Predictors of Exercise Behaviors among Fibromyalgia Patients1

Karen Oliver, M.S.* and Terry Cronan, Ph.D.†,2

Preventive Medicine 35, 383–389 (2002)doi:10.1006/pmed.2002.1084

olo

*SDSU/UCSD Joint Doctoral Program in Clinical Psych

Background. Exercise improves the physical andpsychological well being of patients with fibromyalgiasyndrome (FMS). However, exercise interventions forpatients with FMS have suffered from poor adherence.The purpose of this study was to examine predictors ofexercise for people with FMS.

Methods. Participants were 444 patients with FMSwho were part of a larger study. Hierarchical logisticregression analyses were conducted examining exer-cise behavior at multiple time points. Discriminantanalyses were also used to identify predictor vari-ables.

Results. Engaging in regular exercise and havinghigher exercise self-efficacy significantly predictedcontinued engagement in exercise behavior in peoplewith FMS. Age, employment status, depression, educa-tion level, self-efficacy for managing FMS, and the sizeof one’s social network also demonstrated predictivequalities.

Conclusion. Exercise self-efficacy and continuedparticipation in regular exercise most strongly pre-dicted present and future exercise behavior in pa-tients with FMS. Interventions designed to train FMSpatients in initiating exercise programs while ad-dressing exercise self-efficacy, depression, and socialsupport are warranted. © 2002 American Health Foundation and

Elsevier Science (USA)

Key Words: fibromyalgia; exercise; predictors.

INTRODUCTION

Exercise is important in the management of fibro-myalgia syndrome (FMS). FMS is a chronic, painfularthritis-related condition that affects approximatelythree to six million Americans. Symptoms of FMS in-clude generalized pain and fatigue, chronic headaches,sleep disturbances, stiffness, and depression [1]. Mostpatients with FMS demonstrate extreme physical de-

1 Preparation for this article was supported by NIH grant AR-

383

gy, †San Diego State University, San Diego, California

conditioning [2,3] with 80% of FMS patients reportedas physically unfit [4]. Studies indicate that patientshave an increase in co-contraction of antagonist mus-cles in areas where they experience pain, as well asreduced maximum voluntary contractions in agonistmuscles [5,6]. Additionally, patients show changes inskeletal activity and demonstrate muscle micro-trauma, as displayed by poor recovery from delayedonset muscle soreness, a deficiency in growth hormone,and low levels of somatomedin C [7].

Exercise programs increase the physical and psycho-logical well-being of patients with FMS [8–10]. Pa-tients participating in an exercise program featuringaerobic, flexibility, and strength training reduced theirnumber of tender points and degree of tenderness andincreased their aerobic fitness levels more than a con-trol group [11]. Participants in a 20-week cardiovascu-lar fitness training program had better peak work ca-pacity, total myalgic score, well-being, and physicianreports of well-being than controls [12]. Patients par-ticipating in a pool exercise program significantly im-proved on measures of functional disability, physicalfunctioning, grip strength, pain severity, social func-tioning, psychological distress, and quality of life [13].

While the health benefits of exercise for FMS pa-tients have been established, exercise interventionshave been plagued with poor adherence and retentionrates [11,14], and the predictors of exercise behavioramong patients with FMS remain poorly understood.Among the general population, self-efficacy and exer-cise self-efficacy have frequently been documented assignificant predictors of exercise activity [15–19]. De-mographic characteristics, such as education, income,age, and gender, have also significantly predicted ex-ercise behavior [18,20], as have variables relating tosocial support [19–21] and depression [22,23].

No previous study has examined predictors of exer-cise behavior among patients with FMS. Therefore, the

44020.2 To whom correspondence and reprint requests should be ad-

dressed at 6505 Alvarado Road, Suite 110, San Diego, CA 92120. Fax(619) 594-1247. E-mail: tcronan@sunstroke.sdsu.edu.

present study was designed to identify predictors as-sociated with the initiation and maintenance of regularexercise among a large sample of patients with FMS.

