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Bachman SL, Gomes E, Aryal S, Cella D, Clay I, Lyden K, Leach HJ. Do Measures of Real-World Physical Behavior Provide Insights Into the Well-Being and Physical Function of Cancer Survivors? Cross-Sectional Analysis. JMIR Cancer 2024; 10:e53180. [PMID: 39008350 DOI: 10.2196/53180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 02/26/2024] [Accepted: 04/24/2024] [Indexed: 07/16/2024] Open
Abstract
BACKGROUND As the number of cancer survivors increases, maintaining health-related quality of life in cancer survivorship is a priority. This necessitates accurate and reliable methods to assess how cancer survivors are feeling and functioning. Real-world digital measures derived from wearable sensors offer potential for monitoring well-being and physical function in cancer survivorship, but questions surrounding the clinical utility of these measures remain to be answered. OBJECTIVE In this secondary analysis, we used 2 existing data sets to examine how measures of real-world physical behavior, captured with a wearable accelerometer, were related to aerobic fitness and self-reported well-being and physical function in a sample of individuals who had completed cancer treatment. METHODS Overall, 86 disease-free cancer survivors aged 21-85 years completed self-report assessments of well-being and physical function, as well as a submaximal exercise test that was used to estimate their aerobic fitness, quantified as predicted submaximal oxygen uptake (VO2). A thigh-worn accelerometer was used to monitor participants' real-world physical behavior for 7 days. Accelerometry data were used to calculate average values of the following measures of physical behavior: sedentary time, step counts, time in light and moderate to vigorous physical activity, time and weighted median cadence in stepping bouts over 1 minute, and peak 30-second cadence. RESULTS Spearman correlation analyses indicated that 6 (86%) of the 7 accelerometry-derived measures of real-world physical behavior were not significantly correlated with Functional Assessment of Cancer Therapy-General total well-being or linked Patient-Reported Outcomes Measurement Information System-Physical Function scores (Ps≥.08). In contrast, all but one of the physical behavior measures were significantly correlated with submaximal VO2 (Ps≤.03). Comparing these associations using likelihood ratio tests, we found that step counts, time in stepping bouts over 1 minute, and time in moderate to vigorous activity were more strongly associated with submaximal VO2 than with self-reported well-being or physical function (Ps≤.03). In contrast, cadence in stepping bouts over 1 minute and peak 30-second cadence were not more associated with submaximal VO2 than with the self-reported measures (Ps≥.08). CONCLUSIONS In a sample of disease-free cancer survivors, we found that several measures of real-world physical behavior were more associated with aerobic fitness than with self-reported well-being and physical function. These results highlight the possibility that in individuals who have completed cancer treatment, measures of real-world physical behavior may provide additional information compared with self-reported and performance measures. To advance the appropriate use of digital measures in oncology clinical research, further research evaluating the clinical utility of real-world physical behavior over time in large, representative samples of cancer survivors is warranted. TRIAL REGISTRATION ClinicalTrials.gov NCT03781154; https://clinicaltrials.gov/ct2/show/NCT03781154.
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Affiliation(s)
| | - Emma Gomes
- Department of Health and Exercise Science, Colorado State University, Fort Collins, CO, United States
| | | | - David Cella
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Ieuan Clay
- VivoSense, Inc, Newport Coast, CA, United States
| | - Kate Lyden
- VivoSense, Inc, Newport Coast, CA, United States
| | - Heather J Leach
- Department of Health and Exercise Science, Colorado State University, Fort Collins, CO, United States
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2
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Tibbitts DC, Stoyles SA, Mancini M, El-Gohary M, Horak FB, Dieckmann NF, Winters-Stone KM. The Use of Novel Instrumented Socks to Detect Changes in Daily Life Mobility During an Exercise Intervention in Prostate Cancer Survivors Treated with Androgen Deprivation Therapy. Semin Oncol Nurs 2024:151658. [PMID: 38902183 DOI: 10.1016/j.soncn.2024.151658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 06/22/2024]
Abstract
OBJECTIVES To describe changes in daily life mobility in prostate cancer survivors treated with androgen deprivation therapy (ADT) after a 6-month exercise intervention using novel instrumented socks and to identify characteristics of participants who exhibited changes in daily life mobility. METHODS A subset of participants in a fall prevention exercise trial completed objective tests and patient-reported surveys of physical functioning, and wore instrumented socks for up to 7 days to measure daily life mobility. Changes in cadence, double support proportion, and pitch angle of the foot at toe-off were selected as measures of daily life mobility previously found to be different in men exposed to ADT for prostate cancer versus controls. Daily life mobility was compared from baseline to 6 months using paired t-tests. Characteristics of responders who improved their daily life mobility were compared to nonresponders using two-sample t-tests, Chi-squared proportion tests, or Fisher's Exact Tests. RESULTS Our sample included 35 prostate cancer survivors (mean age 71.6 ± 7.8 years). Mean cadence, double support proportion, and pitch angle at toe-off did not change significantly over 6 months of exercise, but 14 participants (40%) improved in at least two of three daily life mobility measures ("responders"). Responders were characterized by lower physical functioning, lower cadence in daily life, fewer comorbidities, and better social and mental/emotional functioning. CONCLUSIONS Certain daily life mobility measures potentially impacted by ADT could be measured with instrumented socks and improved by exercise. Men who start with lower physical functioning and better social and mental/emotional functioning appear most likely to benefit, possibly because they have more to gain from exercise and are able to engage in a 6-month intervention. IMPLICATIONS FOR NURSING PRACTICE Technology-based approaches could provide nurses with an objective measure of daily life mobility for patients with chronic illness and detect who is responding to rehabilitation.
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Affiliation(s)
- Deanne C Tibbitts
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA; Division of Oncological Sciences, Oregon Health & Science University, Portland, OR, USA
| | - Sydnee A Stoyles
- School of Nursing, Oregon Health & Science University, Portland, OR, USA
| | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | | | - Fay B Horak
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Nathan F Dieckmann
- School of Nursing, Oregon Health & Science University, Portland, OR, USA
| | - Kerri M Winters-Stone
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA; Division of Oncological Sciences, Oregon Health & Science University, Portland, OR, USA.
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3
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Del-Valle-Soto C, Briseño RA, Valdivia LJ, Nolazco-Flores JA. Unveiling wearables: exploring the global landscape of biometric applications and vital signs and behavioral impact. BioData Min 2024; 17:15. [PMID: 38863014 PMCID: PMC11165804 DOI: 10.1186/s13040-024-00368-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 05/28/2024] [Indexed: 06/13/2024] Open
Abstract
The development of neuroscientific techniques enabling the recording of brain and peripheral nervous system activity has fueled research in cognitive science. Recent technological advancements offer new possibilities for inducing behavioral change, particularly through cost-effective Internet-based interventions. However, limitations in laboratory equipment volume have hindered the generalization of results to real-life contexts. The advent of Internet of Things (IoT) devices, such as wearables, equipped with sensors and microchips, has ushered in a new era in behavior change techniques. Wearables, including smartwatches, electronic tattoos, and more, are poised for massive adoption, with an expected annual growth rate of 55% over the next five years. These devices enable personalized instructions, leading to increased productivity and efficiency, particularly in industrial production. Additionally, the healthcare sector has seen a significant demand for wearables, with over 80% of global consumers willing to use them for health monitoring. This research explores the primary biometric applications of wearables and their impact on users' well-being, focusing on the integration of behavior change techniques facilitated by IoT devices. Wearables have revolutionized health monitoring by providing real-time feedback, personalized interventions, and gamification. They encourage positive behavior changes by delivering immediate feedback, tailored recommendations, and gamified experiences, leading to sustained improvements in health. Furthermore, wearables seamlessly integrate with digital platforms, enhancing their impact through social support and connectivity. However, privacy and data security concerns must be addressed to maintain users' trust. As technology continues to advance, the refinement of IoT devices' design and functionality is crucial for promoting behavior change and improving health outcomes. This study aims to investigate the effects of behavior change techniques facilitated by wearables on individuals' health outcomes and the role of wearables in promoting a healthier lifestyle.
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Affiliation(s)
- Carolina Del-Valle-Soto
- Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan, 45010, Jalisco, Mexico.
| | - Ramon A Briseño
- Centro Universitario de Ciencias Económico Administrativas, Universidad de Guadalajara, Zapopan, 45180, Jalisco, Mexico
| | - Leonardo J Valdivia
- Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan, 45010, Jalisco, Mexico
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Almeida D, Umuhire D, Gonzalez-Quevedo R, António A, Burgos JG, Verpillat P, Bere N, Sepodes B, Torre C. Leveraging patient experience data to guide medicines development, regulation, access decisions and clinical care in the EU. Front Med (Lausanne) 2024; 11:1408636. [PMID: 38846141 PMCID: PMC11153762 DOI: 10.3389/fmed.2024.1408636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 05/07/2024] [Indexed: 06/09/2024] Open
Abstract
Patient experience data (PED), provided by patients/their carers without interpretation by clinicians, directly capture what matters more to patients on their medical condition, treatment and impact of healthcare. PED can be collected through different methodologies and these need to be robust and validated for its intended use. Medicine regulators are increasingly encouraging stakeholders to generate, collect and submit PED to support both scientific advice in development programs and regulatory decisions on the approval and use of these medicines. This article reviews the existing definitions and types of PED and demonstrate the potential for use in different settings of medicines' life cycle, focusing on Patient-Reported Outcomes (PRO) and Patient Preferences (PP). Furthermore, it addresses some challenges and opportunities, alluding to important regulatory guidance that has been published, methodological aspects and digitalization, highlighting the lack of guidance as a key hurdle to achieve more systematic inclusion of PED in regulatory submissions. In addition, the article discusses opportunities at European and global level that could be implemented to leverage PED use. New digital tools that allow patients to collect PED in real time could also contribute to these advances, but it is equally important not to overlook the challenges they entail. The numerous and relevant initiatives being developed by various stakeholders in this field, including regulators, show their confidence in PED's value and create an ideal moment to address challenges and consolidate PED use across medicines' life cycle.
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Affiliation(s)
- Diogo Almeida
- Laboratory of Systems Integration Pharmacology, Clinical and Regulatory Science, Research Institute for Medicines (iMed.ULisboa), Lisbon, Portugal
- Faculdade de Farmácia, Universidade de Lisboa, Lisbon, Portugal
| | - Denise Umuhire
- Data Analytics and Methods Task Force, European Medicines Agency, Amsterdam, Netherlands
| | - Rosa Gonzalez-Quevedo
- Public and Stakeholders Engagement Department, European Medicines Agency, Amsterdam, Netherlands
| | - Ana António
- Referrals Office, Quality and Safety of Medicines Department, European Medicines Agency, Amsterdam, Netherlands
| | - Juan Garcia Burgos
- Public and Stakeholders Engagement Department, European Medicines Agency, Amsterdam, Netherlands
| | - Patrice Verpillat
- Data Analytics and Methods Task Force, European Medicines Agency, Amsterdam, Netherlands
| | - Nathalie Bere
- Regulatory Practice and Analysis, Medsafe—New Zealand Medicines and Medical Devices Safety Authority, Wellington, New Zealand
| | - Bruno Sepodes
- Laboratory of Systems Integration Pharmacology, Clinical and Regulatory Science, Research Institute for Medicines (iMed.ULisboa), Lisbon, Portugal
- Faculdade de Farmácia, Universidade de Lisboa, Lisbon, Portugal
| | - Carla Torre
- Laboratory of Systems Integration Pharmacology, Clinical and Regulatory Science, Research Institute for Medicines (iMed.ULisboa), Lisbon, Portugal
- Faculdade de Farmácia, Universidade de Lisboa, Lisbon, Portugal
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Zhou W, Cho Y, Pu J, Shang S. Trends in wearable device use among cancer survivors in the United States from 2019 to 2022. J Geriatr Oncol 2024; 15:101729. [PMID: 38360455 DOI: 10.1016/j.jgo.2024.101729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/29/2024] [Accepted: 02/09/2024] [Indexed: 02/17/2024]
Affiliation(s)
- Weijiao Zhou
- School of Nursing, Peking University, Beijing, China
| | - Youmin Cho
- Youmin Cho, Chungnam National University College of Nursing, Daejeon, South Korea
| | - Junlan Pu
- School of Nursing, Peking University, Beijing, China
| | - Shaomei Shang
- School of Nursing, Peking University, Beijing, China.
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Andreoletti M, Haller L, Vayena E, Blasimme A. Mapping the ethical landscape of digital biomarkers: A scoping review. PLOS DIGITAL HEALTH 2024; 3:e0000519. [PMID: 38753605 PMCID: PMC11098308 DOI: 10.1371/journal.pdig.0000519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 04/22/2024] [Indexed: 05/18/2024]
Abstract
In the evolving landscape of digital medicine, digital biomarkers have emerged as a transformative source of health data, positioning them as an indispensable element for the future of the discipline. This necessitates a comprehensive exploration of the ethical complexities and challenges intrinsic to this cutting-edge technology. To address this imperative, we conducted a scoping review, seeking to distill the scientific literature exploring the ethical dimensions of the use of digital biomarkers. By closely scrutinizing the literature, this review aims to bring to light the underlying ethical issues associated with the development and integration of digital biomarkers into medical practice.
