Background
The aim behind the International Classification of Functioning, Disability, and Health (WHO-ICF) is to provide a standard language and conceptual basis for defining, exploring, and assessing human physio-psycho-social functioning in relation to disability [
1]. Under the WHO-ICF framework, disability is a condition with multiple dimensions that develops as a process with the potential to impair body functions and structures (including both the physiological system or anatomical structures), limit daily activities (i.e., encountering difficulties when attempting to perform individual tasks or actions), and restrict community participation (i.e., experiencing problems during involvement in life situations) [
2,
3]. All of these aspects of disability interact dynamically with the health of individuals, and with their personal and environmental factors [
1]. Among these three aspects of disability, a person’s level of participation restriction is seldom viewed as an indicator of the condition of that person’s health. Thus, it is seldom assessed or explored in either clinical or research settings, particularly where the person under assessment is an older person.
Only a few studies exploring the risk factors of participation restriction among older people in general reported that being older, exhibiting more depressive moods, poor mobility, and a lack of balance confidence were significantly associated with participation restriction [
4‐
6]. It is rational to believe that the prevalence of participation restriction is greater among a frail population. Frailty refers to a physiological state of increased vulnerability to stressors resulting from a decrease and possible dysregulation of reserves in multiple physiological and/or biological systems [
7,
8]. The early stages of frailty may be clinically silent, with 32.3% of frail older people having neither disabilities nor comorbidities and maintaining a certain level of independence [
8].
However, only one study targeted this specific vulnerable group of older people, and reported that about 80% of community-dwelling frail older people had some form of participation restriction in their life [
9]. A multivariate regression analysis generated in Fairhall et al.’s study [
9] showed that grip strength, mood, number of medical conditions, and mobility were significantly associated with participation. However, their multivariate model could only explain 29% of the variance in participation restriction. That indicates that participation restriction in frail older people is complicated and can be influenced by different factors related to bio-physio-psychosocial factors. Therefore, more studies are still necessary to comprehensively explain participation restriction among the frail population. Information related to the severity and features of participation restriction may pave the way to developing interventions to address problems related to participation restriction in frail older people. In view of this, the aim in this cross-sectional study was to identify the prevalence and underlying risk factors associated with participation restriction among community-dwelling frail and pre-frail older people.
Results
Two hundred and ninety-nine community-dwelling frail older people who were mainly female (
n = 223, 74.6%) and whose mean age was 79.5 (SD 7.33) years were recruited. According to the FFI, 160 participants (53.5%) were identified as pre-frail, while 139 participants were classified as frail (46.5%). Among all of the participants, 207 (69.2%) were identified as having at least one participation restriction in at least one aspect of their life based on the C-RNLI. The top three events that participants reported of experiencing restriction mostly were “take trips out of town” (
n = 170; 56.9%), “I assume a role in my family” (
N = 115; 38.5%), and “I can deal with life events as they happen” (
N = 97; 32.4%). On the other hand, “I am comfortable with how my self-care needs are met” had only 15 participants (5.0%) reported of having restriction. The mean C-RNLI was 68.3 (SD 19.43) among all of the participants in this study. Table
1 contains a summary of their characteristics according to whether or not they had experienced participation restrictions. Participants who were identified as having participation restriction were significantly older, frailer, weaker in the sense that they were suffering from more diseases and had a higher level of comorbidity, were of lower self-perceived socioeconomic status, and had weaker social networks, poorer body functions, and more activity limitations.
