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Corresponding author Vegard P. Moen, MSc, Centre for Habilitation and Rehabilitation, Haukeland University Hospital, Østre Nesttunveg 2, Nesttun N-5221, Bergen, Norway.
Centre for Habilitation and Rehabilitation, Haukeland University Hospital, Bergen, NorwayDepartment of Global Public Health and Primary Care, University of Bergen, Bergeny, Norway
Department of Global Public Health and Primary Care, University of Bergen, Bergeny, NorwayCentre for Clinical Research, Haukeland University Hospital, Bergen, Norway
To study relations between sense of coherence (SOC), disability, and mental and physical components of health-related quality of life (HRQOL) among rehabilitation patients.
Design
Survey.
Setting
Rehabilitation centers in secondary care.
Participants
Patients (N=975) from the Western Norway Health Region consented to participate and had valid data of the main outcome measures.
Interventions
Not applicable.
Main Outcome Measures
SOC was measured with the sense of coherence questionnaire (13-item SOC scale [SOC-13]), disability with the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0), and HRQOL with the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36).
Results
Mean scores ± SD were 62.9±12.3 for SOC-13, 30.8±16.2 for WHODAS 2.0, 32.8±9.6 for SF-36 physical component score, and 43.6±11.8 for SF-36 mental component score. Linear regression analysis showed that increased SOC score was associated with reduced disability scores in the following domains with estimated regression coefficients (95% confidence interval) cognition –0.20 (–0.32 to –0.08), getting along –0.36 (–0.52 to –0.25), and participation –0.23 (–0.36 to –0.11). The fit of 2 structural models with the association from SOC to HRQOL and disability or with disability as a mediator was better for the mental versus the physical component of HRQOL. High SOC increased the mental component of HRQOL, consistent for all diagnostic groups. For both models, good fit was reported for circulatory and less good fit for musculoskeletal diseases.
Conclusions
The results indicate that higher SOC decreases disability in mental domains. The effect of SOC on disability and HRQOL might vary between diagnostic groups. SOC could be a target in rehabilitation, especially among patients with circulatory diseases, but prospective studies are needed.
Rehabilitation patients usually have chronic conditions with sensory, cognitive, and mobility impairments, and experience activity limitations as well as participation restrictions. HRQOL is poorer in rehabilitation patients compared with a healthy reference population.
Aron Antonovsky developed a salutogenic model to explain why some people remain healthy, or even improve their health, when experiencing life events (stressors) whereas others become ill.
A key concept in Antonovsky’s model is sense of coherence (SOC), a measure of an individual’s capacity to cope. SOC captures an individual’s perception of life as being comprehensible, manageable, and meaningful.
Association of the sense of coherence with physical and psychosocial health in the rehabilitation of osteoarthritis of the hip and knee: a prospective cohort study.
Better knowledge of SOC and how it affects disability and HRQOL may help to identify subgroups when planning rehabilitation and tailoring interventions.
A protective personal factor against disability and dependence in the elderly: an ordinal regression analysis with nine geographically-defined samples from Spain.
A protective personal factor against disability and dependence in the elderly: an ordinal regression analysis with nine geographically-defined samples from Spain.
A protective personal factor against disability and dependence in the elderly: an ordinal regression analysis with nine geographically-defined samples from Spain.
However, that study only included older adults and used an overall disability score, which may be less relevant in clinical settings than disability domains. To our knowledge, no previous studies have investigated the effect of SOC on disability (as conceptualized in the International Classification of Functioning, Disability and Health
suggesting further investigation of this relation and its direction in other populations.
We have not found any studies investigating the direction of the association from SOC to HRQOL and disability simultaneously, whether SOC has a direct relation to HRQOL and disability, or if the direction of the association from SOC to HRQOL is mediated by disability (fig 1).
