Abstract
Physical activity can improve quality of life (QOL) in breast cancer survivors but little is known about associations of physical activity and QOL during active cancer therapy. We examine associations between activity levels and QOL in a large cohort of breast cancer patients. Women with invasive, non-metastatic breast cancer (n = 2,279) were enrolled between 2006 and 2009 from a managed care organization; assessment were done during active therapy. A physical activity frequency questionnaire was used to calculate the average weekly metabolic equivalent task (MET) hours spent in moderate and vigorous activity during active treatment. QOL was measured by the Functional Assessment of Cancer Therapy-Breast Cancer. Linear regression models tested cross-sectional associations of QOL and functional well-being with physical activity and covariates [socio-demographics, comorbidity, body mass index (BMI), clinical variables, social support, and assessment timing]. Physical activity had a significant positive unadjusted association with all QOL sub-scales (except emotional well-being) (all P values < 0.01). Overall QOL was 4.6 points higher for women in the highest quartile of moderate and vigorous activity versus women in the lowest quartile (P < 0.001). In regression models, higher activity was associated with better overall QOL and functional well-being, controlling for covariates (P < 0.05). Increasing BMI was also independently but inversely associated with overall QOL (P < 0.001) but did not explain the relationship of activity and QOL. White women reported the higher levels of activity than minority women and activity was associated with QOL for Whites but not for minority women. Greater physical activity is associated with small but clinically meaningful increases in QOL during active breast cancer care therapy for Whites but this effect is not seen for minority women. If confirmed in longitudinal analyses, these differences may have implications for disparities research.
Keywords: Breast cancer, Physical activity, Quality of life
Introduction
Breast cancer treatment can have a considerable impact on women’s short-term quality of life (QOL) and functional capacity [1-5]. Physical activity can improve oxygen consumption, reduce fatigue, and improve QOL among breast cancer survivors [6]. As a result, physical activity has been suggested as an important component of cancer rehabilitation [7]. However, almost all of these studies were done among survivors after the completion of active treatment.
In this article, we use cross-sectional baseline data from an on-going cohort of women newly diagnosed with breast cancer to examine whether self-reports of physical activity during the active treatment period is associated with better overall QOL and functional well-being. The results are intended to inform future interventions to improve QOL during the active treatment phase of care.
Methods
The Pathways Study is a prospective cohort study designed to assess the effects of lifestyle factors on breast cancer recurrence and mortality. Women with invasive breast cancer have been recruited from the Kaiser Permanente Northern California (KPNC) patient population since January 2006 [8]. Briefly, cases are ascertained rapidly by scanning of electronic pathology reports. Eligibility criteria for the parent study include current KPNC membership, being at least 21 years of age at diagnosis, and having a first primary invasive breast cancer with no prior history of cancer other than non-melanoma skin cancer. Participants must speak English, Spanish, Cantonese, or Mandarin and reside within a 65-mile radius of a field interviewer. Passive consent was obtained from the patient’s physician of record to contact the patient for study recruitment. Written informed consent was obtained from all participants. The study was approved by all Institutional Review Boards.
Recruitment is ongoing, and as of December 31, 2009, a total of 2,828 patients have been enrolled. We excluded some women from the current analysis for the following reasons: 97 women with missing stage information or stage 4 cancers, 90 with missing information on treatment or hormone receptor status, one who died before treatment, one with inconsistent treatment dates, and 7 who indicated “other race/ethnicity”. Some women were ineligible for more than one reason; total of 183 were excluded. Among the remaining 2,645 women, we further excluded 366 (14%) women who did not complete key measures. The remaining 2,279 women constitute the final analytic sample. With one exception, there were no differences between excluded women and those included in the final sample: women who were excluded were less likely than those included to have received hormonal therapy at baseline. The mean time from pathology-confirmed diagnosis to baseline interview was 2.24 months (SD 0.88). Thus, the baseline time point corresponded to the period of active adjuvant therapy.
