Key result
Brief bedside PPG-HRV strongly predicts sleep disturbance, which amplifies the link between pain and depression.
Why the study?
To evaluate whether brief bedside photoplethysmography-derived heart-rate variability provides objective information regarding pain, sleep, and affect in adults with chronic pain, and whether sleep moderates the pain-emotion association.
Does brief bedside PPG-HRV provide objective information about pain, sleep, and affect in adults with chronic pain?
Cross-Sectional (n=172)
Does brief bedside PPG-HRV provide objective information about pain, sleep, and affect in adults with chronic pain?
Effect estimate: R2_CV 0.844
Brief bedside PPG-HRV, particularly high-frequency indices, may serve as a practical adjunctive marker for assessing sleep disturbance, depression, and pain burden in hospitalized adults with chronic pain.
May support bedside HRV for assessing sleep, depression, and pain burden; leaves open prospective validation before clinical use.
To test whether brief bedside photoplethysmography-derived heart-rate variability (PPG-HRV) provides objective information about pain, sleep, and affect in adults with chronic pain, and whether sleep moderates the pain–emotion association. We conducted a cross-sectional study of consecutive inpatients with chronic pain (duration ≥ 3 months) admitted to an orthopedics/traumatology service from March 31 to September 28, 2025. Adults (≥ 18 years; right-handed) were excluded for severe internal disease or acute trauma, major psychiatric disorders, marked cognitive impairment, or recent use of anxiolytics/hypnotics. At admission, 5-min fingertip PPG-HRV was recorded under standardized bedside conditions, with artifact correction, stationarity checking, and quality-control screening applied before analysis. Outcomes were the Brief Pain Inventory–Short Form (BPI-SF), Hamilton Depression Rating Scale (HAMD-24), Hamilton Anxiety Rating Scale (HAMA-14), and a composite “sleep problems” index computed as the mean of z-scored HAMD insomnia items and the BPI-SF sleep-interference item. Analyses included correlations, cross-validated random-forest regression incorporating logarithmically transformed HF power (lnHF), and Hayes’ PROCESS Model 1 testing sleep as a moderator of the pain→emotion pathway. A total of 172 patients were enrolled (37.2% men; mean age 59.2 years). lnHF was significantly associated with HFnorm, pain, sleep disturbance, and depression, but not anxiety. Random-forest models showed the strongest predictive performance for sleep disturbance (R 2 _CV = 0.844), followed by depression (R 2 _CV = 0.696) and pain-related burden (R 2 _CV = 0.506), whereas anxiety showed limited predictive performance (R 2 _CV = 0.009). HFnorm and lnHF emerged as recurrently important predictors of pain, sleep disturbance, and depressive symptoms. In moderation analyses, pain×sleep interactions were significant for anxiety (model R 2 = 0.465; β = 0.159; ΔR 2 = 0.026; p < 0.01) and depression (model R 2 = 0.513; β = 0.252; ΔR 2 = 0.066; p < 0.001). Brief bedside PPG-HRV, particularly HF-related indices such as lnHF and HFnorm, provides auxiliary physiological information related to pain, sleep disturbance, and depressive symptoms in hospitalized adults with chronic pain. Sleep disturbance amplified the association between pain and emotional symptoms, supporting the clinical relevance of the pain–sleep–emotion pathway. HRV is not suitable for diagnosis or stand-alone decision-making, but may serve as a practical adjunctive marker for risk stratification and symptom monitoring.
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Hu et al. (2026) conducted a cross-sectional in Chronic pain (n=172). Brief bedside photoplethysmography-derived heart-rate variability (PPG-HRV) was evaluated on Predictive performance for sleep disturbance using random-forest models (R2_CV 0.844). Brief bedside PPG-HRV indices strongly predicted sleep disturbance (R2_CV = 0.844), and sleep disturbance amplified the association between pain and depression (β = 0.252, p<0.001).
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