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Affiliation(s)
- Mattia Andreoletti
- Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Luana Haller
- Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Effy Vayena
- Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Alessandro Blasimme
- Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
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Innominato PF, Macdonald JH, Saxton W, Longshaw L, Granger R, Naja I, Allocca C, Edwards R, Rasheed S, Folkvord F, de Batlle J, Ail R, Motta E, Bale C, Fuller C, Mullard AP, Subbe CP, Griffiths D, Wreglesworth NI, Pecchia L, Fico G, Antonini A. Digital Remote Monitoring Using an mHealth Solution for Survivors of Cancer: Protocol for a Pilot Observational Study. JMIR Res Protoc 2024; 13:e52957. [PMID: 38687985 PMCID: PMC11094600 DOI: 10.2196/52957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 01/16/2024] [Accepted: 02/05/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Healthy lifestyle interventions have a positive impact on multiple disease trajectories, including cancer-related outcomes. Specifically, appropriate habitual physical activity, adequate sleep, and a regular wholesome diet are of paramount importance for the wellness and supportive care of survivors of cancer. Mobile health (mHealth) apps have the potential to support novel tailored lifestyle interventions. OBJECTIVE This observational pilot study aims to assess the feasibility of mHealth multidimensional longitudinal monitoring in survivors of cancer. The primary objective is to test the compliance (user engagement) with the monitoring solution. Secondary objectives include recording clinically relevant subjective and objective measures collected through the digital solution. METHODS This is a monocentric pilot study taking place in Bangor, Wales, United Kingdom. We plan to enroll up to 100 adult survivors of cancer not receiving toxic anticancer treatment, who will provide self-reported behavioral data recorded via a dedicated app and validated questionnaires and objective data automatically collected by a paired smartwatch over 16 weeks. The participants will continue with their normal routine surveillance care for their cancer. The primary end point is feasibility (eg, mHealth monitoring acceptability). Composite secondary end points include clinically relevant patient-reported outcome measures (eg, the Edmonton Symptom Assessment System score) and objective physiological measures (eg, step counts). This trial received a favorable ethical review in May 2023 (Integrated Research Application System 301068). RESULTS This study is part of an array of pilots within a European Union funded project, entitled "GATEKEEPER," conducted at different sites across Europe and covering various chronic diseases. Study accrual is anticipated to commence in January 2024 and continue until June 2024. It is hypothesized that mHealth monitoring will be feasible in survivors of cancer; specifically, at least 50% (50/100) of the participants will engage with the app at least once a week in 8 of the 16 study weeks. CONCLUSIONS In a population with potentially complex clinical needs, this pilot study will test the feasibility of multidimensional remote monitoring of patient-reported outcomes and physiological parameters. Satisfactory compliance with the use of the app and smartwatch, whether confirmed or infirmed through this study, will be propaedeutic to the development of innovative mHealth interventions in survivors of cancer. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/52957.
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Affiliation(s)
- Pasquale F Innominato
- Oncology Department, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, United Kingdom
- Warwick Medical School & Cancer Research Centre, University of Warwick, Coventry, United Kingdom
- Chronotherapy, Cancers and Transplantation Research Unit, Faculty of Medicine, Université Paris-Saclay, Villejuif, France
| | - Jamie H Macdonald
- Institute for Applied Human Physiology, School of Psychology and Sports Science, Bangor University, Bangor, United Kingdom
| | - Wendy Saxton
- Research and Development Department, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, United Kingdom
| | - Laura Longshaw
- Research and Development Department, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, United Kingdom
| | - Rachel Granger
- Institute for Applied Human Physiology, School of Psychology and Sports Science, Bangor University, Bangor, United Kingdom
| | - Iman Naja
- Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom
| | | | - Ruth Edwards
- Dietetics Department, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, United Kingdom
| | - Solah Rasheed
- Dietetics Department, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, United Kingdom
| | - Frans Folkvord
- PredictBy, Barcelona, Spain
- Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, Netherlands
| | | | - Rohit Ail
- Health Innovation, Samsung, Staines, United Kingdom
| | - Enrico Motta
- Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom
| | - Catherine Bale
- Oncology Department, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, United Kingdom
| | - Claire Fuller
- Oncology Department, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, United Kingdom
| | - Anna P Mullard
- Oncology Department, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, United Kingdom
| | - Christian P Subbe
- Acute and Critical Care Medicine, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, United Kingdom
- School of Medical Sciences, Bangor University, Bangor, United Kingdom
| | - Dawn Griffiths
- Oncology Department, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, United Kingdom
| | - Nicholas I Wreglesworth
- Oncology Department, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, United Kingdom
- School of Medical Sciences, Bangor University, Bangor, United Kingdom
| | - Leandro Pecchia
- School of Engineering, University of Warwick, Coventry, United Kingdom
- Facoltà Dipartimentale di Ingegneria, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Giuseppe Fico
- Life Supporting Technologies, Escuela Técnica Superior de Ingenieros de Telecomunicaciones, Universidad Politécnica de Madrid, Madrid, Spain
| | - Alessio Antonini
- Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom
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Cristian A, Rubens M, Orada R, DeVries K, Syrkin G, DePiero MT, Estenoz M, Kothakapu S, McGranaghan P, Lindeman PR. Development of a Cancer Rehabilitation Dashboard to Collect Data on Physical Function in Cancer Patients and Survivors. Am J Phys Med Rehabil 2024; 103:S36-S40. [PMID: 38364028 DOI: 10.1097/phm.0000000000002424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2024]
Abstract
OBJECTIVE The aim of the study is to describe the development of a cancer rehabilitation dashboard that collects data on physical function for cancer survivors in a cancer institute. METHODS This project was conducted at the Miami Cancer Institute. The cancer rehabilitation dashboard was developed by a team of physicians, biostatistician, and medical informatics teams to record, report and track the physical function of cancer survivors. A multimodal approach to the measurement of physical function was used and included the Patient-Reported Outcome Measurement Information System-Physical Function short form, Patient-Reported Outcome Measurement Information System-Fatigue short form, Timed Up and Go Test, Sit-to-Stand Test in 30-sec test, four-stage balance test, and grip strength. To develop this system, a Cerner Power Form was developed based on the physical function data. To display the data, a dedicated flowsheet was developed and placed within the Oncology Viewpoint in Cerner Millennium. Thus, from inside any patient record, the flowsheet could easily be accessed by providers without leaving normal clinician workflows. Using native functionality, the data can also be shown in graphical format to facilitate dialog with patients and oncology teams. All patient data from the Cerner Power Form discrete task assays were integrated into an existing Oncology Data Warehouse for all patients. The data elements in the Cerner Power Form were identified in the electronic medical record system, loaded into the Oncology Data Warehouse, and related to the other source systems to develop reports and data visualizations such as the cancer rehabilitation dashboard. The cancer rehabilitation dashboard allows visualization of numerous parameters of physical function in cancer survivors evaluated and treated and their change over time. Rendered in Tableau, the cancer rehabilitation dashboard acts as a centralized, interactive data source to analyze and connect clinicians to near real-time data. RESULTS The cancer rehabilitation dashboard was successfully developed and implemented into a cancer rehabilitation practice in a cancer institute and used to collect and track physical function data for cancer survivors receiving treatment and cancer survivors. This information has been used to direct the treatment plan and educate individual patients about the impact of the cancer and its treatment on physical function as well as oncology teams in a cancer institute. CONCLUSIONS The cancer rehabilitation dashboard provides an insight into the physical function of cancer survivors receiving treatment and cancer survivors using both self-reported and objective metrics. It can be customized to suit the interests of clinicians and researchers wishing to improve the quality of life of this population.
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Affiliation(s)
- Adrian Cristian
- From the Cancer Rehabilitation, Cancer Patient Support Center-Miami Cancer Institute, Miami, Florida (AC); Department of Oncology Research, Miami Cancer Institute, Miami, Florida (MR); Cancer Patient Support Center, Miami Cancer Institute, Miami, Florida (RO); New York Presbyterian Hospital, New York, New York (KD); Weill Cornell Medical College, New York, New York (KD); Rehabilitation Medicine Service, Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, New York (GS); Informatics Analyst, Department of Oncology Informatics, Miami Cancer Institute, Miami, Florida (MTD, ME, PRIL); Baptist Health South Florida, Miami, Florida (SK, PM); and Department of Oncology Informatics
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Pitt JB, Zeineddin S, Carter M, Figueroa A, Park E, Kwon S, Ghomrawi H, Abdullah F. Using Consumer Wearable Devices to Profile Postoperative Complications After Pediatric Appendectomy. J Surg Res 2024; 295:853-861. [PMID: 38052697 DOI: 10.1016/j.jss.2023.08.060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 08/03/2023] [Accepted: 08/31/2023] [Indexed: 12/07/2023]
Abstract
INTRODUCTION Markers of postoperative recovery in pediatric patients are difficult for parents to evaluate after hospital discharge, who use subjective proxies to assess recovery and the onset of complications. Consumer-grade wearable devices (e.g., Fitbit) generate objective recovery data in near real time and thus may provide an opportunity to remotely monitor postoperative patients and identify complications beyond the initial hospitalization. The aim of this study was to use daily step counts from a Fitbit to compare recovery in patients with complications to those without complications after undergoing appendectomy for complicated appendicitis. METHODS Children ages 3-17 years old undergoing laparoscopic appendectomy for complicated appendicitis were recruited. Patients wore a Fitbit device for 21 d after operation. After collection, patient data were included in the analysis if minimum wear-time criteria were achieved. Postoperative complications were identified through chart review, and step count trajectories for patients recovering with and without complications were compared. Additionally, to account for the patients experiencing a complication on different postoperative days, median daily step count for pre- and post-complication were analyzed. RESULTS Eighty-six patients with complicated appendicitis were enrolled in the study, and fourteen children developed a postoperative complication. Three patients were excluded because they did not meet the minimum wear time requirements. Complications were divided into abscesses (n = 7, 64%), surgical site infections (n = 2, 18%), and other, which included small bowel obstruction and Clostridioides difficile infection (n = 2, 18%). Patients presented with a complication on mean postoperative day 8, while deviation from the normative recovery trajectory was evident 4 d prior. When compared to children with normative recovery, the patients with surgical complications experienced a slower increase in step count postoperatively, but the recovery trajectory was specific to each complication type. When corrected for day of presentation with complication, step count remained low prior to the discovery of the complication and increased after treatment resembling the normative recovery trajectory. CONCLUSIONS This study profiled variations from the normative recovery trajectory in patients with complication after appendectomy for complicated appendicitis, with distinct trajectory patterns by complication type. Our findings have potentially profound clinical implications for monitoring pediatric patients postoperatively, particularly in the outpatient setting, thus providing objective data for potentially earlier identification of complications after hospital discharge.
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Affiliation(s)
- J Benjamin Pitt
- Division of Pediatric Surgery, Department of Surgery, Northwestern University Feinberg School of Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois; Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Suhail Zeineddin
- Division of Pediatric Surgery, Department of Surgery, Northwestern University Feinberg School of Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois; Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Michela Carter
- Division of Pediatric Surgery, Department of Surgery, Northwestern University Feinberg School of Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois; Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Angie Figueroa
- Division of Pediatric Surgery, Department of Surgery, Northwestern University Feinberg School of Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois; Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Erica Park
- Division of Pediatric Surgery, Department of Surgery, Northwestern University Feinberg School of Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois; Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Soyang Kwon
- Division of Pediatric Surgery, Department of Surgery, Northwestern University Feinberg School of Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois; Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Hassan Ghomrawi
- Division of Pediatric Surgery, Department of Surgery, Northwestern University Feinberg School of Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois; Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Rheumatology Division, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Fizan Abdullah
- Division of Pediatric Surgery, Department of Surgery, Northwestern University Feinberg School of Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois; Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois.
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Pappot H, Steen-Olsen EB, Holländer-Mieritz C. Experiences with Wearable Sensors in Oncology during Treatment: Lessons Learned from Feasibility Research Projects in Denmark. Diagnostics (Basel) 2024; 14:405. [PMID: 38396444 PMCID: PMC10887889 DOI: 10.3390/diagnostics14040405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 02/02/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND The fraction of elderly people in the population is growing, the incidence of some cancers is increasing, and the number of available cancer treatments is evolving, causing a challenge to healthcare systems. New healthcare tools are needed, and wearable sensors could partly be potential solutions. The aim of this case report is to describe the Danish research experience with wearable sensors in oncology reporting from three oncological wearable research projects. CASE STUDIES Three planned case studies investigating the feasibility of different wearable sensor solutions during cancer treatment are presented, focusing on study design, population, device, aim, and planned outcomes. Further, two actual case studies performed are reported, focusing on patients included, data collected, results achieved, further activities planned, and strengths and limitations. RESULTS Only two of the three planned studies were performed. In general, patients found the technical issues of wearable sensors too challenging to deal with during cancer treatment. However, at the same time it was demonstrated that a large amount of data could be collected if the framework worked efficiently. CONCLUSION Wearable sensors have the potential to help solve challenges in clinical oncology, but for successful research projects and implementation, a setup with minimal effort on the part of patients is requested.
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Affiliation(s)
- Helle Pappot
- Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark (C.H.-M.)
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Emma Balch Steen-Olsen
- Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark (C.H.-M.)
| | - Cecilie Holländer-Mieritz
- Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark (C.H.-M.)