Data collection
Baseline data were collected by the trained staff. Staff conducted in-home interviews lasting 2–4 h. Information was collected on socio-demographics, QOL, social support, and physical activity. Body mass index (BMI) was based on self-reported height and weight.
Measures
We used the Functional Assessment of Cancer Therapy-Breast Cancer (FACT-B) Version 4 to assess health-related QOL from the baseline interview as our primary outcome measure [9]. The instrument has been extensively validated in different race/ethnic groups and languages. It has a total of 36 statements asking respondents to rate how true each statement is for the last 7 days. Responses range from 0 (not at all) to 4 (very much). Statements represent five subscales: physical well-being (PWB, Cronbach’s alpha = 0.88), functional well-being (FWB, Cronbach’s alpha = 0.84), emotional well-being (EWB, Cronbach’s alpha = 0.77), social/family well-being (SWB, Cronbach’s alpha = 0.72), and breast cancer-specific concerns (BCS, Cronbach’s alpha = 0.67). A general score is calculated by summing all sub-scales except the BCS (FACT-G, Cronbach’s alpha = 0.90); the total FACT-B score is calculated by including BCS in the sum (Cronbach’s alpha = 0.91).
Independent variable
The primary correlate of QOL of interest is baseline physical activity encompassing the active treatment period. We used an activity frequency questionnaire based on the Arizona Activity Questionnaire that asked about frequency and duration of different physical activities and sedentary behaviors during the prior 6 months, amount of time spent doing the activities, and how strenuous the activity was; the original asked about the past 1 month, otherwise the tools were the same [10, 11]. Activities include job or work-related activities, recreational activities (e.g., sports, exercise, dance, and sedentary recreation such as reading or socializing), transportation, and activities not related to paid or volunteer work (e.g., household chores, care giving, and home repairs).
Each activity is assigned a standard metabolic equivalent task (MET) value according to the Compendium by Ainsworth et al. [12]. To examine moderate–vigorous physical activity, the primary exposure of interest, a summary variable in MET-hours/week was created by multiplying the MET value of each activity by frequency and duration and summing over all activities with MET value of 3 or more. In sensitivity analyses, we also examined total activity in MET-hours/week, including mild activity. Since the results were qualitatively similar to those with moderate–vigorous physical activity, we present the data for moderate–vigorous physical activity.
Covariates
There are several variables that could confound the relationships between physical activity and QOL, including age (continuous), BMI (continuous), comorbidity, smoking status (current, former and never), clinical factors, timing of assessment, race [Black, White, Hispanic, and Asian American or Pacific Islander (AAPI)], education (less than high school vs. high school+), marital status (married vs. other), depression, and social support. For pre-cancer comorbidity we abstracted common conditions by ICD-9 codes from the electronic medical record (EMR) and used these data to calculate the Charlson score [13]. Based on the distribution of scores, we dichotomized comorbidity scores as 0–1 versus two or more.
Clinical variables include surgical and adjuvant treatment received, stages 1–3, and hormonal receptor status. Using the EMR, data on breast surgery (lumpectomy, mastectomy) were obtained using ICD-9 (85.20–85.23, 85.33–85.48) diagnostic and CPT-4 (19120–19240, 19301–19307, and 19340–19342) procedural codes. Adjuvant therapies were defined from a combination of registry and EMR data. Data on estrogen receptor (ER) and progesterone receptor (PR) status were obtained from the Cancer Registry [14].
Since physical activity may vary by the timing of adjuvant therapy, we used the actual start and projected stop dates of chemotherapy, radiation therapy, and/or hormonal therapy and the dates of the survey to calculate whether the patient was interviewed during adjuvant therapy (yes vs. no or did not receive adjuvant treatment). The CES-D was used to assess depression. We used continuous scores but note that a cut-point of 16 and above is used to define potentially depressed patients [15]. We used the 19-item Medical Outcomes Study (MOS) Social Support Survey to assess perceived social support that might affect activity including emotional/informational support, tangible support, positive social interaction, and affectionate support [16]. The reliability for each of the sub-scales and overall index were excellent (Cronbach’s alpha 0.92–0.96) in our sample.
