Use of Wearable, Mobile, and Sensor Technology in Cancer Clinical Trials

Monitoring Adolescent and Young Adult Patients With Cancer via a Smart T-Shirt: Prospective, Single-Cohort, Mixed Methods Feasibility Study (OncoSmartShirt Study)

Background

Wearables that measure vital parameters can be potential tools for monitoring patients at home during cancer treatment. One type of wearable is a smart T-shirt with embedded sensors. Initially, smart T-shirts were designed to aid athletes in their performance analyses. Recently however, researchers have been investigating the use of smart T-shirts as supportive tools in health care. In general, the knowledge on the use of wearables for symptom monitoring during cancer treatment is limited, and consensus and awareness about compliance or adherence are lacking.

objectives

The aim of this study was to evaluate adherence to and experiences with using a smart T-shirt for the home monitoring of biometric sensor data among adolescent and young adult patients undergoing cancer treatment during a 2-week period.

Methods

This study was a prospective, single-cohort, mixed methods feasibility study. The inclusion criteria were patients aged 18 to 39 years and those who were receiving treatment at Copenhagen University Hospital - Rigshospitalet, Denmark. Consenting patients were asked to wear the Chronolife smart T-shirt for a period of 2 weeks. The smart T-shirt had multiple sensors and electrodes, which engendered the following six measurements: electrocardiogram (ECG) measurements, thoracic respiration, abdominal respiration, thoracic impedance, physical activity (steps), and skin temperature. The primary end point was adherence, which was defined as a wear time of >8 hours per day. The patient experience was investigated via individual, semistructured telephone interviews and a paper questionnaire.

Results

A total of 10 patients were included. The number of days with wear times of >8 hours during the study period (14 d) varied from 0 to 6 (mean 2 d). Further, 3 patients had a mean wear time of >8 hours during each of their days with data registration. The number of days with any data registration ranged from 0 to 10 (mean 6.4 d). The thematic analysis of interviews pointed to the following three main themes: (1) the smart T-shirt is cool but does not fit patients with cancer, (2) the technology limits the use of the smart T-shirt, and (3) the monitoring of data increases the feeling of safety. Results from the questionnaire showed that the patients generally had confidence in the device.

Conclusions

Although the primary end point was not reached, the patients' experiences with using the smart T-shirt resulted in the knowledge that patients acknowledged the need for new technologies that improve supportive cancer care. The patients were positive when asked to wear the smart T-shirt. However, technical and practical challenges in using the device resulted in low adherence. Although wearables might have potential for home monitoring, the present technology is immature for clinical use.

Attributes, Methods, and Frameworks Used to Evaluate Wearables and Their Companion mHealth Apps: Scoping Review

BACKGROUND

Wearable devices, mobile technologies, and their combination have been accepted into clinical use to better assess the physical fitness and quality of life of patients and as preventive measures. Usability is pivotal for overcoming constraints and gaining users' acceptance of technology such as wearables and their companion mobile health (mHealth) apps. However, owing to limitations in design and evaluation, interactive wearables and mHealth apps have often been restricted from their full potential.

OBJECTIVE

This study aims to identify studies that have incorporated wearable devices and determine their frequency of use in conjunction with mHealth apps or their combination. Specifically, this study aims to understand the attributes and evaluation techniques used to evaluate usability in the health care domain for these technologies and their combinations.

METHODS

We conducted an extensive search across 4 electronic databases, spanning the last 30 years up to December 2021. Studies including the keywords "wearable devices," "mobile apps," "mHealth apps," "physiological data," "usability," "user experience," and "user evaluation" were considered for inclusion. A team of 5 reviewers screened the collected publications and charted the features based on the research questions. Subsequently, we categorized these characteristics following existing usability and wearable taxonomies. We applied a methodological framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.

RESULTS

A total of 382 reports were identified from the search strategy, and 68 articles were included. Most of the studies (57/68, 84%) involved the simultaneous use of wearables and connected mobile apps. Wrist-worn commercial consumer devices such as wristbands were the most prevalent, accounting for 66% (45/68) of the wearables identified in our review. Approximately half of the data from the medical domain (32/68, 47%) focused on studies involving participants with chronic illnesses or disorders. Overall, 29 usability attributes were identified, and 5 attributes were frequently used for evaluation: satisfaction (34/68, 50%), ease of use (27/68, 40%), user experience (16/68, 24%), perceived usefulness (18/68, 26%), and effectiveness (15/68, 22%). Only 10% (7/68) of the studies used a user- or human-centered design paradigm for usability evaluation.

CONCLUSIONS

Our scoping review identified the types and categories of wearable devices and mHealth apps, their frequency of use in studies, and their implementation in the medical context. In addition, we examined the usability evaluation of these technologies: methods, attributes, and frameworks. Within the array of available wearables and mHealth apps, health care providers encounter the challenge of selecting devices and companion apps that are effective, user-friendly, and compatible with user interactions. The current gap in usability and user experience in health care research limits our understanding of the strengths and limitations of wearable technologies and their companion apps. Additional research is necessary to overcome these limitations.

Feasibility and acceptability of C-PRIME: A health promotion intervention for family caregivers of patients with colorectal cancer

PURPOSE

This study aimed to test the feasibility and acceptability of a digital health promotion intervention for family caregivers of patients with advanced colorectal cancer and explore the intervention's preliminary efficacy for mitigating the impact of caregiving on health and well-being.

METHODS

We conducted a single-arm pilot feasibility trial of C-PRIME (Caregiver Protocol for Remotely Improving, Monitoring, and Extending Quality of Life), an 8-week digital health-promotion behavioral intervention involving monitoring and visualizing health-promoting behaviors (e.g., objective sleep and physical activity data) and health coaching (NCT05379933). A priori benchmarks were established for feasibility (≥ 50% recruitment and objective data collection; ≥ 75% session engagement, measure completion, and retention) and patient satisfaction (> 3 on a 1-5 scale). Preliminary efficacy was explored with pre- to post-intervention changes in quality of life (QOL), sleep quality, social engagement, and self-efficacy.

RESULTS

Participants (N = 13) were M = 52 years old (SD = 14). Rates of recruitment (72%), session attendance (87%), assessment completion (87%), objective data collection (80%), and retention (100%) all indicated feasibility. All participants rated the intervention as acceptable (M = 4.7; SD = 0.8). Most participants showed improvement or maintenance of QOL (15% and 62%), sleep quality (23% and 62%), social engagement (23% and 69%), and general self-efficacy (23% and 62%).

CONCLUSION

The C-PRIME digital health promotion intervention demonstrated feasibility and acceptability among family caregivers of patients with advanced colorectal cancer. A fully powered randomized controlled trial is needed to test C-PRIME efficacy, mechanisms, and implementation outcomes, barriers, and facilitators in a divserse sample of family caregivers.

TRIAL REGISTRATION

The Caregiver Protocol for Remotely Improving, Monitoring, and Extending Quality of Life (C-PRIME) study was registered on clinicaltrials.gov, NCT05379933, in May 2022.

