Longitudinal associations among physical activity and sitting with endocrine symptoms and quality of life in breast cancer survivors: A latent growth curve analysis

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Lee Franco, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1469636/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Sep, 2023 Read the published version in Cancer Medicine → Version 2 posted You are reading this latest preprint version Show more versions Abstract Purpose We aimed to determine whether physical activity (PA) may mitigate side effects of adjuvant endocrine therapy (AET), or whether prolonged sitting may exacerbate them. AET often causes debilitating endocrine symptoms that compromise quality of life (QOL) in women diagnosed with hormone receptor positive breast cancer (BC). Methods We examined longitudinal patterns among PA and sitting with endocrine symptoms and QOL over 3 years in 554 female BC survivors undergoing AET using parallel process latent growth curve models. Results At baseline, women were a mean age of 59 years, mostly white (72%), with overweight/obesity (67%), and approximately 50% being within 1 year of diagnosis. Unconditional models showed a significant increase in PA (P<.01) over time but no change in sitting. Endocrine symptoms, general and BC-specific QOL all got significantly worse over time (P<.01). Parallel process models showed no cross-sectional or longitudinal associations between PA and endocrine symptoms, but higher PA was associated with higher QOL (P=.01). Increases in PA were not associated with changes in QOL. Conversely, higher baseline sitting time was associated with worse endocrine symptoms, general and BC specific QOL (Ps<.01), yet worse baseline endocrine symptoms were associated with a slower rate of increase in sitting (P<.01). Better baseline QOL was also associated with an increase in sitting (Ps<.01) while an increase in sitting was associated with reductions in symptoms (P=.017). Model fit statistics (x2, CFI, TLI, SRMR) were acceptable. Conclusion Both PA and sitting are important for managing symptoms and maintaining QOL in BC survivors. Breast Cancer Survivorship Physical Activity Lifestyle Symptoms Figures Figure 1 Introduction Early detection and effective treatments have increased survival rates for women diagnosed with breast cancer (BC) [1], with adjuvant endocrine therapy (AET) being an important option for women diagnosed with hormone receptor positive (HR+) BC [2]. Yet, AET can also frequently produce negative side-effects such as joint pain and stiffness [3], loss of bone mineral density [4], and vasomotor and gynecological symptoms that compromise quality of life (QOL). Unfortunately, these symptoms often lead to poor adherence with up to 50% of women either not initiating or completing their recommended 5-year therapy regimens [5, 6]. To optimize their health status, BC survivors should therefore aim to adopt or maintain healthy lifestyle behaviors that include a healthy diet and physical activity [7]. BC survivors who gain > 5% body weight post-diagnosis have a 12% higher all-cause mortality relative to BC survivors who maintain their weight [8]. Weight gain is also strongly associated with the onset of metabolic dysfunction and cardiovascular disease [9] and may be an important factor impacting symptoms associated with AET. Current guidelines recommend at least 150 minutes of moderate to vigorous physical activity (MVPA) or 75 minutes of VPA per week while also avoiding inactivity [10, 11]. Engaging in PA is also an important part of weight management post BC therapy and may be able to mitigate many of the side-effects of AET [12, 13]; however, higher intensity PA may also be challenging [14] due to the impact of BC-treatments on functional capacity and health-related QOL. It should also be recognized that socio-demographic factors and pre-existing comorbidities may make achieving recommended levels of PA difficult [15]. Consequently, independent of PA, BC survivors should aim to reduce sedentary behaviors (e.g., sitting) as a means of increasing light intensity PA that is also important for weight management [16], metabolic and cardiovascular health, and managing symptoms such as fatigue [17]. What is not yet known is how patterns of PA, sitting and endocrine symptoms change over time following the initiation and adherence to AET. Disparities in adherence to AET have been noted among insured BC survivors by age and race [18] and while previous studies have examined PA patterns in BC patients following treatment [19], they do not include representative numbers of Black women. Furthermore, few studies have examined cross-sectional and longitudinal associations among PA and sitting with general and specific aspects of QOL among women actively undergoing AET. The Women’s Hormonal Initiation and Persistence (WHIP) study is an observational cohort study with up to 3 years of follow up data on a diverse group of women and allows for the examination of these questions given its inclusion of clinical- and treatment-related data among BC survivors [20]. We hypothesized that at baseline, higher levels of self-reported PA would be associated with better QOL and fewer endocrine symptoms. Further, we hypothesized that PA would increase over time as women recovered from BC treatment and that increases in PA would be associated with improving QOL and reduced endocrine symptoms. With regards to sedentary sitting time, we also hypothesized both cross-sectional and longitudinal associations with QOL. Specifically, more sitting time at baseline would be associated with worse QOL and endocrine symptoms while increased sitting time would be associated with worsening QOL and symptoms. We utilize a rigorous analytical method, parallel latent growth curve modeling (LGCM), to estimate 1) average changes in PA, sitting, and QOL and endocrine symptoms; and 2) the extent to which changes in PA and sitting explain the change in general and specific aspects of QOL over a 3-year period, while controlling for time-varying confounding effects of the respective behaviors. Materials And Methods The current study is a secondary analysis of the Women’s Hormonal Initiation and Persistence (WHIP) study. WHIP was a longitudinal observational cohort study designed to examine patterns of adherence to AET (R01CA154848). All study procedures were approved by Institutional Review Boards at participating sites. The study design, recruitment and data collection methods have previously been described in detail [20]. Briefly, women diagnosed with BC were identified across 2 primary geographic regions (Midwest, Southeast) from academic medical centers, from integrated health systems, and via community outreach efforts. Eligibility criteria included being within 12 months of a diagnosis of HR + BC (confirmed via laboratory reports), being 21 years old or older, having initiated AET, and being able to speak English or Spanish. Women were deemed potentially eligible on the basis of cancer registries, and via pharmacy records. They were then mailed letters that described the study protocol and were asked to call a toll-free number or to return a stamped self-addressed postcard if they did not want to be contacted about the study. Women recruited via community outreach were screened for eligibility by a clinical research assistant. Research assistants then contacted women who did not opt out within a 2-week time frame, confirmed the level of interest, consented interested participants, and scheduled phone interviews to complete the survey. Data were collected via standardized surveys that were administered according to a woman’s preference either via telephone by a trained clinical research assistant or via a secure online survey (REDCap). The baseline cohort included 594 women, with a number having dropped out across 3 years of follow up visits (year 1 = 162, year 2 = 220, and year 3 = 405) . Women were excluded from the analysis if they had invalid and/or missing data for study outcomes across all years (n = 40), leaving a valid sample of 554 for longitudinal analyses. Measures PA and sitting time were measured with the short form of the International Physical Activity Questionnaire (IPAQ) [21, 22]. Participants were asked to self-report frequency and duration of walking, or participating in moderate-intensity activities (e.g., carrying light loads and bicycling at a regular pace) and vigorous-intensity activities (heavy lifting and aerobics) during the past 7 days. IPAQ data were cleaned by following the rules set by the IPAQ executive committee [23] including the exclusion of unreasonably high levels of PA data (e.g., total PA exceeding 16 hours per day) and truncation of the self-reported duration of each type of PA when exceeding 4 hours per day. Metabolic equivalents of task (METs) of 3.3, 4.0, and 8.0 were subsequently applied to walking, moderate- and vigorous-intensity activities, respectively, to calculate total MET-hours per week. As part of IPAQ, participants reported the average daily time spent in sitting during the past 7 days. QOL was measured with 2 previously validated Functional Assessment of Cancer Therapy (FACT) questionnaires: FACT–Breast Cancer (FACT-B), and FACT–Endocrine Symptoms (FACT-ES), which is a symptom-specific scale. The FACT-General (FACT-G) was also examined for its relationship to PA and sitting. QOL scores (FACT-G, FACT-B) and ES-subscale scores were all assessed as continuous variables. Higher FACT-G and FACT-B scores indicate better health-related QOL and higher ES-subscale scores indicate worse adjuvant endocrine therapy-related symptoms. Previous studies have reported minimally important differences of 5–6 points for the FACT-G and 2–3 points for FACT-B subscale among breast cancer survivors [24]. In the present study, reliability of each measure was acceptable with a Cronbach’s alpha ranging between .87 and .91 (FACT-G), between .89 and .92 (FACT-B), and between .78 and .80 (FACT-ES) across the years. Baseline sociodemographic characteristics were obtained via survey while clinical variables were confirmed via medical records. Sociodemographic variables included age, self-identified race (Black vs. white), marital status (married/partnered vs. others), education (high school or lower vs. college or higher), employment (yes vs. no), household income (<$60k, $60k thru <$100k, $100k thru <$150k and ≥$150k). Clinical variables included BMI (< 25 kg/m 2 , 25 thru 30 kg/m 2 , and ≥ 30 kg/m 2 ), time since diagnosis (< 1 year vs. ≥1 year), BC stage at diagnosis (stage I, II, or III), human epidermal growth factor receptor 2 (HER2) status (negative or positive), time since AET initiation (< 6 months vs. ≥6 months), surgery type (mastectomy vs. lumpectomy), receipt of chemotherapy (yes vs. no), and receipt of radiation (yes vs. no). Statistical analyses Descriptive statistics of the baseline characteristics were calculated and compared by the number of valid visits (i.e., 1–2 vs. 3–4 valid visits) to examine potential baseline characteristics associated with missingness of the study variables (i.e., PA, sitting, QOL, and endocrine symptoms). Descriptive statistics of the study variables were calculated across the visits, and linear trends over time were estimated using an orthogonal polynomial contrast in a linear mixed model with an unstructured covariance structure for repeated measures. A series of latent growth curve models (LGCM) were examined for primary analyses. First, unconditional LGCM was established for each study variable to examine longitudinal trajectories over time. Three latent growth factors, including intercept (i.e., baseline level), slope (i.e., rate of change per year), and correlation between intercept and slope, were estimated from each model. We then examined the longitudinal associations of PA and sitting with QOL and endocrine symptoms using the parallel process LGCM. Six parallel process LGCMs were tested for each pair of PA or sitting with the other study variables. Each of the parallel LGCM’s was adjusted for age (years) and self-identified race (Black vs. white). A schematic diagram of the parallel process LGCM is presented in Fig. 1 . The primary parameters of interest included: (a) Intercept-to-Intercept correlation (i.e., cross-sectional association between the two study variables at baseline); (b) and (c) Intercept-to-Slope coefficient (i.e., the prospective association of the baseline level of one variable with a rate of change in the other variable); and (d) Slope-to-Slope coefficient (i.e., the unidirectional association of the rate of change of PA or Sitting time with the rate of change of the endocrine symptoms and QOL over years). The model-data-fit of the LGCMs was assessed based on the comparative fit index (CFI), Tucker-Lewis index (TLI), and root mean square error of approximation (RMSEA). The model was considered acceptable if the CFI and TLI ≥ .90 and RMSEA < .10 [25, 26]; yet, less emphasis was given to the RMSEA, particularly when evaluating the unconditional LGCM with few degrees of freedom [27]. The modification indices were also assessed to improve the model-data fit with a consideration of theoretical justification and interpretability. The LGCM analyses were examined using full-information maximum likelihood estimator accounting for missing data under the assumption of at least missing at random. We also conducted a follow-up sensitivity analysis by excluding individuals with two or more missing study variables and compared the results. SAS v9.4 (SAS Institute, Cary, NC) was used for data management and the Mplus v7.2 (Muthén & Muthén, Los Angeles, CA) was used for LGCM analyses. Results Sample characteristics The current analyses included 554 women who had