Abstract
Background
As the Japanese population continues to age, functional decline with aging will inevitably become more pronounced. Vision loss and decline in lifestyle and motor functions are the main causes of functional decline. To date, a few studies have examined the relationship between visual and overall physical function. Therefore, this study aimed to quantitatively investigate the relationship between loss of visual acuity and the decline of physical functionality.
Methods
This cross-sectional study was based on the data of 86 community-dwelling older adults (average age ± standard deviation, 75.7 ± 5.5 years; 34 men) who participated in the October 2020–May 2022 baseline survey. Visual function was assessed based on the measurement of binocular near functional visual acuity (NFVA) using the Smart Vision Check application. Physical function tests were performed by measuring grip strength, 30-second chair stand (CS-30), Timed Up and Go (TUG), with eyes open, one-leg standing balance (OLSB), and two-step test. Multivariate regression analysis was performed to examine the association between NFVA and physical function, and partial regression coefficient (β) and 95% confidence interval (95%CI) were calculated.
Results
After adjusting for all covariates, NFVA was significantly associated with grip strength (β: 3.54, 95%CI: 0.39, 6.70). Meanwhile, no association was noted between NFVA and CS-30 (β: -0.35, 95%CI: -2.68, 1.97), TUG (β: -0.23, 95%CI: -0.65, 0.19), OLSB (β: 6.13, 95%CI: -5.09, 17.35), and two-step test (β: 0.01, 95%CI: -0.05, 0.08).
Conclusions
The study showed an association between quantitatively assessed visual and physical functions in community-dwelling older adults living in Japan. Thus, the findings indicate that maintaining and enhancing grip strength and visual function in older adults may be an important factor in leading a healthy and fulfilling life.
Trial registration
Registration number, UMIN000041970.
Keywords: Near functional visual acuity, Visual function, Visual acuity measurement application, Physical function, Older adults
Background
Among the world’s superaged societies, Japan has the most aged population [1]. As of 2021, 28.9% of its population was aged ≥ 65 years [1]. It is presumed that the population will continue to age and that the decline in various physical, mental, and social functions will become more pronounced with age. The extension of healthy life expectancy is an important issue and is addressed in Healthy Japan 21 (second phase) to enable older adults to lead independent lives [2].
As the population of Japan ages over the next 40 years, the prevalence of visual impairment is projected to increase from an estimated 1.6 million in 2007 to approximately 2 million in 2030, and the number of blind people is expected to reach approximately 221,000 [3]. Indeed, visual impairment is a growing problem in Japan and worldwide. Visual impairment causes several health problems in older adults, including an increased likelihood of social isolation [4, 5, 6], falls and fractures [7, 8], and early placement in nursing homes and care homes [9, 10, 11]. Additionally, concerns about possible associated compounding issues, such as cognitive decline [12, 13, 14] and an increased risk of death, have been reported [15, 16, 17]. Furthermore, regarding the social impact, visual impairment has affected more than 1.64 million people in Japan and incurred an economic cost of approximately yen 8785.4 billion (US $72.8 billion), equivalent to 1.7% of Japan’s gross domestic product [18]. This represents a significant social burden worldwide. Reducing the prevalence of visual impairment in Japanese society through increased public awareness of prevention and early diagnosis, more aggressive treatment, low vision care, and research promotion will improve the quality of life of patients and their family members [3].
Physical function, including mobility, muscle strength, and balance, has been clearly reported to decline with age [20, 21]. This decline in physical function has been indicated to be associated with a higher risk of falls and fractures [22], worsening instrumental activity of daily living (IADL) and activity of daily living (ADL) [23], and increased mortality [23, 24, 25]. Therefore, maintaining and improving physical function is crucial for enhancing ADL and the quality of life of people.
