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. 2020 Jul 22;23(2):153–159. doi: 10.1298/ptr.E10028

Association Between Social Network and Physical Function in Community-Dwelling Older Adults in Japan

Keigo IMAMURA 1, Naoto KAMIDE 2, Miki SAKAMOTO 3, Haruhiko SATO 2, Yoshitaka SHIBA 3, Atsuhiko MATSUNAGA 2
PMCID: PMC7814198  PMID: 33489653

Abstract

Objective: A poor social network and the decline of physical function are known to be critical risk factors for functional decline in older adults. The aim of this study was to investigate the relationships between social network and physical function in Japanese community-dwelling older adults. Methods: Participants were 339 adults aged 65 years or older (mean age : 73.0 years, women :70.2%), living independently in their communities. A self-reported questionnaire was used to assess social network on two different scales―the 6-item Lubben Social Network Scale (6LSNS) and frequency of contact with other people. Handgrip strength, knee extension strength, gait speed, Timed Up and Go Test (TUG) results, and 5-repetition chair stand test (CST) scores were used to determine physical function. A multiple regression analysis that adjusted for confounding factors was used to analyze the relationship between the social network scales and each physical function test. Results: According to the results of a multiple regression analysis, a high 6LSNS score was significantly associated with greater handgrip strength (B = 0.63, p = 0.03), faster CST (B = −0.23, p = 0.01), and faster TUG (B = −0.12, p = 0.03), and high frequency of contact was significantly associated with greater handgrip strength (B = 1.08, p = 0.01). Conclusions: Social network was associated with muscle strength and physical performance. Consequently, older adults with poor social networks require an assessment of physical function, since their physical functions have possibly deteriorated.

Keywords: cross-sectional study, functional decline, social network, Physical function, older adults

Although life expectancy continues to increase globally in developed countries, including Japan, there is a difference between life expectancy and a healthy life expectancy1). Healthy life expectancy is defined as the length of a person's life span that is not characterized by limitations in daily activities due to health problems2). In order to extend healthy life expectancy, it is important to prevent functional decline. Physical, cognitive/psychological, and social factors influence the functional capacity of older adults. Walking speed and handgrip strength, for example, are risk factors of functional decline that can also lead to adverse health events, such as hospitalization and mortality3,4). The results of the chair stand test (CST) and the Timed Up and Go Test (TUG) can indicate older adults' risk of falls and functional decline5,6). Depression and cognitive decline are also risk factors of functional decline7-9). Poor social participation and weak social networks, the structural aspects of various social relationships10), have also been associated with functional decline11-13).

It is likely that the physical, cognitive/psychological, and social factors of physical decline are interrelated. Previous studies have shown that social factors are associated with depression and the maintenance of cognitive function14,15). This suggests that social factors influence cognitive/psychological factors. It has also been hypothesized that a decline of social function precedes a decline of physical function16). Therefore, to clarify the hypothesis regarding the association between social and physical functions, an investigation analyzing the influence of cognitive/psychological factors is indispensable. However, few studies have considered the relationship between the adjustment of social and physical factors, after adjusting for the effects of both cognitive/psychological factors and other potential risk factors. This cross-sectional study investigated the relationship between social factors and physical function to indicate the ways in which social factors affect both physical and cognitive/psychological factors. Investigating this relationship could show that social factors can be used to evaluate the functional decline of older adults.

Methods

Participants

Participants in this cross-sectional study included 339 older adults, aged 65 years or older, from Sagamihara city in Kanagawa Prefecture, living independently in their community. We used community newsletters to recruit participants. The inclusion criteria were as follows: older people who were able to perform activities of daily living (ADLs) independently and could independently walk to the location of the research center for this study. Recruitment interviews were used to confirm inclusion criteria. Individuals who did not have a disability certification through long-term care insurance were considered independent and capable of performing ADLs. Participants suspected of having dementia, based on interviews with experienced researchers, were excluded from this study.

The present study was approved by the Institutional Review Board of the School of Allied Health Sciences at Kitasato University (approval number 2016-G021B). Informed consent was obtained from all individual participants.