complete PA, sitting and QOL data at baseline visit. Table 1 details the participant characteristics by the number of follow up visits where data contributed to longitudinal analyses. At baseline, women enrolled in the WHIP study were a mean age of 58.9 years, majority white (72%), mostly married or partnered (65%), generally having a college education or higher (86%). Approximately half of women in our cohort were less than 1 year from their BC diagnosis having started AET within the past 6 months. We found differences in the years of follow up data available by race, education, and household income such that Black women, women with less than a college education and those with the lowest income bracket were less likely to contribute 2 to 3 years of data vs. 1 to 2 years. Descriptive statistics of the outcomes of interest are shown in supplemental table 1. At baseline women reported engaging in an average of 16.94 ± 15.33 MET-hours of PA per week; this increased significantly over time, almost doubling by the year 3 follow up (30.54 ± 19.91, P < .001). In contrast, time spent sitting did not change over time and remained between 6 and 7 hours per day. Baseline QOL and endocrine symptoms scores (FACT-G; 87.55 ± 12.59, FACT-B; 115.82 ± 16.60, FACT-ES subscale; 15.72 ± 9.99) all got significantly worse by an average of 1–3 points over time ( P’s < .01). Table 2 shows the results of the unconditional LGCM’s examining the estimated change in each outcome of interest across the 3 years of follow up. Latent growth parameters estimated from each model showed that PA increased significantly ( Slope ) by approximately 4.7 MET-hours per week each year while sitting time did not change significantly over time. General and BC specific QOL worsened by an estimated 1.3 and 1.9 points per year while endocrine symptoms increased by 1.5 points per year. Table 1. Baseline Characteristics of the Breast Cancer Survivors of the WHIP Study by the Number of Valid Data Points Over 4 Years Total Number of valid data points (max: 4 years) a P -value b 1 – 2 years 2 – 3 years n (%) 554 (100%) 273 (49.28%) 281 (50.72%) Age (years) 58.93±10.96 58.96±12.14 58.90±9.70 .949 Race <.001 Black 157 (28.34%) 103 (37.73%) 54 (19.22%) white 397 (71.66%) 170 (62.27%) 227 (80.78%) Marital status .414 Married/partnered 359 (64.92%) 172 (63.24%) 187 (66.55%) Others 194 (35.08%) 100 (36.76%) 94 (33.45%) Unknown/missing 1 1 0 Education .018 High school or lower 76 (13.87%) 47 (17.41%) 29 (10.43%) College or higher 472 (86.13%) 223 (82.59%) 249 (89.57%) Unknown/missing 6 3 3 Employment status c .532 Yes (employed) 310 (58.82%) 150 (57.47%) 160 (60.15%) No (unemployed) 217 (41.18%) 111 (42.53%) 106 (39.85%) Unknown/missing 27 12 15 Household income .023 <$60k 162 (31.03%) 94 (36.72%) 68 (25.56%) $60k thru <$100k 153 (29.31%) 63 (24.61%) 90 (33.83%) $100k thru <$150k 104 (19.92%) 52 (20.31%) 52 (19.55%) ≥$150k 103 (19.73%) 47 (18.36%) 56 (21.05%) Unknown/missing 32 17 15 Time since diagnosis .276 <1 year 266 (53.63%) 131 (56.22%) 135 (51.33%) ≥1 year 230 (46.37%) 102 (43.78%) 128 (48.67%) Unknown/missing 58 40 18 Stage at diagnosis .069 I 292 (60.71%) 136 (57.63%) 156 (63.67%) II 148 (30.77%) 73 (30.93%) 75 (30.61%) III 41 (8.52%) 27 (11.44%) 14 (5.71%) Unknown/missing 73 37 36 HER2 status .276 Negative 242 (92.02%) 106 (89.07%) 136 (94.44%) Positive 21 (7.98%) 13 (10.92%) 8 (5.55%) Unknown/missing 291 154 137 Time since AET initiation .621 <6 months 234 (49.26%) 116 (50.43%) 118 (48.16%) ≥6 months 241 (50.74%) 114 (49.57%) 127 (51.84%) Unknown/missing 79 43 36 Chemotherapy .600 Yes 204 (39.38%) 99 (40.57%) 105 (38.32%) No 314 (60.62%) 145 (59.43%) 169 (61.68%) Unknown/missing 36 29 7 Radiation treatment .392 Yes 329 (67.14%) 152 (65.24%) 177 (68.87%) No 161 (32.86%) 81 (34.76%) 80 (31.13%) Unknown/missing 64 40 24 Surgery type d .416 Mastectomy 175 (44.76%) 85 (46.96%) 90 (42.86%) Lumpectomy 216 (55.24%) 96 (53.04%) 120 (57.14%) Unknown/missing 163 92 71 Body mass index .239 <25 kg/m2 174 (33.46%) 77 (29.96%) 97 (36.88%) 25 - <30 kg/m2 133 (25.58%) 68 (26.46%) 65 (24.71%) ≥30 kg/m2 213 (40.96%) 112 (43.58%) 101 (38.4%) Unknown/missing 34 16 18 Note: AET = adjuvant endocrine therapy; HER2 – human epidermal growth factor receptor 2. The values are presented as Mean ± standard deviation for continuous variables and n (%) for categorical variables. The unknown/missing cases were not included in the percentage calculation. a number of non-consecutive valid data points out of 4 years (baseline, year 1 thru 3). b P -values are estimated from an independent sample t -test for a continuous variable and x 2 test of independence for a categorical variable. c those who reported working full- and part-time were categorized as ‘employed’. Otherwise (e.g., full-time homemaker, retired, student) were categorized as ‘unemployed’. d women underwent both lumpectomy and mastectomy (n = 16) were categorized as the mastectomy group. Model-data fit indices Latent growth factors Table 2 Unconditional Latent Growth Curve Modeling on Changes in Study Outcomes Over a 4 Year Period (n = 554) x 2 ( df ) CFI TLI RMSEA Intercept (SE) a Slope (SE) a Correlation (SE) b Total MET-hours/week 34.35 (5) ** .809 .781 .103 17.90 (.66) ** 4.66 (.46) ** 0.23 (.39) Sitting time (hours/day) c 4.92 (4) .987 .980 .021 6.83 (.15) ** -0.19 (.13) -0.01 (.40) FACT-ES (subscale only) 38.89 (5) ** .949 .939 .111 16.27 (.42) ** 1.45 (.17) ** 0.32 (.34) FACT-General 28.20 (5) ** .956 .947 .092 87.37 (.54) ** -1.29 (.30) ** 0.16 (.16) FACT-Breast 35.99 (5) ** .947 .936 .106 115.53 (.71) ** -1.88 (.39) ** 0.26 (.16) Note: CFI = comparative fit index; ES = Endocrine symptoms; FACT-General = Functional Assessment of Cancer Therapy-General (Physical + Social/Family + Emotional + Functional); FACT-Breast = Functional Assessment of Cancer Therapy-Breast Cancer (FACT-G + Breast cancer subscale); MET = metabolic equivalent tasks; TLI = Tucker-Lewis index; RMSEA = root mean square error of approximation; a the values are the estimated mean growth factors (standard error). b the correlation between the intercept and slope. c the residual covariance between the year 1 and 3 was added in the model to improve the model-data-fit. * P < .05 ** P < .001 Physical activity, quality of life and endocrine symptoms Table 3 presents the results of the parallel LGCM examining the associations between PA, endocrine symptoms and QOL over time. The results showed there was no significant association between baseline PA and endocrine symptoms. A significant positive cross-sectional association was found between PA and both general ( b = 0.28; P = .002) and BC-specific ( b = 0.30; P = .001) QOL at baseline ( Intercept-to-Intercept correlations) . Prospective associations between baseline levels of PA and changes in endocrine symptoms and QOL ( Intercept 1 -to-Slope 2 ) were not statistically significant. The reverse was also true whereby baseline levels of endocrine symptoms and QOL were not significantly associated with a change in PA over time ( Intercept 2 - to-Slope 1 ). Similarly, changes in PA were not significantly associated with changes in endocrine symptoms or QOL ( Slope 1 - to-Slope 2 ) . FACT-ES a FACT-General FACT-Breast Table 3 The Results of Parallel Process Latent Growth Model Between Total MET-hour/week and Endocrine Symptoms and QOL (FACT) b (SE) P b (SE) P b (SE) P Intercept-to-intercept correlation (a) I 1 ↔ I 2 -0.02 (.08) .810 0.28 (.09) .002 0.30 (.09) .001 Intercept-to-slope coefficients (b) I 1 → S 2 0.30 (.18) .101 -0.13 (.17) .442 -0.16 (.20) .433 (c) I 2 → S 1 -0.08 (.05) .105 0.10 (.06) .095 0.07 (.04) 134 Slope-to-slope coefficient (d) S 1 → S 2 -0.56 (.35) .108 0.39 (.32) .217 0.46 (.38) .232 Model-data fit indices x 2 (df) 91.36 (32) ** 91.16 (30) ** 97.47 (30) ** CFI .933 .917 .916 TLI .908 .879 .876 RMSEA .058 .061 .064 I 1 = intercept growth factor for total MET-hours/week; S 1 = slope growth factor for total MET-hours/week; I 2 = intercept growth factor for FACT-ES subscale scores; S 2 = slope growth factor for FACT-ES subscale scores. CFI = comparative fit index; FACT-ES = Functional Assessment of Cancer Therapy–Endocrine Symptoms (subscale only); TLI = Tucker-Lewis index; S slope of the latent growth factors; RMSEA = root mean square error of approximation; I = intercept of the latent growth factors; a the residual variance of slope (S 2 ) was fixed to zero due to the negative value. * P < .05 ** P < .001 Sitting time, quality of life and endocrine symptoms Table 4 presents the results of the parallel LGCM examining the associations between sitting, endocrine symptoms and QOL over time. There was a significant association between baseline sitting and endocrine symptoms with more sitting time associated with worse endocrine symptoms ( b = 0.29; P = .002). A significant cross-sectional association was also found between sitting and general ( b =-0.49; P < .001) and BC-specific ( b =-0.50; P < .001) QOL ( Intercept-to-Intercept correlations) showing that more sitting was associated with worse QOL. Prospective associations between baseline levels of sitting and changes in endocrine symptoms or QOL were not significant ( Intercept 1 -to-Slope 2 ). In contrast, the reverse ( Intercept 2 -to-Slope 1 ) was true, whereby baseline levels of endocrine symptoms and QOL were significantly associated with a change in sitting over time. Greater endocrine symptoms at baseline were associated with a slower rate of increase in sitting time ( b =-.05; P = .004); yet, greater general ( b = 0.04; P = .010) and BC-specific ( b = 0.04; P = .003) QOL at baseline were associated with greater rate of increases in sitting time. Increases in sitting time were significantly associated with lower rate of increase in endocrine symptoms ( b =-1.81; P = .017, and while also being associated with a decline in general and BC-specific QOL ( Slope 1 - to-Slope 2 ); those associations were not significant. FACT-ES a,b FACT-General a FACT-Breast a Table 4 The Results of Parallel Process Latent Growth Model Between Sitting Time (minutes/day) and Endocrine Symptoms and QOL (FACT) b (SE) P b (SE) P b (SE) P Intercept-to-intercept correlation (a) I 1 ↔ I 2 0.29 (.09) .002 -0.49 (.13) < .001 -0.50 (.13) < .001 Intercept-to-slope coefficients (b) I 1 → S 2 -0.30 (.16) .079 0.59 (.80 .460 -0.39 (.86) .654 (c) I 2 → S 1 -0.05 (.02) .004 0.04 (.02) .010 0.04 (.01) .003 Slope-to-slope coefficient (d) S 1 → S 2 -1.81 (0.76) .017 -2.02 (1.67) .225 -2.67 (2.03) .189 Model-data fit indices x 2 (df) 74.07 (33) ** 54.03 (29) 68.16 (29) CFI .949 .960 .944 TLI .932 .940 .915 RMSEA .047 .039 .049 I 1 = intercept growth factor for sitting time (minutes/day); S 1 = slope growth factor for sitting time (minutes/day); I 2 = intercept growth factor for FACT-ES subscale scores; S 2 = slope growth factor for FACT-ES subscale scores. The schematic diagram of the parallel process model is depicted in Fig. 1. CFI = comparative fit index; FACT-ES = Functional Assessment of Cancer Therapy–Endocrine Symptoms (subscale only); TLI = Tucker-Lewis index; S slope of the latent growth factors; RMSEA = root mean square error of approximation; I = intercept of the latent growth factors; IPAQ = International Physical Activity Questionnaire. a the residual covariance between the year 1 and 3 was added in the model to improve the model-data-fit. b the residual variances of slope (S 1 and S 2 ) were fixed to zero due to the negative values. * P < .05 ** P < .001 Discussion In this secondary analysis of the WHIP Study examining cross-sectional and longitudinal associations among PA, daily sitting time and QOL, we found significant associations between activity levels (and sitting time) with endocrine symptoms and general and BC specific QOL. Self-reported PA increased each year, while sitting time stayed consistent. Endocrine symptoms significantly increased (worsened) while general and BC specific health-related QOL declined slightly over the same period. Importantly, higher levels of baseline PA or change in PA were not associated with changes in endocrine symptoms or health-related QOL over time. We did however find that worse endocrine symptoms were associated with a slower rate of increase in sitting time while having better QOL scores at baseline was associated with a significant increase in the rate of sitting. Overall, both PA and sitting appear to be important behaviors among BC survivors undergoing AET. A number of previous studies examining the relationship between PA, endocrine symptoms and QOL among women with BC have predominantly examined arthralgias or musculoskeletal concerns that develop as a result of aromatase inhibitor (AI) therapy for HR + BC. A recent meta-analysis, focused specifically on musculoskeletal symptoms, examined 9 trials that included 743 participants who were randomized to exercise or usual care [28]. Overall, findings supported exercise as an effective approach for managing pain, stiffness and grip strength. However, another study did not find improvements in QOL domains such as fatigue, endocrine symptoms or total quality of life, despite significant improvements in PA behaviors as a result of intervention. Nyrop and colleagues encouraged women on AIs (n = 20) to walk for at least 30 minutes per day for 5 days a week over a 6-week period, to address arthralgia [29]. While total walking time per week significantly increased over the study, decreases in joint pain, stiffness and fatigue were not significant suggesting the need for a larger sample. The duration of the intervention may be critical however with that study only being 12-weeks [30]. While not all symptoms that result from AET are musculoskeletal in nature, their presence is also linked to reductions in PA among women who take them [31]. Other factors linked to