The remote monitoring of gastrointestinal cancer patients’ performance status and burden of symptoms via a consumer-based activity tracker (Preprint)

2020 ◽  
Author(s):  
Alireza Ghods ◽  
Armin Shahrokni ◽  
Hassan Ghasemzadeh ◽  
Diane Cook
Keyword(s):  
Cancer Patients ◽  
Remote Monitoring ◽  
Gastrointestinal Cancer ◽  
Performance Status ◽  
Step Count ◽  
Remote Monitoring System ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Steps Per Day ◽  
Ecog Ps

BACKGROUND Patients with cancer spend most of their time in their homes, but their condition is under constant change as a result of the treatment they receive. Patients' performance status (PS) and their symptoms assessments have typically only been collected during clinic visits. Developing a secure and reliable remote monitoring system is a necessity that can be achieved with an inexpensive consumer-based activity tracker. The real-time data captured by wearable activity trackers could provide a dynamic view of patients for clinicians to make an informed treatment. OBJECTIVE This study aims to assess the practicality of a consumer-based activity tracker for remote monitoring of gastrointestinal cancer patients. METHODS Twenty-seven consenting patients (63% male, median age 58 years) wore a consumer-based activity tracker seven days before chemotherapy, and fourteen days after receiving their first treatment. The clinician assessed patients' ECOG-PS and Memorial Symptom Assessment Checklist-Short Form (MSAS-SF) for patients pre- and post-chemotherapy. The statistical correlation between ECOG-PS and MSAS-SF of patients with their step count was assessed. RESULTS The daily step count had the highest correlation with the patients' ECOG-PS after chemotherapy (P = 6.4e-11). The patients with higher ECOG-PS experienced a higher fluctuation in their step count. The patients who walked more (mean: 6071 steps per day) pre-chemotherapy and (mean: 5930 steps per day) post-chemotherapy had a lower MSAS score (less burden of symptoms) compared to patients who walked less (mean: 5205 steps per day) pre-chemotherapy and (mean: 4437 steps per day) post-chemotherapy. CONCLUSIONS This study demonstrates the feasibility of inexpensive, consumer-based activity trackers in monitoring patients’ PS and MSAS in the gastrointestinal cancer population. The findings need to be validated in a larger population for generalizability.

What can we do for gastrointestinal cancer patients with poor performance status (PS)?

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 19629-19629
Author(s):  
K. Shitara ◽  
M. Munakata ◽  
O. Muto ◽  
M. Kasai ◽  
Y. Sakata
Keyword(s):  
Cancer Patients ◽  
Tumor Markers ◽  
Gastrointestinal Cancer ◽  
Survival Benefit ◽  
Performance Status ◽  
Poor Performance ◽  
Poor Performance Status ◽  
Ecog Ps ◽  
To Receive ◽  
Measurable Lesion

19629 Background: The prognosis of advanced gastrointestinal cancer patients, especially those with poor PS, is generally dismal. Needless to say, such patients are ineligible for participation in clinical studies. However, there are many patients with poor PS who wish to receive chemotherapy. Methods: From June 2000 to October 2006, a total of 508 patients with advanced cancer, including 304 gastrointestinal cancer patients, were treated by chemotherapy in our hospital. Of these, 110 gastrointestinal cancer patients (gastric=35, colorectal=30, pancreatic=26, biliary tract=11, esophageal=8) had poor PS (ECOG PS 3 = 68 patients, PS 4 = 42 patients). In 103 patients with at least one measurable lesion, a partial response according to RECIST criteria was obtained in 13 patients (12.6%). In 60 patients with ascites (47 patients), pleural effusion (25 patients), or both (12 patients), 11 of the patients (18.3%) achieved decreased fluid accumulation. A decline in tumor markers (>25%) was observed in 28 patients. Improvement in PS was seen in 13 patients (11.8%). As a result, 35 patients (31.8 %, including 9 patients with PS 4) achieved a tumor response, a decrease in accumulated fluid, or a decline in tumor markers, which resulted in a survival benefit compared to the other 75 patients without effect (6.4 months vs. 2.3 months, p<0.001). Alleviation of some symptoms was observed in 28 out of 98 symptomatic patients (30.4%). A better response and/or a decline in tumor markers significantly correlated with alleviation of symptoms (p<0.001). No treatment related death was seen. Conclusions: With regard to response rate, chemotherapy was rarely effective for patients with advanced gastrointestinal cancer with poor PS. However, more than a few patients gained a certain survival benefit and alleviation of symptoms. Thus, chemotherapy may be warranted in cases of patients with advanced gastrointestinal cancer who wish to receive chemotherapy despite the low possibility of response. No significant financial relationships to disclose.