Research on impaired visual and physical functions has revealed that visual impairment is associated with motor impairment, including reduced walking speed [26], mobility impairment [27], decreased ADL [27, 28], decreased grip strength [29], and a higher risk of sarcopenia [30]. Furthermore, older adults with impaired visual function experience significant effects on their overall functioning, directly impacting their ability to perform tasks such as navigating stairs, which is closely linked to an increased risk of falls. This impairment also leads to reduced physical activity, contributing to negative health outcomes [31]. Previous reports have demonstrated that interventions, such as cataract surgery, for impaired visual function can improve gait speed [27, 28, 29], sleep quality [28, 30], and cognitive function [14], suggesting that early detection of visual impairment and intervention can prevent unnecessary declines in physical and mental function. However, most studies to date have relied on self-administered questionnaires and qualitative self-reporting to evaluate visual acuity, introducing the possibility of recall bias and over- or underestimation in the reporting. No objective data exist on the relationship between visual and physical functions, a situation that is poorly understood. This study aimed to fill this gap using quantitative and objective methods to assess visual function.
Conventional visual acuity tests have been performed to assess and screen visual function, while functional visual acuity (FVA) test can measure the timewise changes in continuous measurement over a fixed period, usually for 1 min, providing detailed parameters of visual performance compared with the conventional test. A new smartphone application, Smart Vision Check (SVC), was developed to address the growing need for a simple method for evaluating daily visual functions [32]. The SVC has been validated [32] to assess FVA at a near distance in a manner comparable to conventional FVA measurement systems. Thus, SVC is characterized by its simplicity and the ability to assess visual function in more detail than conventional methods. Another advantage of SVC is that it enables the evaluation of visual function in daily life, which is difficult with conventional visual acuity testing. This is expected to enable a more accurate data-based examination of the relationship between visual and physical functions.
This study aimed to investigate the relationship between visual and physical functions in community-dwelling older adults using the SVC.
Methods
Study design
This cross-sectional study was conducted using baseline data from our longitudinal study, which included a 1-year follow-up analysis to examine gait and fall risk factors in community-dwelling older adults. Between October 2020 and May 2022, we performed a baseline assessment involving community-dwelling older adults aged ≥ 60 years at Nursing and Medical Care, Keio University School, as well as classrooms, public halls, and gymnasiums in Fujisawa city, Kanagawa, Japan.
The participants were recruited through convenience sampling and/or snowball sampling; the recruitment process involved the following: (1) verbal communication and distribution of leaflets at senior club events in the community, (2) outreach activities at city events in Fujisawa city, and (3) website of the community event.
The following three criteria were applied to individuals who fulfilled these requirements:
(1) individuals aged ≥ 60 years who understand the purpose of this study and have given their consent; (2) those without any obvious functional impairment in their physical functions; and (3) those able to express their own willingness to participate in the research.
Overall, 112 community-dwelling older adults (51 men and 61 women) were finally recruited. The participants of this study were 86 participants that had their visual function measured. The study flow chart is depicted in Fig. 1.
Fig. 1.
Study flow chart showing the study participant selection process, including the exclusion and inclusion criteria. Abbreviations: KGRI, Keio University Global Research Institute; CS-30, 30-second chair stand; TUG, Timed Up and Go test; OLSB, with eyes open, one-leg standing balance
The Ethics Review Board approved this study (Approval Number: 20200165; Study Title: Non-contact movement analysis of daily activities and risk factors for falls in community-dwelling older people. The study was conducted in accordance with the Declaration of Helsinki and the ethical guidelines for medical research involving human subjects.
Assessment of physical function
Physical function was assessed by physical activity specialists and physical therapists for older adults, using grip strength, 30-second chair stand (CS-30) test, with eyes open, one-leg standing balance (OLSB), two-step test, and Timed Up and Go (TUG) test.
Grip strength measurements were performed using a digital grip strength meter (Smedley digital grip strength meter, TKK5401, TAKEI, Japan) [33]. The participants held the grip strength meter in an upright position and adjusted their grip so that the second joint of the index finger was almost at a right angle. The maximum grip strength was measured two times, alternately on the left and right sides, while the arm was lowered naturally without touching the body or clothing.
The CS-30 test was conducted using a chair with a height of 40 cm, with the measurer supporting the participant’s chair with care to minimize the risk of falling [34]. The posture at the beginning of the test was uniform: sitting in a chair with both lower limbs shoulder-width apart, back off the backrest, and arms crossed in front of the chest. The participants were instructed to repeat standing and sitting movements as many times as possible in 30 s and to maintain the hip and knee joints in an upright position in the standing posture.
