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Predictors of resilience for people with spinal cord injury over two periods of COVID-19 social distancing restrictions: a 12-month longitudinal study using structural equation modelling

Abstract

Background

The novel coronavirus (COVID-19) pandemic is disproportionately impacting the health of people with disability. Resilience has remained an important health promoting characteristic during periods of social distancing restrictions. Factors promoting resilience for people with disability under the context of the pandemic remains poorly understood. Studies have yet to investigate evidence-based factors that promote resilience over multiple periods of restrictions for people with disability.

Methods

A longitudinal study developed via a collaborative partnership between peer-support workers with lived experience of spinal cord injury (SCI) and university researchers was undertaken to fill knowledge gaps around factors promoting resilience for people with SCI during two periods of stringent social distancing restrictions within Victoria, Australia. Over 12-months, participants with SCI completed two surveys, towards the end of two lockdown periods. Evidence-based factors associated with resilience were measured. The Impact on Participation and Autonomy Questionnaire, the International SCI Quality of Life scale, and the 10-item Conor Davidson Resilience Scale, respectively measured autonomy and participation limitations, life satisfaction and psychological health, and resilience. A structural equation modelling (SEM) approach established factors directly and indirectly associated with resilience.

Results

A model with excellent fit was produced. During two extended lockdowns over the 12-month period, increased family role limitations and favourable psychological health were respectively, negatively (Lockdown 1 [n = 127]: β = -.251, p < .01, Lockdown 2: β = -.400, p < .01) and positively (Lockdown 1: β = .601, p < .01, Lockdown 2 [n = 65]: β = .430, p < .01) associated with resilience. Indirect negative associations between resilience and increased outdoor autonomy limitations (Lockdown 1: β = -.195, p < .01, Lockdown 2: β = -.255, p < .01) and social life limitations (Lockdown 1: β = -.217, p < .01, Lockdown 2: β = -.142, p < .05) existed, and these relationships were moderated by psychological health.

Conclusions

Psychological health, and participation and autonomy are determinants of resilience during periods of crisis. Health and social care providers and public health departments should prioritise programs promoting these domains, to counter the negative impact of social distancing.

Peer Review reports

Introduction

Over a billion people globally live with disability, and as a result of chronic health conditions and demographic trends, this number is increasing (1). The COVID-19 pandemic is disproportionately impacting health outcomes of people with disability (1); compared to people without disability, people with disability face adverse social, economic, and health consequences (2,3,4). Resilience—defined as the ability to cope with and/or adjust to, adversity (5)—is associated with diverse health and wellbeing outcomes for people with disability, including mental, physical and social health outcomes (6), quality of life (7, 8), and activity continuation subsequent to a fall and/or injury (9). Increased resilience is aligned with an increased capacity to handle adversity (10). As those with heightened resilience are less likely to experience adverse health outcomes due to adversity, resilience is a health promoting attribute. For example, research has confirmed that resilience is a mediator between sleep problems and suicidal ideation (11), and increased resilience is associated with improved mental health (12, 13), reduced antidepressant use (14), and improved psychological health (10).

Resilience is especially important during the time of an emergency or crisis (15,16,17). Understandably, a growing body of research has investigated the impact of resilience on the health outcomes of people throughout the COVID-19 pandemic. Studies have confirmed that lower levels of resilience are associated with increased distress symptoms (18), and depression and anxiety (19,20,21). For people with disability, COVID-19 specific research has concluded that increased resilience protects mental health (22), is associated with reduced anxiety symptoms and improved quality of life (23), and supports the ability for people with disability to navigate and make decisions (24). COVID-19 disability specific research around resilience has a carer focus. In this respect, findings conclude that increased carer resilience, is inversely correlated with caregiver depression (25), whilst also associated with reduced stress related behaviour of children with neurodevelopmental disability (26). Limited research has investigated factors associated with and/or promoting resilience for people with disability during COVID-19. Research in this area has confirmed that for young people with disability, parenting self-efficacy (27), and for people with intellectual disability, day structure and routine and relationships (28) both promote resilience.

Research to date has been incredibly valuable and has improved our understanding surrounding the importance of resilience for people disability during periods of crisis. Specifically, current research has improved our understanding surrounding the impact of resilience on health and wellbeing outcomes for people with disability, and less so, surrounding factors promoting resilience for people with disability under the context of the pandemic. Most research to date has focused on caregiver perspectives, and as a result the perspectives of those with lived experience of disability are largely absent. Research to-date has been cross-sectional, collecting perspectives during a single period of social distancing restrictions. Many locations have experienced multiple waves of increased COVID-19 cases and social distancing restrictions. Still, longitudinal designs have yet to be employed within resilience specific COVID-19 research concerning people with disability. Given the protective nature of resilience, it is important to investigate the impact of public policies on resilience (19), and factors promoting resilience among people with disability during times of crisis. This is especially so moving forward from the pandemic (29) as resilience will remain a protective attribute which must be fostered among those who are characterised as vulnerable (30). This study aimed to fill knowledge gaps and investigated factors associated with resilience over multiple periods of increased COVID-19 cases and resulting social distancing restrictions, for people with spinal cord injury (SCI). It is expected that the findings from this research will inform policies and programs that aim to promote resilience during times of crisis (for example a pandemic).

