A proposed multi‐domain, digital model for capturing functional status and health‐related quality of life in oncology
The rapid proliferation of novel anticancer therapies has greatly increased the complexities clinicians and patients face in navigating care pathways. Robust and efficient methods to predict the optimal therapeutic approach for a given patient, monitor the treatment response, and assess and intervene on side effects are needed now more than ever. Whereas traditional oncology clinical trial endpoints, such as progression‐free survival, remain key for establishing novel treatments, impact on daily functioning and quality‐of‐life are increasingly recognized as potential drivers of clinical care decisions. Functional measures and patient‐reported outcomes have consequently been adopted in the research setting, as regulatory agencies often require these data while considering approval for new treatments. Although lacking a consistent definition, the term “functional status” is generally used to describe an individual's ability to complete activities of daily living across domains, including physical, cognitive, and behavioral functioning. The related concept of health‐related quality of life (HRQoL) encompasses self‐reported perceptions of the positive and negative aspects of physical and mental health. Functional status therefore focuses on an individual's abilities, which can be measured via self‐report, physician rating, and a range of task‐based activities. In contrast, HRQoL is always measured through self‐report and focuses on an individual's own assessment of the extent to which their health impacts their ability to live a fulfilling life. In oncology research, assessments of functional status or frailty, the assessments for which touch on similar domains, are often adopted as part of the eligibility criteria determining whether an individual patient is fit for a given therapy. In addition to laboratory‐ and imaging‐based biomarkers, HRQoL is typically used as an exploratory objective to assess the impact of treatment. However, existing methodologies of collecting these data, often deployed as clinical outcome assessment (COA) targeting these constructs suffer from certain limitations, such as greater variability, which have limited their penetration into oncology care management and clinical trials. In this mini‐review, we will describe the strengths and weaknesses of the functional status data collection methods most commonly used in oncology research to date; discuss the applicability of digital health technologies (DHTs) as a means of addressing some of the limitations associated with existing methods; review recent studies that have captured multiple domains of functional status; and, finally, we will propose a device‐agnostic multi‐domain model for capturing functional status for oncology enabled by connected digital tools. 

Various functional status COAs have been adopted in both clinical care and clinical research in cancer, falling into the categories of clinician‐reported outcomes (ClinROs); such as the Karnofsky Performance Status and Eastern Cooperative Oncology Group (ECOG) scales , ; performance outcomes (PerfOs; such as the 6‐minute walk test [6MWT] and measurement of grip strength ), and patient questionnaires, for example, patient‐reported outcomes (PRO), such as the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire Core 30 , (EORTC QLQ‐C30) and Functional Assessment of Cancer Therapy—General (FACT‐G). ClinROs based on performance status have been widely accepted in the oncology community for several decades as they have been extensively validated, and are associated with chemotherapy toxicities and survival across many cancers. In addition, PROs have gained traction in recent years as they have been shown to improve communication, treatment satisfaction, and symptom management. , Advantages of PerfOs include their applicability to activities of daily living and their value as prognostic indicators. , Despite their widespread use, COAs suffer from several important limitations, including a lack of sufficient granularity to interrogate specific aspects of health, raters' variability, and creating redundant data sets with significant burden for patients if multiple questionnaires are deployed. PerfO assessment is usually performed during in‐clinic visits, which limits the frequency of data collection and can be highly dependent on patient motivation ; in addition, although PerfOs are often described as being objective, many rely on human scoring resulting in challenges with inter‐rater variability. ClinROs, particularly those targeting performance status, cannot be verified retrospectively for accuracy. PRO instruments often have a long recall period, are highly subjective in nature, and can present a significant burden to patients when administered frequently. Interpretation of ClinROs and PROs is further complicated by the fact that they may not be concordant with one another; for example, research has demonstrated that oncologists and nurses are more likely to record greater performance status than patients themselves. In contrast to infrequent “snapshot” assessments of functional status collected through traditional COA methods, digitally enabled COAs and biomarkers offer the opportunity to collect comprehensive and contemporaneous data to assess physical, cognitive, and behavioral function among patients with cancer in order to drive personalized medicine and gain insights into the consequences of anticancer therapies. 