Table 1
Characteristics of the participants according to levels of participation restriction
| Total (n = 299) | With restriction (n = 207) | Without restriction (n = 92) | |
| n | (%) | n | (%) | N | (%) |
P value |
Gender | | | | | | | 0.298 |
Male | 76 | (25.4) | 49 | (23.7) | 27 | (29.3) | |
Female | 223 | (74.6) | 158 | (76.3) | 65 | (70.7) | |
Living Alone | | | | | | | 0.859 |
Yes | 118 | (39.5) | 81 | (39.1) | 37 | (40.2) | |
No | 181 | (60.5) | 126 | (60.9) | 55 | (59.8) | |
Number of hospitalizations in the past 12 months | | | | | | | 0.490 |
0 | 243 | (81.3) | 165 | (79.7) | 78 | (84.8) | |
1 | 45 | (15.1) | 33 | (15.9) | 12 | (13.0) | |
2 | 5 | (1.7) | 3 | (1.4) | 2 | (2.2) | |
3 | 5 | (1.7) | 5 | (2.4) | 0 | (0.0) | |
4 | 1 | (0.3) | 1 | (0.5) | 0 | (0.0) | |
Number of falls in the past 12 months | | | | | | | 0.255 |
0 | 242 | (80.9) | 163 | (78.7) | 79 | (85.9) | |
1 | 41 | (13.7) | 29 | (14.0) | 12 | (13.0) | |
2 | 7 | (2.3) | 6 | (2.9) | 1 | (1.1) | |
3 | 8 | (2.7) | 8 | (3.9) | 0 | (0.0) | |
4 | 1 | (0.3) | 1 | (0.5) | 0 | (0.0) | |
| Total (n = 299) | With restriction | Without restriction | |
| Mean | (SD) | Mean | (SD) | Mean | (SD) |
P value |
Personal Factors (Demographic Variables) |
Age | 79.5 | (7.33) | 80.9 | (7.04) | 76.6 | (7.12) | 0.000** |
Frailty phenotype criteria (0–5, 1–2: pre-frail; ≥3: frail) | 2.54 | (0.88) | 2.72 | (0.88) | 2.11 | (0.73) | 0.000** |
Health-related Factors |
Number of diseases | 2.4 | (1.49) | 2.5 | (1.52) | 2.1 | (1.38) | 0.011* |
Number of prescribed medications | 3.1 | (2.63) | 3.2 | (2.61) | 2.7 | (2.66) | 0.130 |
C-CCI (0–43,a higher score means a higher level of comorbidity) | 4.1 | (1.13) | 4.3 | (1.08) | 3.7 | (1.12) | 0.000** |
Environmental Factors |
SSS (0–10, a lower rating means a lower self-perceived socioeconomic status) | 4.5 | (2.18) | 4.3 | (2.12) | 5.1 | (2.25) | 0.006* |
CLSNS (0–50, a higher score means stronger social networks) | 22.5 | (10.23) | 20.9 | (10.25) | 26.2 | (9.25) | 0.000** |
Body Functions and Structures (Impairment) |
Pain assessment (0–11, a higher rating means a higher level of pain) | 3.9 | (3.38) | 4.2 | (3.39) | 3.1 | (3.25) | 0.013* |
CMFI-20 (20–100, a higher score means a higher fatigue level) | 66.5 | (12.17) | 69.4 | (11.24) | 60.1 | (11.79) | 0.000** |
CMNA-SF (0–14, <11 indicates malnutrition) | 12.6 | (1.44) | 12.4 | (1.50) | 13.1 | (1.14) | 0.000** |
C-PSQI (0–21, a higher score means poorer sleep quality) | 7.6 | (4.10) | 8.4 | (4.10) | 5.7 | (3.46) | 0.000** |
CGDS-SF (0-15, ≥6 indicates the presence of depressed mood) | 4.4 | (3.58) | 5.2 | (3.61) | 2.4 | (2.60) | 0.000** |
Activity Limitations |
TUG (a longer time means a weaker physical performance) | 18.4 | (14.06) | 21.4 | (15.88) | 11.8 | (3.59) | 0.000** |
CFES-I (16–64, a higher score means more concern about falling) | 33.3 | (10.77) | 35.8 | (10.83) | 27.4 | (8.05) | 0.000** |
PASE-C (a higher score means a higher physical activity level) | 65.7 | (47.9) | 54.9 | (40.74) | 90.7 | (53.88) | 0.000** |
C-IADL (0–27, a lower score means a higher level of dependence) | 20.4 | (6.15) | 19.0 | (6.32) | 23.5 | (4.35) | 0.000** |
Participation Restriction |
C-RNLI (0–100, a lower score means a higher participation restriction level) | 68.3 | (19.64) | 60.5 | (18.41) | 75.1 | (18.08) | 0.005** |
Table
2 summarizes the results of the multiple logistic regression of participation restriction on all independent variables. A test of the full model with all independent variables against a constant-only model was statistically significant, with
χ2 = 179.49,
p < 0.001, indicating that the variables, as a set, reliably distinguished between those with or without participation restriction. The Nagelkerke R Square of the model was 0.671, and in 84.8% of cases the dependent variable (i.e., participation restriction) was correctly predicted by the model. Both collinearity measures [tolerance and the variance inflation factor (VIF)] were checked with regard to the impact of collinearity on the independent variables in the regression equation. The value of the tolerance ranged from 0.444 to 0.869 and the VIF was between 1.151 and 2.250 in the model, which indicated no small tolerance and a large VIF. In addition, no two independent variables were found with variance proportions greater than 0.50 under a conditioning index greater than 30. Therefore, no multicollinearity was evident.