0091-7435/02 $35.00© 2002 American Health Foundation and Elsevier Science (USA)

All rights reserved.

METHODS

Participants

Participants were 444 members (20 males and 424females) of a health maintenance organization (HMO)who were diagnosed with FMS. The mean age of theparticipants was 53.9 years (SD � 11.2); 86% wereCaucasian and 65% were married. Forty-nine percentof the participants were employed on either a full- orpart-time basis, and 84% had at least some collegeeducation. Their median income fell in the range of$20,000 to $50,000. The mean length of FMS symp-toms reported was 13.9 years (SD � 13.3), and themean number of comorbid conditions was 1.1 (SD �1.0).

Procedure

The procedure for this study was approved by theSan Diego State University institutional review board.All participants were part of a study that was designedto evaluate the effects of social support and educationon health status, quality of well-being, and health carecosts in people with FMS. Patients were recruitedthrough newspaper advertisements, mass mailings toHMO members, flyers in physicians’ offices, and phy-sician referrals. To be eligible, participants had to bediagnosed by a physician and had to meet the Ameri-can College of Rheumatology (ACR) criteria [24] forFMS before entry into the study. A trained examinerperformed the manual tender point exam after in-formed consent was obtained. Participants were re-quired to have pain above and below the waist, on boththe left and right sides of the body, with a minimumduration of 3 months, and pain in 11 of 18 tender pointsites. Participants had to meet the ACR criteria, agreeto attend 10 2-h weekly meetings, followed by 10 2-hmonthly meetings, and agree to participate in periodicassessments.

Participants completed a battery of questionnairesand then were randomly assigned to one of threegroups: social support, social support and education, ora no-treatment control. Group members in the socialsupport group were presented with a series of tasks tocomplete and issues to be discussed; no staff memberswere present. The meetings for the social support andeducation group included the same social support com-ponent for 1 h. In the second hour, a health educatordid a formal presentation that focused on treatingsymptoms of FMS and when to use the health caresystem. While exercise behavior was addressed withinthe education component of the intervention, this topicwas not a major focus of the intervention. Both inter-vention groups met for 2 h once a week for 10 weeks,and then once a month for 10 months. The controlgroup participated only in the assessments, but wassent quarterly newsletters that did not contain any

information relating to FMS. Participants were as-sessed at baseline, and at 6 months, 1 year, and 18months following the baseline assessment. All groupsdemonstrated improvements on variables assessingFMS impact, self-efficacy, and depression. The socialsupport and education group was less helpless after 1year than were the other groups; differential changesfor all other variables were not significant. There wereno time nor differential changes in health care costsamong participants in the intervention and controlgroups [25].

At each assessment, participants were asked to re-spond “true” or “false” to a question asking whetherthey had engaged in regular exercise over the past 6months. Regular exercise was defined as performingmoderate physical activity three times a week for atleast 20 minutes per time [26].

Six hundred participants were recruited for the in-tervention study. However, at the 18-month assess-ment, there was a 19% attrition rate. Of the 484 par-ticipants remaining, 40 participants were omitted fromthe analyses because of missing exercise behavior data,leaving a remainder of 444 participants for the presentstudy. The participants in the present study did notsignificantly differ on any baseline variables fromthose with missing data, nor from those who hadceased to participate in the study.

Measures

Demographic variables. Participants gave a briefmedical history and were asked their age, gender, in-come, marital status, educational level, employmentstatus, and ethnicity.

Health status. Health status was measured by theQuality of Well-being Scale (QWB). This scale wasdesigned to measure general functioning [27]. It in-volves four weighted subscales of function: SymptomComplex, Mobility, Physical Activity, and Social Activ-ity, and assesses the presence of 27 symptoms or prob-lems on a specific day, over the past week. The healthof the respondent is scaled from 0.0 (death) to 1.0(optimum health). Reliability for the QWB has beendemonstrated [28,29], and the QWB’s validity as anoutcome measure has been shown for various condi-tions [30,31], including FMS [32].

