between the IG and CG using the TUG assessment revealed that, following dual-task training, older adults in the IG consistently demonstrated a lower risk of falls than those in the CG, based on the fall risk cutoff of 12 seconds (Figure 2) [8].

DISCUSSION

The present study aimed to compare the effects of dual-task motor and cognitive protocol training in community-dwelling older adults. Our results revealed that participants in the intervention group experienced greater improvements in functional mobility, balance performance, and cognitive function than those in the control group after 8 weeks of training. Although both groups demonstrated improvements in functional mobility (including dual-task motor and cognitive performance) and cognitive function by week 8, neither group exhibited significant improvement in gait performance or quality of life.

Functional balance and mobility in both single-task and dual-task scenarios, including motor and cognitive aspects, improved significantly in the intervention group. However, gait velocity and quality of life measures remained unchanged. This outcome can be explained by the principle of training specificity, which emphasizes that training-induced adaptations are closely linked to the type, frequency, and duration of the prescribed exercise. To ensure optimal outcomes, training regimens must be tailored to target the specific systems involved in the desired activity [19]. Therefore, we surmise that participants in both groups may not have been sufficiently challenged by the dual-task intervention to elicit improvements in gait speed. Nevertheless, both groups demonstrated enhancements in functional mobility (as measured by the TUG test), dual-task TUG test (cognitive and motor), cognitive performance (MoCA), and quality of life (WHOQOL-BREF). To achieve substantial intergroup differences over time and further improve functional balance, it may be necessary to increase the specificity, intensity, and volume of training. The broad range of training environments used in our intervention may also have overwhelmed participants with excessive stimulation, which could have contributed to the lack of improvement in gait speed.

An alternative interpretation of the walking speed results can be found in a recent study by Bortone et al. [20], which reported that walking speed increased more after resistance and multimodal training compared to coordination exercises in this demographic. Considering the characteristics of our experimental design, a longer intervention period may be required to improve gait speed in community-dwelling older adults whose baseline gait performance was already functionally sufficient (equal to or exceeding 1 m/s).

The literature suggests that walking is not an entirely automatic task; it requires attention, particularly in complex and demanding scenarios such as dual-task conditions. In such circumstances, performance in one or both tasks tends to decrease [5]. This explanation aligns with our findings: the increased cognitive demand of dual-task training likely directed participants’ attention toward enhancing cognitive performance. Consequently, it is expected that cognitive-motor functioning will decline under complex conditions due to a reduced cognitive and motor reserve available for task execution [5,9].

The findings related to cognitive performance further support these observations. As measured by the MoCA, both groups showed improvements in executive function, orientation, memory and delayed recall, attention or concentration, calculation, naming, and abstraction. Participants also demonstrated gains in selective attention and executive functions, including cognitive flexibility and resistance to interference. In the intervention group, MoCA scores increased significantly following dual-task training, from a baseline mean±SD of 22.03±4.15 to 26.73±2.59 (p<0.001). These improvements in cognitive reserve contributed to the observed increase in functional mobility with motor and cognitive dual-tasking after the 8-week intervention.

The TUG test is a fundamental motor assessment encompassing essential movements. Several component tasks can be complex, requiring planning, spatial orientation, and organization. Activities such as turning and rising from a chair may challenge cognitive function, suggesting that these seemingly functional motor tasks are not exclusively motor in nature. Our results indicate that components of the TUG test partially engage executive functions. Supporting this, prior studies have reported increased TUG times in older adults and in individuals with cognitive impairment, which suggests that the TUG is not purely a measure of motor performance but also reflects cognitive function [5,7]. This is consistent with findings by Yuzlu et al. [5], who demonstrated that combined dual-task training significantly improved balance and walking performance in older adults.

In the control group, the completion time for the TUG test with an added cognitive task increased significantly from 10.57±2.07 seconds to 13.69±4.60 seconds (p=0.002), indicating a decline in dual-task performance. In contrast, in the dual-task intervention group, the TUG completion time under cognitive load significantly decreased from 10.21±2.12 seconds to 7.61±1.20 seconds (p<0.01), suggesting improved cognitive-motor integration. During walking assessments, the cognitive and motor regions of the brain work together efficiently. The addition of a cognitive task in the TUG test increases process complexity and affects walking speed, which can be explained by capacity theory [8]. Performing 2 tasks simultaneously may result in competition for limited cognitive resources, potentially reducing the performance of one or both tasks. The walking assessment combined with a cognitive task, which requires cognitive flexibility, is especially relevant as cognitive flexibility often signals early cognitive impairment in older adults [21]. Our results show that the TUG-cognitive required longer completion times than the standard TUG, supporting its role as a valuable clinical tool for identifying deficits in dual-task performance and cognitive function in older adults. Eight weeks of simultaneous dual-task training improved balance performance as evaluated by the TUG test with a cognitive task. Similarly, Herman et al. [22] found that TUG completion times increased in individuals with cognitive impairment when combined with cognitive tasks.