DHTs offer several advantages over in‐clinic and/or paper‐based COAs, including the ability to collect objective data reflecting the usual day‐to‐day activities of patients, without the accessibility limitations imposed by the need for hospital visits. As a result of these potential advantages, the deployment of DHTs for capturing clinical research data has attracted attention from drug developers, academic researchers, regulators, and payers for more than a decade. However, the extent of DHT adoption in oncology is somewhat behind other therapeutic areas, such as musculoskeletal and neurological disorders, in part, due to the competing demands associated with managing patients on therapies with significant toxicities, and the fact that many patients suffering from cancer are older. Currently, the most widely used applications of DHTs in oncology are electronic PROs (ePROs) and adverse event diaries administered electronically, along with wearable digital tools for estimating physical activity, and, to a lesser extent, vital signs and sleep parameters before, during, and after anticancer therapies. Table provides a summary of studies reporting impactful conclusions regarding the use of DHTs for capturing functional status in patients with cancer. Administration of ePRO instruments and patient diaries is the most advanced DHT application in both oncology practice and clinical trials. Deployment of these tools is associated with improved survival, potentially due to improved care delivery and better tolerance of chemotherapy as a result. Furthermore, it has been demonstrated that capturing reliable HRQoL data has an impact on physician‐patient communication and provides benefits to certain patients in terms of emotional well‐being. Monitoring physical activity using connected, remote, sensor‐based DHTs, such as wrist‐worn trackers, is a more recent advancement in oncology. Historically, variables describing the amount and intensity of exercise were based on self‐report, which helped establish a strong link between physical activity and cancer prevention and survival. Additionally, the literature suggests that a sedentary lifestyle prior to diagnosis and during treatment is associated with worse outcomes in patients. The availability of extensive clinical validation data supporting physical activity as a meaningful outcome measure means that the transition to sensor‐based DHTs for capturing similar outcomes is a natural progression supported by analytical validation evidence. Certain physical activity parameters, such as daily step count and gait speed, can serve as a strong dynamic predictor of hospitalizations in patients undergoing chemotherapy and chemoradiotherapy in patients with multiple types of solid tumors and can be prognostic of survival in cancer. , Further, it has been demonstrated that patients with cancer tend to overestimate how much they exercise compared with data from sensor‐based DHTs, suggesting that objective data may yield more reliable and reproducible results. Several reports have indicated that other physiological parameters captured by DHTs, such as heart rate variability and orthostatic hypotension, are predictive of survival, falls, and infections in patients with cancer, and may be useful in post‐surgical settings. Although not as widely used in the field of oncology, DHT‐derived measurements of sleep duration, fragmentation, and timing have been assessed using sensor‐based digital tools in breast, gastrointestinal, and lung cancers. Sleep disruption has been well‐documented in patients undergoing chemotherapy, and the literature suggests that individuals experiencing more profound sleep fragmentation tend to have worse outcomes. Examination of circadian rhythms in patients with breast cancer during chemotherapy has indicated disruptive sleep–wake rhythms which worsened with therapy cycle progression. Similar to what has been observed for physical activity outcomes, it has been reported that individuals with lung cancer tend to overestimate sleep duration and sleep efficiency, and underestimate sleep onset latency and wake after sleep onset, again underscoring the potential for capturing more reliable results using digital tools. Although DHTs for remote data collection have been available for several years, their use in both clinical care and research was accelerated during the coronavirus disease 2019 (COVID‐19) pandemic, highlighting the benefits of capturing frequent or semi‐continuous data allowing early intervention in some cases. In the research setting, the increased precision enabled by high‐resolution data makes it possible to draw conclusions based on a smaller sample size than would be required for a traditional pre/post‐analysis. Further, a recent systematic review of digital data captured in oncology clinical trials found that 60–100% of patients were considered adherent to the DHTs deployed, contributing 45–94% evaluable days. Although assessment of adherence across studies of DHTs has been hampered by inconsistent definitions and reporting quality, overall, these data suggest that digital technology deployment for capturing functional status outcomes is feasible in the oncology field, with the potential for further improvement in usability and patient education. Despite these advantages, adoption of digital outcome measures in oncology trials is not yet widespread. A 2023 qualitative study conducted among US‐based clinical trial sponsors identified several barriers to implementing DHT‐derived measures, including the need for further regulatory guidance and concerns with the feasibility of completing the endpoint qualification process. In addition, logistical considerations have required study sponsors to adapt their operational processes, such as the need to identify data integration platforms; process very large amounts of data while maintaining data security and privacy; and monitor technology changes over time. Infrastructure to support digital clinical trial endpoints now largely exists and continues to improve, and regulatory guidance is continuing to emerge ; thus, we anticipate continuing growth in the use of DHTs for oncology research data collection in the coming years. In addition, widespread adoption of new practices in medicine, and therefore in clinical research, often occurs when required or recommended by professional societies or in response to changes in reimbursement policies. For example, electronic prescriptions have been shown to reduce errors, but an act providing financial incentives for electronic prescribing was required to ensure widespread adoption. To our knowledge, no oncology‐focused professional organizations have yet made clear statements about the adoption of DHTs in clinical trials of cancer. 

