A Randomized Controlled Trial of a Physical Activity Intervention for Self-Management of Fatigue in Adolescents and Young Adults with Cancer

Abstract

Background:

Fatigue remains a persistent and troubling symptom for adolescents and young adults (AYAs) with cancer. Physical activity (PA) is recommended as a strategy for self-management of fatigue.

Objective:

The aim of the study was to examine a physical activity (PA) intervention to improve the self-management of fatigue in AYAs during chemotherapy.

Methods:

This randomized controlled trial enrolled AYAs (18–39 years) receiving chemotherapy. AYAs in the intervention group received a 12-week PA intervention integrated into 5 clinic visits that included education, PA tracking, and collaboration. PA was measured with an accelerometer, and participants completed measures of fatigue severity, self-efficacy for PA, and self-regulation at baseline and end of study.

Results:

44 AYAs completed the study. At baseline, AYAs averaged 4290 steps/day (SD=2423) and 14.4 (SD=20.6) minutes/day of moderate- to vigorous-intensity PA; their average PROMIS fatigue score was 55.0 (SD=9.2). At end of study, there were no significant differences between groups in fatigue, self-efficacy, self-regulation, or PA, but trends in the desired direction were observed for each of the variables in the intervention group.

Conclusion:

During chemotherapy, AYAs had variable levels of PA and engaged in mostly light-intensity physical activity. Their average fatigue level was slightly worse than a national comparison group.

Implications for practice:

Tailored interventions are needed to promote PA in AYAs as a self-management strategy for fatigue. Enhancing self-efficacy and self-regulation may be important approaches to promote PA.

Introduction

Over 70,000 adolescents and young adults (AYAs) between the ages of 15 and 39 years are diagnosed with cancer each year in the United States.¹ Although AYAs are a small fraction of the total number diagnosed with cancer, cancer is the leading cause of death from illness in this age group, and outcomes for AYAs with cancer have not improved at the same rate as outcomes for other age groups.^2,3 Historically poorer outcomes in AYAs may be associated with unique biological characteristics of some AYA cancers, AYAs’ low participation and access to clinical trials, a lower likelihood of having adequate health insurance, and the psychosocial aspects of being in a transitional developmental stage. Therefore, a special emphasis is needed to understand the treatment and supportive needs of this special group of patients to in order to eliminate this health disparity and improve their health-related outcomes.

Unlike older adults, AYAs are generally healthy when they are diagnosed with cancer. AYAs are less likely to have comorbid illnesses, such as heart and lung disease, diabetes, and arthritis, which may complicate treatment and the management of symptoms and side effects. Nevertheless, AYAs undergoing cancer treatment report multiple cancer-related symptoms, and they report unmet supportive care needs, such as loss of fertility, financial insecurity, and mental health problems.⁴ AYAs undergoing chemotherapy report an average of 8 symptoms, with nausea, fatigue, drowsiness, and appetite loss as the most frequently reported.⁵

Despite decades of research, fatigue remains a prevalent, severe, and impactful symptom experienced by AYAs with cancer,⁶ and for some AYA cancer survivors, fatigue can persist long after the end of treatment.⁷ Fatigue is implicated as contributing to the poor physical and emotional health-related outcomes for AYAs with cancer.⁶ Physical activity (PA) is recommended as an evidence-based intervention to manage cancer-related fatigue during and after treatment, although most of this evidence comes from studies with adults who have the most common cancers, such as breast and prostate cancer.^8,9 Although the optimal type, intensity, and duration of PA have not been identified and recommendations for exercise need to be individualized for each patient, the overall benefits of PA relate to improved physiologic functions, reduced symptoms, improved psychosocial well-being, and even survival.⁹ Few studies have evaluated interventions to promote PA in AYAs as ways to manage fatigue, especially during the treatment period.^10,11 In addition, AYAs acknowledge unmet needs related to the management of fatigue and concerns about how and when to return to exercise post-treatment.¹²

AYAs should be encouraged and supported by their health providers to engage in PA as a self-management strategy to relieve fatigue and to reinforce healthy lifestyle behaviors that should be maintained during and after cancer treatment. Self-management is the dynamic process by which individuals integrate strategies to manage their disease and associated symptoms.¹³ AYAs with cancer need to learn multiple self-management strategies to cope with the challenges related to the disease, its treatment, and the effects on physical and psychosocial well-being. This study is guided by the Individual and Family Self-Management Theory (IFSMT), which outlines context and process factors that impact the self-management process to improve outcomes.¹⁴ According to the IFSMT, interventions to improve knowledge and beliefs about PA and self-efficacy and self-regulation related to PA are strategies that may improve PA and fatigue outcomes in AYAs (Figure 1).

