Problematic Social Media Use and Its Relationship with Depression or Anxiety: A Systematic Review.

There has been a notable increase in social media and Internet use over recent decades, not only for social interaction or entertainment, but also for working and meeting tools, as seen during the COVID-19 pandemic. A relationship between this usage and the development of mental illness is frequently hypothesized, but a few studies have empirical findings. This study is a systematic review of the relationship between social media use and depression or anxiety. Our Medline search yielded 1,747 papers. Our study found a strong and often bidirectional relationship between social media use and depression or anxiety. This relationship was frequently related to problematic social media use. No definite linear relationship was found between time spent using social media and depressive or anxious symptoms, but usually, the longer the time spent in that activity, the worse the outcomes. Factors related to problematic social media use were often different for men and women. Other variables may also play a role, such as nighttime-specific use, emotional involvement, and whether the individual behaves as an active or passive user. Evidence from this review provides a solid base for recommending cautious use of social media. Intense use and unhealthy habits, evidenced by addiction symptoms, may be problematic in less resilient individuals.

Performance Assessment of a Custom, Portable, and Low-Cost Brain-Computer Interface Platform.

OBJECTIVE: Conventional brain-computer interfaces (BCIs) are often expensive, complex to operate, and lack portability, which confines their use to laboratory settings. Portable, inexpensive BCIs can mitigate these problems, but it remains unclear whether their low-cost design compromises their performance. Therefore, we developed a portable, low-cost BCI and compared its performance to that of a conventional BCI. METHODS: The BCI was assembled by integrating a custom electroencephalogram (EEG) amplifier with an open-source microcontroller and a touchscreen. The function of the amplifier was first validated against a commercial bioamplifier, followed by a head-to-head comparison between the custom BCI (using four EEG channels) and a conventional 32-channel BCI. Specifically, five able-bodied subjects were cued to alternate between hand opening/closing and remaining motionless while the BCI decoded their movement state in real time and provided visual feedback through a light emitting diode. Subjects repeated the above task for a total of 10 trials, and were unaware of which system was being used. The performance in each trial was defined as the temporal correlation between the cues and the decoded states. RESULTS: The EEG data simultaneously acquired with the custom and commercial amplifiers were visually similar and highly correlated ( ρ = 0.79). The decoding performances of the custom and conventional BCIs averaged across trials and subjects were 0.70 ± 0.12 and 0.68 ± 0.10, respectively, and were not significantly different. CONCLUSION: The performance of our portable, low-cost BCI is comparable to that of the conventional BCIs. SIGNIFICANCE: Platforms, such as the one developed here, are suitable for BCI applications outside of a laboratory.