Table 2
Logistic regression of participation restriction with other independent variables systematically related to the WHO-ICF framework
Personal Factors (Demographic Variables) | | Lower | Upper |
Age | 1.05 | 0.97 | 1.14 |
Male | 0.52 | 0.19 | 1.46 |
Frailty phenotype criteria (0–5, 1–2: pre-frail; ≥3: frail) | 2.20* | 1.10 | 4.42 |
Health-related Factors |
Number of diseases | 1.22 | 0.87 | 1.73 |
Number of prescribed medications used | 0.95 | 0.77 | 1.17 |
History of hospitalizations | 0.99 | 0.42 | 2.37 |
History of falls | 1.08 | 0.52 | 2.25 |
C-CCI (0–43, a higher score means a higher level of comorbidity) | 1.14 | 0.67 | 1.96 |
Environmental Factors |
SSS (0–10, a lower rating means a lower self-perceived socioeconomic status) | 0.79* | 0.64 | 0.97 |
CLSNS (0–50, a higher score means stronger social networks) | 1.00 | 0.96 | 1.05 |
Lives alone | 0.71 | 0.28 | 1.81 |
Body Functions and Structures (Impairment) |
Pain assessment (0–11, a higher rating means a higher level of pain) | 0.95 | 0.82 | 1.09 |
CMFI-20 (20–100, a higher score means a higher fatigue level) | 1.00 | 0.96 | 1.04 |
CMNA-SF (0–14, <11 indicates malnutrition) | 0.90 | 0.63 | 1.27 |
C-PSQI (0–21, a higher score means poorer sleep quality) | 1.19* | 1.05 | 1.35 |
CGDS-SF (0–15, ≥6 indicates the presence of depressed mood) | 1.40** | 1.15 | 1.70 |
Activity Limitation |
TUG (a longer time means a weaker physical performance) | 1.21** | 1.06 | 1.38 |
CFES-I (16–64, a higher score means more concern about falling) | 1.05* | 1.00 | 1.11 |
PASE-C (a higher score means a higher physical activity level) | 0.99* | 0.98 | 1.00 |
C-IADL (0–27, a lower score means a higher level of dependence) | 0.92 | 0.82 | 1.04 |
The results show that the status of frailty, mobility evaluated by TUG, the fear of falling as measured by the CFES-I, sleep quality as measured by the C-PSQI, being depressed as measured by the CGDS-SF, subjective social status as measured by SSS, and physical activity level as measured by the PASE-C are significantly associated with participation restriction. The odds ratio of 2.20 (95% CI: 1.10–4.42) on the level of frailty implied that those who were frail and who had more frailty-related characteristics were twice as likely to experience participation restriction than those who were pre-frail. The odds ratio of 1.21 (95% CI: 1.06–1.38) on the TUG test implied that those who took a longer time to complete the TUG test were 1.21 times more likely to have participation restrictions than those could finish the test within a shorter time. Those who exhibited more depressed symptoms, had poorer sleep quality, and were more concerned about falling were more likely to have participation restrictions, with odds ratios of 1.40, 1.19, and 1.05, respectively. Those with lower self-perceived social status (SSS) and a lower physical activity level (PASE-C) tended to be more likely to have participation restrictions. The odds ratio of 0.99 (95% CI: 0.98–1.00) on PASE-C implied that the likelihood of participation restriction decreased by 1% [i.e., (1-0.99) x 100%] for each increase of one score in the PASE-C. The odds ratio of 0.79 (95% CI: 0.64–0.97) on SSS implied that the likelihood of participation restriction decreased by 21% for each increase of one score in social status.
Discussion
Maintaining civil and social involvement by participating in different life events is important if older people are to keep up their satisfaction with life. However, information about the level of participation restriction among older people, particularly the pre-frail or frail, remains scant. The prevalence of participation restrictions among community-dwelling frail older people identified in the current study is about 70%, which is comparable with a previous study involving participants of a similar type [
9] but obviously higher than in another study involving younger participants [
43]. Many studies have in fact identified a positive correlation between age and participation restrictions [
6,
9,
44].
When all factors conceptualized based on the WHO-ICF framework in running the multivariate model are included, 67% of the variance in participation restriction among the participants is explained. With the exception of factors under the health-related component, all components have at least one factor that is significantly associated with participation restriction. This finding supports the view that components in the WHO-ICF framework are interrelated and affect frail people’s levels of participation in daily, life, and social events. This provides further support for the argument that participation restriction is multifactorial in etiology [
9]. The factors that show a significant association with participation restriction include the participants’ status of frailty, their self-perceived social status, level of exhibited depressive mood, sleep quality, mobility, level of fear of falling, and physical activity levels.
