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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Cancer. Author manuscript; available in PMC 2009 March 9.
Published in final edited form as:
PMCID: PMC2653221
NIHMSID: NIHMS90620

Promoting Physical Activity in Childhood Cancer Survivors: Targets for Intervention

Abstract

Purpose

Although physical activity may modify the late effects of childhood cancer treatment, 20%-52% of adult survivors are sedentary. We sought to identify modifiable factors that influence survivors' participation in physical activity.

Methods

Structural equation modeling of data from the Childhood Cancer Survivors Study of adult survivors (current mean age, 30.98 years; mean years since diagnosis, 23.74; mean age at diagnosis, 9.25 years) diagnosed between 1970 and 1986.

Results

Forty percent of the variance in male survivors' recent participation vs. nonparticipation in physical activity was explained directly and/or indirectly by self-reported health fears (P=0.01), perceived primary-care physician (PCP) expertise (P=0.01), baseline exercise frequency (P=<0.001), education level (P=0.01), self-reported stamina (P=0.01), cancer-related pain (P=<0.001), fatigue (P=<0.001), age at diagnosis (P=0.01), cancer-related anxiety (P=<0.001), motivation (P=0.01), affect (P=0.01), and discussion of subsequent cancer risk with the PCP (P=<0.001) (N=256; X2=53.38, df=51, P=0.38, CFI=1.000, TLI=1.000, RMSEA=0.014,WRMR=0.76). Thirty-one percent of the variance in females' recent physical activity participation was explained directly and/or indirectly by self-reported stamina (P=<0.001), fatigue (P=0.01), baseline exercise frequency (P=0.01), cancer-related pain (P=<0.001), cancer-related anxiety (P=0.01), recency of visits with PCP (<0.001), quality of interaction with the PCP (P=0.01), and motivation (P=<0.001) (N=366; X2=67.52 df=55, P=0.12, CFI=0.98, TLI=0.98, RMSEA=0.025, WRMR=0.76).

Conclusions

Gender-tailored intervention strategies in which providers specifically target motivation, fear, and affect may support physical activity in childhood cancer survivors.

Keywords: childhood cancer survivors, sedentary lifestyle

INTRODUCTION

Increases in childhood cancer survival have shifted the care paradigm from one of cure to a long-term emphasis on treatment-related morbidity and quality of life. Among the potential consequences of cancer therapy are pain, fatigue, obesity, osteoporosis, cardiomyopathy, and neuromusculoskeletal complications [1-4]. Lifelong physical activity initiated during cancer therapy is essential to modify or prevent these late effects [5-8].

Recent reports indicate that 20% to 52% of adult survivors of childhood cancer are sedentary, despite their risk of late effects [9-12]; this rate is similar to that in the general population [13, 14]. However, survivors of acute lymphoblastic leukemia (ALL), the most common pediatric cancer, were more likely than members of the general population to report no leisure-time physical activity (OR, 1.74; 95% CI, 1.56-1.94) [15]. Survivors treated with more than 20 Gy of cranial radiotherapy were at particular risk. When the comparison was adjusted for age, race, and ethnicity, ALL survivors were substantially more likely than their healthy counterparts to be inactive (females: OR, 1.86; 95% CI, 1.50-2.31; males: OR, 1.84; 95% CI, 1.45-2.32).

Factors that positively or negatively influence childhood cancer survivors' physical activity have not been documented. To describe modifiable influences on survivors' physical activity participation, we selected a broad-based health behavior model adapted to childhood cancer survivors [14-16] to inform variable selection and relationships and structural equation modeling (SEM).

METHODS

Conceptual Model

The Interaction Model of Client Health Behavior (IMCHB) [16-18], (Fig. 1), has been used [18-21] to identify survivor, provider, and contextual factors that can be targeted by interventions to modify the late effects of cancer therapy. The model comprises three broad elements: (1) the intrapersonal (affect, motivation, cognitive appraisal) and contextual (demographic/social influences, health history, resources) characteristics that uniquely define individuals relative to their health, disease, and treatment; (2) the therapeutic content and process between providers and patients that can support or impede behavior change; and (3) behaviorally related health outcomes. All variables within the data sets corresponding to the conceptual model [17, 18] were evaluated in preliminary models (Figure 1); only those variables that contributed significantly (p≤0.01) to the explained variance in the physical activity outcome were retained in the final models.

