1Department of Rehabilitation Medicine, National Rehabilitation Center, Seoul, Korea
2Department of Education Evaluation, Sungkyunkwan University, Seoul, Korea
Correspondence: Jinhee Jeong Department of Rehabilitation Medicine, National Rehabilitation Center, 58 Samgaksan-ro, Gangbuk-gu, Seoul 01022, Korea. Tel: +82-2-901-1801 Fax: +82-2-901-1800 E-mail: jinzza95@naver.com
• Received: March 31, 2025 • Revised: September 20, 2025 • Accepted: October 13, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
To explore subjective vitality and mental health among individuals with spinal cord injury (SCI) in South Korea; specifically the relationship between subjective vitality and mental health and their associations with SCI-related factors, including health conditions, activity, environmental, and personal factors.
Methods
This cross-sectional study utilized data from the International Spinal Cord Injury Community Survey conducted in South Korea between March and October 2017. Data from 688 community-dwelling individuals with SCI were included in this study. Correlation and multiple regression analyses were conducted to investigate the relationships between vitality, mental health, and their associated factors.
Results
A strong positive correlation was identified between subjective vitality and mental health (r=0.78, p<0.001). In multiple regression analyses, common factors significantly associated with both domains included sleep problems, healthcare-related activities, financial burden, self-efficacy, and belongingness. Bowel problems were associated only with subjective vitality, while pressure injury and perceived social attitudes were associated only with mental health.
Conclusion
These findings highlight the importance of comprehensive approaches that address secondary health complications, promote healthcare education, and alleviate financial burdens to enhance both subjective vitality and mental health in individuals with SCI. Additionally, psychological interventions that foster belongingness and strengthen self-efficacy may further contribute to psychological well-being following SCI. Further research is needed to validate these associations and evaluate the long-term effects of such multidimensional strategies on subjective vitality and overall quality of life following SCI.
Spinal cord injury (SCI) leads to lifelong disability resulting from motor and sensory deficits, as well as autonomic dysfunction. This condition is often accompanied by a range of secondary health complications, including pain, pressure injuries, and bladder and bowel dysfunction [1]. In addition, functional impairments associated with SCI contribute to activity limitations in daily living tasks [2]. The severity of these restrictions is influenced by environmental barriers, including transportation challenges, healthcare accessibility, and economic constraints [3,4]. These factors collectively contribute to the increased prevalence of mental health issues such as depression and anxiety [5,6]. Untreated mental health challenges can significantly diminish the benefits of rehabilitation and adversely impact the overall quality of life [7], underscoring the growing importance of addressing mental health concerns in individuals with SCI.
Subjective vitality, as defined by Ryan and Frederick, refers to a positive psychological state characterized by a sense of aliveness and energy, reflecting a productive and internally driven state of positive energy [8,9]. This does not merely refer to the absence of mental distress, such as anxiety and depression, but rather represents a productive state of positive energy perceived and derived from self [8]. This concept aligns with the World Health Organization’s (WHO) concept of positive dimension of psychological well-being—“a state of well-being whereby individuals recognize their abilities, are able to cope with the normal stresses of life, work productively and fruitfully, and contribute to their communities,” [10] and is, thus, considered one of its predictors [11,12]. As subjective vitality is influenced by both physical and psychological factors [13], individuals with SCI are likely to experience reduced levels of subjective vitality due to their physical limitations and susceptibility to mental health issues [14]. However, subjective vitality has received limited attention in SCI research, particularly regarding its relationship with other psychological constructs and various challenges associated with SCI. Understanding these relationships could provide valuable insights for designing targeted psychological and rehabilitative interventions, thereby enhancing rehabilitation outcomes and promoting psychological well-being in this population. Therefore, in this study, we aimed to (1) investigate the relationship between subjective vitality and mental health in individuals with SCI and (2) explore SCI-related health problems, activity capacity, environmental factors, and personal factors that affect subjective vitality and mental health.
METHODS
Data collection
The International Spinal Cord Injury Community Survey (InSCI) is a multinational, cross-sectional survey based on the International Classification of Functioning, Disability, and Health (ICF) Core Sets for individuals living with SCI [15]. It was developed to collect comprehensive information on the lived experiences of individuals with SCI across 22 countries within six WHO regions [16]. The first round of the InSCI survey, comprising 125 items, was translated into the respective languages of the participating countries and conducted between 2017 and 2019 [17].
In South Korea, the survey was conducted from March to October 2017 in accordance with the InSCI research protocol [15]. From the National Rehabilitation Center and the Korea Spinal Cord Injury Association databases, 6,355 eligible individuals were randomly selected and invited to participate through text messages, emails, in-person interviews, and telephone calls. The survey was administered via the official InSCI website, paper questionnaires, and telephone interviews. A total of 890 participants provided written informed consent and completed the study, and their responses were compiled into the database. The inclusion criteria were as follows: (1) community-dwelling Korean residents aged ≥19 years with traumatic or non-traumatic SCI, and (2) individuals temporarily hospitalized for routine follow-up assessments, all of whom fully understood the study purpose, were able to respond to the survey items, and provided informed consent to participate. The exclusion criteria were as follows: (1) congenital SCI, (2) neurodegenerative disorders such as multiple sclerosis and amyotrophic lateral sclerosis, and (3) peripheral nerve injury such as Guillain–Barré Syndrome. Ethical approval for the secondary use of these data in the present study was obtained from the Institutional Review Board of National Rehabilitation Center (Institutional Review Board No.: NRC-2024-05-036).