Illness impact. The Fibromyalgia Impact Question-naire (FIQ) is a brief, self-administered, reliable, andvalid tool for assessing physical functioning and psy-chological, social, and global well-being in people withFMS [33]. The questionnaire consists of items assess-ing functioning in daily living tasks (e.g., shopping,laundry) pertaining to the activities respondents wereable to engage in during the past week. Respondentsuse a 4-point Likert scale ranging from 0 (always) to 3(never). Other subscales are assessed by single items,

384 OLIVER AND CRONAN

with respondents marking a point on a 100-mm Visualanalog scale (VAS), describing how they felt over thepast week. A total score was used, with higher scoresindicating worse functioning.

Pain. The pain subscale of the FIQ was used as ameasure of participants’ pain severity. Participants areasked to mark a point on a 100-mm VAS, anchored at0 (no pain) and 100 (very severe pain), describing theirpain over the past week.

Depression. The Center for Epidemiologic Studies-Depression Scale (CES-D) [34] was used to measuredepressive symptomatology. Twenty items are rated ona 4-point Likert scale ranging from 0 (rarely or none ofthe time) to 3 (most or all of the time). Reliability hasbeen reported at 0.88. The scale is internally consistent(alpha coefficients range from 0.80 to 0.90), has mod-erate test–retest reliability (r � 0.40 and above), andhigh concurrent and construct validity [34].

Helplessness. The Arthritis Helplessness Index(AHI) [35] was adapted for the FMS population (theterm “arthritis” was changed to “fibromyalgia”). This isa self-administered measure of the participants’ per-ceptions of helplessness in coping with FMS. Partici-pants were asked to rate the 11 items, using a 6-pointLikert scale that ranges from “strongly disagree” to“strongly agree.” Scores are reverse coded so thathigher scores indicate greater helplessness. The scaleis internally consistent (Cronbach’s alpha � 0.69), andhas a test–retest reliability over a 12-month period of0.52 [36].

Self-efficacy for managing FMS. Perceived self-efficacy was measured with the Arthritis Self-EfficacyScale, modified for use with FMS patients by changingthe term “arthritis” to “fibromyalgia” [37]. This self-report scale is comprised of 20 items measuring threecomponents of self-efficacy: perception of control overpain, over daily functioning, and over other symptoms,measured on a scale from 0 (very uncertain) to 100(very certain). Examples of items include confidence inone’s ability to decrease pain or walk 100 feet on flatground in 20 s. This scale has good test–retest reliabil-ity (ranging from 0.71 to 0.85), and has demonstratedconstruct validity [37].

Social support. The Norbeck Social Support Ques-tionnaire (NSSQ) was administered to assess socialsupport. Participants are asked to list all the peoplewho are important to them (12 maximum) and then torate them in different areas of support on a Likert scaleranging from 1 (not at all) to 5 (a great deal) [38].Participants were also asked to rate their overall sat-isfaction with their social support, using this sameLikert scale. For the present study, the total number ofpeople listed was used to quantify the size of theirsocial network. Construct validity for the NSSQ has

been demonstrated, and test–retest reliability rangesfrom 0.58 to 0.78 [38].

Exercise self-efficacy. Participants were asked sevenquestions about their confidence in their ability to ex-ercise under adverse conditions (e.g., tired, in a badmood, lack of time, bad weather, in pain). Responseswere rated on a Likert Scale from 1 (not at all confi-dent) to 5 (extremely confident) [39].