A proposed multi-domain, digital model for capturing functional status and health-related quality of life in oncology

Whereas traditional oncology clinical trial endpoints remain key for assessing novel treatments, capturing patients' functional status is increasingly recognized as an important aspect for supporting clinical decisions and assessing outcomes in clinical trials. Existing functional status assessments suffer from various limitations, some of which may be addressed by adopting digital health technologies (DHTs) as a means of collecting both objective and self-reported outcomes. In this mini-review, we propose a device-agnostic multi-domain model for oncology capturing functional status, which includes physical activity data, vital signs, sleep variables, and measures related to health-related quality of life enabled by connected digital tools. 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. This is complemented by electronic questionnaires administered at intervals appropriate for each instrument. Preliminary testing and practical considerations to address before adoption are also discussed. Finally, we highlight multi-institutional pre-competitive collaborations as a means of successfully transitioning the proposed digitally enabled data collection model from feasibility studies to interventional trials and care management.

Big Data in Oncology Nursing Research: State of the Science

OBJECTIVE

To review the state of oncology nursing science as it pertains to big data. The authors aim to define and characterize big data, describe key considerations for accessing and analyzing big data, provide examples of analyses of big data in oncology nursing science, and highlight ethical considerations related to the collection and analysis of big data.

DATA SOURCES

Peer-reviewed articles published by investigators specializing in oncology, nursing, and related disciplines.

CONCLUSION

Big data is defined as data that are high in volume, velocity, and variety. To date, oncology nurse scientists have used big data to predict patient outcomes from clinician notes, identify distinct symptom phenotypes, and identify predictors of chemotherapy toxicity, among other applications. Although the emergence of big data and advances in computational methods provide new and exciting opportunities to advance oncology nursing science, several challenges are associated with accessing and using big data. Data security, research participant privacy, and the underrepresentation of minoritized individuals in big data are important concerns.

IMPLICATIONS FOR NURSING PRACTICE

With their unique focus on the interplay between the whole person, the environment, and health, nurses bring an indispensable perspective to the interpretation and application of big data research findings. Given the increasing ubiquity of passive data collection, all nurses should be taught the definition, characteristics, applications, and limitations of big data. Nurses who are trained in big data and advanced computational methods will be poised to contribute to guidelines and policies that preserve the rights of human research participants.

The association between wearable device metrics and clinical outcomes in oncology: a systematic review with evidence synthesis and meta-analysis

BACKGROUND

The emerging study of wearable devices (WDs) in patients with cancer provides opportunities to harness real-time patient data for predicting clinical outcomes. We conducted a systematic review with best evidence synthesis to examine the association between WD metrics and clinical outcomes in patients with cancer.

METHODS

MEDLINE and Embase were searched from inception until June 2022. Risk of bias assessment and best evidence synthesis were performed and, If possible, meta-analysis was conducted.

RESULTS

A total of 34 studies was included. We found moderate-to-strong evidence for associations between circadian rest-activity metrics and OS. Disrupted I<O was associated with increased hazard for death (HR 2.08; 95 %CI: 1.50-2.88). For most associations there was insufficient evidence due to lack of studies (n = 32) or inconsistent results (n = 14).

CONCLUSION

Meta-analysis was greatly hampered due to heterogeneity and different methodology used between studies. Studies primarily designed to investigate the association between WD metrics and clinical outcomes are warranted.

Biosensing chips for cancer diagnosis and treatment: a new wave towards clinical innovation

Recent technological advances in nanoscience and material designing have led to the development of point-of-care devices for biomolecule sensing and cancer diagnosis. In situ and portable sensing devices for bedside, diagnosis can effectively improve the patient’s clinical outcomes and reduce the mortality rate. Detection of exosomal RNAs by immuno-biochip with increased sensitivity and specificity to diagnose cancer has raised the understanding of the tumor microenvironment and many other technology-based biosensing devices hold great promise for clinical innovations to conquer the unbeatable fort of cancer metastasis. Electrochemical biosensors are the most sensitive category of biomolecule detection sensors with significantly low concentrations down to the atomic level. In this sense, this review addresses the recent advances in cancer detection and diagnosis by developing significant biological sensing devices that are believed to have better sensing potential than existing facilities.

Feasibility of Monitoring Patients Who Have Cancer With a Smart T-shirt: Protocol for the OncoSmartShirt Study

Background

Studies have shown that there may be dissimilar perceptions on symptoms or side effects between patients with cancer and health care professionals. This may lead to symptomatic patients notifying the clinic irregularly or not telling the clinic at all. Wearables could help identify symptoms earlier. Patients with low socioeconomic status and less self-awareness of their health may benefit from this. A new design of wearables is a smart t-shirt that, with embedded sensors, provides measurement flows such as electrocardiogram, thoracic and abdominal respiration, and temperature.

Objective

This study evaluates the feasibility of using a smart t-shirt for home monitoring of biometric sensor data in adolescent and young adult and elderly patients during cancer treatment.

Methods

The OncoSmartShirt study is an explorative study investigating the feasibility of using the Chronolife smart t-shirt during cancer treatment. This smart t-shirt is designed with multiple fully embedded sensors and electrodes that engender 6 different measurement flows continuously. A total of 20 Danish patients with cancer ≥18 years old in antineoplastic treatment at Department of Oncology Rigshospitalet Denmark will be recruited from all cancer wards, whether patients are in curative or palliative care. Of these 20 patients, 10 (50%) will be <39 years old, defined as adolescent and young adult, and 10 (50%) will be patients >65 years old, defined as elderly. Consenting patients will be asked to wear a smart t-shirt daily for 2 weeks during their treatment course.

Results

The primary outcome is to determine if it is feasible to wear a smart t-shirt throughout the day (preferably 8 hours per day) for 2 weeks. Inclusion of patients started in March 2022.

Conclusions

The study will assess the feasibility of using the Chronolife smart t-shirt for home monitoring of vital parameters in patients with cancer during their treatment and bring new insights into how wearables and biometric data can be used as part of symptom or side-effect recognition in patients with cancer during treatment, with the aim to increase patients’ quality of life.

Trial Registration

ClinicalTrials.gov NCT05235594; https://beta.clinicaltrials.gov/study/NCT05235594

International Registered Report Identifier (IRRID)

PRR1-10.2196/37626

Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN

Random drift error is one of the important factors of MEMS (micro-electro-mechanical-system) sensor output error. Identifying and compensating sensor output error is an important means to improve sensor accuracy. In order to reduce the impact of white noise on neural network modeling, the ensemble empirical mode decomposition (EEMD) method was used to separate white noise from the original signal. The drift signal after noise removal is modeled by GRNN (general regression neural network). In order to achieve a better modeling effect, cross-validation and parameter optimization algorithms were designed to obtain the optimal GRNN model. The algorithm is used to model and compensate errors for the generated random drift signal. The results show that the mean value of original signal decreases from 0.1130 m/s² to -1.2646 × 10^-7 m/s², while the variance decreases from 0.0133 m/s² to 1.0975 × 10^-5 m/s². In addition, the displacement test was carried out by MEMS acceleration sensor. Experimental results show that the displacement measurement accuracy is improved from 95.64% to 98.00% by compensating the output error of MEMS sensor. By comparing the GA-BP (genetic algorithm-back propagation) neural network and the polynomial fitting method, the EEMD-GRNN method proposed in this paper can effectively identify and compensate for complex nonlinear drift signals.

Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data

Mounting clinical evidence suggests that viral infections can lead to detectable changes in an individual's normal physiologic and behavioral metrics, including heart and respiration rates, heart rate variability, temperature, activity, and sleep prior to symptom onset, potentially even in asymptomatic individuals. While the ability of wearable devices to detect viral infections in a real-world setting has yet to be proven, multiple recent studies have established that individual, continuous data from a range of biometric monitoring technologies can be easily acquired and that through the use of machine learning techniques, physiological signals and warning signs can be identified. In this review, we highlight the existing knowledge base supporting the potential for widespread implementation of biometric data to address existing gaps in the diagnosis and treatment of viral illnesses, with a particular focus on the many important lessons learned from the coronavirus disease 2019 pandemic.

Longitudinal Collection of Patient-Reported Outcomes and Activity Data during CAR-T Therapy: Feasibility, Acceptability, and Data Visualization

Background: Clinicians must closely monitor patients for toxicities after chimeric antigen receptor T-cell therapy (CAR-T). Patient-reported outcomes (PROs) (e.g., toxicities, quality of life) and activity data (e.g., steps, sleep) may complement clinicians’ observations. This study tested the feasibility and acceptability of collecting PROs and activity data from patients with hematologic malignancies during CAR-T and explored preliminary data patterns. Methods: Participants wore a Fitbit tracker and completed PROs at several timepoints through 90-days post-infusion. Feasibility was assessed with a priori benchmarks for recruitment (≥50%), retention (≥70%), PRO completion (≥70%), and days wearing the Fitbit (≥50%). Acceptability was assessed with participant satisfaction (a priori benchmark > 2 on a 0−4 scale). Results: Participants (N = 12) were M = 66 years old (SD = 7). Rates of recruitment (68%), retention (83%), PRO completion (85%), and days wearing the Fitbit (85%) indicated feasibility. Satisfaction with completing the PROs (M = 3.2, SD = 0.5) and wearing the Fitbit (M = 2.9, SD = 0.5) indicated acceptability. Preliminary data patterns suggested that participants with better treatment response (vs. progressive disease) had a higher toxicity burden. Conclusions: Longitudinal PRO and activity data collection was feasible and acceptable. Data collected on a larger scale may be used to specify risk prediction models to identify predictors of severe CAR-T-related toxicities and inform early interventions.

Role of decentralized clinical trials in cancer drug development: Results from a survey of oncologists and patients

As a result of the unprecedented challenges imposed by the COVID-19 pandemic on enrollment to cancer clinical trials, there has been an urgency to identify and incorporate new solutions to mitigate these difficulties. The concept of decentralized or hybrid clinical trials has rapidly gained currency, given that it aims to reduce patient burden, increase patient enrollment and retention, and preserve quality of life, while also increasing the efficiency of trial logistics. Therefore, the clinical trial environment is moving toward remote collection and assessment of data, transitioning from the classic site-centric model to one that is more patient-centric.

Management of Treatment-Related Infectious Complications in High-Risk Hemato-Oncological Patients via Telemedicine

Background

Infectious complications, especially febrile neutropenia, in hemato-oncological patients are associated with considerable morbidity, mortality and expenses. Remote monitoring of physiological functions and thus early detection of adverse events via telemedicine could improve the safety of these high-risk patients and save financial resources by shortening the time-to-antibiotics.

Methods

Patients undergoing active cancer treatment in high risk of acquiring severe infection are selected and enrolled in this project. Each patient receives a digital blood pressure monitor, an infrared thermometer and a mobile hub (cell phone). In the comfort of their homes, patients measure their blood pressure/pulse and body temperature regularly or whenever they feel unwell. The obtained data are encrypted and forwarded via the mobile hub to the password-protected portal. The values registered outside the set-up range trigger the alarms, which are immediately sent to the designated physician who can check the portal in real-time from any device with an Internet connection, contact the patient, if need be, and initiate the anti-infective therapy almost instantly after the first symptoms occur.

Results

Fifty hemato-oncological patients were recruited between March 1, 2018 and August 1, 2020. Two hundred ninety-seven alarms of body temperature were registered and checked by the physician and patients were contacted in 18.5% of the cases (55/297). Among these 55 events, 13 required medical assistance, which makes it approximately one-quarter of all conducted telephone interventions (23.4%) and neither septic shock nor death due to treatment-related toxicity occurred.

Conclusion

Telemedicine seems like a useful tool to improve the safety of high risk hemato-oncological patients when treatment-related infectious complications are concerned.

Opportunities and Challenges for Decentralized Clinical Trials: European Regulators' Perspective

Decentralized clinical trials (DCTs) have the potential to improve accessibility, diversity, and retention in clinical trials by moving trial activities to participants’ homes and local surroundings. In this study, we conducted semi‐structured interviews with 20 European regulators to identify regulatory challenges and opportunities for the implementation of DCTs in the European Union. The key opportunities for DCTs that were recognized by regulators include a reduced participation burden, which could facilitate the participation of underserved patients. In addition, regulators indicated that data collected in DCTs are expected to be more representative of the real world. Key challenges recognized by regulators for DCTs include concerns regarding investigator oversight and participants’ safety when physical examinations and face‐to‐face contact are limited. To facilitate future learning, hybrid clinical trials with both on‐site and decentralized elements are proposed by the respondents.

Consumer-grade wearables identify changes in multiple physiological systems during COVID-19 disease progression

Consumer-grade wearables are needed to track disease, especially in the ongoing pandemic, as they can monitor patients in real time. We show that decomposing heart rate from low-cost wearable technologies into signals from different systems can give a multidimensional description of physiological changes due to COVID-19 infection. We find that the separate physiological features of basal heart rate, heart rate response to physical activity, circadian variation in heart rate, and autocorrelation of heart rate are significantly altered and can classify symptomatic versus healthy periods. Increased heart rate and autocorrelation begin at symptom onset, while the heart rate response to activity increases soon after symptom onset and increases more in individuals exhibiting cough. Symptom onset is associated with a blunting of circadian variation in heart rate, as measured by the uncertainty in the phase estimate. This work establishes an innovative data analytic approach to monitor disease progression remotely using consumer-grade wearables.

•

We separate wearable heart rate into cardiopulmonary, circadian, and other signals

•

Parameters from different physiological systems enable disease tracking

•

Individual signals change in distinct ways around COVID-19 symptom onset

•

Together, the parameter changes can distinguish healthy from infection periods

Predictive Modeling for Adverse Events and Risk Stratification Programs for People Receiving Cancer Treatment

Emergency department visits and hospitalizations are common among people receiving cancer treatment, accounting for a large proportion of spending in oncology care and negatively affecting quality of life. As oncology care shifts toward value- and quality-based payment models, there is a need to develop interventions that can prevent these costly and low-value events among people receiving cancer treatment. Risk stratification programs have the potential to address this need and optimally would consist of three components: (1) a risk stratification algorithm that accurately identifies patients with modifiable risk(s), (2) intervention(s) that successfully reduce this risk, and (3) the ability to implement the risk algorithm and intervention(s) in an adaptable and sustainable way. Predictive modeling is a common method of risk stratification, and although a number of predictive models have been developed for use in oncology care, they have rarely been tested alongside corresponding interventions or developed with implementation in clinical practice as an explicit consideration. In this article, we review the available published predictive models for treatment-related toxicity or acute care events among people receiving cancer treatment and highlight challenges faced when attempting to use these models in practice. To move the field of risk-stratified oncology care forward, we argue that it is critical to evaluate predictive models alongside targeted interventions that address modifiable risks and to demonstrate that these two key components can be implemented within clinical practice to avoid unplanned acute care events among people receiving cancer treatment.