Data Source and Sample

The Childhood Cancer Survivors Study (CCSS) was designed to enroll a cohort of survivors sufficiently large and diverse to allow meaningful analysis of the delayed effects of treatment. The surveys and sampling methods have been described in detail by Robison et al.[22] and are available for review at http://www.stjude.org/ccss. In 1994, participants completed a baseline questionnaire that addressed demographic and health-related information; additionally, participating centers provided detailed abstracts of the medical records for consenting subjects. The original CCSS study contacted 20,346 eligible participants from 26 institutions across the United States and Canada who had survived five or more years after treatment for malignant disease diagnosed (before age 21) between 1970 and 1986. A subsequent ancillary study, the Health Care Needs Survey (HCNS), randomly sampled 1600 of approximately 12,872 subjects who remained alive; 978 (61%) completed and returned the survey and 838 (86%) of the 978 returned the total-cohort Follow-up 2 survey within the same data collection period. These 838 survivors were the subjects of this analysis.

Measures

Dependent Variable

Consistent with previous CCSS reports [15], physical activity participation was measured as a binary outcome derived from the Behavioral Risk Factor Surveillance Study [23]. Survivors were asked (yes=1; no=2), “During the past month, did you participate in any physical activities or exercises such as running, calisthenics, golf, bicycling, swimming, wheelchair basketball, or walking for exercise?”

Directly Observed Independent Variables

Eleven directly observed independent variables were significantly associated with recent physical activity as covariates in the final analytical models. All were self-reported by survivors: 1) their primary-care physician's (PCP's) familiarity with cancer-related problems (1= familiar, 2 = not familiar); 2) current pain resulting from cancer or its treatment (1= no pain; 5=excruciating pain); 3) frequency of fatigue(1= all the time; 6=none of the time); 4) whether survivors had discussed the risk of recurrent cancer with their PCP (1=yes, 2=no); 5) baseline frequency of aerobic exercise (defined as sufficient to induce sweating or breathing hard, lasting ≥20 min,) (0 days per week, 7 days per week); 6) age at diagnosis; 7) current anxiety as a result of cancer or its treatment (1=no anxiety, 5=extreme anxiety); 8) current highest school grade completed; and 9) whether the survivor had seen a primary care physician since cancer treatment ended (1=yes, 2=no); 10) modified from the Multidimensional Health Locus of Control Scales [24], for intrinsic motivation, survivors rated 4 items (e.g., “I am in control of my health”; “The main thing which affects my health is what I myself do”) on a 6-point Likert Scale (1=strongly disagree; 6=strongly agree) (α=0.79); 11) for extrinsic motivation, survivors rated 4 items (e.g., “Health professionals control my health”; “Regarding my health, I can only do what my doctor tells me to do”) on a 6-point Likert Scale (1=strongly disagree; 6=strongly agree) (α=0.80).

Latent Independent Variables

In structural equation modeling, latent variables (e.g., depression) are measured indirectly by using a set of observed variables [25]. Our final analyses identified 4 latent measures that contributed (directly or indirectly) to the explained variance in physical activity participation. The strength of the latent measures (i.e., the cohesiveness and fit of their contributory observed variables) was assessed by confirmatory factor analysis. Four strong latent variables (factor score determinacy values >0.80) were derived from the observed variables: survivor-provider interaction (0.94), fear (0.98), affect (0.96), and stamina (0.98).

Stamina

Derived from 5 observed items on the physical function measure of the SF-36 [26], survivors were asked to what extent their physical health limited their ability to climb several flights of stairs, climb 1 flight of stairs, walk more than 1 mile, walk several blocks, and walk 1 block (3=not limited at all, 2=limited a little, 1=limited a lot) (α= 0.924).

Fear

Three observed variables measured on a 5-point Likert Scale assessed the extent of survivors' fear (1=not at all concerned; 5=extremely concerned) about their future health, the return of their cancer, and the discovery of a health problem during a routine check-up (α=0.76).

Affect

Derived from 3 observed measures on the SF-36 Mental Health subscale [26] rated on a 6-point Likert Scale (1=all of the time; 6=none of the time), survivors assessed the frequency of feeling unhappy, downhearted and blue, and not cheerful (α=0.78).

Survivor-Provider Interaction

Four items asked survivors to rate on a 5-point Likert Scale (1=not at all; 5=extremely) to what extent they believed that: their doctor took enough time to answer their questions; they could ask their doctor questions about cancer; their fears and concerns had been addressed by their doctor; and their PCP could handle cancer-related problems (α=0.78).