RESULTS

Logistic Regression Analyses

Several logistic regression analyses were conductedwith exercise behavior (yes/no) as the dependent vari-able. The first logistic regression examined demo-graphic, health, and psychosocial variables as predic-tors of exercise behavior at the baseline assessment.Three additional regressions were computed, one eachat 6, 12, and 18 months. Each additional regressionwas conducted in hierarchical fashion: exercise behav-ior during the previous 6 months was entered on thefirst step, and the predictor variables were entered onthe second step. For all analyses, significance was setat P � 0.05. Please see Table 1 for the logistic regres-sion coefficients, significance values, and odds ratiosfor all predictor variables at each time point.

At baseline, the predictor variables correctly classi-fied 71.02% of the participants (Model �2(15) � 124.7,P � 0.0001). Younger age, unemployment, and higherexercise self-efficacy were significantly related to en-gaging in exercise behavior. No other variables weresignificantly associated with exercise behavior at thebaseline assessment.

At 6 months, exercising at the baseline assessmentcorrectly classified 68.1% of the participants (Model�2(1) � 56.9, P � 0.0001). The addition of the predic-tor variables improved the correct classification to74.2% (Improvement �2(15) � 76.1, P � 0.0001); lowerlevels of depression and higher exercise self-efficacywere significantly associated with engaging in exercisebehavior.

At 1 year, exercise status at the 6-month assessmentcorrectly classified 70.9% of the participants (Model�2(1) � 76.6, P � 0.0001). The addition of the predic-tor variables improved the correct classification to76.1% (Improvement �2(15) � 93.8, P � 0.0001); hav-ing a high school education or less and higher levels ofexercise self-efficacy were significantly associated withengaging in exercise behavior.

At 18 months, exercise status at the 1-year assess-ment correctly classified 68.7% of the participants(Model �2(1) � 61.3, P � 0.0001). Adding the predictorvariables increased the correct classification to 74.8%(Improvement �2(15) � 87.9, P � 0.0001). Of thesevariables, higher levels of self-efficacy for managing

385EXERCISE PREDICTORS

TABLE 1

Logistic Regression Coefficients, Significance, and Odds Ratios

B SE P Exp (B)

Baseline assessmentAge �0.03 0.01 0.01 0.97Income (1) �0.17 0.34 0.62 0.84Income (2) �0.21 0.38 0.58 0.81Education (1) 0.33 0.34 0.33 1.39Education (2) 0.38 0.37 0.30 1.47Employment �0.87 0.27 �0.01 0.42Social support satisfaction 0.05 0.12 0.70 1.05Pain �0.01 0.01 0.77 1.00Illness Impact �0.02 0.01 0.28 0.98Helplessness 0.30 0.22 0.18 1.34Self-efficacy �0.01 0.01 0.57 1.00Health status 0.01 0.01 0.30 1.00Depression �0.01 0.02 0.96 1.00Exercise self-efficacy 1.45 0.18 �0.01 4.28Support network �0.03 0.04 0.46 0.97

6-Month assessmentExercise status at baseline 1.52 0.21 �0.01 4.56Age �0.01 0.01 0.32 0.99Income (1) �0.01 0.35 0.99 1.00Income (2) �0.31 0.39 0.43 0.73Education (1) �0.59 0.35 0.09 0.56Education (2) �0.43 0.38 0.26 0.65Employment �0.10 0.27 0.71 0.90Social support satisfaction �0.02 0.14 0.88 0.98Pain 0.01 0.01 0.66 1.00Illness Impact �0.01 0.01 0.80 1.00Helplessness �0.03 0.23 0.90 0.97Self-efficacy 0.01 0.01 0.84 1.00Health status �0.01 0.01 0.41 1.00Depression �0.04 0.02 0.01 0.96Exercise self-efficacy 1.01 0.16 �0.01 2.74Support network �0.02 0.05 0.71 0.98