Figure 1.

The Individual and Family Self-Management Theory Applied to the Physical Activity (PA) Intervention for AYAs with Cancer

The purpose of this research is to examine the preliminary efficacy of a PA intervention to improve the self-management of fatigue in AYAs receiving chemotherapy. The aim of the study was to determine the impact of the PA intervention on the self-management process variables of self-efficacy and self-regulation and outcomes related to PA and fatigue severity.

Methods

Design

This pilot study used a randomized controlled trial design to examine a PA intervention in AYAs who are receiving chemotherapy. The study was designed to measure key contextual and process variables related to self-management outlined in the IFSMT as well as outcomes related to PA and fatigue. The PA intervention was conceptualized to specifically target the process variables of knowledge and beliefs, self-efficacy, and self-regulation related to PA in AYAs with cancer. The intervention was intended to be integrated into regular clinical visits over approximately 12 weeks and to focus on maximizing home-based physical activity during everyday life.

Sample

Eligible participants were 18 to 39 years old and receiving chemotherapy for a primary diagnosis of any type of cancer. Inclusion criteria were: 1) within the first 2 months of a chemotherapy regimen expected to last at least another 3 months; 2) ambulatory without assistance; 3) had written approval from their physician to participate; 5) had the ability to understand English; and 6) had access to a computer or Smartphone with Internet access. Chemotherapy regimens could be of any intensity; however, because the focus of the intervention was on home-based physical activity, eligible patients were AYAs who were receiving chemotherapy expected to be administered on an out-patient basis or during a short hospital stay.

Setting

The study was conducted at two adjacent academic health systems, one adult and one pediatric, located in a large city in the Midwestern United States.

Ethical considerations

The study was approved by the Institutional Review Boards of both hospitals and the researchers’ university setting.

Study measures

Physical activity (PA) was measured using an accelerometer (ActiGraph wGT3X-BT). Each participant was given instructions to wear the accelerometer around the waist continuously for 24 hours/day for 7 days, in accordance with manufacturer instructions. If participants removed the device for more than a few minutes (e.g. due to discomfort or water activities), they documented actual wear times using a log.

Self-efficacy for physical activity was measured using the Physical Activity Assessment Inventory (PAAI) developed by Haase and Northam.¹⁵ The 13-item scale was developed and tested with women with breast cancer to measure confidence to be physically active under various conditions and demonstrated initial validity and reliability. Responses are measured on a 100-point scale, ranging from 0 (“cannot do at all”) to 100 (“certain can do”), and an average score is calculated. A higher score indicates greater self-efficacy. Reliability for the PAAI using Cronbach alpha in this study was .92.

Self-regulation skills were measured using the Self-Regulation Index for Physical Activity (SRI). The SRI for Physical Activity is a 9-item scale based on an original scale developed and revised by Fleury and colleagues to measure an individual’s level of self-regulation.^16,17 Self-regulation refers to the individual’s ability to process information, monitor performance, and take action in relation to their goals, and the concept is thought to be an important motivator for health behaviors.¹⁸ The SRI uses a 6-point Likert-type scale ranging from 1 (strongly disagree) to 6 (strongly agree), and an average score is calculated, with higher scores indicating a higher level of self-regulation. The scale was developed and tested mainly with patients in cardiac rehabilitation settings with evidence of adequate reliability and validity. Reliability for the ISR using Cronbach alpha in this study was .92.

Fatigue severity was measured using the PROMIS-Fatigue 8a Short Form, an 8-item scale developed and available through the Patient Reported Outcomes Measurement Information System (PROMIS).¹⁹ Participants self-report their fatigue over the past 7 days using a 5-point Likert scale (never to always). Raw scores are converted to standardized T-scores, with higher scores indicating greater fatigue severity. A T-score of 50 represents the average score calibrated with a national sample of the general population.

Demographic characteristics were self-reported by participants using an investigator-developed form.