Remote Monitoring of Patients With Hematologic Malignancies at High Risk of Febrile Neutropenia: Exploratory Study

BACKGROUND

Febrile neutropenia is one of the most common oncologic emergencies and is associated with significant, preventable morbidity and mortality. Most patients who experience a febrile neutropenia episode are hospitalized, resulting in significant economic cost.

OBJECTIVE

This exploratory study implemented a remote monitoring system comprising a digital infrared thermometer and a pulse oximeter with the capability to notify providers in real time of abnormalities in vital signs that could suggest early clinical deterioration and thereby improve clinical outcomes.

METHODS

The remote monitoring system was implemented and compared to standard-of-care vital signs monitoring in hospitalized patients with underlying hematologic malignancies complicated by a febrile neutropenia episode in order to assess the feasibility and validity of the system. Statistical analysis was performed using the intraclass correlation coefficient (ICC) to assess the consistency between the measurements taken using traditional methods and those taken with the remote monitoring system for each of the vital sign parameters (temperature, heart rate, and oxygen saturation). A linear mixed-effects model with a random subject effect was used to estimate the variance components. Bland-Altman plots were created for the parameters to further delineate the direction of any occurring bias.

RESULTS

A total of 23 patients were enrolled in the study (mean age 56, SD 23-75 years; male patients: n=11, 47.8%). ICC analysis confirmed the high repeatability and accuracy of the heart rate assessment (ICC=0.856), acting as a supplement to remote temperature assessment. While the sensitivity and specificity for capturing tachycardia above a rate of 100 bpm were excellent (88% and 97%, respectively), the sensitivity of the remote monitoring system in capturing temperatures >37.8 °C and oxygen saturation <92% was 45% and 50%, respectively.

CONCLUSIONS

Overall, this novel approach using temperature, heart rate, and oxygen saturation assessments successfully provided real-time, clinically valuable feedback to providers. While temperature and oxygen saturation assessments lagged in terms of sensitivity compared to a standard in-hospital system, the heart rate assessment provided highly accurate complementary data. As a whole, the system provided additional information that can be applied to a clinically vulnerable population. By transitioning its application to high-risk patients in the outpatient setting, this system can help prevent additional use of health care services through early provider intervention and potentially improve outcomes.

Feasibility of Wearable-Based Remote Monitoring in Patients During Intensive Treatment for Aggressive Hematologic Malignancies

PURPOSE

Intensive treatment protocols for aggressive hematologic malignancies harbor a high risk of serious clinical complications, such as infections. Current techniques of monitoring vital signs to detect such complications are cumbersome and often fail to diagnose them early. Continuous monitoring of vital signs and physical activity by means of an upper arm medical wearable allowing 24/7 streaming of such parameters may be a promising alternative.

METHODS

This single-arm, single-center observational trial evaluated symptom-related patient-reported outcomes and feasibility of a wearable-based remote patient monitoring. All wearable data were reviewed retrospectively and were not available to the patient or clinical staff. A total of 79 patients (54 inpatients and 25 outpatients) participated and received standard-of-care treatment for a hematologic malignancy. In addition, the wearable was continuously worn and self-managed by the patient to record multiple parameters such as heart rate, oxygen saturation, and physical activity.

RESULTS

Fifty-one patients (94.4%) in the inpatient cohort and 16 (64.0%) in the outpatient cohort reported gastrointestinal symptoms (diarrhea, nausea, and emesis), pain, dyspnea, or shivering in at least one visit. With the wearable, vital signs and physical activity were recorded for a total of 1,304.8 days. Recordings accounted for 78.0% (63.0-88.5; median [interquartile range]) of the potential recording time for the inpatient cohort and 84.6% (76.3-90.2) for the outpatient cohort. Adherence to the wearable was comparable in both cohorts, but decreased moderately over time during the trial.

CONCLUSION

A high adherence to the wearable was observed in patients on intensive treatment protocols for a hematologic malignancy who experience high symptom burden. Remote patient monitoring of vital signs and physical activity was demonstrated to be feasible and of primarily sufficient quality.

Stride Velocity 95th Centile: Insights into Gaining Regulatory Qualification of the First Wearable-Derived Digital Endpoint for use in Duchenne Muscular Dystrophy Trials

In 2019, stride velocity 95th centile (SV95C) became the first wearable-derived digital clinical outcome assessment (COA) qualified by the European Medicines Agency (EMA) for use as a secondary endpoint in trials for Duchenne muscular dystrophy. SV95C was approved via the EMA's qualification pathway for novel methodologies for medicine development, which is a voluntary procedure for assessing the regulatory acceptability of innovative methods used in pharmaceutical research and development. SV95C is an objective, real-world digital ambulation measure of peak performance, representing the speed of the fastest strides taken by the wearer over a recording period of 180 hours. SV95C is correlated with traditional clinic-based assessments of motor function and has greater sensitivity to clinical change over 6 months than other wearable-derived stride variables, for example, median stride length or velocity. SV95C overcomes many limitations of episodic, clinic-based motor function testing, allowing the assessment of ambulation ability between clinic visits and under free-living conditions. Here we highlight considerations and challenges in developing SV95C using evidence generated by a high-performance wearable sensor. We also provide a commentary of the device's technical capabilities, which were a determining factor in the regulatory approval of SV95C. This article aims to provide insights into the methods employed, and the challenges faced, during the regulatory approval process for researchers developing new digital tools for patients with diseases that affect motor function.

Perspectives on emerging technologies, personalised medicine, and clinical research for cancer control in Latin America and the Caribbean

Challenges of health systems in Latin America and the Caribbean include accessibility, inequity, segmentation, and poverty. These challenges are similar in different countries of the region and transcend national borders. The increasing digital transformation of health care holds promise of more precise interventions, improved health outcomes, increased efficiency, and ultimately reduced health-care costs. In Latin America and the Caribbean, the adoption of digital health tools is in early stages and the quality of cancer registries, electronic health records, and structured databases are problematic. Cancer research and innovation in the region are limited due to inadequate academic resources and translational research is almost fully dependent on public funding. Regulatory complexity and extended timelines jeopardise the potential improvement in participation in international studies. Emerging technologies, artificial intelligence, big data, and cancer research represent an opportunity to address the health-care challenges in Latin America and the Caribbean collectively, by optimising national capacities, sharing and comparing best practices, and transferring scientific and technical capabilities.

Digital Health for Patients With Multiple Myeloma: An Unmet Need

Multiple myeloma (MM) is associated with the highest symptom burden and lowest health-related quality of life (HRQoL) among patients with hematologic malignancies. HRQoL in MM is heterogeneous, varying over the course of disease, with the highest burden at diagnosis and relapse. Patients with MM are increasingly being treated with oral maintenance medications at home. As a result, longitudinal monitoring of medication adherence and patient-reported outcomes, including HRQoL, could inform on disease status, therapeutic tolerability, and satisfaction with care. Digital health technologies, including telemedicine, mobile health, and wearable devices, are poised to become an integral part of modern health care, in part due to the surge in telemedicine necessitated by the COVID-19 pandemic. Although the literature has many reports on the use of digital health technologies in other types of cancers, fewer studies report on their application to MM. In the current narrative review, we survey the applications of digital health for MM. Although there is evidence that some are associated with improved health outcomes, challenges exist that must be met to ensure more widespread adoption. These include the need for increased awareness by patients and health care providers, lack of access by the typical older patient with MM, absence of randomized clinical trials, and low integration with current workflows such as electronic health records. Following our summary of technologies that could benefit patients with MM, we end by describing our vision for how they can be integrated into each phase of the patient journey.

