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The Effect of Social Support on Functional Recovery and Well-Being in Older Adults Following Joint Arthroplasty
Projections by the U.S. Census Bureau indicate a continual rise in the population of older adults. Along with increased dependency among older adults, chronic illness and aging may have attendant social and personal concerns in the areas of health care, community health services, and quality of life. Direct and indirect costs of osteoarthritis are $120 billion per year in medical treatment and lost wages. Every year more than 300,000 knee replacements and 120,000 hip replacements are performed in the United States (Sapountzi-Krepia et al., 2007). A large percentage of joint replacement patients have now assumed responsibility for their recovery process. This exploratory study assessed and measured social support and evaluated its impact on functional recovery and well-being in older adults after joint arthroplasty. Although social support, associated with the covariates of “living arrangements” and “age,” demonstrated a positive relationship with perceived well-being, no relationship was demonstrated with high or low levels of social support and functional recovery.
Osteoarthritis is currently one of the most prevalent chronic illnesses in the United States, and its incidence is expected to increase as the population ages. One-third of the U.S. population is affected by osteoarthritis (Centers for Disease Control and Prevention [CDC], 2008). Osteoarthritis is also the leading cause of disability in the United States accounting for 17.5% of all of those on disability. The total cost resulting from osteoarthritis is more than $120 billion per year in medical treatment and lost wages (CDC). Osteoarthritis has a significant impact on the well-being of older adults. Individuals affected by degenerative joint disease begin to feel that they are becoming a burden to their family and friends (Koyama et al., 2007). As conservative therapy fails to work on pain relief, sleeping patterns suffer and social isolation increases. Surgical intervention is rapidly becoming the treatment of choice for degenerative joint disease (Hamel, Toth, Legedza, & Rosen, 2008; Koyama et al.). Total joint arthroplasty has been successful in pain relief, improved physical function, and enhanced feelings of well-being (Montin, Leino-Kilpi, Suominen, & Lepisto, 2008). The majority of patients proceed through surgery and recovery without complication. Following surgery, physical and occupational therapy are used to restore motion and function with results lasting approximately 15 years (Lucas, 2007).
More than 300,000 knee replacements and 120,000 hip replacements are performed annually in the United States (Sapountzi-Krepia et al., 2007). Surgical intervention, although safe and successful, is not without a rigorous recovery period and the possibility of complications for the patient. It is vital that these individuals and members of their support systems are educated and prepared for the challenges of the postoperative rehabilitation period, the ultimate goal being to return individuals to their highest level of functional ability in the shortest time possible.
Managed healthcare criteria and changes in insurance reimbursement have greatly affected the way care following joint arthroplasty is delivered to the older adult population (Fielden, Scott, & Horne, 2003). A large percentage of joint replacement patients have assumed responsibility for their rehabilitation. It is vital is that these changes do not result in negative patient outcomes.
A shortened hospital stay with discharge to home requires having a social support system in place to provide both the personal and professional assistance that these patients will require in the early stages of their recovery. The provision of personal and professional social support is critical to a patient’s independence and well-being (Lucas, 2007).
The world population is aging. The U.S. Census Bureau (2004) projects that by 2030, adults 65 years and older will constitute 20% of the population (Williams, Dunning, & Manias, 2007). As the number of older Americans increases at an unprecedented rate, there is concern that the number of people with disabilities may also be increasing (Freedman & Martin, 1998). The aging process gradually increases an older adult’s vulnerability to chronic illness, which is accompanied by numerous challenges and restrictions (Hickey & Stillwell, 1992). At least 80% of people older than 65 years report one chronic condition (Quinn, 2008). Nearly 50% of the aged population is unable to perform some activities of daily living (ADLs), such as bathing, dressing, eating, toileting, transferring, and ambulation (Talkowski, Lenze, Munin, Harrison, & Brach, 2009). In addition, 7.6 million older adults need assistance with daily activities, including the preparation of meals, shopping, money management, and household cleaning and maintenance—the instrumental ADLs (IADLs; Talkowski et al.). Increased dependency resulting from chronic illness and aging have attendant social and personal concerns in the areas of health care, community and home health services, and quality-of-life issues (Blixen & Kippes, 1999). A clearer understanding of the extent to which changes in functional ability reflect changes in the underlying physiological capability of older Americans may offer insight into future patterns of disability (Talkowski et al.).
