98%
921
2 minutes
20
The current article details a position statement and recommendations for future research and practice on planning and implementation intentions in health contexts endorsed by the Synergy Expert Group. The group comprised world-leading researchers in health and social psychology and behavioural medicine who convened to discuss priority issues in planning interventions in health contexts and develop a set of recommendations for future research and practice. The expert group adopted a nominal groups approach and voting system to elicit and structure priority issues in planning interventions and implementation intentions research. Forty-two priority issues identified in initial discussions were further condensed to 18 key issues, including definitions of planning and implementation intentions and 17 priority research areas. Each issue was subjected to voting for consensus among group members and formed the basis of the position statement and recommendations. Specifically, the expert group endorsed statements and recommendations in the following areas: generic definition of planning and specific definition of implementation intentions, recommendations for better testing of mechanisms, guidance on testing the effects of moderators of planning interventions, recommendations on the social aspects of planning interventions, identification of the preconditions that moderate effectiveness of planning interventions and recommendations for research on how people use plans.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1080/08870446.2016.1146719 | DOI Listing |
Alzheimers Dement
September 2025
Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea.
Introduction: We developed and validated age-related amyloid beta (Aβ) positron emission tomography (PET) trajectories using a statistical model in cognitively unimpaired (CU) individuals.
Methods: We analyzed 849 CU Korean and 521 CU non-Hispanic White (NHW) participants after propensity score matching. Aβ PET trajectories were modeled using the generalized additive model for location, scale, and shape (GAMLSS) based on baseline data and validated with longitudinal data.
Stroke
September 2025
Department of Medicine, University of Melbourne, Parkville, Victoria, Australia. (V.Y., B.C.V.C., L.C., L.O., M.W.P.).
Background: To assess the efficacy and safety of tenecteplase in patients presenting within 24 hours of symptom onset with a large vessel occlusion and target mismatch on perfusion computed tomography.
Methods: ETERNAL-LVO was a prospective, randomized, open-label, blinded end point, phase 3, superiority trial where adult participants with a large vessel occlusion, presenting within 24 hours of onset with salvageable tissue on computed tomography perfusion, were randomized to tenecteplase 0.25 mg/kg or standard care across 11 primary and comprehensive stroke centers in Australia.
Muscle Nerve
September 2025
Department of Neurology, Seoul Hospital, Ewha Womans University College of Medicine, Seoul, South Korea.
Introduction/aims: There is a lack of up-to-date information on the burden of motor neuron diseases (MNDs) in the United States (US). This study aimed to estimate trends in the prevalence, incidence, mortality, and disability-adjusted life years (DALYs) for MNDs in the US from 1990 to 2021.
Methods: We performed a secondary analysis of MNDs in the US using estimates of prevalence, incidence, and mortality obtained from analyses of the Global Burden of Disease 2021 dataset.
Eur J Case Rep Intern Med
August 2025
Hanoi Heart Hospital, Hanoi, Vietnam.
Background: Perforation of artery causing bleeding is a rare but serious complication of percutaneous coronary intervention (PCI), with potentially life-threatening consequences. Prompt recognition and management are crucial, particularly in high-risk patients or complex procedures. Coils are essential tools for sealing perforated or ruptured vessels, preventing further haemorrhage and stabilising the patient.
View Article and Find Full Text PDFFront Rehabil Sci
August 2025
Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, United States.
Introduction: Spinal cord injury (SCI) presents a significant burden to patients, families, and the healthcare system. The ability to accurately predict functional outcomes for SCI patients is essential for optimizing rehabilitation strategies, guiding patient and family decision making, and improving patient care.
Methods: We conducted a retrospective analysis of 589 SCI patients admitted to a single acute rehabilitation facility and used the dataset to train advanced machine learning algorithms to predict patients' rehabilitation outcomes.