Digital health interventions (DHIs) are prevalent and have been shown to help some people with long-term conditions (LTCs) to manage their condition. There are myriad options for digital delivery yet limited understanding of what modes of delivery are acceptable to people with LTCs. It is important to understand the acceptability of delivery methods of DHIs to inform future DHI development and promote engagement. This scoping review aims to explore the acceptability of the delivery of DHIs for people with LTCs.
This review will follow the Joanna Briggs Institute guidance for scoping reviews and will be reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Scoping reviews extension checklist. Databases including MEDLINE, PubMed, CINAHL, AHMED and PsycINFO will be searched for primary studies that provide data on preferences for delivery methods of DHIs by people with LTCs. Narrative analysis is anticipated, and a summary of the findings will be presented in a tabulated format.
Ethical approval will not be required for this scoping review. The findings will be disseminated via appropriate peer-reviewed journals and conferences and PhD theses.
This study aimed to explore what intervention specificities or attributes newly diagnosed individuals with multiple sclerosis (MS) find important and to explore possible reasons behind their evaluations.
A stepwise approach began with a systematic literature review to identify significant attributes. Patients with MS then assessed these attributes through an online survey, which included a ranking exercise and open-ended questions. Finally, the results were evaluated by the clinical team to select the most relevant factors for personalised care.
From June 2023 to December 2023, all consecutive patients referred to the MS Center of Careggi University Hospital were screened for inclusion. Following recruitment, cognitive and physical assessments were administered at the Don Gnocchi Centre. All participants were interviewed by an experienced neuropsychologist.
Participants were enrolled in the RELIABLE clinical trial, which included a ranking exercise and open-ended question. In the ranking exercise, patients prioritised levels of treatment attributes: treatment effects, methods of intervention, type of monitoring, monitoring, mode and mental support. The open-ended questions addressed the reasons behind the level rankings.
Participants’ rankings revealed the most important levels of each attribute. The highest-ranked method of intervention was disease-modifying treatment, which received 164 points. For mental support, individual psychotherapy was deemed most important with 149 points. Preservation of cognitive function, a key treatment effect, received 144 points. Clinical check-ups were the top type of monitoring with 129 points. Lastly, the hybrid mode of monitoring (half remote/half in-person) was ranked with 77 points. Open-ended responses provided insights into the reasons behind these preferences, emphasising the importance of maintaining mobility, cognitive function and emotional well-being. The clinical team evaluated these findings, confirming that the selected attributes were both clinically relevant and aligned with patient priorities. This evaluation process ensured that the treatment specificities chosen for individualised care were comprehensive and reflective of patient needs.
By identifying and prioritising key treatment attributes, this research highlights the multifaceted nature of MS management and emphasises the importance of aligning treatment options with patient preferences. Addressing these factors through further quantitative preference assessments is essential for preventative MS care, improving patient outcomes and promoting a more patient-centred approach to treatment.
by Janet Poplawski, Tony Montina, Gerlinde A. S. Metz
The developing nervous system displays remarkable plasticity in response to sensory stimulation during critical periods of development. Critical periods may also increase the brain’s vulnerability to adverse experiences. Here we show that early-life stress (ELS) in mice shifts the timing of critical periods in the visual cortex. ELS induced by animal transportation on postnatal day 12 accelerated the opening and closing of the visual cortex critical period along with earlier maturation of visual acuity. Staining of a molecular correlate that marks the end of critical period plasticity revealed premature emergence of inhibitory perineuronal nets (PNNs) following ELS. ELS also drove lasting changes in visual cortex mRNA expression affecting genes linked to psychiatric disease risk, with hemispheric asymmetries favoring the right side. NMR spectroscopy and a metabolomics approach revealed that ELS was accompanied by activated energy metabolism and protein biosynthesis. Thus, ELS may accelerate visual system development, resulting in premature opening and closing of critical period plasticity. Overall, the data suggest that ELS desynchronizes the orchestrated temporal sequence of regional brain development potentially leading to long-term functional deficiencies. These observations provide new insights into a neurodevelopmental expense to adaptative brain plasticity. These findings also suggest that shipment of laboratory animals during vulnerable developmental ages may result in long lasting phenotypes, introducing critical confounds to the experimental design.by Michele Salvagno, Alessandro De Cassai, Stefano Zorzi, Mario Zaccarelli, Marco Pasetto, Elda Diletta Sterchele, Dmytro Chumachenko, Alberto Giovanni Gerli, Razvan Azamfirei, Fabio Silvio Taccone
Natural Language Processing (NLP) is a subset of artificial intelligence that enables machines to understand and respond to human language through Large Language Models (LLMs)‥ These models have diverse applications in fields such as medical research, scientific writing, and publishing, but concerns such as hallucination, ethical issues, bias, and cybersecurity need to be addressed. To understand the scientific community’s understanding and perspective on the role of Artificial Intelligence (AI) in research and authorship, a survey was designed for corresponding authors in top medical journals. An online survey was conducted from July 13th, 2023, to September 1st, 2023, using the SurveyMonkey web instrument, and the population of interest were corresponding authors who published in 2022 in the 15 highest-impact medical journals, as ranked by the Journal Citation Report. The survey link has been sent to all the identified corresponding authors by mail. A total of 266 authors answered, and 236 entered the final analysis. Most of the researchers (40.6%) reported having moderate familiarity with artificial intelligence, while a minority (4.4%) had no associated knowledge. Furthermore, the vast majority (79.0%) believe that artificial intelligence will play a major role in the future of research. Of note, no correlation between academic metrics and artificial intelligence knowledge or confidence was found. The results indicate that although researchers have varying degrees of familiarity with artificial intelligence, its use in scientific research is still in its early phases. Despite lacking formal AI training, many scholars publishing in high-impact journals have started integrating such technologies into their projects, including rephrasing, translation, and proofreading tasks. Efforts should focus on providing training for their effective use, establishing guidelines by journal editors, and creating software applications that bundle multiple integrated tools into a single platform.