Statistical analysis
We examined unadjusted bivariate associations between physical activity grouped into quartiles of METs per week of moderate or vigorous activity and study variables using χ2 tests. Next, we compared the QOL sub-scale scores and overall QOL by physical activity quartile using t tests and one-way analysis of variance. We then constructed preliminary linear regression models for the continuous outcomes of interest using physical activity as our primary predictor and controlling for covariates that were significantly associated with QOL in bivariate analysis. Variables that were not significant in the preliminary models were not included in the final model except that comorbidity was retained in final models for face validity purposes. Finally, we conducted a sensitivity analysis, via interactions, to test if conclusions varied based on race or comorbidity level (based on bivariate results). We also explored the possibility of including a higher order functional form for physical activity and BMI; since these were not significant they were not included in the final models. The R2 statistic was used as an estimate of the variation in QOL explained by the variables in the final regression models. All analyses were conducted using SAS software Version 9.2 (SAS Institute, Inc., Cary, NC).
Results
Women were interviewed a median of 2 months post-diagnosis. Ninety-two percent of women were interviewed during active treatment (chemotherapy, radiation, or hormonal treatment); 37% while actually on-treatment, 55% who had just completed treatment, and 8% received no treatment beyond surgery.
These breast cancer patients were very active, reporting a median of 20.2 MET-hours of moderate or vigorous activity per week during the period just before and including the active treatment phase of care. Activity levels were inversely and linearly related to age with younger women reporting higher levels of activity, P = <0.0001 (Table 1). Interestingly, there were also race/ethnicity variations in activity with 27.1% of White patients reporting the highest quartiles of activity versus 18.7% of Blacks, 21.4% of Hispanics, and 17.2% of AAPIs (P = <0.0001). Women taking aromatase inhibitors as their type of hormonal therapy were significantly less likely to be in the highest activity quartile than women on tamoxifen (21.7 vs. 29.8%, P = 0.0026). Finally, there was a non-significant trend for smokers to be less likely to be in the highest quartile of activity (16.8 vs. 25.3 and 24.7% for current, former, and never smokers, respectively).
Table 1.
Breast cancer patient characteristics by activity level
| Overall % (N) | Quartiles of moderate–vigorous activity (in MET-hours/week)
|
P value | ||||
|---|---|---|---|---|---|---|
| 1 (<7.8) % (N) | 2 (7.8 to <20.2) % (N) | 3 (20.2 to <39.7) % (N) | 4 (≥39.7)% (N) | |||
| Age, mean (SD) | 59.7 (11.6) | 62.0 (12.1) | 60.6 (11.6) | 58.8 (11.0) | 57.2 (11.2) | <0.0001 |