declines of PA following use of AIs included BMI. In the HOPE study [32], a 12-month clinical exercise trial among women with AI-induced arthralgia, 121 BC survivors with at least mild arthralgias were randomized to a supervised exercise or control group. At 12 months follow up, women who participated in aerobic and resistance training reported greater improvements in overall, BC-specific and endocrine symptom subscales compared to the control participants. Study authors concluded that given the frequency of side-effects from AIs and the risk for treatment non-adherence, that non-pharmacological approaches like exercise training could be valuable. Interestingly our results did not show an improvement in QOL (general or BC-specific) or endocrine symptoms to be associated with total PA, perhaps due to the fact that the most common activity reported in the current analyses is walking. This is likely due to the non-specific nature of the IPAQ regarding resistance training, which was a primary component of the exercise intervention in the HOPE study. It is well established that resistance training is critical for the maintenance of physical functioning, including among older persons and those who experience chronic pain. Therefore, guidance to increase physical activity should specifically include the guideline-based recommendations of at least 2-days of resistance training per week in addition to 150 minutes of aerobic training. It should be noted that a barrier to recommending resistance training relates to access to facilities and supervision in the safe performance of certain exercises. However, the recent COVID-19 pandemic has increased the availability of remote and online programs at low cost and these may be valuable in this regard. A unique aspect and strength of this study is the examination of sitting time and its association with QOL and endocrine symptoms cross-sectionally and over time. The fact that more sitting time at baseline was associated with worse general, BC-specific, and endocrine symptoms at baseline but reduced endocrine symptoms over time was somewhat unexpected. Hartman and colleagues [33], examining both moderate to vigorous PA (MVPA) and sedentary time measured with accelerometry among 134 post-menopausal BC survivors, found that more time spent sitting in longer bouts was associated with worse physical QOL, especially in women who did little MVPA. While we did not specifically assess whether there was effect modification in our analyses, it may be that women who were doing more PA were also resting more outside of those bouts of activity. A separate study [34], conducted among 195 post-systemic therapy BC survivors (50% on hormonal therapy), found that more time spent in accelerometer-assessed sitting was associated with worse pain, fatigue and depression, especially among women with low levels of PA. Women with better QOL at baseline may have increased their sitting as they began to experience side-effects of treatment. Women who experience pain at either at rest or at onset of activity, are likely to attribute this discomfort to activity and therefore do less. Unfortunately, this only serves to further reduce their functional capacity increasing the likelihood that when they engage in activity, they will experience discomfort. Similarly, it is often counter-intuitive that women who are fatigued can benefit from activity. Most often patients who are fatigued believe they should do less and rest, which again leads to declines in function and worsening fatigue over time. The only way to combat this is to engage in activity to strengthen the musculoskeletal system. Indeed , our prior findings indicated that women who were the most adherent to AET had the highest levels of sitting time [35], perhaps suggesting that these women would experience side effects of AET early and these symptoms would then level off or increase more slowly over time. These findings highlight that both behaviors are independently important for the management of symptoms in BC survivors and they should be advised to both increase exercise and also reduce long periods of sitting time. Other strengths of our study are the inclusion of a racially diverse sample of HR + BC survivors. This study does have a few limitations. First, only self-reported measures were used for collecting PA and sitting data. While important for clarifying context, there are known issues with over-reporting of PA and under-reporting of sitting time in the IPAQ questionnaire, particularly for BC survivors [36–38]. Furthermore, use of objective measures of PA would allow for the assessment of lower intensities of PA in the range of 1.5–2.9 METs, capturing movement and daily activities of living which are lost in self-reported measures but still important for health among BC survivors [16]. Fortunately, because the primary outcome of interest in the WHIP parent study was adherence to AET and not specifically PA there may have been less inclination to overreport activity. Nevertheless, the use of accelerometers would have improved the accuracy of data capture in that regard. Second, there were a relatively large number of participants who were lost to follow up in this study. We did find that certain baseline characteristics (race, education and income) seemed to be correlated with missingness of study variables; however, in sensitivity analyses the primary findings were not altered after excluding those with two or more missing visits. Thus, we cannot fully rule out the possibility of survival bias where the missingness may be related to the symptoms or prognosis after treatment. Further, we only included the treatment type at the time of the baseline visit in these models and did not explore whether women changed the type of therapy used over time such as in a switch strategy. This implies that caution is needed, particularly when interpreting the results from an unconditional LGCM showing the shape of longitudinal changes in study variables over time. Lastly, the model-data fits of the LGCMs were below or marginally above the acceptable levels for model fit parameters in structural equation modelling frameworks . The greater model-data fits are necessary to obtain the valid parameter estimates from the LGCM model, and thus future studies are warranted to test the proposed LGCM model in different settings. In summary, our findings from a large and diverse cohort of BC survivors undergoing AET, shows that both PA and sitting time were important with respect to managing symptoms and maintaining QOL. Clinician guidance towards the adoption of PA that includes both aerobic and resistance exercise and a reduction of sedentary behaviors should take into account the patient-level variations in symptoms such as fatigue or pain, that may make activity challenging. Regular activity can also help with weight management efforts, which is important for a population of women who are at risk for weight gain and therefore further comorbid conditions. Given baseline levels of activity/sitting are more strongly associated with baseline symptoms and QOL, it is important that women are encouraged to adopt appropriate activity behaviors as early as possible, despite the challenges they may face as a result of treatment and associated side-effects. Future research is needed to more accurately delineate the optimal prescription of PA (e.g., type, dose, intensity, volume) coupled with management of prolonged periods of inactivity (sedentary behaviors), especially in consideration of the types of treatment a women may receive for HER + BC. Declarations Funding: This research was funded by the National Institutes of Health (R01CA154848) and the Bank of America (to Vanessa B. Sheppard). It was also supported in part by the National Cancer Institute (2T32CA093423 to Vanessa B. Sheppard), the National Institutes of Health/National Cancer Institute (Cancer Center Support Grant P30 CA016059), and the National Center for Advancing Translational Sciences (Translational Science Award UL1TR002649). Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the National Institutes of Health. Competing Interests: The authors have no relevant financial or non-financial interests to disclose. Author Contributions: Alexander Lucas: Conceptualization, methodology and writing–original draft. Youngdeok Kim: Data curation, methodology, writing–original draft, and writing–review and editing. Autumn Lanoye: writing–original draft and writing–review and editing. Arnethea L. Sutton: Writing–original draft and writing–review and editing. Robert Lee Franco: Writing–original draft and writing–review and editing. Jessica G LaRose: Writing–original draft and writing–review and editing. Masey Ross: Supervision. Vanessa B. Sheppard: Funding acquisition, writing– original draft, and writing–review and editing. Data availability: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Siegel, R.L., et al. (2021) Cancer Statistics, 2021 . CA Cancer J Clin 71:7-33. https://doi.org/10.3322/caac.21654 Early Breast Cancer Trialists' Collaborative, G. (2005) Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials . Lancet 365:1687-717. https://doi.org/10.1016/S0140-6736(05)66544-0 Laroche, F., et al. (2017) Quality of life and impact of pain in women treated with aromatase inhibitors for breast cancer. A multicenter cohort study . PLoS One 12:e0187165. https://doi.org/10.1371/journal.pone.0187165 Saad, F., et al. (2008) Cancer treatment-induced bone loss in breast and prostate cancer . J Clin Oncol 26:5465-76. https://doi.org/10.1200/JCO.2008.18.4184 Hershman, D.L., et al. (2011) Early discontinuation and non-adherence to adjuvant hormonal therapy are associated with increased mortality in women with breast cancer . Breast Cancer Res Treat 126:529-37. https://doi.org/10.1007/s10549-010-1132-4 Chlebowski, R.T., J. Kim, and R. Haque (2014) Adherence to endocrine therapy in breast cancer adjuvant and prevention settings . Cancer Prev Res (Phila) 7:378-87. https://doi.org/10.1158/1940-6207.CAPR-13-0389 Rock, C.L., et al. (2012) Nutrition and physical activity guidelines for cancer survivors . CA Cancer J Clin 62:243-74. https://doi.org/10.3322/caac.21142 Playdon, M.C., et al. (2015) Weight Gain After Breast Cancer Diagnosis and All-Cause Mortality: Systematic Review and Meta-Analysis . J Natl Cancer Inst 107:djv275. https://doi.org/10.1093/jnci/djv275 McTiernan, A., et al. (2003) Recreational physical activity and the risk of breast cancer in postmenopausal women: the Women's Health Initiative Cohort Study . JAMA 290:1331-6. https://doi.org/10.1001/jama.290.10.1331 Campbell, K.L., et al. (2019) Exercise Guidelines for Cancer Survivors: Consensus Statement from International Multidisciplinary Roundtable . Med Sci Sports Exerc 51:2375-2390. https://doi.org/10.1249/MSS.0000000000002116 Piercy, K.L., et al. (2018) The Physical Activity Guidelines for Americans . JAMA 320:2020-2028. https://doi.org/10.1001/jama.2018.14854 Thomas, G.A., et al. (2017) The effect of exercise on body composition and bone mineral density in breast cancer survivors taking aromatase inhibitors . Obesity (Silver Spring) 25:346-351. https://doi.org/10.1002/oby.21729 Sheppard, V.B., et al. (2020) Physical activity, health-related quality of life, and adjuvant endocrine therapy-related symptoms in women with hormone receptor-positive breast cancer . Cancer 126:4059-4066. https://doi.org/10.1002/cncr.33054 McNeil, J., et al. (2021) Adherence to a lower versus higher intensity physical activity intervention in the Breast Cancer & Physical Activity Level (BC-PAL) Trial . J Cancer Surviv. https://doi.org/10.1007/s11764-021-01030-w Kim, R.B., et al. (2013) Physical activity and sedentary behavior of cancer survivors and non-cancer individuals: results from a national survey . PLoS One 8:e57598. https://doi.org/10.1371/journal.pone.0057598 Lynch, B.M., et al. (2010) Objectively measured physical activity and sedentary time of breast cancer survivors, and associations with adiposity: findings from NHANES (2003-2006) . Cancer Causes Control 21:283-8. https://doi.org/10.1007/s10552-009-9460-6 Phillips, S.M., et al. (2015) Objectively measured physical activity and sedentary behavior and quality of life indicators in survivors of breast cancer . Cancer 121:4044-52. https://doi.org/10.1002/cncr.29620 Sheppard, V.B., et al. (2019) Adherence to Adjuvant Endocrine Therapy in Insured Black and White Breast Cancer Survivors: Exploring Adherence Measures in Patient Data . J Manag Care Spec Pharm 25:578-586. https://doi.org/10.18553/jmcp.2019.25.5.578 Lucas, A.R., B.J. Levine, and N.E. Avis (2017) Posttreatment trajectories of physical activity in breast cancer survivors . Cancer 123:2773-2780. https://doi.org/10.1002/cncr.30641 Sheppard, V.B., et al. (2018) Biospecimen donation among black and white breast cancer survivors: opportunities to promote precision medicine . J Cancer Surviv 12:74-81. https://doi.org/10.1007/s11764-017-0646-8 Craig, C.L., et al. (2003) International physical activity questionnaire: 12-country reliability and validity . Med Sci Sports Exerc 35:1381-95. https://doi.org/10.1249/01.MSS.0000078924.61453.FB Lee, P.H., et al. (2011) Validity of the International Physical Activity Questionnaire Short Form (IPAQ-SF): a systematic review . Int J Behav Nutr Phys Act 8:115. https://doi.org/10.1186/1479-5868-8-115 IPAQ Research Committee. IPAQ scoring protocol . 