Wearable activity trackers and artificial intelligence in the management of rheumatic diseases : Where are we in 2021?

Wearable activity trackers are playing an increasingly important role in healthcare. In the field of rheumatic and musculoskeletal diseases (RMDs), various applications are currently possible. This review will present the use of activity trackers to promote physical activity levels in rheumatology, as well as the use of trackers to measure health parameters and detect flares using artificial intelligence. Challenges and limitations of the use of artificial intelligence will be discussed, as well as technical issues when using activity trackers in clinical practice.

Natural Language Processing to Ascertain Cancer Outcomes From Medical Oncologist Notes

PURPOSE

Cancer research using electronic health records and genomic data sets requires clinical outcomes data, which may be recorded only in unstructured text by treating oncologists. Natural language processing (NLP) could substantially accelerate extraction of this information.

METHODS

Patients with lung cancer who had tumor sequencing as part of a single-institution precision oncology study from 2013 to 2018 were identified. Medical oncologists' progress notes for these patients were reviewed. For each note, curators recorded whether the assessment/plan indicated any cancer, progression/worsening of disease, and/or response to therapy or improving disease. Next, a recurrent neural network was trained using unlabeled notes to extract the assessment/plan from each note. Finally, convolutional neural networks were trained on labeled assessments/plans to predict the probability that each curated outcome was present. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) among a held-out test set of 10% of patients. Associations between curated response or progression end points and overall survival were measured using Cox models among patients receiving palliative-intent systemic therapy.

RESULTS

Medical oncologist notes (n = 7,597) were manually curated for 919 patients. In the 10% test set, NLP models replicated human curation with AUROCs of 0.94 for the any-cancer outcome, 0.86 for the progression outcome, and 0.90 for the response outcome. Progression/worsening events identified using NLP models were associated with shortened survival (hazard ratio [HR] for mortality, 2.49; 95% CI, 2.00 to 3.09); response/improvement events were associated with improved survival (HR, 0.45; 95% CI, 0.30 to 0.67).

CONCLUSION

NLP models based on neural networks can extract meaningful outcomes from oncologist notes at scale. Such models may facilitate identification of clinical and genomic features associated with response to cancer treatment.

Use of Telehealth for Domiciliary Follow-up After Hematopoietic Cell Transplantation During the COVID-19 Pandemic: Prospective Pilot Study

Background

Patients who have recently received a hematopoietic cell transplant (HCT) are at higher risk of acute complications in the first weeks after discharge, especially during the COVID-19 pandemic.

Objective

The aim of this study was to test the use of a telehealth platform for the follow-up of HCT patients during the first two weeks after discharge.

Methods

In total, 21 patients who received autologous or allogeneic HCT for hematological malignancies were screened from April 30, 2020, to July 15, 2020. The telehealth platform assisted in the daily collection of vital signs as well as physical and psychological symptoms for two weeks after hospital discharge. The required medical devices (oximeter and blood pressure monitor) were given to patients and a dedicated smartphone app was developed to collect this data. The data were reviewed daily through web-based software by a hematologist specializing in HCT.

Results

Only 12 of 21 patients were able to join and complete the study. Technological barriers were the most frequent limiting factor in this study. Among the 12 patients who completed the study, adherence to data reporting was high. The patients’ experience of using such a system was considered good. In two cases, the system enabled the early recognition of acute complications.

Conclusions

This pilot study showed that telehealth systems can be applied in the early posttransplant setting, with evident advantages for physicians and patients for both medical and psychological aspects. Technological issues still represent a challenge for the applicability of such a system, especially for older adult patients. Easier-to-use technologies could help to expand the use of telehealth systems in this setting in the future.

Wearable Devices: Current Status and Opportunities in Pain Assessment and Management

INTRODUCTION

We investigated the possibilities and opportunities for using wearable devices that measure physical activity and physiometric signals in conjunction with ecological momentary assessment (EMA) data to improve the assessment and treatment of pain.

METHODS

We considered studies with cross-sectional and longitudinal designs as well as interventional or observational studies correlating pain scores with measures derived from wearable devices. A search was also performed on studies that investigated physical activity and physiometric signals among patients with pain.

RESULTS

Few studies have assessed the possibility of incorporating wearable devices as objective tools for contextualizing pain and physical function in free-living environments. Of the studies that have been conducted, most focus solely on physical activity and functional outcomes as measured by a wearable accelerometer. Several studies report promising correlations between pain scores and signals derived from wearable devices, objectively measured physical activity, and physical function. In addition, there is a known association between physiologic signals that can be measured by wearable devices and pain, though studies using wearable devices to measure these signals and associate them with pain in free-living environments are limited.

CONCLUSION

There exists a great opportunity to study the complex interplay between physiometric signals, physical function, and pain in a real-time fashion in free-living environments. The literature supports the hypothesis that wearable devices can be used to develop reproducible biosignals that correlate with pain. The combination of wearable devices and EMA will likely lead to the development of clinically meaningful endpoints that will transform how we understand and treat pain patients.

Optimizing electronic capture of patient-reported outcome measures in oncology clinical trials: lessons learned from a qualitative study

Aim: To understand the impact of anticancer treatment on oncology patients' ability to use electronic solutions for completing patient-reported outcomes (ePRO). Materials & methods: Semi-structured interviews were conducted with seven individuals who had experienced a cancer diagnosis and treatment. Results: Participants reported that the following would impact the ability to interact with an ePRO solution: peripheral neuropathy of the hands (4/7), fatigue and/or concentration and memory issues (6/7), where they are in a treatment cycle (5/7). Approaches to improve usability included: larger, well-spaced buttons to deal with finger numbness, the ability to pause a survey and complete at a later point and presenting the recall period with every question to reduce reliance on memory. Conclusion: Symptoms associated with cancers and anticancer treatments can impact the use of technologies. The recommendations for optimizing the electronic implementation of patient-reported outcome instruments in this population provides the potential to improve data quality in oncology trials and places patient needs at the forefront to ensure 'fit-for-purpose' solutions.

The Use of Wearables in Clinical Trials During Cancer Treatment: Systematic Review

Background

Interest in the use of wearables in medical care is increasing. Wearables can be used to monitor different variables, such as vital signs and physical activity. A crucial point for using wearables in oncology is if patients already under the burden of severe disease and oncological treatment can accept and adhere to the device. At present, there are no specific recommendations for the use of wearables in oncology, and little research has examined the purpose of using wearables in oncology.

Objective

The purpose of this review is to explore the use of wearables in clinical trials during cancer treatment, with a special focus on adherence.

Methods

PubMed and EMBASE databases were searched prior and up to October 3, 2019, with no limitation in the date of publication. The search strategy was aimed at studies using wearables for monitoring adult patients with cancer during active antineoplastic treatment. Studies were screened independently by 2 reviewers by title and abstract, selected for inclusion and exclusion, and the full-text was assessed for eligibility. Data on study design, type of wearable used, primary outcome, adherence, and device outcome were extracted. Results were presented descriptively.

