Travel and Social Isolation of People With Spinal Cord Injury

Abstract

Background

People with spinal cord injury (SCI) are at high risk for social isolation because they face barriers to social participation owing to limitations in physical functioning, secondary health conditions, and environmental barriers. Research has shown that social isolation, an objective lack of contact with others, can lead to loneliness, mental health issues, and low life satisfaction. Leisure travel, which involves interactions with others, may have the potential to reduce social isolation.

Objectives

To explore the association of travel participation with social isolation among people with SCI.

Methods

A total of 11,091 cases from 2016-2021 in the SCI Model Systems database were included in the study. Participants were categorized into low and high social isolation groups. A hierarchical logistic regression was performed with social isolation as the dependent variable and travel groups as the independent variable, while controlling for age, family income, and health conditions.

Results

Travel participation is negatively associated with social isolation. People with SCI who traveled for one to two nights (odd ratio [OR] 0.52, 95% CI 0.40-0.67), three to four nights (OR 0.56, 95% CI 0.43-0.72), or more than five nights (OR 0.41, 95% CI 0.35-0.49) in the past 12 months are less likely to be socially isolated compared to those who have not traveled in the past 12 months.

Conclusion

Travel participation may help reduce the social isolation of individuals with SCI. Therapists and rehabilitation professionals should design effective travel training programs that encourage patients with SCI to travel more often.

Introduction

Healthy social relationships contribute greatly to the optimal health and well-being of individuals.¹ Social isolation, on the other hand, may lead to loneliness, mental health issues, and low life satisfaction.^2,3 Social isolation, representing an objective lack of contact with others and a small or nonexistent social network,⁴ has been associated with negative health outcomes such as depression, poor cognitive functioning, reduced physical functioning, cardiovascular disease, poor self-rated health,⁵ elevated cortisol level, and blood pressure.⁶

Given these adverse effects of social isolation, and the US Surgeon General’s advisory of a “National Strategy to Advance Social Connection,” there is increasing call for research devoted to understanding the risk factors for social isolation.⁷ It is thus of great importance to identify ways to reduce social isolation for more vulnerable people in society, such as people with spinal cord injury⁶ (SCI). People with SCI are at higher risk for social isolation as they face barriers to social participation owing to limitations in physical functioning, secondary health conditions, and environmental barriers.^2,3 In addition, disability, caused by life-changing events such as SCI, may lead individuals to engage in “self-alienation” and consequently, increase their risk for social isolation.⁸ Research is therefore needed to identify effective methods, programs, and activities for people living with SCI to increase their social participation, encourage social connectedness, and enhance their physical and mental well-being. This study is intended to investigate the association between travel participation and social isolation in this population.

Previous studies have found that physical activities,⁹ community engagement programs,¹⁰ education and social activity interventions,¹¹ and certain group and leisure activities¹² can alleviate the adverse health effects of social isolation and improve quality of life.¹³ Leisure travel as an activity in the leisure and recreational dimension of life has long been recognized as an effective means of socialization.^14-16 Travel activity generally requires various social interactions that bolster social connectedness.¹⁷ For example, a few qualitative studies have found that bus travel within and outside home communities, short trips to visit places with social contacts, and shopping trips can prevent social isolation and loneliness among older adults.^18,19 However, empirical evidence of the association between travel participation and social isolation for people with SCI is still limited.

Travel can be challenging for people after SCI, and leisure travel as a volitional activity may not have been considered accessible for people with SCI. Research on the travel experience of people with SCI is scarce. Limited research that exists on the travel participation of people with SCI has focused on identifying barriers,^20,21 and the potential benefits of leisure travel are rarely studied. Although a lowered level of social participation among individuals after SCI is well documented in the literature,^22,23 most studies have focused on investigating approaches for social and community integration after SCI,^24,25 whereas social isolation is less discussed. To bridge this gap in the literature, this study aims to explore the association of leisure travel with social isolation among individuals living with SCI. Specifically, the study has two objectives: (1) to investigate the demographic characteristics and health conditions of individuals with SCI with high social isolation and those with low social isolation, and (2) to examine whether the level of leisure travel participation of people with SCI is associated with social isolation levels.