Statistical Analysis

Structural equation models of the data were analyzed using Mplus 4.2 software [25]. Male (N=256) and female (N=366) subsamples with complete data comprised the final sample for the SEM analysis. We chose to use samples with complete data rather than to use data imputation in order to avoid potentially distorting coefficients of association and correlation relating variables [27]. A sample size >200 is considered large in SEM [28]. The best-fitting model is one that is conceptually sound, has parameter estimates that significantly correspond to the hypothesized relationships, meets the established SEM fit criteria (Figures 2 and and3,3, Appendix 1), and offers the highest percentage of explained variance for the outcome.

RESULTS

Sample

There were no demographic or cancer-related differences between the total sample with missing data (N=838) and the SEM subsamples with no missing data (Table 1). Significant differences observed between the gender subsamples and the total sample (using Bonferonni correction factor) were observed in the subsamples with complete data: males reported higher income categories; females were more likely to have visited their PCP since completion of cancer treatment (Table 1). The typical respondent was representative of the entire CCSS cohort [22, 29, 30]: white, female, college graduate, earning $20,000-$60,000, and covered by health insurance(Table 1). Approximately 25% percent of respondents in the total sample reported no leisure-time physical activity; females in the SEM subsample were slightly more active (NS). Subsample females were more likely to be fearful of finding a problem at check-up than were subsample males. Subsample males and females reported less fatigue than the total sample, with males reporting less fatigue than females. Subsample females were less extrinically motivated than subsample males (Appendix 2). Because gender differences in physical activity are reported in the general population [31-34] and were observed in the study's independent variables (Appendix 2), we estimated separate models for male and female survivors rather than test model equivalency between genders.

Table 1
Descriptive Summary of Total Samplea and SEM Subsamples

The Physical Activity Model for Males

The likelihood of male survivors' physical activity participation during the past month was predicted by the perception that their PCP was familiar with cancer-related problems, greater fear about future health, more education, and by greater baseline frequency of aerobic exercise (Fig. 2 and Appendix 1). Younger age at diagnosis, infrequent fatigue, little or no cancer pain, and infrequent negative affect predicted greater stamina. Less fear about future health, infrequent fatigue, and less anxiety about cancer predicted a more positive affect. Greater fear was predicted by more cancer pain, having discussed the risk of cancer in the future with their PCP, higher levels of extrinsic motivation, and lower levels of intrinsic motivation. Perceptions of less stamina, greater cancer anxiety, physical activity non-participation, and greater cancer pain predicted frequent fatigue. Greater fear about future health, greater cancer pain, and less intrinsic motivation predicted greater cancer anxiety (correlate of physical activity participation).

The Physical Activity Model for Females

A greater likelihood that female survivors had recently participated in physical activity (Appendix 1 and Figure 3) was directly predicted by greater reported stamina, less fatigue, and more frequent baseline aerobic exercise. Higher levels of intrinsic motivation (P=0.01) was a significant indirect predictor of physical activity through their effect on stamina. Greater stamina was predicted by less fatigue, less cancer-related pain, more frequent exercise at baseline, and greater intrinsic motivation. Greater fear about health was associated with having seen a PCP since completion of cancer therapy, greater fatigue, and greater extrinsic motivation. A more positive perception of survivor-PCP interaction was predicted by less fear about future health and greater extrinsic motivation. Greater intrinsic motivation was predicted by more positive perceptions of interaction with the provider. Greater fatigue was predicted by greater cancer-related pain; greater anxiety was predicted by greater cancer-related pain and greater fear about future health.

DISCUSSION

While this study identified some of the same direct predictors (education, fatigue, fear, stamina) of physical activity participation as those reported for adult cancer survivors [35, 36], the impact of these variables were gender specific and they simultaneously indirectly predicted physical activity participation through their impact on other variables. A higher level of education [37] predicted physical activity among males. Perceptions of greater stamina predicted physical activity participation in female survivors, consistent with the hypothesis [38] that low exercise capacity directly accounts for a more sedentary lifestyle among survivors. As with survivors of adult cancer [39, 40], fatigue predicted less physical activity among females; conversely, physical activity participation predicted less fatigue in males.