- Department of Oncology, Zealand University Hospital, 4700 Naestved, Denmark
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11
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Hong YA. Mobile health and behavior tracking (mHBT) among cancer survivors: results from a large and diverse sample. Mhealth 2024; 10:5. [PMID: 38323153 PMCID: PMC10839504 DOI: 10.21037/mhealth-23-58] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 12/08/2023] [Indexed: 02/08/2024] Open
Abstract
This study investigates the prevalence of mobile health and behavior tracking (mHBT) among a large and diverse sample of cancer survivors in the United States focusing on different mHBT adoption based on socio-demographic factors. Data was drawn from the 2020 to 2021 Health Information National Trends Survey (HINTS), which over-sampled cancer survivors from three cancer registries. Data analyses revealed that out of 1,234 cancer survivors studied, 39% had adopted mHBT, with 52% of these users engaging in daily tracking. A significant majority (86%) mHBT users were willing to share their data with healthcare providers. Notably, mHBT adoption was independently associated with younger age, female gender, college education, and better perceived health; but it was not significantly associated with race, income level, or state of residence after controlling for confounders. Cancer survivors' high rates of mHBT use and willingness to share data suggest the potential of using patient-generated data for personalized care and monitoring. However, caution is needed to address the digital divide and its impact on health disparities.
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12
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Dobson R, Stowell M, Warren J, Tane T, Ni L, Gu Y, McCool J, Whittaker R. Use of Consumer Wearables in Health Research: Issues and Considerations. J Med Internet Res 2023; 25:e52444. [PMID: 37988147 DOI: 10.2196/52444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/23/2023] [Accepted: 10/26/2023] [Indexed: 11/22/2023] Open
Abstract
As wearable devices, which allow individuals to track and self-manage their health, become more ubiquitous, the opportunities are growing for researchers to use these sensors within interventions and for data collection. They offer access to data that are captured continuously, passively, and pragmatically with minimal user burden, providing huge advantages for health research. However, the growth in their use must be coupled with consideration of their potential limitations, in particular, digital inclusion, data availability, privacy, ethics of third-party involvement, data quality, and potential for adverse consequences. In this paper, we discuss these issues and strategies used to prevent or mitigate them and recommendations for researchers using wearables as part of interventions or for data collection.
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Affiliation(s)
- Rosie Dobson
- School of Population Health, University of Auckland, Auckland, New Zealand
- Institute for Innovation and Improvement, Te Whatu Ora Waitematā, Auckland, New Zealand
| | - Melanie Stowell
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Jim Warren
- School of Computer Science, University of Auckland, Auckland, New Zealand
| | - Taria Tane
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Lin Ni
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Yulong Gu
- School of Health Sciences, Stockton University, Galloway, NJ, United States
| | - Judith McCool
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Robyn Whittaker
- School of Population Health, University of Auckland, Auckland, New Zealand
- Institute for Innovation and Improvement, Te Whatu Ora Waitematā, Auckland, New Zealand
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13
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van Grootel JWM, Bor P, Netjes JA, Veenhof C, Valkenet K. Improving physical activity in hospitalized patients: The preliminary effectiveness of a goal-directed movement intervention. Clin Rehabil 2023; 37:1501-1509. [PMID: 37487188 PMCID: PMC10492426 DOI: 10.1177/02692155231189607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 07/05/2023] [Indexed: 07/26/2023]
Abstract
OBJECTIVE To evaluate the preliminary effectiveness of a goal-directed movement intervention using a movement sensor on physical activity of hospitalized patients. DESIGN Prospective, pre-post study. SETTING A university medical center. PARTICIPANTS Patients admitted to the pulmonology and nephrology/gastro-enterology wards. INTERVENTION The movement intervention consisted of (1) self-monitoring of patients' physical activity, (2) setting daily movement goals and (3) posters with exercises and walking routes. Physical activity was measured with a movement sensor (PAM AM400) which measures active minutes per day. MAIN MEASURES Primary outcome was the mean difference in active minutes per day pre- and post-implementation. Secondary outcomes were length of stay, discharge destination, immobility-related complications, physical functioning, perceived difficulty to move, 30-day readmission, 30-day mortality and the adoption of the intervention. RESULTS A total of 61 patients was included pre-implementation, and a total of 56 patients was included post-implementation. Pre-implementation, patients were active 38 ± 21 minutes (mean ± SD) per day, and post-implementation 50 ± 31 minutes per day (Δ12, P = 0.031). Perceived difficulty to move decreased from 3.4 to 1.7 (0-10) (Δ1.7, P = 0.008). No significant differences were found in other secondary outcomes. CONCLUSIONS The goal-directed movement intervention seems to increase physical activity levels during hospitalization. Therefore, this intervention might be useful for other hospitals to stimulate inpatient physical activity.
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Affiliation(s)
- JWM van Grootel
- Department of Rehabilitation, Physiotherapy Science & Sport, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Utrecht, The Netherlands
- Research Center of Healthy and Sustainable Living, Research group Innovation of Movement Care, HU University of Applied Sciences Utrecht, Utrecht, Utrecht, The Netherlands
| | - P Bor
- Department of Rehabilitation, Physiotherapy Science & Sport, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Utrecht, The Netherlands
| | - JA Netjes
- Department of Rehabilitation, Physiotherapy Science & Sport, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Utrecht, The Netherlands
| | - C Veenhof
- Department of Rehabilitation, Physiotherapy Science & Sport, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Utrecht, The Netherlands
- Research Center of Healthy and Sustainable Living, Research group Innovation of Movement Care, HU University of Applied Sciences Utrecht, Utrecht, Utrecht, The Netherlands
| | - K Valkenet
- Department of Rehabilitation, Physiotherapy Science & Sport, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Utrecht, The Netherlands
- Research Center of Healthy and Sustainable Living, Research group Innovation of Movement Care, HU University of Applied Sciences Utrecht, Utrecht, Utrecht, The Netherlands
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14
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Schwab N, Wu CY, Galler J, DeRamus T, Ford A, Gerber J, Kitchen R, Rashid B, Riley M, Sather L, Wang X, Young C, Yang L, Dodge HH, Arnold SE. Feasibility of common, enjoyable game play for assessing daily cognitive functioning in older adults. Front Neurol 2023; 14:1258216. [PMID: 37900599 PMCID: PMC10602782 DOI: 10.3389/fneur.2023.1258216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 09/26/2023] [Indexed: 10/31/2023] Open
Abstract
Background Frequent digital monitoring of cognition is a promising approach for assessing endpoints in prevention and treatment trials of Alzheimer's disease and related dementias (ADRD). This study evaluated the feasibility of the MIND GamePack© for recurrent semi-passive assessment of cognition across a longitudinal interval. Methods The MIND GamePack consists of four iPad-based games selected to be both familiar and enjoyable: Word Scramble, Block Drop, FreeCell, and Memory Match. Participants were asked to play 20 min/day for 5 days (100 min) for 4 months. Feasibility of use by older adults was assessed by measuring gameplay time and game performance. We also evaluated compliance through semi-structured surveys. A linear generalized estimating equation (GEE) model was used to analyze changes in gameplay time, and a regression tree model was employed to estimate the days it took for game performance to plateau. Subjective and environmental factors associated with gameplay time and performance were examined, including daily self-reported questions of memory and thinking ability, mood, sleep, energy, current location, and distractions prior to gameplay. Results Twenty-six cognitively-unimpaired older adults participated (mean age ± SD = 71.9 ± 8.6; 73% female). Gameplay time remained stable throughout the 4-months, with an average compliance rate of 91% ± 11% (1946 days of data across all participants) and weekly average playtime of 210 ± 132 min per participant. We observed an initial learning curve of improving game performance which on average, plateaued after 22-39 days, depending on the game. Higher levels of self-reported memory and thinking ability were associated with more gameplay time and sessions. Conclusion MIND GamePack is a feasible and well-designed semi-passive cognitive assessment platform which may provide complementary data to traditional neuropsychological testing in research on aging and dementia.
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Affiliation(s)
- Nadine Schwab
- Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Chao-Yi Wu
- Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Jake Galler
- Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Thomas DeRamus
- Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Abaigeal Ford
- Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Jessica Gerber
- Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Robert Kitchen
- Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Barnaly Rashid
- Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Misha Riley
- Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Lauren Sather
- Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Xifeng Wang
- AbbVie, Inc., North Chicago, IL, United States
| | - Cathrine Young
- Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | | | - Hiroko H. Dodge
- Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Steven E. Arnold
- Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
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15
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Li X, Mao JJ, Garland SN, Root J, Li SQ, Ahles T, Liou KT. Comparing sleep measures in cancer survivors: Self-reported sleep diary versus objective wearable sleep tracker. RESEARCH SQUARE 2023:rs.3.rs-3407984. [PMID: 37886444 PMCID: PMC10602054 DOI: 10.21203/rs.3.rs-3407984/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Purpose Cancer survivors are increasingly using wearable fitness trackers, but it's unclear if they match traditional self-reported sleep diaries. We aimed to compare sleep data from Fitbit and the Consensus Sleep Diary (CSD) in this group. Methods We analyzed data from two randomized clinical trials, using both CSD and Fitbit to collect sleep outcomes: total sleep time (TST), wake time after sleep onset (WASO), number of awakenings (NWAK), time in bed (TIB) and sleep efficiency (SE). Insomnia severity was measured by Insomnia Severity Index (ISI). We used the Wilcoxon Singed Ranks Test, Spearman's rank correlation coefficients, and the Mann-Whitney Test to compare sleep outcomes and assess their ability to distinguish insomnia severity levels between CSD and Fitbit data. Results Among 62 participants, compared to CSD, Fitbit recorded longer TST by an average of 14.6 (SD = 84.9) minutes, longer WASO by an average of 28.7 (SD = 40.5) minutes, more NWAK by an average of 16.7 (SD = 6.6) times per night, and higher SE by an average of 7.1% (SD = 14.4); but shorter TIB by an average of 24.4 (SD = 71.5) minutes. All the differences were statistically significant (all p < 0.05), except for TST (p = 0.38). Moderate correlations were found for TST (r = 0.41, p = 0.001) and TIB (r = 0.44, p < 0.001). Compared to no/mild insomnia group, participants with clinical insomnia reported more NWAK (p = 0.009) and lower SE (p = 0.029) as measured by CSD, but Fitbit outcomes didn't. Conclusions TST was the only similar outcome between Fitbit and CSD. Our study highlights the advantages, disadvantages, and clinical utilization of sleep trackers in oncology.
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Affiliation(s)
| | - Jun J Mao
- Memorial Sloan Kettering Cancer Center
| | | | | | | | - Tim Ahles
- Memorial Sloan Kettering Cancer Center
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16
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Franzoi MA, Gillanders E, Vaz-Luis I. Unlocking digitally enabled research in oncology: the time is now. ESMO Open 2023; 8:101633. [PMID: 37660408 PMCID: PMC10482746 DOI: 10.1016/j.esmoop.2023.101633] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 08/07/2023] [Indexed: 09/05/2023] Open
Affiliation(s)
- M A Franzoi
- Cancer Survivorship Group, Inserm Unit 981, Gustave Roussy, Villejuif
| | - E Gillanders
- Cancer Survivorship Group, Inserm Unit 981, Gustave Roussy, Villejuif
| | - I Vaz-Luis
- Cancer Survivorship Group, Inserm Unit 981, Gustave Roussy, Villejuif; Department for the Organization of Patient Pathways, DIOPP, Gustave Roussy, Villejuif, France.
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17
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Viana JN, Pilbeam C, Howard M, Scholz B, Ge Z, Fisser C, Mitchell I, Raman S, Leach J. Maintaining High-Touch in High-Tech Digital Health Monitoring and Multi-Omics Prognostication: Ethical, Equity, and Societal Considerations in Precision Health for Palliative Care. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2023; 27:461-473. [PMID: 37861713 DOI: 10.1089/omi.2023.0120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Advances in digital health, systems biology, environmental monitoring, and artificial intelligence (AI) continue to revolutionize health care, ushering a precision health future. More than disease treatment and prevention, precision health aims at maintaining good health throughout the lifespan. However, how can precision health impact care for people with a terminal or life-limiting condition? We examine here the ethical, equity, and societal/relational implications of two precision health modalities, (1) integrated systems biology/multi-omics analysis for disease prognostication and (2) digital health technologies for health status monitoring and communication. We focus on three main ethical and societal considerations: benefits and risks associated with integration of these modalities into the palliative care system; inclusion of underrepresented and marginalized groups in technology development and deployment; and the impact of high-tech modalities on palliative care's highly personalized and "high-touch" practice. We conclude with 10 recommendations for ensuring that precision health technologies, such as multi-omics prognostication and digital health monitoring, for palliative care are developed, tested, and implemented ethically, inclusively, and equitably.
