Introduction
The time spent seeking cancer care poses a substantial burden on patients’ quality of life, especially for those with incurable malignancies [ 1 - 3 ]. Compared with the general population, oncology patients typically utilize the healthcare more frequently. For example, individuals in the USA visit their primary care physician on average three times a year according to the Centers for Disease Control and Prevention (CDC) data, whereas some cancer patients receive weekly chemotherapy infusions with laboratory tests. Time toxicity describes the cumulative burden of time patients endure while pursuing medical care, including travel time, obtaining diagnostic scans and biopsies, office visits, time spent receiving infusion or radiation, hospitalizations, laboratory visits, follow-up appointments, and side effect management [ 3 , 4 ]. Time toxicity exists for all aspects of healthcare, and was previously described as time cost, lost day, healthcare contact day, and opportunity cost [ 5 - 8 ]. Time toxicity can significantly affect patients’ lives beyond the clinical impact of cancer itself. Frequent appointments, long travel times, and extended hours spent in waiting rooms or undergoing tests often disrupt daily routines and reduce the amount of time patients can spend at home or engaging in meaningful activities. This loss of autonomy over one’s time can be emotionally distressing, particularly for patients with a limited life expectancy who may value quality time with loved ones over marginal clinical gains. Despite the tremendous progress made in cancer treatment, most of the guideline-recommended palliative treatment options convey short survival benefits of approximately 3 - 6 months when compared with supportive care [ 9 ]. While medication toxicities are meticulously reviewed with patients, the cumulative impact of time is rarely included in the discussion, partly due to the lack of standardized measurement of time toxicity, and the lack of data from clinical trials. Thus, there is a compelling need for the quantification of time toxicity to guide clinical practice and patient preference.
Prior studies have confirmed that individuals with cancer face a large burden of time dealing with the health care system. A Mexican study demonstrated that palliative systemic therapy consumed on average 1 in 5 days in patients older than 65 years with metastatic cancer during the first 6 months of treatment, with higher toxicity in the cohorts receiving radiotherapy or chemotherapy [ 1 ]. A Canadian study of 5,785 patients with stage IV non-small cell lung cancer estimated that 1 in 3 days were spent interacting with the health care system outside of home, with fewer days consumed when receiving targeted therapy [ 2 ]. Similar patterns were also found in advanced gastrointestinal cancer patients, with patients spending approximately 30% of their days seeking treatment [ 8 , 10 ]. A distinctive U-shaped trajectory was observed by both Gupta and Johnson with a greater time toxicity following the initial diagnosis of metastatic cancer, and at the end of life [ 2 , 10 ], which reflects the staging workup, multidisciplinary consultations in the beginning, and the demanding and increasing difficulty of symptom management towards the end of treatment. A matched, retrospective cohort study in Canada revealed that the degree of time toxicity is similar between more recent time and historic eras [ 11 ].
In this study, we summarized the methods and findings of the time toxicity studies in the oncology field ( Table 1 [ 1 , 2 , 7 , 10 - 18 ]). We reported the results of a retrospective study conducted in our general oncology clinic using a modified concept of treatment-related time toxicity (TrTT) to quantify the burden of time among individuals with advanced-stage cancers. We explored patterns between different cancers and treatment types, provided information to help guide clinicians in patient-centered discussions, and to ultimately decrease unnecessary disruption in patients’ lives.
Table 1
Summary of the Literature on Time Toxicity in Oncology
CCTG: Canadian Cancer Trials Group; NSCLC: non-small cell lung cancer; OS: overall survival.