Fig 1Hypothesized structural models, including the results from SEM among 975 patients accepted for specialized somatic rehabilitation in the Western Norway Health Region during the first half of 2015.*Estimated standard regression coefficients with 95% CIs for model including MCS of HRQOL. †Estimated standard regression coefficients with 95% confidence intervals for model including PCS of HRQOL.
Variables associated with disability in male and female long-term survivors from acute myocardial infarction. Results from the MONICA/KORA Myocardial Infarction Registry.
This study aimed to increase the understanding of SOC, disability, and HRQOL in rehabilitation patients. Specific objectives were to (1) describe the simultaneous distribution of SOC, disability, and HRQOL; (2) investigate possible effects of SOC on disability domains; and (3) investigate hypothesized structural models for SOC, disability, and HRQOL. Analyses were also performed specifically for diagnostic groups to enhance clinical significance.
Methods
Design, sample, and procedure
The study used a cross-sectional design. All patients in the Western Norway Health Region accepted for inpatient or outpatient rehabilitation at a rehabilitation center in secondary care during the first half of 2015, and who were referred from hospitals or general practitioners, were invited by mail or at admittance. A flow chart showing participant inclusion and exclusion is shown in fig 2. Further details are provided in a previous paper.
Fig 2Flowchart of patients accepted for rehabilitation at a rehabilitation center in secondary care in the Western Norway Health Region during the first half of 2015.
Patient-reported data were linked to individual public register data obtained from Statistics Norway, on educational attainment, residence municipality, and marital status.
Ethics
This study was approved by the Regional Committee for Medical Research Ethics in Western Norway, REK-No. 2014-1636. Written informed consent, including linkage to public register data, was obtained from study participants.
Main variables
The 36-item World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) assesses disability across 6 domains
: cognition (6 items), mobility (5 items), self-care (4 items), getting along (5 items), life activities (8 items), and participation (8 items). Four life activities items relate to household and 4 to work or study. Responses are on a 5-point Likert scale with 2 anchor responses (“none” and “extreme or cannot do”). Domain scores and a total disability score are calculated using complex scoring according to the manual, ranging from 0 (no disability) to 100 (full disability). An algorithm enabled calculation of a score for the life activities domain and a total score (regardless of whether the 4 items related to work or study were answered). The instrument has satisfactory reliability and moderate validity for use in rehabilitation services.
The scale contains 36 items in 8 domains: mental health, vitality, bodily pain, general health, social functioning, physical functioning, role limitation related to physical problems, and role limitation related to emotional problems. In addition, 1 item assesses changes in general health over the past year. The 8 domain scores can be summarized to give a mental component score (MCS) and a physical component score (PCS), which were used in this study. Scores range from 0 to 100, with higher scores indicating better HRQOL. The instrument is a valid measure of health status for a range of patients
scale comprises items in 3 subscales: comprehensibility, manageability, and meaningfulness. Each item is scored on a 7-point Likert scale with 2 anchor responses (“never” and “very often”). After reversing 5 negatively formulated items, all items are summed to give a total score of 13-91; higher scores indicate stronger SOC. The SOC-13 has generally acceptable reliability and validity.
which comprises 14 items on 2 subscales: anxiety and depression. Each subscale has 7 items. Scores range from 0 to 21, higher scores representing higher severity. HADS performs well as a screening instrument in assessing symptom severity in somatic patients
Diagnostic groups were categorized based on referral diagnoses (registered according to the International Classification of Diseases-10 chapter without any further details) into musculoskeletal, circulatory, and neurologic diseases, neoplasms, and other (including various health conditions with <50 patients).
Multimorbidity was defined as the coexistence of more than 1 self-reported chronic conditions in the same individual
This instrument comprises 5 questions and a health rating scale. The questions assess physical activity, psychological distress, and pain or discomfort. Pain or discomfort has 5 possible responses, from no pain or discomfort to extreme pain or discomfort. Measurement properties of the instrument have been tested extensively.
