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Keywords:

  • Arthritis;
  • Employment;
  • Spillover;
  • Role conflict;
  • Disability;
  • Occupational stress

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PARTICIPANTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

Objective

To examine employed individuals' perceptions of arthritis-work spillover (AWS), the reciprocal influence of arthritis on work and work on arthritis, and the demographic, illness, and work context factors associated with AWS.

Methods

The study group comprised 492 employed individuals with osteoarthritis or inflammatory arthritis. Participants completed an interview-administered, structured questionnaire assessing AWS, demographic (e.g., age, sex), illness (e.g., disease type, pain, activity limitations), and work context (e.g., workplace control, hours of work) variables. Principal components analysis, reliability analysis, and multiple linear regression were used to analyze the data.

Results

A single factor solution emerged for AWS. The scale had an internal reliability of 0.88. Respondents were more likely to report that work interfered with caring for their arthritis than they were to report that their disease affected their work performance. Younger respondents, those with more fatigue and workplace activity limitations, and those working in trades and transportation reported more AWS. Individuals with more control over their work schedules reported less AWS.

Conclusion

The results of this study extend research on arthritis by reexamining the interface between arthritis and employment. This study introduces a new measure of AWS that enhances the range of tools available to researchers and clinicians examining the impact of arthritis in individuals' lives.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PARTICIPANTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

Research on arthritis and employment has provided consistent evidence that having arthritis increases the risk of job loss (1–12). In exploring this relationship, studies have focused on the impact that disease characteristics such as pain, fatigue, and activity limitations have on meeting job demands, and the role of work context and demographic variables. Among individuals with arthritis, a greater impact on employment has been found with greater disease duration and symptom severity and when work demands are physical, the pace of work is high, and persons with arthritis are older and have less education (5, 6, 8–10, 12–16). Other research has focused on individual and social aspects of work among persons who remain employed. These studies emphasize workplace disability; job accommodations; help from others, especially family support; and coping efforts such as planning, pacing, modifying activities, and making work changes as important to the work experiences of individuals with arthritis (1, 7, 9, 14, 17–24).

The emphasis on the impact of arthritis on employment has resulted in relatively little research examining whether persons with arthritis perceive work as having an impact on their health. Elsewhere, however, employment studies have found that exposure to adverse work conditions is related to the health of workers, including increased risk of musculoskeletal pain, cardiovascular disease, hypertension, and depression (25–29). This finding suggests the potential for a reciprocal impact of arthritis on work and work on arthritis.

The notion of a reciprocal impact has been labeled “spillover” in previous research and has been explained in terms of multiple-role stress (30). Spillover is believed to occur when the stresses experienced in one area of life (e.g., work) lead to stresses in another area (e.g., managing arthritis) and vice versa. To date, spillover has been studied extensively in the work-family area (30–33). Research has not looked at perceptions of spillover in managing employment and chronic illnesses such as arthritis. The purpose of this study was to examine individuals' perceptions of arthritis-work spillover (AWS) and the demographic, illness, and work context factors that may be associated with it.

In research on work and family, spillover has sometimes been found to reflect 2 separate dimensions: family interference with work and work interference with family (31, 33, 34). In other research, spillover has reflected a single dimension of work-family conflict (35). Across a number of studies, spillover between work and family has been associated with decreased well-being, greater depression, poorer physical health, absenteeism, job dissatisfaction, marital conflict, and alcohol consumption (31–36). However, at least 1 study found that work context factors such as having more control over the work environment and greater latitude to make decisions were associated with less spillover and more positive work-family experiences (32).

These studies suggest the need to examine perceptions of spillover between arthritis and employment. Previous research has underscored the importance of studying individuals' perceptions and appraisals. Individuals' perceptions can intervene in the stress and disease process and aggravate or ameliorate the impact of potential stressors on health. In addition, individuals' perceptions provide insight and a better basis for designing interventions that can enhance individual self-management and coping strategies (37). Currently, with few exceptions, workplace interventions aimed at helping employed individuals with arthritis remain employed are lacking (19). Research on AWS may provide valuable insights into the interface between arthritis and employment and inform intervention efforts aimed at reducing the stresses of working and living with a chronic illness.