Methods

Study population and data source

We utilized secondary data collected by the SCI Model Systems (SCIMS) in the United States. There are 14 SCI Model Systems in the nation, which are sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. The SCIMS collects patients’ baseline and follow-up data on year 1 of their injury and every fifth anniversary thereafter. Data collected are managed by the National Spinal Cord Injury Statistical Center (NSCISC) housed in the University of Alabama-Birmingham’s Department of Physical Medicine and Rehabilitation.²⁶ This study includes the most recent wave of SCIMS data where 15,167 cases were captured using the follow-up anniversary survey from 2016 to 2021. As the same survey was used in the data collection from 2016 to 2021, we treated the data using a cross-sectional approach.

Case inclusion criteria for the current investigation included individuals aged 18 years or older at the time of injury, those who reported their social isolation status and travel participation, and those with less than 5% missing data. Of the 15,167 cases collected from 2016 to 2021, 4076 had missing data on these key variables or more than 5% overall missing data, which were not included in subsequent analysis. The final dataset consists of 11,091 valid cases.

Measures

Outcome variable: social isolation

There is no standard measure for social isolation, but researchers agree that frequency of social contact is an important indicator.²⁷ Based on the Steptoe Social Isolation Index,²⁸ we used the marital status item and the following four items used in the Craig Handicap Assessment and Reporting Technique (CHART)²⁹: “living with spouse/significant other,” “business contacts,” “contacts with friends,” and “conversation with strangers.” Social isolation was transformed and categorized into a binary variable. Participants who were unmarried (including participants who were single, separated, divorced, or widowed) were given a score of 1, while those who were married or living with significant others were given a score of 0. Similarly, participants who were living alone were scored 1, while those living with a spouse, relatives, or roommates were given a score of 0. Participants who had no contact with business or organizational associates were given a score of 1, whereas those who had at least one visit or contact with such associates per month were given a score of 0. Similarly, participants who had no contact with friends or family were given a score of 1, and those who had such contact were given a score of 0. Finally, participants who had not initiated a conversation with a stranger in the past month were given a score of 1, and those who had done so were given a score of 0. The resulting social isolation values ranged from 0 to 5, with higher values (3-5) categorized into the high social isolation group and lower values (0-2) categorized into the low social isolation group based on previous research.²⁸

Exposure variable: travel participation

Travel participation was measured by the question in CHART: “In the last year, how many nights have you spent away from home? Nights spent away from home include on vacation, visiting family, holiday stays, etc. but do not include nights at the Hospital.” Response options included “none,” “1-2 nights,” “3-4 nights,” or “5 or more nights.” As people with SCI often face many barriers to travel, where their average travel frequency is generally low,³⁰ we kept all original response options from the SCIMS dataset to capture the maximum variance in travel participation levels among individual with SCI.²⁶

Covariates

Covariates were selected based on previous studies showing associations of these variables with the exposure and outcomes of interest. The covariates include age, gender, family income, and current health issues (diabetes, hypertension, hyperlipidemia, arthritis, and depression). Participants were asked to report their gender (male or female), age, and annual family income level (1 = less than $25,000, 2 = $25,000-49,999, 3 = $50,000-74,999, and 4 = $75,000 or more). Participants were also asked whether they currently have the following health issues (0 = no, 1 = yes): diabetes, hypertension, hyperlipidemia, and arthritis. Depression is the sum of responses from the Patient Health Questionnaire that quantified depression symptoms by asking how often (0 = not at all, 1 = several days, 2 = more than half the days, 3 = nearly every day) participants have been bothered by the following in the past 2 weeks: “little interest or pleasure,” “feeling down, depressed or hopeless,” “trouble sleeping,” “feeling tired,” “poor appetite or overeating,” “feeling bad about yourself,” “trouble concentrating,” “moving slowly,” and “hurting yourself.” The total score of depression ranges from 0 to 27, with higher scores indicating more severe depression symptoms.