Both males and females who reported more frequent exercise at baseline were more likely to report recent physical activity, suggesting that physical activity was a previously established behavior. For both genders, cancer-related pain, anxiety, fear, and fatigue were powerful indirect influences on physical activity participation. Pain contributed to increased fatigue and anxiety, and reduced stamina. Perceptions of decreased stamina reinforced perceptions of increased fatigue. Higher fear levels increased cancer-related anxiety.

In contrast to anxiety which is a negative influence on positive health behavior, fear and worry can support positive health behavior [41]. Consistent with survivors of adult cancer, [40], greater fear about health predicted physical activity participation among male childhood cancer survivors.

Having discussed cancer recurrence with their PCP predicted men's greater fear. Males often view physician visits as necessary only in a crisis [30, 42, 43]; physician contact may therefore reflect specific problems that contribute to a more negative affect. In contrast, females tend to view interaction with their physician as supportive [44, 45].

Perceptions of survivor-provider interaction were relevant to recent physical activity in the female but not the male model; the provider's perceived competence was predictive in the male but not the female model. These findings further demonstrate significant sex differences in health-care expectations, in which males seek minimal physician input but value physician competence, whereas females rely on physician input for their health care decisions and value the quality of the relationship with the physician [30, 42-45].

Both models revealed complex relationships between fear, motivation, and affect. Males who were more extrinsically motivated had greater fear about their future health. Extrinsically motivated individuals are known to be more worried and fearful about their health, to see themselves as less in control of health matters, and to rely more on health professionals for direction [18, 19, 46]. Higher levels of intrinsic motivation, supported by positive perceptions of the survivor-provider relationship, predicted greater stamina in females, and indirectly predicted physical activity participation through stamina. Individuals with greater intrinsic motivation are more often self-reliant and self-directed, rather than provider-directed [19, 21], in their health behavior choices.

CLINICAL IMPLICATIONS

Regardless of the length of time since a survivor's cancer diagnosis and treatment [29, 47], primary care physicians are encouraged to specifically inquire about any treatment-related symptoms, particularly pain, fatigue, and anxiety. These symptoms may share common biological mechanisms [48, 49] and, until they are addressed, may remain significant barriers to physical activity in survivors. Physical activity programs have reduced fatigue and decreased depression and anxiety in survivors of adult cancer [6, 7, 50-52], and they may similarly affect childhood cancer survivors.

Pediatric cancer patients experience problems with pain, physical function, and fatigue throughout therapy [53-55]; as survivors, many must confront these persistent symptoms as well as the late effects of radiation and chemotherapy [56, 57]. In order to modify these late effects, it is important to introduce individually tailored physical activity during treatment and support survivors to maintain their physical activity throughout their lives. Exercise programs during treatment include passive range of motion exercises, progressing to strength and endurance activities [58-62]. The concurrent demands of treatment, together with the patient's physical, psychological, and motivational preparedness must be considered in tailoring physical activities [63].

As in the general population, sex should be considered in tailoring physical activity interventions to survivors of childhood cancer [64-74]. For some male survivors, personalized risk information conveyed by print, video, and web-based media [71, 74] may help to increase health concern and support motivation for physical activity in the absence of frequent physician interaction[69-71, 74]. In some female survivors, direct contact with the PCP may be more effective in supporting physical activity than would a media intervention. Finally, interventions that are individually tailored to the influence of fear, affect, and motivation [65, 67, 69] are more likely to be effective in supporting behavior change than are interventions that only address changing knowledge.

CONCLUSIONS

The study sample reflects a subset of the overall CCSS population - those who responded to the Health Care Needs and CCSS Follow-up 2 Surveys; therefore, survivors included in the current analysis may not be fully representative of the population from which they were derived. The physical activity outcome, as well as the independent measures, was based upon self-reported data.

Despite the study limitations, we are able to report three novel findings about factors that predict the likelihood of childhood cancer survivors' physical activity and suggestions for considering these factors in managing survivors' long-term care: (1) physical activity is influenced by pain, anxiety, fatigue, and stamina independent of gender; (2) the sexes differ in which factors predict physical activity and how they do so; (3) affect, fear, motivation, and survivor-physician interaction are modifiable predictors of the likelihood of physical activity. Interventions that consider these multiple factors have the potential to support positive behavior change in childhood cancer survivors.

Acknowledgements

The authors acknowledge the contributions of Sharon Naron and Vani Shanker (for editorial assistance), Deborah Sherrill-Mittleman, PhD (for assistance with data analysis), and Kelly Shempert and Dawn Silcott (for illustrations).