Study context

The state of Victoria, Australia experienced two periods (in 2020 and 2021) of heightened social distancing restrictions due to increased COVID-19 cases. During these periods, the restrictions were amongst the most stringent globally (31). Metropolitan Melbourne was under a strict lockdown from 30th June 2020 (31, 32) until 28th October 2020 (31, 33). From 30th June to 2nd August, Metropolitan Melbourne was under Stage 3 restrictions. A ‘state of disaster’ was declared on 2nd August 2020, and during this period Stage 4 social distancing restrictions were enacted (34). These Stage 4 restrictions consisted of limited movement with a 5- km radius of the home, and an 8PM curfew. Residents could only leave home four essential reasons which consisted of exercise for up to 1-h a day, essential shopping, work and/or medical care. Mask wearing was mandatory within indoor and outdoor settings (except when undertaking rigorous exercise). Members from different households were unable to visit eachother. Stage 3 restrictions were comparable to Stage 4 restrictions as residents could only leave home for the four essential reasons, and there were no home visitors allowed (except for medical/care purpose). Stage 3 restrictions differed as residents were not required to remain within a 5-km radius of their home, and were not bound by a curfew (34). On 28th October 2020, with eased cases, these stringent restrictions were lifted. However, with an increase in COVID-19 cases during 2021, social distancing measures were reenacted. These included short lockdowns in May, June and July, 2021 (28th May to 10th June, 16th July to 27th July) with an extended lockdown from 5th August until the end of October 2021 [31, 35]. It is important to highlight that during the suggested periods, regional Victorian residents lived under Stage 3 restrictions, comparable to Stage 4 restrictions (see [36] for details). For details around the stringency of measures please consider the seminal Blavatnik School of Government Working Paper, where policy responses to COVID-19 across all Australian States and Territories have been documented [31]. Given the stringent level of restrictions within Victoria, Australia, it is an ideal location to investigate the factors which contribute to resilience during periods of lockdown or crisis.

Study overview

A longitudinal study developed via a collaborative research partnership between peer-support workers with lived experience of SCI and university researchers in Australia, was undertaken. The study aimed to fill knowledge gaps around factors promoting resilience for people with mobile disability, people with SCI, during periods of increased social distancing restrictions and periods of isolation. In particular, it aimed to test for indirect and direct relationships between evidence-based factors associated with resilience for people with SCI. Factors had to meet two criteria and had to be (i) identified as significantly associated with resilience within a 2021 integrative review of 11 studies which aimed to establish the factors associated with resilience for people with SCI [37], and (ii) identified as important to consider by co-investigators with lived experience of SCI. The considered factors were psychosocial (social support, life satisfaction, and psychological health), and demographic (functional independence, employment status, age, gender and living location). Given relationships established within an integrated review [37], it was expected that being older, being employed, living in an urban location, having favourable psychological health, having increased social support, and having higher levels of functional independence would be associated with increased resilience. It was expected that considering the diverse factors contributing to resilience during a time of crisis, could inform public health policy and promotion activities.

Methodology

This research was conducted in accordance with the Declaration of Helsinki, and received ethical approval from the La Trobe University Human Research Ethics Committee (Protocol ID: HEC20197). The study design and methods were developed in collaboration by the co-investigator team, consisting of peer-support workers with lived experience of SCI and university researchers. Specifically, the six-stage collaborative process model for university-community organization partnership was followed [38]. Within Step 1, through consultations between researchers and people with lived-experience of SCI, research aims and goals were established. During Step 2, researchers reviewed academic and grey literature for research in the area/s specified throughout consultations undertaken during Step 1. During Step 3, researchers presented collated information identified during Step 2, and participated in a consultation with people with lived experience of SCI, with the aim of confirming cross-sectional survey questions. Information presented during Step 3, was provided to people with lived-experience of SCI two-weeks prior to the session, to ensure adequate time for consideration. During Step 4, a consultation was held where the cross-sectional survey was presented to people with lived-experience of SCI, and feedback sought. (Similarly, information was provided two weeks prior to the consultation.) During Step 5, the revised cross-sectional survey accounting for feedback provided during Step 4, was presented to people with lived-experience of SCI, and any final feedback gathered and considered. During Step 6, the finalised survey was used.

Study design

A longitudinal approach was utilised. An online survey was completed at three time-points. Participants completed the T1 survey, during September and October 2020. Participants responded to questions in light of their experience since the initiation of COVID-19 restrictions in March 2020. For a considerable portion of the reference period, participants were in stringent lockdown (experiencing either Stage 4 and Stage 3 restrictions during the latter three to four months). Participants completed a second survey between April and May 2021 (T2). Similarly, participants responded to questions in light of their experience over the previous six months, which coincided with a period where stringent social distancing restrictions had been lifted. Finally, between November and December 2021, participants completed a third survey (T3), where they provided their responses given their experience over the previous six months. During the majority (up to 4 months) of this six month period participants were living in stringent lockdown (experiencing either Stage 4 and Stage 3 restrictions). See Edwards, Barnes [31] for details around the extent of restrictions across the referenced periods. Given the study aims of establishing factors contributing to resilience during periods of social distancing restrictions, data from T1 and T3 have been utilised within this study (respectively referenced as Lockdown 1 and Lockdown 2 moving forward). This is the reasonable approach as it considers data collected over a 12-month period where participants provided responses given their experience whilst in two periods of heightened restrictions.