The medical community has long recognized that functional status is a complex aspect of health, requiring assessments across several concepts of interest. Different approaches to multi‐domain functional status data collection have been proposed and tested in observational studies providing important results on patient adherence and clinical utility of the data, laying the groundwork for future use (see Table ). For example, Pergolotti and Sattar (2021) proposed a combination of patient‐ or proxy‐reported instruments alongside traditional (non‐digital) task‐based assessments, such as gait speed, timed‐up‐and‐go, sit‐to‐stand, and grip strength for assessment of older individuals with cancer. Levi et al. (2020) tested the feasibility of remote data collection using two digital tools (wrist‐worn accelerometry and chest‐strap accelerometry) among 25 participants with gastrointestinal cancer, reporting 98% adherence to the study protocol. A more complex combination of measurements was tested by Wood et al. (2023) who captured in‐laboratory 6MWT and cardiopulmonary exercise testing (CPET) followed by 6 weeks of remote data collection using a wrist‐worn DHT (containing an accelerometer, altimeter, and optical light sensor) with intermittent ePROs. Usable 6MWT and CPET data were obtained in 68% of patients undergoing chemotherapy or hematopoietic stem cell transplant; in contrast, 84% had usable data from the wrist‐worn DHT and 73% had usable data from both the wrist‐worn DHT and ePROs. Finally, in the upcoming “In4m” feasibility and usability study, Thanarajasingam et al. propose a multi‐domain data collection protocol among 200 patients with breast cancer or lymphoma undergoing chemotherapy (pre‐print only). Data capture will be enabled by an online platform designed to consolidate data from electronic health records (including study‐specific in‐clinic 6MWT and ECOG data), weekly ePROs (several instruments focused on physical activity), and a wrist‐worn wearable (to estimate outcomes reflecting steps/distance walked, heart rate, and activity levels). The clinical concepts of interest and outcome measures adopted in these studies are therefore conceptually aligned with multiple aspects of functional status specific to cancer, and include some combination of ClinRO, PRO, and PerfO assessments. 

Building on the clinical validation evidence supporting the capture of functional status and HRQoL outcome measures in cancer, the emerging technological infrastructure required for deployment of DHTs, and the more recent emphasis on multi‐domain assessments, we now propose a digital measurement approach for capturing functional status in patients with cancer that includes four distinct domains. By using DHTs for all aspects of data collection, our proposed model allows for high‐resolution measurement of objective data as patients navigate their daily lives outside of the hospital setting, complemented by ePROs administered at intervals appropriate for each selected instrument. As depicted in Figure , the first domain covers the concepts of physical activity and mobility, with outcome measures including hourly and daily step count, gait speed, and metabolic equivalent of task. The second domain covers vital signs, with outcome measures, including heart rate, heart rate variability, blood pressure, and potentially pulse oximetry, if applicable, to a specific tumor type. Although vital signs are biomarkers and are not patient‐centered outcomes by definition, they are directly related to physiological processes associated with wellbeing, and are important for informing treatment decisions and assessing a patient's treatment response and side effects of therapy. The third domain covers the concept of sleep, with outcome measures including sleep staging, sleep duration, sleep onset latency, wake after sleep onset, sleep efficiency, and sleep timing. The fourth and final domain covers HRQoL captured using validated instruments and diaries via ePRO platforms, assessing aspects of health that are meaningful to individuals living with cancer, such as disease‐ and treatment‐related symptoms and the impact their physical and mental health may have on activities of daily living, their work life, and socializing. Well‐constructed PRO instruments have demonstrated psychometric properties comparable to physiological measurements commonly used to assess medical interventions, and including this domain consisting of self‐reported data captured directly from patients and caregivers provides context to objectively measured aspects of physical health that clinicians are more familiar with. Our proposed multi‐domain, DHT‐supported model for remote monitoring in cancer therefore includes a range of concepts supported by a large body of clinical validation evidence demonstrating that they are meaningful to patients and informative to clinicians, all of which can be captured using technology available today. Existing data suggest that the proposed domains would be relevant to patients with cancer receiving chemotherapy as well as other established therapies, such as radiotherapy or surgery. , We are confident that similar data would be relevant for patients receiving novel cancer treatments, although the specific metrics that are most critical might vary depending on the treatment time. Real‐time temperature monitoring, for example, could be particularly important for patients who receive CAR‐T therapy. Importantly, we present a device‐agnostic model to allow for flexibility depending on study hypotheses, characteristics of the study sample, and different data collection environments. Moreover, an agnostic approach to technology selection has been demonstrated to be critical for broader technology adoption in multi‐center studies, contingent on the demonstration of data comparability across different tools. When determining which combination of DHTs may be useful for capturing outcomes aligned to our proposed model, several considerations must be addressed. First, robust validation data are required to support sensor‐based DHTs (capturing vital signs, physical activity, and sleep outcomes), with the latter gathered from study samples that are representative of, or generalizable to, the target population of interest. Although the purpose of this review is not to recommend specific DHT makes/models and form factors, we are aware of several wrist‐worn DHTs – typically containing a combination of accelerometer, gyroscope, and optical sensors – which are capable of capturing estimated physical activity, sleep, and vital signs metrics within a single tool. In some scenarios, it may be beneficial to capture more detailed sleep data using a mobile electroencephalogram typically adopting a headband form factor. The performance of sensor‐based DHTs compared with a reference (gold) standard can vary substantially according to technology type, body position, testing protocol, disease state, patient characteristics, such as age, and other factors. For example, analytical validation studies of DHTs assessing step count against laboratory‐based treadmill tests have reported median accuracy (defined as the mean absolute percentage error, with lower values indicating better accuracy) of 0.5%, 0.8%, 1.1%, 6.0%, and 6.6% for DHTs placed on the thigh, waist, ankle, upper‐arm, and wrist, respectively. Within waist‐worn tools, average accuracy varied from 0.8–81.7% across different treadmill speeds. As such, an evidence‐driven approach to selecting an appropriate tool for a particular context of use is required, aided by open‐access databases where appropriate. As the number of DHTs administered to an individual patient increases, greater emphasis must be placed on studies demonstrating the feasibility of deploying multiple DHTs simultaneously within the proposed use environment, in order to assess usability and patient experience, adherence, and the privacy, security, and integrity of data transfer. The burden imposed on patients and caregivers through the use of multiple DHTs, all with different instructions for using, charging, and cleaning, may be eased to some extent by using a single data collection hub, such as a smartphone or tablet application capable of synching data from multiple tools which may be produced by different manufacturers. Data hubs in the form of smartphone apps may offer additional advantages, such as the ability to develop friendly reminders to complete point‐in‐time tasks and ePROs, and providing technical troubleshooting and/or clinical support in multiple languages. Once a suitable combination of DHTs has been assembled, pilot testing is an important step for determining optimal study design including frequency and duration of data collection, and the usefulness of particular outcomes for the clinical question of interest, particularly with respect to co‐linearity and redundancy. It is recognized that some redundancy across domains may be helpful for confirming the directionality of change; for example, increased heart rate variability would be expected to be correlated with improved physical fitness and sleep architecture. In addition, the complexities associated with basing clinical decisions on multi‐dimensional data requires careful consideration. The reproducibility of DHT‐derived PerfO outcomes based on sensor inputs without manual interpretation or scoring is a major advantage in terms of standardizing clinical decisions, particularly in multi‐site settings. Study sponsors may opt to define evidence‐driven parameters for clinical decisions based on data ranges alongside clinical interpretation, for example, and may consider adoption of a central reading center for processing and interpreting DHT signals/outcomes to support standardization across investigators and sites. Depending on the logistical questions of interest, pilot studies addressing these topics to inform subsequent clinical trial design may be incorporated into studies designed primarily for analytical validation and/or usability testing. 