Subjective vitality and mental health
Two subscales from the 36-Item Short Form Health Survey (SF-36), version 2, vitality subscale and mental health subscale, were used in the InSCI questionnaire to evaluate subjective vitality and mental health, respectively. The vitality subscale captures an individual’s energy level and fatigue and corresponds to the “energy and drive functions” category of ICF, while the mental health subscale specifically measures emotional states such as anxiety and depression, corresponding to the “emotional functions” category of ICF [16]. The vitality subscale asks participants how much of the time during the past four weeks they “felt full of life,” “had a lot of energy,” felt worn out,” and “felt tired.” The mental health subscale assesses participants’ experiences over the past 4 weeks by asking how often they: “felt very nervous,” “felt so down in the dumps that nothing could cheer them up,” “felt calm and peaceful,” “felt downhearted and depressed,” and “felt happy.” For both subscales, raw scores were transformed to a 0–100-point scale following the scoring manual, where higher scores indicate greater vitality and better mental health [18].
Health problems, activity-related problems, environmental factors, and personal factors
To analyze associations with subjective vitality and mental health, each independent variable was assessed using a single item drawn from relevant categories in the InSCI questionnaire, including health problems, activity-related problems, environmental factors, and personal factors. Health problems, including sleep problems, bowel problems, pressure injuries, and pain, were assessed using items from the Spinal Cord Injury Secondary Health Conditions Scale [19]. Each item was rated on a 5-point scale, ranging from “no problem” (1) to “extreme problem” (5). Activity-related problems were evaluated using items derived from the Model Disability Survey (MDS) [20] and the Spinal Cord Independence Measure for Self-report (SCIM-SR) [21]. Activities related to fine motor skills, healthcare, and household tasks were scored on a 5-point Likert scale ranging from “no problem” (1) to “extreme problem” (5). The ability to sit unsupported was dichotomized as “no” or “yes,” while bed-to-wheelchair transfer was scored from 1 to 4 points, and mobility for moderate distances was scored from 1 to 8 points, following SCIM-SR scoring system, with higher scores indicating better functional abilities. Environmental factors, including public place accessibility, transportation accessibility, social attitudes, financial situation, and medical supplies, were assessed using the Nottwil Environmental Factors Inventory [22]. Ratings were based on a 4-point Likert scale as follows: “not applicable” (1), “no influence” (2), “made my life a little harder” (3), and “made my life a lot harder” (4). To simplify the analysis, responses were recoded into a 3-point scale by combining “not applicable” and “no influence,” consistent with prior research demonstrating good internal consistency (Cronbach α=.82) [22]. Lastly, three variables for personal factors—self-efficacy, autonomy, and belongingness—were selected, each representing basic psychological needs identified as key determinants of subjective vitality [9]. Survey questions for personal factors were derived from multiple validated sources [15]. Self-efficacy was assessed by asking participants about their confidence in handling unexpected events; autonomy was evaluated through perceived control over major life decisions; belongingness was measured from the extent to which participants felt included in social settings. Each item was rated on a 5-point Likert scale ranging from “not at all” (1) to “completely” (5), with higher scores indicating more positive attributes.
Confounders
To identify potential confounding variables, a preliminary analysis was conducted, which assessed associations of the two outcome variables with demographic factors, including age, sex, and marital status, and injury-related factors, including the level of injury, completeness, and the etiology of injury. Age was analyzed using Pearson’s correlation analysis, while other dichotomous variables were evaluated using point-biserial analysis. Variables showing significant associations with each outcome were included as confounders in the subsequent multiple regression models.
Statistical analysis
Statistical analyses were conducted using SPSS, version 27.0 (IBM Corp.). Sociodemographic data and clinical characteristics are summarized as percentages or means with standard deviations (SDs). Pearson’s correlation analysis was employed to examine the relationship between subjective vitality and mental health. To explore variables associated with subjective vitality and mental health, including demographic and injury-related factors as possible confounders, an initial correlation analysis was performed. Variables measured with 5-point Likert scale or higher scales were treated as continuous variables and analyzed using Pearson’s correlation analysis, as their distributions approximated normality [23]; the maximum absolute skewness (0.58) and kurtosis (1.58) did not exceed the thresholds (|skewness|<2, |kurtosis|<7) suggested in a previous study [24]. For ordinal variables measured using 3- or 4-point scales, Spearman’s rank correlation analysis was applied. Dichotomous variables were analyzed using point-biserial correlation. Subsequently, variables demonstrating statistically significant correlations were selected as independent variables for multiple regression analysis to further explore the factors influencing subjective vitality and mental health. Variables identified as confounders in the initial correlation analyses were included in the corresponding regression model for each outcome. Independent variables measured using 5-point or higher Likert scales were treated as continuous, whereas those measured using 3- or 4-point Likert scales were dummy coded. Both crude and covariate-adjusted models were constructed to assess the independent effects of predictor domains. To enhance reliability, surveys with missing data on the analyzed variables were excluded through listwise deletion, yielding a final sample size of 688 participants for the analysis. Statistical significance was set at p<0.05 for all analyses.