Participants and recruitment

A convenience sampling approach was employed. Members and clients from a single health and social service organisation were recruited to participate in the study. The health and social service organisation provides advocacy services, and also assists people with lived experience organise their allied health support services. The health and social service organisation sent a personalised email to 1100 people with SCI. The information sheet about the study was attached to the email, and a weblink to complete the survey was included within the text of the email. The survey was completed via the REDCap online survey platform. Participants had to have an SCI and resided in Victoria to participate in this study. For participating, participants were entered into a draw to receive a $50 grocery stoe gift voucher (there were 10 vouchers available for each survey round). One hundred and twenty-seven people completed the Lockdown 1 survey and 65 of these participants completed the Lockdown 2 survey.

Data collection

Participants completed the Impact on Participation and Autonomy Questionnaire (IPAQ) [39], which measures the level of difficulty that people with neurological disability, including SCI [40,41,42,43], have across the following domains: Autonomy Outdoors, Autonomy Indoors, Social Life and Relationships, Family role, and Work and Education. For the overall scale and each domain, a higher score is suggestive of having greater difficulty completing the domain. As previously reported [44] Lockdown 1 Cronbach’s alpha values were similar to Cardol, de Haan [40] suggesting the subscales are reliable. In relation to Lockdown 2 responses, Cronbach’s alpha values were similar to Lockdown 1 and Cardol, de Haan [40] suggesting the scales are reliable (see alpha in brackets): Autonomy Outdoors (0.83), Autonomy Indoors (0.92), Social Life and Relationships (0.87), Family Role (0.91), and Work and Education (0.89). Participants completed the International SCI Quality of Life (SCI-QOL) measure [45]. This measure is reliable for people with SCI [46], and includes three questions measuring satisfaction across, psychological health, physical health, and overall wellbeing. For each question participants are required to indicate how satisfied they are (0 [completely dissatisfied] to 10 [completely satisfied]). Cronbach’s alpha values for Lockdown 1 have been reported elsewhere [44] and were similar to findings by New, Tate [46]. Similar to Lockdown 1 and New, Tate [46] Cronbach’s alpha for Lockdown 2 indicated that the scale was reliable (0.87).

Two questions with dichotomous response options were utilised to establish whether participants received formal and informal peer-support (responses were coded as 1 [yes] or 0 [no]). Formal peer-support was described as support provided by someone with lived experience of SCI who was employed by a health and/or social service organisation (for example discussions with a peer-support worker). Informal peer-support was described as support provided by people with lived experience of SCI however not employed with a health and/or social service (for example conversations with someone with living with SCI). Additionally, two questions sought to establish how satisfied participants were with formal and informal support they received. Response options for these questions ranged from: 0 (completely dissatisfied) to 10 (completely satisfied).

The 10-Item Conor Davidson Resilience Scale [5] was used to measure resilience (a reliable measure for people with SCI [47]). The measure requires participants to indicate how much they agree with ten statements (scored from 1 [not true at all], to 5 [true nearly all the time]). A higher score is indicative of increased resilience. Cronbach’s alpha for Lockdown 1 and Lockdown 2 respectively were 0.92 and 0.87, mirroring reliability produced by Kuiper, van Leeuwen [47], and indicative of the scale being reliable.

Data analysis

Measures aligning with psychosocial (social support, life satisfaction, and psychological health), and demographic factors (functional independence, employment status, age, gender and living location) were (i) identified as significantly associated with resilience throughout a 2021 integrative review of 11 studies [37], and (ii) identified as important to consider by co-investigators with lived experience of SCI. The factors and corresponding questions/measures have been included in Table 1 below.

Table 1 Factors and measures

Inferential analysis was progressed via a combination of IBM’s SPSS 26 and IBM’s AMOS 28. A three-stage approach was conducted. Initially, using Lockdown 1 data, Spearman’s rank-order correlation statistic was used to clarify whether a bivariate relationship between individual psychosocial and demographic factors and resilience existed. After, with IBM’s AMOS 28, a structural equation modelling (SEM) approach was undertaken to establish variables which were directly and/or indirectly associated with resilience. Variables which produced a significant bivariate relationship with resilience were included within the model. The SEM procedure was progressed as follows. Using Lockdown 1 data, a saturated model was developed where demographic and social support measures were included as level one variables, life satisfaction and psychological health measures included as level two variables, and resilience as the third-level outcome variable. The approach followed the modelling logic of Amato [48] and included demographic variables as the first block of variables. Social support was grouped with demographic factors as research involving people with SCI has confirmed that social support is associated with life satisfaction and psychosocial health [49], and further confirmed by study co-investigators with lived experience as important to group with demographic factors. Within the saturated model, all level one variables had a unidirectional link with level two variables and the single level three variable (resilience), and two level two variables had a unidirectional link to the single level three variable. All links which were non-significant or did not trend towards significance (p < 0.1) were removed, with the aim of identifying a parsimonious model which was intelligible. Once developed, the same model was applied to Lockdown 2 data.

Model fit was assessed  via the chi-square test for absolute fit (p > 0.05 indicative of the model fitting the data), and Comparative Fit Index (CFI) (≥ 0.95 indicative of an excellent fitting model) and Root Mean Square Error of Approximation (RMSEA) (≤ 0.06 indicative of an excellent fitting model) tests for relative fit.

Findings

Demographic information for both groups has been included in Table 2 below. One hundred and twenty-seven completed the Lockdown 1 survey, and 65 people completed the Lockdown 2 survey.