Results

Our systematic search identified 1269 studies, of which 25 studies met our inclusion criteria. The types of cancer represented in the studies were breast (7/25), gastrointestinal (4/25), lung (4/25), and gynecologic (1/25); 9 studies had multiple types of cancer. Oncologic treatment was primarily chemotherapy (17/25). The study-type distribution was pilot/feasibility study (12/25), observational study (10/25), and randomized controlled trial (3/25). The median sample size was 40 patients (range 7-180). All studies used a wearable with an accelerometer. Adherence varied across studies, from 60%-100% for patients wearing the wearable/evaluable sensor data and 45%-94% for evaluable days, but was differently measured and reported. Of the 25 studies, the most frequent duration for planned monitoring with a wearable was 8-30 days (13/25). Topics for wearable outcomes were physical activity (19/25), circadian rhythm (8/25), sleep (6/25), and skin temperature (1/25). Patient-reported outcomes (PRO) were used in 17 studies; of the 17 PRO studies, only 9 studies reported correlations between the wearable outcome and the PRO.

Conclusions

We found that definitions of outcome measures and adherence varied across studies, and limited consensus among studies existed on which variables to monitor during treatment. Less heterogeneity, better consensus in terms of the use of wearables, and established standards for the definitions of wearable outcomes and adherence would improve comparisons of outcomes from studies using wearables. Adherence, and the definition of such, seems crucial to conclude on data from wearable studies in oncology. Additionally, research using advanced wearable devices and active use of the data are encouraged to further explore the potential of wearables in oncology during treatment. Particularly, randomized clinical studies are warranted to create consensus on when and how to implement in oncological practice.

Use of wearable biometric monitoring devices to measure outcomes in randomized clinical trials: a methodological systematic review

BACKGROUND

Wearable biometric monitoring devices (BMDs) have the potential to transform the conduct of randomized controlled trials (RCTs) by shifting the collection of outcome data from single measurements at predefined time points to dense continuous measurements.

METHODS

Methodological systematic review to understand how recent RCTs used BMDs to measure outcomes and to describe the reporting of these RCTs. Electronic search was performed in the Cochrane Central Register of Controlled Trials, PubMed, and EMBASE and completed a page-by-page hand search in five leading medical journals between January 1, 2018, and December 31, 2018. Three reviewers independently extracted all primary and secondary outcomes collected using BMDs, and assessed (1) the definitions used to summarize BMD outcome data; (2) whether the validity, reliability, and responsiveness of sensors was reported; (3) the discrepancy with outcomes prespecified in public clinical trial registries; and (4) the methods used to manage missing and incomplete BMD outcome data.

RESULTS

Of the 4562 records screened, 75 RCTs were eligible. Among them, 24% tested a pharmacological intervention and 57% used an inertial measurement sensor to measure physical activity. Included trials involved 464 outcomes (average of 6 [SD = 8] outcomes per trial). In total, 35 trials used a BMD to measure a primary outcome. Several issues affected the value and transparency of trials using BMDs to measure outcomes. First, the definition of outcomes used in the trials was highly heterogeneous (e.g., 21 diabetes trials had 266 outcomes and 153 had different unique definitions to measure diabetes control), which limited the combination and comparison of results. Second, information on the validity, reliability, and responsiveness of sensors used was lacking in 74% of trials. Third, half (53%) of the outcomes measured with BMDs had not been prespecified, with a high risk of outcome reporting bias. Finally, reporting on the management of incomplete outcome data (e.g., due to suboptimal compliance with the BMD) was absent in 68% of RCTs.

CONCLUSIONS

Use of BMDs to measure outcomes is becoming the norm rather than the exception in many fields. Yet, trialists need to account for several methodological issues when specifying and conducting RCTs using these novel tools.

Accuracy of Mobile Applications versus Wearable Devices in Long-Term Step Measurements

Fitness sensors and health systems are paving the way toward improving the quality of medical care by exploiting the benefits of new technology. For example, the great amount of patient-generated health data available today gives new opportunities to measure life parameters in real time and create a revolution in communication for professionals and patients. In this work, we concentrated on the basic parameter typically measured by fitness applications and devices-the number of steps taken daily. In particular, the main goal of this study was to compare the accuracy and precision of smartphone applications versus those of wearable devices to give users an idea about what can be expected regarding the relative difference in measurements achieved using different system typologies. In particular, the data obtained showed a difference of approximately 30%, proving that smartphone applications provide inaccurate measurements in long-term analysis, while wearable devices are precise and accurate. Accordingly, we challenge the reliability of previous studies reporting data collected with phone-based applications, and besides discussing the current limitations, we support the use of wearable devices for mHealth.

Harnessing consumer smartphone and wearable sensors for clinical cancer research

As smartphones and consumer wearable devices become more ubiquitous, there is a growing opportunity to capture rich mobile sensor data continuously, passively, and in real-world settings with minimal burden. In the context of cancer, changes in these passively sensed digital biomarkers may reflect meaningful variation in functional status, symptom burden, quality of life, and risk for adverse clinical outcomes. These data could enable real-time remote monitoring of patients between clinical encounters and more proactive, comprehensive, and personalized care. Over the past few years, small studies across a variety of cancer populations support the feasibility and potential clinical value of mobile sensors in oncology. Barriers to implementing mobile sensing in clinical oncology care include the challenges of managing and making sense of continuous sensor data, patient engagement issues, difficulty integrating sensor data into existing electronic health systems and clinical workflows, and ethical and privacy concerns. Multidisciplinary collaboration is needed to develop mobile sensing frameworks that overcome these barriers and that can be implemented at large-scale for remote monitoring of deteriorating health during or after cancer treatment or for promotion and tailoring of lifestyle or symptom management interventions. Leveraging digital technology has the potential to enrich scientific understanding of how cancer and its treatment affect patient lives, to use this understanding to offer more timely and personalized support to patients, and to improve clinical oncology outcomes.

Quantified Kinematics to Evaluate Patient Chemotherapy Risks in Clinic

PURPOSE

Performance status (PS) is a key factor in oncologic decision making, but conventional scales used to measure PS vary among observers. Consumer-grade biometric sensors have previously been identified as objective alternatives to the assessment of PS. Here, we investigate how one such biometric sensor can be used during a clinic visit to identify patients who are at risk for complications, particularly unexpected hospitalizations that may delay treatment or result in low physical activity. We aim to provide a novel and objective means of predicting tolerability to chemotherapy.

METHODS

Thirty-eight patients across three centers in the United States who were diagnosed with a solid tumor with plans for treatment with two cycles of highly emetogenic chemotherapy were included in this single-arm, observational prospective study. A noninvasive motion-capture system quantified patient movement from chair to table and during the get-up-and-walk test. Activity levels were recorded using a wearable sensor over a 2-month period. Changes in kinematics from two motion-capture data points pre- and post-treatment were tested for correlation with unexpected hospitalizations and physical activity levels as measured by a wearable activity sensor.

RESULTS

Among 38 patients (mean age, 48.3 years; 53% female), kinematic features from chair to table were the best predictors for unexpected health care encounters (area under the curve, 0.775 ± 0.029) and physical activity (area under the curve, 0.830 ± 0.080). Chair-to-table acceleration of the nonpivoting knee (t = 3.39; P = .002) was most correlated with unexpected health care encounters. Get-up-and-walk kinematics were most correlated with physical activity, particularly the right knee acceleration (t = -2.95; P = .006) and left arm angular velocity (t = -2.4; P = .025).

CONCLUSION

Chair-to-table kinematics are good predictors of unexpected hospitalizations, whereas the get-up-and-walk kinematics are good predictors of low physical activity.