| Race/ethnicity | ||||||
| White | 73.3 (1671) | 21.1 (352) | 24.7 (413) | 27.1 (453) | 27.1 (453) | <0.0001 |
| Black | 6.6 (150) | 40.7 (61) | 26.0 (39) | 14.7 (22) | 18.7 (28) | |
| Hispanic | 8.6 (196) | 34.7 (68) | 22.5 (44) | 21.4 (42) | 21.4 (42) | |
| Asian-PI | 11.5 (262) | 34.4 (90) | 27.9 (73) | 20.6 (54) | 17.2 (45) | |
| Education | ||||||
| <High school | 3.4 (78) | 51.3 (40) | 29.5 (23) | 12.8 (10) | 6.4 (5) | <0.0001 |
| ≥High school | 84.6 (1929) (262) | 24.8 (478) | 24.6 (474) | 25.4 (489) | 25.3 (488) | |
| Missing | 11.9 (272) | 53 | 72 | 72 | 75 | |
| Marital status | ||||||
| Married | 55.2 (1259) | 23.4 (294) | 24.8 (312) | 25.7 (324) | 26.1 (329) | 0.0066 |
| Not married | 32.7 (746) | 29.9 (223) | 24.8 (185) | 23.5 (175) | 21.9 (163) | |
| Missing | 12.0 (274) | 54 | 72 | 72 | 76 | |
| Employment | ||||||
| No | 38.4 (876) | 27.1 (237) | 26.0 (228) | 24.1 (211) | 22.8 (200) | 0.1144 |
| Yes | 61.6 (1403) | 23.8 (334) | 24.3 (341) | 25.7 (360) | 26.2 (368) | |
| AJCC stage | ||||||
| I | 54.3 (1238) | 24.3 (301) | 24.2 (300) | 25.5 (316) | 25.9 (321) | 0.6768 |
| II | 35.5 (809) | 25.6 (207) | 25.6 (207) | 24.2 (196) | 24.6 (199) | |
| III | 10.2 (232) | 27.2 (63) | 26.7 (62) | 25.4 (59) | 20.7 (48) | |
| ER status | ||||||
| Negative | 17.2 (391) | 23.5 (92) | 26.1 (102) | 26.1 (102) | 24.3 (95) | 0.8132 |
| Positive | 82.8 (1888) | 25.4 (479) | 24.7 (467) | 24.8 (469) | 25.1 (473) | |
| Surgery | ||||||
| Breast conserving | 60.3 (1375) | 26.0 (357) | 24.4 (335) | 25.2 (347) | 24.4 (336) | 0.8548 |
| Mastectomy | 38.8 (885) | 23.7 (210) | 26.0 (230) | 24.6 (218) | 25.7 (227) | |
| None | 0.8 (19) | 21.1 (4) | 21.1 (4) | 31.6 (6) | 26.3 (5) | |
| Hormonal therapy | ||||||
| Aromatase inhibitors | 43.1 (982) | 26.2 (257) | 27.5 (270) | 24.6 (242) | 21.7 (213) | 0.0133 |
| Tamoxifen | 20.5 (467) | 21.6 (101) | 22.9 (107) | 25.7 (120) | 29.8 (139) | |
| None | 35.4 (807) | 25.7 (207) | 23.2 (187) | 25.4 (205) | 25.8 (208) | |
| Unknown | 1.0 (23) | 6 | 5 | 4 | 8 | |
| Chemotherapy | ||||||
| No | 53.8 (1225) | 27.1 (332) | 24.4 (299) | 24.7 (302) | 23.8 (292) | 0.1028 |
| Yes | 46.2 (1054) | 22.7 (239) | 25.6 (270) | 25.5 (269) | 26.2 (276) | |
| Radiation | ||||||
| No | 63.8 (1453) | 24.7 (359) | 24.8 (360) | 24.8 (360) | 25.7 (374) | 0.6942 |
| Yes | 36.2 (826) | 25.7 (212) | 25.3 (209) | 25.5 (211) | 23.5 (194) | |
| Comorbidity | ||||||
| 0, 1 | 92.4 (2105) | 23.7 (499) | 24.5 (516) | 25.8 (542) | 26.0 (548) | <0.0001 |
| 2+ | 7.6 (174) | 41.4 (72) | 30.5 (53) | 16.7 (29) | 11.5 (20) | |
| Smoking | ||||||
| Current | 4.4 (101) | 36.6 (37) | 27.7 (28) | 18.8 (19) | 16.8 (17) | 0.1489 |
| Former | 35.7 (814) | 25.2 (205) | 24.6 (200) | 24.9 (203) | 25.3 (206) | |
| Never | 47.9 (1091) | 25.3 (276) | 24.6 (268) | 25.4 (277) | 24.8 (270) | |
| Missing | 12.0 (273) | 53 | 73 | 72 | 75 | |
| Mean CES-D score (SD) | 11.1 (9.0) | 12.0 (10.1) | 11.0 (8.2) | 11.0 (8.9) | 10.6 (8.5) | 0.0688 |
