Remote Monitoring of the Performance Status and Burden of Symptoms of Patients With Gastrointestinal Cancer Via a Consumer-Based Activity Tracker: Quantitative Cohort Study

Abstract

Background The number of older patients with gastrointestinal cancer is increasing due to an aging global population. Minimizing reliance on an in-clinic patient performance status test to determine a patient’s prognosis and course of treatment can improve resource utilization. Further, current performance status measurements cannot capture patients' constant changes. These measurements also rely on self-reports, which are subjective and subject to bias. Real-time monitoring of patients' activities may allow for a more accurate assessment of patients’ performance status while minimizing resource utilization. Objective This study investigates the validity of consumer-based activity trackers for monitoring the performance status of patients with gastrointestinal cancer. Methods A total of 27 consenting patients (63% male, median age 58 years) wore a consumer-based activity tracker 7 days before chemotherapy and 14 days after receiving their first treatment. The provider assessed patients using the Eastern Cooperative Oncology Group Performance Status (ECOG-PS) scale and Memorial Symptom Assessment Scale-Short Form (MSAS-SF) before and after chemotherapy visits. The statistical correlations between ECOG-PS and MSAS-SF scores and patients’ daily step counts were assessed. Results The daily step counts yielded the highest correlation with the patients' ECOG-PS scores after chemotherapy (P<.001). The patients with higher ECOG-PS scores experienced a higher fluctuation in their step counts. The patients who walked more prechemotherapy (mean 6071 steps per day) and postchemotherapy (mean 5930 steps per day) had a lower MSAS-SF score (lower burden of symptoms) compared to patients who walked less prechemotherapy (mean 5205 steps per day) and postchemotherapy (mean 4437 steps per day). Conclusions This study demonstrates the feasibility of using inexpensive, consumer-based activity trackers for the remote monitoring of performance status in the gastrointestinal cancer population. The findings need to be validated in a larger population for generalizability.

A Scoping Review to Assess Adherence to and Clinical Outcomes of Wearable Devices in the Cancer Population

“…The studies included in this review article employed different interventional approaches to improve the clinical outcomes of the participants. Most of the studies used a mobile application to provide the intervention [ 22 , 34 , 35 , 39 , 40 , 46 , 53 , 54 , 57 ]. Of the 38 studies, 3 provided the intervention by sending text messages [ 22 , 45 , 57 ], and 4 conducted a general group session or included a virtual support group [ 39 , 41 , 44 ].…”

“…The average adherence percentage was calculated for each segment. The intervention-based segments included studies with weeks 1 [ 48 , 49 ], 2 [ 27 , 55 ], 3 [ 50 , 52 , 57 ], 4 [ 36 , 37 , 53 ], 7 [ 28 ], 8 [ 32 , 33 , 34 ], 10 [ 24 , 30 , 41 ], 12 [ 20 , 21 , 23 , 25 , 26 , 38 , 39 , 40 , 42 , 43 , 44 , 45 , 46 , 47 , 54 , 56 ], 17 [ 29 ], 24 [ 22 , 31 ] and 43 [ 35 ]. In the graph, (1.84) indicates the studies grouped in this segment that had a one-week intervention with an average adherence of 84%.…”

“…These studies showed an average adherence rate of 91% [ 20 , 21 , 25 , 38 , 43 ]. Studies with an intervention period of 4 or 7 weeks also observed high adherence to WDs (i.e., 92% and 93%, respectively) [ 28 , 36 , 37 , 53 ] ( Figure 2 ). There is one unique study that enrolled patients across four seasons of the year.…”

The Use of Wearable Devices in Oncology Patients: A Systematic Review

“…The studies included in this review inform prognostication by examining physical activity levels and its correlates to identify potential patient profiles and their association with various clinical outcomes. These outcomes include physical well-being measures such as anthropometric measures, 31 , 45 , 51 , 63 , 64 , 70 , 82 , 106 , 109 , 113 , 124 , 179 performance status, 26 , 39 , 56 , 62 , 67 , 85 , 120 , 128 motor function, 71 , 92 , 208 functional recovery, 17 , 117 bone health, 162 and symptoms or pain, 12 , 27 , 38 , 39 , 41 , 51 , 56 , 63 , 99 , 126 , 163 quality of life 13-16 , 18 , 24 , 25 , 29 , 32 , 34 , 37 , 39 , 41 , 46 , 47 , 53 , 60 , 67 , 69 , 75 , 80 , 87 , 93 , 101 , 111 , 113 , 114 , …”

“…As mentioned earlier, wearable devices can increase physical activity levels at a relatively lower cost to individuals than facility- or membership-based interventions. 54 , 56 , 160 It can also decrease the cost for healthcare providers by serving as remote monitoring and rehabilitation, thereby lowering the cost of resources related to more rigorous clinical testing (eg, administering the 6-minute walk test) 21 and travel/clinical visits. 56 Finally, wearable devices can be used to support increased physical activity.…”