App monitors symptom severity in patients undergoing chemotherapy
Smartphone sensor data allow doctors to better detect worsening side effects from cancer treatment.
Using a smartphone and Fitbit device, cancer patients undergoing chemotherapy were able to be remotely monitored in real time by their physicians in order to better detect severe or worsening symptoms from the treatment and to intervene earlier between clinic visits.
The goal is preventing unnecessary doctor or hospital visits and improving patient quality of life, according to researchers from the University of Pittsburgh who led the study conducted last year.
Over a four-week period, 14 patients receiving chemotherapy for gastrointestinal cancer at UPMC Hillman Cancer Center carried an Android smartphone and wore a Fitbit device which provided data to doctors on patient mobility and activity, sleep, phone usage, as well as communication.
In addition, participants in the study completed daily severity ratings of 12 common symptoms, which were tabulated to create a total symptom burden score for each day.
“We found that on days when the patients reported worse-than-average symptoms, they tended to spend more time being sedentary, moved the phone more slowly and spent more minutes using apps on the phone,” said Carissa Low, assistant professor of medicine and psychology at the University of Pittsburgh, and lead author of the study.
Also See: Patients with metastatic cancer live longer using web-based tool
“Collecting these objective behavioral measures from smartphone sensors requires no additional effort from patients, and they could prove beneficial for long-term monitoring of those undergoing arduous cancer treatments or those with other chronic illnesses,” added Low.
A smartphone app developed by researchers passively collected data on patient behavior patterns, which was then used to develop an algorithm that could identify and correlate the patient’s high-symptom, average-symptom and low-symptom days with 88 percent accuracy. Overall, researchers collected 295 days of symptom and sensor data.
Results of the study, published this week in the Journal of Medical Internet Research, also show that the accuracy of individual models ranged from 78.1 percent to 100 percent (with a mean of 88.4 percent).
“These findings highlight opportunities for long-term monitoring of cancer patients during chemotherapy with minimal patient burden as well as real-time adaptive interventions aimed at early management of worsening or severe symptoms,” conclude the authors of the study, which was funded by research grants from the National Cancer Institute and a Manners Faculty Development Award from the University of Pittsburgh Center for Social and Urban Research.
“Passive detection of worsening physical and psychological symptoms also enables technology-supported just-in-time adaptive interventions aimed at symptom management,” states the article. “For example, when relatively increased levels of symptoms are detected, an alert could be automatically sent to the clinical care team or self-management instructions texted to patients. Such personalized real-time intervention could improve quality of life and the ability of patients to withstand life-prolonging cancer treatments.”
According to the researchers, they are conducting follow-up studies to determine whether the same passive sensing approach using smartphones could be used to identify complications arising from cancer surgery. In addition, they are working with clinicians to better understand how to integrate the data into clinical workflows.
The goal is preventing unnecessary doctor or hospital visits and improving patient quality of life, according to researchers from the University of Pittsburgh who led the study conducted last year.
Over a four-week period, 14 patients receiving chemotherapy for gastrointestinal cancer at UPMC Hillman Cancer Center carried an Android smartphone and wore a Fitbit device which provided data to doctors on patient mobility and activity, sleep, phone usage, as well as communication.
In addition, participants in the study completed daily severity ratings of 12 common symptoms, which were tabulated to create a total symptom burden score for each day. “We found that on days when the patients reported worse-than-average symptoms, they tended to spend more time being sedentary, moved the phone more slowly and spent more minutes using apps on the phone,” said Carissa Low, assistant professor of medicine and psychology at the University of Pittsburgh, and lead author of the study.
Also See: Patients with metastatic cancer live longer using web-based tool
“Collecting these objective behavioral measures from smartphone sensors requires no additional effort from patients, and they could prove beneficial for long-term monitoring of those undergoing arduous cancer treatments or those with other chronic illnesses,” added Low.
A smartphone app developed by researchers passively collected data on patient behavior patterns, which was then used to develop an algorithm that could identify and correlate the patient’s high-symptom, average-symptom and low-symptom days with 88 percent accuracy. Overall, researchers collected 295 days of symptom and sensor data.
Results of the study, published this week in the Journal of Medical Internet Research, also show that the accuracy of individual models ranged from 78.1 percent to 100 percent (with a mean of 88.4 percent).
“These findings highlight opportunities for long-term monitoring of cancer patients during chemotherapy with minimal patient burden as well as real-time adaptive interventions aimed at early management of worsening or severe symptoms,” conclude the authors of the study, which was funded by research grants from the National Cancer Institute and a Manners Faculty Development Award from the University of Pittsburgh Center for Social and Urban Research.
“Passive detection of worsening physical and psychological symptoms also enables technology-supported just-in-time adaptive interventions aimed at symptom management,” states the article. “For example, when relatively increased levels of symptoms are detected, an alert could be automatically sent to the clinical care team or self-management instructions texted to patients. Such personalized real-time intervention could improve quality of life and the ability of patients to withstand life-prolonging cancer treatments.”
According to the researchers, they are conducting follow-up studies to determine whether the same passive sensing approach using smartphones could be used to identify complications arising from cancer surgery. In addition, they are working with clinicians to better understand how to integrate the data into clinical workflows.
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