| Mean BMI (SD) | 28.4 (6.6) | 31.1 (7.6) | 28.8 (6.7) | 27.2 (5.6) | 26.4 (5.0) | <0.0001 |
| Social support | ||||||
| Mean (SD) | ||||||
| Emotion/info | 78.8 (18.7) | 76.8 (21.1) | 78.4 (19.5) | 78.7 (16.9) | 81.5 (6.6) | <0.0001 |
| Tangible | 78.8 (22.8) | 76.9 (24.9) | 79.1 (21.7) | 79.0 (21.9) | 80.2 (22.7) | 0.003 |
| Affectionate | 86.4 (20.8) | 84.0 (23.5) | 86.6 (19.8) | 86.6 (19.6) | 88.3 (19.8) | <0.0001 |
| Social interaction | 82.8 (21.0) | 80.5 (24.3) | 82.1 (19.9) | 82.3 (20.5) | 86.5 (18.8) | <0.0001 |
| Overall | 80.8 (17.6) | 78.6 (20.2) | 80.5 (17.1) | 80.7 (16.2) | 83.3 (16.3) | <0.0001 |
Missing data are not used in calculation of P values
In unadjusted analyses, physical activity was associated with all QOL sub-scales, except for emotional well-being. The absolute differences from the least to the greatest amount of activity were small (2–5 points increase in QOL per sub-scale) (Table 2). Cumulatively, QOL was 4.6 points higher for women in the highest quartile of moderate and vigorous activity compared to women in the lowest quartile (P = <0.0001).
Table 2.
QOL sub-scales by physical activity quartile during active breast cancer treatment [Mean (SD)]
| Overall sample | Moderate–vigorous activity
|
P valuea | ||||
|---|---|---|---|---|---|---|
| Quartiles of MET-hours/week
| ||||||
| 1 N (%) (<7.8) | 2 N (%) (7.8 to <20.2) | 3 N (%) (20.2 to <39.7) | 4 N (%) (≥39.7) | |||
| Physical well being | 22.1 (5.8) | 21.6 (6.1) | 22.1 (5.6) | 22.4 (5.6) | 22.3 (5.8) | 0.006 |
| Social well being | 24.4 (4.0) | 23.9 (4.5) | 24.7 (3.8) | 24.4 (3.8) | 24.8 (3.9) | 0.003 |
| Emotional well being | 19.1 (4.1) | 18.8 (4.8) | 19.3 (3.7) | 19.1 (4.1) | 19.2 (4.0) | 0.371 |
| Functional well being | 20.6 (5.5) | 19.8 (6.1) | 20.5 (5.3) | 20.9 (5.2) | 21.4 (5.3) | <0.0001 |
| Breast cancer well being | 25.6 (5.8) | 25.1 (5.8) | 25.4 (5.8) | 25.8 (5.6) | 26.1 (5.8) | 0.004 |
| General well being | 86.3 (14.6) | 84.2 (16.2) | 86.8 (13.4) | 86.8 (14.4) | 87.7 (14.2) | <0.0001 |
| Overall QOL score | 111.8 (18.8) | 109.2 (20.3) | 112.0 (17.8) | 112.5 (18.6) | 113.7 (18.4) | <0.0001 |
P values from 1-way ANOVAs
Physical activity remained significantly associated with overall QOL after considering other covariates, with women in the highest quartile of physical activity reporting significantly higher adjusted QOL than women in the lowest quartile (P = 0.010) (Table 3). As BMI increased, QOL decreased (P < 0.001). BMI did not explain the relationship between physical activity and QOL, however, since both activity and BMI were independently associated with QOL. As age increased QOL increased (P < 0.001), despite the fact that older women reported lower activity levels. As expected, women who were actively undergoing chemotherapy, radiation, or hormonal therapy had lower QOL scores than those who has not yet started or had recently completed these modalities. The model explained 53% of the variation in QOL.