OLSB was performed on a non-slip floor with the participant observed attentively by the operator to minimize the risk of falling [35]. With both eyes open, the participants placed both hands on their hips and, after being asked to “raise one leg,” raised either their left or right leg by approximately 5 cm. The time taken for the raised leg to touch the floor was measured for up to 60 s.
For the two-step test, the maximum two-step length (stride) that could be achieved without losing balance was measured, and the two-step value was divided by height [36]. The distance from the toes of both sides of the starting limb to the toe of the final limb was measured to the nearest cm using a measuring tape. The length was measured two times for each leg, and the maximum value was used. After sufficient practice before the measurements, measurements were obtained under proximal monitoring to minimize the risk of falling.
The TUG is used to measure the time in seconds taken to rise from a standard chair without armrests, walk 3 m, turn, return to the chair, and sit down again [37]. At the start, the participants were placed in a posture with their legs shoulder-width apart, the tips of their feet together, and their hands placed on the front of their thighs. They did one from the right foot and one from the left foot, right and left turns, respectively. The time of the fastest movement was used.
Assessments of physical function were conducted in the same environment for each participant. They were also assessed using a standardized procedure by experienced evaluators, with sufficient breaks to enable participant invasiveness.
Assessment of visual function
Near functional visual acuity (NFVA) was measured to evaluate visual function with glasses for the near distance that the patient used daily, using the SVC application as previously described by Hanyuda et al. [32]. If the patient did not use glasses for near sightedness, NFVA was measured without glasses. Near visual acuity (NVA) was also measured to evaluate conventional visual acuity at near distances before the measurement of NFVA using SVC.
NFVA is a method for evaluating FVA at near distances, and SVC can evaluate NFVA comparable to conventional FVA tests with high reproducibility [32]. The FVA test was first developed to detect impaired visual function in daily life in dry eye syndrome. It is measured as the average of visual acuity measured over a short period each day, thereby reflecting daily visual acuity more efficiently than measuring visual acuity at a single specific point on a single day [38, 39]. Previous studies have shown that the FVA test can detect subtle differences in visual function compared with conventional visual acuity; therefore, we chose this method to evaluate visual function in this study [40, 41, 42, 43, 44]. This application is the first validated smartphone-based NFVA test, which enabled us to detect dynamic changes in visual acuity that are closely linked to the ocular surface condition and quality of life [32].
The SVC application can be uploaded and used on an Apple iPhone SE2 (Apple Inc, Cupertino, California, 2020) to provide a flexible measurement of NFVA. The iPhone SE2 screen size measures 138.4 × 67.3 mm; the pixel resolution is 750 × 1,334 pixels with a resolution of 326 ppi. SVC comprises a computing unit and display. Before implementation, the participants underwent measurements by holding the smartphone in front of them at a distance of 40 cm from their eyes at maximum brightness while wearing their glasses for near vision (if used). The distance between the smartphone and the participants’ eyes was evaluated by a trained orthoptist using a measuring tape at the start of the test and monitored for any changes throughout the test. All participants were checked and confirmed to have sufficient proficiency with digital devices and the ability to use the SVC using another mode of the SVC to measure the conventional near visual acuity before assessing the NFVA. When the participant tapped the start button, SVC displayed the optotype. Participants tapped an icon indicating the direction of the Landolt ring that automatically appears on the iPhone screen within 2 s; failure to respond within 2 s was considered an incorrect response. The optotype was automatically enlarged by one decimal degree for each incorrect answer or non-response.
If the participant responded correctly, an optotype of the same size was displayed randomly. The optotype decreased by one size in decimal visual acuity when the next answer was correct. The SVC test was completed in 60 s and automatically calculated NFVA, specifically the average of the NVA values for 60 s. The display of SVC is depicted in Fig. 2.
Fig. 2.