RESULTS
Demographics and clinical characteristics
The demographic and injury characteristics of the 688 participants are summarized in Table 1. The mean age of participants was 47.4 years (SD=11.7), and 518 (75.3%) were male. A total of 327 participants (47.5%) were married, and 194 (28.2%) were employed. Paraplegia was present in 404 participants (58.7%), and 406 participants (59.0%) reported complete injuries. A traumatic etiology was noted for 644 participants (93.6%). Descriptive statistics for subjective vitality, mental health, and covariates are presented in Table 2. The mean subjective vitality score was 46.39 (SD=21.6), while the mean mental health score was 57.67 (SD=20.6).
Correlation between vitality and mental health domains
The results of the correlation analysis between subjective vitality and mental health domains are illustrated in Fig. 1. A strong positive correlation was identified between the two domains (r=0.78, p<0.001). Histograms and scatter plots show that the data distributions for both variables approximate normal distributions and confirm the positive linear relationship between the variables.
Correlations of vitality/mental health domains with demographic, injury-related, and explanatory variables
Table 3 presents the correlations of various factors with subjective vitality and the mental health domain. Demographic and injury-related variables were additionally examined to identify potential confounders, alongside explanatory variables, including health problems and activity-related, environmental, and personal factors. As a result, sex, marital status, employment status, completeness, and etiology were significantly associated with the mental health domain, whereas only employment status was significantly associated with the vitality domain. These variables were subsequently included as covariates in the regression analysis.
Among the explanatory variables, all secondary health problems showed significantly negative correlations with both subjective vitality and mental health, with sleep problems demonstrating the strongest associations. Fine motor skills, healthcare activities, and household tasks showed negative correlations with subjective vitality and mental health, while transfer ability was positively correlated with both outcomes. Meanwhile, sitting ability and mobility were not significantly correlated with either domain. Environmental factors, including public place accessibility, transportation accessibility, social attitudes, medical supplies, and financial situation, were all significantly negatively correlated with both outcomes. Lastly, all three personal factors, including self-efficacy, autonomy, and belongingness, exhibited significant positive correlations with both outcomes.
Multiple regression of vitality/mental health domain on correlated variables
Tables 4 and 5 present the results of multiple regression analyses for the subjective vitality and mental health domains. Analyses were conducted using both unadjusted and covariate-adjusted models. Covariates were selected based on variables significantly correlated with each outcome domain, as identified in the preceding correlation analyses. Variance inflation factors for the entire models ranged from 1.07 to 2.53, and all values were well below the conventional threshold of 5, indicating no evidence of problematic multicollinearity.
In the covariate-adjusted model for subjective vitality (Table 4), sleep problems (B=-3.79, β=-0.25, p<0.001), bowel problems (B=-1.64, β=-0.10, p=0.004), and healthcare issues (B=-3.31, β=-0.19, p<0.001) demonstrated significant negative associations. Among environmental factors, financial situation with severe burden (reference: no burden; B=-6.45, β=-0.13, p<0.001) remained a significant negative predictor of subjective vitality. Regarding personal factors, self-efficacy (B=2.99, β=0.15, p<0.001) and belongingness (B=4.00, β=0.20, p<0.001) were significant positive predictors of subjective vitality.
In the adjusted model for mental health (Table 5), sleep problems (B=-3.23, β=-0.22, p<0.001), pressure injury (B=-0.99, β=-0.07, p=0.03), and healthcare issues (B=-2.76, β=-0.17, p<0.001) showed significant negative associations. Among environmental factors, financial situation with severe burden (reference: no burden; B=-7.51, β=-0.16, p<0.001) and the negative perception of social attitude with little burden (reference: no burden; B=-3.66, β=-0.09, p=0.02) were significantly associated with poor mental health. With regard to personal factors, self-efficacy (B=2.18, β=0.12, p=0.001) and belongingness (B=4.54, β=0.24, p<0.001) were positively associated with mental health.
DISCUSSION
In this study, we aimed to explore subjective vitality and mental health among individuals with SCI in South Korea and examine their relationship and associations with SCI-related factors. The results indicated a strong positive correlation (r=0.78, p<0.001) between subjective vitality and mental health, both influenced by common factors such as sleep problems, healthcare-related activities, financial situation, self-efficacy, and belongingness. Bowel problems were associated with subjective vitality only, while pressure injury and social attitude were associated with mental health only.