Analysis

All data cleaning and data analyses were conducted using IBM SPSS 28. Characteristics of the study population were summarized with descriptive statistics. Age and depression scores were compared between the high social isolation group and low social isolation group using independent t tests (two-tailed). Travel participation, demographic characteristics, and health conditions were compared between the high social isolation group and low social isolation group using Pearson’s chi-square analysis. The frequencies with percentages [N (%)] of the exposure and covariate variables were also presented for the two groups.

Next, to examine the association between travel participation and the outcome variable, hierarchical logistic regression was conducted with travel participation as the independent variable, while adjusting the confounders of demographic variables and health conditions. To assess the effect of travel participation on social isolation status, we utilized a hierarchical logistical regression method that controls for other variables including age, family income, and health conditions. Parameter estimates, odds ratios (OR), and their 95% CI were obtained. Variables with p values ≤ .05 in the current analysis were considered as significant factors associated with the outcome variable.

Results

We defined participants with a social isolation score of 0-2 as having low social isolation (N₁ = 9227, 83.2%) and those with a score of 3-5 as having high social isolation (N₂ = 1864, 16.8%). Table 1 summarizes the sample characteristics in relation to their social isolation status. The average age of the sample was around 50 years old (SD = 15.28, range, 17 to 97 years). Independent t test results suggest that age was significantly different between the individuals with SCI in the high social isolation and low social isolation (t = 9.13, p < .001). The average depression score for the whole sample was 4.82 (SD = 4.93), whereas the average score for highly isolated participants with SCI was 6.04 (SD = 5.91) and the average score for low isolation participants was 4.58 (SD = 4.68). The independent t test of depression and social isolation shows the severity of depression was different between the two groups (t = 160.30, p < .001), suggesting participants in the high social isolation group had significantly more depression symptoms.

Table 1.

Sample characteristics in relation to social isolation

	Participants
	Total (N = 11,091)	Social Isolation
		Low social isolation (n = 9927)	High social isolation (n = 1864)	χ2
Age, years, mean (SD)	49.70 (15.28)	49.10 (15.29)	52.63 (14.88)
Travel participation				χ2(3) = 724.68*
None	4740, 42.7%	3430, 37.2%	1310, 70.3%
1-2 nights	819, 7.4%	704, 7.6%	115, 6.2%
3-4 nights	977, 8.8%	864, 9.4%	113, 6.1%
5 or more nights	4555, 41.1%	4229, 45.8%	326, 17.5%
Gender				χ2(1) = 0.08
Male	8683, 78.3%	7219, 78.2%	1464, 78.5%
Female	2408, 21.7%	2008, 21.8%	400, 21.5%
Income				χ2(3) = 941.22*
Less than $25,000	3607, 32.5%	2505, 27.1%	1102, 59.1%
$25,000-49,999	2167, 19.5%	1901, 20.6%	266, 14.3%
$50,000-74,999	1254, 11.3%	1180, 12.8%	74, 4.0%
$75,000 or more	2328, 21.0%	2246, 24.3%	82, 4.4%
Marital status				χ2(1) = 1131.93*
Married/living with significant others	4495, 40.5%	4390, 47.6%	105, 5.6%
Unmarried	6596, 59.5%	4837, 52.4%	1759, 94.4%
Health issues
Diabetes	1239, 11.2%	942, 10.2%	297, 15.9%	χ2(1) = 52.53*
Hypertension	2160, 19.5%	1694, 18.4%	466, 25.0%	χ2(1) = 46.25*
Hyperlipidemia	1629, 14.7%	1331, 14.4%	298, 16.0%	χ2(1) = 4.07*
Arthritis	2883, 26.0%	2293, 24.9%	590, 31.7%	χ2(1) = 41.53*
Depression, mean (SD)	4.82 (5.91)	4.58 (4.68)	6.04 (5.91)

Note: Data are presented as the number (percentage of participants), unless otherwise indicated.