Table 3.
Adjusted estimates of the associations between physical activity (and other covariates) and overall QOL among breast cancer patients during active therapy
| Beta | Std. error | P value | |
|---|---|---|---|
| Age (per 1 year increase in age) | 0.21 | 0.02 | <0.001 |
| Activity level (quartiles) | |||
| 1 (referent) | |||
| 2 | 0.72 | 0.68 | 0.286 |
| 3 | 1.50 | 0.69 | 0.030 |
| 4 | 1.81 | 0.71 | 0.010 |
| BMI (per kg/m2 unit increase) | −0.14 | 0.04 | <0.001 |
| Depression (per 1 point increase)a | −1.43 | 0.03 | <0.001 |
| Summary social support (per 1 point increase) | 0.15 | 0.01 | <0.001 |
| Race/ethnicity | |||
| White (referent) | |||
| Asian-PI | −3.45 | 0.79 | <0.001 |
| Black | 0.84 | 0.99 | 0.394 |
| Hispanic | −4.91 | 0.87 | <0.001 |
| ER status | |||
| Positive (referent) | |||
| Negative | −1.35 | 0.64 | 0.035 |
| Comorbidity score | |||
| 0, 1 (referent) | |||
| 2+ | −1.73 | 0.93 | 0.062 |
| AJCC stage | |||
| III (referent) | |||
| I | 6.12 | 0.82 | <0.001 |
| II | 3.49 | 0.85 | <0.001 |
| On adjuvant treatment (no vs. yes) | 1.18 | 0.51 | 0.020 |
| Time from diagnosis to interview (months) | −0.40 | 0.28 | 0.158 |
Each variable is adjusted for the effects of the other variables in the table in a linear regression model
Model R2 53%
Higher depression score indicates greater depression
In race-stratified models (not shown), physical activity was significantly associated with QOL for Whites but not for minorities. In analyses examining FACT-B subscales, results for functional well-being during active treatment mirrored those for overall QOL (Table 4), except that BMI was not related to functional well-being.
Table 4.
Adjusted estimates of the associations between physical activity and functional well-being among breast cancer patient during the active phase of therapy
| Beta | Std. error | P value | |
|---|---|---|---|
| Age (per 1 year increase in age) | 0.02 | 0.01 | 0.026 |
| Activity level (quartiles) | |||
| 1 (referent) | |||
| 2 | 0.33 | 0.25 | 0.189 |
| 3 | 0.88 | 0.26 | <0.001 |
| 4 | 0.95 | 0.26 | <0.001 |
| BMI (per kg/m2 unit increase) | −0.00 | 0.01 | 0.938 |
| Depression (per 1 point increase)a | −0.36 | 0.01 | <0.001 |
| Summary social support (per 1 point increase) | 0.04 | 0.01 | <0.001 |
| Race/ethnicity | |||
| White (referent) | |||
| Asian-PI | 0.07 | 0.29 | 0.801 |
| Black | 0.05 | 0.37 | 0.900 |
| Hispanic | −1.30 | 0.32 | <0.001 |
| ER status | |||
| Positive (referent) | – | – | – |
| Negative | −0.13 | 0.24 | 0.572 |
| Comorbidity score | |||
| 0, 1 (referent) | – | – | – |
| 2+ | −0.79 | 0.34 | 0.021 |
| AJCC stage | |||
| III (referent) | – | – | – |
| I | 1.41 | 0.30 | <0.001 |
| II | 0.92 | 0.31 | 0.003 |
| On adjuvant treatment (no vs. yes) | 0.22 | 0.19 | 0.231 |
| Time from diagnosis to interview (months) | 0.00 | 0.10 | 0.999 |
Each variable is adjusted for the effects of the other variables in the table in a linear regression model
Model R2 36%
Higher score on depression scale indicates greater depression
Discussion
It is widely accepted that physical activity improves QOL in cancer survivors [6, 17-19]. This study uses a large and racially/ethnically diverse cohort to extend our knowledge about physical activity to the early phases of active adjuvant treatment. We found that women who reported the highest levels of moderate and vigorous activity had the highest QOL in this period. Increasing BMI was also independently but inversely associated with QOL, but did not explain the physical activity findings. There were some differences between activity levels and race/ethnicity and relationship to QOL but differences by type of hormonal agent were eliminated after considering age.