Display of the Smart Vision Check test. (A) Photo of near functional visual acuity measurement using the Smart Vision Check (SVC) application. (B) The iPhone screen during measurement. Participants tapped an icon indicating the direction of the Landolt ring that automatically appears on the iPhone screen within 2 s. (C) A representative screen displaying the measurement results. The green line denotes the decimal near visual acuity (NVA) with habitual correction that corresponds to the initial visual acuity. The purple line shows the timewise changes in NVA with habitual correction during testing. The pink line denotes mean NVA over 60 s, defined as near functional visual acuity (NFVA). The mean NVA value was calculated after converting the decimal visual acuity to logarithm and then converting that value back to decimal visual acuity. The purple open circles show the correct responses. In this study, NFVA was used to evaluate visual function
Other assessment items
A self-administered questionnaire was used to determine the characteristics of the participants by collecting data on age, sex, residential status, work status, treatment status, and cognitive function. Height and weight were measured during the study survey. Body mass index (BMI, kg/m2) was calculated from height and weight measured during the study. For the residential status, responses were obtained to the question: “Number of people living with you (including yourself).” Work status was obtained based on responses to the question: “Do you work?” (yes or no). Treatment status was assessed based on responses “Yes” or “No” to the question: “Do you currently have a medical condition for which you are receiving treatment?” If the answer was “Yes”, the respondents selected the applicable category from the following categories; hypertension, heart disease, cancer, cerebrovascular disease, diabetes, respiratory disease, prostate disease, bone and joint disease, and others. Those who selected “others” self-reported the name of the diseases in the free-text field. Cognitive function was assessed based on the CADi2, which has been correlated and validated with the Mini-Mental State Examination [45].
Statistical analysis
For the basic attributes of the participants, the mean (standard deviation [SD]), median (1st − 3rd quartile), and percentage were calculated according to the distribution of the descriptive statistics. Additionally, effect sizes were calculated based on the characteristics of each group. All measured data of NFVA were evaluated and analyzed after the conversion to the logarithm of the minimum angle of resolution units from decimal values.
Continuous variables are presented as mean ± SD or median (1st − 3rd quartile), and categorical variables are expressed as percentages. Continuous variables were checked for distribution using histograms or scatterplots. For the basic attributes of the good and poor vision groups, the Student’s t-test or the Mann–Whitney U test was performed according to data distribution. A χ2 test was performed for the proportions. Spearman correlations were performed according to data distribution. A multivariate regression analysis was conducted to calculate the partial regression coefficient (β) and 95% confidence interval (95%CI) for the association between visual and physical functions. The dependent variable was physical function (grip strength, CS-30, OLSB, two-step test, and TUG), and the explanatory variables were NFVA (converted to binary using the median NFVA: good/poor) and covariates adjusted for age (continuous variable), sex (male/female), BMI (continuous variable), residential status (alone/other), work (yes/no), treatment (yes/no), and cognitive function (continuous variable).
In the crude model, univariate regression was performed using NFVA as the input. Model 1 was the crude model adjusted for age and sex. Model 2 was Model 1 further adjusted for BMI, residential status, work, and treatment. Multicollinearity was considered by calculating the variance inflation factor (VIF).
Only participants who consented to participate in the study were included; those with missing data on visual and physical function were excluded from statistical analyses. All statistical analyses were performed using IBM SPSS Statistics for Windows, version 27 (IBM Corp., Armonk, NY, USA). Statistical significance was defined as a two-sided p-value of < 0.05.
Results
After applying the inclusion and exclusion criteria, we finally included 86 (34, 39.5% men) participants with a mean (SD) age of 75.71 (5.52) years. The participants’ NFVA [median (1st − 3rd quartile)] was 0.512 (0.376–0.642). Two of the participants self-reported “cataract” in the free-text field for other diseases they were suffering from, and two self-reported “glaucoma”. A weak correlation was observed between NFVA and grip strength (ρ = -0.241, p = 0.02). The descriptive statistics, as presented in Table 1, indicate that the mean age tended to be higher in people with poor vision than in those with good vision. Other physical function test results showed that patients with good vision tended to perform better on physical function tests than those with poor vision. Particularly, participants with good vision had a higher grip strength (good vision group: 28.0 kg, poor vision group: 22.6 kg) and better visual acuity maintenance (good vision group: 0.83, poor vision group: 0.70) than those with poor vision.
Table 1.