State of eHealth in Cancer Care: Review of the Benefits and Limitations of eHealth Tools

BACKGROUND

eHealth has the potential to improve patient access to care through the use of various tools.

OBJECTIVES

This article provides a review of some eHealth technologies, including a discussion of their benefits and limitations. An overview of studies using eHealth technologies are summarized, and future directions are explored.

METHODS

A review of the eHealth literature was conducted, with a focus on outcomes of telehealth interventions in cancer care.

FINDINGS

eHealth can transform health care by expanding the reach of clinical cancer care. Examples of this expansion of care include patients who live in remote areas with limited access to oncology providers, patients who find travel challenging, and patients who prefer the convenience of communicating with their provider from their home. Such telehealth interventions can increase patient satisfaction, but additional research is needed to further evaluate patient outcomes.

Patient-generated health data collection using a wearable activity tracker in cancer patients-a feasibility study

PURPOSE

Incorporation of patient-generated health data (PGHD) into clinical research requires an investigation of the validity of outcomes and feasibility of implementation. This single-arm pilot trial investigated the feasibility of using a commercially available activity tracking wearable device in cancer patients to assess adherence to the device and real-time PGHD collection in a clinical research setting.

METHODS

From July to November 2017, enrolled adult patients were asked to wear a wristband-style device. Brief Fatigue Inventory (BFI) and MD Anderson Symptom Inventory (MDASI) were assessed at baseline and on day 29. Furthermore, 29-day Pittsburgh Sleep Quality Index, global impression of the devices, and NCI CTCAE v4 were evaluated.

RESULTS

Of 30 patients (mean age, 58.6 years; male, 21 [70%]), 15 (50%) and 11 (36.7%) had gastrointestinal and lung cancer, respectively, and 27 (90%, 95% CI: 0.74-0.98) were well adhered (> 70%) to the device for 28 days. The mean adherence was 84.9% (range: 41.7-95.2%). More frequent PGHD synchronization tended to show better device adherence, with moderate correlation (r = 0.62, 95% CI: 0.33-0.80, p < 000.1).

CONCLUSIONS

The feasibility of using a wearable activity tracker was confirmed in cancer patients receiving chemotherapy for a month. For future implementation in clinical trials, there is a need for further comprehensive assessment of the validity and reliability of wearable activity trackers.

TRIAL REGISTRATION

This trial was registered at the University Hospital Medical Information Network Clinical Trials Registry as UMIN: UMIN000027575.

Patients' Perspective on Digital Technologies in Advanced Genitourinary Cancers

INTRODUCTION

Digital technologies allow for the remote monitoring of cancer patients and thereby close an important care gap. Despite a variety of upcoming digital health-tech solutions, there is little knowledge on uro-oncologic patients' perception of digital technologies in clinical care and cancer trials.

PATIENTS AND METHODS

A questionnaire was developed to evaluate patients' current use, preferences, and expectations of digital health technology. Patients receiving systemic treatment for urothelial, prostate, and renal-cell carcinoma were included during outpatient visits.

RESULTS

Ninety-seven patients undergoing systemic therapy for metastatic renal-cell, urothelial, or prostate cancer were included in the final analysis. Internet, smartphone, and wearable user rates were significantly higher in younger patients (100% user rate in age group 40-49 years vs. 38% in age group 80-89 years). Patients were more likely to use wearables in clinical trials when they received the generated data (2.9/5) than when they did not (2.3/5, P < .0001). Interest in activity data (3.7/5) was higher than sleeping data (2.7/5, P < .0001), but desire for sleeping data increases with advancement of treatment lines (3.9, P = .008). Patients prefer a digital follow-up every 2.6 days; younger patients and those receiving advanced therapy lines prefer less frequent follow-up (respectively, every 3.3 days, P = .050, and every 4.0 days, P = .0001). Patients allow a maximum of an average of 2.2 minutes daily for digital follow-up.

CONCLUSION

We observed high engagement in digital technologies and interest in the data generated by digital devices. However, for the development of future health care applications, aspects such as patient age, gender, and therapy line need to be considered in uro-oncologic patients.

Real-time, personalized medicine through wearable sensors and dynamic predictive modeling: a new paradigm for clinical medicine

Accurately predicting the onset and course of a disease in an individual is a major unmet challenge in medicine due to the complex and dynamic nature of disease progression. Continuous data from wearable technologies and biomarker data with a fine time resolution provide a unique opportunity to learn more about disease evolution and to usher in a new era of personalized and real-time medicine. Herein, we propose the potential of real-time, continuously measured physiological data as a noninvasive biomarker approach for detecting disease transitions, using allogeneic hematopoietic stem cell transplant (HCT) patient care as an example. Additionally, we review a recent computational technique, the landscape dynamic network biomarker method, that uses biomarker data to identify transition states in disease progression and explore how to use it with both biomarker and physiological data for earlier detection of graft-versus-host disease specifically. Throughout, we argue that increased collaboration across multiple fields is essential to realizing the full potential of wearable and biomarker data in a new paradigm of personalized and real-time medicine.

The day after: correlates of patient-reported outcomes with actigraphy-assessed sleep in cancer patients at home (inCASA project)

Subjective sleep assessment in cancer patients poorly correlates with actigraphy parameters that usually encompass multiple nights. We aimed to determine the objective actigraphy measures that best correlated with subjective sleep ratings on a night-by-night basis in cancer patients. Thirty-one cancer patients daily self-rated sleep disturbances using the single dedicated item of the MD Anderson Symptom Inventory (0-10 scale) with 18 other items, and continuously wore a wrist actigraph for 30 days. Objective sleep parameters were computed from the actigraphy nighttime series, and correlated with subjective sleep disturbances reported on the following day, using repeated measures correlations. Multilevel Poisson regression analysis was performed to identify the objective and subjective parameters that affected subjective sleep rating. Poor subjective sleep score was correlated with poor sleep efficiency (rrm = -0.13, p = 0.002) and large number of wake episodes (rrm = 0.12, p = 0.005) on the rated night. Multilevel analysis demonstrated that the expected sleep disturbance score was affected by the joint contribution of the wake episodes (exp(β) = 1.01, 95% confidence interval = 1.00 to 1.02, p = 0.016), fatigue (exp(β) = 1.35, 95% confidence interval = 1.15 to 1.55, p < 0.001) and drowsiness (exp(β) = 1.70, 95% confidence interval = 1.19 to 2.62, p = 0.018), self-rated the following evening, and sleep disturbance experienced one night before (exp(β) = 1.77, 95% confidence interval = 1.41 to 2.22, p < 0.001). The night-by-night approach within a multidimensional home tele-monitoring framework mainly identified the objective number of wake episodes computed from actigraphy records as the main determinant of the severity of sleep complaint in cancer patients on chemotherapy. This quantitative information remotely obtained in real time from cancer patients provides a novel framework for streamlining and evaluating interventions toward sleep improvement in cancer patients.

Machine Learning Algorithms Can Use Wearable Sensor Data to Accurately Predict Six-Week Patient-Reported Outcome Scores Following Joint Replacement in a Prospective Trial

BACKGROUND

Tracking patient-generated health data (PGHD) following total joint arthroplasty (TJA) may enable data-driven early intervention to improve clinical results. We aim to demonstrate the feasibility of combining machine learning (ML) with PGHD in TJA to predict patient-reported outcome measures (PROMs).