In the present study, we designed an AWS measure to examine the extent to which employed individuals with arthritis reported that their arthritis affected their ability to work, as well as the extent to which work affected their ability to manage their condition. Although researchers have focused almost exclusively on the impact of arthritis on employment, we expected that individuals would perceive a greater impact of employment on arthritis than arthritis on employment. This is in keeping with research on work-family conflict demonstrating that individuals report a greater negative impact of work on other areas of life than the opposite (33, 34). The concept of spillover also suggests that illness, disability, and work context factors will be associated with AWS. That is, greater spillover should be reported when individuals have more pain and fatigue, a longer disease duration, more joints involved, and greater disability at work and at home. Individuals with arthritis should also report more spillover when they work a greater number of hours; have physically demanding work; have additional job responsibilities such as overtime, travel, and variable hours (e.g., shift work); and when they have less control over the work environment.

PARTICIPANTS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PARTICIPANTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

Participants.

Study participants were 383 women and 109 men with inflammatory arthritis or osteoarthritis (OA). All were employed. Participants resided in southwestern Ontario, Canada. To ensure a wide range of occupations and that persons receiving fewer health care services were not systematically excluded from the study, the sample was a purposive one. Participants were recruited from rheumatology clinics; The Arthritis Society, Ontario Division; rehabilitation clinics; and community newspaper advertisements. Eligibility criteria were as follows: a physician diagnosis of inflammatory arthritis or OA, having had arthritis for at least 1 year, paid employment, no comorbid conditions causing physical disability, and fluency in English. Of 694 screening questionnaires administered, 492 individuals (70%) were eligible and were subsequently interviewed. Nonparticipants were not employed or retired (3.6%); sought information, treatment, and/or compensation for participation (8.3%); did not have a physician diagnosis of arthritis or had comorbid conditions resulting in disability (3.6%); or were unavailable at call back or declined to participate after hearing the study details (e.g., time involved) (14.5%).

Procedure.

An in-depth, structured questionnaire was administered to participants at home or at a location of their choice and lasted, on average, 2 hours. The questionnaire consisted of standardized scales, measures from our previous research, and items developed for this study. The questionnaire was pilot tested with 7 individuals. The data comprise the first phase of a 5-year longitudinal study examining the coping and adaptation efforts that persons with arthritis use to remain employed. Interviewers completed an in-depth, standardized training session supplemented by regular meetings. Questionnaires were monitored for data quality and completeness. Informed, written consent was obtained from all participants.

Measures.

Demographics.

Data on participants' sex, age, marital status, living arrangements, education, and total household income in the preceding 12 months were collected.

Arthritis type and duration.

Participants were asked about the type(s) of arthritis their physician had diagnosed and the time since diagnosis. Arthritis type was coded in 3 categories: inflammatory arthritis (rheumatoid arthritis, psoriatic arthritis), OA, or both inflammatory arthritis and OA. In cases of multiple diagnoses, the longest duration was used in statistical analyses.

Symptom severity.

Pain and fatigue were measured over the preceding month. Our previous pilot work and ongoing research suggested that the frequency of severe pain was more relevant to persons working with arthritis than pain severity alone without reference to its timing (38). Therefore, pain was assessed using the following question: “How often did you have severe pain from your arthritis?” Fatigue was measured by asking, “How often have you felt fatigue as a result of your arthritis?” Participants responded using a 5-point Likert-type scale (where 1 = no days, 2 = a few days, 3 = some days, 4 = most days, and 5 = all days).

Joints affected.

A joint homunculus displaying major joints affected by arthritis was used to calculate the total number of joints affected (39).

At-home activity limitations.

Eighteen items assessed difficulty with activities in the home environment. The activities were drawn from the Stanford Arthritis Center Health Assessment Questionnaire (40) and the Multidimensional Functional Assessment Questionnaire (41). When answering, respondents were asked to think about performing the activity “without any help from another person or from a special gadget or piece of equipment.” Four domains of activity were tapped: personal care (5 items, e.g., dressing/undressing, eating), in-home mobility (6 items, e.g., standing up from an armless chair, getting in/out of bed), community mobility (3 items, e.g., getting in and around the community, getting in/out of a car), and household activities (4 items, e.g., preparing a meal, light and heavy housework). Items were scored on a 4-point Likert-type scale (where 0 = no difficulty, 1 = some difficulty, 2 = a lot of difficulty, and 3 = not able to do). Participants indicating that an activity was not something that they performed (e.g., cooking) were assigned a score of 0 (no difficulty) for that activity. Responses across the items were summed for a total at-home activity limitations score. Cronbach's alpha, a measure of the internal consistency of the items, was 0.90 (mean ± SD 11.6 ± 7.8).