Age was categorized by decades. Marital status was dichotomized as married or unmarried. The highest completed education level was categorized as primary school, high school, or college or university. Smoking status was dichotomized as current smoking or not. Residence was dichotomized as rural or urban, with the cutoff being 20,000 inhabitants in the municipality. Rehabilitation was dichotomized as initial (referred by a general practitioner) or ongoing management (referred by a hospital).
Statistical analysis
For descriptive statistics, mean and SD are reported. To compare the female proportion and age distribution between participants and nonparticipants, exact chi-square and Mann-Whitney U tests were used.
Multiple linear regression analysis was used to study the effect of SOC on domain-specific disability. Results are reported as estimated regression coefficients with 95% confidence intervals (95% CI) and P values from the F test. The distribution of residuals was checked for adherence to assumptions of linearity, normality, and variance homogeneity. Analysis of variance was performed using the F test to investigate differences in SOC scores for variables with more than 2 categories. Tukey post hoc test was used for subgroup comparisons.
Path analysis using structural equational modeling (SEM) was performed for 2 hypothesized models (see fig 1). Satisfactory model fit was defined as a comparative fit index close to 0.95 or higher, Tucker-Lewis index close to 0.95 or higher, a root mean square error of approximation close to <0.06 or lower and cutoff close to 0.08 or lower, and standardized root mean square residual close to 0.08 or lower.
Regression coefficients were examined for statistical significance. Estimated model parameters are given with 95% CI.
All analyses were performed for the full sample and separately for diagnostic groups. However, the structural models were estimable only in 3 diagnostic groups because the other groups were too small for valid analysis.
Multiple imputations for missing items were applied according to the WHODAS 2.0 manual,
with the number of imputation sets=5. If the rate of missing WHODAS 2.0 items was >50%, the data were excluded. Missing items in the SF-36 were managed according to the SF-36 manual.
For the HADS and SOC-13, scores for patients with fewer than 3 missing questions per subscale were included. For missing data, scores were imputed based on the mean across each person’s available responses for each subscale. For SEM analysis, listwise deletion was used, and no further imputation or adjustments were applied. The criterion for statistical significance was set at 5%. SPSS version 23a was used for all statistical analyses except SEM where RStudio version 1.0.143b with the lavaan package 0.5-23.1097 was used.
Results
In total, 984 of eligible patients provided responses and data of 975 patients were included in the analyses (response rate, 34.6%). The mean age ± SD was 57.6±14.0 years and 63.2% of participants were women. Among nonparticipants the mean age ± SD was 55.6±16.7 years (P<.001) and 67.2% were women (P<.05).
Main outcome measures could not be calculated for 9 patients. After imputation for missing items, a WHODAS 2.0 overall disability score could be calculated for 967 patients, SF-36 PCS and MCS scores for 885 patients, and SOC scores for 933 patients. For all scales (and variables), missing values for items ranged from 0.4% to 4.2%, except for 1 WHODAS 2.0 item concerning sexual activities (12.8%).
WHODAS 2.0, SF-36 (PCS and MCS), and SOC-13 scores are shown in table 1. Men had significantly higher SOC scores than women (P<.001), and participants aged ≥50 years had significantly higher SOC scores than those aged <50 years (P≤.01). Participants with circulatory diseases scored significantly higher than those with musculoskeletal diseases (P=.001).
Table 1Distribution of WHODAS score, SF-36 component scores, and SOC score among 975 patients accepted for specialized somatic rehabilitation in the Western Norway Health Region during the first half of 2015
Diseases included the following: endocrine, nutritional, and metabolic diseases (n=36), respiratory diseases (n=37), injuries and external causes (n=27), skin diseases (n=24), factors influencing health status and contact with health services (n=23), mental and behavioral disorders (n=12), symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified (n=9); codes for special purposes (n=7); diseases of the digestive system (n=6); diseases of the blood and blood-forming organs, and certain disorders involving the immune mechanism (n=5); diseases of the ear and the mastoid process (n=3); diseases of the genitourinary system (n=3); congenital malformations, deformations, and chromosomal abnormalities (n=5); and certain infectious and parasitic diseases (n=2).