Procedures

Following the informed consent process, participants completed baseline measures on a study iPad (Table 1). Each participant was given an accelerometer to wear beginning that evening for 7 days, which corresponded to the 7-day period they were at home following receipt of chemotherapy. They were asked to return the accelerometer to the lab using a prepaid envelope. Following baseline data collection, participants were randomized to either the intervention group (IG) or the attention control group (ACG) using a previously created computer-generated randomization list.

Table 1.

Measures for Major Outcome Variables

IFSMT factor	Variable	Measure	Baseline	Post
Process	Self-efficacy for physical activity	Physical Activity Assessment Inventory (Haas & Northam, 2010)	X	X
Process	Self-regulation for physical activity	Index for Self-Regulation (Fleury et al., 1998, 2001)	X	X
Proximal outcome	Physical Activity	Accelerometer (ActiGraph wGT3X-BT)	X	X
Proximal outcome	Fatigue severity	PROMIS Fatigue 8a SF	X	X

Intervention group (IG).

Participants in the intervention group were seen by a study facilitator at their next regularly scheduled clinic visit to begin the intervention. The intervention consisted of 3 components: 1) education about the benefits of PA to prevent and reduce fatigue using published professional materials, including the Oncology Nursing Society General Exercise Brochure²⁰ and the National Comprehensive Cancer Network (NCCN) Guidelines for Exercise;²¹ 2) a PA tracker (Fitbit Flex®)²² to set walking goals, measure steps/day, and provide feedback on walking goals; and 3) coaching and support to identify barriers and facilitators to PA and strategies to overcome barriers to reach walking goals. The study facilitator reviewed use of the Fitbit Flex® with participants, including how to set up the app on their Smartphone. The study facilitator used baseline accelerometer data to guide the participant to set a goal for steps/day and discussed their current level of PA, barriers to PA, and strategies to maintain or increase PA.

The study facilitator met with each participant in person approximately every 3–4 weeks during the next 4 regularly scheduled clinic visits. At each visit, the facilitator reviewed the Fitbit Flex® data with the participant and discussed whether the steps/day goal had been met, barriers to PA, and self-management strategies to overcome these barriers. If the participant met the goal of steps/day for the majority of days for that period, the facilitator and participant collaborated to set an increased goal (e.g. 10% increase) in steps/day and encouraged continued PA strategies. If goals were not met, the facilitator and participant discussed barriers to PA, other strategies to try to overcome barriers to PA, and whether to adjust the steps/day goal.

Attention Control group (ACG).

Participants who were assigned to the comparison group were also seen by a study facilitator at their next regularly scheduled clinic visit and received the Oncology Nursing Society General Exercise Brochure.²⁰ The study facilitator met with participants in person at their next 4 regularly scheduled visits at approximately 3–4 week intervals and asked them to describe their PA levels. This group did not receive any coaching related to PA.

Outcomes were assessed at the end of the last study visit. Participants completed post-study measures and wore the accelerometer using the protocol described above.

Data management and analysis

Data collection instruments for the study were built using Research Electronic Data Capture (REDCap), a secure web application for building and managing online surveys and databases.²³ Data were collected online and stored using REDCap and were later downloaded into SPSS software version 26.0 (IBM, Armonk, NY) for analysis.

Accelerometer (Actigraph wGT3X-BT) data were collected at 100 Hz and analyzed using the ActiLife software (v13.3) in 60 second epochs (Actigraph, Pensacola, FL). Freedson adult cutpoints (counts per minute [cpm]) were applied to assess time spent in sedentary (0–99 cpm), light (100–1951 cpm), and moderate-to-vigorous intensity PA (> 1952 cpm).^24,25 Activity monitor wear time was determined by a combination of the Choi algorithm and the wear log.^26,27 Participants were only included in the analysis if they had at least four valid days of accelerometer wear time, defined as providing 10 hours of wear time data per day.²⁸

Descriptive statistics were used to summarize the variables. Baseline characteristics of the participants were compared using t-test and chi-square test. Differences within and between groups in baseline and post-study measures of self-efficacy, self-regulation, fatigue severity, and PA variables were compared using t-tests. Differences between groups over time were analyzed using a general linear model.

Results

Enrollment

The study was conducted between February 2017 and June 2019. Out of 67 AYAs who met eligibility and were invited to participate in the study, 47 enrolled and entered the study (70% enrollment rate), with 25 randomized to the IG and 22 to the ACG. The most common reason for declining enrollment was lack of interest in the study. Three participants in the ACG withdrew after completing the baseline measurements (6% attrition).