Workplace activity limitations scale.

Eleven items gauged physical functioning and arthritis-related activity limitations in the workplace (20, 21). Participants were asked to think about performing each activity “without any help from another person or without the help of a special gadget or piece of equipment.” Items asked respondents about how much difficulty they had getting to, from, and around the workplace; sitting and standing for long periods; lifting; working with one's hands; crouching, bending, kneeling, and reaching; the schedule, hours, and pace of work; and meeting current job demands. Items were scored on a 4-point Likert-type scale (where 0 = no difficulty, 1 = some difficulty, 2 = a lot of difficulty, and 3 = not able to do). Participants indicating that an activity was not applicable to their job were assigned a score of 0 (no difficulty) for that activity. Responses across the 11 items were summed for a total workplace activity limitations score. Reliability for the workplace activity limitation scale using Cronbach's alpha was 0.79 (mean ± SD 6.39 ± 4.37, range 0–20).

Hours of work and workplace control.

Participants were asked about the number of hours they worked in an average week. Participants were also asked to what extent they had control over their work schedule (where 1 = not at all and 5 = a great deal).

Occupation.

Occupation was classified using the Human Resources Development Canada National Occupational Classification Matrix 2001 (42). For the purposes of analysis, the occupations were collapsed into 4 groups: business, finance, and administration; health, science, art, and sport; sales and service; and trades, transportation, and equipment operation.

Additional job responsibilities.

Three questions assessed additional job responsibilities. They asked whether respondents worked overtime, variable hours (e.g., shift work), or traveled for business. Responses were yes/no and were summed, with higher scores indicating greater additional job responsibilities.

Arthritis-work spillover.

Six items assessed the extent to which the demands of arthritis interfered with work performance and the extent to which work interfered with managing arthritis. The measure was similar in construction to those used in the work-family area (33, 34). Items were scored on a 5-point Likert-type scale (where 1 = strongly disagree and 5 = strongly agree). Three items assessed the impact of arthritis on work: “The demands of my arthritis make it difficult for me to do as good a job at my work as I would like,” “The quality of my work suffers because of the demands of my arthritis,” and “It takes a great deal of my energy and time to manage the demands of my condition.” An additional 3 items assessed the impact of employment on arthritis: “The demands of my job make it difficult for me to take good care of my arthritis,” “My condition suffers because of the demands of my work,” and “It takes a great deal of my energy and time to manage my work demands.”

Statistical analyses.

Frequencies, means, and SDs were calculated for all variables. Principal components analysis was used to assess the factor structure of the AWS questions. Reliability analysis using Cronbach's alpha coefficients examined the internal coherence of the items. Categorical variables were dummy coded. Reference groups for sex, marital status, education, diagnosis, and job sector were: men; married or living as married; postgraduate education; inflammatory arthritis; and business, finance, and administration, respectively. Pearson's correlation coefficients examined the relationship between AWS and demographic, illness, and work variables. Bivariate analyses of categorical independent variables with AWS used t-tests and analysis of variance tests, and simple regression was used for continuous variables. Multivariate linear regression analysis examined the relationship between participants' AWS and the independent variables significant at the bivariate level of analysis (P < 0.10). Variables were entered simultaneously into the analysis. Analyses were conducted using the SPSS version 12.0.1 for Windows (SPSS, Chicago, IL).

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PARTICIPANTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

The sample characteristics for the study are presented in Table 1. Approximately three-quarters of participants were women (77.8%); the mean ± SD sample age was 50.9 ± 9.3 years. More than half of participants were married and most were well educated, with only 17.3% having a secondary school education or less. One-third of respondents reported a diagnosis of inflammatory arthritis (mostly rheumatoid arthritis; 95.7%), 56.5% reported OA, and 10% reported both inflammatory arthritis and OA. The mean ± SD duration of arthritis was 9.2 ± 8.8 years. On average, respondents reported 8.4 joints affected by arthritis, ∼50% reported fatigue on most or all days, and one-third reported severe pain on most or all days. Approximately two-thirds of respondents worked either in business, finance, or administration or in health, science, art, or sport. The average hours worked in a week were 38.1, and 76% of respondents reported ≥1 additional job responsibilities.