The correlation between subjective vitality and mental health in SCI sample aligns with previous research that examined psychometric properties of SF-36 in individuals with SCI and found a moderate correlation between vitality and mental health subscales (r=0.53) [25]. However, vitality and mental health remain conceptually distinct domains of health—the vitality score of the InSCI survey reflects the “energy and drive functions” category of ICF, while the mental health score corresponds to the “emotional functions” domain of ICF [16]. Activity limitations or medical conditions may reduce vitality without mood depression, and conversely, individuals may experience minimal fatigue while still exhibiting depressive mood. Therefore, the strong association between subjective vitality and mental health in our study suggests pronounced interconnectedness of these constructs in the context of SCI. Furthermore, the present study demonstrates that subjective vitality and mental health are influenced by a common set of SCI-related factors, which suggests potential synergistic benefits from interventions targeting these factors. Each significant factor is discussed in detail below.
Sleep problems significantly impacted both vitality and mental health in this study. Previous studies have consistently shown a strong association between poor sleep quality and mental health outcomes in individuals with SCI [26]. Insufficient nighttime sleep leads to daytime fatigue, which is considered a physical factor that hampers activation and diminishes subjective vitality [27]. Additionally, sleep problems are closely associated with other secondary health issues following SCI, such as pain, bowel and bladder symptoms, and spasms, which collectively contribute to exacerbated mental health outcomes [26,28]. Therefore, addressing sleep problems, alongside the comprehensive management of related health issues, is crucial for enhancing subjective vitality and mental health in individuals with SCI.
Problems with healthcare-related activities were found to negatively impact both mental health and subjective vitality. “Healthcare-related activities” are defined as activities related to managing one’s health, eating well, exercising, and taking medication as outlined in the MDS survey [20]. Sertkaya et al. [29] reported a significant association of health literacy among individuals with SCI with mental health outcomes. These findings collectively highlight the importance of the knowledge and ability to manage one’s health after SCI. Effective health management fosters a sense of control and self-efficacy [30], alleviating feelings of helplessness and dependency after SCI [31], thereby enhancing subjective vitality and mental health outcomes. Therefore, incorporating health management education programs [32] into rehabilitation settings is recommended to support the psychological well-being of individuals with SCI.
Financial burden emerged as a significant environmental barrier affecting both subjective vitality and mental health in the present study. Financial burden is a major environmental barrier perceived by SCI patients globally [33]. Zürcher et al. [34] reported that financial strain is an independent predictor of mental health among individuals with SCI, identifying it as a novel indicator of socioeconomic conditions in this population. Consistent with these findings, our results underscore the importance of mitigating the economic burdens associated with SCI through, for example, policy interventions, to improve mental health and subjective vitality in this population.
Finally, belongingness and self-efficacy emerged as significant personal factors influencing both subjective vitality and mental health. The need for belongingness, or relatedness, represents the desire to feel connected and valued in social relationships [9]. Consistent with our findings, Fekete et al. [35] reported belongingness as one of significant social factors associated with better mental health in the SCI population. Self-efficacy, or competence, is the belief in one’s capabilities to perform actions required to produce specific outcomes [36]. van Leeuwen et al. [31] demonstrated that self-efficacy is directly related to mental health and indirectly influences the quality of life in individuals with SCI. Aligned with these findings, our results suggest that psychological interventions targeting self-efficacy and belongingness, such as cognitive behavioral therapy [37,38], could play a meaningful role in enhancing subjective vitality as well. Further research is warranted to assess the effectiveness of such interventions on subjective vitality and their subsequent benefits in this population.
It is also noteworthy that certain predictors were exclusively associated with one outcome domain. Bowel problems were related only to subjective vitality. Given the chronic nature of neurogenic bowel dysfunction after SCI [39], individuals may gradually adapt to its emotional burden over time; however, the resulting loss of self-efficacy and restrictions in daily participation and activity may be more sensitively reflected in subjective vitality. In contrast, pressure injuries were associated only with mental health, likely because of their episodic and chronic course, which may result in more immediate emotional distress and anxiety [40] rather than directly diminishing one’s sense of energy and aliveness. Finally, negative social attitudes were linked only to mental health, suggesting that unfavorable societal perceptions of individuals with SCI are primarily internalized as emotional distress. This finding underscores the social implications of stigma and highlights the need for policy and community-level interventions. Interestingly, statistical significance was observed only at the intermediate level, not at the most severe level. This non-linear effect may reflect the distribution of our sample, where negative attitudes with little burden were more frequently reported (49%) than were those with severe burden (18%), resulting in greater statistical power in this category.
Limitations and strengths
This study has certain limitations. First, owing to its cross-sectional survey design, establishing clear causality is challenging. Potential causal pathways should be further examined in longitudinal settings to design and validate appropriate interventions. Second, the participants were limited to individuals living in the Korean community, limiting the generalizability of the findings to populations in other countries. Third, although this study included a relatively large sample size, the possibility of non-responder bias cannot be excluded. Fourth, although this study employed SF-36 subscales as outcome measures, the lack of validated psychometric tools specifically designed for assessing subjective vitality in SCI populations remains a limitation. Finally, potential interaction effects among predictors were not examined. The possibility of synergistic or moderating relationships between factors cannot be excluded and warrants further investigation.