^*p < .001.

Results of the chi-square test also show significant differences between the two groups in their travel participation, income, marital status, and health issues including diabetes, hypertension, and hyperlipidemia (as shown in Table 1). Compared to those in the low social isolation group, individuals with SCI in the high social isolation group were more likely to be nontravelers, have less family income, and be single. The two groups did not differ in terms of gender.

The hierarchical logistic regression was conducted next, with social isolation as the dependent variable and travel participation as the categorical independent variable, while controlling for age, family income, and health issues of diabetes, hypertension, hyperlipidemia, arthritis, and depression. Prior to the analysis, the assumptions for logistic regression were assessed. Multicollinearity was evaluated through Spearman correlation analysis among the independent variables (including the covariates). The correlation coefficients ranged from −.114 to .355, all of which fell below the threshold of .70,³¹ indicating no high intercorrelations were found among the independent variables. A scatter plot was examined to identify potential outliers for the continuous independent variables, age and depression, and no extreme outliers were observed. The linearity between the continuous predictors (age and depression variables) and their logits was tested using the Box-Tidwell test. Both interaction terms (age and natural log of age, depression and natural log of depression) did not demonstrate statistical significance. Consequently, the dataset satisfied the assumptions for logistic regression. Age was added as the first step of the hierarchical logistic regression (χ² = 68.10, p < .001), family income was added as step 2 (χ² =921.98, p < .001), health issues was added as step 3 (χ² = 45.67, p < .001), and travel participation was added as step 4 (χ² = 1293.127, p < .001). As shown in Table 2, the final model (R²_Nagelkerke= 0.239) explains about 23.9% of the variance in social isolation.

Table 2.

Hierarchical logistic regression

Variables	B	SE	Wald	df	p	ORa (95% CI)	χ2	df a	Rn2b	Classification percentage
Step 1							68.10*	1	0.02	84.30
Age	0.02	0.00	66.76	1.00	0.00	1.02 (1.01-1.02)
Step 2							921.98*	3	0.21	84.20
Age	0.03	0.00	143.75	1.00	0.00	1.03 (1.02-1.03)
Family income (ref: <$25,000)			676.28	3.00	0.00
$25,000-49,999	-1.26	0.08	222.62	1.00	0.00	0.28 (0.24-0.34)
$50,000-74,999	-2.17	0.15	221.71	1.00	0.00	0.11 (0.09-0.15)
$75,000+	-2.71	0.13	411.19	1.00	0.00	0.07 (0.05-0.09)
Step 3							45.67*	5	0.21	84.30
Age	0.03	0.00	104.51	1.00	0.00	1.03 (1.02-1.03)
Family income (ref: < $25,000)			634.76	3.00	0.00
$25,000-49,999	-1.23	0.08	211.18	1.00	0.00	0.29 (0.25-0.34)
$50,000-74,999	-2.13	0.15	211.91	1.00	0.00	0.12 (0.09-0.16)
$75,000+	-2.64	0.13	384.16	1.00	0.00	0.07 (0.06-0.09)
Diabetes (ref: no diabetes)	0.14	0.10	1.84	1.00	0.17	1.15 (0.94-1.40)
Hypertension (ref: no hypertension)	0.14	0.08	3.04	1.00	0.08	1.15 (0.98-1.35)
Hyperlipidemia (ref: no hyperlipidemia)	-0.14	0.09	2.27	1.00	0.13	0.87 (0.73-1.04)
Arthritis (ref: no arthritis)	0.09	0.07	1.33	1.00	0.25	1.09 (0.94-1.26)
Depression	0.04	0.01	34.79	1.00	0.00	1.04 (1.03-1.05)
Step 4							129.127*	3	0.24	84.40
Age	0.02	0.00	67.61	1.00	0.00	1.02 (1.02-1.03)
Family income (ref: <$25,000)			469.97	3.00	0.00
$25,000-49,999	-1.12	0.09	169.59	1.00	0.00	0.33 (0.28-0.39)
$50,000-74,999	-1.90	0.15	162.61	1.00	0.00	0.15 (0.11-0.20)
$75,000+	-2.32	0.14	283.82	1.00	0.00	0.10 (0.07-0.13)
Diabetes (ref: no diabetes)	0.11	0.10	1.24	1.00	0.27	1.12(0.92-1.37)
Hypertension (ref: no hypertension)	0.16	0.08	3.82	1.00	0.051	1.18 (1.00-1.38)
Hyperlipidemia (ref: no hyperlipidemia)	-0.16	0.09	3.04	1.00	0.08	0.85 (0.71-1.02)
Arthritis (ref: no arthritis)	0.09	0.08	1.42	1.00	0.23	1.09 (0.94-1.27)
Depression	0.03	0.01	30.31	1.00	0.00	1.04 (1.02-1.05)
Travel participation (ref: None)			124.34	3.00	0.00
1-2 nights	-0.65	0.13	24.65	1.00	0.00	0.52 (0.40-0.67)
3-4 nights	-0.58	0.13	19.42	1.00	0.00	0.56 (0.43-0.72)
5+ nights	-0.89	0.09	108.92	1.00	0.00	0.41 (0.35-.049)
Constant	-1.89	0.14	172.95	1.00	0.00	0.15