Social Network

In this study, we measured social network to assess social factors. We used a previously developed assessment scale, with validity and reliability, to measure social network17-19). Previous studies have reported that the type of social network can influence differentially disability and mortality20-23); therefore, in the present study, we used two types of scales to assess social network. First, we assessed participants' six-item Lubben social network scores (6LSNS)18). Several cross-national and cross-cultural validation studies have demonstrated that the 6LSNS can be used to evaluate social networks among community-dwelling older adults10,17). The 6LSNS measures the size of participants' active and intimate networks of family and friends, with whom the participants could talk or upon whom they could call for help. Each 6LSNS question was scored from 0 to 5 points. The total score is an equally weighted sum of these six questions, with scores ranging from 0 to 30 points. Higher scores indicate a larger social network. According to a previous study, we categorized respondents with scores of <12 points as being at risk of social isolation17).

We also assessed the participants' frequency of contact with others19) using an additional social network scale. The frequency of contact with others was measured by: (i) frequency of face-to-face contact with non-co-resident family and relatives; (ii) frequency of face-to-face contact with friends and neighbors; (iii) frequency of non-face-to-face contact (i.e., via telephone, email, letters) with non-co-resident family and relatives; and (iv) frequency of face-to-face contact with friends and neighbors. An 8-point Likert scale was used to measure the frequency of face-to-face and non-face-to-face contact as follows: (1) every day (more than nine times a week); (2) once a day (fewer than nine times a week); (3) fewer than six times a week; (4) fewer than four times a week; (5) fewer than three times a week; (6) less than twice a week; (7) less than once a week; or (8) less than once a month. In a previous study19), social isolation status was defined as engagement in every type of social contact less than once a week, and this definition predicted inverse health outcomes. We similarly determined that respondents who engaged in social interaction less than once a week were experiencing social isolation due to their lack of frequent contact with others.

Physical Function Measurements

To evaluate participants' physical function, we measured 5 meter (m) walking time at a comfortable pace, 5 m walking time at maximum effort, CST24), grip strength, knee extensor strength, and scores on the TUG25). To assess walking time, a stopwatch was used to measure the time at which participants passed 5 m on a 9 m walkway with 2 m acceleration and 2 m deceleration zones (ALBA W072; Seiko Watch Corporation, Tokyo, Japan). For CST, a stopwatch was used to measure how long it took the participant to stand up from a sitting position and sit down with arms crossed in front of chest five times as quickly as possible (ALBA W072; Seiko Watch Corporation, Tokyo, Japan). A Smedley-type dynamometer was used to measure the handgrip strength of each participant's dominant hand (T.K.K.5401, TAKEI Scientific Instruments Co., Ltd., Niigata, Japan). A handheld dynamometer was used to measure the isometric contraction of knee extensor strength in the right leg (μ-Tas F-1, Anima Co., Tokyo, Japan), while the participant was in a sitting position with knee and hip joints flexed at 90 degrees. For the TUG, researchers instructed the participants to stand up from a chair without hand support, walk 3 m as quickly as possible, turn around, walk back, and then sit down again26). A stopwatch was used to measure the time that the participant required to complete this task (ALBA W072; Seiko Watch Corporation). All measurements were measured twice, and the best value from both measurements were used for analysis.

Measurements for Confounding Factors

The age, sex, body mass index, and medical history of participants were recorded as potential confounding factors. Self-reported questionnaires were used to obtain information on comorbidity in participants (hypertension, cerebrovascular disorder, heart disease, diabetes mellitus, liver disease, kidney disease, respiratory disease and other disease), and medications. The Tokyo Metropolitan Institute of Gerontology Index of Competence (TMIG-IC)30) was used to assess functional capacity greater than ADL. Scores on the TMIG-IC range from 0 to 13 points, with a higher score indicating better function. To assess psychological factors, we evaluated participants' depressive symptoms and self-rated health (SRH). Depressive symptoms were measured using the 5-item version of the Geriatric Depression Scale (5-GDS)27). Scores on the 5-GDS ranged from 0 to 5 points. Participants with two points or more on the 5-GDS were identified as having depressive symptoms, while those with zero or one point did not have depressive symptoms27). SRH was measured using a 4-point Likert scale as follows: (1) excellent, (2) good, (3) fair, and (4) poor. Responses were transformed into dichotomous variables: excellent/good responses = good, and fair/poor responses = poor28). To assess cognitive function, participants used a tablet to complete part A of the Trail Making Test, the reliability and validity of which has been confirmed29).