Despite these limitations, to the best of our knowledge, this study is among the first to explore subjective vitality, an under-recognized aspect of well-being in people with SCI, and its associations with SCI-related factors in a large, nationally representative sample. By highlighting the interrelated nature of subjective vitality and mental health following SCI, the findings offer novel insights into their shared determinant and the relevance of including subjective vitality as a meaningful psychological outcome in SCI research. These results may inform future research aimed at developing evidence-based interventions to enhance psychological well-being in this population.
Conclusion
Our findings support the strong correlation between vitality and mental health domains among individuals with SCI, as well as the significant influences of secondary health problems, healthcare activities, financial burdens, belongingness, and self-efficacy on both domains. Developing and implementing strategies—such as regular healthcare services, educational programs, supportive policy frameworks, and targeted psychological interventions—could enhance subjective vitality and mental health, optimize rehabilitation outcomes, and improve psychological well-being and overall life satisfaction for individuals living with SCI.
CONFLICTS OF INTEREST
No potential conflict of interest relevant to this article was reported.
FUNDING INFORMATION
None.
AUTHOR CONTRIBUTION
Conceptualization: Kim O, Jeong J. Data curation: Lim J. Formal analysis: Lim J. Investigation: Kim O. Methodology: Jeong J. Project administration: Kim O. Supervision: Kim O. Validation: Jeong J. Visualization: Lim J. Writing – original draft: Kim O, Jeong J. Writing – review & editing: Kim O, Jeong J. Approval of final manuscript: all authors.
ACKNOWLEDGEMENTS
This research was supported by the International Cooperation Team of the National Rehabilitation Center, Republic of Korea.
Fig. 1.
Results of correlation analysis between vitality domain and mental health domain. The histograms and scatter plot indicate that the data distributions of both variables approximate normal distributions. The numeric value indicates the Pearson correlation coefficient (r=0.78, ***p<0.001) indicating a strong positive correlation.
Table 1.
Demographic and injury-related characteristics of the participants (n=688)
Variable
Value
Age (yr)
47.4 (11.7)
Sex
Male
518 (75.3)
Female
170 (24.7)
Marital status
Married
327 (47.5)
Single, widowed, separated/divorced or cohabiting/partnership
361 (52.5)
Employment status
Yes
194 (28.2)
No
494 (71.8)
Level of injury
Tetraplegia
284 (41.3)
Paraplegia
404 (58.7)
Completeness
Complete
406 (59.0)
Incomplete
282 (41.0)
Etiology of injury
Traumatic
644 (93.6)
Non-traumatic
44 (6.4)
Values are presented as mean (standard deviation) or number (%).
Table 2.
Descriptive statistics for subjective vitality, mental health, and explanatory variables (n=688)
Variable (scale)
Value
Explanatory variable
Health problem
Sleep problem (1–5)
2.65 (1.4)
Bowel problem (1–5)
3.05 (1.4)
Pressure injury (1–5)
2.44 (1.5)
Pain (1–5)
3.45 (1.3)
Activity
Fine motor (1–5)
2.77 (1.6)
Health care (1–5)
2.97 (1.3)
Household (1–5)
3.76 (1.2)
Able-to-sit; Yes
328 (47.7)
Able-to-sit; No
360 (52.3)
Transfer (1–4)
2.32 (1.0)
Mobility (1–8)
2.58 (1.5)
Environmental factor
Accessibility in public places (1–3)
1.87 (0.7)
Public transport accessibility (1–3)
2.06 (0.8)
Social attitude (1–3)
1.84 (0.7)
Medical supplies (1–3)
1.67 (0.7)
Financial situation (1–3)
1.98 (0.8)
Personal factor
Self-efficacy (1–5)
3.08 (1.1)
Autonomy (1–5)
3.40 (1.2)
Belongingness (1–5)
3.31 (1.1)
Outcome measurement
Vitality domain (0–100)
46.39 (21.6)
Mental Health domain (0–100)
57.67 (20.6)
Values are presented as mean (standard deviation) or number (%).
Higher scores for health problems and environmental factors indicate greater difficulties experienced by participants. Higher scores for personal factors reflect more positive attributes. Regarding activity measures, higher scores for fine motor skills, healthcare activities, and household tasks indicate greater difficulties, while higher scores for transfer and mobility for moderate distances indicate better abilities.
Table 3.
Results of Pearson’sa), point biserialb), and Spearman’s rank correlation analysis (n=688)
Crude model: F (23, 664)=19.68, p<0.001, adjusted R2=38.5%, Durbin–Watson d-value=1.85.
Covariate-adjusted model: adjusted for employment status. F (24, 663)=18.85, p<0.001, adjusted R2=38.4%, Durbin–Watson d-value=1.85.