The study revealed a significant correlation between TUG-cognitive performance and cognitive function, as assessed by the MoCA, a widely recognized tool in both clinical and research contexts [14]. However, some studies have noted that the MoCA’s limitations in assessing executive function and attention should be carefully considered [14]. We investigated the relationships among visual attention, task switching, and reaction time for stepping. The results demonstrated a significant correlation between TUG-cognitive performance and measures of cognitive function, visual attention, task switching, and reaction time. In older adults, early attention and executive dysfunctions often stem from basal ganglia pathology, which can lead to difficulties in performing dual cognitive tasks. It is important to recognize the interconnectedness of dual-task performance, executive function, and attention [23]. These abilities are fundamental components of executive function, underlying will, self-awareness, planning, focus, response monitoring, and sustained attention. Impairments in one or more components of executive function can negatively impact an individual’s ability to walk safely and effectively [23]. Therefore, impaired executive function contributes to difficulties in dual-task scenarios and increases the risk of falls. Li et al. [24] reported that multimodal training enhances the effect of dual-task training on cognitive performance. Specifically, interventions that combine physical and intellectual activities can significantly delay dementia progression and improve cognitive abilities in older adults. Research by Yuzlu et al. [5] further demonstrated that dual-task training, whether motor-motor or motor-cognitive, resulted in improved balance and increased walking speed, as well as clear improvements in brain functions related to thinking, processing, and responding to stimuli.

In addition to cognitive benefits, dual-task intervention protocols also improved functional balance performance. Physical exercise enhances structural plasticity in the human brain, which in turn improves cognitive functions related to balance, including visual and vestibular abilities [19]. A decline in balance is associated with reduced physical functioning and may adversely affect the performance of specific activities of daily living [19,25]. From a motor control perspective, both training protocols in this study produced notable enhancements in both static and dynamic balance.

The strengths of this study include (1) its rigorous randomized controlled trial methodology and (2) a training protocol that integrated physical performance, cognitive function, and aerobic exercise, maintaining participant engagement throughout the 8-week intervention. However, several limitations should be acknowledged. The findings apply specifically to older adults with mild cognitive impairment, and the intervention timing differed between groups, which may have influenced outcome measures. Future studies should include longer follow-up periods of 3 months to 6 months and recruit individuals who have experienced recurrent falls or post-fall syndrome. Additionally, assessments should include evaluations of participants’ fear of falling.

Integrating combined cognitive and motor dual-task training into fall prevention programs can play a crucial role in reducing falls among older adults. Such training enhances functional stability and balance, which are vital for safely navigating complex environments. It also bolsters cognitive resilience, enabling individuals to respond more effectively to unpredictable situations that could lead to falls. The development of evidence-based, sustainable, and engaging training protocols will contribute to improved safety and quality of life in the aging population.

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

This study was supported by the National Sciences, Research and Innovation Fund (NSRF) of Thailand (Fund ID: 682A10006) and Mae Fah Luang University.

Acknowledgements

The authors express their gratitude to Tessaban Tumbun Nanglae, Chiang Rai, Thailand, for facilitating the venue and organization.

Author Contributions

Conceptualization: Jattanond W, Sutalangka C, Namkorn P, Chaiut W. Data curation: Jattanond W, Sutalangka C, Namkorn P, Chaiut W. Formal analysis: Jattanond W, Sutalangka C, Namkorn P, Chaiut W. Funding acquisition: Jattanond W, Sutalangka C, Chaiut W. Methodology: Jattanond W, Sutalangka C, Namkorn P, Chaiut W, Sitthipornvorakul E. Project administration: Jattanond W, Sutalangka C, Chaiut W. Visualization: Jattanond W, Sutalangka C, Namkorn P, Chaiut W, Sitthipornvorakul E, Atsawakaewmonghkhon S. Writing – original draft: Jattanond W, Sutalangka C, Namkorn P, Chaiut W, Sitthipornvorakul E, Atsawakaewmonghkhon S, Suwannakul B, Ganogpichayagrai A, Kongkratoke S, Taniguchi R. Writing – review & editing: Jattanond W, Sutalangka C, Namkorn P, Chaiut W, Sitthipornvorakul E, Atsawakaewmonghkhon S, Suwannakul B, Ganogpichayagrai A, Kongkratoke S, Taniguchi R.

Figure. 1.

Study flow chart.

Figure. 2.

Characteristics of the elderly’s balance ability (Timed Up and Go: TUG) in 3 conditions: (A) Conventional TUG, (B) TUG with cognitive task, and (C) TUG with motor task when compared between the exercise group and the control group. The blue dashed line represents the intervention group (IG) and the green dotted line represents the control group (CG). The red solid line represent cut-out points of a fall risk in elderly at 12 seconds.

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