| Author and reference | Year of publication | Study design | Country of study | Sample size | Study population | Time toxicity metric | Findings |
| Guy and Richardson [ 13 ] | 2012 | Payment claim data from the National Ambulatory Medical Care Survey | USA | 2,470 | Any cancer diagnosis | Minutes spent with oncologists | Mean of 23 min per oncology visit |
| Ray et al [ 14 ] | 2015 | Analysis using The American Time Use Survey data | USA | 3,787 | Patients with any conditions were included | Reported minutes spent seeking medical care per day | Mean of 123 min spent per medical visit |
| Rocque et al [ 15 ] | 2020 | A collection of direct observations, patient survey, and administrative claims | USA | 132 | Metastatic breast cancer | Minutes spent | Average 7-10% of the initial 3 months of treatment were spent on health care |
| Bange et al [ 12 ] | 2020 | Retrospective cohort study using medical records | USA | 362 | Metastatic pancreatic cancer | Days spent | 10% of survival days spent receiving palliative chemotherapy |
| Gupta et al [ 16 ] | 2023 | Secondary analysis of the CCTG CO.17 trial | Canada | 572 | Advanced colorectal cancer receiving cetuximab vs. supportive care | Home days as days spent at home | Despite a 6-week OS benefit from cetuximab, home days were equal between treatment and supportive care |
| Bateni et al [ 11 ] | 2023 | Retrospective, matched cohort study between two historical eras | Canada | 731 per cohort | Stage II-IV melanoma | In-person days with health care visits for the 1st year since treatment | Average 1 in 7 days for stage IV melanoma |
| Gupta et al [ 2 ] | 2024 | Retrospective cohort study | Canada | 5,785 | Stage IV NSCLC | Health care contact days | Median contact days were 36, median weekly percentage of 33.3% |
| Baltussen et al [ 1 ] | 2024 | Prospective study | Mexico | 158 | Metastatic cancer, ≥ 65 years old | Health care contact days within the first 6 months | Mean of 21% of days spent with healthcare contact |
| Gupta et al [ 17 ] | 2024 | Retrospective study using Medicare survey data | USA | 1,168 | Cancer survivors ≥ 65 years old | Health care contact days | Cancer survivors spent 1 month of the year receiving health care |
| Kagalwalla et al [ 7 ] | 2024 | Retrospective study using real-time location badge data | USA | 435 | Adult patients with any cancer diagnosis | Minutes spent for oncology-related ambulatory care | Median oncology clinic time: 119 min per visit |
| Banerjee et al [ 18 ] | 2024 | Single-center, cross-sectional survey | USA | 252 | Multiple myeloma after stem cell transplantation | TimeTox+, defined as ≥ 1 contact per week | 40% of all multiple myeloma patients were positive for time toxicity |
| Johnson et al [ 10 ] | 2024 | Retrospective cohort study using the VA Clinical Cancer Registry | USA | 468 | Stage IV gastrointestinal cancer | Contact day | 1 in 3 days, with a “U-shaped” pattern with an initial peak after diagnosis, and a rise near the end of life |
Materials and Methods
A total of 150 patients who met the inclusion criteria were selected from a single-center, retrospective cohort. Data were collected via an electronic health record review under our institutional ethics guidelines. No identifiable patient information was collected. No in-person interviews or assessments were conducted. This study was conducted at the Ascension Seton Infusion Center, an oncology clinic site affiliated with the Dell Seton Medical Center at the University of Texas at Austin, TX, USA (the safety net hospital for Travis County) between January 1, 2021 and January 1, 2024. This study was conducted in compliance with the ethical standards of our institutional ethical guidelines. Institutional Review Board approval was not required for this type of study.
We selected individuals who received palliative treatment at our center for metastatic, incurable solid malignancies. Staging was based on the current National Comprehensive Cancer Network (NCCN) guidelines. Anonymous data, including cancer types, sociodemographic information, Eastern Cooperative Oncology Group (ECOG) performance status (PS), and types of cancer-directed therapies, were collected from the electronic health records. In-person health care contact records were counted and analyzed by our authors, including clinic visits, treatment visits, inpatient admissions, emergency department visits, laboratory tests, and radiology appointments. Telehealth visits and noncancer-related health care encounters were not included in the data collection. We restricted the analysis to the first 12 weeks from the initiation of the first line palliative therapy to minimize the chance for changes in therapy. We selected this initial 12-week window because it generally represents the most intensive treatment phase, during which patients face the highest density of visits and decisions regarding ongoing care.