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Affiliation(s)
- John Noel Viana
- Australian National Centre for the Public Awareness of Science, College of Science, The Australian National University, Canberra, Australia
- Responsible Innovation Future Science Platform, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia
| | - Caitlin Pilbeam
- School of Medicine and Psychology, College of Health and Medicine, The Australian National University, Canberra, Australia
| | - Mark Howard
- Monash Data Futures Institute, Monash University, Clayton, Australia
- Department of Philosophy, School of Philosophical, Historical and International Studies, Monash University, Clayton, Australia
| | - Brett Scholz
- School of Medicine and Psychology, College of Health and Medicine, The Australian National University, Canberra, Australia
| | - Zongyuan Ge
- Monash Data Futures Institute, Monash University, Clayton, Australia
- Department of Data Science & AI, Monash University, Clayton, Australia
| | - Carys Fisser
- Australian National Centre for the Public Awareness of Science, College of Science, The Australian National University, Canberra, Australia
- School of Medicine and Psychology, College of Health and Medicine, The Australian National University, Canberra, Australia
| | - Imogen Mitchell
- School of Medicine and Psychology, College of Health and Medicine, The Australian National University, Canberra, Australia
- Intensive Care Unit, Canberra Hospital, Canberra, Australia
| | - Sujatha Raman
- Australian National Centre for the Public Awareness of Science, College of Science, The Australian National University, Canberra, Australia
| | - Joan Leach
- Australian National Centre for the Public Awareness of Science, College of Science, The Australian National University, Canberra, Australia
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18
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Liu JH, Shih CY, Huang HL, Peng JK, Cheng SY, Tsai JS, Lai F. Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study. J Med Internet Res 2023; 25:e47366. [PMID: 37594793 PMCID: PMC10474512 DOI: 10.2196/47366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 07/02/2023] [Accepted: 07/14/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND An accurate prediction of mortality in end-of-life care is crucial but presents challenges. Existing prognostic tools demonstrate moderate performance in predicting survival across various time frames, primarily in in-hospital settings and single-time evaluations. However, these tools may fail to capture the individualized and diverse trajectories of patients. Limited evidence exists regarding the use of artificial intelligence (AI) and wearable devices, specifically among patients with cancer at the end of life. OBJECTIVE This study aimed to investigate the potential of using wearable devices and AI to predict death events among patients with cancer at the end of life. Our hypothesis was that continuous monitoring through smartwatches can offer valuable insights into the progression of patients at the end of life and enable the prediction of changes in their condition, which could ultimately enhance personalized care, particularly in outpatient or home care settings. METHODS This prospective study was conducted at the National Taiwan University Hospital. Patients diagnosed with cancer and receiving end-of-life care were invited to enroll in wards, outpatient clinics, and home-based care settings. Each participant was given a smartwatch to collect physiological data, including steps taken, heart rate, sleep time, and blood oxygen saturation. Clinical assessments were conducted weekly. The participants were followed until the end of life or up to 52 weeks. With these input features, we evaluated the prediction performance of several machine learning-based classifiers and a deep neural network in 7-day death events. We used area under the receiver operating characteristic curve (AUROC), F1-score, accuracy, and specificity as evaluation metrics. A Shapley additive explanations value analysis was performed to further explore the models with good performance. RESULTS From September 2021 to August 2022, overall, 1657 data points were collected from 40 patients with a median survival time of 34 days, with the detection of 28 death events. Among the proposed models, extreme gradient boost (XGBoost) yielded the best result, with an AUROC of 96%, F1-score of 78.5%, accuracy of 93%, and specificity of 97% on the testing set. The Shapley additive explanations value analysis identified the average heart rate as the most important feature. Other important features included steps taken, appetite, urination status, and clinical care phase. CONCLUSIONS We demonstrated the successful prediction of patient deaths within the next 7 days using a combination of wearable devices and AI. Our findings highlight the potential of integrating AI and wearable technology into clinical end-of-life care, offering valuable insights and supporting clinical decision-making for personalized patient care. It is important to acknowledge that our study was conducted in a relatively small cohort; thus, further research is needed to validate our approach and assess its impact on clinical care. TRIAL REGISTRATION ClinicalTrials.gov NCT05054907; https://classic.clinicaltrials.gov/ct2/show/NCT05054907.
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Affiliation(s)
- Jen-Hsuan Liu
- Department of Family Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
| | - Chih-Yuan Shih
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Hsien-Liang Huang
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Jen-Kuei Peng
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Shao-Yi Cheng
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Jaw-Shiun Tsai
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Feipei Lai
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
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Ghomrawi HMK, O'Brien MK, Carter M, Macaluso R, Khazanchi R, Fanton M, DeBoer C, Linton SC, Zeineddin S, Pitt JB, Bouchard M, Figueroa A, Kwon S, Holl JL, Jayaraman A, Abdullah F. Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy. NPJ Digit Med 2023; 6:148. [PMID: 37587211 PMCID: PMC10432429 DOI: 10.1038/s41746-023-00890-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 08/01/2023] [Indexed: 08/18/2023] Open
Abstract
When children are discharged from the hospital after surgery, their caregivers often rely on subjective assessments (e.g., appetite, fatigue) to monitor postoperative recovery as objective assessment tools are scarce at home. Such imprecise and one-dimensional evaluations can result in unwarranted emergency department visits or delayed care. To address this gap in postoperative monitoring, we evaluated the ability of a consumer-grade wearable device, Fitbit, which records multimodal data about daily physical activity, heart rate, and sleep, in detecting abnormal recovery early in children recovering after appendectomy. One hundred and sixty-two children, ages 3-17 years old, who underwent an appendectomy (86 complicated and 76 simple cases of appendicitis) wore a Fitbit device on their wrist for 21 days postoperatively. Abnormal recovery events (i.e., abnormal symptoms or confirmed postoperative complications) that arose during this period were gathered from medical records and patient reports. Fitbit-derived measures, as well as demographic and clinical characteristics, were used to train machine learning models to retrospectively detect abnormal recovery in the two days leading up to the event for patients with complicated and simple appendicitis. A balanced random forest classifier accurately detected 83% of these abnormal recovery days in complicated appendicitis and 70% of abnormal recovery days in simple appendicitis prior to the true report of a symptom/complication. These results support the development of machine learning algorithms to predict onset of abnormal symptoms and complications in children undergoing surgery, and the use of consumer wearables as monitoring tools for early detection of postoperative events.
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Affiliation(s)
- Hassan M K Ghomrawi
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Health Services and Outcomes Research, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Global Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Medicine (Rheumatology), Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Michela Carter
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | | | - Rushmin Khazanchi
- Shirley Ryan AbilityLab, Chicago, IL, USA
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Christopher DeBoer
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Samuel C Linton
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Suhail Zeineddin
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - J Benjamin Pitt
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Megan Bouchard
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Angie Figueroa
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Soyang Kwon
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Pediatrics, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Jane L Holl
- Department of Neurology and Center for Healthcare Delivery Science and Innovation, Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Arun Jayaraman
- Shirley Ryan AbilityLab, Chicago, IL, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Physical Therapy and Human Movement Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Fizan Abdullah
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Center for Global Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, 225 East Chicago Avenue, Box 63, Chicago, IL, 60611, USA.
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Gouldthorpe C, Ancoli-Israel S, Cash E, Innominato P, Jakobsen G, Lévi F, Miaskowski C, Parganiha A, Pati AK, Pereira D, Revell V, Zeitzer JM, Davies A. International e-Delphi Consensus Recommendations for the Assessment and Diagnosis of Circadian rest-Activity Rhythm Disorders (CARDs) in Patients with Cancer. Cancers (Basel) 2023; 15:3784. [PMID: 37568600 PMCID: PMC10416864 DOI: 10.3390/cancers15153784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 07/19/2023] [Accepted: 07/21/2023] [Indexed: 08/13/2023] Open
Abstract
PURPOSE Circadian rest-Activity Rhythm Disorders (CARDs) are common in patients with cancer, particularly in advanced disease. CARDs are associated with increased symptom burden, poorer quality of life, and shorter survival. Research and reporting practices lack standardization, and formal diagnostic criteria do not exist. This electronic Delphi (e-Delphi) study aimed to formulate international recommendations for the assessment and diagnosis of CARDs in patients with cancer. METHODS An international e-Delphi was performed using an online platform (Welphi). Round 1 developed statements regarding circadian rest-activity rhythms, diagnostic criteria, and assessment techniques. Rounds 2 and 3 involved participants rating their level of agreement with the statements and providing comments until consensus (defined internally as 67%) and stability between rounds were achieved. Recommendations were then created and distributed to participants for comments before being finalized. RESULTS Sixteen participants from nine different clinical specialties and seven different countries, with 5-35 years of relevant research experience, were recruited, and thirteen participants completed all three rounds. Of the 164 generated statements, 66% achieved consensus, and responses were stable between the final two rounds. CONCLUSIONS The e-Delphi resulted in international recommendations for assessing and diagnosing CARDs in patients with cancer. These recommendations should ensure standardized research and reporting practices in future studies.
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Affiliation(s)
- Craig Gouldthorpe
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- Academic Department of Palliative Medicine, Our Lady’s Hospice & Care Services, D6W RY72 Dublin, Ireland
| | - Sonia Ancoli-Israel
- Department of Psychiatry, University of California San Diego School of Medicine, La Jolla, CA 92093, USA
| | - Elizabeth Cash
- Department of Otolaryngology-Head & Neck Surgery & Communicative Disorders, UofL Healthcare-Brown Cancer Center, University of Louisville School of Medicine, Louisville, KY 40202, USA
| | - Pasquale Innominato
- Oncology Department, Alaw, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor LL57 2PW, UK
- Cancer Research Centre, Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
- UPR “Chronotherapy, Cancers and Transplantation”, Faculty of Medicine, Paris-Saclay University, 94800 Villejuif, France
| | - Gunnhild Jakobsen
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway
- Cancer Clinic, St. Olavs Hospital, Trondheim University Hospital, 7006 Trondheim, Norway
| | - Francis Lévi
- UPR “Chronotherapy, Cancers and Transplantation”, Faculty of Medicine, Paris-Saclay University, 94800 Villejuif, France
- Gastro-Intestinal and General Oncology Service, Paul Brousse Hospital, Assistance Publique-Hôpitaux de Paris, 94800 Villejuif, France
- Division of Biomedical Sciences, Cancer Chronotherapy Team, Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
| | - Christine Miaskowski
- Department of Physiological Nursing, University of California, San Francisco, CA 94143, USA
| | - Arti Parganiha
- School of Studies in Life Science & Centre for Translational Chronobiology, Pandit Ravishankar Shukla University, Raipur 492010, India
| | - Atanu Kumar Pati
- School of Studies in Life Science & Centre for Translational Chronobiology, Pandit Ravishankar Shukla University, Raipur 492010, India
- Odisha State Higher Education Council, Government of Odisha, Bhubaneswar 751001, India
- Kalinga Institute of Social Sciences, Bhubaneswar 751024, India
| | - Deidre Pereira
- Department of Clinical and Health Psychology, University of Florida Health, Gainesville, FL 32610, USA
| | - Victoria Revell
- Surrey Sleep Research Centre, University of Surrey, Surrey GU2 7XH, UK
| | - Jamie M. Zeitzer
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
- Mental Illness Research Education and Clinical Center, VA Palo Alto Health Care System, Palo Alto, CA 94304, USA
| | - Andrew Davies
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- Academic Department of Palliative Medicine, Our Lady’s Hospice & Care Services, D6W RY72 Dublin, Ireland
- School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
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21
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Jacobsen M, Gholamipoor R, Dembek TA, Rottmann P, Verket M, Brandts J, Jäger P, Baermann BN, Kondakci M, Heinemann L, Gerke AL, Marx N, Müller-Wieland D, Möllenhoff K, Seyfarth M, Kollmann M, Kobbe G. Wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of Hematologic malignancies. NPJ Digit Med 2023; 6:105. [PMID: 37268734 DOI: 10.1038/s41746-023-00847-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 05/19/2023] [Indexed: 06/04/2023] Open
Abstract
Serious clinical complications (SCC; CTCAE grade ≥ 3) occur frequently in patients treated for hematological malignancies. Early diagnosis and treatment of SCC are essential to improve outcomes. Here we report a deep learning model-derived SCC-Score to detect and predict SCC from time-series data recorded continuously by a medical wearable. In this single-arm, single-center, observational cohort study, vital signs and physical activity were recorded with a wearable for 31,234 h in 79 patients (54 Inpatient Cohort (IC)/25 Outpatient Cohort (OC)). Hours with normal physical functioning without evidence of SCC (regular hours) were presented to a deep neural network that was trained by a self-supervised contrastive learning objective to extract features from the time series that are typical in regular periods. The model was used to calculate a SCC-Score that measures the dissimilarity to regular features. Detection and prediction performance of the SCC-Score was compared to clinical documentation of SCC (AUROC ± SD). In total 124 clinically documented SCC occurred in the IC, 16 in the OC. Detection of SCC was achieved in the IC with a sensitivity of 79.7% and specificity of 87.9%, with AUROC of 0.91 ± 0.01 (OC sensitivity 77.4%, specificity 81.8%, AUROC 0.87 ± 0.02). Prediction of infectious SCC was possible up to 2 days before clinical diagnosis (AUROC 0.90 at -24 h and 0.88 at -48 h). We provide proof of principle for the detection and prediction of SCC in patients treated for hematological malignancies using wearable data and a deep learning model. As a consequence, remote patient monitoring may enable pre-emptive complication management.
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Affiliation(s)
- Malte Jacobsen
- Faculty of Health, University Witten/Herdecke, 58448, Witten, Germany.
- Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, 52074, Aachen, Germany.
| | - Rahil Gholamipoor
- Department of Computer Science, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Till A Dembek
- Department of Neurology, Faculty of Medicine, University of Cologne, 50937, Cologne, Germany
| | - Pauline Rottmann
- Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Marlo Verket
- Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, 52074, Aachen, Germany
| | - Julia Brandts
- Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, 52074, Aachen, Germany
| | - Paul Jäger
- Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Ben-Niklas Baermann
- Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Mustafa Kondakci
- Department of Oncology and Hematology, St. Lukas Hospital Solingen, 42697, Solingen, Germany
| | | | - Anna L Gerke
- Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Nikolaus Marx
- Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, 52074, Aachen, Germany
| | - Dirk Müller-Wieland
- Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, 52074, Aachen, Germany
| | - Kathrin Möllenhoff
- Mathematical Institute, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Melchior Seyfarth
- Faculty of Health, University Witten/Herdecke, 58448, Witten, Germany
- Department of Cardiology, Helios University Hospital Wuppertal, 42117, Wuppertal, Germany
| | - Markus Kollmann
- Department of Biology, Heinrich Heine University Düsseldorf, Düsseldorf, 40225, Germany.
| | - Guido Kobbe
- Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
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22
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Papachristou N, Kotronoulas G, Dikaios N, Allison SJ, Eleftherochorinou H, Rai T, Kunz H, Barnaghi P, Miaskowski C, Bamidis PD. Digital Transformation of Cancer Care in the Era of Big Data, Artificial Intelligence and Data-Driven Interventions: Navigating the Field. Semin Oncol Nurs 2023; 39:151433. [PMID: 37137770 DOI: 10.1016/j.soncn.2023.151433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 03/29/2023] [Indexed: 05/05/2023]
Abstract
OBJECTIVES To navigate the field of digital cancer care and define and discuss key aspects and applications of big data analytics, artificial intelligence (AI), and data-driven interventions. DATA SOURCES Peer-reviewed scientific publications and expert opinion. CONCLUSION The digital transformation of cancer care, enabled by big data analytics, AI, and data-driven interventions, presents a significant opportunity to revolutionize the field. An increased understanding of the lifecycle and ethics of data-driven interventions will enhance development of innovative and applicable products to advance digital cancer care services. IMPLICATIONS FOR NURSING PRACTICE As digital technologies become integrated into cancer care, nurse practitioners and scientists will be required to increase their knowledge and skills to effectively use these tools to the patient's benefit. An enhanced understanding of the core concepts of AI and big data, confident use of digital health platforms, and ability to interpret the outputs of data-driven interventions are key competencies. Nurses in oncology will play a crucial role in patient education around big data and AI, with a focus on addressing any arising questions, concerns, or misconceptions to foster trust in these technologies. Successful integration of data-driven innovations into oncology nursing practice will empower practitioners to deliver more personalized, effective, and evidence-based care.
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Affiliation(s)
- Nikolaos Papachristou
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | | | - Nikolaos Dikaios
- Centre for Vision Speech and Signal Processing, University of Surrey, Guildford, UK; Mathematics Research Centre, Academy of Athens, Athens, Greece
| | - Sarah J Allison
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Newcastle, UK; School of Bioscience and Medicine, Faculty of Health & Medical Sciences, University of Surrey, Guildford, UK
| | | | - Taranpreet Rai
- Centre for Vision Speech and Signal Processing, University of Surrey, Guildford, UK; Datalab, The Veterinary Health Innovation Engine (vHive), Guildford, UK
| | - Holger Kunz
- Institute of Health Informatics, University College London, London, UK
| | - Payam Barnaghi
- UK Dementia Research Institute Care Research and Technology Centre, Imperial College London, London, UK
| | - Christine Miaskowski
- School of Nursing, University California San Francisco, San Francisco, California, USA
| | - Panagiotis D Bamidis
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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23
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Kos M, Brouwer CG, van Laarhoven HW, Hopman MT, van Oijen MG, Buffart LM. The association between wearable device metrics and clinical outcomes in oncology: a systematic review with evidence synthesis and meta-analysis. Crit Rev Oncol Hematol 2023; 185:103979. [PMID: 37001837 DOI: 10.1016/j.critrevonc.2023.103979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 03/24/2023] [Accepted: 03/26/2023] [Indexed: 03/31/2023] Open
Abstract
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.
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24
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Wood WA, Dilip D, Derkach A, Grover NS, Elemento O, Levine R, Thanarajasingam G, Batsis JA, Bailey C, Kannappan A, Devine SM, Artz AS, Ligibel JA, Basch E, Kent E, Glass J. Wearable sensor-based performance status assessment in cancer: A pilot multicenter study from the Alliance for Clinical Trials in Oncology (A19_Pilot2). PLOS DIGITAL HEALTH 2023; 2:e0000178. [PMID: 36812616 PMCID: PMC9931326 DOI: 10.1371/journal.pdig.0000178] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 12/06/2022] [Indexed: 01/27/2023]
Abstract
Clinical performance status is designed to be a measure of overall health, reflecting a patient's physiological reserve and ability to tolerate various forms of therapy. Currently, it is measured by a combination of subjective clinician assessment and patient-reported exercise tolerance in the context of daily living activities. In this study, we assess the feasibility of combining objective data sources and patient-generated health data (PGHD) to improve the accuracy of performance status assessment during routine cancer care. Patients undergoing routine chemotherapy for solid tumors, routine chemotherapy for hematologic malignancies, or hematopoietic stem cell transplant (HCT) at one of four sites in a cancer clinical trials cooperative group were consented to a six-week prospective observational clinical trial (NCT02786628). Baseline data acquisition included cardiopulmonary exercise testing (CPET) and a six-minute walk test (6MWT). Weekly PGHD included patient-reported physical function and symptom burden. Continuous data capture included use of a Fitbit Charge HR (sensor). Baseline CPET and 6MWT could only be obtained in 68% of study patients, suggesting low feasibility during routine cancer treatment. In contrast, 84% of patients had usable fitness tracker data, 93% completed baseline patient-reported surveys, and overall, 73% of patients had overlapping sensor and survey data that could be used for modeling. A linear model with repeated measures was constructed to predict the patient-reported physical function. Sensor-derived daily activity, sensor-derived median heart rate, and patient-reported symptom burden emerged as strong predictors of physical function (marginal R2 0.429-0.433, conditional R2 0.816-0.822). Trial Registration: Clinicaltrials.gov Id NCT02786628.
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Affiliation(s)
- William A. Wood
- Lineberger Comprehensive Cancer Center and School of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Deepika Dilip
- Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Andriy Derkach
- Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Natalie S. Grover
- Lineberger Comprehensive Cancer Center and School of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Olivier Elemento
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, United States of America
| | - Ross Levine
- Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Gita Thanarajasingam
- Division of Hematology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - John A. Batsis
- School of Medicine and Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Charlotte Bailey
- Lineberger Comprehensive Cancer Center and School of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Arun Kannappan
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado, Aurora, Colorado, United States of America
| | - Steven M. Devine
- National Marrow Donor Program (NMDP)/Be The Match, Minneapolis, Minnesota, United States of America
| | - Andrew S. Artz
- Department of Hematology and Hematopoietic Cell Transplantation, City of Hope, Duarte, California
| | - Jennifer A. Ligibel
- Dana Farber Cancer Institute, Boston, Massachusetts, United States of America
| | - Ethan Basch
- Lineberger Comprehensive Cancer Center and School of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Erin Kent
- Lineberger Comprehensive Cancer Center and Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Jacob Glass
- Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
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25
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Holmgren JG, Morrow A, Coffee AK, Nahod PM, Santora SH, Schwartz B, Stiegmann RA, Zanetti CA. Utilizing digital predictive biomarkers to identify Veteran suicide risk. Front Digit Health 2022; 4:913590. [PMID: 36329831 PMCID: PMC9624222 DOI: 10.3389/fdgth.2022.913590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 09/12/2022] [Indexed: 12/02/2022] Open
Abstract
Veteran suicide is one of the most complex and pressing health issues in the United States. According to the 2020 National Veteran Suicide Prevention Annual Report, since 2018 an average of 17.2 Veterans died by suicide each day. Veteran suicide risk screening is currently limited to suicide hotlines, patient reporting, patient visits, and family or friend reporting. As a result of these limitations, innovative approaches in suicide screening are increasingly garnering attention. An essential feature of these innovative methods includes better incorporation of risk factors that might indicate higher risk for tracking suicidal ideation based on personal behavior. Digital technologies create a means through which measuring these risk factors more reliably, with higher fidelity, and more frequently throughout daily life is possible, with the capacity to identify potentially telling behavior patterns. In this review, digital predictive biomarkers are discussed as they pertain to suicide risk, such as sleep vital signs, sleep disturbance, sleep quality, and speech pattern recognition. Various digital predictive biomarkers are reviewed and evaluated as well as their potential utility in predicting and diagnosing Veteran suicidal ideation in real time. In the future, these digital biomarkers could be combined to generate further suicide screening for diagnosis and severity assessments, allowing healthcare providers and healthcare teams to intervene more optimally.
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Affiliation(s)
- Jackson G. Holmgren
- Rocky Vista University College of Osteopathic Medicine, Ivins, UT, United States,Correspondence: Jackson G. Holmgren
| | - Adelene Morrow
- Rocky Vista University College of Osteopathic Medicine, Ivins, UT, United States
| | - Ali K. Coffee
- Rocky Vista University College of Osteopathic Medicine, Ivins, UT, United States
| | - Paige M. Nahod
- Rocky Vista University College of Osteopathic Medicine, Parker, CO, United States
| | - Samantha H. Santora
- Rocky Vista University College of Osteopathic Medicine, Parker, CO, United States
| | - Brian Schwartz
- Department of Medical Humanities, Rocky Vista University College of Osteopathic Medicine, Parker, CO, United States
| | - Regan A. Stiegmann
- Department of Tracks and Special Programs, Rocky Vista University College of Osteopathic Medicine, Parker, CO, United States,Flight Medicine, US Air Force Academy, Colorado Springs, CO, United States
| | - Cole A. Zanetti
- Department of Tracks and Special Programs, Rocky Vista University College of Osteopathic Medicine, Parker, CO, United States,Chief Health Informatics Officer, Ralph H Johnson VA Health System, Charleston, SC, United States
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26
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Lipkova J, Chen RJ, Chen B, Lu MY, Barbieri M, Shao D, Vaidya AJ, Chen C, Zhuang L, Williamson DFK, Shaban M, Chen TY, Mahmood F. Artificial intelligence for multimodal data integration in oncology. Cancer Cell 2022; 40:1095-1110. [PMID: 36220072 PMCID: PMC10655164 DOI: 10.1016/j.ccell.2022.09.012] [Citation(s) in RCA: 87] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 07/12/2022] [Accepted: 09/15/2022] [Indexed: 02/07/2023]
Abstract
In oncology, the patient state is characterized by a whole spectrum of modalities, ranging from radiology, histology, and genomics to electronic health records. Current artificial intelligence (AI) models operate mainly in the realm of a single modality, neglecting the broader clinical context, which inevitably diminishes their potential. Integration of different data modalities provides opportunities to increase robustness and accuracy of diagnostic and prognostic models, bringing AI closer to clinical practice. AI models are also capable of discovering novel patterns within and across modalities suitable for explaining differences in patient outcomes or treatment resistance. The insights gleaned from such models can guide exploration studies and contribute to the discovery of novel biomarkers and therapeutic targets. To support these advances, here we present a synopsis of AI methods and strategies for multimodal data fusion and association discovery. We outline approaches for AI interpretability and directions for AI-driven exploration through multimodal data interconnections. We examine challenges in clinical adoption and discuss emerging solutions.
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Affiliation(s)
- Jana Lipkova
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Richard J Chen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Bowen Chen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Computer Science, Harvard University, Cambridge, MA, USA
| | - Ming Y Lu
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Matteo Barbieri
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniel Shao
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Harvard-MIT Health Sciences and Technology (HST), Cambridge, MA, USA
| | - Anurag J Vaidya
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Harvard-MIT Health Sciences and Technology (HST), Cambridge, MA, USA
| | - Chengkuan Chen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Luoting Zhuang
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Drew F K Williamson
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Muhammad Shaban
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Tiffany Y Chen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA; Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA.
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27
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Fahed M, McManus K, Vahia IV, Offodile AC. Digital Phenotyping of Behavioral Symptoms as the Next Frontier for Personalized and Proactive Cancer Care. JCO Clin Cancer Inform 2022; 6:e2200095. [DOI: 10.1200/cci.22.00095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Mario Fahed
- Department of Psychiatry, University of Connecticut, Farmington, CT
- Department of Psychiatry, Yale University, New Haven, CT
| | - Kaitlin McManus
- Division of Geriatric Psychiatry, McLean Hospital, Belmont, MA
| | - Ipsit V. Vahia
- Division of Geriatric Psychiatry, McLean Hospital, Belmont, MA
- Department of Psychiatry, Harvard Medical School, Boston, MA
| | - Anaeze C. Offodile
- Department of Plastic Surgery, UT MD Anderson Cancer Center, Houston, TX
- Department of Health Services Research, UT MD Anderson Cancer Center, Houston, TX
- Baker Institute for Public Policy, Rice University, Houston, TX
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28
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Wearable smart devices in cancer diagnosis and remote clinical trial monitoring: Transforming the healthcare applications. Drug Discov Today 2022; 27:103314. [PMID: 35798227 DOI: 10.1016/j.drudis.2022.06.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 05/25/2022] [Accepted: 06/29/2022] [Indexed: 12/15/2022]
Abstract
During the past two decades, the era of digitalization in pharmaceutical device manufacturing has gained significant momentum for maintaining human health. From various available technologies, internet of things (IoT) sensors are being increasingly used as wearable devices (e.g., smart watches, wrist bands, mobile phones, tablets, implantable pumps, etc.) that enable real-time monitoring of data. Such devices are integrated with smart materials that typically monitor the real-time data (blood pressure, blood sugar, heart and pulse rate, cytokine levels, etc.) to advise patients and physicians. Hence, there has been a great demand for wearable devices as potential tools for remote clinical trial monitoring in cancers and other diseases and they are proving to be very cost-effective.