Table 2 Demographic information

Descriptive statistics for scaled variables during both Lockdown 1 and Lockdown 2 periods are provided in Table 3. Spearman’s rank-order correlation statistic was used for Lockdown 1 data to establish variables to consider within SEMs. As clarified within Table 4, the following variables were significantly associated with resilience: Satisfaction with Formal Peer Support, Receiving Informal Peer Support, IPAQ—Social Support, SCI-QOL—Overall Wellbeing, SCI-QOL—Psychological Health, IPAQ—Work and Education, IPAQ—Family Role, IPAQ—Autonomy Indoors, and IPAQ—Autonomy Outdoors.

Table 3 Descriptive statistics for scaled variables
Table 4 Correlation coefficients

Lockdown 1 data underpinning the suggested variables were required to meet the following assumptions (as detailed by Aminu and Shariff [50]) necessary for SEM: not including a substantial percentage of missing responses, normality requirements, and having an absence of multicollinearity. There is no universal cut-off for the level of missing data which would inhibit inferential analysis [51], however, greater than 10% of missing data for a set variable can contribute to biased findings [52]. The following variables had greater than 10% missing data and were consequently excluded from SEM analysis (with percentage of missing data in brackets): IPAQ – Work and Education (27.6), and Satisfaction with Formal Peer Support (40.9). The level of missing data across both variables was likely because not everyone was working and/or receiving education and, not everyone engaged with formal peer-support. The remaining variables all had less than 7% missing data. Given both Lockdown 1 and Lockdown 2 data, the remaining variables were suitable for SEM analysis as they met normality requirements and multicollinearity was non-existent. All data fell within the acceptable skewness and kurtosis threshold [50, 53], respectively < 3 and < 10, and consequently met the criteria for normal distribution (a requirement for a SEM approach [53]). Furthermore, the remaining variables were not correlated at a level above a threshold of 0.9 [53], nor breaching tolerance (< 0.10) and Variance Inflation Factor (> 10) thresholds [53, 54], thus multicollinearity among independent variables was non-existent.

The SEMs for Lockdown 1 and Lockdown 2 data are illustrated (with standardized coefficients) within Figs. 1 and 2 respectively. Both models exemplified excellent fit. Fit statistics for both models are as follows (with statistics in brackets): Lockdown 1 Resilience ([X2 [3] = 1.684, p = 0.640], CFI = 1.00, RMSEA = 0.000 [90% CI: 0.000, 0.120]), and Lockdown 2 Resilience ([X2 [3] = 2.672, p = 0.445], CFI = 1.000, RMSEA = 0.000 [90% CI: 0.000, 0.144]). Direct and indirect relationships between variables have been included in Table 5 and correlation coefficients are  provided in Table 6. For both Lockdown 1 and Lockdown 2 data, increased family role limitations and favourable psychological health were respectively negatively and positively associated with resilience. Increased outdoor autonomy and social life limitations were negatively associated with psychological health, and indirectly negatively associated with resilience. In summary, experiencing increased limitations inhibiting independence, is associated with lower levels of resilience, whilst having favourable psychological health, is associated with improved resilience. The relationships between increased limitations impacting outdoor autonomy and social engagement, and resilience, are moderated by psychological health.

Fig. 1
figure 1

Structural equation model with Lockdown 1 Resilience as the outcome variable. Note: IPAQ-Family Role: Family Role Limitation, IPAQ-Outdoor Autonomy: Outdoor Autonomy Limitation, IPAQ-Social Role: Social Life Limitation, Er: Resilience Residual Error, Ep: Psychological Health Residual Error

Fig. 2
figure 2

Structural equation model with Lockdown 2 Resilience as the outcome variable. Note: IPAQ-Family Role: Family Role Limitation, IPAQ-Outdoor Autonomy: Outdoor Autonomy Limitation, IPAQ-Social Role: Social Life Limitation, Er: Resilience Residual Error, Ep: Psychological Health Residual Error

Table 5 Structural equation modelling direct and indirect effect coefficients
Table 6 Structural equation modelling correlation coefficients

Discussion

This is the first longitudinal study which utilised data from two COVID-19 lockdown periods, and tested for indirect and direct relationships between evidence-based factors associated with resilience for people with SCI. The findings from this study are robust as they involved identifying factors associated with resilience, and then confirming the appropriateness of those factors based on follow-up data. Initially a model of factors associated with resilience during a period of crisis (the first period of stringent social distancing restrictions in light of the COVID-19 pandemic) was developed. After modelled relationships were confirmed, based on data collected one year later during a second period of stringent restrictions. Thus, the factors indirectly (outdoor autonomy limitations and social life limitations) and directly (family role limitation and psychological health) associated with resilience for people with SCI during a time of crisis, identified within this study, should be considered reliable.

The factors were derived from a synthesis of the literature [37] and recognised as important to consider by co-investigators with lived experience of SCI. Findings confirm that psychological health, social health and functional independence contribute to resilience during stringent social distancing restrictions. When coupled with cross-sectional research focusing on resilience for people with disability and/or their carers during social distancing restrictions, and longitudinal research which considered data collected during social distancing restrictions and prior, the findings confirm factors important to consider for targeted health promotion interventions, and the nuanced relationship between psychosocial and demographic factors, and resilience.

