Unhappy and addicted to your phone?

With mobile phones becoming central parts of our lives, mobile technology gets criticized for its negative impact on people's well-being. Studies generally report negative associations between mobile phone use (MPU) and well-being. However, few studies contrast the relationship of MPU with different concepts of positive psychology. The aim of this study was to investigate the relationship between MPU and different concepts of positive psychology: life satisfaction, well-being, and mindfulness. Data from 461 German speaking participants answering an online-questionnaire were analyzed. Overall, results suggest that participants who use their mobile phones more often report lower well-being, life satisfaction, and mindfulness scores. Furthermore, results imply that the relationships between positive psychology concepts and MPU differ between men and women. Results indicate that MPU and its associations with concepts of positive psychology are relevant areas for research and deserve more attention

Glucose Increases Risky Behavior and Attitudes in People Low in Self-Control: A pilot study

People low in self-control have a strong proclivity toward risk-taking. Risk-taking behavior provides an opportunity to obtain some form of reward. Glucose, on the other hand, seems to facilitate reward and goal-directed behavior. In a pilot study executed in the laboratory, we investigated whether consuming a glucose drink would increase risky behavior and attitudes in people low in self-control. Our findings revealed that a dose of glucose compared to placebo increased risk-taking on a behavioral and cognitive level in participants low in self-control but not in participants high in self-control. The findings may shed some light on the psychological underpinnings of glucose: By showing glucose's association with high-risk behavior, they support the assumption of glucose driving a goal-directed motivation

Effects of fact‐checking warning labels and social endorsement cues on climate change fake news credibility and engagement on social media

Online fake news can have noxious consequences. Social media platforms are experimenting with different interventions to curb fake news' spread, often employing them simultaneously. However, research investigating the interaction of these interventions is limited. Here, we use the heuristic-systematic model of information processing (HSM) as a theoretical framework to jointly test two interventions against fake news that are implemented at scale by social media platforms: (1) adding warning labels from fact checkers to initiate systematic processing and (2) removing social endorsement cues (e.g., engagement counts) to reduce the influence of this heuristic cue. Moreover, we accounted for dispositions previously found to affect a person's response to fake news through motivated reasoning or cognitive style. An online experiment in Germany (N = 571) confirmed that warning labels reduced the perceived credibility of a fake news post exaggerating the consequences of climate change. Warning labels also lowered the (self-reported) likelihood to amplify fake news. Removing social endorsement cues did not have an effect. In line with research on motivated reasoning, left-leaning individuals perceived the climate fake news to be more credible and reported a higher likelihood to amplify it. Supporting research on cognitive style, participants with lower educational levels and a less analytic thinking style also reported a higher likelihood of amplification. Elaboration likelihood was associated only with age, involvement, and political leaning, but not affected by warning labels. Our findings contribute to the mounting evidence for the effectiveness of warning labels while questioning their relevance for systematic processing

DEVELOPMENT OF A LC-MS/MS METHOD FOR THE DETECTION OF SNAKE VENOM TOXINS IN HUMAN PLASMA

Medicine: Clinical Pharmacolog

Electromechanical tuning of vertically-coupled photonic crystal nanobeams

We present the design, the fabrication and the characterization of a tunable one-dimensional (1D) photonic crystal cavity (PCC) etched on two vertically-coupled GaAs nanobeams. A novel fabrication method which prevents their adhesion under capillary forces is introduced. We discuss a design to increase the flexibility of the structure and we demonstrate a large reversible and controllable electromechanical wavelength tuning (> 15 nm) of the cavity modes.Comment: 11 pages, 4 figure

Enhanced spontaneous emission from quantum dots in short photonic crystal waveguides

We report a study of the quantum dot emission in short photonic crystal waveguides. We observe that the quantum dot photoluminescence intensity and decay rate are strongly enhanced when the emission energy is in resonance with Fabry-Perot cavity modes in the slow-light regime of the dispersion curve. The experimental results are in agreement with previous theoretical predictions and further supported by three-dimensional finite element simulation. Our results show that the combination of slow group velocity and Fabry-Perot cavity resonance provides an avenue to efficiently channel photons from quantum dots into waveguides for integrated quantum photonic applications.Comment: 12 pages, 4 figure

Attitudes Toward the Adoption of 2 Artificial Intelligence-Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study

BACKGROUND: Despite growing efforts to develop user-friendly artificial intelligence (AI) applications for clinical care, their adoption remains limited because of the barriers at individual, organizational, and system levels. There is limited research on the intention to use AI systems in mental health care. OBJECTIVE: This study aimed to address this gap by examining the predictors of psychology students' and early practitioners' intention to use 2 specific AI-enabled mental health tools based on the Unified Theory of Acceptance and Use of Technology. METHODS: This cross-sectional study included 206 psychology students and psychotherapists in training to examine the predictors of their intention to use 2 AI-enabled mental health care tools. The first tool provides feedback to the psychotherapist on their adherence to motivational interviewing techniques. The second tool uses patient voice samples to derive mood scores that the therapists may use for treatment decisions. Participants were presented with graphic depictions of the tools' functioning mechanisms before measuring the variables of the extended Unified Theory of Acceptance and Use of Technology. In total, 2 structural equation models (1 for each tool) were specified, which included direct and mediated paths for predicting tool use intentions. RESULTS: Perceived usefulness and social influence had a positive effect on the intention to use the feedback tool (P<.001) and the treatment recommendation tool (perceived usefulness, P=.01 and social influence, P<.001). However, trust was unrelated to use intentions for both the tools. Moreover, perceived ease of use was unrelated (feedback tool) and even negatively related (treatment recommendation tool) to use intentions when considering all predictors (P=.004). In addition, a positive relationship between cognitive technology readiness (P=.02) and the intention to use the feedback tool and a negative relationship between AI anxiety and the intention to use the feedback tool (P=.001) and the treatment recommendation tool (P<.001) were observed. CONCLUSIONS: The results shed light on the general and tool-dependent drivers of AI technology adoption in mental health care. Future research may explore the technological and user group characteristics that influence the adoption of AI-enabled tools in mental health care

Advancing mental health care with AI-enabled precision psychiatry tools: A patent review

The review provides an overview of patents on AI-enabled precision psychiatry tools published between 2015 and mid-October 2022. Multiple analytic approaches, such as graphic network analysis and topic modeling, are used to analyze the scope, content, and trends of the retained patents. The included tools aim to provide accurate diagnoses according to established psychometric criteria, predict the response to specific treatment approaches, suggest optimal treatments, and make prognoses regarding disorder courses without intervention. About one-third of the tools recommend treatment options or include treatment administration related to digital therapeutics, pharmacotherapy, and electrotherapy. Data sources used to make predictions include behavioral data collected through mobile devices, neuroimaging, and electronic health records. The complexity of technology combinations used in the included devices has increased until 2021. The topics extracted from the patent data illuminate current trends and potential future developments in AI-enabled precision psychiatry. The most impactful patents and associated available products reveal relevant commercialization possibilities and likely future developments. Overall, the review highlights the potential of adopting AI-enabled precision psychiatry tools in practice