199
61.3
27.4±16.0
NA
34.8±11.0
NA
44.1±11.7
NA
62.8±12.3
NA
Multimorbidity
NA
NA
NA
P<.05 (F test).
NA
P<.05 (F test).
NA
P<.01 (F test).
NA
P<.05 (F test).
Yes
635
65.7
32.9±16.7
NA
31.7±9.1
NA
42.7±12.3
NA
61.8±12.8
NA
No
349
59.3
27.1±14.5
NA
34.9±10.2
NA
45.3±10.8
NA
65.2±11.0
NA
Rehabilitation urgency
NA
NA
NA
P<.001 (F test).
NA
P<.01 (F test).
NA
NA
NA
P<.01 (F test).
Elective
653
68.0
31.9±15.9
NA
32.3±9.3
NA
42.8±12.1
NA
61.6±12.6
NA
Acute
274
53.6
27.6±16.3
NA
34.6±10.2
NA
45.8±11.1
NA
66.5±10.7
NA
Unknown
57
57.9
34.9±15.6
NA
29.9±9.8
NA
43.2±10.9
NA
61.1±12.2
NA
Marital status
NA
NA
NA
P<.001 (F test).
NA
NA
NA
P<.05 (F test).
NA
P<.05 (F test).
Unmarried
455
69.9
32.3±16.4
NA
32.6±9.7
NA
41.4±12.3
NA
60.9±12.9
NA
Married
525
57.9
29.6±15.8
NA
33.0±9.6
NA
45.5±11.1
NA
64.7±11.5
NA
Unknown
4
NA
23.7±16.9
NA
31.2±10.3
NA
47.6±6.0
NA
65.5±11.2
NA
Educational level
NA
NA
NA
P<.001 (F test).
NA
P<.01 (F test).
NA
NA
NA
P<.01 (F test).
Primary school
205
68.8
33.6±17.1
NA
30.6±9.6
NA
43.6±11.8
NA
60.5±12.8
NA
Secondary school
490
60.2
30.7±16.1
NA
32.9±9.9
NA
43.5±12.5
NA
63.0±12.2
NA
College/university
278
66.2
29.0±15.5
NA
34.3±10.8
NA
43.8±10.8
NA
64.5±11.9
NA
Unknown
11
66.2
32.9±14.3
NA
30.7±9.1
NA
43.8±7.9
NA
64.4±12.4
NA
Smoking
NA
NA
NA
P<.01 (F test).
NA
NA
NA
P<.05 (F test).
NA
P<.05 (F test).
Yes
184
70.7
34.1±14.6
NA
31.9±8.6
NA
39.2±12.3
NA
58.1±12.6
NA
No
788
61.4
30.7±16.4
NA
33.0±9.9
NA
44.7±11.5
NA
64.1±12.0
NA
Unknown
12
61.4
29.6±16.0
NA
36.1±4.9
NA
41.9±9.5
NA
62.3±8.7
NA
Living area
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Urban
519
65.1
31.3±16.1
NA
33.0±10.0
NA
42.9±11.7
NA
62.9±12.3
NA
Rural
465
61.5
30.3±16.2
NA
32.7±9.3
NA
44.4±11.9
NA
63.0±12.3
NA
Abbreviations: ICD-10, International Classification of Diseases-10; NA, not applicable.
∗ 0=lowest score of disability, 100=highest score of disability.
# Diseases included the following: endocrine, nutritional, and metabolic diseases (n=36), respiratory diseases (n=37), injuries and external causes (n=27), skin diseases (n=24), factors influencing health status and contact with health services (n=23), mental and behavioral disorders (n=12), symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified (n=9); codes for special purposes (n=7); diseases of the digestive system (n=6); diseases of the blood and blood-forming organs, and certain disorders involving the immune mechanism (n=5); diseases of the ear and the mastoid process (n=3); diseases of the genitourinary system (n=3); congenital malformations, deformations, and chromosomal abnormalities (n=5); and certain infectious and parasitic diseases (n=2).