Among the different components of the WHO-ICF, “activity limitation” contains the largest number of risk factors significantly associated with participation restriction. Participants who took more time to complete the TUG test were more likely to have participation restrictions. Mobility in terms of the ability to move around in the nearby community was consistently identified in a previous study as a factor strongly associated with participation [
6]. Beside mobility, balance confidence is another important factor in ensuring that older people are able to maintain their independence [
45]. In this study, both the level of mobility and the fear of falling were significantly associated with participation restriction. This adds further support to the current evidence that for frail older people these are the two keys factors in maintaining a substantial degree of participation in different physical activities [
4,
9]. Having lower levels of mobility and being overly concerned about falling will affect the physical activity levels of older people, and these three inter-related factors have been identified in this study as having a significant association with participation restriction.
Depressive mood, as reflected by the C-GDS, is the second-largest unique contributor to the variance in participation restriction in the current study. There is strong evidence of a relationship between depressive mood and restricted social participation, which is in agreement with previous findings that older people with depression have considerably higher odds of experiencing participation restriction [
6,
9,
43]. The association between the quality of sleep of older people and their participation restriction has thus far seldom been explored. This study found that participants who reported having poorer sleep quality, as reflected by the C-PSQI, tended to have more participation restrictions. Poor sleep quality leads to tiredness during daytime, which may manifest as difficulty in sustaining a high level of functioning and reduced participation in different life events [
46].
Frailty and disability were identified as two distinct but somewhat overlapping conditions commonly found in older people [
8]. Frailty and disability coexist in about 67% of frail people [
8]. Disability refers to a condition in which a person experiences substantial limitations in one or more major life activities, ranging from daily self-care activities (skills that are essential to living independently) to pursuits that are important to an individual’s life satisfaction [
1]. In the WHO-ICF framework, participation restriction is regarded as an aspect of disability. Frailty has been identified as a personal risk factor that is significantly associated with participation restriction [
9]. In this study, the participants’ status of frailty explains much of the variance in participation restriction.
Although a variety of factors were considered and all factors were included in running the regression model in this study, 33% of the variance in participation restriction was not explained by the multivariate model. In fact, each component in the WHO-ICF model is multi-dimensional in nature and contains many different factors. Those factors might not have been examined in this study. This may explain why no health-related factors were identified in this study as having a signification association with participation restriction.
The multifactorial nature of participation suggests that interventions should target the problems listed in the ICF framework in terms of functional levels. Fortunately, the majority of the risk factors identified in this study are modifiable. Thus, they can potentially be targeted in the effort to develop a multifactorial intervention to maintain or reduce participation restriction among frail older people. Depression, mobility, the fear of falling, and sleep quality are manageable in frail older people, and studies are warranted to investigate the productive values of all of these factors and the effect of targeting them during interventions aimed at enhancing the social participation of frail older people.
The findings should be interpreted with caution as this study has several limitations. First, levels of participation restriction in frail older people are a complicated construct in a complex population; they can never be completely captured by C-RNLI, which is a simple instrument. Some important elements under the concept of participation, such as learning and applying knowledge, may be restricted for different reasons that cannot be reflected in the current study. Second, another limitation in the current study is that there is limited empirical evidence to support the use of a cut-off value of 4 in any one the items of the C-RNLI as indicating the presence of participation restriction. Fortunately, a significant difference was identified in the majority of the data related to demographics, health, body functions, and activity levels, suggesting that the cut-off point of the C-RNLI used in this study can distinguish between two significantly different groups of older people with regard to their participation restriction. Third, due to a lack of instruments designed explicitly for the WHO-ICF conceptual framework, it is possible that factors have been misclassified under different components. Four, there is the potential limitation of sample selection bias. Participants were recruited from a district community or day care center where it can be presumed that participation in the survey involved some element of self-selection. This made it likely that some older people with participation restriction who never joined in any of the events / services offered by the district center would be excluded from this study. Also, older people with cognitive impairment were excluded from this study. All of these factors would limit the generalizability of its findings. Last, but not least, the cross-sectional design of this study prevented us from determining the casual relationship between participation restriction and the identified risk factors. Future studies are warranted to study the casual relationship between participation restriction and other risk factors that have been established in cross-sectional studies.
Acknowledgments
The author would like to thank all of the centers that took part in coordinating and collecting the data, as well as the participants who took part in the study for their help in providing useful information for this study. The author would also like to thank the statistician, Kenny Chin, for his advice on analyzing the data.