The primary outcome was TrTT, defined as the number of days with any cancer-related health care encounters during the initial 12 weeks of palliative treatment. Days spent in the healthcare system not related to active cancer treatment were not included in this analysis (such as routine primary care visits, subspecialty visits, vaccinations, dental appointments, and social work visits). When two encounters occurred on the same date, we counted them as 1 day. We excluded patients with cancer of unknown primary, patients with stage IV cancer with oligometastasis who received curative surgical resection or curative radiotherapy, and patients who died within the 12-week period. Mild, moderate, and severe time toxicity were defined as < 10 days/12 weeks, 10 - 30 days/12 weeks, and > 30 days/12 weeks, respectively.
We stratified the TrTT by the type of cancer, type of systemic therapy received, and the ECOG PS. We plotted the total number of days per 12 weeks from starting palliative treatment using medians and interquartile ranges (IQRs). P values were calculated via the Kruskal-Wallis test for comparisons across more than two groups (e.g., different treatment types or cancer types), followed by post hoc pairwise Mann-Whitney tests when significant. The Graphpad Prism software was used for statistical analysis and plot generation.
Results
The median age of the 150 patients included in this study was 61 years (ranging from 23 to 86). Of the patients, 9.3% were older than 75 years, and 52.7% were biological male. Hispanic patients comprised 53% of the total population. The majority of patients had an ECOG score of 0 - 2 (74.6%). The sociodemographic and clinical characteristics are summarized in Table 2 .
Table 2
Sociodemographic and Clinical Characteristics of the Patients
ECOG: Eastern Cooperative Oncology Group; IQR: interquartile range; PS: performance status.
| Variable | Number of patients, No. (%) | Median time toxicity (days/12 weeks), No. (IQR) |
| All patients | 150 | 13 (7 - 23) |
| Age (years) | ||
| 18 - 50 | 47 (31.3) | 18 (7 - 26.5) |
| 51 - 75 | 89 (59.3) | 11.5 (6 - 22) |
| > 75 | 14 (9.3) | 9 (7.3 - 14.5) |
| Sex | ||
| Male | 79 (52.7) | 15 (5 - 24) |
| Female | 71 (47.3) | 12 (7 - 21.8) |
| Primary cancer site | ||
| Breast | 20 (13.3) | 12 (8 - 21.8) |
| Thoracic | 35 (23.3) | 10 (6.3 - 12.5) |
| Gastrointestinal | 55 (36.7) | 23 (18 - 35) |
| Genitourinary | 24 (16) | 4.5 (2 - 10) |
| Others | 16 (10.6) | 7.5 (4 - 8.5) |
| Type of therapy | ||
| Cytotoxic chemotherapy | 39 (26) | 22 (15.5 - 33.5) |
| Immunotherapy | 24 (16) | 7 (4 - 10) |
| Hormone therapy | 20 (13.3) | 3 (2 - 7) |
| Targeted therapy | 32 (21.3) | 8 (3.5 - 10.5) |
| Combination therapy | 35 (23.3) | 22 (17 - 27) |
| ECOG PS | ||
| 0 - 2 | 112 (74.6) | 9.5 (4 - 18.5) |
| 3 - 4 | 38 (25.4) | 22 (15.3 - 38) |
The TrTT was highly variable among individuals. While scheduled office/treatment visits are fairly standardized and predictable, hospital visits varied greatly depending on the type of cancer, tumor burden, and ECOG PS. The median TrTT was 13 days/12 weeks (IQR 7 - 23). The majority of our patients experienced mild to moderate time toxicity (39% and 46%, respectively), with 14% experiencing severe toxicity, as defined by spending more than 1 out of 3 days pursuing treatment.
The type of treatment and cancer affects time toxicity. We found that immunotherapy was associated with significantly less TrTT (median = 7 days, IQR 4 - 10) compared with traditional chemotherapy (median = 22 days, P < 0.0001), while targeted therapy (median TrTT = 8, P < 0.0001) and hormone therapy (median = 3, P < 0.0001) also have lower TrTT than chemotherapy ( Fig. 1 ). Among different cancer types, gastrointestinal malignancies were associated with the highest time toxicity with a median TrTT of 23 days (IQR 18 - 35) when compared with breast, thoracic, or genitourinary cancers ( Fig. 1 , P < 0.05).