Characteristics of the study participants
| Overall (n = 86) | Good (n = 43) | Poor (n = 43) | p | Effect size | |
|---|---|---|---|---|---|
| Age, years, mean (SD)† | 75.7 (5.5) | 74.7 (5.2) | 76.7 (5.7) | 0.10 | 0.38 |
| Sex male (%)* | 34 (39.5) | 19 (44.2) | 15 (34.9) | 0.38 | 0.12 |
| BMI§1, kg/m2, mean (SD)† | 23.5 (2.8) | 23.2 (2.4) | 23.9 (3.3) | 0.20 | 0.28 |
| Living alone, yes (%)* | 19 (22.1) | 8 (18.6) | 11 (25.6) | 0.60 | 0.17 |
| Employment, yes (%)* | 18 (20.9) | 8 (18.6) | 10 (23.3) | 0.60 | 0.12 |
| Treatment, yes (%)* | 69 (76.1) | 33 (76.7) | 36 (83.7) | 0.45 | 0.18 |
|
Grip strength, kg, median (1st − 3rd quartile)‡ |
25.5 (20.1–31.2) |
27.2 (21.9–32.8) |
22.6 (18.8–28.2) |
0.01 | 0.62 |
| Two-step test, mean (SD)† | 1.29 (0.15) | 1.32 (0.15) | 1.27 (0.16) | 0.06 | 0.36 |
|
OLSB§2, sec, median (1st − 3rd quartile)‡ |
14.0 (6.0–41.5) |
16.0 (11.5–50.0) |
9.0 (3.5–28.5) |
0.03 | 0.41 |
| TUG§3, sec, mean (SD)† | 6.8 (1.0) | 6.6 (0.9) | 7.1 (1.1) | 0.02 | 0.50 |
| CS-30§4, mean (SD)† | 19.0 (5.0) | 19.1 (5.8) | 19.1 (4.2) | 0.82 | 0.41 |
|
NVA§5 (logMAR) (1st − 3rd quartile)‡ |
0.154 (0.097–0.325) |
0.097 (0.046–0.155) |
0.301 (0.155–0.398) |
< 0.01 | 1.25 |
|
NFVA§6 (logMAR) (1st − 3rd quartile)‡ |
0.512 (0.376–0.642) |
0.380 (0.254–0.441) |
0.642 (0.571–0.721) |
< 0.01 | 2.59 |
†t-test, ‡Mann–Whitney U test, *Chi-squared test, SD, Standard Deviation
§1BMI: Body Mass Index
§2WOLSB: With eyes open, One-Leg Standing Balance
§3TUG: Timed Up and Go test
§4CS-30: 30-second Chair Stand test
§5NVA: Near Visual Acuity
§6NFVA: Near Functional Visual Acuity
Table 2 shows the results of the multivariate regression analysis of visual and physical functions. The association between NFVA and grip strength was β: 4.25 (95%CI: 1.25, 7.04) for participants with good visual function (crude model). After adjusting for age and sex covariates, the association between NFVA and grip strength was β: 3.85 (95%CI: 0.88, 6.83) for participants with good vision (Model 1). After adjusting for other covariates, the association between NFVA and grip strength was β: 3.54 (95%CI: 0.39, 6.70) for participants with good vision (Model 2). Regarding the association between NFVA and grip strength, the β tended to increase from the crude model to Model 2, and those with better vision tended to have higher grip strength after adjusting for all covariates. VIF was < 2, and multicollinearity was not a concern.
Table 2.
