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Triggering just-in-time adaptive interventions based on real-time detection of daily-life stress: Methodological development and longitudinal multicenter evaluation
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326193.pdf (Publisher’s version ) (Closed access)Stress-related disorders present a significant global burden, highlighting the need for effective, preventive measures. Mobile just-in-time adaptive interventions (JITAI) can be applied in real time and context-specifically, precisely when individuals need them most. Yet, they are rarely applied in stress research. This study introduces a novel approach by performing real-time analysis of both psychological and physiological data to trigger interventions during moments of high stress. We evaluated the feasibility of this JITAI algorithm, which integrates ecological momentary assessments (EMA) and ecological physiological assessments (EPA) to generate a stress score that triggers interventions in real time by relating the score to a personalized stress threshold. The feasibility of the technical implementation, participant adherence, and user experience were assessed within a multicenter study with 215 participants conducted across five research sites. The JITAI algorithm successfully processed EMA and EPA data to trigger real-time interventions. A total of 68% (standard deviation [SD] = 29%) of EMA beeps contained extracted EPA features, demonstrating technical feasibility. The algorithm triggered 1.61 (SD = 1.26) interventions per day, with 43% (SD = 27%) of EMA beeps per week leading to triggered interventions. Compliance rates of 43% (SD = 22%) for EMA and 43% (SD = 30%) for the JITAI were achieved, with feedback indicating areas for improvement, particularly for daily-life integration. Our findings provide preliminary support for the feasibility of the developed JITAI algorithm, demonstrating effective data processing and intervention triggering in real time, while also highlighting areas for improvement. Future research should focus on minimizing participant burden, including the intensity of EMA protocols, to improve participant adherence and acceptability while maintaining the benefits of real-time intervention delivery.21 p
Pathogenesis of mtDNA point mutation m.10191T>C affecting complex I function is a multifactorial process leading to metabolic remodeling of mitochondria
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325349.pdf (Publisher’s version ) (Open Access)19 p
Membership Privacy Evaluation in Deep Spiking Neural Networks
No full textItem does not contain fulltextComputer Security – Esorics 202
Cerebellar transcranial direct current stimulation in spinocerebellar ataxia type 3: An electric field modelling study
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326074.pdf (Publisher’s version ) (Open Access)15 p
Time-Distributed Backdoor Attacks on Federated Spiking Learning
No full textItem does not contain fulltextComputer Security – ESORICS 202
A tipping point in word recognition? Investigating the relationship between root and form frequency across visual and auditory modalities
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324976.pdf (Publisher’s version ) (Open Access)34 p
Exploring the Central Dogma Through the Lens of Gene Expression Noise
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326577.pdf (Publisher’s version ) (Open Access
MLTradeOps: Embedding Trade-Off Management into the MLOps Workflow
No full textItem does not contain fulltextSEAA 202
How Residents Develop Virtues: A Qualitative Longitudinal Study
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326055.pdf (Publisher’s version ) (Open Access)14 p
Teacher-led robot intervention in early primary school classrooms improves pupil and teacher outcomes
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326121.pdf (Publisher’s version ) (Open Access)Programming is often taught through robots in early primary education to support young children's computational thinking (CT), but many teachers lack the confidence and training to use them effectively. This paper presents a school-based robot intervention for children aged 4-7 (n = 430) and their classroom teachers (n = 17), delivered under three conditions: Intervention (robot intervention only), Intervention+ (intervention plus teacher education), and Control (no intervention). The two intervention groups assessed whether teacher education, in addition to classroom robot experience, influenced pupils' prediction and debugging, transferable skills (programming transfer and picture sequencing), and teachers' beliefs (enjoyment, relevance, self-efficacy, anxiety). The intervention improved children's prediction and debugging scores significantly, but only Intervention+ significantly outperformed Control for both prediction and debugging. Performance on the programming transfer and picture sequencing tasks improved across all groups. Teachers in both intervention groups reported improved relevance beliefs, though only Intervention+ showed a significant difference from Control. Self-efficacy also improved significantly in Intervention+ only. These findings offer practical guidance for embedding programming with robots in primary education and underscore the importance of teacher education for significant impact.16 p