Today’s older population is significantly different from that of just a decade ago. Besides functioning better, today’s older generation is likely to be better educated. This increased education may be associated with beneficial changes in lifestyle, access to care, ability to comply with physicians’ instructions, and the ability to modify ones’ environment (Freedman & Martin, 1998).
Aging can be related to a decline in fitness and health (Stevens et al., 2007). Functional health has been viewed as a requirement for success in independent living (Hogue, 1984). A functional limitation is a decline in which the individual becomes dependent in daily activities (Landefeld et al., 1995). Functional limitation has been considered the inability to carry out normal tasks and roles (Huang et al., 1998). The assessment of functional status is critical when caring for older adults. Normal aging changes, acute illness, worsening chronic illness, and hospitalization can contribute to a decline in the ability to perform tasks necessary to live independently in the community. The information from a functional assessment can provide objective data to assist with targeting individualized rehabilitation needs or to plan for specific in-home services (Gallo & Paveza, 2006).
Blixen and Kippes (1999) stressed the importance of the role of social support in managing the long course of chronic illness. Three types of social support include: (1) emotional support by comforting with physical affection or expressing concern for well-being; (2) guidance support by giving knowledge by instruction or suggesting some action; and (3) tangible support by providing housing, money, transportation, or physical assistance. Theoretically, social support is any exchange of resources between two or more individuals perceived by each to enhance the well-being of the recipient (Blixen & Kippes). The formal social support network consists of physical and occupational therapists, housecleaning services, and nutritional services. Informal social support networks include spouses, children, and friends. During periods of physical limitation for older adults, families are the major source of instrumental and emotional support. Involvement with informal support network members plays an important role in the rehabilitation and recovery process. Older adults with osteoarthritis struggle pre- and postoperatively with the need for dependence on others for physical and emotional support while striving to maintain independence (Jacobson et al., 2008).
The many definitions of well-being suggest that it is an intangible and amorphous concept that is perceived differently depending on the person (Wilcock et al., 1998). Well-being can be defined in terms of an individual’s physical, mental, social, and environmental status with each aspect interacting with the other and each status having differing levels of importance and impact according to the individual. Well-being tends to be very individualistic in that self-perception is the defining factor for how one views their “well-being” (Wilcock et al., 1998).
The primary indicator of health and well-being in the elderly is the ability to perform ADLs with relative ease. The presence of disease no longer completely defines the level of health for an aging person. It is now recognized that the elderly are far less concerned about medical diagnosis than their ability to perform necessary personal and household chores and go about their daily errands and social activities (Young & Resnick, 2009).
For some individuals dealing with the joint pain and functional limitation of osteoarthritis and the progression of symptoms affects how these individuals view themselves and their belief of how others view them (Jacobson et al., 2008). Some patients consider themselves active people forced into inactivity due to their symptoms. Feelings of well-being are affected as functional limitations constrict and control their lives (Jacobson et al.).
This exploratory study assessed and measured social support and evaluated its impact on functional recovery and well-being in older adults following joint arthroplasty. The determination of patient outcomes following total joint arthroplasty can produce knowledge for use in clinical nursing for patient and family education. The central hypothesis of this study is that the presence of social support will positively affect functional recovery and well-being of older adults after joint arthroplasty.
A licensed 666-bed mid-Atlantic tertiary healthcare hospital was chosen as the site for participant enrollment for this research study because of the high volume of joint replacement surgery completed at the facility. A convenience sample (n = 125) was recruited from preoperative classes for joint replacement candidates. There were 61 participants in the high social support group and 64 participants in the low social support group based on answers to demographic data questioning about assistance received. The demographic questions were directly related to items on the functional recovery and social support scales used for data collection with documented reliability and validity for this study. Topics addressed items such as self-reported health, marital status, living arrangements, availability of social support, and the number of ADLs requiring assistance. Institutional review board approval was obtained prior to data collection. The principal investigator, a certified rehabilitation registered nurse with 18 years of clinical experience, was solely responsible for data collection.