Most oncology clinical trials include only patients with ECOG PS scale ranging from 0 to 2, as defined as those who are capable of all self-care and ambulatory more than 50% of waking hours. In our study, 25.4% of patients had ECOG PS from 3 to 4 (unable to perform full selfcare). These patients experienced more than double the TrTT than those with ECOG PS of 0 - 2 (median TrTT 22 vs. 9.5, P < 0.0001, Table 2 ). The differences in time toxicity between different subgroups are visualized in Figure 1 using the box and whisker plot.
Discussion
In this article, we present a modified concept of the TrTT as burden of time related to active and palliative cancer treatment, excluding time consumption that is not associated with cancer care, such as healthcare visits for other subspecialties or other comorbidities, dental visit, and routine vaccination. The TrTT is a more accurate metric for quantifying the negative impact of time in the palliative treatment setting. In our regional oncology clinic, 14% of patients with incurable advanced stage solid malignancies experienced severe TrTT. Newer generations of cancer treatments (immunotherapy, targeted therapy, and hormonal therapy) are associated with significantly lower time toxicity than chemotherapy. Patients with poorer PS or with gastrointestinal malignancies face higher time toxicity. Our findings are largely consistent with the published literature. For example, Baltussen et al demonstrated that older patients with metastatic cancer spend 1 in 5 days with healthcare contact during the first 6 months of treatment, with greater time toxicity associated with radiotherapy, chemotherapy, and high frailty [ 1 ]. The study design, patient population, and key findings from prior articles on time toxicity in oncology are summarized in Table 1 .
There is a noticeable and alarming trend of using progression-free survival (PFS) as the primary endpoint in modern oncological phase III trials [ 19 ]. The percentage of trials using PFS as the primary endpoint in oncology climbed exponentially from 0% to 42% (from 1995 to 2009) [ 20 ]. These parameters are defined as the time from intervention to an event, such as disease progression on scans or death. PFS does not always reflect what truly matters to patients living with advanced cancer. The value of therapy often lies not solely in prolonging life, but also in preserving quality, time, and independence, especially for individuals living with incurable diseases. Many US Food and Drug Administration (FDA)-approved oncology interventions are based on small PFS benefits without any significant improvement in overall survival [ 19 , 20 ]. The advantages of utilizing such approach are obvious: faster outcomes often lead to the acceleration of drug approvals. While PFS is an important factor to consider, many other components, including time toxicity, need to be considered to guide individualized decision making and provide meaningful clinical benefit to patients. Gupta et al proposed the “home day” metric as a simple way to measure meaningful treatment benefit, as identified as total survival days minus time toxicity [ 4 ]. Future clinical trials should consider including quality-adjusted survival benefits in their study design. The majority of patients declined treatments that offered only PFS benefits without overall survival gains when given hypothetical choices in the palliative oncology setting, especially when additional toxicity was expected [ 21 - 23 ]. These studies did not involve time toxicity discussion. One could extrapolate that the added time toxicity would make the proposed treatment choice even less attractive. In real-life scenarios, patient preferences are heavily influenced by physicians’ recommendations and their degrees of understanding of the realistic risk and benefit. Time toxicity should be included in these conversations. Time toxicity not only affects the patients, but also their caregivers and families. A total of 78% of patients in our clinic (calculated during a 1-week period) were accompanied by their caregivers. Patients with poorer PS often require external help for transportation and for keeping up with the complex oncology schedules.