Table 1. Sample characteristics (n = 492)*
CharacteristicValue
  • *

    Total number may be <492 due to item nonresponse. Values are the number (percentage) unless otherwise indicated.

  • Additional work responsibilities represents the number of “yes” items to overtime, variable hours of work, and business travel.

Demographic variables 
 Age, mean ± SD years50.9 ± 9.3
 Sex 
  Female383 (77.8)
  Male109 (22.2)
 Education 
  Secondary school or less85 (17.3)
  Some postsecondary113 (23.0)
  Postsecondary197 (40.0)
  Postgraduate95 (19.3)
 Marital status 
  Married/living as married298 (60.6)
  Divorced/separated/widowed118 (24.0)
  Never married76 (15.4)
 Lives alone124 (25.2)
 Household income ($ Canadian) 
  ≤$39,99991 (18.5)
  $40,000–$69,999147 (29.9)
  $70,000–$99,999119 (24.2)
  >$100,00093 (18.9)
Illness-related variables 
 Arthritis type 
  Inflammatory arthritis165 (33.5)
  Osteoarthritis278 (56.5)
  Both49 (10.0)
 Duration, mean ± SD years9.2 ± 8.8
 Joints affected, mean ± SD8.41 ± 4.71
 Pain 
  No days87 (17.7)
  A few days120 (24.4)
  Some days117 (23.8)
  Most days110 (22.4)
  All days54 (11.0)
 Fatigue 
  No days76 (15.4)
  A few days73 (14.8)
  Some days96 (19.5)
  Most days121 (24.6)
  All days123 (25.0)
Activity limitations 
 At-home activity limitations (18 items), mean ± SD11.6 ± 7.8
 Workplace activity limitations (11 items), mean ± SD6.4 ± 4.4
Work context variables 
 Job sector 
  Business, finance, administration164 (33.3)
  Health, science, art, sport175 (35.6)
  Sales and service102 (20.7)
  Trades, transportation, equipment operator50 (10.2)
 Average hours of work per week, mean ± SD38.1 ± 11.4
 Additional job responsibilities 
  0118 (24.0)
  1180 (36.6)
  2130 (26.4)
  364 (13.0)
 Control over work schedule, mean ± SD3.0 ± 1.5

The means, SDs, inter-item correlations, and principal component analysis factor loadings for the AWS scale are presented in Table 2. The means for the 6 items indicated that overall, respondents were somewhat more likely to disagree with items asking whether arthritis interfered with work demands (items 4–6) and were somewhat more likely to agree that work interfered with caring for one's arthritis (items 1–3). When the 3 items reflecting arthritis interference with work performance were summed and compared with the 3 items assessing work interference with arthritis, these differences emerged as significant (t[465] = −3.90, P < 0.001).

Table 2. Time 1 arthritis-work spillover inter-item correlations and scale internal consistency*
ItemMean ± SDInter-item correlation matrixFactor loading
123456
  • *

    Cronbach's alpha = 0.88.

  • P < 0.01 among all items.

1. The demands of my job make it difficult for me to take good care of my arthritis.2.99 ± 1.251.00     0.81
2. It takes a great deal of my energy and time to manage my work demands.3.56 ± 1.220.611.00    0.78
3. My condition suffers because of my work.3.06 ± 1.260.740.681.00   0.87
4. The demands of my arthritis make it difficult for me to do as good a job at my work as I would like.2.91 ± 1.310.480.420.581.00  0.77
5. It takes a great deal of my energy and time to manage the demands of my condition.3.09 ± 1.240.550.570.610.561.00 0.80
6. The quality of my work suffers because of the demands of my arthritis.2.29 ± 1.160.400.360.470.640.471.000.69

Inter-item correlations ranged from 0.36 to 0.74 and were significant at P < 0.01. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.85 and indicated the appropriateness of conducting principal components factor analysis with this sample (43, 44). A varimax rotation was used and yielded a single factor solution with all factor loadings ≥0.69. The factor accounted for 62.1% of the variance. Cronbach's alpha, a measure of the reliability of the scale, was 0.88.

Bivariate analyses examined the relationship of AWS and demographic, illness, activity limitations, and work context variables (Table 3). The results revealed that younger participants, those with more joints affected, and those with greater pain and fatigue reported more AWS. Individuals with more at-home and workplace activity limitations also reported significantly more spillover. Work context factors were related to AWS, with job type, longer work hours, and additional job responsibilities such as overtime, variable hours, or business travel being related to spillover. Greater control over the work schedule was associated with less AWS.

