Monitoring physical impact and recovery of pancreatic cancer treatment using consumer wearable health data: A case report.
Cees P van der SchansSimon van der SchansJurjen van der SchansCaspar MyliusJoost KlaasePublished in: Digital health (2023)
Consumer wearables health data may reflect the impact of pancreatic cancer and its treatment on cardiorespiratory fitness and the subsequent recovery after treatment. The patient is a 65-year-old male treated for borderline resectable pancreatic cancer. Treatment consisted of four courses of FOLFIRINOX neoadjuvant chemotherapy, a Whipple procedure with a right hemicolectomy and venous segment resection, and eight courses of adjuvant FOLFIRINOX chemotherapy. Physical activity and moderate to vigorous physical activity declined after the onset of symptoms, increased in the weeks before surgery, declined after surgery and then gradually recovered during and after adjuvant chemotherapy. Estimated VO 2 max remained stable during neoadjuvant chemotherapy, sharply decreased after surgery and then gradually recovered. Heart rate at rest increased and heart rate variability decreased after the onset of symptoms reaching their highest and lowest values after surgery. Both gradually returned to baseline seven months after the last course of chemotherapy. The physical impact of pancreatic cancer and its treatment and recovery was in this case reflected on consumer wearable health data. Seven months after the last chemotherapy recovery was close to baseline values.
Keyphrases
- locally advanced
- neoadjuvant chemotherapy
- heart rate
- physical activity
- heart rate variability
- rectal cancer
- mental health
- squamous cell carcinoma
- health information
- healthcare
- public health
- radiation therapy
- blood pressure
- lymph node
- minimally invasive
- big data
- electronic health record
- combination therapy
- sleep quality
- depressive symptoms
- coronary artery disease
- risk assessment
- coronary artery bypass
- percutaneous coronary intervention
- high intensity
- human health
- social media
- machine learning
- surgical site infection