Examining the impact of internet-enabled nursing practices, guided by specialist nurses on patients with hypertension and their caregivers.

PURPOSE: To aim of this study is to assess the impact of an internet-enabled nursing model, led by specialized nurses within a framework of multidisciplinary collaboration, on both, patients diagnosed with hypertension, and their respective caregivers. METHODS: A total of 158 patients with hypertension, along with their corresponding caregivers, were meticulously selected and paired. By using a random number table method, participants were allocated into either a control group or an observation group. The control group received conventional health education, blood pressure monitoring, and routine telephone follow-ups administered by designated nurses. Conversely, patients in the observation group underwent treatment within an internet-enabled nursing model, led by specialist nurses within a multidisciplinary collaborative framework. Parameters including systolic and diastolic blood pressure readings of the patients, as well as their scores in compliance with the hypertension treatment, quality of life, and caregiving proficiency of family members, which were meticulously documented prior to intervention (T0), as well as at 3- and 6-month intervals post-intervention (T1 and T2). RESULTS: Statistically significant differences were observed in both systolic and diastolic blood pressure levels among patients, as well as in their scores reflecting compliance with hypertension treatment, quality of life, and caregiving proficiency of family members, when comparing pre- and post-intervention periods within each group, across groups, and within the interaction effect (p < 0.05). Also, there were statistically significant differences in the aforementioned parameters between the two groups at adjacent time points (p < 0.05). Specifically, patients within the observation group exhibited notable reductions in systolic and diastolic blood pressure levels at both T1 and T2, alongside heightened scores indicative of enhanced compliance with hypertension treatment, enhanced quality of life, and increased caregiving proficiency among family members, when compared to patients within the control group (p < 0.05). CONCLUSION: The implementation of an internet-enabled nursing model, overseen by specialized nurses within a framework of multidisciplinary collaboration, demonstrates superior efficacy in preserving the stability of blood pressure among patients with hypertension. This model significantly enhances patient compliance with treatment regimens, enhances their overall quality of life, and fosters heightened caregiving proficiency among their respective caregivers.

Automatic recognition of depression based on audio and video: A review.

Depression is a common mental health disorder. With current depression detection methods, specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression assessment. Non-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively utilized. Specialized physicians usually require extensive training and experience to capture changes in these features. Advancements in deep learning technology have provided technical support for capturing non-biological markers. Several researchers have proposed automatic depression estimation (ADE) systems based on sounds and videos to assist physicians in capturing these features and conducting depression screening. This article summarizes commonly used public datasets and recent research on audio- and video-based ADE based on three perspectives: Datasets, deficiencies in existing research, and future development directions.