scholarly journals Ubiquitous Assessment of the Recovery of Cancer Patients Using Consumer-Level Activity Trackers

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1204
Author(s):  
Salvador Moreno ◽  
Miguel Damas ◽  
Ignacio Rojas ◽  
Victor Amezcua ◽  
Pilar Gutierrez-Pastor ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Performance Status ◽  
Recovery Process ◽  
Breast Cancer Patients ◽  
Treatment Needs ◽  
Commercial Activity ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Intervention Trials

Performance Status (PS) variability is a powerful tool to evaluate overall condition, treatment needs and survival chances of cancer patients. Traditionally, its assessment has relied on the experience of oncologists when interpreting results of clinical tests and when interviewing the patients. Meanwhile, consumer-level activity trackers have obtained good results in behavior-change oriented intervention trials and Fitbit devices have demonstrated enough reliability to provide objective data related to physical activity, but the clinical possibilities of the data collected has been neglected. This work presents a system design for ubiquitous assessment of PS by means of objective and quantifiable data from different sources: medical history, self-reported quality-of-life questionnaires and a commercial activity tracker Fitbit Alta HR. The system proposed aims to contextualize and model the recovery process of breast cancer patients during chemotherapy treatment.

scholarly journals Neural Network–Based Algorithm for Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers: Retrospective Observation and Algorithm Development Study

10.2196/18142 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e18142
Author(s):  
Ramin Mohammadi ◽  
Mursal Atif ◽  
Amanda Jayne Centi ◽  
Stephen Agboola ◽  
Kamal Jethwani ◽  
...  
Keyword(s):  
Neural Network ◽  
Physical Activity ◽  
Machine Learning ◽  
Learning Model ◽  
Step Count ◽  
Daily Step ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Machine Learning Model ◽  
Daily Step Count

Background It is well established that lack of physical activity is detrimental to the overall health of an individual. Modern-day activity trackers enable individuals to monitor their daily activities to meet and maintain targets. This is expected to promote activity encouraging behavior, but the benefits of activity trackers attenuate over time due to waning adherence. One of the key approaches to improving adherence to goals is to motivate individuals to improve on their historic performance metrics. Objective The aim of this work was to build a machine learning model to predict an achievable weekly activity target by considering (1) patterns in the user’s activity tracker data in the previous week and (2) behavior and environment characteristics. By setting realistic goals, ones that are neither too easy nor too difficult to achieve, activity tracker users can be encouraged to continue to meet these goals, and at the same time, to find utility in their activity tracker. Methods We built a neural network model that prescribes a weekly activity target for an individual that can be realistically achieved. The inputs to the model were user-specific personal, social, and environmental factors, daily step count from the previous 7 days, and an entropy measure that characterized the pattern of daily step count. Data for training and evaluating the machine learning model were collected over a duration of 9 weeks. Results Of 30 individuals who were enrolled, data from 20 participants were used. The model predicted target daily count with a mean absolute error of 1545 (95% CI 1383-1706) steps for an 8-week period. Conclusions Artificial intelligence applied to physical activity data combined with behavioral data can be used to set personalized goals in accordance with the individual’s level of activity and thereby improve adherence to a fitness tracker; this could be used to increase engagement with activity trackers. A follow-up prospective study is ongoing to determine the performance of the engagement algorithm.

Differences in performance status assessments between palliative care specialists, nurses, and oncologists.