Support: NIH, NINR RO3 NR009203 (Cox, C.L., PI); Robert Wood Johnson Foundation (Oeffinger, K.C., PI); NIH NCI U24 CA55727 (Robison, L.L., PI); American Lebanese Syrian Associated Charities (ALSAC).

Appendix 1

Table thumbnail
SEM Results for Male and Female Survivors

Appendix 2

Table thumbnail
Comparison of Total Sample and SEM Subsamples on Study Measures

Footnotes

Prior presentation: Podium presentation at 4th Biennial Cancer Survivorship Research Conference, June 19, 2008

References

1. Harila-Saari AH, Vainionpaa LK, Kovala TT, Tolonen EU, Lanning BM. Nerve lesions after therapy for childhood acute lymphoblastic leukemia. Cancer. 1998;82:200–207. [PubMed]
2. Mattano LA, Sather HN, Trigg ME, et al. Osteonecrosis as a complication of treating acute lymphoblastic leukemia in children: a report from the children's cancer group. Clinical Oncology. 2000;18:3262–3272. [PubMed]
3. Reilly J, Bentham JC, Ralston JM, et al. Reduced energy expenditure in pre-obese children treated for acute lymphoblastic leukemia. Pediatric Research. 1998;44:557–562. [PubMed]
4. Reinders-Messelink H, Schoemaker M, Snijders T, et al. Motor performance of children during treatment for acute lymphoblastic leukemia. Med Pediatr Oncol. 1999;33:545–550. [PubMed]
5. Physical activity and health: a report of the surgeon general in Centers of Disease Control and Prevention, National Centers for Chronic Disease Prevention and Health Promotion. US Department of Health and Human Services; Atlanta, GA: 1996.
6. Courneya KS. Exercise interventions during cancer treatment: biopsychosocial outcomes. Exerc Sport Sci Rev. 2001;29:60–4. Review. [PubMed]
7. Dimeo FC, Stieglitz RD, Novelli-Fischer U, Fetscher S, Keul J. Effects of physical activity on the fatigue and psychologic status of cancer patients during chemotherapy. Cancer. 1999;85:2273–7. [PubMed]
8. Young-McCaughan S, Mays MZ, Arzola SM, et al. Research and commentary: change in exercise tolerance, activity and sleep patterns, and quality of life in patients with cancer participating in a structured exercise program. Oncol Nurs Forum. 2003;30:441–454. [PubMed]
9. Butterfield RM, Park ER, Puleo E, et al. Multiple risk behaviors among smokers in the Childhood Cancer Survivors Study cohort. Psycho-Oncology. 2004;13:619–629. [PubMed]
10. Demark-Wahnefried W, Werner C, Clipp EC, Guill AB, Bonner M, Jones LW, Rosoff PM. Survivors of childhood cancer and their guardians. Cancer. 2005;103(10):2171–2180. [PubMed]
11. Pinto BM, Trunzo JJ. Health behaviors during and after a cancer diagnosis. Cancer. 2005;104(11 Suppl):2614–2623. [PubMed]
12. Tercyak KP, Donze JR, Prahlad S, Mosher RB, Shad AT. Multiple behavioral risk factors among adolescent survivors of childhood cancer in the survivor health and resilience education (SHARE) program. Ped Blood Cancer. 2006;47:825–830. [PubMed]
13. Centers for Disease Control and Prevention, editor. Morbidity and Mortality Weekly Report. 2003. Prevalence of physical activity, including lifestyle activities among adults - United States, 2000-2001; pp. 764–769. [PubMed]
14. US Department of Health and Human Services In: Leisure-time physical activity among adults: United States, 1997-1998. US Department of Health and Human Services Centers for Disease Control and Prevention National Center for Health Statistics, editor. 2002.
15. Florin TA, Fryer GE, Miyoshi T, et al. Physical inactivity in adult survivors of childhood acute lymphoblastic leukemia: A report from the Childhood Cancer Survivor Study. Cancer Epidemiol Biomarkers Prev. 2007;16:1356–63. [PubMed]
16. Carter K, Kulbok P. Evaluation of the interaction model of client health behavior through the first decade of research. Adv Nurs Sci. 1995;18:62–73. [PubMed]
17. Cox CL. An interaction model of client health behavior: theoretical prescription for nursing. ANS Adv Nurs Sci. 1982;5(1):41–56. [PubMed]