2005 [cited 2021 November 17]; Available from: https://sites.google.com/site/theipaq/scoring-protocol. Eton, D.T., et al. (2004) A combination of distribution- and anchor-based approaches determined minimally important differences (MIDs) for four endpoints in a breast cancer scale . J Clin Epidemiol 57:898-910. https://doi.org/10.1016/j.jclinepi.2004.01.012 Browne, M.W. and R. Cudeck (1992) Alternative Ways of Assessing Model Fit . Sociological Methods & Research 21:230-258. https://doi.org/10.1177/0049124192021002005 Bentler, P.M. (1990) Comparative fit indexes in structural models . Psychol Bull 107:238-46. https://doi.org/10.1037/0033-2909.107.2.238 Kenny, D.A., B. Kaniskan, and D.B. McCoach (2014) The Performance of RMSEA in Models With Small Degrees of Freedom . Sociological Methods & Research 44:486-507. https://doi.org/10.1177/0049124114543236 Lu, G., J. Zheng, and L. Zhang (2020) The effect of exercise on aromatase inhibitor-induced musculoskeletal symptoms in breast cancer survivors :a systematic review and meta-analysis . Support Care Cancer 28:1587-1596. https://doi.org/10.1007/s00520-019-05186-1 Nyrop, K.A., et al. (2014) Feasibility and promise of a 6-week program to encourage physical activity and reduce joint symptoms among elderly breast cancer survivors on aromatase inhibitor therapy . J Geriatr Oncol 5:148-55. https://doi.org/10.1016/j.jgo.2013.12.002 Rogers, L.Q., et al. (2009) Physical activity and health outcomes three months after completing a physical activity behavior change intervention: persistent and delayed effects . Cancer Epidemiol Biomarkers Prev 18:1410-8. https://doi.org/10.1158/1055-9965.EPI-08-1045 Brown, J.C., et al. (2014) Aromatase inhibitor associated musculoskeletal symptoms are associated with reduced physical activity among breast cancer survivors . Breast J 20:22-8. https://doi.org/10.1111/tbj.12202 Baglia, M.L., et al. (2019) Endocrine-related quality of life in a randomized trial of exercise on aromatase inhibitor-induced arthralgias in breast cancer survivors . Cancer 125:2262-2271. https://doi.org/10.1002/cncr.32051 Hartman, S.J., et al. (2017) Objectively measured sedentary behavior and quality of life among survivors of early stage breast cancer . Support Care Cancer 25:2495-2503. https://doi.org/10.1007/s00520-017-3657-0 Trinh, L., et al. (2015) Physical and psychological health among breast cancer survivors: interactions with sedentary behavior and physical activity . Psycho-Oncology 24:1279-1285. https://doi.org/10.1002/pon.3872 Sheppard, V.B., et al. (2021) Race and Patient-reported Symptoms in Adherence to Adjuvant Endocrine Therapy: A Report from the Women's Hormonal Initiation and Persistence Study . Cancer Epidemiol Biomarkers Prev. https://doi.org/10.1158/1055-9965.EPI-20-0604 Deliens, T., et al. (2021) Misreporting of Physical Activity and Sedentary Behavior in Parents-to-Be: A Validation Study across Sex . Int J Environ Res Public Health 18. https://doi.org/10.3390/ijerph18094654 Johnson-Kozlow, M., et al. (2006) Comparative validation of the IPAQ and the 7-Day PAR among women diagnosed with breast cancer . Int J Behav Nutr Phys Act 3:7. https://doi.org/10.1186/1479-5868-3-7 Ruiz-Casado, A., et al. (2016) Validity of the Physical Activity Questionnaires IPAQ-SF and GPAQ for Cancer Survivors: Insights from a Spanish Cohort . Int J Sports Med 37:979-985. https://doi.org/10.1055/s-0042-103967 Supplemental Table 1 Supplemental Table 1 is not available with this version Cite Share Download PDF Status: Published Journal Publication published 28 Sep, 2023 Read the published version in Cancer Medicine → Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1469636","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":148362210,"identity":"43b5fd8d-18b2-4e99-bcc9-a65044670579","order_by":0,"name":"Alexander R Lucas","email":"","orcid":"","institution":"Virginia Commonwealth University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"R","lastName":"Lucas","suffix":""},{"id":148362211,"identity":"15cdc22b-0924-4848-9a2d-ae5b0fd178ec","order_by":1,"name":"Youngdeok Kim","email":"","orcid":"","institution":"Virginia Commonwealth University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Youngdeok","middleName":"","lastName":"Kim","suffix":""},{"id":148362212,"identity":"7ced8d08-aeb3-4edd-9e96-f23989b18a5c","order_by":2,"name":"Autumn Lanoye","email":"","orcid":"","institution":"Virginia Commonwealth University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Autumn","middleName":"","lastName":"Lanoye","suffix":""},{"id":148362213,"identity":"09bb4975-77fe-436d-ade3-35c714b8c4b6","order_by":3,"name":"R. 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Yet, AET can also frequently produce negative side-effects such as joint pain and stiffness [3], loss of bone mineral density [4], and vasomotor and gynecological symptoms that compromise quality of life (QOL). Unfortunately, these symptoms often lead to poor adherence with up to 50% of women either not initiating or completing their recommended 5-year therapy regimens [5, 6].\u003c/p\u003e \u003cp\u003eTo optimize their health status, BC survivors should therefore aim to adopt or maintain healthy lifestyle behaviors that include a healthy diet and physical activity [7]. BC survivors who gain\u0026thinsp;\u0026gt;\u0026thinsp;5% body weight post-diagnosis have a 12% higher all-cause mortality relative to BC survivors who maintain their weight [8]. Weight gain is also strongly associated with the onset of metabolic dysfunction and cardiovascular disease [9] and may be an important factor impacting symptoms associated with AET. Current guidelines recommend at least 150 minutes of moderate to vigorous physical activity (MVPA) or 75 minutes of VPA per week while also avoiding inactivity [10, 11]. Engaging in PA is also an important part of weight management post BC therapy and may be able to mitigate many of the side-effects of AET [12, 13]; however, higher intensity PA may also be challenging [14] due to the impact of BC-treatments on functional capacity and health-related QOL. It should also be recognized that socio-demographic factors and pre-existing comorbidities may make achieving recommended levels of PA difficult [15]. Consequently, independent of PA, BC survivors should aim to reduce sedentary behaviors (e.g., sitting) as a means of increasing light intensity PA that is also important for weight management [16], metabolic and cardiovascular health, and managing symptoms such as fatigue [17]. What is not yet known is how patterns of PA, sitting and endocrine symptoms change over time following the initiation and adherence to AET.\u003c/p\u003e \u003cp\u003eDisparities in adherence to AET have been noted among insured BC survivors by age and race [18] and while previous studies have examined PA patterns in BC patients following treatment [19], they do not include representative numbers of Black women. Furthermore, few studies have examined cross-sectional and longitudinal associations among PA and sitting with general and specific aspects of QOL among women actively undergoing AET. The Women\u0026rsquo;s Hormonal Initiation and Persistence (WHIP) study is an observational cohort study with up to 3 years of follow up data on a diverse group of women and allows for the examination of these questions given its inclusion of clinical- and treatment-related data among BC survivors [20]. We hypothesized that at baseline, higher levels of self-reported PA would be associated with better QOL and fewer endocrine symptoms. Further, we hypothesized that PA would increase over time as women recovered from BC treatment and that increases in PA would be associated with improving QOL and reduced endocrine symptoms. With regards to sedentary sitting time, we also hypothesized both cross-sectional and longitudinal associations with QOL. Specifically, more sitting time at baseline would be associated with worse QOL and endocrine symptoms while increased sitting time would be associated with worsening QOL and symptoms. We utilize a rigorous analytical method, parallel latent growth curve modeling (LGCM), to estimate 1) average changes in PA, sitting, and QOL and endocrine symptoms; and 2) the extent to which changes in PA and sitting explain the change in general and specific aspects of QOL over a 3-year period, while controlling for time-varying confounding effects of the respective behaviors.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003eThe current study is a secondary analysis of the Women\u0026rsquo;s Hormonal Initiation and Persistence (WHIP) study. WHIP was a longitudinal observational cohort study designed to examine patterns of adherence to AET (R01CA154848). All study procedures were approved by Institutional Review Boards at participating sites. The study design, recruitment and data collection methods have previously been described in detail [20]. Briefly, women diagnosed with BC were identified across 2 primary geographic regions (Midwest, Southeast) from academic medical centers, from integrated health systems, and via community outreach efforts. Eligibility criteria included being within 12 months of a diagnosis of HR\u0026thinsp;+\u0026thinsp;BC (confirmed via laboratory reports), being 21 years old or older, having initiated AET, and being able to speak English or Spanish. Women were deemed potentially eligible on the basis of cancer registries, and via pharmacy records. They were then mailed letters that described the study protocol and were asked to call a toll-free number or to return a stamped self-addressed postcard if they did not want to be contacted about the study. Women recruited via community outreach were screened for eligibility by a clinical research assistant. Research assistants then contacted women who did not opt out within a 2-week time frame, confirmed the level of interest, consented interested participants, and scheduled phone interviews to complete the survey. Data were collected via standardized surveys that were administered according to a woman\u0026rsquo;s preference either via telephone by a trained clinical research assistant or via a secure online survey (REDCap). The baseline cohort included 594 women, with a number having dropped out across 3 years of follow up visits \u003cspan class=\"Underline\" name=\"Emphasis\" type=\"Underline\"\u003e(year 1\u0026thinsp;=\u0026thinsp;162, year 2\u0026thinsp;=\u0026thinsp;220, and year 3\u0026thinsp;=\u0026thinsp;405)\u003c/span\u003e. Women were excluded from the analysis if they had invalid and/or missing data for study outcomes across all years (n\u0026thinsp;=\u0026thinsp;40), leaving a valid sample of 554 for longitudinal analyses.\u003c/p\u003e\n\u003cdiv class=\"Section2\"\u003e\n \u003ch2\u003eMeasures\u003c/h2\u003e\n \u003cp\u003ePA and sitting time were measured with the short form of the International Physical Activity Questionnaire (IPAQ) [21, 22]. Participants were asked to self-report frequency and duration of walking, or participating in moderate-intensity activities (e.g., carrying light loads and bicycling at a regular pace) and vigorous-intensity activities (heavy lifting and aerobics) during the past 7 days. IPAQ data were cleaned by following the rules set by the IPAQ executive committee [23] including the exclusion of unreasonably high levels of PA data (e.g., total PA exceeding 16 hours per day) and truncation of the self-reported duration of each type of PA when exceeding 4 hours per day. Metabolic equivalents of task (METs) of 3.3, 4.0, and 8.0 were subsequently applied to walking, moderate- and vigorous-intensity activities, respectively, to calculate total MET-hours per week. As part of IPAQ, participants reported the average daily time spent in sitting during the past 7 days.\u003c/p\u003e\n \u003cp\u003eQOL was measured with 2 previously validated Functional Assessment of Cancer Therapy (FACT) questionnaires: FACT\u0026ndash;Breast Cancer (FACT-B), and FACT\u0026ndash;Endocrine Symptoms (FACT-ES), which is a symptom-specific scale. The FACT-General (FACT-G) was also examined for its relationship to PA and sitting. QOL scores (FACT-G, FACT-B) and ES-subscale scores were all assessed as continuous variables. Higher FACT-G and FACT-B scores indicate better health-related QOL and higher ES-subscale scores indicate worse adjuvant endocrine therapy-related symptoms. \u003cspan class=\"Underline\" name=\"Emphasis\" type=\"Underline\"\u003ePrevious studies have reported minimally important differences of 5\u0026ndash;6 points for the FACT-G and 2\u0026ndash;3 points for FACT-B subscale among breast cancer survivors [24].\u003c/span\u003e In the present study, reliability of each measure was acceptable with a Cronbach\u0026rsquo;s alpha ranging between .87 and .91 (FACT-G), between .89 and .92 (FACT-B), and between .78 and .80 (FACT-ES) across the years. Baseline sociodemographic characteristics were obtained via survey while clinical variables were confirmed via medical records. Sociodemographic variables included age, self-identified race (Black vs. white), marital status (married/partnered vs. others), education (high school or lower vs. college or higher), employment (yes vs. no), household income (\u0026lt;$60k, $60k thru \u0026lt;$100k, $100k thru \u0026lt;$150k and \u0026ge;$150k). Clinical variables included BMI (\u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e, 25 thru 30 kg/m\u003csup\u003e2\u003c/sup\u003e, and \u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e), time since diagnosis (\u0026lt;\u0026thinsp;1 year vs. \u0026ge;1 year), BC stage at diagnosis (stage I, II, or III), human epidermal growth factor receptor 2 (HER2) status (negative or positive), time since AET initiation (\u0026lt;\u0026thinsp;6 months vs. \u0026ge;6 months), surgery type (mastectomy vs. lumpectomy), \u003cspan class=\"Underline\" name=\"Emphasis\" type=\"Underline\"\u003ereceipt of chemotherapy (yes vs. no), and receipt of radiation (yes vs. no).