The following variables were dummy-coded for regression analysis. The binary variable (e.g. employment status) was entered as 0/1 variables (unemployed=0, employed=1). Environmental factors (1=no burden, 2=little burden, 3=severe burden) were dummy-coded with “no burden (1)” as the reference. Transfer ability (1–4 scale) was dummy-coded with “(1)” (lowest ability) as the reference and higher scores indicate better transfer ability.
*p-values of statistical significance (p<0.05).
Table 5.
Results of multiple regression analysis of variables correlated with mental health (n=688)
Crude model: F (23, 664)=21.59, p<0.001, adjusted R2=40.8%, Durbin–Watson d-value=1.94.
Covariate-adjusted model: adjusted for sex, marital status, employment status, completeness, and etiology. F (28, 659)=18.41, p<0.001, adjusted R2=41.5%, Durbin–Watson d-value=1.95.
The following variables were dummy-coded for regression analysis. Binary variables (e.g. sex, marital status, employment status, completeness, and etiology) were entered as 0/1 variables. (male=1, female=0; unemployed=0, employed=1; incomplete injury=0, complete injury=1; non-traumatic etiology=0, traumatic etiology=1). Environmental factors (1=no burden, 2=little burden, 3=severe burden) were dummy-coded with “no burden (1)” as the reference. Transfer ability (1–4 scale) was dummy-coded with “(1)” (lowest ability) as the reference and higher scores indicate better transfer ability.
*p-values of statistical significance (p<0.05).
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Factors Affecting Subjective Vitality and Mental Health After Spinal Cord Injury: A Cross-Sectional Study
Fig. 1. Results of correlation analysis between vitality domain and mental health domain. The histograms and scatter plot indicate that the data distributions of both variables approximate normal distributions. The numeric value indicates the Pearson correlation coefficient (r=0.78, ***p<0.001) indicating a strong positive correlation.
Graphical abstract
Fig. 1.
Graphical abstract
Factors Affecting Subjective Vitality and Mental Health After Spinal Cord Injury: A Cross-Sectional Study
Variable
Value
Age (yr)
47.4 (11.7)
Sex
Male
518 (75.3)
Female
170 (24.7)
Marital status
Married
327 (47.5)
Single, widowed, separated/divorced or cohabiting/partnership
361 (52.5)
Employment status
Yes
194 (28.2)
No
494 (71.8)
Level of injury
Tetraplegia
284 (41.3)
Paraplegia
404 (58.7)
Completeness
Complete
406 (59.0)
Incomplete
282 (41.0)
Etiology of injury
Traumatic
644 (93.6)
Non-traumatic
44 (6.4)
Variable (scale)
Value
Explanatory variable
Health problem
Sleep problem (1–5)
2.65 (1.4)
Bowel problem (1–5)
3.05 (1.4)
Pressure injury (1–5)
2.44 (1.5)
Pain (1–5)
3.45 (1.3)
Activity
Fine motor (1–5)
2.77 (1.6)
Health care (1–5)
2.97 (1.3)
Household (1–5)
3.76 (1.2)
Able-to-sit; Yes
328 (47.7)
Able-to-sit; No
360 (52.3)
Transfer (1–4)
2.32 (1.0)
Mobility (1–8)
2.58 (1.5)
Environmental factor
Accessibility in public places (1–3)
1.87 (0.7)
Public transport accessibility (1–3)
2.06 (0.8)
Social attitude (1–3)
1.84 (0.7)
Medical supplies (1–3)
1.67 (0.7)
Financial situation (1–3)
1.98 (0.8)
Personal factor
Self-efficacy (1–5)
3.08 (1.1)
Autonomy (1–5)
3.40 (1.2)
Belongingness (1–5)
3.31 (1.1)
Outcome measurement
Vitality domain (0–100)
46.39 (21.6)
Mental Health domain (0–100)
57.67 (20.6)
Vitality domain
Mental health domain
Correlation coefficient
p-value
Correlation coefficient
p-value
Demographic and injury-related variable
Agea)
-0.031
0.413
-0.059
0.120
Sexb)
0.015
0.703
0.688
0.006*
Marital statusb)
0.072
0.058
0.100
0.009*
Employment statusb)
0.128
<0.001*
0.127
<0.001*
Level of injuryb)
-0.072
0.059
-0.035
0.362
Completenessb)
0.071
0.061
0.115
0.002*
Etiology of injuryb)
0.051
0.182
0.080
0.04*
Health problem
Sleep problema)
-0.434
<0.001*
-0.415
<0.001*
Bowel problema)
-0.357
<0.001*
-0.325
<0.001*