Note: Outcome variable: social isolation with 1 indicating high social isolation and 0 indicating low social isolation. B coefficients and ORs that are statistically significant (p <.001) are presented in bold. df = degree of freedom; OR = odds ratio; ref: = reference level; SE = standard error.

^aDegree of freedom for the model fit.

^bNagelkerke R square.

^*p < .001.

Based on the pseudo-r squared and the chi-square tests of each step with added variable, after adding the travel participation variable, the model demonstrated significant change and can explain moderate amount of variance in the dataset. The final model shows that comparing people who had not traveled in the past 12 months, the level of people’s travel participation was negatively associated with the levels of social isolation, adjusting for age, family income, and health issues. Compared to nontravel participants, participants who had traveled one to two nights were less likely to live in isolation (B = −0.65, OR 0.52, 95% CI 0.40, 0.67, p < .001). The same was found for participants who traveled three to four nights (B = −0.58, OR 0.56, 95% CI 0.43-0.72, p < .001). Participants who traveled more than five nights were significantly less likely to be socially isolated (B = −0.89, OR 0.41, 95% CI 0.35-0.49, p < .001).

As for the covariates used as independent variables, family income was found to be significantly negatively associated with social isolation. Participants with higher annual family income (more than $75,000) were less likely to be socially isolated compared to participants with family income of less than $25,000 (income of $75,000+: B = −1.12, OR 0.10, 95% CI 0.08-0.13, p < .001; income of $50,000-74,999: B = −1.90, OR 0.15, 95% CI 0.11-0.20, p < .001; income of $25,000-49,999: B = −2.32, OR 0.33, 95% CI 0.28-0.39, p < .001). We also found that age had a significant positive association with social isolation, indicating the older people with SCI were, more likely to be socially isolated (B = 0.02, OR 1.02, 95% CI 1.02-1.03, p < .001). Additionally, results show that depression symptoms were significantly positively associated with being socially isolated (B = 0.03, OR 1.04, 95% CI 1.02-1.05, p < .001). No significant association was found, however, between social isolation and any of the other health problems (i.e., diabetes, hypertension, hyperlipidemia, and arthritis).

A sensitivity test was conducted with travel participation treated as a continuous independent variable (range, 0 to 5). A logistic regression analysis was conducted with social isolation as the dependent variable, controlling for the same confounders. The logistic regression modified model fit (eTable 1) was found to be similar to the original model (χ² =1150.417, p < .001, R²_Nagelkerke= 0.24). Parameter estimate for travel participation (B = −0.17, OR 0.84, 95% CI 0.81-0.87, p < .001) in the sensitivity test is in the same direction as the original hierarchical logistic regression. The sensitivity test shows that the original analysis is robust, confirming that the results can be interpreted with confidence. Further research could investigate the stability of the results across different samples or populations.