METHODS

Twenty-two TJA patients were recruited for this pilot study. Three activity trackers collected 35 features from 4 weeks before to 6 weeks following surgery. PROMs were collected at both endpoints (Hip and Knee Disability and Osteoarthritis Outcome Score, Knee Osteoarthritis Outcome Score, and Veterans RAND 12-Item Health Survey Physical Component Score). We used ML to identify features with the highest correlation with PROMs. The algorithm trained on a subset of patients and used 3 feature sets (A, B, and C) to group the rest into one of the 3 PROM clusters.

RESULTS

Fifteen patients completed the study and collected 3 million data points. Three sets of features with the highest R² values relative to PROMs were selected (A, B and C). Data collected through the 11th day had the highest predictive value. The ML algorithm grouped patients into 3 clusters predictive of 6-week PROM results, yielding total sum of squares values ranging from 3.86 (A) to 1.86 (C).

CONCLUSION

This small but critical proof-of-concept study demonstrates that ML can be used in combination with PGHD to predict 6-week PROM data as early as 11 days following TJA surgery. Further study is needed to confirm these findings and their clinical value.

Optimal Sampling Frequency for Wearable Sensor Data in Arthroplasty Outcomes Research. A Prospective Observational Cohort Trial

BACKGROUND

Wearable sensors can track patient activity after surgery. The optimal data sampling frequency to identify an association between patient-reported outcome measures (PROMs) and sensor data is unknown. Most commercial grade sensors report 24-hour average data. We hypothesize that increasing the frequency of data collection may improve the correlation with PROM data.

METHODS

Twenty-two total joint arthroplasty (TJA) patients were prospectively recruited and provided wearable sensors. Second-by-second (Raw) and 24-hour average data (24Hr) were collected on 7 gait metrics on the 1st, 7th, 14th, 21st, and 42nd days postoperatively. The average for each metric as well as the slope of a linear regression for 24Hr data (24HrLR) was calculated. The R2 associations were calculated using machine learning algorithms against individual PROM results at 6 weeks. The resulting R2 values were defined having a mild, moderate, or strong fit (R2 ≥ 0.2, ≥0.3, and ≥0.6, respectively) with PROM results. The difference in frequency of fit was analyzed with the McNemar's test.

RESULTS

The frequency of at least a mild fit (R2 ≥ 0.2) for any data point at any time frame relative to either of the PROMs measured was higher for Raw data (42%) than 24Hr data (32%; P = .041). There was no difference in frequency of fit for 24hrLR data (32%) and 24Hr data values (32%; P > .05). Longer data collection improved frequency of fit.

CONCLUSION

In this prospective trial, increasing sampling frequency above the standard 24Hr average provided by consumer grade activity sensors improves the ability of machine learning algorithms to predict 6-week PROMs in our total joint arthroplasty cohort.

A Mobile System to Improve Quality of Life Via Energy Balance in Breast Cancer Survivors (BENECA mHealth): Prospective Test-Retest Quasiexperimental Feasibility Study

Background

Energy balance is defined as the difference between energy expenditure and energy intake. The current state of knowledge supports the need to better integrate mechanistic approaches through effective studies of energy balance in the cancer population because of an observed significant lack of adherence to healthy lifestyle recommendations. To stimulate changes in breast cancer survivors’ lifestyles based on energy balance, our group developed the BENECA (Energy Balance on Cancer) mHealth app. BENECA has been previously validated as a reliable energy balance monitoring system.

Objective

Based on our previous results, the goal of this study was to investigate the feasibility of BENECA mHealth in an ecological clinical setting with breast cancer survivors, by studying (1) its feasibility and (2) pretest-posttest differences with regard to breast cancer survivor lifestyles, quality of life (QoL), and physical activity (PA) motivation.

Methods

Eighty breast cancer survivors diagnosed with stage I to IIIA and with a body mass index over 25 kg/m2 were enrolled in this prospective test-retest quasi-experimental study. Patients used BENECA mHealth for 8 weeks and were assessed at baseline and the postintervention period. Feasibility main outcomes included percentage of adoption, usage, and attrition; user app quality perception measured with the Mobile App Rating Scale (MARS); satisfaction with the Net Promoter Score (NPS); and barriers and facilitators of its use. Clinical main outcomes included measuring QoL with the European Organization for Research and Treatment of Cancer QoL Questionnaire Core 30 (EORT QLQ-C30), PA assessment with accelerometry, PA motivation measure with a Spanish self-efficacy scale for physical activity (EAF), and body composition with dual-energy x-ray absorptiometry. Statistical tests (using paired-sample t tests) and Kaplan-Meier survival curves were analyzed.

Results

BENECA was considered feasible by the breast cancer survivors in terms of use (76%, 58/76), adoption (69%, 80/116), and satisfaction (positive NPS). The app quality score did not make it one of the best-rated apps (mean 3.71, SD 0.47 points out of 5). BENECA mHealth improved the QoL of participants (global health mean difference [MD] 12.83, 95% CI 8.95-16.71, P<.001), and EAF score (global MD 36.99, 95% CI 25.52-48.46, P<.001), daily moderate-to-vigorous PA (MD 7.38, 95% CI 0.39-14.37, P=.04), and reduced body weight (MD −1.42, 95% CI −1.97 to −0.87, P<.001).

Conclusions

BENECA mHealth can be considered feasible in a real clinical context to promote behavioral changes in the lifestyles of breast cancer survivors, but it needs to be enhanced to improve user satisfaction with use and functionality. This study highlights the importance of the use of mobile apps based on energy balance and how the QoL of breast cancer survivors can be improved via monitoring.

The Future of Wearable Technologies and Remote Monitoring in Health Care

Mobile technology has become a ubiquitous part of everyday life and is changing the way we offer clinical care and perform clinical research. We have unprecedented access to data for one's self-care as well as for sharing with health care providers. Meeting the challenge posed by the influx of wearable device data requires a multidisciplinary team of researchers, clinicians, software developers, information technologists, and statisticians. Although the possibility of what can be achieved with the ever-evolving wearable technologies seems to be unlimited, regulatory agencies have provided a framework to establish standards for clinical applications, which will also affect research applications. Clinical programs and electronic medical records vendors should prepare to establish a framework to implement these technologies into clinicians' workflow and to allow feedback to measure the impact on clinical outcome. In this article, we discuss how a new brand of multidisciplinary care is evolving around mobile health devices and present a vision of up-and-coming technology in this space.

Smartphone Sensors for Monitoring Cancer-Related Quality of Life: App Design, EORTC QLQ-C30 Mapping and Feasibility Study in Healthy Subjects

Quality of life (QoL) indicators are now being adopted as clinical outcomes in clinical trials on cancer treatments. Technology-free daily monitoring of patients is complicated, time-consuming and expensive due to the need for vast amounts of resources and personnel. The alternative method of using the patients' own phones could reduce the burden of continuous monitoring of cancer patients in clinical trials. This paper proposes monitoring the patients' QoL by gathering data from their own phones. We considered that the continuous multiparametric acquisition of movement, location, phone calls, conversations and data use could be employed to simultaneously monitor their physical, psychological, social and environmental aspects. An open access phone app was developed (Human Dynamics Reporting Service (HDRS)) to implement this approach. We here propose a novel mapping between the standardized QoL items for these patients, the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) and define HDRS monitoring indicators. A pilot study with university volunteers verified the plausibility of detecting human activity indicators directly related to QoL.