\u003c/span\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analyses\u003c/h2\u003e\n \u003cp\u003eDescriptive statistics of the baseline characteristics were calculated and compared by the number of valid visits (i.e., 1\u0026ndash;2 vs. 3\u0026ndash;4 valid visits) to examine potential baseline characteristics associated with missingness of the study variables (i.e., PA, sitting, QOL, and endocrine symptoms). Descriptive statistics of the study variables were calculated across the visits, and linear trends over time were estimated using an orthogonal polynomial contrast in a linear mixed model with an unstructured covariance structure for repeated measures.\u003c/p\u003e\n \u003cp\u003eA series of latent growth curve models (LGCM) were examined for primary analyses. First, unconditional LGCM was established for each study variable to examine longitudinal trajectories over time. Three latent growth factors, including intercept (i.e., baseline level), slope (i.e., rate of change per year), and correlation between intercept and slope, were estimated from each model. We then examined the longitudinal associations of PA and sitting with QOL and endocrine symptoms using the parallel process LGCM. Six parallel process LGCMs were tested for each pair of PA or sitting with the other study variables. \u003cspan class=\"Underline\" name=\"Emphasis\" type=\"Underline\"\u003eEach of the parallel LGCM\u0026rsquo;s was adjusted for age (years) and self-identified race (Black vs. white).\u003c/span\u003e A schematic diagram of the parallel process LGCM is presented in \u003cstrong\u003eFig.\u0026nbsp;1\u003c/strong\u003e. The primary parameters of interest included: (a) \u003cem\u003eIntercept-to-Intercept\u003c/em\u003e correlation (i.e., cross-sectional association between the two study variables at baseline); (b) and (c) \u003cem\u003eIntercept-to-Slope\u003c/em\u003e coefficient (i.e., the prospective association of the baseline level of one variable with a rate of change in the other variable); and (d) \u003cem\u003eSlope-to-Slope\u003c/em\u003e coefficient (i.e., the unidirectional association of the rate of change of PA or Sitting time with the rate of change of the endocrine symptoms and QOL over years). The model-data-fit of the LGCMs was assessed based on the comparative fit index (CFI), Tucker-Lewis index (TLI), and root mean square error of approximation (RMSEA). The model was considered acceptable if the CFI and TLI\u0026thinsp;\u0026ge;\u0026thinsp;.90 and RMSEA\u0026thinsp;\u0026lt;\u0026thinsp;.10 [25, 26]; yet, less emphasis was given to the RMSEA, particularly when evaluating the unconditional LGCM with few degrees of freedom [27]. The modification indices were also assessed to improve the model-data fit with a consideration of theoretical justification and interpretability. The LGCM analyses were examined using full-information maximum likelihood estimator accounting for missing data under the assumption of at least missing at random. We also conducted a follow-up sensitivity analysis by excluding individuals with two or more missing study variables and compared the results. SAS v9.4 (SAS Institute, Cary, NC) was used for data management and the Mplus v7.2 (Muth\u0026eacute;n \u0026amp; Muth\u0026eacute;n, Los Angeles, CA) was used for LGCM analyses.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003c/p\u003e\n\u003ch2\u003eSample characteristics\u003c/h2\u003e\n\u003cp\u003eThe current analyses included 554 women who had complete PA, sitting and QOL data at baseline visit. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e details the participant characteristics by the number of follow up visits where data contributed to longitudinal analyses. At baseline, women enrolled in the WHIP study were a mean age of 58.9 years, majority white (72%), mostly married or partnered (65%), generally having a college education or higher (86%). Approximately half of women in our cohort were less than 1 year from their BC diagnosis having started AET within the past 6 months. We found differences in the years of follow up data available by race, education, and household income such that Black women, women with less than a college education and those with the lowest income bracket were less likely to contribute 2 to 3 years of data vs. 1 to 2 years.\u003c/p\u003e\n\u003cp\u003eDescriptive statistics of the outcomes of interest are shown in supplemental table 1. At baseline women reported engaging in an average of 16.94\u0026thinsp;\u0026plusmn;\u0026thinsp;15.33 MET-hours of PA per week; this increased significantly over time, almost doubling by the year 3 follow up (30.54\u0026thinsp;\u0026plusmn;\u0026thinsp;19.91, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). In contrast, time spent sitting did not change over time and remained between 6 and 7 hours per day. Baseline QOL and endocrine symptoms scores \u003cspan class=\"Underline\" name=\"Emphasis\" type=\"Underline\"\u003e(FACT-G; 87.55\u0026thinsp;\u0026plusmn;\u0026thinsp;12.59, FACT-B; 115.82\u0026thinsp;\u0026plusmn;\u0026thinsp;16.60, FACT-ES subscale; 15.72\u0026thinsp;\u0026plusmn;\u0026thinsp;9.99) all got significantly worse by an average of 1\u0026ndash;3 points over time\u003c/span\u003e (\u003cem\u003eP\u0026rsquo;s\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01). Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of the unconditional LGCM\u0026rsquo;s examining the estimated change in each outcome of interest across the 3 years of follow up. Latent growth parameters estimated from each model showed that PA increased significantly (\u003cem\u003eSlope\u003c/em\u003e) by approximately 4.7 MET-hours per week each year while sitting time did not change significantly over time. General and BC specific QOL worsened by an estimated 1.3 and 1.9 points per year while endocrine symptoms increased by 1.5 points per year.\u003c/p\u003e\n\u003ch3\u003eTable 1. \u0026nbsp;Baseline Characteristics of the Breast Cancer Survivors of the WHIP Study by the Number of Valid Data Points Over 4 Years \u0026nbsp;\u003c/h3\u003e\u0026nbsp;\u003cp\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"510\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"30%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"38.23529411764706%\"\u003e\n \u003cp\u003eNumber of valid data points (max: 4 years) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.93103448275862%\"\u003e\n \u003cp\u003e1 \u0026ndash; 2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.7816091954023%\"\u003e\n \u003cp\u003e2 \u0026ndash; 3 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.28735632183908%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e554 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e273 (49.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e281 (50.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e58.93\u0026plusmn;10.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e58.96\u0026plusmn;12.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e58.90\u0026plusmn;9.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e.949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Black\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e157 (28.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e103 (37.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e54 (19.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;white\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e397 (71.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e170 (62.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e227 (80.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e.414\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Married/partnered\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e359 (64.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e172 (63.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e187 (66.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Others\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e194 (35.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e100 (36.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e94 (33.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Unknown/missing\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e\u003cstrong\u003e.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;High school or lower\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e76 (13.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e47 (17.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e29 (10.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;College or higher\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e472 (86.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e223 (82.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e249 (89.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Unknown/missing\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003eEmployment status \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e.532\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Yes (employed)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e310 (58.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e150 (57.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e160 (60.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;No (unemployed)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e217 (41.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e111 (42.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e106 (39.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Unknown/missing\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e27\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e\u003cem\u003e12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e15\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003eHousehold income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e\u003cstrong\u003e.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;$60k\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e162 (31.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e94 (36.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e68 (25.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;$60k thru \u0026lt;$100k\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e153 (29.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e63 (24.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e90 (33.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;$100k thru \u0026lt;$150k\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e104 (19.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e52 (20.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e52 (19.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026ge;$150k\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e103 (19.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e47 (18.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e56 (21.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Unknown/missing\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e32\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e\u003cem\u003e17\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e15\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003eTime since diagnosis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e.276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;1 year\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e266 (53.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e131 (56.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e135 (51.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026ge;1 year\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e230 (46.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e102 (43.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e128 (48.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Unknown/missing\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e58\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e\u003cem\u003e40\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e18\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003eStage at diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;I\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e292 (60.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e136 (57.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e156 (63.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;II\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e148 (30.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e73 (30.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e75 (30.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;III\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e41 (8.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e27 (11.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e14 (5.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Unknown/missing\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e73\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e\u003cem\u003e37\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e36\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003eHER2 status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e.276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Negative\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e242 (92.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e106 (89.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e136 (94.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Positive\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e21 (7.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e13 (10.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e8 (5.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Unknown/missing\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e291\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e\u003cem\u003e154\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e137\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" width=\"48.8235294117647%\"\u003e\n \u003cp\u003eTime since AET initiation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;6 months\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e234 (49.