Background Cybersuicide refers to suicidal behaviour influenced by online activities, particularly cyberbullying and cyber-victimisation. Digital harassment is strongly linked to mental health problems that can precipitate suicidal ideation and attempts, especially among adolescents, who experience multiple forms of online abuse leading to depression, anxiety and stress. Gender minority populations may face greater risk due to minority stress and disproportionate online harassment. A comprehensive review is needed to understand this emerging public health challenge and inform targeted prevention and mental health interventions. Summary A systematic search following PRISMA guidelines was conducted on 17 March 2025 across PubMed, Scopus, ScienceDirect and Cochrane CENTRAL after registering protocol on PROSPERO (CRD420251061647). Of 400 studies identified, 21 involved adolescents, youth or university students, and 7 examined gender minority populations. The pooled odds ratio for the association between cyberbullying and suicidality across 9 studies was 3.22 (95% CI: 1.9–5.5; z = 4.33, p < .0001). Pooled cyberbullying prevalence was 13.9% (95% CI: 7.8–23.5) pre-2020 and 13.2% (95% CI: 6.7–24.1) post-2020 (I² = 99.7%). Among gender minority groups, pooled prevalence was 39.89% (95% CI: 18.56–65.91). Pooled suicidal ideation prevalence was 21.39% (95% CI: 13.30–32.56). Key Message Cyberbullying is a consistent risk factor for suicidal behaviour, amplified by depression, minority stress and low social support. Strengthening digital literacy, integrating cyberbullying prevention into adolescent mental health programmes and implementing inclusive online monitoring protocols are essential to mitigate these risks.
Background Heart rate variability (HRV) and cardiovascular autonomic reflex tests (CARTs) serve as non-invasive clinical indicators of central autonomic network function and cardiovagal regulation. Purpose This study aimed to establish regional normative reference values for HRV and CART parameters in healthy adults from northwestern India to evaluate the modulating effects of age, biological sex and subclinical cardiometabolic risk factors. Methods This cross-sectional study evaluated 142 healthy subjects (74 males, 68 females) aged 18–70 years. Baseline vital signs and time-domain HRV parameters (standard deviation of normal-to-normal intervals (SDNN) and root mean square of successive differences (RMSSD)) were recorded. Autonomic reactivity was assessed using deep breathing (expiration-to-inspiration (E:I) ratio), the Valsalva manoeuvre, isometric handgrip and orthostatic challenge. A high-risk sub-cohort ( n = 21) with subclinical obesity or elevated blood pressure was analysed. Adherence to Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines was maintained. Results The total study sample comprised 142 participants, including 74 males (52.1%) and 68 females (47.9%). The high-risk subset comprised 11 males (52.4%) and 10 females (47.6%). Resting heart rate was higher in females (74.6 ± 8.2 bpm) than in males (70.1 ± 7.6 bpm; t (140) = 3.39, p = .02), while blood pressure was higher in males ( p = .03). Time-domain HRV and reflex indices declined significantly with age ( p < .001), with optimal thresholds for autonomic senescence occurring at 47 years (SDNN, area under the curve (AUC) = 0.82) and 49 years (Valsalva, AUC = 0.79). Healthy females exhibited enhanced cardiovagal responses (E:I, Valsalva and 30:15 ratios; p < .05). This female autonomic advantage was entirely blunted in the high-risk subset ( n = 21), where autonomic parameters fell below the 5th percentile limits (SDNN = 25 ms, RMSSD = 18 ms, Valsalva ratio = 1.09). Conclusion Healthy adults exhibit an age-related decline in parasympathetic function and a female-specific cardiovagal advantage. Subclinical cardiometabolic risk factors override these physiological buffers, blunting gender-related protection and accelerating autonomic senescence.
Background motion recognition using electroencephalography (EEG) has gained significant attention in the fields of affective computing and human–computer interaction due to its ability to capture real-time neural responses. EEG signal variations associated with emotional states can be analysed using wavelet-based signal processing within the arousal–valence framework. Purpose This study aims to analyse EEG signal variations associated with emotional states using a wavelet-based signal processing approach within the arousal–valence framework. Methods A prospective observational study was conducted on 48 healthy participants. Emotional states were induced using image, video and music stimuli categorised under the arousal–valence model. EEG signals were recorded using the standard 10–20 electrode placement system. The acquired signals were pre-processed and analysed using discrete wavelet transform (DWT) to extract time–frequency features, including energy and entropy. Statistical analysis was performed to evaluate hemispheric differences and gender-based variations. Results Differences in EEG activity were observed between the left and right hemispheres across multiple emotional states (p < .05). Video and music stimuli produced stronger and more distinguishable EEG responses compared to image-based stimuli. Negative emotions demonstrated higher discriminative patterns than positive emotions. Gender-based variations were also observed in EEG responses. Conclusion Wavelet-based EEG feature analysis provides meaningful and discriminative information for emotion recognition and may provide a robust feature set for future machine learning-based emotion recognition systems. Further studies incorporating classification algorithms and larger data sets are required to validate their predictive performance.