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Psaltos DJ, Mamashli F, Adamusiak T, Demanuele C, Santamaria M, Czech MD. Wearable-Based Stair Climb Power Estimation and Activity Classification. SENSORS (BASEL, SWITZERLAND) 2022; 22:6600. [PMID: 36081058 PMCID: PMC9459813 DOI: 10.3390/s22176600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/25/2022] [Accepted: 08/26/2022] [Indexed: 06/15/2023]
Abstract
Stair climb power (SCP) is a clinical measure of leg muscular function assessed in-clinic via the Stair Climb Power Test (SCPT). This method is subject to human error and cannot provide continuous remote monitoring. Continuous monitoring using wearable sensors may provide a more comprehensive assessment of lower-limb muscular function. In this work, we propose an algorithm to classify stair climbing periods and estimate SCP from a lower-back worn accelerometer, which strongly agrees with the clinical standard (r = 0.92, p < 0.001; ICC = 0.90, [0.82, 0.94]). Data were collected in-lab from healthy adults (n = 65) performing the four-step SCPT and a walking assessment while instrumented (accelerometer + gyroscope), which allowed us to investigate tradeoffs between sensor modalities. Using two classifiers, we were able to identify periods of stair ascent with >89% accuracy [sensitivity = >0.89, specificity = >0.90] using two ensemble machine learning algorithms, trained on accelerometer signal features. Minimal changes in model performances were observed using the gyroscope alone (±0−6% accuracy) versus the accelerometer model. While we observed a slight increase in accuracy when combining gyroscope and accelerometer (about +3−6% accuracy), this is tolerable to preserve battery life in the at-home environment. This work is impactful as it shows potential for an accelerometer-based at-home assessment of SCP.
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LeBaron V. Challenges and Opportunities in Designing and Deploying Remote Health Monitoring Technology for Older Adults With Cancer. Innov Aging 2022; 6:igac057. [PMID: 36452048 PMCID: PMC9701055 DOI: 10.1093/geroni/igac057] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Indexed: 09/02/2023] Open
Abstract
Remote health monitoring (RHM) technologies (eg, wearables, smart phones, embedded sensors, and telehealth platforms) offer significant opportunities to improve health and wellness for older adults facing serious illness. This article highlights key challenges and opportunities for designing and deploying RHM systems in the context of caring for older adults with cancer, with an emphasis on the key role nurses can play in this work. Focal topics include user-centered design, interdisciplinary collaboration, addressing health inequities and disparities, privacy and data security, participant recruitment and burden, personalized and tailored care, rapid technological change, family caregiver perspectives, and naturalistic data collection. It is critical for nurses to be aware of both challenges and opportunities within each of these areas in order to develop RHM systems that are optimally beneficial for patients, family caregivers, clinicians, and organizations. By leveraging their unique knowledge of the illness experience from the patient, family, and health care provider perspective, nurses can make essential clinical and scientific contributions to advance the field of RHM.
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Affiliation(s)
- Virginia LeBaron
- School of Nursing, University of Virginia, Charlottesville, Virginia, USA
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31
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Faro JM, Yue KL, Singh A, Soni A, Ding EY, Shi Q, McManus DD. Wearable device use and technology preferences in cancer survivors with or at risk for atrial fibrillation. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2022; 3:S23-S27. [PMID: 36589761 PMCID: PMC9795259 DOI: 10.1016/j.cvdhj.2022.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Background Cancer survivors face increased risk of heart disease, including atrial fibrillation (AF). Certain types of technology, such as consumer wearable devices, can be useful to monitor for AF, but little is known about wearables and AF monitoring in cancer survivor populations. Objective The purpose of this study was to understand technology usage and preferences in cancer survivors with or at risk for AF, and to describe demographic factors associated with wearable device ownership in this population. Methods Eligible patients completed a remote survey assessment regarding use of commercial wearable devices. The survey contained questions designed to assess commercial wearable device use, electronic health communications, and perceptions regarding the participant's cardiac health. Results A total of 424 cancer survivors (mean age 74.2 years; 53.1% female; 98.8% white) were studied. Although most participants owned a smartphone (85.9%), only 31.8% owned a wearable device. Over half (53.5%) of cancer survivors were worried about their heart health. Overall, patients believed arrhythmias (79.7%) were the most important heart condition for a wearable to detect. Survivors reported being most willing to share blood pressure (95.6%) and heart rate (95.3%) data with their providers and were least willing to share information about their diet, weight, and physical activity using these devices. Conclusion Understanding factors such as device ownership, usage, and heart health concerns in cancer survivors can play an important role in improving cardiovascular monitoring and its accessibility. Long-term patient outcomes may be improved by incorporating wearable devices into routine care of cancer survivors.
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Affiliation(s)
- Jamie M. Faro
- Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, Massachusetts,Address reprint requests and correspondence: Dr Jamie Faro, Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, 368 Plantation St, Worcester, MA 01605.
| | - Kai-Lou Yue
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts
| | - Aditi Singh
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts
| | - Apurv Soni
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts
| | - Eric Y. Ding
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts
| | - Qiming Shi
- Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, Massachusetts
| | - David D. McManus
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts
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de Leeuwerk ME, Botjes M, van Vliet V, Geleijn E, de Groot V, van Wegen E, van der Schaaf M, Tuynman J, Dickhoff C, van der Leeden M. Self-monitoring of Physical Activity After Hospital Discharge in Patients Who Have Undergone Gastrointestinal or Lung Cancer Surgery: Mixed Methods Feasibility Study. JMIR Cancer 2022; 8:e35694. [PMID: 35749165 PMCID: PMC9270713 DOI: 10.2196/35694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/31/2022] [Accepted: 04/10/2022] [Indexed: 11/19/2022] Open
Abstract
Background Self-monitoring of physical activity (PA) using an accelerometer is a promising intervention to stimulate PA after hospital discharge. Objective This study aimed to evaluate the feasibility of PA self-monitoring after discharge in patients who have undergone gastrointestinal or lung cancer surgery. Methods A mixed methods study was conducted in which 41 patients with cancer scheduled for lobectomy, esophageal resection, or hyperthermic intraperitoneal chemotherapy were included. Preoperatively, patients received an ankle-worn accelerometer and the corresponding mobile health app to familiarize themselves with its use. The use was continued for up to 6 weeks after surgery. Feasibility criteria related to the study procedures, the System Usability Scale, and user experiences were established. In addition, 6 patients were selected to participate in semistructured interviews. Results The percentage of patients willing to participate in the study (68/90, 76%) and the final participation rate (57/90, 63%) were considered good. The retention rate was acceptable (41/57, 72%), whereas the rate of missing accelerometer data was relatively high (31%). The mean System Usability Scale score was good (77.3). Interviewed patients mentioned that the accelerometer and app were easy to use, motivated them to be more physically active, and provided postdischarge support. The technical shortcomings and comfort of the ankle straps should be improved. Conclusions Self-monitoring of PA after discharge appears to be feasible based on good system usability and predominantly positive user experiences in patients with cancer after lobectomy, esophageal resection, or hyperthermic intraperitoneal chemotherapy. Solving technical problems and improving the comfort of the ankle strap may reduce the number of dropouts and missing data in clinical use and follow-up studies.
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Affiliation(s)
- Marijke Elizabeth de Leeuwerk
- Rehabilitation Medicine, Amsterdam University Medical Centers location Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Ageing & Vitality, Amsterdam Movement Sciences, Amsterdam, Netherlands
| | - Martine Botjes
- Rehabilitation Medicine, Amsterdam University Medical Centers location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Vincent van Vliet
- Rehabilitation Medicine, Amsterdam University Medical Centers location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Edwin Geleijn
- Rehabilitation Medicine, Amsterdam University Medical Centers location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Vincent de Groot
- Rehabilitation Medicine, Amsterdam University Medical Centers location Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Rehabilitation & Development, Amsterdam Movement Sciences, Amsterdam, Netherlands
| | - Erwin van Wegen
- Rehabilitation Medicine, Amsterdam University Medical Centers location Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Ageing & Vitality, Amsterdam Movement Sciences, Amsterdam, Netherlands.,Rehabilitation & Development, Amsterdam Movement Sciences, Amsterdam, Netherlands
| | - Marike van der Schaaf
- Ageing & Vitality, Amsterdam Movement Sciences, Amsterdam, Netherlands.,Rehabilitation Medicine, Amsterdam University Medical Centers location University of Amsterdam, Amsterdam, Netherlands.,Center of Expertise Urban Vitality, Faculty of Health, Amsterdam University of Applied Sciences, Amsterdam, Netherlands
| | - Jurriaan Tuynman
- General Surgery, Amsterdam University Medical Centers location Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Cancer Centre Amsterdam, Treatment and Quality of Life, Amsterdam, Netherlands
| | - Chris Dickhoff
- Cancer Centre Amsterdam, Treatment and Quality of Life, Amsterdam, Netherlands.,Cardio-Thoracic Surgery, Amsterdam University Medical Centers location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Marike van der Leeden
- Rehabilitation Medicine, Amsterdam University Medical Centers location Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Ageing & Vitality, Amsterdam Movement Sciences, Amsterdam, Netherlands
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Goergen CJ, Tweardy MJ, Steinhubl SR, Wegerich SW, Singh K, Mieloszyk RJ, Dunn J. Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data. Annu Rev Biomed Eng 2022; 24:1-27. [PMID: 34932906 PMCID: PMC9218991 DOI: 10.1146/annurev-bioeng-103020-040136] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
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.
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Affiliation(s)
- Craig J Goergen
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | | | - Steven R Steinhubl
- physIQ Inc., Chicago, Illinois, USA
- Scripps Research Translational Institute, La Jolla, California, USA
| | | | - Karnika Singh
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | | | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
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de Leeuwerk ME, Bor P, van der Ploeg HP, de Groot V, van der Schaaf M, van der Leeden M. The effectiveness of physical activity interventions using activity trackers during or after inpatient care: a systematic review and meta-analysis of randomized controlled trials. Int J Behav Nutr Phys Act 2022; 19:59. [PMID: 35606852 PMCID: PMC9125831 DOI: 10.1186/s12966-022-01261-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 01/24/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Promoting physical activity (PA) in patients during and/or after an inpatient stay appears important but challenging. Interventions using activity trackers seem promising to increase PA and enhance recovery of physical functioning. OBJECTIVE To review the effectiveness of physical activity interventions using activity trackers on improving PA and physical functioning, compared to usual care in patients during and/or after inpatient care. In addition, it was determined whether the following intervention characteristics increase the effectiveness of these interventions: the number of behaviour change techniques (BCTs) used, the use of a theoretical model or the addition of coaching by a health professional. DESIGN Systematic review and meta-analysis. DATA SOURCES PubMed, EMBASE, Cinahl, SportDiscus and Web of Science databases were searched in March 2020 and updated in March 2021. ELIGIBILITY CRITERIA FOR SELECTING STUDIES Randomized controlled trials (RCTs) including interventions using activity trackers and feedback on PA in adult patients during, or less than 3 months after, hospitalization or inpatient rehabilitation. METHODS Following database search and title and abstract screening, articles were screened on full text for eligibility and then assessed for risk of bias by using the Physiotherapy Evidence Database (PEDro) scale. Meta-analyses, including subgroup analysis on intervention characteristics, were conducted for the outcomes PA and physical functioning. RESULTS Overall, 21 RCTs totalling 2355 patients were included. The trials covered a variety of clinical areas. There was considerable heterogeneity between studies. For the 13 studies that measured PA as an outcome variable(N = 1435), a significant small positive effect in favour of the intervention was found (standardized mean difference (SMD) = 0.34; 95%CI 0.12-0.56). For the 13 studies that measured physical functioning as an outcome variable (N = 1415) no significant effect was found (SMD = 0.09; 95%CI -0.02 - 0.19). Effectiveness on PA seems to improve by providing the intervention both during and after the inpatient period and by using a theoretical model, multiple BCTs and coaching by a health professional. CONCLUSION Interventions using activity trackers during and/or after inpatient care can be effective in increasing the level of PA. However, these improvements did not necessarily translate into improvements in physical functioning. Several intervention characteristics were found to increase the effectiveness of PA interventions. TRIAL REGISTRATION Registered in PROSPERO ( CRD42020175977 ) on March 23th, 2020.