Disability domains with mental components were associated with SOC, with lower disability scores for higher SOC scores (table 2). The estimated regression coefficients (95% CI) of SOC on cognition, getting along, and participation were –0.20 (–0.32 to –0.08), –0.38 (–0.52 to –0.25) and –0.23 (–0.36 to –0.11), respectively. No significant interactions were found, and the reported results were based on analyses with no interaction terms included.
Table 2Results of a fully adjusted linear regression analysis for predicting WHODAS 2.0 domain scores among 975 patients accepted for specialized somatic rehabilitation in the Western Norway Health Region during the first half of 2015
Diseases included the following: endocrine, nutritional, and metabolic diseases (n=37), respiratory diseases (n=36), injuries and external causes (n=26), factors influencing health status and contact with health services (n=23), mental and behavioral disorders (n=13), symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified (n=9); codes for special purposes (n=7); diseases of the digestive system (n=6); diseases of the blood and blood-forming organs, and certain disorders involving the immune mechanism (n=5); diseases of the ear and the mastoid process (n=3); diseases of the genitourinary system (n=3); congenital malformations, deformations, and chromosomal abnormalities (n=3); and certain infectious and parasitic diseases (n=2).
Abbreviations: B, unstandardized estimated regression coefficient; EQ-5D, EuroQol EQ-5D; HADS-A, Hospital Anxiety and Depression scale, anxiety subscale; HADS-D, Hospital Anxiety and Depression scale, depression subscale; ref, reference.
∗ Diseases included the following: endocrine, nutritional, and metabolic diseases (n=37), respiratory diseases (n=36), injuries and external causes (n=26), factors influencing health status and contact with health services (n=23), mental and behavioral disorders (n=13), symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified (n=9); codes for special purposes (n=7); diseases of the digestive system (n=6); diseases of the blood and blood-forming organs, and certain disorders involving the immune mechanism (n=5); diseases of the ear and the mastoid process (n=3); diseases of the genitourinary system (n=3); congenital malformations, deformations, and chromosomal abnormalities (n=3); and certain infectious and parasitic diseases (n=2).
† From no pain or discomfort to extreme pain or discomfort, 5 categories.
‡ 0=lowest score of depressive symptoms, 21=highest score of depressive symptoms.
§ 0=lowest score of anxiety symptoms, 21=highest score of anxiety symptoms.
The disability domain getting along was associated with SOC for most diagnostic groups, with lower disability score for higher SOC score, and associations with SOC were present in some other domains for some diagnostic groups (table 3).
Table 3Results of a fully adjusted linear regression analysis for predicting WHODAS 2.0 domain scores in main groups of diseases among 975 patients accepted for specialized somatic rehabilitation in the Western Norway Health Region during the first half of 2015
13=lowest score, 91=highest score (best). Diseases included the following: endocrine, nutritional, and metabolic diseases (n=37); respiratory diseases (n=36); injuries and external causes (n=26); factors influencing health status and contact with health services (n=23); mental and behavioral disorders (n=13); symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified (n=9); codes for special purposes (n=7); diseases of the digestive system (n=6); diseases of the blood and blood-forming organs, and certain disorders involving the immune mechanism (n=5); diseases of the ear and the mastoid process (n=3); diseases of the genitourinary system (n=3); congenital malformations, deformations, and chromosomal abnormalities (n=3); and certain infectious and parasitic diseases (n=2).
∗ Adjusted for sex, age groups, multimorbidity, rehabilitation urgency, marital status, education, smoking, urbanity, pain/discomfort, depressive symptoms, and anxiety symptoms.