This group of breast cancer patients had a higher level of moderate and vigorous activity than reported in other past studies of breast cancer patients [10, 20, 21]. This is likely due to the more detailed physical activity assessment in our study, resulting in possible overestimation of energy expenditure. Also, since activity levels have increased over time, our cohort may have been more physically active than groups reported in the past. Increasing awareness of the benefits of exercise may have also led to higher self-reported activity levels. Our sample was comparable to other similar breast cancer populations, however, in ratings of QOL during treatment [22].
The magnitude of observed differences in QOL scores between the lowest and highest quartile of physical activity was small but in the range reported as important clinically in other studies (2 points on sub-scales and 5 points for the overall score) [22-24]. Similar magnitude of benefits have been observed in the few published [1, 18, 25-27] studies; a few clinical trials [28, 29] and observational studies [30] of exercise during active treatment are currently in process and shown provide additional evidence to support using active treatment as a “teachable moment” for lifestyle changes in the lives of breast cancer patients [31].
The association of physical activity was not explained by BMI, although BMI was also associated with QOL. Higher BMI may be associated with poorer body image, although we did not measure this construct [32]. High BMI, especially BMI of 30 or more, has been linked to increased risk of breast cancer, diminished treatment effectiveness and lower survival [33-35]. It is thought that these effects are in part mediated through insulin-like growth factor-1 (IGF-1) axis and altered production of proinflammatory cytokines [36]. These same paths have been linked to cancer fatigue [37], which could, in turn, lower QOL. These biological mechanisms are also postulated to partially explain the pathways whereby physical activity improves QOL [38]. We will be examining inflammatory markers in this cohort in our future study to better understand these pathways in breast cancer QOL and prognosis.
In this sample, White women were more likely to report the highest levels of activity and better QOL compared to minority women. A similar result has been noted in other breast cancer studies [39]. Of note, physical activity was associated with QOL for Whites but not for minority women. Moreover, even after considering activity level, social support, comorbidity, and clinical factors, Whites maintained a higher QOL during active treatment than minority women. These results suggest that minority women may have different experiences during active therapy than Whites, or that there are unmeasured differences in correlates of QOL by race/ethnicity. Alternatively, minority women may be judging their cancer diagnosis against a different contextual backdrop that leads them to rate QOL at lower levels during the active treatment phase of care (e.g., feeling that “cancer is like a death sentence”) [40]. Others have noted poorer QOL in Blacks (vs. Whites) among longer-term survivors [41]. It will be important to explore treatment experiences further, especially when considering physical activity interventions for minority groups.
While aromatase inhibitors have equivalent or better survival benefits than tamoxifen [42], they have different side-effect profiles, many with the potential to affect physical activity, and QOL. For instance, aromatase inhibitors have been noted to cause greater fatigue, arthralgias, and myaglias than tamoxifen, side effects that could affect both activity level and QOL [43]. We noted that women on aromatase inhibitors were less active than women on tamoxifen, but this effect disappeared after considering age and the activity-QOL association did not differ by type of hormonal therapy received. It is possible, however, that activity levels were affected by side effects of specific non-hormonal agents (e.g., taxanes and muscle pain and neuropathy). Unfortunately, we do not have data on specific agents and doses or self-reported toxicities; this will be important to consider in future investigations. Although, depression may influence both activity levels and QOL; depression did not alter the association of physical activity with QOL.