The current study confirmed that psychosocial and functional independence variables (outdoor autonomy and family role limitations) were associated and contributed to a model which best fit resilience. Demographic variables were not significantly associated with resilience, and consequently, were not considered. In this respect, findings confirm contemporary longitudinal research in the area. In their study which aimed to assess the impact of the 2020 lockdown in Italy on the resilience of people living with multiple sclerosis Sbragia, Colombo [55], tested for a relationship between psychological health, functional independence, and demographic factors collected prior to the lockdown, and resilience as measured during the lockdown. Favourable psychological health and greater functional independence were associated with higher resilience, whilst demographic factors were not. In combination, it appears as though for people with disability, including people with SCI, functional independence and psychological health are resilience promoting characteristics during periods of social distancing, and perhaps in general times of crisis.

As to a large degree, psychological health and functional independence are modifiable determinants of resilience, it is worthwhile for health and social care providers, communities and public health departments, to consider interventions and programs which can promote psychological health and functional independence in general, especially during periods of crisis. In relation to promoting psychological health, some favourable interventions are web based, thus have particular value during periods of social distancing restrictions, or during times where physical contact is not possible (for example, natural disasters including extreme weather events). Some examples include engaging with: natural environments delivered via virtual reality [56], web-based guided cognitive behavioural therapy [57], and web-based health coaching [58]. In relation to functional independence, providing allied health support [59, 60], removing environmental barriers [61] (physical and social at home and community levels) and implementing assistive technologies [62] (at home and community levels), may promote functional independence outcomes. Again, such initiatives can be particularly valuable during periods of social isolation or crisis.

The findings confirm that social engagement has an indirect effect on resilience, moderated via psychological health. Increased social life limitations were negatively associated with psychological health and resilience. These longitudinal findings are confirmed by cross-sectional research investigating factors associated with resilience among young people during the initiation of social distancing restrictions in Canada [27]. In their cross-sectional study, Yusuf, Wright [27] found that increased parent support in accessing school was associated with resilience among young people (demographic variables were not). In combination, the findings suggest that programs which promote social participation can contribute to increased resilience during times of crisis. Given our understanding of factors associated with social participation for people with a SCI, programs which assist in health condition management [42] and contribute peer-support engagement [63] may work to reduce social life limitations.

Throughout multiple periods of social distancing restrictions, factors associated with resilience remained consistent. Study findings confirmed that direct and indirect relationships between factors associated with resilience identified during Lockdown 1, were comparable to Lockdown 2 (based on β coefficients and p-values); the notable exception being the relationship between social life limitations and psychological health, which was significant during Lockdown 1, while trending towards significance during Lockdown 2. Furthermore, correlation coefficients and p-values during both periods were comparable. These findings confirm that factors promoting resilience among people with SCI during consecutive periods of crisis (for example lockdowns), may remain the same. Thus, for people with SCI, targeted efforts to promote resilience during an initial period of crisis may still be worthwhile during a subsequent period.

It is important to consider study limitations as they impact the implications of findings. Data underpinning this study were derived during two periods of social distancing restrictions, and at the time, these restrictions were amongst the most stringent globally, and certainly within Australia [31]. Consequently, the factors contributing to resilience and aligned recommendations are likely relevant for regions which have experienced relatively stringent restrictions, and in the future, regions which experience extreme isolation because of crisis. The current study first involved, developing a parsimonious intelligible model based on Lockdown 1 data (n = 127), and applying this same model and calculating parameter estimates using Lockdown 2 data (n = 65). There is no universally understood method to calculate the sample size required for a structural equation model [64,65,66]. The 10:1 cases (samples) to parameter ratio has been tested and identified as acceptable [64, 66]. The model utilised within the current study, includes seven parameters, thus the analysis based on Lockdown 1 data is well powered (n = 127 vs the sample of 70 required), while the analysis based on Lockdown 2 data is near to well powered (n = 65 vs the sample of 70 required). Given the restricted and distinct sample (people with spinal cord injury), and unprecedented scenario (experience of multiple stringent lockdowns, which at the time, were amongst the most stringent globally, in light of a global pandemic), estimates derived from Lockdown 2 data, should be considered. This aligns with the perspective provided by Barrett [67], suggesting that SEM sample size concessions should be made for small samples, when the population is restricted. Given the unique scenario that the study investigated, it is expected that confirmation of these findings would be extremely difficult, thus the current findings are valuable.

Conclusions

Compared to people without disability, people with disability, including SCI, can experience adverse participation and wellbeing outcomes. Such differences can be heightened during times of crisis, necessitating targeted support to promote health. Resilience is a determinant of health, and programs which aim to promote resilience are exceptionally important. Ongoing efforts to improve determinants of resilience, including functional independence, psychological health and social participation, are necessary. It is expected that such efforts will be especially beneficial during times of crisis and worthwhile to consider as it may reduce the amplified health and wellbeing gap between people with and without disability that exists during times of crisis.

Availability of data and materials

The data underpinning the current study is not publicly available. Identifiable information was collected, and participants consented to the raw data being accessible to the research team only. If someone wishes to request data from this study, please contact Dr Ali Lakhani (a.lakhani@latrobe.edu.au).

References

  1. World Health Organization. Disability and Health. 2021 13th December 2021. Available from: https://www.who.int/news-room/fact-sheets/detail/disability-and-health.