Results of multivariate regression analysis of NFVA and physical function
| Crude model | Model 1 | Model 2 | |||||||
|---|---|---|---|---|---|---|---|---|---|
| β | 95%CI | p-value | β | 95%CI | p-value | β | 95%CI | p-value | |
| A. Grip strength | |||||||||
| NFVA | 4.25 | 1.25, 7.04 | < 0.01 | 3.85 | 0.88, 6.83 | 0.01 | 3.54 | 0.39, 6.70 | 0.03 |
| Age | -0.06 | -0.33, 0.22 | 0.68 | -0,08 | -0.39, 0.22 | 0.60 | |||
| Sex | -1.94 | -4.94, 1.07 | 0.20 | -2.35 | -5.83, 1.13 | 0.18 | |||
| BMI | -0.40 | -0.96, 0.17 | 0.17 | ||||||
| Living alone | 0.84 | -2.85, 4.54 | 0.65 | ||||||
| Work | 2.46 | 1.26, 6.19 | 0.19 | ||||||
| Treatment | -0.02 | -3.87, 3.83 | 0.99 | ||||||
| Cognitive function | -0.39 | -1.92, 1.15 | 0.62 | ||||||
| B. CS-30 | |||||||||
| NFVA | 0.09 | -2.08, 2.27 | 0.93 | -0.43 | -2.62, 1.76 | 0.70 | -0.35 | -2.68, 1.97 | 0.76 |
| Age | -0.23 | -0.43, -0.03 | 0.02 | -0.22 | -0.44, 0.01 | 0.06 | |||
| Sex | -0.42 | -2.63, 1.79 | 0.71 | -0.20 | -2.77, 2.37 | 0.88 | |||
| BMI | -0.26 | -0.68, 0.16 | 0.22 | ||||||
| Living alone | 0.98 | -1.75, 3.71 | 0.48 | ||||||
| Work | 0.01 | -2.74, 2.76 | 1.00 | ||||||
| Treatment | 0.48 | -2.36, 3.32 | 0.74 | ||||||
| Cognitive function | 0.61 | -0.53, 1.74 | 0.29 | ||||||
| C. Two-step- test | |||||||||
| NFVA | 0.06 | -0.01, 0.12 | 0.10 | 0.03 | -0.03, 0.09 | 0.37 | 0.01 | -0.05, 0.08 | 0.69 |
| Age | -0.01 | -0.02, -0.01 | < 0.01 | -0.01 | -0.02, -0.01 | < 0.01 | |||
| Sex | -0.06 | -0.12, 0.01 | 0.09 | -0.07 | -0.14, 0.00 | 0.05 | |||
| BMI | -0.01 | -0.03, -0.003 | 0.02 | ||||||
| Living alone | 0.16 | -0.06, 0.09 | 0.68 | ||||||
| Work | 0.02 | -0.06, 0.10 | 0.60 | ||||||
| Treatment | -0.04 | -0.12, 0.04 | 0.31 | ||||||
| Cognitive function | -0.002 | -0.03, 0.03 | 0.91 | ||||||
| D. TUG | |||||||||
| NFVA | -0.51 | -0.94, -0.07 | 0.02 | -0.29 | -0.68, 0.10 | 0.15 | -0.23 | -0.65, 0.19 | 0.28 |
| Age | 0.93 | 0.06, 0.13 | < 0.01 | 0.10 | 0.06, 0.14 | < 0.01 | |||
| Sex | 0.27 | -0.13, 0.66 | 0.19 | 0.33 | -0.13, 0.79 | 0.16 | |||
| BMI | 0.05 | -0.02, 0.13 | 0.17 | ||||||
| Living alone | -0.06 | -0.55, 0.43 | 0.80 | ||||||
| Work | 0.09 | -0.40, 0.59 | 0.71 | ||||||
| Treatment | 0.16 | -0.34, 0.69 | 0.50 | ||||||
| Cognitive function | -0.02 | -0.23, 0.18 | 0.84 | ||||||
| E. OLSB | |||||||||
| NFVA | 9.04 | -2.86, 20.95 | 0.13 | 6.58 | -4.67, 17.83 | 0.25 | 6.13 | -5.09, 17.35 | 0.28 |
| Age | -1.53 | -2.52, -0.54 | < 0.01 | -1.42 | -2.40, -0.43 | 0.01 | |||
| Sex | -5.73 | -17.52, 6.06 | 0.33 | -6.67 | -18.82, 5.49 | 0.27 | |||
| BMI | -1.50 | -3.38, 0.39 | 0.12 | ||||||
| Living alone | 5.04 | -7.40, 17.48 | 0.42 | ||||||
| Work | 13.78 | -2.28, 29.84 | 0.09 | ||||||
| Treatment | -13.70 | -29.81, 2.41 | 0.09 | ||||||
| Cognitive function | 1.73 | -3.16, 6.63 | 0.48 | ||||||
Multiple regression analysis, β: Partial regression coefficient, CI: Confidence Interval,
Adjusted for age, sex, BMI, living arrangement, employment, treatment and cognitive function
NFVA: Near Functional Visual Acuity, BMI: Body Mass Index, TUG: Timed Up and Go test, CS-30: 30-second Chair Stand test, OLSB: with eyes open, One-Leg Standing Balance
The association between NFVA and TUG was β: -0.51 (95%CI: -0.94, -0.07) for participants with good visual function (crude model). However, after adjusting for other covariates, no significant association was found (p = 0.28). Furthermore, no significant associations were found for the other CS-30 (p = 0.76), two-step test (p = 0.69), and OLSB (p = 0.28) for all models from the crude model to Model 2.