Missing data and intervention fidelity

Baseline accelerometer data were missing for 2 participants in the IG and 6 in the ACG, and post-study accelerometer data were missing for 7 participants in the IG and 7 in the ACG. Reasons for missing accelerometer data included not using the device, not returning the device, insufficient wear time, and device malfunction. There were 0.35% missing data in the survey responses. The study flow is presented in Figure 2.

Figure 2.

Consort diagram

The IG participants completed 96% of the study visits, and the ACG completed 92% of the study visits. The most common reason for missed visits was changes in the chemotherapy administration schedule. Of the 25 participants in the IG, 21 (84%) used the Fitbit Flex® consistently as a PA tracking device so that the intervention could be fully delivered as intended (74/96 study visits, or 74%). Reasons for not using the Fitbit Flex® offered by participants were trouble syncing the device to their Smartphone to access data, forgetting to wear the device after taking it off for charging, and choosing not to wear it due to activities, travel, lack of interest, or not feeling well. For those participants who did not wear the device, the facilitator still delivered the other components of the intervention during the study visit; namely, this included reinforcing the benefits of PA, discussing barriers to PA, and identifying strategies to overcome barriers to PA.

Sample characteristics

The characteristics of 47 AYAs who enrolled in the study are summarized in Table 2. Most of the sample were female (79%) and Caucasian (81%), and their mean age was 32.3 years (SD = 5.2). Of the total participants, 38% had breast cancer. There were no differences between groups except there were more married participants in the IG. Because of the small numbers, group comparisons by diagnosis were not possible.

Table 2.

Sample Characteristics (n = 47)

Variable	Attention Control Group		Intervention Group		P
	n = 22	%	n = 25	%
Age (years)					.60
Mean	32.77		31.96
SD	5.79		4.71
Range	20–39		21–39
Gender					.82
Male	5	23	5	20
Female	17	77	20	80
Race					.39
White	19	86	19	76
Black	3	14	4	16
Asian	0	0	2	8
Ethnicity					.28
Non-Hispanic	21	95	25	100
Hispanic	1	5	0	0
Marital Status					.002
Single	14	64	5	20
Married/ partnered	8	36	20	80
Children					.51
Yes	12	55	16	64
No	10	45	8	32
Diagnosis
Breast Cancer	7	32	11	44
ALL	4	18	1	4
Hodgkin Lymphoma	1	5	5	20
Non-Hodgkin Lymphoma	3	14	1	4
AML	1	5	0	0
Other	3	14	4	16

Abbreviations: ALL: Acute lymphoblastic leukemia; AML: Acute myelocytic leukemia SD: Standard deviation

Fatigue and Physical Activity Outcomes

AYAs reported fatigue severity that was about average when compared to the general population, with mean group scores on the PROMIS Fatigue SF ranging from 54.9 −55.1 at baseline. At baseline, the AYAs engaged in averages of approximately 216 minutes per day in light physical activity, 16 minutes per day in moderate physical activity, and no vigorous physical activity. Their average scores for self-efficacy for physical activity, as measured by the PAAI, were 62.4–62.7 at baseline, which indicates they were at least moderately confident they could perform usual physical activity under competing or challenging situations. Finally, their average ISR score at baseline was 4.3 out of 6, indicating they ‘somewhat agree’ they possess self-regulation skills and abilities. Table 3 presents the differences in PA and fatigue outcomes within and between study groups from baseline to the end-of-study. Within each group, no significant changes were measured in the amount of PA (light, moderate, vigorous, total, steps/day). Within the ACG, there was a significant decrease in self-efficacy (as measured by the PAAI). Overall, there were no significant differences between the groups on any of the variables.

Table 3.