18. Cox CL. Online exclusive: a model of health behavior to guide studies of childhood cancer survivors. Oncol Nurs Forum. 2003;30:E92–9. [PubMed]
19. Cox CL, McLaughlin RA, Rai SN, Steen BD, Hudson MM. Adolescent survivors: a secondary analysis of a clinical trial targeting behavior change. Pediatric Blood and Cancer. 2005;45:144–154. [PubMed]
20. Cox CL, McLaughlin RA, Steen BD, Hudson MM. Predicting and modifying substance use in childhood cancer survivors: application of a conceptual model. Oncol Nurs Forum. 2006;33:51–60. [PubMed]
21. Cox CL, Montgomery M, Rai SN, McLaughlin R, Steen BD, Hudson MM. Supporting breast self-examination in female childhood cancer survivors: A secondary analysis of a behavioral intervention. Oncology Nursing Forum. 2008;35:423–430. [PubMed]
22. Robison LL, Mertens AC, Boice JD, Breslow NE, Donaldson SS, Green DM, Li FP, Meadows AT, Mulvihill JJ, et al. Study design and cohort characteristics of the Childhood Cancer Survivor Study: A multi-institutional collaborative project. Medical and Pediatric Oncology. 2002;38:229–239. [PubMed]
23. Centers for Disease Control and Prevention National Center for Chronic Disease Prevention and Health Promotion Behavioral Risk Factor Surveillance System. 2008. Available from: http://www.cdc.gov/brfss/index.htm.
24. Wallston KA, Wallston BS, DeVellis R. Development of the Multidimensional Health Locus of Control (MHLC) Scales. Health Educ Monogr. 1978;6:160–170. [PubMed]
25. Muthen K, Muthen BO. Mplus User's Guide. 4th ed. Muthen & Muthen; Los Angeles, CA: 19982007.
26. Ware JE, Snow KK, Kosinski M. SF-36 Health Survey: Manual and Interpretation Guide. QualityMetric Incorporated; Lincoln, RI: 1993 2000.
27. Kalton G, Kasprzyk D. Survey Methodology. Vol. 12. 1986. The treatment of missing survey data; pp. 1–16.
28. Kline RB. Principles and Practice of Structural Equation Modeling (Methodology in the Social Sciences) 2nd ed. The Guilford Press; New York, NY: 2005.
29. Hudson MM, Mertens AC, Yasui Y, Hobbie W, Chen H, Gurney JG, et al. Health status of adult long-term survivors of childhood cancer: A report from the childhood cancer survivor study. Journal of the American Medical Association. 2003;290(12):1583–1592. [PubMed]
30. Oeffinger KC, Mertens AC, Hudson MM, Gurney JG, Casillas J, Chen H, et al. Health care of young adult survivors of childhood cancer: a report from the Childhood Cancer Survivor Study. Ann Fam Med. 2004;2(1):61–70. [PubMed]
31. Jago R, Anderson CB, Baranowski T, Watson K. Adolescent patterns of physical activity differences by gender, day, and time of day. Am J Prev Med. 2005;28:447–452. [PubMed]
32. Sallis JF. Epidemiology of physical activity and fitness in children and adolescents. Crit Rev Food Sci Nutr. 1993;33:403–408. [PubMed]
33. Thompson AM, Baxter-Jones AD, Mirwald RL, Bailey DA. Comparison of physical activity in male and female children: does maturation matter? Med Sci Sports Exerc. 2003;35:1684–1690. [PubMed]
34. Trost SG, Pate RR, Sallis JF, et al. Age and gender differences in objectively measured physical activity in youth. Med Sci Sports Exerc. 2002;34:350–355. [PubMed]
35. Courneya KS, Vallance JKH, Jones LW, Reiman T. Correlates of exercise intentions in non-Hodgkin's lymphoma survivors: An application of the Theory of Planned Behavior. Journal of Sport & Exercise Psychology. 2005;27:335–349.
36. Courneya KS, Friedenreich CM, Reid RD, Gelmon K, Mackey JR, Ladha AB, et al. Predictors of follow-up exercise behavior 6 months after a randomized trial of exercise training during breast cancer chemotherapy. Breast Cancer Research and Treatment. 2008 Epub. [PubMed]
37. Patterson RE, Neuhouser ML, Hedderson MM, Schwartz SM, Standish LJ, Bowen DJ. Changes in diet, physical activity, and supplement use among adults diagnosed with cancer. J Am Diet Assoc. 2003;103:323–328. [PubMed]