We acknowledge that our findings should be interpreted in the context of several limitations. This study did not include patients undergoing clinical trials. Depending on the study design, clinical trials can generate significantly greater time toxicity due to more frequent visits, imaging scans, and longer commute to cancer centers. Our TrTT data are likely underestimated given that some but few of the hospital and emergency room visits were outside of our health system that were not available for collection. Notably, most of our patients did not have private insurance and utilized the county-funded medical access program; thus, a vast majority of these visits should be captured. Additionally, because we analyzed only the first 12 weeks of palliative therapy, our findings primarily capture the early, high-intensity phase of care. The time burden for patients receiving prolonged therapies such as maintenance immunotherapy or oral targeted agents may decrease over time as visit frequency becomes less intense, and future studies should investigate longitudinal changes in TrTT. Not all time toxicities are equally disruptive. A well-tolerated 6-h chemotherapy infusion session can be more pleasant than a 3-h emergency room visit for intractable vomiting. In an ideal world, we could assign severity to the time duration for a more accurate depiction of the time burden by patient-reported surveys. This retrospective data collection analysis was not designed to do so. Several studies were able to calculate the hours and minutes that patients spent per visit via objective measurements. Kagalwalla et al used real-time location system badge data to dissect clinic time into clinician, lab, infusion visits, and parking [ 7 ], which revealed that patients on average spent around 2 h per visit. While it is well recognized that patients with poor PS and those with gastrointestinal malignancies have higher symptom burden and health care utilization, our study quantifies this burden in a standardized manner, providing a metric that can be compared across populations and treatment modalities. We believe this quantitative approach may guide clinicians in balancing treatment intensity with quality-of-life considerations. Our sample size is modest but reflects a real-world safety-net population that is frequently underrepresented in clinical trials; these data may therefore be particularly relevant for practices caring for socioeconomically disadvantaged patients. Despite the inevitable limitations, we believe that our estimation provides valuable insights into the frequency of health care utilization.
Minimizing time toxicity requires changes in how oncology care is delivered. Small incremental improvements at each step of healthcare can lead to substantial gains in the quality of life for patients. When choosing between different types of therapies, time toxicity should be considered. Our study demonstrated that traditional chemotherapy posed significantly greater time toxicity when compared to immunotherapy, targeted therapy, or hormone therapy. This finding is consistent with our real-life experience, where chemotherapy tends to encompass more frequent visits, adverse effects, and hospitalizations. Prevention and management of aggressive adverse effects are crucial during cancer treatment. Patients with unaddressed side effects are more likely to seek urgent or emergent care, contributing further to unplanned time toxicity. To detect complications effectively, early symptom monitoring systems, such as through a patient-reported portal or regular nurse check-ins, are crucial and allow for outpatient rather than inpatient management. Technological advancements have transformed nearly every aspect of medicine. Telemedicine and home-based interventions reduce travel time and minimize disruption to a patient’s daily life. In our clinic, we implemented care-bundling to reduce time toxicity. In practice, this means scheduling pre-infusion laboratory tests on the morning of the infusion, followed immediately by the provider visit, and treatment in the same facility on the same day. This approach reduces the number of separate trips patients must make to the clinic and consolidates multiple encounters into one visit. Bange et al demonstrated that using digital text messaging tools can substantially decrease the burden of pre-infusion visits, saving patients over an hour per session without compromising safety [ 12 ]. Wearable monitoring devices, such as smartwatches and biosensors, have been proven to be useful in various oncology settings [ 24 , 25 ]. Objective data from wearables related to sleep quality, physical activity, and caloric intake, can help oncologists monitor tolerance and compliance, and tailor treatment intensity. High risk changes in vital signs or symptoms can be flagged to allow early detection of conditions such as neutropenic fever, arrhythmias, and pulmonary embolism, and result in timely interventions and fewer hospital visits. The incorporation of artificial intelligence (AI) could provide powerful tools in time toxicity reduction. Cancellations and rescheduled appointments are especially common in the oncology offices. Fitting recurrent infusion visits of different lengths, frequencies, and durations are challenging tasks for the clinic staff. AI-powered scheduling systems can reduce wait times, improve translations and communications, handle scheduling changes, and optimize the infusion room utilization efficiency. By implementing strategies that preserve quality of life while minimizing disruptions, oncology providers can choose treatments that best align with the goals of their patients and their families. As cancer treatments continue to evolve rapidly, it is crucial that we consider the time toxicity of these new options.
Conclusion
TrTT is an important and often overlooked aspect of the overall treatment burden in oncology. As the complexity and duration of cancer treatments increase, addressing time toxicity becomes crucial to improving patients’ quality of life and overall outcomes. Our study revealed that severe time toxicity occurred in one in seven individuals with advanced stage solid malignancies, particularly in frail patients with gastrointestinal cancers or those receiving traditional chemotherapy. By recognizing and addressing time toxicity through better care coordination, telemedicine, and patient-centered approaches, the healthcare system can reduce this burden on cancer patients, allowing them to focus more on their goal of life and less on the logistical challenges of their treatment. Continued research and patient feedback will be essential in refining strategies to mitigate time toxicity and ensuring that the time demands of cancer treatment do not unduly compromise patients’ well-being.