  2. Nzeribe E, et al. COVID-19 pandemic and people living with disability. J Disabil Stud. 2021;7(1):2021.

    Google Scholar 

  3. Kendall E, et al. Immediate and Long-Term Implications of the COVID-19 Pandemic for People With Disabilities. Am J Public Health. 2020;110(12):1774–9.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Lindsay S, Ahmed H, Apostolopoulos D. Facilitators for Coping With the COVID-19 Pandemic: Comparing Youth With and Without Disabilities. Arch Phys Med Rehabil. 2021;102(10):e106.

    Google Scholar 

  5. Connor KM, Davidson JR. Development of a new resilience scale: the Connor-Davidson Resilience Scale (CD-RISC). Depress Anxiety. 2003;18(2):76–82.

    Article  PubMed  Google Scholar 

  6. Silverman AM, et al. Resilience predicts functional outcomes in people aging with disability: a longitudinal investigation. Arch Phys Med Rehabil. 2015;96(7):1262–8.

    Article  PubMed  Google Scholar 

  7. Kasser SL, Zia A. Mediating role of resilience on quality of life in individuals with multiple sclerosis: a structural equation modeling approach. Arch Phys Med Rehabil. 2020;101(7):1152–61.

    Article  PubMed  Google Scholar 

  8. Dening KH, Jones L, Sampson EL. Preferences for end-of-life care: A nominal group study of people with dementia and their family carers. Palliat Med. 2012;27(5):409–17.

    Article  PubMed  Google Scholar 

  9. Martin S, Kasser SL. The role of resilience: Physical activity continuation after falling in adults with multiple sclerosis. Disabil Health J. 2021;14(2):101046.

    Article  PubMed  Google Scholar 

  10. van der Meulen E, et al. Longitudinal associations of psychological resilience with mental health and functioning among military personnel: a meta-analysis of prospective studies. Soc Sci Med. 2020;255:112814.

    Article  PubMed  Google Scholar 

  11. Chang L-Y, et al. Resilience buffers the effects of sleep problems on the trajectory of suicidal ideation from adolescence through young adulthood. Soc Sci Med. 2021;279:114020.

    Article  PubMed  Google Scholar 

  12. Wu Q, et al. Acculturation, resilience, and the mental health of migrant youth: a cross-country comparative study. Public Health. 2018;162:63–70.

    Article  CAS  PubMed  Google Scholar 

  13. Lensch T, et al. Adverse childhood experiences and co-occurring psychological distress and substance abuse among juvenile offenders: the role of protective factors. Public Health. 2021;194:42–7.

    Article  CAS  PubMed  Google Scholar 

  14. Hiyoshi A, et al. Stress resilience in adolescence and subsequent antidepressant and anxiolytic medication in middle aged men: Swedish cohort study. Soc Sci Med. 2015;134:43–9.

    Article  PubMed  Google Scholar 

  15. Xu J, Ou L. Resilience and quality of life among Wenchuan earthquake survivors: the mediating role of social support. Public Health. 2014;128(5):430–7.

    Article  CAS  PubMed  Google Scholar 

  16. Guyer, P., C. van Koot – Hodges, and B. Weijermars, Are Expanded Resilience Capacities Associated with Better Quality-of-Life Outcomes? Evidence from Poor Households Grappling with Climate Change in Bangladesh, Chad, India and Nepal, in Handbook of Quality of Life and Sustainability, J. Martinez, C.A. Mikkelsen, and R. Phillips, Editors. 2021, Springer International Publishing: Cham. p. 157–178.

  17. Herron RV, et al. Rural older adults’ resilience in the context of COVID-19. Soc Sci Med. 2022;306:115153.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Kimhi S, et al. Resilience and demographic characteristics predicting distress during the COVID-19 crisis. Soc Sci Med. 2020;265:113389.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Ran L, et al. Psychological resilience, depression, anxiety, and somatization symptoms in response to COVID-19: a study of the general population in China at the peak of its epidemic. Soc Sci Med. 2020;262:113261.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Barzilay R, et al. Resilience, COVID-19-related stress, anxiety and depression during the pandemic in a large population enriched for healthcare providers. Transl Psychiatry. 2020;10(1):291.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. To QG, et al. The association of resilience with depression, anxiety, stress and physical activity during the COVID-19 pandemic. BMC Public Health. 2022;22(1):491.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Na L, Yang L. Psychological and behavioral responses during the COVID-19 pandemic among individuals with mobility and/or self-care disabilities. Disabil Health J. 2022;15(1):101216.

    Article  PubMed  Google Scholar 

  23. Mikolajczyk B, et al. Resilience and mental health in individuals with spinal cord injury during the COVID-19 pandemic. Spinal Cord. 2021;59(12):1261–7.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Spencer P, et al. “It’s ok, mom. I got it!”: Exploring the experiences of young adults with intellectual disabilities in a postsecondary program affected by the COVID-19 pandemic from their perspective and their families’ perspective. J Intellect Disabil. 2021;25(3):405–14.

    Article  PubMed  Google Scholar 

  25. Lim TSH, et al. Factors contributing to psychological ill-effects and resilience of caregivers of children with developmental disabilities during a nation-wide lockdown during the COVID-19 pandemic. J Autism Dev Disord. 2022;52(7):3015–25.

    Article  PubMed  Google Scholar 

  26. Montirosso R, et al. Stress symptoms and resilience factors in children with neurodevelopmental disabilities and their parents during the COVID-19 pandemic. Health Psychol. 2021;40(7):428–38.