38. Aziz NM. Cancer Survivorship Research: Challenge and Opportunity. The Journal of Nutrition. 2002;132:3494S–3503S. [PubMed]
39. Courneya KS, Friedenreich CM, Quinney HA, Fields ALA, Jones LW, Vallance JKH, Fairey AS. A Longitudinal Study of Exercise Barriers in Colorectal Cancer Survivors Participating in a Randomized Controlled Trial. Annals of Behavioral Medicine. 2005;29(2):147–153. [PubMed]
40. Stolley MR, Sharp LK, Wells AM, Simon N, Schiffer L. Health Behaviors and Breast Cancer: Experiences of Urban African-American Women. Health Education and Behavior. 2006;33(5):604–624. [PubMed]
41. Sallis JF, Prochaska JJ, Tayler WC. A review of correlates of physical activity of children and adolescents. Med Sci Sports Exerc. 2000;32(5):963–975. [PubMed]
42. Shaw AK, Pogany L, Speechley KN, Maunsell E, Barrera M, Mery LS. Use of health care services by survivors of childhood and adolescent cancer in Canada. Cancer. 2006;106(8):1829–1837. [PubMed]
43. Xu KT, Borders TF. Gender, health, and physician visits among adults in the United States. American Journal of Public Health. 2003;93(7):1076–1079. [PubMed]
44. Hall JA, Irish JT, Roter DL, Ehrlich CM, Miller LH. Satisfaction, gender, and communication in medical visits. Med Care. 1994;32:1216–1231. [PubMed]
45. Hall JA, Roter DL. Patient gender and communication with physicians: Results of a community-based study. Womens Health. 1995;1:77–95. [PubMed]
46. Deci EL, Ryan RM. Handbook of Self-Determination Research. University of Rochester Press; Rochester, NY: 2002.
47. Meeske K, Siegel S, Globe D, Mack W, Bernstein L. Prevalence and correlates of fatigue in long-term survivors of childhood leukemia. J Clin Oncol. 2005;23:5501–5510. [PubMed]
48. Cleeland CS, Bennett GJ, Dantzer R, et al. Are the symptoms of cancer and cancer treatment due to a shared biologic mechanism? Cancer. 2003;97:2919–2925. [PubMed]
49. Lee BN, Dantzer R, Langley KE, et al. A cytokine-based neuroimmunologic mechanism of cancer-related symptoms. Neuroimmunomodulation. 2004;11:279–292. [PubMed]
50. Courneya KS, Friedenreich CM. Physical exercise and quality of life following cancer diagnosis: a literature review. Ann Behav Med. 1999;21:171–9. Review. [PubMed]
51. Courneya KS, Mackey JR, Bell GJ, Jones LW, Field CJ, Fairey AS. Randomized controlled trial of exercise training in postmenopausal breast cancer survivors: Cardiopulmonary and quality of life outcomes. J Clin Oncol. 2003;21:1660–1668. [PubMed]
52. Mock V, Frangakis C, Davidson NE, et al. Exercise manages fatigue during breast cancer treatment: A randomized controlled trial. Psychooncology. 2005;14:464–477. [PubMed]
53. Cox C, Rai SN, Rosenthal D, Phipps S, Hudson MM. Subclinical late cardiac toxicity in childhood cancer survivors: impact on quality of life. Cancer. 2008;112:1835–1844. [PubMed]
54. Karimova EJ, Rai SN, Howard SC, Neel M, Britton L, Pui CH, Kaste SC. Femoral head osteonecrosis in pediatric and young adult patients with leukemia or lymphoma. Journal of Clincal Oncology. 2007;25:1525–1531. [PubMed]
55. Vainionpaa L, Kovala T, Tolonen U, Lanning M. Vincristine therapy for children with acute lymphoblastic leukemia impairs conduction in the entire peripheral nerve. Pediatr Neurol. 1995;13:314–318. [PubMed]
56. Kaste SC, Rai SN, Fleming K, et al. Changes in bone mineral density in survivors of childhood acute lymphoblastic leukemia. Pediatr Blood Cancer. 2006;46(1):77. [PubMed]
57. Oeffinger K, Eshelman D, Tomlinson G, et al. Grading of late effects in young adult survivors of childhood cancer followed in an ambulatory adult setting. Cancer. 2000;88:1687–1695. [PubMed]
58. Aznar S, Webster AL, San Juan AF, Chamorro-Vina C, Mate-Munoz JL, Moral S, Perez M, Garcia-Castro J, Ramirez M, Madero L, Lucia A. Physical activity during treatment in children with leukemia: a pilot study. Appl Physiol Nutr Metab. 2006;31:407–413. [PubMed]