The authors thank Dr. Kathleen T. Li (University of Texas, Austin, TX) for her expertise in statistical analysis and thoughtful editorial assistance.
Conflict of Interest
All authors report no conflict of interest.
Informed Consent
Not applicable.
Author Contributions
Yuhang Zhou, Kyaw Aung, Om Pandey, and Boone Goodgame conceived and designed the analysis; Yuhang Zhou, Madeline Fitzpatrick, Sujata Ojha, Marisabel Hurtado-Castillo, and Williams Sessions collected the data; Yuhang Zhou and Madeline Fitzpatrick performed the analysis; Yuhang Zhou, Madeline Fitzpatrick, Sujata Ojha, Marisabel Hurtado-Castillo, Williams Sessions, Kyaw Aung, and Boone Goodgame wrote the paper.
Data Availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Abbreviations
artificial intelligence
Centers for Disease Control and Prevention
Eastern Cooperative Oncology Group
US Food and Drug Administration
National Comprehensive Cancer Network
progression-free survival
treatment-related time toxicity
References
1 Baltussen JC Cardenas-Reyes P Chavarri-Guerra Y Ramirez-Fontes A Morales-Alfaro A Portielje JEA Ramos-Lopez WA et al Time toxicity among older patients with cancer treated with palliative systemic therapy Support Care Cancer 2024 32 9 621 10.1007/s00520-024-08844-1 39212749
2 Gupta A Nguyen P Kain D Robinson AG Kulkarni AA Johnson DH Presley CJ et al Trajectories of health care contact days for patients with stage IV non-small cell lung cancer JAMA Netw Open 2024 7 4 e244278 10.1001/jamanetworkopen.2024.4278 38587847 PMC11002696
3 Gupta A Jensen EH Virnig BA Beg MS Time-related burdens of cancer care JCO Oncol Pract 2022 18 4 245 246 10.1200/OP.21.00662 34709950
4 Gupta A Eisenhauer EA Booth CM The time toxicity of cancer treatment J Clin Oncol 2022 40 15 1611 1615 10.1200/JCO.21.02810 35235366
5 Prasad V Olivier T Chen EY Haslam A Estimation of time cost of anti-cancer drugs approved based on comparisons to best supportive care: A cross sectional analysis J Cancer Policy 2022 34 100363 10.1016/j.jcpo.2022.100363 36162749
6 Lim SA Hao SB Boyd BA Mitsakos A Irish W Burke AM Parikh AA et al Opportunity costs of surgical resection and perioperative chemotherapy for locoregional pancreatic adenocarcinoma JCO Oncol Pract 2022 18 4 302 309 10.1200/OP.21.00311 34709961
7 Kagalwalla S Tsai AK George M Waldock A Davis S Jewett P Vogel RI et al Consuming patients' days: time spent on ambulatory appointments by people with cancer Oncologist 2024 29 5 400 406 10.1093/oncolo/oyae016 38339991 PMC11067814
8 Patel VR Ramesh V Tsai AK Sedhom R Westanmo AD Blaes AH Vogel RI et al Health care contact days experienced by decedents with advanced GI cancer JCO Oncol Pract 2023 19 11 1031 1038 10.1200/OP.23.00232 37738532 PMC10667015
9 Ioannidis JP Pavlidis N Levels of absolute survival benefit for systemic therapies of advanced cancer. a call for standards Eur J Cancer 2003 39 9 1194 1198 10.1016/s0959-8049(03)00119-9 12763206
10 Johnson WV Phung QH Patel VR Tsai AK Arora N Klein MA Westanmo AD et al Trajectory of healthcare contact days for veterans with advanced gastrointestinal malignancy Oncologist 2024 29 2 e290 e293 10.1093/oncolo/oyad313 38016182 PMC10836304
11 Bateni SB Nguyen P Eskander A Seung SJ Mittmann N Jalink M Gupta A et al Changes in health care costs, survival, and time toxicity in the era of immunotherapy and targeted systemic therapy for melanoma JAMA Dermatol 2023 159 11 1195 1204 10.1001/jamadermatol.2023.3179 37672282 PMC10483386