1-Year assessmentExercise status at 6-months 1.78 0.21 �0.01 5.94Age 0.02 0.01 0.08 1.02Income (1) �0.18 0.37 0.63 0.84Income (2) 0.07 0.41 0.85 1.08Education (1) 0.90 0.36 0.01 2.45Education (2) 0.50 0.39 0.21 1.64Employment 0.15 0.29 0.60 1.16Social support satisfaction 0.09 0.15 0.52 1.10Pain 0.01 0.01 0.33 1.01Illness Impact �0.01 0.01 0.37 0.99Helplessness �0.03 0.26 0.89 0.97Self-efficacy �0.01 0.01 0.60 0.99Health status 0.01 0.01 0.31 1.00Depression 0.01 0.02 0.79 1.01Exercise self-efficacy 1.24 0.17 �0.01 3.44Support network 0.04 0.05 0.42 1.04

18-Month assessmentExercise status at 1-year 1.58 0.21 �0.01 4.86Age 0.01 0.01 0.39 1.01Income (1) �0.21 0.36 0.56 0.81Income (2) �0.06 0.40 0.88 0.94Education (1) �0.26 0.35 0.45 0.77Education (2) �0.27 0.38 0.48 0.76Employment �0.21 0.27 0.43 0.81Social support satisfaction �0.06 0.14 0.68 0.94Pain �0.01 0.01 0.56 1.00Illness Impact 0.01 0.01 0.40 1.01Helplessness 0.19 0.24 0.43 1.21Self-efficacy 0.02 0.01 0.04 1.02

386 OLIVER AND CRONAN

FMS and exercise-self-efficacy were significantly asso-ciated with engaging in exercise behavior.

Discriminant Analysis

To further explore predictors of exercise behavior,the participants were divided into two groups based ontheir exercise behavior. Group 1 consisted of exercisers(those who reported exercising regularly when theyentered the study and at each assessment period, orthose who did not exercise regularly when they enteredthe study, but who started exercising regularly withinthe first 6 months, and continued to exercise through-out the study period; n � 117). Group 2 consisted ofnon-exercisers (not exercising regularly when they en-tered the study or at any assessment period; n � 94).Multivariate analyses of variance and �2 analyses in-dicated that there were no significant demographicdifferences among the two groups. Stepwise discrimi-nant analysis was then used to identify variables thatbest discriminate members of the groups from one an-other [40]. All variables were entered into the discrimi-nant function analysis together; the stepwise proce-dure than added discriminating variables one at a timeor eliminated weak or redundant variables in succes-sive steps, thereby producing an optimal model. Theorder of selection of discriminating variables was basedon minimizing the overall Wilks’ Lambda, a multivar-iate measure of group differences over all variables inthe model [41].

Results of the discriminant analysis are presented inTable 2. The model that best predicted exercise behav-ior included three discriminating variables: exerciseself-efficacy, depression, and the size of the social sup-port network. Specifically, lower depression scores,

higher exercise self-efficacy, and larger social supportnetworks predicted classification as an exerciser. Themodel correctly classified 78.7% of all cases, and thediscriminant function was a significant predictor ofexercise behavior (�2(3) � 100.3, P � 0.001), with anoverall Wilks’ Lambda of 0.62.

DISCUSSION

The results of the present study indicate that engag-ing in regular exercise and having higher exercise self-efficacy significantly predict continued engagement inexercise behavior in people with FMS. Factors such asage, employment status, depression, education level,self-efficacy for managing FMS, and the size of one’ssocial network also demonstrate predictive qualities,and can discriminate between those who regularly en-gage in an exercise program and those who do notreport exercising at all. Health status variables such aspain do not appear to play a role in FMS patients’decisions to engage in exercise behavior.

While it is interesting to ascertain the predictivevalue of demographic characteristics such as age andeducation level, it is difficult for behavioral researchersto intervene with these variables. Thus, their utility islower than that of variables that are subject to manip-ulation. In particular, exercise self-efficacy was astrong and consistent predictor of exercise behavior incross-sectional and longitudinal analyses. Bandura[42] defined self-efficacy as an individual’s perceivedsense of control in performing certain tasks and man-aging his or her life or environment. The results of thepresent study indicate that patients with FMS need tobelieve in their ability to engage in exercise behaviorsin order for them to do so. Further, it appears thatprevious exercise, which consistently predicted futureexercise, strengthens confidence in one’s ability to con-tinue engaging in exercise behavior [43]. Thus, inter-ventions that can assist FMS patients in beginning aregular exercise program should enhance exercise-self-efficacy, particularly if these interventions are tailoredto address participants’ individual needs [44].