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Affiliation(s)
- Marijke E de Leeuwerk
- Amsterdam UMC location Vrije Universiteit Amsterdam, Rehabilitation Medicine, de Boelelaan, 1117, Amsterdam, the Netherlands. .,Amsterdam Movement Sciences, Ageing & Vitality, Amsterdam, The Netherlands.
| | - Petra Bor
- University Medical Centre Utrecht, Utrecht University, Department of Rehabilitation, Physical Therapy Science & Sports, Heidelberglaan, 100, Utrecht, the Netherlands
| | - Hidde P van der Ploeg
- Amsterdam UMC location Vrije universiteit Amsterdam, Public and Occupational Health, de Boelelaan, 1117, Amsterdam, the Netherlands
| | - Vincent de Groot
- Amsterdam UMC location Vrije Universiteit Amsterdam, Rehabilitation Medicine, de Boelelaan, 1117, Amsterdam, the Netherlands.,Amsterdam Public Health, Health Behaviors & Chronic Diseases, Amsterdam, The Netherlands.,Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, The Netherlands
| | - Marike van der Schaaf
- Amsterdam Movement Sciences, Ageing & Vitality, Amsterdam, The Netherlands.,Amsterdam UMC location University of Amsterdam, Rehabilitation Medicine, Meibergdreef 9, Amsterdam, the Netherlands.,Faculty of Health, Center of Expertise Urban Vitality, Amsterdam University of Applied Sciences, Amsterdam, the Netherlands
| | - Marike van der Leeden
- Amsterdam UMC location Vrije Universiteit Amsterdam, Rehabilitation Medicine, de Boelelaan, 1117, Amsterdam, the Netherlands.,Amsterdam Movement Sciences, Ageing & Vitality, Amsterdam, The Netherlands
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Lonsdale H, Gray GM, Ahumada LM, Yates HM, Varughese A, Rehman MA. The Perioperative Human Digital Twin. Anesth Analg 2022; 134:885-892. [PMID: 35299215 DOI: 10.1213/ane.0000000000005916] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Hannah Lonsdale
- From the Department of Anesthesiology and Critical Care Medicine, Johns Hopkins Hospital, Baltimore, Maryland
| | | | - Luis M Ahumada
- Center for Pediatric Data Science and Analytics Methodology
| | - Hannah M Yates
- Department of Anesthesia and Pain Medicine, Johns Hopkins All Children's Hospital, St Petersburg, Florida
| | - Anna Varughese
- Department of Anesthesia and Pain Medicine, Johns Hopkins All Children's Hospital, St Petersburg, Florida
| | - Mohamed A Rehman
- Department of Anesthesia and Pain Medicine, Johns Hopkins All Children's Hospital, St Petersburg, Florida
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36
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Zhou J, Xin H. Emerging artificial intelligence methods for fighting lung cancer: a survey. CLINICAL EHEALTH 2022. [DOI: 10.1016/j.ceh.2022.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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Fitzgerald RC, Antoniou AC, Fruk L, Rosenfeld N. The future of early cancer detection. Nat Med 2022; 28:666-677. [PMID: 35440720 DOI: 10.1038/s41591-022-01746-x] [Citation(s) in RCA: 89] [Impact Index Per Article: 44.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/15/2022] [Indexed: 12/22/2022]
Abstract
A proactive approach to detecting cancer at an early stage can make treatments more effective, with fewer side effects and improved long-term survival. However, as detection methods become increasingly sensitive, it can be difficult to distinguish inconsequential changes from lesions that will lead to life-threatening cancer. Progress relies on a detailed understanding of individualized risk, clear delineation of cancer development stages, a range of testing methods with optimal performance characteristics, and robust evaluation of the implications for individuals and society. In the future, advances in sensors, contrast agents, molecular methods, and artificial intelligence will help detect cancer-specific signals in real time. To reduce the burden of cancer on society, risk-based detection and prevention needs to be cost effective and widely accessible.
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Affiliation(s)
- Rebecca C Fitzgerald
- Early Detection Programme, Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health & Primary Care, University of Cambridge, Cambridge, UK
| | - Ljiljana Fruk
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Nitzan Rosenfeld
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
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Osterman CK, Sanoff HK, Wood WA, Fasold M, Lafata JE. Predictive Modeling for Adverse Events and Risk Stratification Programs for People Receiving Cancer Treatment. JCO Oncol Pract 2022; 18:127-136. [PMID: 34469180 PMCID: PMC9213197 DOI: 10.1200/op.21.00198] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
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.
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Affiliation(s)
- Chelsea K. Osterman
- Division of Oncology, Department of Medicine, University of North Carolina, Chapel Hill, NC
| | - Hanna K. Sanoff
- Division of Oncology, Department of Medicine, University of North Carolina, Chapel Hill, NC,Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC
| | - William A. Wood
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC,Division of Hematology, Department of Medicine, University of North Carolina, Chapel Hill, NC
| | - Megan Fasold
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC
| | - Jennifer Elston Lafata
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC,Division of Pharmaceutical Outcomes and Policy, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC,Jennifer Elston Lafata, PhD, University of North Carolina, 2214 Kerr Hall, CB# 7573, Chapel Hill, NC 27599; e-mail:
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Jacobsen M, Rottmann P, Dembek TA, Gerke AL, Gholamipoor R, Blum C, Hartmann NU, Verket M, Kaivers J, Jäger P, Baermann BN, Heinemann L, Marx N, Müller-Wieland D, Kollmann M, Seyfarth M, Kobbe G. Feasibility of Wearable-Based Remote Monitoring in Patients During Intensive Treatment for Aggressive Hematologic Malignancies. JCO Clin Cancer Inform 2022; 6:e2100126. [PMID: 35025669 DOI: 10.1200/cci.21.00126] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
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.
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Affiliation(s)
- Malte Jacobsen
- Faculty of Health, University Witten/Herdecke, Witten, Germany.,Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, Aachen, Germany
| | - Pauline Rottmann
- Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Düsseldorf, Germany.,Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Till A Dembek
- Department of Neurology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Anna L Gerke
- Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Düsseldorf, Germany.,Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Rahil Gholamipoor
- Department of Computer Science, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christopher Blum
- Department of Biology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Niels-Ulrik Hartmann
- Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, Aachen, Germany
| | - Marlo Verket
- Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, Aachen, Germany
| | - Jennifer Kaivers
- Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Düsseldorf, Germany.,Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Paul Jäger
- Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Düsseldorf, Germany.,Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Ben-Niklas Baermann
- Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Düsseldorf, Germany.,Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | | | - Nikolaus Marx
- Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, Aachen, Germany
| | - Dirk Müller-Wieland
- Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, Aachen, Germany
| | - Markus Kollmann
- Department of Biology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Melchior Seyfarth
- Faculty of Health, University Witten/Herdecke, Witten, Germany.,Department of Cardiology, Helios University Hospital of Wuppertal, Wuppertal, Germany
| | - Guido Kobbe
- Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Düsseldorf, Germany.,Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Gibb M, Winter H, Komarzynski S, Wreglesworth NI, Innominato PF. Holistic Needs Assessment of Cancer Survivors-Supporting the Process Through Digital Monitoring of Circadian Physiology. Integr Cancer Ther 2022; 21:15347354221123525. [PMID: 36154506 PMCID: PMC9520145 DOI: 10.1177/15347354221123525] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
The year 2022 could represent a significant juncture in the incorporation of mHealth solutions in routine cancer care. With the recent global COVID-19 pandemic leading a surge in both observation- and intervention-based studies predominantly aimed at remote monitoring there has been huge intellectual investment in developing platforms able to provide real time analytics that are readily usable. Another fallout from the pandemic has seen record waiting times and delayed access to cancer therapies leading to exhausting pressures on global healthcare providers. It seems an opportune time to utilize this boom in platforms to offer more efficient “at home” clinical assessments and less “in department” time for patients. Here, we will focus specifically on the role of digital tools around cancer survivorship, a relevant aspect of the cancer journey, particularly benefiting from integrative approaches. Within that context a further concept will be introduced and that is of the likely upsurge in circadian-based interpretation of continuous monitoring and the engendered therapeutic modifications. Chronobiology across the 24-hour span has long been understood to control key bodily aspects and circadian dysregulation plays a significant role in the risk of cancer and also the response to therapy and therefore progressive outcome. The rapid improvement in minimally invasive monitoring devices is, in the opinion of the authors, likely to advance introducing chronobiological amendments to routine clinical practices with positive impact on cancer survivors.
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Affiliation(s)
- Max Gibb
- Cancer Services, Betsi Cadwaladr University Health Board, Bodelwyddan, UK
| | - Hannah Winter
- Respiratory Medicine, Betsi Cadwaladr University Health Board, Bangor, UK
| | | | - Nicholas I Wreglesworth
- Cancer Services, Betsi Cadwaladr University Health Board, Bodelwyddan, UK.,Bangor University, Bangor, UK
| | - Pasquale F Innominato
- Cancer Services, Betsi Cadwaladr University Health Board, Bodelwyddan, UK.,University of Warwick, Coventry, UK.,Paris-Saclay University, Villejuif, France
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Blum S, Hölle D, Bleichner MG, Debener S. Pocketable Labs for Everyone: Synchronized Multi-Sensor Data Streaming and Recording on Smartphones with the Lab Streaming Layer. SENSORS (BASEL, SWITZERLAND) 2021; 21:8135. [PMID: 34884139 PMCID: PMC8662410 DOI: 10.3390/s21238135] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/16/2021] [Accepted: 12/01/2021] [Indexed: 12/14/2022]
Abstract
The streaming and recording of smartphone sensor signals is desirable for mHealth, telemedicine, environmental monitoring and other applications. Time series data gathered in these fields typically benefit from the time-synchronized integration of different sensor signals. However, solutions required for this synchronization are mostly available for stationary setups. We hope to contribute to the important emerging field of portable data acquisition by presenting open-source Android applications both for the synchronized streaming (Send-a) and recording (Record-a) of multiple sensor data streams. We validate the applications in terms of functionality, flexibility and precision in fully mobile setups and in hybrid setups combining mobile and desktop hardware. Our results show that the fully mobile solution is equivalent to well-established desktop versions. With the streaming application Send-a and the recording application Record-a, purely smartphone-based setups for mobile research and personal health settings can be realized on off-the-shelf Android devices.
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Affiliation(s)
- Sarah Blum
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, 26111 Oldenburg, Germany;
- Cluster of Excellence Hearing4all, 26111 Oldenburg, Germany
| | - Daniel Hölle
- Neurophysiology of Everyday Life Group, Department of Psychology, University of Oldenburg, 26111 Oldenburg, Germany; (D.H.); (M.G.B.)
| | - Martin Georg Bleichner
- Neurophysiology of Everyday Life Group, Department of Psychology, University of Oldenburg, 26111 Oldenburg, Germany; (D.H.); (M.G.B.)
| | - Stefan Debener
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, 26111 Oldenburg, Germany;
- Cluster of Excellence Hearing4all, 26111 Oldenburg, Germany
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Fonseka LN, Woo BK. Consumer Wearables and the Integration of New Objective Measures in Oncology: Patient and Provider Perspectives. JMIR Mhealth Uhealth 2021; 9:e28664. [PMID: 34264191 PMCID: PMC8323022 DOI: 10.2196/28664] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 04/12/2021] [Accepted: 06/11/2021] [Indexed: 12/15/2022] Open
Abstract
With one in five adults in the United States owning a smartwatch or fitness tracker, these devices are poised to impact all aspects of medicine by offering a more objective approach to replace self-reported data. Oncology has proved to be a prototypical example, and wearables offer immediate benefits to patients and oncologists with the ability to track symptoms and health metrics in real time. We aimed to review the recent literature on consumer-grade wearables and its current applications in cancer from the perspective of both the patient and the provider. The relevant studies suggested that these devices offer benefits, such as improved medication adherence and accuracy of symptom tracking over self-reported data, as well as insights that increase patient empowerment. Physical activity is consistently correlated with stronger patient outcomes, and a patient's real-time metrics were found to be capable of tracking medication side effects and toxicity. Studies have made associations between wearable data and telomere shortening, cardiovascular disease, alcohol consumption, sleep apnea, and other conditions. The objective data obtained by the wearable presents a more complete picture of an individual's health than the snapshot of a 15-minute office visit and a single set of vital signs. Real-time metrics can be translated into a digital phenotype that identifies risk factors specific to each patient, and shared risk factors across one's social network may uncover common environmental exposures detrimental to one's health. Wearable data and its upcoming integration with social media will be the foundation for the next generation of personalized medicine.
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Affiliation(s)
- Lakshan N Fonseka
- College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, Pomona, CA, United States
| | - Benjamin Kp Woo
- Olive View-University of California, Los Angeles Medical Center, Sylmar, CA, United States
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Moses JC, Adibi S, Shariful Islam SM, Wickramasinghe N, Nguyen L. Application of Smartphone Technologies in Disease Monitoring: A Systematic Review. Healthcare (Basel) 2021; 9:889. [PMID: 34356267 PMCID: PMC8303662 DOI: 10.3390/healthcare9070889] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/03/2021] [Accepted: 07/09/2021] [Indexed: 12/21/2022] Open
Abstract
Technologies play an essential role in monitoring, managing, and self-management of chronic diseases. Since chronic patients rely on life-long healthcare systems and the current COVID-19 pandemic has placed limits on hospital care, there is a need to explore disease monitoring and management technologies and examine their acceptance by chronic patients. We systematically examined the use of smartphone applications (apps) in chronic disease monitoring and management in databases, namely, Medline, Web of Science, Embase, and Proquest, published from 2010 to 2020. Results showed that app-based weight management programs had a significant effect on healthy eating and physical activity (p = 0.002), eating behaviours (p < 0.001) and dietary intake pattern (p < 0.001), decreased mean body weight (p = 0.008), mean Body Mass Index (BMI) (p = 0.002) and mean waist circumference (p < 0.001). App intervention assisted in decreasing the stress levels (paired t-test = 3.18; p < 0.05). Among cancer patients, we observed a high acceptance of technology (76%) and a moderately positive correlation between non-invasive electronic monitoring data and questionnaire (r = 0.6, p < 0.0001). We found a significant relationship between app use and standard clinical evaluation and high acceptance of the use of apps to monitor the disease. Our findings provide insights into critical issues, including technology acceptance along with regulatory guidelines to be considered when designing, developing, and deploying smartphone solutions targeted for chronic patients.