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e116 (50.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e118 (48.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026ge;6 months\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e241 (50.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e114 (49.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e127 (51.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Unknown/missing\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e79\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e\u003cem\u003e43\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e36\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e.600\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Yes\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e204 (39.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e99 (40.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e105 (38.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;No\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e314 (60.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e145 (59.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e169 (61.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Unknown/missing\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e36\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e\u003cem\u003e29\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003eRadiation treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e.392\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Yes\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e329 (67.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e152 (65.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e177 (68.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;No\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e161 (32.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e81 (34.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e80 (31.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Unknown/missing\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e64\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e\u003cem\u003e40\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e24\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003eSurgery type\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e.416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mastectomy\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e175 (44.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e85 (46.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e90 (42.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Lumpectomy\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e216 (55.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e96 (53.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e120 (57.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Unknown/missing\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e163\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e\u003cem\u003e92\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e71\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e.239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt;25 kg/m2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e174 (33.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e77 (29.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e97 (36.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;25 - \u0026lt;30 kg/m2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e133 (25.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e68 (26.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e65 (24.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026ge;30 kg/m2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e213 (40.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e112 (43.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e101 (38.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Unknown/missing\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e34\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.41176470588235%\"\u003e\n \u003cp\u003e\u003cem\u003e16\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.823529411764707%\"\u003e\n \u003cp\u003e\u003cem\u003e18\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: AET = adjuvant endocrine therapy; HER2 \u0026ndash; human epidermal growth factor receptor 2. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe values are presented as Mean \u0026plusmn; standard deviation for continuous variables and n (%) for categorical variables. The unknown/missing cases were not included in the percentage calculation. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e number of non-consecutive valid data points out of 4 years (baseline, year 1 thru 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e-values are estimated from an independent sample \u003cem\u003et\u003c/em\u003e-test for a continuous variable and \u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e test of independence for a categorical variable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003ethose who reported working full- and part-time were categorized as \u0026lsquo;employed\u0026rsquo;. Otherwise (e.g., full-time homemaker, retired, student) were categorized as \u0026lsquo;unemployed\u0026rsquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e women underwent both lumpectomy and mastectomy (n = 16) were categorized as the mastectomy group.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eModel-data fit indices\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eLatent growth factors\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnconditional Latent Growth Curve Modeling on Changes in Study Outcomes Over a 4 Year Period (n\u0026thinsp;=\u0026thinsp;554)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (\u003cem\u003edf\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept (SE)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlope (SE)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorrelation (SE)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal MET-hours/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.35 (5) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.90 (.66) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.66 (.46) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23 (.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSitting time (hours/day) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.92 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.83 (.15) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.19 (.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01 (.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFACT-ES (subscale only)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.89 (5) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.27 (.42) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45 (.17) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32 (.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFACT-General\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.20 (5) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.37 (.54) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.29 (.30) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16 (.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFACT-Breast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.99 (5) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.53 (.71) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.88 (.39) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26 (.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eNote: CFI\u0026thinsp;=\u0026thinsp;comparative fit index; ES\u0026thinsp;=\u0026thinsp;Endocrine symptoms; FACT-General\u0026thinsp;=\u0026thinsp;Functional Assessment of Cancer Therapy-General (Physical\u0026thinsp;+\u0026thinsp;Social/Family\u0026thinsp;+\u0026thinsp;Emotional\u0026thinsp;+\u0026thinsp;Functional); FACT-Breast\u0026thinsp;=\u0026thinsp;Functional Assessment of Cancer Therapy-Breast Cancer (FACT-G\u0026thinsp;+\u0026thinsp;Breast cancer subscale); MET\u0026thinsp;=\u0026thinsp;metabolic equivalent tasks; TLI\u0026thinsp;=\u0026thinsp;Tucker-Lewis index; RMSEA\u0026thinsp;=\u0026thinsp;root mean square error of approximation;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e the values are the estimated mean growth factors (standard error).\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003e the correlation between the intercept and slope.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003ec\u003c/sup\u003e the residual covariance between the year 1 and 3 was added in the model to improve the model-data-fit.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e**\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003ePhysical activity, quality of life and endocrine symptoms\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of the parallel LGCM examining the associations between PA, endocrine symptoms and QOL over time. The results showed there was no significant association between baseline PA and endocrine symptoms. A significant positive cross-sectional association was found between PA and both general (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.28; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002) and BC-specific (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.30; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001) QOL at baseline (\u003cem\u003eIntercept-to-Intercept correlations)\u003c/em\u003e. Prospective associations between baseline levels of PA and changes in endocrine symptoms and QOL (\u003cem\u003eIntercept\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-to-Slope\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e) were not statistically significant. The reverse was also true whereby baseline levels of endocrine symptoms and QOL were not significantly associated with a change in PA over time (\u003cem\u003eIntercept\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e- to-Slope\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e). Similarly, changes in PA were not significantly associated with changes in endocrine symptoms or QOL (\u003cem\u003eSlope\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e- to-Slope\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFACT-ES\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFACT-General\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFACT-Breast\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe Results of Parallel Process Latent Growth Model Between Total MET-hour/week and Endocrine Symptoms and QOL (FACT)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eb\u003c/em\u003e (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eb\u003c/em\u003e (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eb\u003c/em\u003e (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cem\u003eIntercept-to-intercept correlation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(a) I\u003csub\u003e1\u003c/sub\u003e \u0026harr; I\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02 (.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28 (.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30 (.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cem\u003eIntercept-to-slope coefficients\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(b) I\u003csub\u003e1\u003c/sub\u003e \u0026rarr; S\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30 (.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.13 (.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.16 (.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(c) I\u003csub\u003e2\u003c/sub\u003e \u0026rarr; S\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.08 (.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10 (.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07 (.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cem\u003eSlope-to-slope coefficient\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(d) S\u003csub\u003e1\u003c/sub\u003e \u0026rarr; S\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.56 (.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39 (.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46 (.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eModel-data fit indices\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (df)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e91.36 (32) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e91.16 (30) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e97.47 (30) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.916\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eI\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;intercept growth factor for total MET-hours/week; S\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;slope growth factor for total MET-hours/week; I\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;intercept growth factor for FACT-ES subscale scores; S\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;slope growth factor for FACT-ES subscale scores. CFI\u0026thinsp;=\u0026thinsp;comparative fit index; FACT-ES\u0026thinsp;=\u0026thinsp;Functional Assessment of Cancer Therapy\u0026ndash;Endocrine Symptoms (subscale only); TLI\u0026thinsp;=\u0026thinsp;Tucker-Lewis index; S slope of the latent growth factors; RMSEA\u0026thinsp;=\u0026thinsp;root mean square error of approximation; I\u0026thinsp;=\u0026thinsp;intercept of the latent growth factors;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e the residual variance of slope (S\u003csub\u003e2\u003c/sub\u003e) was fixed to zero due to the negative value.