The results of the discriminant analysis suggest thatan intervention program designed to decrease depres-sion, enhance self-efficacy, and promote regular exer-

B SE P Exp (B)

18-Month assessmentHealth status 0.01 0.01 0.56 1.00Depression 0.01 0.02 0.83 1.00Exercise self-efficacy 1.20 0.17 �0.01 3.33Support network 0.01 0.05 0.82 1.01

Note: Income (1) � Below $20,000; Income (2) � $20,000–$50,000; Education (1) � high school or less; Education (2) � some college.

TABLE 2

Discriminant Function Summary for PredictingExercise Group Membership

VariableF toenter

Wilks’lambda

Standardizedcoefficient P

Exercise self-efficacy 107.09 0.66 0.88 �0.0001Depression 62.35 0.63 �0.39 �0.0001Social support network 42.89 0.62 0.19 �0.0001

387EXERCISE PREDICTORS

TABLE 1—Continued

cise within groups would be beneficial for patients withFMS. Once begun, the probable reciprocal effects ofexercise, depression, and self-efficacy should tend toperpetuate exercise behaviors. Additionally, FMS pa-tients who exercise tend to experience benefits in bothphysical and psychological functioning [8,13], whichshould further enhance the desire to engage in regularexercise. The challenge for interventions is to find thecorrect exercise “prescription” for each patient [45], aswell as to get the patients to exercise long enough torealize its benefits.

The present study used a volunteer, conveniencesample that was obtained from a single HMO. Thus,the participants may not be a representative sample ofFMS patients, but they are likely similar to FMS pa-tients from other HMOs. Additionally, people who vol-unteer to participate may differ from those who do not;however, the participants are likely to represent vol-unteers in other behaviorally based intervention stud-ies. In addition, only one self-report measure was usedto assess exercise behavior in the study participants;no exercise diary or other method of assessing exercisebehavior was used. It is, therefore, possible that socialdesirability may have led to over-reporting exercisebehavior. However, over-reporting of exercise shouldreduce, rather than create, differences between groups.

In conclusion, exercise self-efficacy and continuedparticipation in exercise programs most strongly pre-dicted present and future exercise behavior in patientswith FMS. Additionally, depression and the size ofone’s social network can further discriminate amongthose who exercise regularly and those who do not.Interventions designed to train FMS patients in initi-ating exercise programs while addressing exercise self-efficacy, depression, and social support are warranted.

ACKNOWLEDGMENTS

We thank the people at Kaiser Permanente for their cooperationand assistance in conducting this study. We also thank Dr. WilliamHillix for his comments on earlier drafts.

REFERENCES

1. Arthritis Foundation. Managing your fibromyalgia. Atlanta: Ar-thritis Foundation, 2000:1–2.

2. Keel P. Pain management strategies and team approach. Bail-lieres Best Pract Res Clin Rheumatol 1999;13:493–506.

3. Sim J, Adams N. Physical and other non-pharmacological inter-ventions for fibromyalgia. Baillieres Best Pract Res Clin Rheu-matol 1999;13:507–23.

4. Bennett RM, Clark SR, Goldberg L, Nelson D, Bonafede RP,Porter J, et al. Aerobic fitness in patients with fibrositis. Acontrolled study of respiratory gas exchange and 133xenon clear-ance from exercising muscle. Arthritis Rheum 1989;32:454–60.

5. Lund JP, Stohler CS, Widmer CG. The relationship betweenpain and muscle activity in fibromyalgia and similar conditions.In: Vaeroy H, Merskey H, editors. Progress in fibromaylgia andmyofascial pain. Amsterdam: Elsevier Science, 1993:311–27.