    Article  PubMed  Google Scholar 

  27. Yusuf A, et al. Factors associated with resilience among children and youths with disability during the COVID-19 pandemic. PLoS One. 2022;17(7):e0271229.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Scheffers F, Moonen X, van Vugt E. Assessing the quality of support and discovering sources of resilience during COVID-19 measures in people with intellectual disabilities by professional carers. Res Dev Disabil. 2021;111:103889.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Verdolini N, et al. Resilience and mental health during the COVID-19 pandemic. J Affect Disord. 2021;283:156–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chen L-K. Older adults and COVID-19 pandemic: Resilience matters. Arch Gerontol Geriatr. 2020;89:104124–104124.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Edwards B, et al. Variation in policy response to COVID-19 across Australian states and territories: BSG-WP-2022/046. 2022.

    Google Scholar 

  32. Premier of Victoria. Statement From The Premier: 30 June 2020. 2020. https://www.premier.vic.gov.au/statement-premier-72.

  33. Department of Health and Human Services. Statement from the Premier: 26 October 2020. 2020. https://www.dhhs.vic.gov.au/updates/coronavirus-covid-19/statement-premier-26-october-2020.

  34. Department of Health and Human Services. Premier's statement on changes to Melbourne’s restrictions. 2020. https://www.dhhs.vic.gov.au/updates/coronavirus-covid-19/premiers-statement-changes-melbournes-restriction-2-august-2020.

  35. Premier of Victoria. News and updates from Dan Andrews and his team. 2022. Available from: https://www.premier.vic.gov.au/?q=lockdown&page=1.

    Google Scholar 

  36. Victoria State Government. Victorian Government COVID-19 - DPC - Stage 3 Stay at Home Restrictionss - Regional Victoria - Stakeholder Pack. 2020. Available from: https://www.vichealth.vic.gov.au/-/media/DPC-COVID19--Stage-3-Regional-Victoria-Stakeholder-Pack-l-1008.pdf?la=en&hash=3C3C5539E828D2E54D246AA3565CA0FC1B2CA246.

    Google Scholar 

  37. Bhattarai M, Smedema SM, Maneewat K. An Integrative Review of Factors Associated With Resilience Post-Spinal Cord Injury. Rehabil Couns Bull. 2020;64(2):118–27.

    Article  Google Scholar 

  38. Lakhani A, Parekh S, Watling DP, Grimbeek P, Duncan R, Charlifue S, et al. Acces and engagement with places in the community, and the quality of life among people with spinal cord damage. J Spinal Cord Med. 2021;1–9. https://doi.org/10.1080/10790268.2020.1860867.

  39. Cardol M, De Jong BA, Ward CD. On autonomy and participation in rehabilitation. Disabil Rehabil. 2002;24(18):970–4 discussion 975-1004.

    Article  CAS  PubMed  Google Scholar 

  40. Cardol M, et al. Psychometric properties of the impact on Participation and Autonomy Questionnaire. Arch Phys Med Rehabil. 2001;82(2):210–6.

    Article  CAS  PubMed  Google Scholar 

  41. Bombardier C, et al. Collaborative Care for Pain, Depression and Physical Inactivity in an Outpatient SCI Clinic: the SCI-CARE Study…2016 ACRM / American Congress of Rehabilitation Medicine Annual Conference 30 October - 4 November 2016, Chicago, IL. Arch Phys Med Rehabil. 2016;97(10):e78–9.

    Article  Google Scholar 

  42. Craig A, et al. Adjustment following chronic spinal cord injury: Determining factors that contribute to social participation. Br J Health Psychol. 2015;20(4):807–23.

    Article  PubMed  Google Scholar 

  43. Cardol M, et al. Beyond disability: perceived participation in people with a chronic disabling condition. Clin Rehabil. 2002;16(1):27–35.

    Article  PubMed  Google Scholar 

  44. Lakhani A, Dema S, Hose J, Erdem N, Wollersheim D, Grimbeek P, Charlifue S. What happens post-lockdown for people with disability? Autonomy, quality of life, service access and health changes for people with spinal cord injury in Victoria, Australia after COVID-19 social distancing restrictions. Health Soc Care Community. 2022.  https://doi.org/10.1111/hsc.13958.

  45. Charlifue S, Post MW, Biering-Sorensen F, Catz A, Dijkers M, Geyh S, et al. International Spinal Cord Injury Quality of Life Basic Data Set. Spinal Cord. 2012;50(9):672–5. https://doi.org/10.1038/sc.2012.27.

  46. New PW, et al. Preliminary psychometric analyses of the International Spinal Cord Injury Quality of Life Basic Data Set. Spinal Cord. 2019;57(9):789–95.

    Article  PubMed  Google Scholar 

  47. Kuiper H, et al. Measuring resilience with the Connor-Davidson Resilience Scale (CD-RISC): which version to choose? Spinal Cord. 2019;57(5):360–6.

    Article  PubMed  Google Scholar 

  48. Amato S. Effects of environmental factors on sleep patterns in traumatic brain injured adults in the rehabilitation setting, in College of Nursing. USA: Kent State University; 2018.