Pressure injurya)
-0.171
<0.001*
-0.227
<0.001*
Paina)
-0.275
<0.001*
-0.228
<0.001*
Activity
Fine motora)
-0.109
0.004*
-0.115
0.003*
Health carea)
-0.422
<0.001*
-0.422
<0.001*
Householda)
-0.252
<0.001*
-0.256
<0.001*
Able-to-sitb)
0.056
0.143
0.037
0.335
Transfer
0.110
0.004*
0.138
<0.001*
Mobilitya)
-0.013
0.738
0.045
0.240
Environmental factor
Accessibility in public places
-0.200
<0.001*
-0.216
<0.001*
Public transport accessibility
-0.236
<0.001*
-0.252
<0.001*
Social attitude
-0.250
<0.001*
-0.308
<0.001*
Medical supplies
-0.246
<0.001*
-0.282
<0.001*
Financial situation
-0.289
<0.001*
-0.333
<0.001*
Personal factor
Self-efficacya)
0.348
<0.001*
0.359
<0.001*
Autonomya)
0.194
<0.001*
0.263
<0.001*
Belongingnessa)
0.336
<0.001*
0.395
<0.001*
Crude model
Covariate-adjusted model
B
β
t
p-value
B
β
t
p-value
(Constant)
57.08
11.97
57.22
11.98
Covariate
Employment status
-0.85
-0.02
-0.55
0.580
Health problem
Sleep problem
-3.78
-0.25
-7.13
<0.001*
-3.79
-0.25
-7.14
<0.001*
Bowel problem
-1.63
-0.10
-2.85
0.004*
-1.64
-0.10
-2.87
0.004*
Pressure injury
0.12
0.01
0.26
0.794
0.13
0.01
0.27
0.789
Pain
-0.76
-0.05
-1.36
0.175
-0.80
-0.05
-1.42
0.156
Activity
Fine motor
0.52
0.04
0.99
0.324
0.52
0.04
1.00
0.320
Health care
-3.33
-0.20
-4.82
<0.001*
-3.31
-0.19
-4.78
<0.001*
Household
0.26
0.01
0.36
0.720
0.24
0.01
0.33
0.745
Transfer (reference: 1)
Transfer (2)
1.39
0.03
0.71
0.480
1.45
0.03
0.74
0.462
Transfer (3)
2.10
0.05
1.04
0.300
2.24
0.05
1.10
0.272
Transfer (4)
-5.40
-0.07
-1.80
0.072
-5.18
-0.06
-1.71
0.087
Environmental factor
Accessibility in public place (reference: 1)
Accessibility in public places (2)
1.15
0.03
0.70
0.487
1.22
0.03
0.74
0.463
Accessibility in public places (3)
-3.29
-0.06
-1.53
0.126
-3.26
-0.06
-1.52
0.130
Public transport accessibility (reference: 1)
Public transport accessibility (2)
-0.32
-0.01
-0.17
0.861
-0.32
-0.01
-0.18
0.859
Public transport accessibility (3)
-1.57
-0.03
-0.75
0.454
-1.61
-0.03
-0.76
0.445
Social attitude (reference: 1)
Social attitude (2)
-1.40
-0.03
-0.86
0.391
-1.40
-0.03
-0.86
0.390
Social attitude (3)
1.69
0.03
0.73
0.463
1.66
0.03
0.72
0.472
Medical supplies (reference: 1)
Medical supplies (2)
-2.55
-0.06
-1.59
0.113
-2.49
-0.06
-1.54
0.124
Medical supplies (3)
0.95
0.02
0.39
0.696
0.96
0.02
0.39
0.693
Financial situation (reference: 1)
Financial situation (2)
-1.57
-0.04
-0.94
0.348
-1.62
-0.04
-0.97
0.334
Financial situation (3)
-6.41
-0.13
-3.17
0.002*
-6.45
-0.13
-3.19
0.001*
Personal factor
Self-efficacy
2.96
0.15
4.27
<0.001*
2.99
0.15
4.30
<0.001*
Autonomy
-1.26
-0.07
-1.95
0.051
-1.23
-0.07
-1.90
0.058
Belongingness
3.99
0.20
5.39
<0.001*
4.00
0.20
5.39
<0.001*
Crude model
Covariate-adjusted model
B
β
t
p-value
B
β
t
p-value
(Constant)
60.23
13.49
56.88
12.27
Covariate
Sex
-0.72
-0.02
-0.49
0.627
Marital status
2.38
0.06
1.91
0.057
Employment status
-1.24
-0.03
-0.86
0.390
Completeness
3.17
0.08
2.41
0.02*
Etiology
2.13
0.04
1.41
0.158
Health problem
Sleep problem
-3.32
-0.23
-6.70
<0.001*
-3.23
-0.22
-6.49
<0.001*
Bowel problem
-0.88
-0.06
-1.65
0.100
-0.99
-0.07
-1.85
0.064
Pressure injury
-0.82
-0.06
-1.86
0.063
-0.99
-0.07
-2.20
0.03*
Pain
0.09
0.01
0.17
0.863
-0.08
-0.01
-0.15
0.878
Activity
Fine motor
0.34
0.03
0.69
0.492
0.45
0.04
0.91
0.363
Health care
-2.87
-0.18
-4.43
<0.001*
-2.76
-0.17
-4.27
<0.001*
Household
0.45
0.03
0.65
0.517
0.39
0.02
0.57
0.571
Transfer (reference : 1)
Transfer (2)
1.78
0.04
0.97
0.335
1.88
0.04
1.02
0.307
Transfer (3)
1.35
0.03
0.71
0.478
1.55
0.04
0.81
0.419
Transfer (4)
-1.62
-0.02
-0.58
0.563
-0.19
0.00
-0.07
0.947
Environmental factor
Accessibility in public place (reference : 1)
Accessibility in public places (2)
1.00
0.02
0.64
0.519
0.82
0.02
0.53
0.594
Accessibility in public places (3)
-2.01
-0.04
-1.00
0.318
-2.09
-0.04
-1.04
0.298
Public transport accessibility (reference: 1)