Discussion and Conclusion

The aim of the study is to explore the association of travel participation with social isolation of people with SCI. Our findings suggest travel participation is negatively associated with social isolation, after controlling for age, family income, and health issues including diabetes, hypertension, hyperlipidemia, and depression. People with SCI who traveled for one to two nights, three to four nights, and more than five nights are less likely to be socially isolated than those who did not traveled in the past 12 months. The finding confirms an association between travel participation and social isolation, which highlights a potential area for intervention that can target socially isolated individuals living with SCI. This finding aligns with previous research indicating that increased engagement in leisure activities is correlated with reduced social isolation.¹² Travel activities, like many other leisure activities, thus has potential to reduce social isolation.¹² Further research is needed to identify the effect of travel-based interventions on socially isolated individuals with SCI. As suggested by previous study, travel participation can provide opportunity for social interaction among family and friends, with travel service employees, or with other travelers and acquaintances. Future research can focus on examining the feasibility and potential strategies of travel programs designed to serve as an intervention to reduce social isolation among individuals with SCI.

Based on the sample studied and the independent t test, age was found to be significant different between the high social isolation group and low social isolation group, where elder participants experienced more isolation. This finding aligns with previous research indicating that individuals become more susceptible to social isolation as they age.¹¹ Furthermore, the demographic characteristics analyzed through chi-square tests (as presented in Table 1) revealed that compared to the low social isolation group, participants with SCI in the high social isolation group were more likely to have lower family income, unmarried status, live alone, and experience a higher prevalence of health issues such as diabetes, hypertension, hyperlipidemia, and arthritis. The covariates used in the regression model also suggest family income and depression are associated with social isolation. This finding is in line with a previous study showing that as people get older, they are more likely to experience social isolation.¹¹ It also confirms that people with lower income levels who often have fewer opportunities for full participation in society are more likely to be socially isolated.² In addition, the results of the study have shown that participants with SCI who are socially isolated tend to have significantly more symptoms of depression compared to those who are not socially isolated. This finding further confirms that symptoms of depression are also closely associated with social isolation.⁴

Examination of the demographic characteristics of the study sample suggests shows that socially isolated individuals with SCI are more likely to be older, have lower family income, and experience more health issues. These outcomes imply that when promoting travel for people with SCI, researchers, health providers, and mental health specialists need to take these factors into consideration. Policy makers and tourism services providers should also be included in designing travel programs for socially isolated individuals with SCI in order to make travel program more affordable and accessible to them.

Limitations

While the current study provides valuable insights into the relationship between travel participation and social isolation among individuals with SCI, it has several limitations. First, the study relied on self-reported data, which may be subject to biases and inaccuracies. That is self-reported travel participation is categorical instead of continuous, which did not capture all variances in the travel frequency of these individuals living with SCI. Additionally, the study did not assess the types of travel activities or destinations. Because the travel experiences for participants may vary significantly, the relationship between travel participation and social isolation can be dynamic. Another limitation is the use of cross-sectional data. The results of this research should thus be interpreted cautiously, as causal effect cannot be interpreted from this data. Future studies may need to consider using random controlled trials and repeated experiments to identify the effect of travel participation on socially isolated individuals with SCI.

Overall, the findings of this study provide initial evidence for a negative relationship between travel participation and social isolation. Future studies can adopt more robust research designs to further investigate the potentially complex relations between travel and social isolation. As more research is confirming the adverse effects of social isolation on people’s physical and cognitive health, and with new US government recognition of the threat that loneliness and social isolation poses to the health and wellbeing of many,⁷ there is an imminent need to develop programs that have the potential to interrupt social isolation. Travel, especially leisure travel, can be an enjoyable activity that inherently promotes social connectedness. More research on the benefits of leisure travel participation is therefore warranted. Findings in this line of research can offer insights and guidelines for therapists and rehabilitation professionals and for the travel industry to design travel training programs and provide resources for people with SCI when they choose to travel and to encourage them to travel.