Statistical Analyses

The participants were classified into two groups according to their 6LSNS score and their frequency of contact with others: no social isolation (6LSNS) and social isolation (6LSNS); and no social isolation (frequency of contact) and social isolation (frequency of contact). The Shapiro-Wilk test was used to analyze variables that had non-normal distributions. The unpaired Student t-test, Mann-Whitney U-test, and chi-square test were used to compare participant characteristics, physical function, psychological factors, and cognitive function. To investigate the association between social isolation (6LSNS and frequency of contact) and physical function, we conducted a multiple regression analysis to adjust for confounding factors. In the multiple regression analysis, each physical function (usual/max walking speed, CST, handgrip strength, knee extensor strength, and TUG) was set as the dependent variable, and each social isolation score was set as the independent variable. Model 1 of the multiple regression analysis was adjusted for age, sex, and BMI. In Model 2, self-rated health, medications, comorbidities, depression, TMIG-IC score, and Trail Making Test (TMT) A were added.

JMPⓇ Pro version 13.2 (SAS Institute Inc., Cary, NC) and R version 3.5.2 (R Foundation for Statistical Computing, Vienna, Austria) were used to analyze the data, with the level of statistical significance set at 5%.

Results

Participants included 238 (70.2%) women with 217 (64.0%) under 75 years of age. Regarding functional capacity, 142 (41.9%) participants had the highest possible score (13 points) on the TMIG-IC. The mean score for 6LSNS was 15.9 ± 5.0 and, based on 6LSNS scores (<12 points), 18.6% of participants experienced social isolation. The mean frequency of contact with others was 25.4 ± 16.8 times/month and, based on the frequency of contact with others, 8.0% of participants experienced social isolation (Table 1). Spearman's rank correlation coefficient test between 6LSNS and frequency of contact yielded a correlation coefficient of 0.38 (p < 0.001), and the kappa coefficient between the two social isolation categories was 0.25 (p < 0.001). Participants who experienced social isolation (6LSNS) had a lower TMIG-IC score and greater prevalence of depressive symptoms compared to participants who were not socially isolated. Participants who experienced social isolation (frequency of contact) had a lower TMIG-IC score than participants who were not socially isolated.

Table 1.

Summary of participants' characteristics

Participants (n=339) Mean (± SD)
aTMIG-IC: Tokyo Metropolitan Institute of Gerontology Index of Competence
Age, years 73.0 (4.8)
Body Mass Index (kg/m2) 22.2 (2.9)
Number of medications 1.9 (2.1)
Number of comorbidities 1.1 (1.0)
aTMIG-IC score 11.9 (1.3)
Trail Making Test-A (sec) 54.9 (16.2)
5-repetition chair stand test (sec) 5.9 (1.3)
Timed Up and Go Test (sec) 5.6 (0.9)
Handgrip strength (kg) 26.2 (6.6)
Knee extensor strength (kgf) 28.8 (9.6)
Usual walking speed (m/sec) 1.6 (0.2)
Maximum walking speed (m/sec) 2.0 (0.3)
Lubben Social Network Scale-6 (points) 15.9 (5.0)
Frequency of contact with others (times/month) 25.4 (16.8)
Participants (n=339) Number (%)
Women 238 (70.2)
Self-rated health (poor) 15 (4.4)
Depressive symptoms (yes) 41 (12.1)
Hypertension 115 (33.9)
Cerebrovascular disorder 12 (3.5)
Heart disease 27 (8.0)
Diabetes mellitus 32 (9.4)
Liver disease 7 (2.1)
Kidney disease 16 (4.7)
Respiratory disease 22 (6.5)
Other 51 (15.0)
Social isolation (6LSNS) 63 (18.6)
Social isolation (frequency of contact) 27 (8.0)
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The results of social isolation categorized by 6LSNS scores showed that participants with social isolation had a significantly slower CST (6.4 sec vs 5.8 sec, p<0.01) and TUG (5.8 sec vs 5.5 sec, p=0.013) than those without social isolation. Handgrip strength (24.9 kg vs 26.5 kg, p= 0.081) and knee extensor strength (27.0 kgf vs 29.3 kgf, p = 0.099) were lower in social isolation. Furthermore, based on the results of social isolation defined by frequency of contact, participants with social isolation had a significantly slower CST (6.5 sec vs 5.9 sec, p=0.028) than those without social isolation; however there were no significant differences between the presence and absence of social isolation in other physical functions.