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Affiliation(s)
- Jeban Chandir Moses
- School of Information Technology, Deakin University, 1 Gheringhap St, Geelong, VIC 3220, Australia;
| | - Sasan Adibi
- School of Information Technology, Deakin University, 1 Gheringhap St, Geelong, VIC 3220, Australia;
| | | | - Nilmini Wickramasinghe
- Iverson Health Innovation Research Institute, Swinburne University of Technology, Hawthorn, VIC 3122, Australia;
| | - Lemai Nguyen
- Department of Information Systems and Business Analytics, Deakin Business School, 221 Burwood Highway, Burwood, VIC 3125, Australia;
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Balachandran DD, Miller MA, Faiz SA, Yennurajalingam S, Innominato PF. Evaluation and Management of Sleep and Circadian Rhythm Disturbance in Cancer. Curr Treat Options Oncol 2021; 22:81. [PMID: 34213651 DOI: 10.1007/s11864-021-00872-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2021] [Indexed: 12/16/2022]
Abstract
OPINION STATEMENT Sleep and circadian rhythm disturbance are among the most commonly experienced symptoms in patients with cancer. These disturbances occur throughout the spectrum of cancer care from diagnosis, treatment, and long into survivorship. The pathogenesis of these symptoms and disturbances is based on common inflammatory pathways related to cancer and its' treatments. The evaluation of sleep and circadian disorders requires an understanding of how these symptoms cluster with other cancer-related symptoms and potentiate each other. A thorough evaluation of these symptoms and disorders utilizing validated diagnostic tools, directed review of clinical information, and diagnostic testing is recommended. Treatment of sleep and circadian disturbance in cancer patients should be based on the findings of a detailed evaluation, including specific treatment of primary sleep and circadian disorders, and utilize integrative and personalised management of cancer-related symptoms through multiple pharmacologic and non-pharmacologic modalities. Recognition, evaluation, and treatment of sleep and circadian rhythm disturbance in cancer may lead to improved symptom management, quality of life, and outcomes.
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Affiliation(s)
- Diwakar D Balachandran
- Department of Pulmonary Medicine, Division of Internal Medicine, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street. Unit 1462, Houston, TX, 77030-4009, USA.
| | - Michelle A Miller
- Division of Health Sciences (Mental Health & Wellbeing), University of Warwick, Warwick Medical School, Gibbet Hill, Coventry, UK
| | - Saadia A Faiz
- Department of Pulmonary Medicine, Division of Internal Medicine, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street. Unit 1462, Houston, TX, 77030-4009, USA
| | - Sriram Yennurajalingam
- Department of Palliative, Rehabilitation, and Integrative Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Pasquale F Innominato
- North Wales Cancer Treatment Centre, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, UK
- Cancer Chronotherapy Team, Warwick Medical School, Coventry, UK
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45
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Kent EE, Park EM, Wood WA, Bryant AL, Mollica MA. Survivorship Care of Older Adults With Cancer: Priority Areas for Clinical Practice, Training, Research, and Policy. J Clin Oncol 2021; 39:2175-2184. [PMID: 34043450 PMCID: PMC8260922 DOI: 10.1200/jco.21.00226] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 03/09/2021] [Accepted: 03/23/2021] [Indexed: 12/25/2022] Open
Affiliation(s)
- Erin E. Kent
- University of North Carolina at Chapel Hill, Chapel Hill, NC
- Lineberger Comprehensive Cancer Center, Chapel Hill, NC
| | - Eliza M. Park
- University of North Carolina at Chapel Hill, Chapel Hill, NC
- Lineberger Comprehensive Cancer Center, Chapel Hill, NC
| | - William A. Wood
- University of North Carolina at Chapel Hill, Chapel Hill, NC
- Lineberger Comprehensive Cancer Center, Chapel Hill, NC
| | - Ashley Leak Bryant
- University of North Carolina at Chapel Hill, Chapel Hill, NC
- Lineberger Comprehensive Cancer Center, Chapel Hill, NC
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Baglione AN, Cai L, Bahrini A, Posey I, Boukhechba M, Chow PI. Understanding the Relationship between Mood Symptoms and Mobile App Engagement Among Breast Cancer Patients: A Machine Learning Process (Preprint). JMIR Med Inform 2021; 10:e30712. [PMID: 35653183 PMCID: PMC9204571 DOI: 10.2196/30712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 02/07/2022] [Accepted: 03/11/2022] [Indexed: 12/04/2022] Open
Abstract
Background Health interventions delivered via smart devices are increasingly being used to address mental health challenges associated with cancer treatment. Engagement with mobile interventions has been associated with treatment success; however, the relationship between mood and engagement among patients with cancer remains poorly understood. A reason for this is the lack of a data-driven process for analyzing mood and app engagement data for patients with cancer. Objective This study aimed to provide a step-by-step process for using app engagement metrics to predict continuously assessed mood outcomes in patients with breast cancer. Methods We described the steps involved in data preprocessing, feature extraction, and data modeling and prediction. We applied this process as a case study to data collected from patients with breast cancer who engaged with a mobile mental health app intervention (IntelliCare) over 7 weeks. We compared engagement patterns over time (eg, frequency and days of use) between participants with high and low anxiety and between participants with high and low depression. We then used a linear mixed model to identify significant effects and evaluate the performance of the random forest and XGBoost classifiers in predicting weekly mood from baseline affect and engagement features. Results We observed differences in engagement patterns between the participants with high and low levels of anxiety and depression. The linear mixed model results varied by the feature set; these results revealed weak effects for several features of engagement, including duration-based metrics and frequency. The accuracy of predicting depressed mood varied according to the feature set and classifier. The feature set containing survey features and overall app engagement features achieved the best performance (accuracy: 84.6%; precision: 82.5%; recall: 64.4%; F1 score: 67.8%) when used with a random forest classifier. Conclusions The results from the case study support the feasibility and potential of our analytic process for understanding the relationship between app engagement and mood outcomes in patients with breast cancer. The ability to leverage both self-report and engagement features to analyze and predict mood during an intervention could be used to enhance decision-making for researchers and clinicians and assist in developing more personalized interventions for patients with breast cancer.
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Affiliation(s)
- Anna N Baglione
- Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, United States
| | - Lihua Cai
- Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, United States
| | - Aram Bahrini
- Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, United States
| | - Isabella Posey
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, United States
| | - Mehdi Boukhechba
- Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, United States
| | - Philip I Chow
- Center for Behavioral Health & Technology, School of Medicine, University of Virginia, Charlottesville, VA, United States
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47
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Low CA, Li M, Vega J, Durica KC, Ferreira D, Tam V, Hogg M, Zeh Iii H, Doryab A, Dey AK. Digital Biomarkers of Symptom Burden Self-Reported by Perioperative Patients Undergoing Pancreatic Surgery: Prospective Longitudinal Study. JMIR Cancer 2021; 7:e27975. [PMID: 33904822 PMCID: PMC8114161 DOI: 10.2196/27975] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/17/2021] [Accepted: 03/29/2021] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Cancer treatments can cause a variety of symptoms that impair quality of life and functioning but are frequently missed by clinicians. Smartphone and wearable sensors may capture behavioral and physiological changes indicative of symptom burden, enabling passive and remote real-time monitoring of fluctuating symptoms. OBJECTIVE The aim of this study was to examine whether smartphone and Fitbit data could be used to estimate daily symptom burden before and after pancreatic surgery. METHODS A total of 44 patients scheduled for pancreatic surgery participated in this prospective longitudinal study and provided sufficient sensor and self-reported symptom data for analyses. Participants collected smartphone sensor and Fitbit data and completed daily symptom ratings starting at least two weeks before surgery, throughout their inpatient recovery, and for up to 60 days after postoperative discharge. Day-level behavioral features reflecting mobility and activity patterns, sleep, screen time, heart rate, and communication were extracted from raw smartphone and Fitbit data and used to classify the next day as high or low symptom burden, adjusted for each individual's typical level of reported symptoms. In addition to the overall symptom burden, we examined pain, fatigue, and diarrhea specifically. RESULTS Models using light gradient boosting machine (LightGBM) were able to correctly predict whether the next day would be a high symptom day with 73.5% accuracy, surpassing baseline models. The most important sensor features for discriminating high symptom days were related to physical activity bouts, sleep, heart rate, and location. LightGBM models predicting next-day diarrhea (79.0% accuracy), fatigue (75.8% accuracy), and pain (79.6% accuracy) performed similarly. CONCLUSIONS Results suggest that digital biomarkers may be useful in predicting patient-reported symptom burden before and after cancer surgery. Although model performance in this small sample may not be adequate for clinical implementation, findings support the feasibility of collecting mobile sensor data from older patients who are acutely ill as well as the potential clinical value of mobile sensing for passive monitoring of patients with cancer and suggest that data from devices that many patients already own and use may be useful in detecting worsening perioperative symptoms and triggering just-in-time symptom management interventions.
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Affiliation(s)
- Carissa A Low
- Mobile Sensing + Health Institute, Center for Behavioral Health, Media, and Technology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Meng Li
- Mobile Sensing + Health Institute, Center for Behavioral Health, Media, and Technology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Julio Vega
- Mobile Sensing + Health Institute, Center for Behavioral Health, Media, and Technology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Krina C Durica
- Mobile Sensing + Health Institute, Center for Behavioral Health, Media, and Technology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Denzil Ferreira
- Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
| | - Vernissia Tam
- Department of Surgery, New York-Presbyterian Hospital & Weill Cornell Medical College, New York, NY, United States
| | - Melissa Hogg
- NorthShore University HealthSystem, Evanston, IL, United States
| | - Herbert Zeh Iii
- Department of Surgery, UT Southwestern Medical Center, Dallas, TX, United States
| | - Afsaneh Doryab
- Systems and Information Engineering, University of Virginia, Charlottesville, VA, United States
| | - Anind K Dey
- Information School, University of Washington, Seattle, WA, United States
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48
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Komarzynski S, Wreglesworth NI, Griffiths D, Pecchia L, Subbe CP, Hughes SF, Davies EH, Innominato PF. Embracing Change: Learnings From Implementing Multidimensional Digital Remote Monitoring in Oncology Patients at a District General Hospital During the COVID-19 Pandemic. JCO Clin Cancer Inform 2021; 5:216-220. [PMID: 33606562 DOI: 10.1200/cci.20.00136] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Affiliation(s)
| | - Nicholas I Wreglesworth
- Oncology Department, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, UK.,School of Medical Sciences, Bangor University, Bangor, UK
| | - Dawn Griffiths
- Oncology Department, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, UK
| | | | - Christian P Subbe
- School of Medical Sciences, Bangor University, Bangor, UK.,Acute and Critical Care Medicine, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, UK
| | - Stephen F Hughes
- North Wales Clinical Research Centre, Betsi Cadwaladr University Health Board, Wrexham, UK
| | | | - Pasquale F Innominato
- Oncology Department, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, UK.,Cancer Chronotherapy Team, Warwick Medical School, University of Warwick, Coventry, UK.,European Laboratory U935, Institut National de la Santé et de la Recherche Médicale (INSERM), Paris-Saclay University, Villejuif, France
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49
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Koenig C, Ammann RA, Kuehni CE, Roessler J, Brack E. Continuous recording of vital signs with a wearable device in pediatric patients undergoing chemotherapy for cancer-an operational feasibility study. Support Care Cancer 2021; 29:5283-5292. [PMID: 33655413 PMCID: PMC7925259 DOI: 10.1007/s00520-021-06099-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 02/19/2021] [Indexed: 11/29/2022]
Abstract
PURPOSE Pediatric patients with cancer are at high risk for severe infections. Infections can trigger changes of vital signs long before clinical symptoms arise. Continuous recording may detect such changes earlier than discrete measurements. We aimed to assess the feasibility of continuous recording of vital signs by a wearable device (WD) in pediatric patients undergoing chemotherapy for cancer. METHODS In this prospective, observational single-center study, pediatric patients under chemotherapy wore the Everion® WD for 14 days. The predefined patient-specific goal was heart rate recorded in good quality during ≥18/24 h per day, on ≥7 consecutive days. The predefined criterion to claim feasibility was ≥15/20 patients fulfilling this patient-specific goal. RESULTS Twenty patients were included (median age, 6 years; range, 2-16). Six patients aged 3-16 years fulfilled the patient-specific goal. Quality of heart rate recording was good during 3992 of 6576 (61%) hours studied and poor during 300 (5%) hours, and no data was recorded during 2284 (35%) hours. Eighteen of 20 participants indicated that this WD is acceptable to measure vital signs in children under chemotherapy. CONCLUSION The predefined feasibility criterion was not fulfilled. This was mainly due to important compliance problems and independent of the WD itself. However, continuous recording of vital signs was possible across a very wide age range in pediatric patients undergoing chemotherapy for cancer. We recommend to study feasibility in the Everion® again, plus in further WDs, applying measures to enhance compliance. TRIAL REGISTRATION ClinicalTrials.gov (NCT04134429) on October 22, 2019.
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Affiliation(s)
- Christa Koenig
- Pediatric Hematology/Oncology, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
| | - Roland A Ammann
- Pediatric Hematology/Oncology, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.,Kinderaerzte KurWerk, Burgdorf, Switzerland
| | - Claudia E Kuehni
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Jochen Roessler
- Pediatric Hematology/Oncology, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Eva Brack
- Pediatric Hematology/Oncology, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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