Creating a unified definition of functional status and collecting corresponding data from patients with cancer has the potential to inform improvements in health care delivery for all individuals with cancer, as well as the design and implementation of clinical trials assessing the efficacy and effectiveness of anticancer therapy. In some scenarios, the greatest benefits might be seen when selecting patients who are fit for intense treatment regimens and/or in monitoring those patients for acute toxicities during therapy. In other situations, outcomes might be improved most dramatically by utilizing digital data to promote healthful lifestyles in the survivorship period. In the end‐of‐life setting, digitally enabled data might help guide the implementation of palliative interventions. Here, we propose a multi‐domain model of data capture covering HRQoL, sleep, physical activity, and vital signs, supported by ePRO and sensor‐based DHTs. Although complex, consisting of multiple measurement tools capturing interrelated but distinct aspects of health, we have described some high‐level considerations regarding technology selection, assessment, and pilot testing. The model proposed here is entirely reliant on remote data collection, thus increasing accessibility to a broader and more diverse representation of patients while also promoting more equitable access to care. , Finally, we emphasize that all components of our multi‐domain model have already been deployed individually in the oncology field, , as well as by patients themselves using over‐the‐counter products. Although validation, usability, and pilot testing is important when assessing and selecting suitable DHTs, moving our proposed model beyond feasibility studies will require multi‐institutional efforts that are most successful via precompetitive collaborations. 

No funding was received for this work. 

E.S.I., J.A.W., and R.E., are employees of Koneksa Health and may own company stock. J.P.B. reports financial interests in Philips, Signifier Medical Technologies, Apnimed, and Koneksa Health, and salary from the non‐profit Digital Medicine Society; her interests were reviewed and are managed by Brigham and Women's Hospital and Mass General Brigham in accordance with their conflicts of interest policy. N.O. is employed by Albert Einstein College of Medicine; has a consulting or advisory role with Merck and Genentech; receives research funding from Merck, Reflexion Medical, and AstraZeneca. R.K. has nothing to declare. As Editor‐in‐Chief of Clinical and Translational Science , John Wagner was not involved in the review or decision process for this paper. 

