Högskolan i Skövde

Background: Delivery of interventions within student mental health services has undergone considerable digital transformation in recent years. Traditional face-to-face meetings are being substituted with autonomous digital tools with evident advantages in terms of accessibility and scalability. Despite an increasing array of digital options, there is also a growing recognition that digital tools offer limited effectiveness without some degree of human support. For example, for mental well-being, completely digitally delivered interventions show approximately half the effect sizes of interventions delivered in a traditional format. Blended forms of delivery that use both digital advantages and recognized effects of human contact are therefore promising. Hitherto, the effects of blended delivery have not been evaluated for mental well-being. Hence, investigating how digitalization in intervention delivery impacts intervention effects on mental well-being is important. This is especially relevant among emerging adults enrolled in higher education, going through a critical, transformative life phase.

Objective: This systematic review and meta-analysis will primarily aim to investigate differences in effect due to the degree of digitalization in the mode of delivery of interventions on mental well-being among higher education students.

Methods: This work will adhere to the Cochrane Collaboration methodology, and results will be reported according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A systematic literature search will be conducted across 9 databases (Scopus, MEDLINE, PubMed, PsycINFO, ERIC, CINAHL, Web of Science, Cochrane, and International Clinical Trials Registry Platform). The population, intervention, comparator, and outcome framework will inform both the search strategy and eligibility criteria. For inclusion, studies should be randomized controlled trials investigating the effect of individually delivered interventions on positive affect or life satisfaction among mentally healthy higher education students aged 18 to 29 years. Studies will be independently screened, and data will be extracted, including the standardized mean difference as the effect measure. Risk of bias assessment will be conducted using the Cochrane Risk of Bias 2 instrument. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis will be applied, and study biases will be analyzed by funnel-plot assessment and Egger test. Finally, certainty of evidence for positive affect and life satisfaction will be assessed using the Grading of Recommendations Assessment, Development, and Evaluation approach. Results will be presented in a summary of findings table.

Results: The search was finalized in March 2026, generating 6603 records after duplicate removal. Study selection and data extraction were conducted during April 2026, resulting in 41 eligible studies. Risk of bias assessment, data analysis, and manuscript preparation are planned before submission for peer review in August 2026.

Conclusions: The principal findings of this study will highlight differences in effect due to the mode of delivery of interventions on mental well-being among higher education students. This will be relevant for the management of student mental health promotion services.

Trial Registration: PROSPERO CRD420251131950; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251131950

International Registered Report Identifier (IRRID): PRR1-10.2196/88458

The purpose of this research was to answer the following central questions: 1) How accurate are human observers at predicting the behavior of cyclists as the cyclists approached a crossing? 2) If the accuracy is reliably better than chance, what cues were used to make the predictions? 3) At what distance from the crossing did the most critical cues occur? 4) Can the cues be used in a model that can reliably predict cyclist intent? We present results that show a number of indicators that can be used in to predict the intention of a cyclist, i.e., future actions of a cyclist, e.g., “left turn” or “continue forward” etc.

Results of empirical studies show that humans are reasonably good at this type of prediction for a majority of the situations studied. However, some situations seem to contain conflicting information. The results also suggested that human prediction of intention is to a large extent relying on a single “strong” indicator, e.g., that the cyclist makes a clear “head movement”. Several “weaker" indicators that together could be a strong “combined indicator”, or equivalently strong evidence, is likely to be missed or too complex to be handled by humans in real-time. We suggest this line of research can be used to create decision support systems that predict the behavior of cyclists in traffic.

Genom att titta på ett fåtal bestämda signaler kan man med god träffsäkerhet förutsäga cyklisters beteende, vilket tyder på att de identifierade signalerna är betydelsefulla. Vetskapen om dessa kan, bland annat, praktiskt användas för att utveckla enkla hjälpmedel – såsom medveten placering av fluorescerande eller reflekterande material på leder och/eller införande av olikfärgade hjälmsidor. Dylika kan förväntas förstärka kommunikationen av viktiga signaler. Vetskapen kan även användas för att utbilda oerfarna bilförare. Båda fallen kan i förlängningen ge en säkrare trafikmiljö för oskyddade trafikanter.