Discussion
This study aimed to investigate the relationship between visual and physical function in older people living in the community using SVC. As a result, descriptive statistics revealed that participants with good vision tended to have better results in each physical function than those with poor vision. The results of the multivariate regression analysis also showed an association between quantitatively assessed visual function and grip strength, even after adjusting for the covariates of age, sex, BMI, residential status, work, treatment status, and cognitive function. Thus, this study suggests that, among community-dwelling older adults in Japan, those with good vision tend to have a higher grip strength.
Individuals with impaired visual function are more likely to experience impairments in daily living and motor function, such as reduced walking speed and decreased ADLs [26, 27, 28, 46, 47, 48, 49, 50, 51]. Additionally, studies based on self-administered questionnaires have suggested an association between impaired visual function and decreased grip strength, which may be associated with an increased risk of sarcopenia [29, 30]. In contrast, our study used quantitative measures and found that participants with good visual function tended to have higher grip strength, which is generally consistent with these earlier findings. Previously, studies have been conducted in Western countries, focusing on patients with ophthalmological diseases [26, 27, 28, 29, 30, 46, 47, 48, 49]. However, the differences in results between studies may be due to variations in the population studied, measurement methods, and the consideration of confounding factors.
Older adults with impaired visual function are at an increased risk of restricted physical functioning, significantly reduced engagement in leisure activities [52], decreased social participation [53], and an increased risk of falls [54]. Regarding physical activity, individuals with impaired visual function have been found to be 30% less moderately to vigorously physically active and spend more time sitting [55, 56, 57]. In this study, NFVA was associated with grip strength, which is considered a simplified indicator of whole-body muscle strength. Previous studies have reported that individuals with impaired visual function may have reduced physical activity, resulting in a concomitant decrease in whole-body muscle mass [29, 30, 58, 59, 60]. Particularly, for grip strength in older people, it has been shown that the decline in grip strength is more gradual with higher levels of physical activity [61]. Those with good vision may be more physically active as they can perform daily activities without restrictions. Additionally, since the hands are frequently used in daily life, it is plausible that the decline in grip strength may be more gradual than other physical functions. Furthermore, the possibility of measurement error should also be considered. Grip strength is widely recognized as a useful indicator of overall health. It has been reported that grip strength in the elderly declines with age, with an annual decline of 1.13 and 0.06 kg in men and women, respectively [61]. It has also been shown that a 5 kg decrease in grip strength is associated with a significant increase in cardiovascular risk (overall hazard ratio 5.98) [62]. Considering this, the result of 3.54 kg higher grip strength in those with good vision shown in our study is not only statistically significant but may also have clinical implications. In this study, we found no association between the parameters of visual and other physical functions. Age was identified to be significantly associated with physical function analyses. Previous studies have reported that loss of muscle mass, functional disability in ADLs, and sarcopenia risk tend to increase with age [63, 64, 65]. In this study, physical function tended to worsen with age, suggesting that age is related to these parameters. Furthermore, considering the effect size, approximately 180 participants might be required for parameters other than grip strength. Therefore, the impact of sample size should also be considered. Thus, large-scale, longitudinal studies are required to further consider these associations. We consider it important to promote physical activity to preserve and promote physical function, even in people with impaired visual function. According to the World Health Organization, physical activity results in health benefits even when the recommended levels are not attained [66]. Therefore, physical activity should be promoted among individuals with impaired visual function to ensure that they can maintain and improve their motor functions and lead independent lives. Previous studies have shown that various programs can promote physical activity in individuals with visual impairments, improving aspects such as lifestyle and motor functions [67, 68, 69, 70]. Moreover, low vision rehabilitation for those with impaired visual function may improve impaired visual function [71]. Cataracts and uncorrected refractive errors, which are major causes of visual impairment in older adults [19], can be effectively treated with surgery and glasses, respectively [72]. Interventions for visual impairment, such as cataract surgery, may be associated with improved physical function and sleep quality [46, 47, 48, 49]. Since this was a cross-sectional study and did not adequately consider cataracts, further large-scale studies are required to investigate the relevance.