Comparing Differences Within and Between Groups in Physical Activity, Self-efficacy, Self-regulation, and Fatigue Scores

Variable	Attention Control Group Mean (SD)	P within attention control group	Intervention Group Mean (SD)	P within intervention group	P between groups
n	15		16
Light physical activity (min/day)					.75
Baseline	215.9 (54.7)		216.1 (67.2)
End of study	205.7 (81.2)		214.0 (85.7)
Mean difference (SD)	−10.2 (60.6)	.53	−2.1 (79.6)	.92
n	15		16
Moderate physical activity (min/day)					.43
Baseline	16.3 (20.4)		15.6 (22.8)
End of study	14.4 (17.0)		17.5 (24.4)
Mean difference (SD)	−1.9 (17.4)	.69	1.9 (6.5)	.27
n	15		16
Vigorous physical activity (min/day)					.28
Baseline	0.45 (1.7)		0 (0)
End of study	0.67 (2.1)		0.55 (1.1)
Mean difference (SD)	0.22 (0.5)	.12	0.55 (1.1)	.06
n	15		16
Total physical activity (min/day)					.64
Baseline	232.8 (59.6)		231.7 (76.8)
End of study	220.8 (93.1)		232.1 (108.1)
Mean difference (SD)	−12.0 (62.2)	.47	0.4 (83.1)	.98
n	15		16
Steps/day					.17
Baseline	4370(2487)		4263 (2847)
End of study	3991(2428)		4720 (3772)
Mean difference (SD)	−480 (1902)	.35	457 (1785)	.32
n	18		25
Physical Activity Appraisal Inventory (PAAI)					.22
Baseline	62.4 (21.1)		62.7 (23.4)
End of study	56.2 (24.4)		62.6 (22.9)
Mean difference (SD)	−6.2 (10.3)	.02*	−0.1 (18.6)	.97
n	17		25
Index of Self-Regulation (ISR)					.40
Baseline	4.3 (0.9)		4.3 (1.1)
End of study	4.4 (1.1)		4.7 (0.8)
Mean difference (SD)	1.1 (1.4)	.75	0.4 (1.2)	.06
n	18		25
PROMIS Fatigue SF
Baseline	55.1 (7.7)		54.9 (10.4)
End of study	57.4 (8.9)		53.6 (7.2)		.20
Mean difference (SD)	2.3 (10.3)	.36	−1.3 (7.5)	.41

Abbreviations: SD, standard deviation

^*p < .05

A general linear model was also used to examine the group effect on the changes in self-efficacy, self-regulation, fatigue severity, and total PA from baseline to post-study. These results are illustrated in Figure 3. While none of the group by time interactions were statistically significant, trends were in the desired direction for the IG. Self-efficacy (PAAI scores) decreased in the ACG group but remained the same in the IG (F = 1.567, p = 0.218). Self-regulation (ISR scores) increased in both groups (F = 0.733, p = 0.397). The total PA (min/day) decreased in ACG but increased in the IG (F = 0.223, p = 0.641). Finally, the PROMIS Fatigue score increased in ACG while decreased in the IG (F = 1.682, p = 0.202).

Figure 3.

Differences in self-regulation, self-efficacy, total physical activity, and fatigue

Discussion

This study examined the effect of an intervention to promote physical activity in AYAs receiving chemotherapy as a self-management strategy for cancer-related fatigue. Fatigue remains one of the most persistent symptoms self-reported by AYAs with cancer, and while PA is specifically recommended to alleviate fatigue, PA has a number of other benefits for cancer survivors, including improved cardiorespiratory fitness, strength, health-related quality of life, and disease-free survival for some cohorts.²⁹ Physical activity is one component of a healthy lifestyle, which, along with counseling about nutrition, smoking, and alcohol use, is an important aspect of care for all those with cancer. Unfortunately, there is limited evidence of PA interventions that have been developed and examined for AYAs as ways to manage cancer-related fatigue, especially during treatment.

The high enrollment rate (70%) shows that AYAs are interested in PA and willing to participate in a study related to PA and fatigue, even as they were at the beginning of chemotherapy for a new diagnosis of cancer. AYAs with cancer continue to report they receive no or only brief information about PA from health professionals, and they desire age-appropriate information about healthy lifestyle topics, including as strategies to reduce their future cancer risk and to improve their well-being.³⁰ At the same time, while the attrition rate was low (3%) and self-report data was nearly complete, the high amount of missing PA data from the accelerometers points to problems with participant adherence and possible burden related to wearing and returning the devices, especially during treatment. Although objective measurement of PA is recommended, most other studies with AYAs have used only self-report instruments.²⁹ Further study is needed to determine the best devices and strategies for measurement of PA in this population.