To qualify for inclusion, participants had to be between 55 and 95 years of age and have undergone an elective total joint replacement. Exclusion criteria included preexisting neuromuscular conditions, infection, deep venous thrombosis, joint revisions, hemiarthroplasties, or emergency arthroplasties. A $25 American Express gift card was given to each participant following completion of all survey forms.
The data collection procedure developed for this study consisted of three parts: (1) demographic information, (2) items and scales that measured functional abilities, and (3) items and scales that measured psychosocial variables potentially related to the recovery process. The Groningen Orthopaedic Social Support Scale (GOSS) with a Cronbach’s alpha of 0.89, was used to measure perceived personal and instrumental social support (van den Akker-Scheek, Stevens, Spriensma, & van Horn, 2004). The Perceived Well-Being Scale (PWBS), with an overall internal consistency reliability coefficient of 0.85, was used to assess physical and psychological well-being (Reker & Wong, 1984). The Groningen Activity Restriction Scale (GARS), with significant correlation to the Health Assessment Questionnaire control (p < .001), was used to measure functional ability with the ADLs and the IADLs (van den Akker-Scheek et al.). Participants were recruited 2 weeks prior to surgery during joint replacement education classes and follow-up took place 2 weeks postoperatively at physical therapy sessions or physician visits. Data collection tools contained numbered subject identifiers to ensure confidentiality. Data were analyzed using SPSS-PC 16.0 (Cary, NC) software to test the two research hypotheses: (1) higher levels of social support will be associated with higher levels of functional recovery, and (2) higher levels of social support will be associated with higher levels of well-being.
Results from multivariate statistical analyses are generally superior to outcomes from univariate analyses because they better capture the full network of correlations among independent and dependent variables (Stevens, 2002; Tabachnick & Fidell, 2007). Therefore, data were analyzed using a direct-entry (standard), binary logistic regression analysis (multivariate regression analysis).
There were three primary explanatory (i.e., independent) variables. All three variables were on the interval scale of measurement: (a) the PWBS (higher numbers indicated higher levels of well-being); (b) the GOSS (higher numbers indicated higher levels of social support); and (c) the GARS (higher numbers corresponded to greater levels of disability). The upper part of Table 1 presents distributional statistics—mean and standard deviation (SD)—for the PWBS, GOSS, and GARS and separates the scales into two levels of social support. The covariates were age, gender, living arrangements, previous joint replacement, stair climbing, race/ethnicity, and significant health history. The fourth line of Table 1 presents the distributional statistics for age.
The sample contained more women (64.8%) than men (35.2%). Nevertheless, as suggested by the percentages in Table 1, and confirmed by a univariate statistical comparison, the two social support groups showed no significant gender differences (r2 = 0.897, df = 1, p = .344). The ethnic composition of the sample was exclusively non-Hispanic White, non-Hispanic Black, and Hispanic. Less than 10% of the sample (5.6%) came from either the Non-Hispanic Black or Hispanic groups. There was no significant difference between the two social support groups with respect to previous joint replacements (r2 = 0.025, df = 1, p = .875), the presence of stairs in the home (r2 = 0.325, df = 1, p = .569), or significant health history (r2 = 0.048, df = 1, p = .827).
Results from the analysis of “living arrangements” showed that 32% of the sample lived alone and that 68% lived with someone else, such as a nonworking spouse, an adult working child, or a working spouse. A univariate statistical comparison between the two social support groups showed a significant difference; fewer people in the high social support group lived alone (r2 = 26.896, df = 1, p = .001).
Regression diagnostics were performed to evaluate whether the overall model met underlying assumptions (Meyers, Gamst, & Guarina, 2006; Tabachnick & Fidell, 2007). Results from the logistic analysis indicated that the 10-predictor model provided a statistically significant improvement over the constant-only model, (r2 = 58.029, df = 10, p = .001). The Nagelkerke pseudo R2 indicated that the model accounted for approximately 49.2% of the total variance. The pseudo R2 was converted to Cohen’s (1988) f2 statistic, in which 0.02 equals a small effect size, values of 0.15 identify a medium effect, and values of 0.35 and above connote a large effect. Therefore, the obtained f2 (0.98) suggested the presence of a very large effect size and it indicated that, as a set, the 10 design variables were excellent in discriminating between individuals with high and low levels of social support.