Preventing the deterioration of visual function through early detection and intervention is critical, as living with poor vision is associated with many risks. Furthermore, physical activity should also be promoted to maintain and enhance ADLs and motor function and to improve quality of life and well-being.
A key strength of this study is its novelty in quantifying the relationship between visual and physical functions using administered assessments rather than self-reported measures in community-dwelling older adults. Our approach ensured the reliability of the results by avoiding recall bias and over- or underestimation by respondents.
Nevertheless, this study also had some limitations. First, this was a cross-sectional study, making it difficult to refer to causal relationships. Second, we only used NFVA to screen visual function and did not evaluate visual function at a distance, which might also influence ADLs. Moreover, we could not exclude the influence of unmeasured confounding factors. Particularly, the study considered confounding factors wherever possible; however, confounding factors such as socio-economic factors, physical activity levels, and certain comorbidity conditions were not adequately considered. These factors are likely to significantly impact the validity of this study. Therefore, we believe that these factors should be fully considered in future studies. Third, the sample size was small, as this study was conducted in one city in Japan in the suburbs of the Tokyo metropolitan area. Thus, this study’s results may not be generalizable to other populations. Furthermore, as multiple physical function outcomes were analyzed in this study, caution should be exercised in interpreting the results, given the potential risk of Type I error due to multiple comparisons and the wide CI.
In the future, large-scale longitudinal studies should be conducted to gain a deeper understanding of causal relationships and mechanisms. Furthermore, it is considered extremely important to examine the mediating factors involved in the association between visual and physical functions.
Conclusions
This study quantitatively demonstrated an association between assessed visual and physical functions in community-dwelling older adults in Japan. Particularly, an association was found between visual function and grip strength, suggesting that people with good visual function tended to have better grip strength. Grip strength is an indicator of overall muscle strength and health status, while visual function plays an important role in obtaining information in daily life. Therefore, maintaining and enhancing grip strength and visual function is important in leading a healthy and fulfilling life. Our results also suggested that using digital devices, such as SVCs, to assess visual acuity helps early detection of visual acuity decline.
Acknowledgements
The authors would like to thank Professors Masaki Takahashi, Yasumichi Arai, Hiroko Komatsu, Instructor Ami Ogawa, and Sachiko Masui. The authors would also like to express their gratitude to all the participants and staff involved in this study.
Abbreviations
- ADL
Activity of Daily Living
- BMI
Body Mass Index
- CI
Confidence Intervals
- CS-30
30-second Chair Stand test
- IADL
Instrumental Activity of Daily Living
- KGRI
Keio university Global Research Institute
- NFVA
Near Functional Visual Acuity
- NVA
Near Visual Acuity
- OLSB
with eyes open, One-Leg Standing Balance
- OR
Odds Ratios
- SD
Standard Deviation
- SVC
Smart Vision Check
- TUG
Timed Up and Go
- VMR
Visual Maintenance Ratio
Author contributions
KY and YO designed the study. YO, KN, MS, and KY coordinated the study. YO, TM, KT, KN, MS, and KY performed the data collection. YO and TM performed the data analysis. YO, TM, KN, and KY interpreted the data; TM and YO drafted the manuscript. All authors read and approved the final version of the manuscript.
Funding
This research was conducted with funds allocated by the KGRI, Keio University Global Research Institute and the IoT Healthy Life Consortium.
Data availability
The datasets generated and/or analyzed during the current study are not publicly available due to research funding restrictions but are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The study was approved by the Institutional Review Board of the Keio University School of Medicine (No. 20200165). All procedures of the study were conducted in accordance with the Declaration of Helsinki. We obtained written and verbal informed consent from all participants.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analyzed during the current study are not publicly available due to research funding restrictions but are available from the corresponding author on reasonable request.