† 13=lowest score, 91=highest score (best). Diseases included the following: endocrine, nutritional, and metabolic diseases (n=37); respiratory diseases (n=36); injuries and external causes (n=26); factors influencing health status and contact with health services (n=23); mental and behavioral disorders (n=13); symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified (n=9); codes for special purposes (n=7); diseases of the digestive system (n=6); diseases of the blood and blood-forming organs, and certain disorders involving the immune mechanism (n=5); diseases of the ear and the mastoid process (n=3); diseases of the genitourinary system (n=3); congenital malformations, deformations, and chromosomal abnormalities (n=3); and certain infectious and parasitic diseases (n=2).
The results from the SEM are shown in fig 1 and table 4. SOC had a positive association with both HRQOL measures, mostly mediated by disability because better SOC led to reduced disability which led to better HRQOL. The model fit was best for the subpopulation with circulatory diseases. All models were significantly better than the independent model.
Table 4Results from SEM for prior hypothesized structural models of SOC, disability, and HRQOL among 975 patients accepted for specialized somatic rehabilitation in the Western Norway Health Region during the first half of 2015
Satisfactory fit of a model was defined by a CFI and TLI close to 0.95 or higher, an RMSEA close to 0.06 or lower, and a standardized root mean square residual close to 0.08 or lower.42
Abbreviations: CFI, comparative fit index; RMSEA, root mean square error of approximation; SRMR, standardized root mean square residual; TLI, Tucker-Lewis index.
∗ Partially mediated.
† Direct relation only.
‡ Satisfactory fit of a model was defined by a CFI and TLI close to 0.95 or higher, an RMSEA close to 0.06 or lower, and a standardized root mean square residual close to 0.08 or lower.
To our knowledge this is the first study to assess the relations between SOC, domain-specific disability, and HRQOL in a large sample of rehabilitation patients with diagnoses that are common in rehabilitation centers in secondary care. SEM was performed, in which 2 structural models were investigated. The largest diagnostic groups were analyzed separately to enhance the clinical relevance.
In comparisons with previous Norwegian studies, the mean SOC-13 score found in this study was slightly lower than in a population of women after myocardial infarction
consistent with the lower SOC-13 scores for patients with musculoskeletal diseases compared to patients with circulatory diseases in the present study. The mean SF-36 domain scores in the present study (data not shown) were lower compared with a Dutch study of patients in a rehabilitation center.
However, the Dutch study was postrehabilitation, 6-12 months after discharge. In our study population, overall disability scores were higher than in similar populations of other studies.
From a theoretical perspective, Antonovsky argued for an association between SOC and both mental and physical components of health, with better health according to stronger SOC. However, the lack of association between SOC and the physical domains of disability (mobility, self-care, life activities) found in the present study is consistent with a previous systematic review.
An association between mental disability domains and SOC was found in all diagnostic groups and implies that rehabilitation patients with better capacity to cope report less disability in mental domains, also consistent with the same review.
Some items in the participation domain assess attitudes, reactions, and actions from significant persons, which may represent aspects of social support that is positively related to SOC.
The results from the SEM in the full sample showed better fit of both hypothesized models, including the mental components of HRQOL, than the physical components, also in line with previous studies.
This suggests that rehabilitation patients who are able to mobilize available resources to manage challenges of everyday life, and who find this meaningful, may have improved mental health. This was also found in a study where higher levels of SOC predicted better mental health in men 3-6 months after a myocardial infarction.
The fit indices for the hypothesized models found in patients with circulatory diseases support a direction of association from SOC to disability and mental components of HRQOL, consistent with a previous study among adolescents with congenital heart disease.
The fit indices were also adequate considering the physical component of HRQOL. To our knowledge, this has not been reported previously and further investigation using longitudinal studies is needed to confirm that SOC actually improves the physical component of HRQOL among patients with circulatory diseases.