6. Norregaard J, Bulow P, Lykkegaard JJ, Mehlsen J, Danneskiold-Samsooe B. Muscle strength, working capacity and effort inpatients with fibromyalgia. Scand J Rehabil Med 1997;29:97–102.

7. Bennett RM, Clark SR, Campbell SM, Burckhardt CS. Somato-medin C levels in patients with fibromyalgia syndrome: a possi-ble link between sleep and muscle pain. Arthritis Rheum 1992;35:1113–6.

8. DaCosta D, Dobkin PL, Dritsa M, Fitzcharles M. The relation-ship between exercise participation and depressed mood inwomen with fibromyalgia. Psychol Health Med 2001;6:301–11.

9. Meiworm L, Jakob E, Walker U, Peter HH, Keul J. Patients withfibromyalgia benefit from aerobic endurance exercise. Clin Rheu-matol 2000;19:253–7.

10. Nichols DS, Glenn TM. Effects of aerobic exercise on pain per-ception, affect, and level of disability in individuals with fibro-myalgia. Phys Ther 1993;74:327–32.

11. Martin L, Nutting A, Macintosh BR, Edworthy SM, ButterwickD, Cook J. An exercise program in the treatment of fibromyalgia.J Rheumatol 1996;23:1050–3.

12. McCain GA, Bell DA, Mai FM, Halliday PD. A controlled studyof the effects of a supervised cardiovascular fitness trainingprogram on the manifestations of primary fibromyalgia. Arthri-tis Rheum 1988;3:1135–41.

13. Mannerkorpi K, Nyberg B, Ahlmen M, Ekdahl C. Pool exercisecombined with an education program for patients with fibromy-algia syndrome. A prospective, randomized study. J Rheumatol2000;27:2473–81.

14. Wigers SH, Stiles TC, Vogel PA. Effects of aerobic exerciseversus stress management treatment in fibromyalgia. ScandRheumatol 1996;25:77–86.

15. Alewijnse D, Mesters I, Metsemakers J, Adriaans J, van denBorne B. Predictors of intention to adhere to physiotherapyamong women with urinary incontinence. Health Educ Res 2001;16:173–86.

16. Gyurcsik N, Brawley LR. Mindful deliberation about exercise:influence of acute positive and negative thinking. J Appl SocPsychol 2000;30:2513–33.

17. McAuley E, Courneya KS, Rudolph DL, Cox CL. Enhancingexercise adherence in middle-aged males and females. Prev Med1994;23:498–506.

18. Rimal RN. Longitudinal influences of knowledge and self-efficacy on exercise behavior: tests of a mutual reinforcementmodel. J Health Psychol 2001;6:31–46.

19. Wallace LS, Buckworth J, Kirby TE, Sherman WM. Character-istics of exercise behavior among college students: application ofsocial cognitive theory to predicting stage of change. Prev Med2000;31:494–505.

20. Steptoe A, Rink E, Kerry S. Psychosocial predictors of changes inphysical activity in overweight sedentary adults following coun-seling in primary care. Prev Med 2000;31:183–94.

21. Wankel LM, Mummery WK, Stephens T, Craig CL. Prediction ofphysical activity intention from social psychological variables:results from the Campbell’s Survey of Well-Being. J Sport ExercPsychol 1994;16:56–69.

22. Jette AM, Rooks D, Lachman M, Lin TH, Levenson C, HeisleinD, et al. Home-based resistance training: predictors of participa-tion and adherence. Gerontologist 1998;38:412–21.

23. Williams P, Lord SR. Predictors of adherence to a structuredexercise program for older women. Psychol Aging 1995;10:617–24.

24. Wolfe F, Smythe HA, Yunus MB, Bennett RM, Bombardier C,Goldenberg DL, et al. The American College of Rheumatology1990 criteria for the classification of fibromyalgia. ArthritisRheum 1990;33:160–72.