2014 ◽  
Vol 32 (31_suppl) ◽  
pp. 19-19
Author(s):  
YuJung Kim ◽  
Yi Zhang ◽  
Ji Chan Park ◽  
David Hui ◽  
Gary B. Chisholm ◽  
...  
Keyword(s):  
Palliative Care ◽  
Cancer Patients ◽  
Advanced Cancer ◽  
Performance Status ◽  
Well Being ◽  
Assessment Tools ◽  
Advanced Cancer Patients ◽  
Care Clinic ◽  
Edmonton Symptom Assessment Scale ◽  
Ecog Ps

19 Background: The Eastern Cooperative Oncology Group (ECOG) performance status (PS) is one of the most commonly used assessment tools among oncologists and palliative care specialists caring advanced cancer patients. However, the inter-observer difference between the oncologist and palliative care specialist has never been reported. Methods: We retrospectively reviewed the medical records of all patients who were first referred to an outpatient palliative care clinic in 2013 and identified 278 eligible patients. The ECOG PS assessments by palliative care specialists, nurses, and oncologists, and the symptom burden measured by Edmonton Symptom Assessment Scale (ESAS) were analyzed. Results: According to the pairwise comparisons using Sign tests, palliative care specialists rated the ECOG PS grade significantly higher than oncologists (median 0.5 grade, P<0.0001) and nurses also rated significantly higher (median 1.0 grade, P<0.0001). The assessments of palliative care specialists and nurses were not significantly different (P=0.10). Weighted kappa values for inter-observer agreement were 0.26 between palliative care specialists and oncologists, and 0.61 between palliative care specialists and nurses. Palliative care specialists’ assessments showed a moderate correlation with fatigue, dyspnea, anorexia, feeling of well-being, and symptom distress score measured by ESAS. The ECOG PS assessments by all three groups were significantly associated with survival (P<0.001), but the assessments by oncologists could not distinguish survival of patients with PS 2 from 3. Independent predictors of discordance in PS assessments between palliative care specialists and oncologists were the presence of an effective treatment option (odds ratio [OR] 2.39, 95% confidence interval [CI] 1.09-5.23) and poor feeling of well-being (≥4) by ESAS (OR 2.38, 95% CI 1.34-4.21). Conclusions: ECOG PS assessments by the palliative care specialists and nurses were significantly different from the oncologists. Systematic efforts to increase regular interdisciplinary meetings and communications might be crucial to bridge the gap and establish a best care plan for each advanced cancer patients.

Digitally captured step counts for evaluating performance status in advanced cancer patients: A single cohort, prospective trial (Digi-STEPS).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. TPS6651-TPS6651
Author(s):  
Gillian Gresham ◽  
Andrew Eugene Hendifar ◽  
Jun Gong ◽  
Arash Asher ◽  
Christine S. Walsh ◽  
...  
Keyword(s):  
Adverse Events ◽  
Cancer Patients ◽  
Advanced Cancer ◽  
Performance Status ◽  
Advanced Cancer Patients ◽  
Step Counts ◽  
Wide Range ◽  
Ecog Ps ◽  
Single Cohort

TPS6651 Background: Advanced cancer patients undergo dynamic changes in their functionality and physical activity over the course of their treatment. Monitoring patient function is important because it can inform treatment decisions and allow for timely and appropriate intervention. Current scales that assess patient function, such as the ECOG Performance Status (PS), are limited in their ability to capture the wide range in activity that cancer patients can experience on a daily basis outside of the clinic setting. Given recent technological advances in wearable activity monitors, we can collect real-time, objective information about a patient’s daily activity including steps, stairs, heart rate, sleep, and activity intensity. Thus, the primary objective of this study is to determine whether longitudinal changes in objectively-assessed activity are associated with change in physician-rated ECOG PS. Methods: This is a prospective, single cohort trial being conducted at Cedars-Sinai Medical Center. Stage 3/4 cancer patients who are English or Spanish-speaking, ambulatory (assistive walking devices are allowed) and expected to be seen for treatment or follow-up with their oncologist at least every 8 weeks are eligible for study. Consenting patients will be asked to wear a Fitbit Charge HR continuously for 8 weeks during the study period and for one week prior to the 6 month and 1 year follow-up visits. Primary outcomes are change in average daily step counts and ECOG PS at 8 weeks from baseline. Secondary outcomes include: 1) Change in NIH PROMIS patient-reported outcomes (physical function, pain, sleep, emotional distress, and fatigue), 2) Change in frailty status at 8 weeks, 3) Occurrence of adverse events, and 4) 6-month and 1-year survival outcomes. Baseline assessments include a physical exam, medical history, and frailty assessment. The attending oncologist will rate the patient's ECOG PS at baseline and at the end-of-study visit. Weekly NIH PROMIS questionnaires will be administered online over the 8-week study and again at 6 months and 1 year follow-up. The occurrence of serious cancer-related adverse events, chemotherapy-associated toxicities, and hospitalizations will be documented up to 12 weeks from baseline. Survival will be assessed at 6 months and 1 year. Accrual is ongoing with 20 patients currently enrolled of a target sample size of 60 patients. Clinical trial information: NCT03757182.