Table 3. Bivariate analysis of variance/t-test/regression for explanatory variables by total arthritis work spillover score
Variablet/FP
  • *

    Excess work responsibilities represents the number of “yes” items to overtime, variable hours of work, and business travel.

Demographic variables  
 Age−3.050.00
 Sex−0.400.69
 Education0.250.86
 Marital status0.300.74
 Live alone0.260.61
Illness-related variables  
 Arthritis type0.710.49
 Duration−0.170.87
 Number of joints affected5.120.00
 Pain6.830.00
 Fatigue9.880.00
Activity limitations  
 At-home activity limitations11.380.00
 Workplace activity limitations14.460.00
Work context variables  
 Job sector3.390.02
 Average hours of work per week3.370.00
 Additional job responsibilities*2.180.03
 Control over work schedule−5.560.00

Standard multivariate linear regression examined the associations of all the predictor variables to AWS. Variables significant at the P < 0.10 level in the bivariate analyses were included in the multivariate analyses. The final model accounted for 42% of the variance (Table 4). Age accounted for 2% of the variance in AWS, with younger respondents reporting more spillover. Fatigue and workplace activity limitations explained the greatest amount of variance in the model, accounting for 21% and 15% of the variance, respectively. Individuals with arthritis who worked in trades and transportation and those with less control over their work schedules also reported significantly more AWS. These work context variables accounted for 6% of the variance in the final model.

Table 4. Multivariate unstandardized (b) and standardized (β) regression coefficients for explanatory variables by arthritis work spillover*
VariableR2 changebβP
  • *

    Total adjusted R2 = 0.42.

  • Reference category: business, finance, administration.

Demographic variables0.02   
 Age −0.07−0.110.00
Illness-related variables0.21   
 Number of joints affected 1.610.060.15
 Pain 0.120.030.53
 Fatigue 0.760.180.00
Activity limitations0.15   
 At-home activity limitations 0.980.070.20
 Workplace activity limitations 0.470.350.00
Work context variables0.06   
 Job sector    
  Health, science, art, sport −0.68−0.060.15
  Sales and service 0.950.060.10
  Trades and transportation 2.110.160.00
 Average hours of work per week 0.020.030.42
 Additional job responsibilities 0.300.050.21
 Control over work schedule −0.65−0.160.00

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PARTICIPANTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

Research on arthritis and employment has focused almost exclusively on the impact of the disease on employment and not the potential reciprocal impact of work on arthritis. This study created a new measure of AWS that assessed individuals' perceptions of the interface between arthritis and employment. It also examined the relationship of demographic, illness, activity limitations, and work context variables to this perceived spillover. The findings provide evidence for the value of reconceptualizing the relationship between arthritis and work to include the reciprocal effects of arthritis and employment on one another, and they suggest new avenues of research that include both perceptions of spillover between work and arthritis and the potential impact of work on arthritis disease severity.

The AWS measure created for this research drew on previous studies in the work-family conflict area and assessed 2 dimensions of spillover: arthritis interference with work performance and work interference with caring for one's arthritis. Preliminary descriptive analyses revealed that individuals were more likely to perceive that work interfered with the care of their arthritis than arthritis interfered with work performance. However, factor analyses confirmed that, conceptually, the scale is best conceived of as a single dimension. That is, trying to determine whether it is arthritis or work that is more likely to affect the other is not as relevant as assessing the global perception of spillover and conflict between these 2 areas of life. The potential to quickly and easily tap perceptions of spillover enhances the range of tools available to researchers to examine the role of arthritis in individuals' lives. Previous studies have noted the importance of examining a broader range of variables to understand why some persons with arthritis remain working while others give up employment (5, 6, 9, 11, 20, 45). In general, however, arthritis-specific measures that assess the impact of arthritis and individuals' perceptions of that impact have been lacking. Measuring AWS provides a conceptually clear and useful means of examining the nexus between arthritis and work from the perspective of persons living with the disease.