388 OLIVER AND CRONAN

25. Oliver K, Cronan TA, Walen HR, Tomita M. The effects of socialsupport and education on health care costs fibromyalgia pa-tients. J Rheumatol 2002;28:2711–19.

26. Marcus BH, Simkin LR. The stages of exercise behavior. J SportsMed Phys Fitness 1993;33:83–8.

27. Kaplan RM, Anderson JP. An integrated approach to quality oflife assessments: the general health policy model. In: Spilker B,editor. Quality of life in clinical studies. New York: Raven, 1990.

28. Anderson JP, Kaplan RM, Berry CC, Bush JW, Rumbaut RG.Interday reliability of function assessment for a health statusmeasure. The Quality of Well-Being scale. Med Care 1989;27:1076–83.

29. Kaplan RM, Bush JW, Berry CC. Health status index: categoryrating versus magnitude estimation for measuring levels of well-being. Med Care 1979;5:501–23.

30. Kaplan R, Anderson J, Wu A, Mathews W, Kozin F, Orenstein D.The Quality of Well-being Scale: application in AIDS, cysticfibrosis, and arthritis. Med Care 1989;27:S27–43.

31. Kaplan R, Atkins C, Timms R. Validity of a Quality of Well-beingScale as an outcome measure in chronic obstructive pulmonarydisease. J Chronic Dis 1984;37:85–95.

32. Kaplan RM, Schmidt SM, Cronan TA. Quality of well being inpatients with fibromyalgia. J Rheumatol 2000;27:785–9.

33. Burckhardt CS, Clark SR, Bennett RM. The Fibromyalgia Im-pact Questionnaire: development and validation. J Rheumatol1991;18:728–33.

34. Radloff LS. The CES-D scale; a self-report depression scale forresearch in the general population. Appl Psychol Measure 1977;1:385–401.

35. Nicassio PM, Wallston KA, Callahan LF, Herbert M, Pincus T.

The measurement of helplessness in rheumatoid arthritis: thedevelopment of the arthritis helplessness index. J Rheumatol1985;12:462–7.

36. Stein MJ, Wallston KA, Nicassio PM. Factor structure of thearthritis helplessness index. J Rheumatol 1987;15:427–32.

37. Lorig K, Chastain RL, Ung E, Shoor S, Holman HR. Develop-ment and evaluation of a scale to measure perceived self-efficacyin people with arthritis. Arthritis Rheum 1989;32:37–44.

38. Norbeck JS, Lindsey AM, Carrieri VC. Further development ofthe Norbeck Social Support Questionnaire: normative data andvalidity testing. Nurs Res 1981;32:4–9.

39. Marcus BH, Selby VC, Niaura RS, Rossi JS. Self-efficacy and thestages of exercise behavior change. Res Q Exerc Sport 1992;63:60–6.

40. Silva PD, Stam A. Discriminant analysis. In: Grimm LG,Yarnold PR, editors. Reading and understanding multivariatestatistics. Washington DC: American Psychological Association,1998.

41. Klecka WR. Discriminant analysis. Beverly Hills: Sage, 1980.42. Bandura A. Self-efficacy: toward a unifying theory of behavioral

change. Psychol Rev 1977;84:191–215.43. Doyle-Baker PK. Fibromyalgia syndrome patient’s intention to

exercise: an application of the theory of planned behavior. DissAbstracts Int B 2001;61:4671.

44. Oliver K, Cronan TA, Walen HR. A review of multidisciplinaryinterventions for fibromyalgia patients: where do we go fromhere? J Musculosk Pain 2001;9:63–80.

45. Clark SR, Jones KD, Burckhardt CS, Bennett RM. Exercise forpatients with fibromyalgia: risks versus benefits. Curr Rheuma-tol Rep 2001;3:135–40.

389EXERCISE PREDICTORS