Surprisingly, while they had lower levels of activity, older women reported higher QOL during active therapy than younger women despite having higher risks of toxicity and decreased functional status during chemotherapy than younger women [31, 44]. It is possible that older women were more accustomed to chronic illness so that health decrements during treatment were viewed less negatively than by younger women. It is unclear, however, if increasing activity levels can enhance physiological reserve and QOL in those with comorbidities (e.g., by diminishing fatigue or increasing oxygen consumption). This will be an important area for future research.
There are several caveats that should be noted in considering our results. First, we measured activity and QOL at the same time, so we cannot make any inferences about causality. It is logical that greater activity levels would lead to improved QOL, yet it is also plausible that women with greater QOL felt more energetic or had greater exercise self-efficacy and, thus, were able to be more active [45, 46]. Also, a related issue is that our measure of activity asks about the past 6 months, a period that spans the treatment period, but may be skewed toward higher activity levels from pre-diagnosis and does not capture decrements due to breast cancer. We are collecting follow-up assessments of both physical activity and QOL, and will be able to examine temporal relationships in future analyses. Next, we rely on self-reported activity levels, although the instrument that our tool was based on has been validated against doubly labeled water [11]. Our sample was drawn from a managed care population and findings may therefore be limited in generalizability to other care settings or the uninsured. However, it is unlikely that type of health care coverage affects physical activity patterns per se, although there may be effects on QOL. In addition, women who are diagnosed with breast cancer in this setting appear to be largely similar demographically and diagnostically to the breast cancer population overall. Despite these limits, the study has several strengths, including the very large and diverse sample, use of standard, validated measures of QOL and physical activity, and assessment of important covariates that captured a very high proportion of variance in QOL.
Physical activity, improves QOL for survivors, may decrease breast cancer mortality [36, 47, 48] and diminishes all-cause mortality rates [10, 49, 50]. Our study demonstrates that physical activity is also associated with QOL during the active treatment phase of breast cancer care. If causal relationships are demonstrated in future research, our results suggest that physical activity “prescriptions” might be considered early in cancer therapy and continue into survivorship care planning [48]. Value for minorities and for the growing older population with comorbidities remains to be established and should receive priority for future investigation.
Acknowledgments
This research was supported, in part by National Cancer Institute Grants R01 CA105274 and R01 CA124924 to LK; BC043120 (Department of Defense) to LK; National Cancer Institute Grants RO1 CA 127617, U10 CA 84131, RO1 CA124924, and KO5 CA96940 to JSM; R21 CA 149996 to LAC, and the Biostatistics and Bioinformatics Shared Resources at Lombardi Comprehensive Cancer Center under National Cancer Institute Grant #P30 CA51008 covering GL.
Footnotes
Conflict of interest None.
Contributor Information
Jeanne S. Mandelblatt, Email: mandelbj@georgetown.edu, Cancer Control Program, Lombardi Cancer Center and Department of Oncology, Georgetown University Medical Center, 3300 Whitehaven St. NW, Ste 4100, Washington, DC 20007, USA.
Gheorghe Luta, Department of Biostatistics, Bioinformatics and Biomathematics and Lombardi Cancer Center, Georgetown University Medical Center, Washington, DC, USA.
Marilyn L. Kwan, Division of Research, Kaiser Permanente, Oakland, CA, USA
Solomon B. Makgoeng, Cancer Control Program, Lombardi Cancer Center and Department of Oncology, Georgetown University Medical Center, 3300 Whitehaven St. NW, Ste 4100, Washington, DC 20007, USA
Isaac J. Ergas, Division of Research, Kaiser Permanente, Oakland, CA, USA
Janise M. Roh, Division of Research, Kaiser Permanente, Oakland, CA, USA
Barbara Sternfeld, Division of Research, Kaiser Permanente, Oakland, CA, USA.
Lucile L. Adams-Campbell, Carcinogenesis, Biomarkers and Epidemiology Program, Lombardi Cancer Center and Department of Oncology, Georgetown University Medical Center, Washington, DC, USA
Lawrence H. Kushi, Division of Research, Kaiser Permanente, Oakland, CA, USA
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