Analyses that examine the relationship of AWS to other variables provide insights into the factors associated with spillover. Interestingly, younger, not older, adults reported more spillover. At first glance this seems counterintuitive because older adults have been found in other research to be more likely to give up employment than younger adults (45, 46). Greater perceptions of spillover would seem to suggest greater likelihood of giving up employment. However, research also finds that younger adults are more likely to make arthritis-related work changes such as changing the hours of work or the type and nature of their employment (20). This raises the possibility that perceptions of AWS might have positive effects in some cases and may motivate individuals to seek new ways to manage their arthritis and employment. Alternatively, older adults may view their arthritis as age normative and perceive less spillover of their condition and work than younger adults (20). Future research needs to examine perceptions of AWS as a mediating variable that may influence treatment and health utilization, as well as workplace changes and additional coping and self-management efforts.

The illness-related variables examined in this study included the type of arthritis reported, disease duration, number of joints affected, pain, and fatigue. Although most of these variables were not significant in predicting AWS at the multivariate level, further research is warranted in light of studies that have found employment retention differences among persons who vary in their type of inflammatory arthritis (8). In this study, the small number of respondents with an inflammatory disease other than rheumatoid arthritis precluded analyses differentiating perceptions among individuals with different inflammatory disease diagnoses.

Multivariate analyses did highlight the importance of fatigue in perceptions of AWS. Fatigue has been documented as a common symptom among persons with arthritis, especially those with rheumatoid arthritis (47, 48). These data suggest that fatigue from arthritis and also from trying to meet multiple role demands plays an important role in understanding individuals' perceptions of spillover. Intervention and treatment efforts to address fatigue are lacking in the arthritis and workplace literature. However, reducing fatigue may be critical in enabling persons to better manage multiple roles and in sustaining their involvement in employment.

Workplace activity limitations were also related to AWS, whereas at-home activity limitations with activities such as self care, mobility, and household tasks were not. These findings provide some initial convergent and discriminant validity for the AWS measure. Specifically, we expected that the difficulties arthritis created in managing workplace activities such as bending, reaching, moving around, standing or sitting for long periods of time, grasping objects, and meeting the pace of job demands would be related to greater perceptions of spillover between arthritis and work. Similar difficulties with activities outside of work may have an impact on individuals' general well-being but should not affect perceptions of AWS. These findings support this hypothesis.

Job type was related to AWS, with respondents who worked in trades, transportation, and equipment operation being more likely to report spillover. Typically, these jobs are physically demanding and potentially more difficult for persons with arthritis to manage. In turn, a more physically demanding job can make taking care of one's condition more difficult. As a result, perceptions of AWS may be intensified. Previous research has found that individuals with arthritis who work in physically demanding jobs are at risk for leaving employment (1, 10, 11, 49). The perception of greater spillover among these individuals suggests the need to also examine whether physically demanding work puts persons with arthritis at risk for adverse disease consequences.

Finally, the importance of having control in the workplace has been emphasized in numerous studies and is related to reports of fewer adverse health effects (25, 28). In arthritis research, greater control has been associated with remaining employed (50). In the present study, greater control over one's work schedule was associated with less AWS. The findings again confirm the importance of workplace context, in addition to treatment, when designing interventions to keep individuals with arthritis employed. Specifically, workplace benefits that include work arrangements such as flexible hours or flexibility in arranging one's work tasks can provide greater control and may be particularly important to individuals with arthritis in reducing perceptions of spillover.

Several limitations in this research need to be addressed in future studies. First, although our recruitment strategy enabled us to generalize beyond a clinical sample and was comparable with other samples in terms of age, marital status, education, income, and occupation of participants, the sample was a purposive one. The extent of AWS experienced by working adults with arthritis should be replicated with other samples. The findings revealed that our AWS measure demonstrated a high degree of internal consistency and evidence of convergent and discriminant validity. Longitudinal research is needed to examine the stability and sensitivity of the measure over time as well as its predictive validity. Finally, much of the variance in predicting perceptions of AWS was unaccounted for in the regression analyses. Future research needs to examine a wider range of potential predictors of AWS including the availability of support, treatment variables, and more detailed information about the work context.

Having acknowledged some of its limitations, this study extends research on arthritis and employment by focusing on the potential conflict that can occur between these 2 areas of life and by generating a measure of individuals' perceptions of AWS. The findings highlight the importance of examining the broader, reciprocal influences of different roles in the lives of persons with arthritis. Although only a first step in this process, this study illustrates the considerable promise of examining AWS.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PARTICIPANTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

Our thanks to Jennifer Boyle, Novlette Fraser, Cristina Mattison, the interviewers and coders who participated in this research, and 2 anonymous reviewers for their helpful comments.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PARTICIPANTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES
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