scholarly journals Wearable Activity Trackers in the Management of Rheumatic Diseases: Where Are We in 2020?

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4797
Author(s):  
Thomas Davergne ◽  
Antsa Rakotozafiarison ◽  
Hervé Servy ◽  
Laure Gossec
Keyword(s):  
Physical Activity ◽  
Time Management ◽  
Automatic Monitoring ◽  
Great Accuracy ◽  
Self Monitoring ◽  
Activity Tracker ◽  
External Monitoring ◽  
Activity Trackers ◽  
Steps Per Day ◽  
Wearable Activity Trackers

In healthcare, physical activity can be monitored in two ways: self-monitoring by the patient himself or external monitoring by health professionals. Regarding self-monitoring, wearable activity trackers allow automated passive data collection that educate and motivate patients. Wearing an activity tracker can improve walking time by around 1500 steps per day. However, there are concerns about measurement accuracy (e.g., lack of a common validation protocol or measurement discrepancies between different devices). For external monitoring, many innovative electronic tools are currently used in rheumatology to help support physician time management, to reduce the burden on clinic time, and to prioritize patients who may need further attention. In inflammatory arthritis, such as rheumatoid arthritis, regular monitoring of patients to detect disease flares improves outcomes. In a pilot study applying machine learning to activity tracker steps, we showed that physical activity was strongly linked to disease flares and that patterns of physical activity could be used to predict flares with great accuracy, with a sensitivity and specificity above 95%. Thus, automatic monitoring of steps may lead to improved disease control through potential early identification of disease flares. However, activity trackers have some limitations when applied to rheumatic patients, such as tracker adherence, lack of clarity on long-term effectiveness, or the potential multiplicity of trackers.

Comparing Activity Trackers With vs. Without Alarms to Increase Postoperative Ambulation: A Randomized Control Trial

2020 ◽  
pp. 000313482097336
Author(s):  
Giacomo C. Waller ◽  
Tesia G. Kim ◽  
Sebastian Perez ◽  
Gregory J. Esper ◽  
Jahnavi K. Srinivasan ◽  
...  
Keyword(s):  
Enhanced Recovery ◽  
Intervention Group ◽  
Postoperative Recovery ◽  
Control Group ◽  
Data Set ◽  
Early Ambulation ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Sex Risk ◽  
Steps Per Day

Early ambulation is a key component to postoperative recovery; however, measuring steps taken is often inconsistent and nonstandardized. This study aimed to determine whether an activity tracker with alarms would increase postoperative ambulation in patients after elective colorectal procedures. Forty-eight patients were randomly assigned to either trackers with 5 daily alarms or activity trackers alone. Over 223 total patient days, the trackers recorded a complete data set for 216 patient days (96.9%). Increasing the postoperative day significantly affected the number of steps taken, while age, sex, Risk Analysis Index score, and approach (laparoscopic versus open) did not show a significant effect. The mean steps per day in the intervention group were 1468 (median 495; interquartile range (IQR) 1345) and in the control group was 1645 (median 1014; IQR 2498). The use of trackers with alarms did not significantly affect the number of daily steps compared to trackers alone (ANOVA, P = .93). Although activity trackers with alarms did not increase postoperative ambulation compared with trackers with no alarms, we demonstrated a strategy to operationalize the use of trackers into postoperative care to provide a quantitative value for ambulation. This enables quantification of a key component in the Enhanced Recovery After Surgery protocol.

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