59. Hinds PS, Hockenberry M, Rai SN, Zhang L, Razzouk BI, Cremer L, et al. Clinical field testing of an enhanced-activity intervention in hospitalized children with cancer. Journal of Pain and Symptom Management. 2007;33:686–697. [PubMed]
60. Marchese VG, Chiarello LA, Lange BJ. Effects of physical therapy intervention for children with acute lymphoblastic leukemia. Pediatr Blood Cancer. 2004;42:127–133. [PubMed]
61. San Juan AF, Fleck SJ, Chamorro-Vina C, Mate-Munoz JL, Moral S, Perez M, Cardona C, Del Valle MF, Hernandez M, Ramirez M, Madero L, Lucia A. Effects of an intrahospital exercise program intervention for children with leukemia. Med Sci Sports Exercise. 2007;39:13–21. [PubMed]
62. San Juan AF, Fleck SJ, Chamorro-Vina C, Mate-Munoz JL, Garcia-Castro J, Ramirez M, Madero L, Lucia A. Early-phase adaptations to intrahospital training in strength and functional mobility of children with leukemia. Journal of Strength and Conditioning Research. 2007;21:173–177. [PubMed]
63. Stull VB, Snyder DC, Demark-Wahnefried W. Lifestyle interventions in cancer survivors: Designing programs that meet the needs of this vulnerable and growing population. The Journal of Nutrition. 2007;137:243S–248S. [PubMed]
64. de Nooijer J, Lechner L, Candel M, de Vries H. Short- and long-term effects of tailored information versus general information on determinants and intentions related to early detection of cancer. Preventive Medicine. 2004;38(6):694–703. [PubMed]
65. Dijker AJ, Koomen W, Kok G. Interpersonal determinants of fear of people with AIDS: the moderating role of predictable behavior. Basic and Applied Social Psychology. 1997;19:61–79.
66. Eakin EG, Glasgow RE, Riley KM. Review of primary care-based physical activity intervention studies: effectiveness and implications for practice and future research. J Fam Pract. 2000;49(2):158–168. [PubMed]
67. Geller ES. Designing an effective fear appeal. n.d. [cited August 20, 2007]; Available from: http://www.safetyperformance.com/pdf/Articles/2001/DesigninganEffectiveFearAppeal.pdf.
68. Glasgow RE, Goldstein MG, Ockene JK, Pronk NP. Translating what we have learned into practice. Principles and hypotheses for interventions addressing multiple behaviors in primary care. Am J Prev Med. 2004;27(2 Suppl):88–101. [PubMed]
69. Hoskins SL, van Hooff JC. Motivation and ability: which students use online learning and what influence does it have on their achievement? British Journal of Educational Technology. 2005;36(2):177–192.
70. Lee MH, Tsai CC. Exploring high school students' and teachers' preferences toward the constructivist internet-based learning environments in Taiwan. Educational Studies. 2005;31(2):149–167.
71. Passig D, Levin H. Gender preferences for multimedia interfaces. Journal of Computer Assisted Learning. 2000;16(1):64–71.
72. Revere D, Dunbar PJ. Review of computer-generated outpatient health behavior interventions: clinical encounters "in absentia". J Am Med Inform Assoc. 2001;8(1):62–79. [PMC free article] [PubMed]
73. Skinner CS, Strecher VJ, Hospers H. Physicians' recommendations for mammography: Do tailored messages make a difference? American Journal of Public Health. 1994;84:43–49. [PubMed]
74. Young BJ. Gender differences in student attitudes toward computers. Journal of Research on Computing in Education. 2000;33(2):204–216.
75. Bentler PM. Comparative fit indexes in structural models. Psychol Bull. 1990;107:238–246. [PubMed]
76. Bollen K. Overall fit in covariance structure models: two types of sample size effects. Psychol Bull. 1990;107:256–259.
77. Browne M, Cudeck R. Alternative ways of assessing model fit. In: Bollen KA, Long JA, editors. Testing structural equation models. Sage; Newbury Park, CA: 1993.
78. Hu L, Bentler P. Cutoff criteria for fit indices in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling. 1999;6:1–55.