    Google Scholar 

  49. Zürcher C, et al. Mental health in individuals with spinal cord injury: The role of socioeconomic conditions and social relationships. PLoS One. 2019;14(2):e0206069.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Aminu IM, Shariff M. Strategic orientation, access to finance, business environment and SMEs performance in nigeria: data screening and preliminary analysis. Eur J Bus Manag. 2014;6:124–31.

    Google Scholar 

  51. Dong Y, Peng CY. Principled missing data methods for researchers. Springerplus. 2013;2(1):222.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Bennett DA. How can I deal with missing data in my study? Aust N Z J Public Health. 2001;25(5):464–9.

    Article  CAS  PubMed  Google Scholar 

  53. Hair JF, et al. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 3rd ed. Thousand Oaks: Sage; 2022.

    Google Scholar 

  54. Pallant, J., SPSS Survival Manual: A Step by Step Guide to Data Analysis Using IBM SPSS (7th ed.). London: Routledge; 2002.

  55. Sbragia E, et al. Embracing resilience in multiple sclerosis: a new perspective from COVID-19 pandemic. Psychol Health Med. 2022;27(2):352–60.

    Article  PubMed  Google Scholar 

  56. Lakhani A, Martin K, Gray L, Mallison J, Grimbeek P, Hollins I, Mackareth C. What Is the Impact of Engaging With Natural Environments Delivered Via Virtual Reality on the Psycho-emotional Health of People With Spinal Cord Injury Receiving Rehabilitation in Hospital? Findings From a Pilot Randomized Controlled Trial. Arch Phys Med Rehabil. 2020;101(9):1532–40. https://doi.org/10.1016/j.apmr.2020.05.013.

  57. Mehta S, et al. Guided internet-delivered cognitive-behaviour therapy for persons with spinal cord injury: a feasibility trial. Spinal Cord. 2020;58(5):544–52.

    Article  PubMed  Google Scholar 

  58. Allin S, et al. Web-based health coaching for spinal cord injury: results from a mixed methods feasibility evaluation. JMIR Rehabil Assist Technol. 2020;7(2):e16351.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Harvey LA, Glinsky JV, Chu J. Do any physiotherapy interventions increase spinal cord independence measure or functional independence measure scores in people with spinal cord injuries? A systematic review. Spinal Cord. 2021;59(7):705–15.

    Article  PubMed  Google Scholar 

  60. Ávila A, et al. Promoting functional independence in people with Alzheimer’s disease: Outcomes of a home-based occupational therapy intervention in Spain. Health Soc Care Community. 2018;26(5):734–43.

    Article  Google Scholar 

  61. Abou, L. and L.A. Rice, Predictors of participation enfranchisement of wheelchair users with spinal cord injury in the United States. J Spinal Cord Med. 2022:1–9. https://www.tandfonline.com/doi/pdf/10.1080/10790268.2022.2087336?casa_token=2oQdDD1lGKgAAAAA:FoiwAno-B5EWmn2ziTZ-mDJN7RJwjvjGZiK5_5ZzUGCAzxHUj_6zLY1deM4EACIJweav2A7uBmBxbA.

  62. Jackson, H., et al., Change in care hours, cost, and functional independence following continence and assistive technology intervention in an acquired brain injury population. Disabil Rehabil. 2022;45(7):1–12.

  63. Barclay L, et al. Facilitators and barriers to social and community participation following spinal cord injury. Aust Occup Ther J. 2016;63(1):19–28.

    Article  PubMed  Google Scholar 

  64. Jackson DL. Revisiting sample size and number of parameter estimates: some support for the N:q hypothesis. Struct Equ Modeling. 2003;10(1):128–41.

    Article  Google Scholar 

  65. Kline RB. Principles and Practice of Structural Equation Modeling. ProQuest. 3rd ed. New York: New York : Guilford Publications, Inc; 2010.

    Google Scholar 

  66. Wolf EJ, et al. Sample size requirements for structural equation models: an evaluation of power, bias, and solution propriety. Educ Psychol Meas. 2013;76(6):913–34.

    Article  PubMed  Google Scholar 

  67. Barrett P. Structural equation modelling: Adjudging model fit. Personality Individ Differ. 2007;42(5):815–24.

    Article  Google Scholar 

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Acknowledgements

The authors would like to acknowledge that the research questions and methods were informed by the larger peer-support team at AQA-Victoria. 

Funding

This research was funded by a La Trobe University, College of Science, Health and Engineering Start-Up Grant.

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Contributions

AL led all aspects of this study and the development of the manuscript including: study conceptualization, data collection and analysis, and writing for publication. SD, JH, and NE co-led the survey development, led recruitment, and contributed to finding interpretations and the development of implications. PG and DW, respectfully assisted with data analysis and data collection, and both assisted with interpreting the findings. AG, and SC provided a critical review of the manuscript, and contributed to implication and discussion sections. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ali Lakhani.

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Ethics approval and consent to participate

This research was conducted in accordance with the Declaration of Helsinki, and received ethical approval from the La Trobe University Human Research Ethics Committee. The first online survey question was a consent question, where participants were requested to provide written informed consent for their longitudinal data to be collected.

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Not applicable.

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The authors declare no competing interests.

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Lakhani, A., Dema, S., Hose, J. et al. Predictors of resilience for people with spinal cord injury over two periods of COVID-19 social distancing restrictions: a 12-month longitudinal study using structural equation modelling. BMC Public Health 23, 1334 (2023). https://doi.org/10.1186/s12889-023-16238-x

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