12 Bange EM Coughlin K Li W Moriarty E Brown TJ Shulman LN Mamtani R Accuracy of a text intervention to minimize the burden of cancer care among patients treated with immune checkpoint inhibitors JAMA Netw Open 2022 5 8 e2228452 10.1001/jamanetworkopen.2022.28452 36036938 PMC9425147
13 Guy GP Jr Richardson LC Visit duration for outpatient physician office visits among patients with cancer J Oncol Pract 2012 8 3 Suppl 2s 8s 10.1200/JOP.2011.000493 22942822 PMC3348598
14 Ray KN Chari AV Engberg J Bertolet M Mehrotra A Disparities in time spent seeking medical care in the United States JAMA Intern Med 2015 175 12 1983 1986 10.1001/jamainternmed.2015.4468 26437386 PMC5055855
15 Rocque GB Williams CP Ingram SA Azuero A Mennemeyer ST Young Pierce J Nipp RD et al Health care-related time costs in patients with metastatic breast cancer Cancer Med 2020 9 22 8423 8431 10.1002/cam4.3461 32955793 PMC7666754
16 Gupta A O'Callaghan CJ Zhu L Jonker DJ Wong RPW Colwell B Moore MJ et al Evaluating the time toxicity of cancer treatment in the CCTG CO.17 trial JCO Oncol Pract 2023 19 6 e859 e866 10.1200/OP.22.00737 36881786 PMC10337749
17 Gupta A Chant ED Mohile S Vogel RI Parsons HM Blaes AH Booth CM et al Health care contact days among older cancer survivors JCO Oncol Pract 2024 20 7 943 952 10.1200/OP.23.00590 38452315 PMC11268556
18 Banerjee R Cowan AJ Ortega M Missimer C Carpenter PA Oshima MU Salit RB et al Financial toxicity, time toxicity, and quality of life in multiple myeloma Clin Lymphoma Myeloma Leuk 2024 24 7 446 454.e443 10.1016/j.clml.2024.02.013 38521640 PMC11189752
19 Walia A Tuia J Prasad V Progression-free survival, disease-free survival and other composite end points in oncology: improved reporting is needed Nat Rev Clin Oncol 2023 20 12 885 895 10.1038/s41571-023-00823-5 37828154
20 Del Paggio JC Berry JS Hopman WM Eisenhauer EA Prasad V Gyawali B Booth CM Evolution of the Randomized Clinical Trial in the Era of Precision Oncology JAMA Oncol 2021 7 5 728 734 10.1001/jamaoncol.2021.0379 33764385 PMC7995135
21 Brundage MD Booth CM Eisenhauer EA Galica J Kankesan J Karim S Koven R et al Patients' attitudes and preferences toward delayed disease progression in the absence of improved survival J Natl Cancer Inst 2023 115 12 1526 1534 10.1093/jnci/djad138 37458509 PMC10699849
22 Robinson AG O'Donnell J Booth C Koven R Eisenhauer E Brundage M Patient perspectives of value of delayed disease progression on imaging (imaging PFS). A treatment trade-off experiment J Cancer Policy 2021 30 100301 10.1016/j.jcpo.2021.100301 35559797
23 Raphael MJ Robinson A Booth CM O'Donnell J Palmer M Eisenhauer E Brundage M The value of progression-free survival as a treatment end point among patients with advanced cancer: a systematic review and qualitative assessment of the literature JAMA Oncol 2019 5 12 1779 1789 10.1001/jamaoncol.2019.3338 31556921
24 Hunter H Bennington-McKay N Sher J Psutka SP Lin C Emerging role of mobile applications and wearable devices for prehabilitation in urologic oncology Eur Urol Focus 2024 10 1 20 22 10.1016/j.euf.2023.10.010 37923631
25 Chow R Drkulec H Im JHB Tsai J Nafees A Kumar S Hou T et al The use of wearable devices in oncology patients: a systematic review Oncologist 2024 29 4 e419 e430 10.1093/oncolo/oyad305 37971410 PMC10994271