Abstract
Biofeedback therapy is mainly based on the analysis of physiological features to improve an individual’s affective state. There are insufficient objective indicators to assess symptom improvement after biofeedback. In addition to psychological and physiological features, speech features can precisely convey information about emotions. The use of speech features can improve the objectivity of psychiatric assessments. Therefore, biofeedback based on subjective symptom scales, objective speech, and physiological features to evaluate efficacy provides a new approach for early screening and treatment of emotional problems in college students. A 4-week, randomized, controlled, parallel biofeedback therapy study was conducted with college students with symptoms of anxiety or depression. Speech samples, physiological samples, and clinical symptoms were collected at baseline and at the end of treatment, and the extracted speech features and physiological features were used for between-group comparisons and correlation analyses between the biofeedback and wait-list groups. Based on the speech features with differences between the biofeedback intervention and wait-list groups, an artificial neural network was used to predict the therapeutic effect and response after biofeedback therapy. Through biofeedback therapy, improvements in depression (p = 0.001), anxiety (p = 0.001), insomnia (p = 0.013), and stress (p = 0.004) severity were observed in college-going students (n = 52). The speech and physiological features in the biofeedback group also changed significantly compared to the waitlist group (n = 52) and were related to the change in symptoms. The energy parameters and Mel-Frequency Cepstral Coefficients (MFCC) of speech features can predict whether biofeedback intervention effectively improves anxiety and insomnia symptoms and treatment response. The accuracy of the classification model built using the artificial neural network (ANN) for treatment response and non-response was approximately 60%. The results of this study provide valuable information about biofeedback in improving the mental health of college-going students. The study identified speech features, such as the energy parameters, and MFCC as more accurate and objective indicators for tracking biofeedback therapy response and predicting efficacy. Trial Registration ClinicalTrials.gov ChiCTR2100045542.
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Acknowledgements
The authors thank all participants who took part in this study. The authors thank all participants who took part in this study. The support to recruitment by personnel in Xinxiang Medical University is acknowledged. The authors thank Xinxiang Medical University and The Affiliated Brain Hospital of Nanjing Medical University for excellent research assistance.
Funding
This study was funded by National Science Fund for Distinguished Young Scholars (81725005 to Fei Wang), National Natural Science Foundation Regional Innovation and Development Joint Fund (U20A6005 to Fei Wang), Jiangsu Provincial Key Research and Development Program (BE2021617), National Natural Science Foundation of China (62176129 to Xizhe Zhang) and Key Project supported by Medical Science and Technology Development Foundation, Jiangsu Commission of Health (ZD2021026 to Zhu Rongxin).
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Wang, L., Liu, R., Wang, Y. et al. Effectiveness of a Biofeedback Intervention Targeting Mental and Physical Health Among College Students Through Speech and Physiology as Biomarkers Using Machine Learning: A Randomized Controlled Trial. Appl Psychophysiol Biofeedback 49, 71–83 (2024). https://doi.org/10.1007/s10484-023-09612-3
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DOI: https://doi.org/10.1007/s10484-023-09612-3