Use of Wearable Activity Tracker in Patients With Cancer Undergoing Chemotherapy: Toward Evaluating Risk of Unplanned Health Care Encounters.

PURPOSE: Unplanned health care encounters (UHEs) such as emergency room visits can occur commonly during cancer chemotherapy treatments. Patients at an increased risk of UHEs are typically identified by clinicians using performance status (PS) assessments based on a descriptive scale, such as the Eastern Cooperative Oncology Group (ECOG) scale. Such assessments can be bias prone, resulting in PS score disagreements between assessors. We therefore propose to evaluate PS using physical activity measurements (eg, energy expenditure) from wearable activity trackers. Specifically, we examined the feasibility of using a wristband (band) and a smartphone app for PS assessments. METHODS: We conducted an observational study on a cohort of patients with solid tumor receiving highly emetogenic chemotherapy. Patients were instructed to wear the band for a 60-day activity-tracking period. During clinic visits, we obtained ECOG scores assessed by physicians, coordinators, and patients themselves. UHEs occurring during the activity-tracking period plus a 90-day follow-up period were later compiled. We defined our primary outcome as the percentage of patients adherent to band-wear ≥ 80% of 10 am to 8 pm for ≥ 80% of the activity-tracking period. In an exploratory analysis, we computed hourly metabolic equivalent of task (MET) and counted 10 am to 8 pm hours with > 1.5 METs as nonsedentary physical activity hours. RESULTS: Forty-one patients completed the study (56.1% female; 61.0% age 40-60 years); 68% were adherent to band-wear. ECOG score disagreement between assessors ranged from 35.3% to 50.0%. In our exploratory analysis, lower average METs and nonsedentary hours, but not higher ECOG scores, were associated with higher 150-day UHEs. CONCLUSION: The use of a wearable activity tracker is generally feasible in a similar population of patients with cancer. A larger randomized controlled trial should be conducted to confirm the association between lower nonsedentary hours and higher UHEs.

Quantified Kinematics to Evaluate Patient Chemotherapy Risks in Clinic.

PURPOSE: Performance status (PS) is a key factor in oncologic decision making, but conventional scales used to measure PS vary among observers. Consumer-grade biometric sensors have previously been identified as objective alternatives to the assessment of PS. Here, we investigate how one such biometric sensor can be used during a clinic visit to identify patients who are at risk for complications, particularly unexpected hospitalizations that may delay treatment or result in low physical activity. We aim to provide a novel and objective means of predicting tolerability to chemotherapy. METHODS: Thirty-eight patients across three centers in the United States who were diagnosed with a solid tumor with plans for treatment with two cycles of highly emetogenic chemotherapy were included in this single-arm, observational prospective study. A noninvasive motion-capture system quantified patient movement from chair to table and during the get-up-and-walk test. Activity levels were recorded using a wearable sensor over a 2-month period. Changes in kinematics from two motion-capture data points pre- and post-treatment were tested for correlation with unexpected hospitalizations and physical activity levels as measured by a wearable activity sensor. RESULTS: Among 38 patients (mean age, 48.3 years; 53% female), kinematic features from chair to table were the best predictors for unexpected health care encounters (area under the curve, 0.775 ± 0.029) and physical activity (area under the curve, 0.830 ± 0.080). Chair-to-table acceleration of the nonpivoting knee (t = 3.39; P = .002) was most correlated with unexpected health care encounters. Get-up-and-walk kinematics were most correlated with physical activity, particularly the right knee acceleration (t = -2.95; P = .006) and left arm angular velocity (t = -2.4; P = .025). CONCLUSION: Chair-to-table kinematics are good predictors of unexpected hospitalizations, whereas the get-up-and-walk kinematics are good predictors of low physical activity.

Patient reported outcomes can improve performance status assessment: a pilot study.

BACKGROUND: Patient performance status is routinely used in oncology to estimate physical functioning, an important factor in clinical treatment decisions and eligibility for clinical trials. However, validity and reliability data for ratings of performance status have not been optimal. This study recruited oncology patients who were about to begin emetogenic palliative or adjuvant chemotherapy for treatment of solid tumors. We employed actigraphy as the gold standard for physical activity level. Correspondences between actigraphy and oncologists' and patients' ratings of performance status were examined and compared with the correspondences of actigraphy and several patient reported outcomes (PROs). The study was designed to determine feasibility of the measurement approaches and if PROs can improve the accuracy of assessment of performance status. METHODS: Oncologists and patients made performance status ratings at visit 1. Patients wore an actigraph and entered weekly PROs on a smartphone app. Data for days 1-14 after visit 1 were analyzed. Chart reviews were conducted to tabulate all unexpected medical events across days 1-150. RESULTS: Neither oncologist nor patient ratings of performance status predicted steps/hour (actigraphy). The PROMIS® Physical Function PRO (average of Days 1, 7, 14) was associated with steps/hour at high (for men) and moderate (for women) levels; the PROMIS® Fatigue PRO predicted steps for men, but not for women. Unexpected medical events occurred in 57% of patients. Only body weight in female patients predicted events; oncologist and patient performance status ratings, steps/hour, and other PROs did not. CONCLUSIONS: PROMIS® Physical Function and Fatigue PROs show good correspondence with steps/hour making them easy, useful tools for oncologists to improve their assessment of performance status, especially for male patients. Female patients had lower levels of steps/hour than males and lower correlations among the predictors, suggesting the need for further work to improve performance status assessment in women. Assessment of pre-morbid sedentary behavior alongside current Physical Functioning and Fatigue PROs may allow for a more valid determination of disease-related activity level and performance status.

Low-dimensional dynamical characterization of human performance of cancer patients using motion data.

BACKGROUND: Biomechanical characterization of human performance with respect to fatigue and fitness is relevant in many settings, however is usually limited to either fully qualitative assessments or invasive methods which require a significant experimental setup consisting of numerous sensors, force plates, and motion detectors. Qualitative assessments are difficult to standardize due to their intrinsic subjective nature, on the other hand, invasive methods provide reliable metrics but are not feasible for large scale applications. METHODS: Presented here is a dynamical toolset for detecting performance groups using a non-invasive system based on the Microsoft Kinect motion capture sensor, and a case study of 37 cancer patients performing two clinically monitored tasks before and after therapy regimens. Dynamical features are extracted from the motion time series data and evaluated based on their ability to i) cluster patients into coherent fitness groups using unsupervised learning algorithms and to ii) predict Eastern Cooperative Oncology Group performance status via supervised learning. FINDINGS: The unsupervised patient clustering is comparable to clustering based on physician assigned Eastern Cooperative Oncology Group status in that they both have similar concordance with change in weight before and after therapy as well as unexpected hospitalizations throughout the study. The extracted dynamical features can predict physician, coordinator, and patient Eastern Cooperative Oncology Group status with an accuracy of approximately 80%. INTERPRETATION: The non-invasive Microsoft Kinect sensor and the proposed dynamical toolset comprised of data preprocessing, feature extraction, dimensionality reduction, and machine learning offers a low-cost and general method for performance segregation and can complement existing qualitative clinical assessments.