Table 2 shows the results of the multiple regression analysis of the relationship between each of the two social isolation types and each physical function. In Model 1, which was adjusted for age, sex, and body mass index, social isolation (6LSNS) was significantly associated with CST, TUG, and handgrip strength. In Model 2, which was adjusted for all potential confounding factors, social isolation (6LSNS) was significantly associated with CST, TUG, and handgrip. However, gait speed (comfortable/maximum) and knee extensor strength were not significantly associated with social isolation. In both Model 1 and Model 2, social isolation (frequency of contact) was associated only with handgrip strength.

Table 2.

Association between social network and physical function using multiple linear regression

Dependent variables Social isolation (6LSNS) Social isolation (frequency of contact)
Model 1c Model 2d Model 1c Model 2d
Ba SEb p value Ba SEb p value Ba SEb p value Ba SEb p value
Ba: Unstandardized coefficient, SEb: standard error
Model 1c: adjusted for age, sex, body mass index
Model 2d: adjusted for Model 1 + self-rated health, medications, comorbidities, depression, TMIG-IC score, TMT-A
5-repetition chair stand test (sec) -0.27 0.09 0.003 -0.23 0.09 0.013 -0.22 0.13 0.084 -0.23 0.13 0.076
Timed Up and Go Test (sec) -0.13 0.05 0.019 -0.12 0.06 0.031 -0.08 0.08 0.293 -0.07 0.08 0.358
Handgrip strength (kg) 0.70 0.28 0.011 0.63 0.29 0.032 0.87 0.40 0.030 1.08 0.41 0.009
Knee extensor strength (kg) 0.89 0.60 0.139 0.62 0.64 0.330 -0.06 0.87 0.944 0.24 0.91 0.790
Usual walking speed (m/sec) 0.01 0.01 0.370 <0.01 0.02 0.864 <0.01 0.02 0.918 <-0.01 0.02 0.806
Maximum walking speed (m/sec) 0.01 0.02 0.546 <0.01 0.02 0.898 0.02 0.03 0.461 0.02 0.03 0.579
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Discussion

In the present study, we sought to clarify the relationship between social network and physical function in community-dwelling older adults. The mean total score for 6LSNS in this study was 15.9 points, and based on 6LSNS, the prevalence of social isolation was 18.6%. In previous studies, the mean score of 6LSNS ranged between 12.5 and 17.9 points, and the prevalence of social isolation ranged from 11% to 20%10,17,18,31). In the present study, the prevalence of social isolation based on frequency of contact was 8.0%. A previous study that aimed at clarifying the relationship between health risk and social isolation in community-dwelling older adults reported that the prevalence of social isolation was 15.8%19). Compared to the previous study, participants in the present study tended to interact more frequently with specific others19), but there was a similar degree of social isolation (as measured by the 6LSNS) in both studies10,17,18,31).