Predictive success was also evaluated for each case used to develop the model. The overall classification accuracy was impressive (82.4%). Even more so, findings for sensitivity and specificity were impressive. Sensitivity is the ability of the ten design variables (predictors), as a set, to correctly identify individuals with high levels of social support (Streiner, 2003). Alternatively, specificity is the ability of the ten predictors to correctly identify individuals with low levels of social support. In the current case, sensitivity was very high (85.25%), meaning that the design variables would correctly identify 85.25% of the people with high levels of social support. Likewise, specificity was very high (76.69%) and showed that the design variables would correctly identify 76.69% of the people with low levels of social support.
Table 2 presents regression coefficients (B), Wald statistics, significance levels, odds ratios, and 95% confidence limits for the odds ratio for each predictor in the model. The Wald test revealed that three design variables (predictors) were statistically significant: (1) whether patients lived with someone else (p = .001); (2) age (p = .002); and (3) scores from the PWBS (p = .001). None of the other seven design variables contributed to the model.
The variable “lives with someone else” is binary. Therefore, interpreting its odds ratio of 19.049 is straightforward. Patients who live with someone else are 19 times (i.e., 19.049 times) more likely to be in the high social support group than patients who live by themselves. This is the unique effect of living with someone else. In other words, living with someone else predicts high social support after controlling for the effects of all of the other predictors in the model (i.e., age, PWB, GOSS, GARS, race/ethnicity, gender, previous joint replacement, stairs in the home, and significant health history). The other two significant design variables—age and PWB—were on the interval scale of measurement. It is necessary to take into consideration the mean and SD when interpreting the effects of interval-level predictors (Hosmer & Lemeshow, 2000).
The mean age for the sample was 67.80 years (SD = 8.38 years). Therefore, a person who is 76.18 years old (i.e., one SD above the average age of the sample) is more than nine times as likely to be in the high social support group than someone who is at the mean age of the sample (i.e., 1.102 x 8.38 = 9.235). This is the unique effect of age after controlling for the effects of all of the other predictors in the model.
The mean PWB score was 75.0 (SD = 13.13). Therefore, a person whose PWB score is 88.13 (i.e., one SD above the average of the sample) is more than 14 times as likely to be in the high social support group than someone who is at the mean PWB score of the sample (i.e., 1.077 x 13.13 = 14.141). This is the unique effect of PWB after controlling for the effects of all of the other predictors in the model.
Study results clearly indicate that a relationship does exist between the presence or absence of social support and the perceived well-being of individuals following joint replacement surgery. Individuals with high well-being scores were more likely to be in the high social support group as opposed to individuals with lower perceived well-being scores. Interestingly, the variables of age and living arrangements were the only two covariates that affected this relationship. Those individuals who lived with a spouse, adult child, or friend were much more likely to have higher levels of social support than those individuals who lived alone. Similarly, as the individual age increased above the mean, individuals were more likely to be in the high social support group than those individuals at the mean age of the sample. The covariates of gender, previous joint replacement, stair climbing, race/ethnicity, and significant health history demonstrated little relationship to group social support placement and subsequently to well-being or functional recovery. On a whole, the very large effect size (0.98) indicated that as a set, the ten design variables were excellent in discriminating between individuals with both high and low levels of social support. Sensitivity levels of 85.25% clearly indicate that the design variables correctly identify individuals with high levels of social support. Alternately, specificity levels of 76.69% show that the design variables correctly identify those individuals with low levels of social support. Limited sample size, limited diversity, single-site data collection, and gender were the limitations of this study.
