This research study is for participants diagnosed with leptomeningeal metastasis disease (LMM), a condition where cancer has spread to the fluid and membranes surround the brain and spinal cord. The purpose of this study is to find out whether a consumer Smartwatch can continuously monitor the participant's health and detect early signs of neurological decline and whether sending an automatic alert to the participant's doctor when a change is detected can help reduce the number of participant hospitalizations.
LMM is a condition where cancer has spread to the fluid and membranes surrounding the brain and spinal cord. It is a common complication of cancer and affects about 5-15% of all cancer patients. It causes rapid neurological decline and survival is usually around 6-12 months. LMM is treated with chemotherapy radiation, but this does not improve neurological function or quality of life. Many of the current assessments for LMM are not good detecting progression and symptoms are often missed. There will be two parts to this study. Smartwatches will be used to continuously capture data such as gait stability, heart rate variability (HRV), step count, and sleep, which are affected by LMM. The first part of this study is to learn whether this can help detect early signs of neurological decline. The second part of this study will test whether sending an automatic alert to the participant's doctor when a change is detected can help reduce the amount of times the participants are admitted to the hospital unexpectedly.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
DEVICE_FEASIBILITY
Masking
NONE
Enrollment
48
Apple Watch Series 6+ or Samsung Galaxy Watch 4+ worn 10+ hours/day for 4+ days/week for 6 months. Passive wearable data collection from in-house cross-platform app to find out patient-specific digital signal thresholds for neurological decline, symptom logging, daily sync reminders, and monthly EORTC-QLQ-C30 surveys.
Apple Watch Series 6+ or Samsung Galaxy Watch 4+ worn 10+ hours/day for 4+ days/week for 5 months. Active monitoring with alerts using the data collected from the in-house cross-platform app and sent to study neuro-oncologist.
Cleveland Clinic, Case Comprehensive Cancer Center
Cleveland, Ohio, United States
Predicting Karnofsky Performance Scale (KPS) decline
Karnofsky Performance scale of 100-0. Sensitivity, specificity, Positive Predictive Value (PPV), and lead time of participant-specific thresholds. Assessed from calibration phase data if there is a greater than10-point KPS decline.
Time frame: 6 months
Predicting neurological progression
Sensitivity, specificity, PPV, and lead time of participant-specific thresholds. Assessed from calibration phase data.
Time frame: 6 months
Days in hospital per participant-month during silent monitoring periods
Taken from EMR by staff blinded to alert status. Defined as incidence rate ratios (IRRs) with 95% CIs using mixed-effects negative binomial regression.
Time frame: 5 months
Days in hospital per participant-month during active alerting periods
Taken from EMR by staff blinded to alert status. Defined as incidence rate ratios (IRRs) with 95% CIs using mixed-effects negative binomial regression.
Time frame: 5 months
Participant completion
80% or more of participants completing 3 months or more of active monitoring with median wear of 10 or more hours on 4 or more days/week
Time frame: 5 months
Recruitment rate
number of participants enrolled within 9-12 months
Time frame: 12 months
Dropout rate
Number of participants who do not complete study, assessed at study completion
Time frame: 12 months
Alert pathway feasibility
Alert frequency per participant-month and proportion judged clinically relevant.
Time frame: 5 months
Number of unplanned hospitalizations and ED visits per participant-month
Evaluate the efficacy of alert pathway in reducing unplanned hospitalization and ED visits. EMR review throughout Phase 2 during active vs. silent periods.
Time frame: 5 months
European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 ( EORTC-QLQ-C30) total and subscale scores
EORTC-QLQ-C30 is a 30 item validated questionnaire to determine the participant's quality of life. The questions are in a 4- point scale from 1 ("Not at all)" to 4 ("Very much"). A higher functional or global health score represents a higher quality of life, while a higher symptom score means a higher level of reported symptoms. Change in EORTC-QLQ-C30 will be measured from baseline at each time point.
Time frame: 6 months
Progression-free survival (PFS)
Time from enrollment to first radiographic or clinical progression; assessed throughout treatment period
Time frame: 12 months
Overall survival (OS)
Time from enrollment to death; assessed throughout treatment period and via medical record review
Time frame: 12 months
Wearable data quantity across iOS and Android platforms
Assessed at each treatment visit per participant per month (mean, variance)
Time frame: 12 months
Time from alert trigger to clinician-documented neurological progression
Evaluate the efficacy of alert pathway in reducing unplanned hospitalization and ED visits. EMR review throughout Phase 2 during active vs. silent periods.
Time frame: 5 months
Wearable data quality across iOS and Android platforms
Assessed at each treatment visit per participant per month (mean, variance)
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.
Time frame: 12 months
Participant symptoms
Logged per type per participant per month. Assessed at each treatment visit
Time frame: 12 months
Variance in accelerometry
This device is used to track changes in physical activity and sleep patterns. Assessed at study completion
Time frame: 12 months
Variance in heart rate variability (HRV)
This device assess HRV, which is where the amount of time between heartbeats fluctuate slightly. The participant's heart data will be collected and used to see if there any changes from the participant's month 1 mean. Assessed at study completion
Time frame: 12 months
Variance in gait metrics
This device assesses the speed, step length and asymmetry to see if changes these can predict any clinical events by days to weeks.
Time frame: 12 months
Reasons for dropout
Collected via exit questionnaire at withdrawal or study completion
Time frame: 12 months
Participant device wear
Collected via exit survey and structured questionnaires at each visit for participant reported quality of life (QOL)
Time frame: 12 months
Participant symptom logging
Collected via exit survey and structured questionnaires at each visit for participant reported quality of life (QOL).
Time frame: 12 months
Participant app usage
Collected via exit survey and structured questionnaires at each visit for participant reported quality of life (QOL).
Time frame: 12 months