Social isolation (6LSNS) was associated with CST, TUG, and handgrip strength in the present study. Social isolation (frequency of contact) was also associated with handgrip strength. This relationship was significant even after we adjusted for psychological factors, such as self-rated health, depression, functioning, and cognitive function. In this study, both social network types were significantly associated with handgrip strength. Handgrip strength is widely used as an index to diagnose frailty and sarcopenia32-34), and it is associated with death and functional decline3,35). A previous study reported that a lower handgrip strength was correlated with poor social health36). In addition, handgrip strength was a more useful predictor of a decline in higher-level competence of community-dwelling older adults than walking speed35). It is believed that social network reflected higher-level competence in community-dwelling older adults; thus, the results of this study showed that handgrip strength was closely related to social isolation. Further, TUG, which included tasks such as standing and turning around in addition to walking, is believed to be a relatively difficult performance test. Therefore, TUG was less likely to have a ceiling effect in comparison to walking speed and may have a tendency to be related to higher-level competence, such as social network. CST was correlated with lower limb muscle strength24). As discussed, handgrip strength was closely related to social network, thus suggesting that muscle strength was one of the physical functions associated with social network. Therefore, the relationship between muscle strength and social network reflect the correlation between CST and social isolation.

On the other hand, the reason walking speed was not associated with social network was because the influence of the ceiling effect. In fact, the participants of this study performed independently in ADL and had a high functional capacity (41.9% had a full-score in TMIG-IC score). Therefore, walking speed was not an appropriate performance test for our study regarding difficulty of task. Regarding knee extensor strength, a previous study reported that measurement errors were larger than grip strength37). Further, larger measurement errors occurred, because the belt was not used to fix the trunk and thigh during measurements of the knee extensor strength in this study. Moreover, knee extensor strength was not converted into torque. It was possible that these factors influenced the relationship between knee extensor strength and social network in this study.

The two social network scales used in the present study showed different associations between social isolation and physical function. The 6LSNS scale indicated that social isolation was associated with some physical functions; however, social isolation, based on frequency of contact, was associated only with handgrip strength. This suggests that the type of social network can influence the associations between social isolation and physical function. Previous studies reported that the influence of social network on functional capacity differed according to the type of social network20-23). Indeed, the results of the correlation coefficient and kappa coefficient showed that there was a low degree of concordance between the 6LSNS and frequency of contact. These results suggest that the two social network scales used in the present study reflected specifics of each type of social network. To assess frequency of contact, this study used non-face-to-face contact, such as telephone, email, and letters; however, the 6LSNS had some items that did not clearly define whether they were face-to-face or non-face-to-face contact. The maintenance of non-face-to-face contact with other people does not always require high physical function; this might explain why, in the present study, frequency of contact was related only to handgrip strength. However, we could not clarify why social isolation (6LSNS) was strongly associated with physical function but frequency of contact was associated with only handgrip strength. Further research is needed to clarify this point.

Our study has some limitations. Participants in this study had good physical function, and older adults who have frailty, sarcopenia, or require long-term care would not yield the same results. Further, this study did not obtain socioeconomic data, such as income and education level, and therefore, it could not evaluate the impact of income or education level on social isolation or physical function. A participant with cognitive decline, such as mild cognitive impairment, could have been included in this study, because the exclusion criteria was not set using objective cognitive function tests. Further, cognitive function was assessed only using TMT-A because of feasibility of the data collection for this study; thus, estimation of the influence of cognitive function may be insufficient. Furthermore, not only social network but also social support was important with regards to social function for older adults. We determined the relationship between social network and physical functions in this study; however, the relationship between social support and physical functions could not be clarified. Finally, this was a cross-sectional study, which cannot show causality. Future studies should investigate the relationship between social network and physical function longitudinally.

This study clarified the relationship between social network and physical function in community-dwelling older adults with good physical function. It was hypothesized that social factors would decline earlier than physical function16,38). The results of the present study confirmed the relationship between social network and physical function. Older adults with poor social networks require an assessment of physical function, since their physical functions have possibly deteriorated. This study suggests that older adults with poor social networks should engage in physical exercise and activity to avoid further decline. Furthermore, older adults with poor social networks are also more likely not to participate in a center or a laboratory based physical function assessment. Therefore, it was suggested that development and establishment of methodology for outreach-type or home-based physical function assessment is necessary.

Conflict of Interest

The authors have no conflicts of interest to disclose.

Acknowledgments

This study was supported by Japan Society for the Promotion of Science KAKENHI (Grant No. 16K21348). The funders played no role in the design, methods, subject recruitment, data collection, analysis, or preparation of the paper.

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