Significance to Nursing
The assessment of functional status is critical when caring for older adults. Normal aging changes, acute illness, worsening chronic illness, and hospitalization can contribute to a decline in the ability to perform tasks necessary to live independently in the community. The information from a functional assessment can provide objective data to assist with targeting individualized rehabilitation needs or to plan for specific social support in-home services such as meal preparation, nursing care, homemaker services, personal care, or continuous supervision. A functional assessment can also assist the nursing professional to focus on the person’s baseline capabilities, facilitating early recognition of changes that may signify a need either for additional resources or for a medical work-up (Gallo & Paveza, 2006). Results of this study clearly indicate that it is imperative that older adult patients receive a thorough functional assessment prior to discharge. The discharge environment must also be evaluated for safety and possible limitations. The rehabilitation home care nurse is ideally placed to provide the follow-up assessment in the home setting. This assessment is critical for individuals whose living arrangements require them to be alone. Low social support increases the risk of negative patient outcomes.
Nurses are ideally placed to inform health policy through practice and research. Patient advocacy helps restore faith in a healthcare system that has demonstrated difficulty in meeting patient needs. Patient and family education becomes a key component in discharge planning for patients undergoing total joint arthroplasty. The education process begins well before the surgery takes place. It is vital that these individuals and members of their support systems are educated and prepared for the challenges of the postoperative rehabilitation period. Nurses need to help patients and their support systems develop safe, effective exercise regimes that are practiced regularly to promote cardiovascular fitness and added joint flexibility and balance. The nurse’s ultimate goal is to return these individuals to their highest level of functional ability in the shortest time possible.
The results of this study add to current data determining whether the current plan of care for joint replacement patients demonstrates positive outcomes. It is also essential to determine whether the current treatment plan leads to complications that lengthen the recovery time and require rehospitalization (and added costs). These data can also help benchmark patient progress and lead to prognostic treatment decisions for recovery and rehabilitative needs. This knowledge will result in improved care planning standards and will advance clinical outcomes. The application of evidence to practice can promote more informed decision making and foster the use of best practices and maximal care delivery outcomes in the home healthcare setting (Chimenti & Ingersoll, 2007). Patient expectations and knowledge of the rehabilitation process are key determinants in discharge placement. Realistic expectations are paramount to achieve successful outcomes on a maximal functional status recovery timetable that proves acceptable to patients and their families.
Decreased lengths of stay in both the orthopedic and acute rehabilitation settings coupled with decreased home care reimbursement necessitate greater research to optimize the effectiveness of theory-based interventions in regard to cost effectiveness and the provision of the greatest benefits to functional recovery in the older adult population following total joint replacement. Validating the effects of the various community reintegration interventions is essential as the nursing profession continues to embrace the concept of evidence-based practice.
Recommendations for Future Research
Further research using qualitative methods can produce subjective knowledge about joint replacement outcomes. Knowledge of the determinants of outcome can help in the development of interventions with the goal of altering outcome behaviors by influencing the significant determinants. Self-efficacy and resilience may be considered two of these determinants. Uncertainty with resultant anxiety may provide a unique contribution as another determinant to successful rehabilitation (Kagan & Bar-Tal, 2008). Uncertainty promotes feelings of discomfort and uneasiness. It arises from low confidence and lack of control (Kagan & Bar-Tal). Mishel’s model of uncertainty (1988) paints uncertainty in illness as a threat or danger. Further research into this concept can add to the current knowledge base and provide education to patients and family members to allay fears and promote confident decision making.
Further exploration of the role of positive affect or well-being in maintaining physical functioning in older age may lead to the development of more comprehensive and effective ways to promote the continued independence of the older adult population (Ostir, Markides, Black, & Goodwin, 2000).
Qualitative analysis of patient rationale for wait times may reveal a fuller range of experiences providing particular strengths in uncovering evidence revealing significant, unanticipated factors resultant in patients “putting up and putting off,” “waiting and worrying,” and “hurting and hoping” (Jacobson et al., 2008).
Future research about total joint arthroplasty would be beneficial. The projected increase in the number of joint replacements and joint replacement revisions will result in a large population of individuals contemplating their options for dealing with the disabilities of osteoarthritis. Having an increased knowledge base in the areas of patient expectations and outcomes will better prepare healthcare professionals to address these issues and lead to positive outcomes.
About the Author
Ruth Ann Kiefer, DrNP RN CRRN CNE, is a member of the faculty at the Dixon School of Nursing at Abington Memorial Hospital in Abington, PA, and continues to practice as a CRRN in the hospital’s rehabilitation unit. Address correspondence to her at email@example.com.
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