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003csup\u003e**\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003eSitting time, quality of life and endocrine symptoms\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results of the parallel LGCM examining the associations between sitting, endocrine symptoms and QOL over time. There was a significant association between baseline sitting and endocrine symptoms with more sitting time associated with worse endocrine symptoms (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.29; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002). A significant cross-sectional association was also found between sitting and general (\u003cem\u003eb\u003c/em\u003e=-0.49; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and BC-specific (\u003cem\u003eb\u003c/em\u003e=-0.50; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) QOL (\u003cem\u003eIntercept-to-Intercept correlations)\u003c/em\u003e showing that more sitting was associated with worse QOL. Prospective associations between baseline levels of sitting and changes in endocrine symptoms or QOL were not significant (\u003cem\u003eIntercept\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-to-Slope\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e). In contrast, the reverse (\u003cem\u003eIntercept\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-to-Slope\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e) was true, whereby baseline levels of endocrine symptoms and QOL were significantly associated with a change in sitting over time. Greater endocrine symptoms at baseline were associated with a slower rate of increase in sitting time (\u003cem\u003eb\u003c/em\u003e =-.05; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.004); yet, greater general (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.010) and BC-specific (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003) QOL at baseline were associated with greater rate of increases in sitting time. Increases in sitting time were significantly associated with lower rate of increase in endocrine symptoms (\u003cem\u003eb\u003c/em\u003e=-1.81; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.017, and while also being associated with a decline in general and BC-specific QOL (\u003cem\u003eSlope\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e- to-Slope\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e); those associations were not significant.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFACT-ES\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFACT-General\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFACT-Breast\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe Results of Parallel Process Latent Growth Model Between Sitting Time (minutes/day) and Endocrine Symptoms and QOL (FACT)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eb\u003c/em\u003e (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eb\u003c/em\u003e (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eb\u003c/em\u003e (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eIntercept-to-intercept correlation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(a) I\u003csub\u003e1\u003c/sub\u003e \u0026harr; I\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29 (.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.49 (.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.50 (.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eIntercept-to-slope coefficients\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(b) I\u003csub\u003e1\u003c/sub\u003e \u0026rarr; S\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.30 (.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59 (.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.39 (.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(c) I\u003csub\u003e2\u003c/sub\u003e \u0026rarr; S\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05 (.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04 (.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04 (.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eSlope-to-slope coefficient\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(d) S\u003csub\u003e1\u003c/sub\u003e \u0026rarr; S\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.81 (0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.02 (1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.67 (2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eModel-data fit indices\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (df)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e74.07 (33) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e54.03 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e68.16 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eI\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;intercept growth factor for sitting time (minutes/day); S\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;slope growth factor for sitting time (minutes/day); I\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;intercept growth factor for FACT-ES subscale scores; S\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;slope growth factor for FACT-ES subscale scores. The schematic diagram of the parallel process model is depicted in Fig.\u0026nbsp;1. CFI\u0026thinsp;=\u0026thinsp;comparative fit index; FACT-ES\u0026thinsp;=\u0026thinsp;Functional Assessment of Cancer Therapy\u0026ndash;Endocrine Symptoms (subscale only); TLI\u0026thinsp;=\u0026thinsp;Tucker-Lewis index; S slope of the latent growth factors; RMSEA\u0026thinsp;=\u0026thinsp;root mean square error of approximation; I\u0026thinsp;=\u0026thinsp;intercept of the latent growth factors; IPAQ\u0026thinsp;=\u0026thinsp;International Physical Activity Questionnaire.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e the residual covariance between the year 1 and 3 was added in the model to improve the model-data-fit.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003e the residual variances of slope (S\u003csub\u003e1\u003c/sub\u003e and S\u003csub\u003e2\u003c/sub\u003e) were fixed to zero due to the negative values.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003e**\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this secondary analysis of the WHIP Study examining cross-sectional and longitudinal associations among PA, daily sitting time and QOL, we found significant associations between activity levels (and sitting time) with endocrine symptoms and general and BC specific QOL. Self-reported PA increased each year, while sitting time stayed consistent. Endocrine symptoms significantly increased (worsened) while general and BC specific health-related QOL declined slightly over the same period. Importantly, higher levels of baseline PA or change in PA were not associated with changes in endocrine symptoms or health-related QOL over time. We did however find that worse endocrine symptoms were associated with a slower rate of increase in sitting time while having better QOL scores at baseline was associated with a significant increase in the rate of sitting. Overall, both PA and sitting appear to be important behaviors among BC survivors undergoing AET.\u003c/p\u003e\n\u003cp\u003eA number of previous studies examining the relationship between PA, endocrine symptoms and QOL among women with BC have predominantly examined arthralgias or musculoskeletal concerns that develop as a result of aromatase inhibitor (AI) therapy for HR\u0026thinsp;+\u0026thinsp;BC. A recent meta-analysis, focused specifically on musculoskeletal symptoms, examined 9 trials that included 743 participants who were randomized to exercise or usual care [28]. Overall, findings supported exercise as an effective approach for managing pain, stiffness and grip strength. However, another study did not find improvements in QOL domains such as fatigue, endocrine symptoms or total quality of life, despite significant improvements in PA behaviors as a result of intervention. Nyrop and colleagues encouraged women on AIs (n\u0026thinsp;=\u0026thinsp;20) to walk for at least 30 minutes per day for 5 days a week over a 6-week period, to address arthralgia [29]. While total walking time per week significantly increased over the study, decreases in joint pain, stiffness and fatigue were not significant suggesting the need for a larger sample. The duration of the intervention may be critical however with that study only being 12-weeks [30]. While not all symptoms that result from AET are musculoskeletal in nature, their presence is also linked to reductions in PA among women who take them [31]. Other factors linked to declines of PA following use of AIs included BMI. In the HOPE study [32], a 12-month clinical exercise trial among women with AI-induced arthralgia, 121 BC survivors with at least mild arthralgias were randomized to a supervised exercise or control group. At 12 months follow up, women who participated in aerobic and resistance training reported greater improvements in overall, BC-specific and endocrine symptom subscales compared to the control participants. Study authors concluded that given the frequency of side-effects from AIs and the risk for treatment non-adherence, that non-pharmacological approaches like exercise training could be valuable. Interestingly our results did not show an improvement in QOL (general or BC-specific) or endocrine symptoms to be associated with total PA, \u003cspan class=\"Underline\" name=\"Emphasis\" type=\"Underline\"\u003eperhaps due to the fact that the most common activity reported in the current analyses is walking.\u003c/span\u003e This is likely due to the non-specific nature of the IPAQ regarding resistance training, which was a primary component of the exercise intervention in the HOPE study. \u003cspan class=\"Underline\" name=\"Emphasis\" type=\"Underline\"\u003eIt is well established that resistance training is critical for the maintenance of physical functioning, including among older persons and those who experience chronic pain. Therefore, guidance to increase physical activity should specifically include the guideline-based recommendations of at least 2-days of resistance training per week in addition to 150 minutes of aerobic training. It should be noted that a barrier to recommending resistance training relates to access to facilities and supervision in the safe performance of certain exercises. However, the recent COVID-19 pandemic has increased the availability of remote and online programs at low cost and these may be valuable in this regard.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eA unique aspect and strength of this study is the examination of sitting time and its association with QOL and endocrine symptoms cross-sectionally and over time. The fact that more sitting time at baseline was associated with worse general, BC-specific, and endocrine symptoms at baseline but reduced endocrine symptoms over time was somewhat unexpected. Hartman and colleagues [33], examining both moderate to vigorous PA (MVPA) and sedentary time measured with accelerometry among 134 post-menopausal BC survivors, found that more time spent sitting in longer bouts was associated with worse physical QOL, especially in women who did little MVPA. While we did not specifically assess whether there was effect modification in our analyses, it may be that women who were doing more PA were also resting more outside of those bouts of activity. A separate study [34], conducted among 195 post-systemic therapy BC survivors (50% on hormonal therapy), found that more time spent in accelerometer-assessed sitting was associated with worse pain, fatigue and depression, especially among women with low levels of PA. Women with better QOL at baseline may have increased their sitting as they began to experience side-effects of treatment. \u003cspan class=\"Underline\" name=\"Emphasis\" type=\"Underline\"\u003eWomen who experience pain at either at rest or at onset of activity, are likely to attribute this discomfort to activity and therefore do less. Unfortunately, this only serves to further reduce their functional capacity increasing the likelihood that when they engage in activity, they will experience discomfort. Similarly, it is often counter-intuitive that women who are fatigued can benefit from activity. Most often patients who are fatigued believe they should do less and rest, which again leads to declines in function and worsening fatigue over time. The only way to combat this is to engage in activity to strengthen the musculoskeletal system. Indeed\u003c/span\u003e, our prior findings indicated that women who were the most adherent to AET had the highest levels of sitting time [35], perhaps suggesting that these women would experience side effects of AET early and these symptoms would then level off or increase more slowly over time. These findings highlight that both behaviors are independently important for the management of symptoms in BC survivors and they should be advised to both increase exercise and also reduce long periods of sitting time. Other strengths of our study are the inclusion of a racially diverse sample of HR\u0026thinsp;+\u0026thinsp;BC survivors.