Key result
Wearable HRV signals classify mild-to-moderate cancer-related fatigue with ~81% accuracy.
Why the study?
Cancer-related fatigue is a distressing side effect affecting survival, but most patients do not report their fatigue level, prompting the development of an objective assessment method using heart rate variability.
Does heart rate variability monitoring via wearables accurately classify cancer-related fatigue in lung cancer patients?
Observational (n=60)
No
Does heart rate variability monitoring via wearables accurately classify cancer-related fatigue in lung cancer patients?
A 24-hour HRV monitoring device can effectively classify mild and moderate cancer-related fatigue in lung cancer patients, offering an objective assessment method.
May support objective wearable HRV monitoring for cancer-related fatigue; hypothesis-generating and requires prospective validation before practice change.
Background: Cancer-related fatigue (CRF) is the most distressing side effect in cancer patients and affects the survival rate. However, most patients do not report their fatigue level. This study is aimed to develop an objective CRF assessment method based on heart rate variability (HRV). Methods: In this study, patients with lung cancer who received chemotherapy or target therapy were enrolled. Patients wore wearable devices with photoplethysmography that regularly recorded HRV parameters for seven consecutive days and completed the Brief Fatigue Inventory (BFI) questionnaire. The collected parameters were divided into the active and sleep phase parameters to allow tracking of fatigue variation. Statistical analysis was used to identify correlations between fatigue scores and HRV parameters. Findings: In this study, 60 patients with lung cancer were enrolled. The HRV parameters including the low-frequency/high-frequency (LF/HF) ratio and the LF/HF disorder ratio in the active phase and the sleep phase were extracted. A linear classifier with HRV-based cutoff points achieved correct classification rates of 73 and 88% for mild and moderate fatigue levels, respectively. Conclusion: Fatigue was effectively identified, and the data were effectively classified using a 24-h HRV device. This objective fatigue monitoring method may enable clinicians to effectively handle fatigue problems.
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Shih et al. (2023) conducted an observational in Cancer-related fatigue in lung cancer (n=60). Wearable photoplethysmography (PPG) device vs. Brief Fatigue Inventory (BFI) questionnaire was evaluated on Total correct classification rate of mild and moderate fatigue levels using a linear classifier. A linear classifier based on heart rate variability signals from a wearable device achieved correct classification rates of 73% and 88% for mild and moderate cancer-related fatigue, respectively.
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