The intent of this unsupervised PA intervention was to integrate a low-intensity, low-burden, and low-cost self-management intervention into the clinical care of AYAs. The high completion of study visits showed that an approach to integrate PA coaching into clinical visits is feasible. The intervention encouraged any type of PA - mainly being “up and about” and avoiding inactivity as much as possible. This approach may result in better integration of PA into the lifestyles of AYAs who are in active treatment, who may have days when they are highly symptomatic, and who may be unwilling or unable to attend additional supervised sessions or travel to alternate locations.^31,32 Many AYAs with cancer describe lifestyles that include work, school, and parenting activities, and they share the challenges of maintaining usual activities during treatment. However, this intervention may not have provided enough support or intensity to overcome barriers to PA during treatment. This sample of AYAs in treatment included participants with active disease and treatment complications, such as infections that necessitated unplanned hospitalizations, and some who had disease progression; therefore, increased PA may not have been a realistic goal for some participants. Finally, not every participant in the study used the Fitbit Flex® to track their PA activity; therefore, an intervention that includes PA tracking with a wearable device may not be a fit for every AYA.³³

While the intervention did not significantly affect fatigue, self-management variables, or PA study outcomes in this sample, encouraging trends in the desired direction were observed for each of these variables. Of note, a significant decrease in the self-management process variable of self-efficacy was noted in the ACG but remained stable in the IG over the weeks of the study. Self-efficacy has been shown to be a critical predictor of self-management behaviors. In one study of adults receiving chemotherapy, for example, self-efficacy was found to mediate fatigue severity, which predicted functional outcomes.³⁴ In addition, an increase in self-regulation for PA approached significance in the IG, while it remained stable in the comparison group. Future interventions for AYAs that enhance self-efficacy and self-regulation for PA should be examined as strategies to promote PA as a self-management behavior, which may alleviate troubling symptoms, such as fatigue, and promote development of healthier lifestyle habits.

The AYAs in this sample had a wide range of daily PA, averaging over 3.5 hours of light PA, 15 minutes of moderate PA per day, and 4300 steps/day, as measured by accelerometry. While there are limited studies with AYAs using objective PA measurement for comparison, these counts are lower than counts previously published for AYAs in active treatment²⁹ and lower than other samples of adults receiving chemotherapy.³⁵ In addition, these counts are also much lower than the PA reported in a healthy cohort of young adults, where males (age 20–29 years) averaged 37.9 minutes of moderate activity per day and females averaged 19.9 minutes of moderate activity.²⁸ The lower levels of PA in this study may be partly explained by the timing of accelerometry, which occurred during the week after chemotherapy administration for these AYAs when symptom severity and periods of inactivity may be higher. The study does provide evidence, however, that many AYAs have low levels of physical activity as they begin chemotherapy, and that without any intervention or encouragement, AYAs will likely become even less active and more sedentary during the months of treatment. Providers need to intentionally and regularly ask patients about their physical activity, provide encouragement and advice about staying as active as possible, and reinforce the value of physical activity, not just to manage cancer-related fatigue but to fully experience other short- and long-term health benefits.

Limitations

Limitations of the study included the small sample size and missing PA data, and the majority of the participants were white women with breast cancer. The study enrolled participants regardless of cancer diagnosis, prior PA level, or baseline fatigue severity. In addition, the self-report questionnaires to measure self-efficacy and self-regulation had limited use in studies with the AYA oncology population. Nevertheless, the study still provides valuable evidence about objectively measured PA levels in AYAs and the feasibility of conducting this research during their treatment period.

Conclusion

This study confirms that AYAs with cancer are interested and willing to participate in research related to PA and fatigue during their treatment period, and preliminary data show that interventions to encourage PA are needed in this population. While some AYAs in this study maintained high levels of PA during treatment, the problem of inactivity was evident for many. Self-reported fatigue levels were also variable in these AYAs, and the cycle of fatigue leading to decreased PA which contributes to increased fatigue severity needs close examination. Future studies need to involve AYAs in planning and conducting PA trials to minimize participant burden and maximize adherence to study procedures and intervention fidelity.³⁶ Researchers need to develop and test developmentally-appropriate interventions that are tailored to AYAs’ lifestyles - for example, considering the involvement of spouses and children and utilizing acceptable wearable devices and mHealth tools. Staying physically active can benefit AYAs with cancer by alleviating treatment-related symptoms, such as fatigue, promoting quality of life during and after treatment, and reducing the risk of long-term comorbidities.