\u003c/p\u003e\n\u003cp\u003eThis study does have a few limitations. First, only self-reported measures were used for collecting PA and sitting data. While important for clarifying context, there are known issues with over-reporting of PA and under-reporting of sitting time in the IPAQ questionnaire, particularly for BC survivors [36\u0026ndash;38]. Furthermore, use of objective measures of PA would allow for the assessment of lower intensities of PA in the range of 1.5\u0026ndash;2.9 METs, capturing movement and daily activities of living which are lost in self-reported measures but still important for health among BC survivors [16]. Fortunately, because the primary outcome of interest in the WHIP parent study was adherence to AET and not specifically PA there may have been less inclination to overreport activity. Nevertheless, the use of accelerometers would have improved the accuracy of data capture in that regard. Second, there were a relatively large number of participants who were lost to follow up in this study. We did find that certain baseline characteristics (race, education and income) seemed to be correlated with missingness of study variables; however, in sensitivity analyses the primary findings were not altered after excluding those with two or more missing visits. Thus, we cannot fully rule out the possibility of survival bias where the missingness may be related to the symptoms or prognosis after treatment. \u003cspan class=\"Underline\" name=\"Emphasis\" type=\"Underline\"\u003eFurther, we only included the treatment type at the time of the baseline visit in these models and did not explore whether women changed the type of therapy used over time such as in a switch strategy.\u003c/span\u003e This implies that caution is needed, particularly when interpreting the results from an unconditional LGCM showing the shape of longitudinal changes in study variables over time. Lastly, the model-data fits of the LGCMs were below or marginally above the acceptable levels \u003cspan class=\"Underline\" name=\"Emphasis\" type=\"Underline\"\u003efor model fit parameters in structural equation modelling frameworks\u003c/span\u003e. The greater model-data fits are necessary to obtain the valid parameter estimates from the LGCM model, and thus future studies are warranted to test the proposed LGCM model in different settings.\u003c/p\u003e\n\u003cp\u003eIn summary, our findings from a large and diverse cohort of BC survivors undergoing AET, shows that both PA and sitting time were important with respect to managing symptoms and maintaining QOL. Clinician guidance towards the adoption of PA that includes both aerobic and resistance exercise and a reduction of sedentary behaviors should take into account the patient-level variations in symptoms such as fatigue or pain, that may make activity challenging. \u003cspan class=\"Underline\" name=\"Emphasis\" type=\"Underline\"\u003eRegular activity can also help with weight management efforts, which is important for a population of women who are at risk for weight gain and therefore further comorbid conditions. Given baseline levels of activity/sitting are more strongly associated with baseline symptoms and QOL, it is important that women are encouraged to adopt appropriate activity behaviors as early as possible, despite the challenges they may face as a result of treatment and associated side-effects.\u003c/span\u003e Future research is needed to more accurately delineate the optimal prescription of PA (e.g., type, dose, intensity, volume) coupled with management of prolonged periods of inactivity (sedentary behaviors), especially in consideration of the types of treatment a women may receive for HER\u0026thinsp;+\u0026thinsp;BC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eFunding:\u003c/h3\u003e\n\u003cp\u003eThis research was funded by the National Institutes of Health (R01CA154848) and the Bank of America (to Vanessa B. Sheppard). It was also supported in part by the National Cancer Institute (2T32CA093423 to Vanessa B. Sheppard), the National Institutes of Health/National Cancer Institute (Cancer Center Support Grant P30 CA016059), and the National Center for Advancing Translational Sciences (Translational Science Award UL1TR002649). Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the National Institutes of Health.\u003c/p\u003e\n\u003ch3\u003eCompeting Interests:\u003c/h3\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch3\u003eAuthor Contributions:\u003c/h3\u003e\n\u003cp\u003eAlexander Lucas: Conceptualization, methodology and writing\u0026ndash;original draft. Youngdeok Kim: Data curation, methodology, writing\u0026ndash;original draft, and writing\u0026ndash;review and editing. Autumn Lanoye: writing\u0026ndash;original draft and writing\u0026ndash;review and editing. Arnethea L. Sutton: Writing\u0026ndash;original draft and writing\u0026ndash;review and editing. Robert Lee Franco: Writing\u0026ndash;original draft and writing\u0026ndash;review and editing. Jessica G LaRose: Writing\u0026ndash;original draft and writing\u0026ndash;review and editing. Masey Ross: Supervision. Vanessa B. Sheppard: Funding acquisition, writing\u0026ndash; original draft, and writing\u0026ndash;review and editing.\u003c/p\u003e\n\u003ch3\u003eData availability:\u003c/h3\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel, R.L., et al. (2021) Cancer Statistics, 2021\u003cem\u003e.\u003c/em\u003e CA Cancer J Clin 71:7-33. https://doi.org/10.3322/caac.21654\u003c/li\u003e\n\u003cli\u003eEarly Breast Cancer Trialists\u0026apos; Collaborative, G. 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(2018) Biospecimen donation among black and white breast cancer survivors: opportunities to promote precision medicine\u003cem\u003e.\u003c/em\u003e J Cancer Surviv 12:74-81. https://doi.org/10.1007/s11764-017-0646-8\u003c/li\u003e\n\u003cli\u003eCraig, C.L., et al. (2003) International physical activity questionnaire: 12-country reliability and validity\u003cem\u003e.\u003c/em\u003e Med Sci Sports Exerc 35:1381-95. https://doi.org/10.1249/01.MSS.0000078924.61453.FB\u003c/li\u003e\n\u003cli\u003eLee, P.H., et al. (2011) Validity of the International Physical Activity Questionnaire Short Form (IPAQ-SF): a systematic review\u003cem\u003e.\u003c/em\u003e Int J Behav Nutr Phys Act 8:115. https://doi.org/10.1186/1479-5868-8-115\u003c/li\u003e\n\u003cli\u003eIPAQ Research Committee. \u003cem\u003eIPAQ scoring protocol\u003c/em\u003e. 2005 [cited 2021 November 17]; Available from: https://sites.google.com/site/theipaq/scoring-protocol.\u003c/li\u003e\n\u003cli\u003eEton, D.T., et al. (2004) A combination of distribution- and anchor-based approaches determined minimally important differences (MIDs) for four endpoints in a breast cancer scale\u003cem\u003e.\u003c/em\u003e J Clin Epidemiol 57:898-910. https://doi.org/10.1016/j.jclinepi.2004.01.012\u003c/li\u003e\n\u003cli\u003eBrowne, M.W. and R. Cudeck (1992) Alternative Ways of Assessing Model Fit\u003cem\u003e.\u003c/em\u003e Sociological Methods \u0026amp; Research 21:230-258. https://doi.org/10.1177/0049124192021002005\u003c/li\u003e\n\u003cli\u003eBentler, P.M. (1990) Comparative fit indexes in structural models\u003cem\u003e.\u003c/em\u003e Psychol Bull 107:238-46. https://doi.org/10.1037/0033-2909.107.2.238\u003c/li\u003e\n\u003cli\u003eKenny, D.A., B. Kaniskan, and D.B. McCoach (2014) The Performance of RMSEA in Models With Small Degrees of Freedom\u003cem\u003e.\u003c/em\u003e Sociological Methods \u0026amp; Research 44:486-507. https://doi.org/10.1177/0049124114543236\u003c/li\u003e\n\u003cli\u003eLu, G., J. Zheng, and L. Zhang (2020) The effect of exercise on aromatase inhibitor-induced musculoskeletal symptoms in breast cancer survivors :a systematic review and meta-analysis\u003cem\u003e.\u003c/em\u003e Support Care Cancer 28:1587-1596. https://doi.org/10.1007/s00520-019-05186-1\u003c/li\u003e\n\u003cli\u003eNyrop, K.A., et al. (2014) Feasibility and promise of a 6-week program to encourage physical activity and reduce joint symptoms among elderly breast cancer survivors on aromatase inhibitor therapy\u003cem\u003e.\u003c/em\u003e J Geriatr Oncol 5:148-55. https://doi.org/10.1016/j.jgo.2013.12.002\u003c/li\u003e\n\u003cli\u003eRogers, L.Q., et al. (2009) Physical activity and health outcomes three months after completing a physical activity behavior change intervention: persistent and delayed effects\u003cem\u003e.\u003c/em\u003e Cancer Epidemiol Biomarkers Prev 18:1410-8. https://doi.org/10.1158/1055-9965.EPI-08-1045\u003c/li\u003e\n\u003cli\u003eBrown, J.C., et al. (2014) Aromatase inhibitor associated musculoskeletal symptoms are associated with reduced physical activity among breast cancer survivors\u003cem\u003e.\u003c/em\u003e Breast J 20:22-8. https://doi.org/10.1111/tbj.12202\u003c/li\u003e\n\u003cli\u003eBaglia, M.L., et al. (2019) Endocrine-related quality of life in a randomized trial of exercise on aromatase inhibitor-induced arthralgias in breast cancer survivors\u003cem\u003e.\u003c/em\u003e Cancer 125:2262-2271. https://doi.org/10.1002/cncr.32051\u003c/li\u003e\n\u003cli\u003eHartman, S.J., et al. (2017) Objectively measured sedentary behavior and quality of life among survivors of early stage breast cancer\u003cem\u003e.\u003c/em\u003e Support Care Cancer 25:2495-2503. https://doi.org/10.1007/s00520-017-3657-0\u003c/li\u003e\n\u003cli\u003eTrinh, L., et al. (2015) Physical and psychological health among breast cancer survivors: interactions with sedentary behavior and physical activity\u003cem\u003e.\u003c/em\u003e Psycho-Oncology 24:1279-1285. https://doi.org/10.1002/pon.3872\u003c/li\u003e\n\u003cli\u003eSheppard, V.B., et al. (2021) Race and Patient-reported Symptoms in Adherence to Adjuvant Endocrine Therapy: A Report from the Women\u0026apos;s Hormonal Initiation and Persistence Study\u003cem\u003e.\u003c/em\u003e Cancer Epidemiol Biomarkers Prev. https://doi.org/10.1158/1055-9965.EPI-20-0604\u003c/li\u003e\n\u003cli\u003eDeliens, T., et al. (2021) Misreporting of Physical Activity and Sedentary Behavior in Parents-to-Be: A Validation Study across Sex\u003cem\u003e.\u003c/em\u003e Int J Environ Res Public Health 18. https://doi.org/10.3390/ijerph18094654\u003c/li\u003e\n\u003cli\u003eJohnson-Kozlow, M., et al. (2006) Comparative validation of the IPAQ and the 7-Day PAR among women diagnosed with breast cancer\u003cem\u003e.\u003c/em\u003e Int J Behav Nutr Phys Act 3:7. https://doi.org/10.1186/1479-5868-3-7\u003c/li\u003e\n\u003cli\u003eRuiz-Casado, A., et al. (2016) Validity of the Physical Activity Questionnaires IPAQ-SF and GPAQ for Cancer Survivors: Insights from a Spanish Cohort\u003cem\u003e.\u003c/em\u003e Int J Sports Med 37:979-985. https://doi.org/10.1055/s-0042-103967\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplemental Table 1","content":"\u003cp\u003eSupplemental Table 1 is not available with this version\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Breast Cancer, Survivorship, Physical Activity, Lifestyle, Symptoms ","lastPublishedDoi":"10.21203/rs.3.rs-1469636/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1469636/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003eWe aimed to determine whether physical activity (PA) may mitigate side effects of adjuvant endocrine therapy (AET), or whether prolonged sitting may exacerbate them. AET often causes debilitating endocrine symptoms that compromise quality of life (QOL) in women diagnosed with hormone receptor positive breast cancer (BC).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e We examined longitudinal patterns among PA and sitting with endocrine symptoms and QOL over 3 years in 554 female BC survivors undergoing AET using parallel process latent growth curve models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e At baseline, women were a mean age of 59 years, mostly white (72%), with overweight/obesity (67%), and approximately 50% being within 1 year of diagnosis. Unconditional models showed a significant increase in PA (P\u0026lt;.01) over time but no change in sitting. Endocrine symptoms, general and BC-specific QOL all got significantly worse over time (P\u0026lt;.01). Parallel process models showed no cross-sectional or longitudinal associations between PA and endocrine symptoms, but higher PA was associated with higher QOL (P=.01). Increases in PA were not associated with changes in QOL. Conversely, higher baseline sitting time was associated with worse endocrine symptoms, general and BC specific QOL (Ps\u0026lt;.01), yet worse baseline endocrine symptoms were associated with a slower rate of increase in sitting (P\u0026lt;.01). Better baseline QOL was also associated with an increase in sitting (Ps\u0026lt;.01) while an increase in sitting was associated with reductions in symptoms (P=.017). Model fit statistics (x2, CFI, TLI, SRMR) were acceptable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e Both PA and sitting are important for managing symptoms and maintaining QOL in BC survivors.\u003c/p\u003e","manuscriptTitle":"Longitudinal associations among physical activity and sitting with endocrine symptoms and quality of life in breast cancer survivors: A latent growth curve analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2022-11-02 14:32:14","doi":"10.21203/rs.3.rs-1469636/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2022-03-30 19:14:32","doi":"10.21203/rs.3.rs-1469636/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"40faa596-31c1-46a6-af67-c2ab8a7f9d3c","owner":[],"postedDate":"November 2nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-18T06:40:50+00:00","versionOfRecord":{"articleIdentity":"rs-1469636","link":"https://doi.org/10.1002/cam4.6581","journal":{"identity":"cancer-medicine","isVorOnly":true,"title":"Cancer Medicine"},"publishedOn":"2023-09-28 06:40:50","publishedOnDateReadable":"September 28th, 2023"},"versionCreatedAt":"2022-11-02 14:32:14","video":"","vorDoi":"10.1002/cam4.6581","vorDoiUrl":"https://doi.org/10.1002/cam4.6581","workflowStages":[]},"version":"v2","identity":"rs-1469636","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1469636","identity":"rs-1469636","version":["v2"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}