Public transport accessibility (2)
1.26
0.03
0.74
0.460
1.35
0.03
0.80
0.427
Public transport accessibility (3)
-0.64
-0.01
-0.33
0.745
-0.72
-0.02
-0.36
0.717
Social attitude (reference: 1)
Social attitude (2)
-3.74
-0.09
-2.46
0.01*
-3.66
-0.09
-2.42
0.02*
Social attitude (3)
-2.92
-0.05
-1.36
0.176
-2.82
-0.05
-1.31
0.190
Medical supplies (reference: 1)
Medical supplies (2)
-1.54
-0.04
-1.02
0.308
-1.49
-0.04
-0.99
0.324
Medical supplies (3)
-0.70
-0.01
-0.31
0.757
-0.92
-0.02
-0.41
0.684
Financial situation (reference: 1)
Financial situation (2)
-1.35
-0.03
-0.86
0.388
-1.44
-0.03
-0.92
0.356
Financial situation (3)
-8.25
-0.18
-4.36
<0.001*
-7.51
-0.16
-3.95
<0.001*
Personal factor
Self-efficacy
2.27
0.12
3.50
<0.001*
2.18
0.12
3.35
0.001*
Autonomy
-0.18
-0.01
-0.30
0.765
-0.15
-0.01
-0.24
0.808
Belongingness
4.52
0.24
6.52
<0.001*
4.54
0.24
6.56
<0.001*
Table 1. Demographic and injury-related characteristics of the participants (n=688)
Values are presented as mean (standard deviation) or number (%).
Table 2. Descriptive statistics for subjective vitality, mental health, and explanatory variables (n=688)
Values are presented as mean (standard deviation) or number (%).
Higher scores for health problems and environmental factors indicate greater difficulties experienced by participants. Higher scores for personal factors reflect more positive attributes. Regarding activity measures, higher scores for fine motor skills, healthcare activities, and household tasks indicate greater difficulties, while higher scores for transfer and mobility for moderate distances indicate better abilities.
Table 3. Results of Pearson’sa), point biserialb), and Spearman’s rank correlation analysis (n=688)
For binary variables, the following reference coding was used: female=0, male=1; unmarried=0, married=1; unemployed=0, employed=1; incomplete injury=0, complete injury=1; non-traumatic etiology=0, traumatic etiology=1.
Pearson’s correlation analysis;
point-biserial correlation analysis; all other values correspond to Spearman’s rank correlation analysis.
p-values of statistical significance (p<0.05).
Table 4. Results of multiple regression analysis of variables correlated with subjective vitality (n=688)
Crude model: F (23, 664)=19.68, p<0.001, adjusted R2=38.5%, Durbin–Watson d-value=1.85.
Covariate-adjusted model: adjusted for employment status. F (24, 663)=18.85, p<0.001, adjusted R2=38.4%, Durbin–Watson d-value=1.85.
The following variables were dummy-coded for regression analysis. The binary variable (e.g. employment status) was entered as 0/1 variables (unemployed=0, employed=1). Environmental factors (1=no burden, 2=little burden, 3=severe burden) were dummy-coded with “no burden (1)” as the reference. Transfer ability (1–4 scale) was dummy-coded with “(1)” (lowest ability) as the reference and higher scores indicate better transfer ability.
p-values of statistical significance (p<0.05).
Table 5. Results of multiple regression analysis of variables correlated with mental health (n=688)
Crude model: F (23, 664)=21.59, p<0.001, adjusted R2=40.8%, Durbin–Watson d-value=1.94.
Covariate-adjusted model: adjusted for sex, marital status, employment status, completeness, and etiology. F (28, 659)=18.41, p<0.001, adjusted R2=41.5%, Durbin–Watson d-value=1.95.
The following variables were dummy-coded for regression analysis. Binary variables (e.g. sex, marital status, employment status, completeness, and etiology) were entered as 0/1 variables. (male=1, female=0; unemployed=0, employed=1; incomplete injury=0, complete injury=1; non-traumatic etiology=0, traumatic etiology=1). Environmental factors (1=no burden, 2=little burden, 3=severe burden) were dummy-coded with “no burden (1)” as the reference. Transfer ability (1–4 scale) was dummy-coded with “(1)” (lowest ability) as the reference and higher scores indicate better transfer ability.