Among patients with musculoskeletal diseases, the results from the path analysis did not support the hypothesized models. We have not found any studies explaining this directly, and future studies should investigate if SOC-related constructs such as pain, depression, and anxiety can explain why these relations vary among different diseases. However, a study among patients with long-term musculoskeletal pain showed no association between SOC and work reentry.
Thus, the relation between SOC, disability, HRQOL, and other important rehabilitation outcomes should be further investigated especially in this diagnostic group.
Although the WHODAS 2.0 measures the restriction on daily life activities and social participation and the SF-36 addresses patient’s physical and mental health, these constructs overlap. Nevertheless, the results from the present SEM, which were numerically similar, imply a different causal role of SOC.
Contrary to our hypothesized structural model, with the direction of association from SOC to disability, the authors of a study investigating the association between SOC and disability among elderly adults suggested an opposite direction.
A protective personal factor against disability and dependence in the elderly: an ordinal regression analysis with nine geographically-defined samples from Spain.
A protective personal factor against disability and dependence in the elderly: an ordinal regression analysis with nine geographically-defined samples from Spain.
; the authors do not further specify the type of this disability. Although Antonovsky postulated SOC to be relatively stable, he considered that SOC could change under certain conditions.
Rehabilitation patients with activity limitations and participation restrictions caused by their health condition may have their SOC weakened, consistent with findings from a 5-year prospective population-based study showing that people with certain disease were among those with the largest decrease in SOC score over time.
Longitudinal studies are needed to assess whether SOC might be decreased before rehabilitation, and if rehabilitation efforts can restore the previous SOC.
Study limitations
The main limitation of this study is the cross-sectional design. We used our hypothesized models to investigate whether data were consistent with causal links between the main outcome measures, disability, HRQOL, and SOC as the main predictors. However, the limitations of a cross-sectional design are well known, and the present findings can only contribute to other evidence. Further investigations in this research area are needed to clarify the importance of SOC in rehabilitation.
One-third of invited patients consented to participate and a large number of survey instruments were completed, indicating an acceptable response rate compared with other large-scale surveys among rehabilitation patients
The large number of instruments used may explain some of the attrition. Nevertheless, the lack of data from 65% of eligible participants limits the validity. The age of participants was slightly higher than among nonrespondents, which may lead to an overestimation of SOC scores because these scores were highest among older adults. However, a lack of information on nonparticipants makes it difficult to determine whether participants were actually healthier or had stronger SOC. Further research should include larger samples with younger patients and with other diseases. Most importantly, only a prospective design can give valid proof of causal mechanisms.
Conclusion
The present study indicates that SOC is related to mental domains of disability as measured by WHODAS 2.0. However, the role of SOC in relation to disability and HRQOL seemed to vary between the diagnostic groups. We believe that targeting SOC in the rehabilitation setting, especially in patients with circulatory diseases, could improve the mental components of disability and HRQOL. Strengthening SOC involves enhancing patients’ understanding and reflection on stressful situations and the available resources and might help the patient to engage in the rehabilitation process and take control of their own life. Future prospective studies might clarify the role of SOC in achieving important outcomes in rehabilitation.
Suppliers
a.
SPSS, version 23; IBM Corporation.
b.
RStudio, version 1.0.143; RStudio.
Acknowledgments
We thank the World Health Organization for technical support in obtaining the WHODAS 2.0. Furthermore, we thank all rehabilitation institutions (Åstveit Health Center, Red Cross Haugland Rehabilitation Centre, Ravneberghaugen Rehabilitation Centre, LHL Clinics Bergen, LHL Clinics Nærland, Rehabilitering Vest Rehabilitation Centre) and participating staff for recruiting patients for this study. We thank Analisa Avila, ELS, from Edanz Group (www.edanzediting.com/ac) for editing a draft of this manuscript.
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A protective personal factor against disability and dependence in the elderly: an ordinal regression analysis with nine geographically-defined samples from Spain.
Variables associated with disability in male and female long-term survivors from acute myocardial infarction. Results from the MONICA/KORA Myocardial Infarction Registry.
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