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Background: Physical activity (PA) improves many facets of health. Despite this, the majority of American adults are insufficiently active. Adults who visit a physician complaining of chest pain and related cardiovascular symptoms are often referred for further testing. However, when this testing does not reveal an underlying disease or pathology, patients typically receive no additional standard care services. A PA intervention delivered within the clinic setting may be an effective strategy for improving the health of this population at a time when they may be motivated to take preventive action.
Objective: Our aim was to determine the effectiveness of a tailored, computer-based, interactive personal action planning session to initiate PA among a group of sedentary cardiac patients following exercise treadmill testing (ETT).
Methods: This study was part of a larger 2x2 randomized controlled trial to determine the impact of environmental and social-cognitive intervention approaches on the initiation and maintenance of weekly PA for patients post ETT. Participants who were referred to an ETT center but had a negative-test (ie, stress tests results indicated no apparent cardiac issues) were randomized to one of four treatment arms: (1) increased environmental accessibility to PA resources via the provision of a free voucher to a fitness facility in close proximity to their home or workplace (ENV), (2) a tailored social cognitive intervention (SC) using a "5 As"-based (ask, advise, assess, assist, and arrange) personal action planning tool, (3) combined intervention of both ENV and SC approaches (COMBO), or (4) a matched contact nutrition control (CON). Each intervention was delivered using a computer-based interactive session. A general linear model for repeated measures was conducted with change in PA behavior from baseline to 1-month post interactive computer session as the primary outcome.
Results: Sedentary participants (n=452; 34.7% participation rate) without a gym membership (mean age 58.57 years; 59% female, 78% white, 12% black, 11% Hispanic) completed a baseline assessment and an interactive computer session. PA increased across the study sample (F1,441=30.03, P<.001). However, a time by condition interaction (F3,441=8.33, P<.001) followed by post hoc analyses indicated that SC participants exhibited a significant increase in weekly PA participation (mean 45.1, SD 10.2) compared to CON (mean -2.5, SD 10.8, P=.004) and ENV (mean 8.3, SD 8.1, P<.05). Additionally, COMBO participants exhibited a significant increase in weekly PA participation (mean 53.4, SD 8.9) compared to CON (P<.001) and ENV (P=.003) participants. There were no significant differences between ENV and CON or between SC and COMBO.
Conclusions: A brief, computer-based, interactive personal action planning session may be an effective tool to initiate PA within a health care setting, in particular as part of the ETT system.
Trial Registration: Clinicaltrials.gov NCT00432133, http://clinicaltrials.gov/ct2/show/NCT00432133 (Archived by WebCite at http://www.webcitation.org/6aa8X3mw1).
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http://dx.doi.org/10.2196/jmir.3759 | DOI Listing |
Genome Biol
September 2025
Department of Biology, Plant-Microbe Interactions, Science for Life, Utrecht University, Utrecht, 3584CH, The Netherlands.
Background: Plant roots release root exudates to attract microbes that form root communities, which in turn promote plant health and growth. Root community assembly arises from millions of interactions between microbes and the plant, leading to robust and stable microbial networks. To manage the complexity of natural root microbiomes for research purposes, scientists have developed reductionist approaches using synthetic microbial inocula (SynComs).
View Article and Find Full Text PDFNat Microbiol
September 2025
Division of Computational Pathology, Brigham and Women's Hospital, Boston, MA, USA.
Although dynamical systems models are a powerful tool for analysing microbial ecosystems, challenges in learning these models from complex microbiome datasets and interpreting their outputs limit use. We introduce the Microbial Dynamical Systems Inference Engine 2 (MDSINE2), a Bayesian method that learns compact and interpretable ecosystems-scale dynamical systems models from microbiome timeseries data. Microbial dynamics are modelled as stochastic processes driven by interaction modules, or groups of microbes with similar interaction structure and responses to perturbations, and additionally, noise characteristics of data are modelled.
View Article and Find Full Text PDFDig Liver Dis
September 2025
Department of Gastroenterology, Valduce Hospital, Como, Italy. Electronic address:
Objectives: Computer-aided detection (CADe) systems improve adenoma detection during colonoscopy, but the influence of bowel preparation quality on CADe performance is unclear. This study assessed whether different levels of adequate bowel preparation affect CADe effectiveness.
Methods: A post-hoc pooled analysis was conducted using individual patient data from three randomized controlled trials comparing CADe-assisted colonoscopy to standard colonoscopy (SC).
Med Eng Phys
October 2025
Department of Engineering Science, University of Oxford, United Kingdom. Electronic address:
Traditionally, clinical devices are designed, tested and improved through lengthy and expensive laboratory experiments and clinical trials [1]. More recently, computational methods have allowed for rapid testing, speeding up the design process and enabling far more complete searches of design space. While computational models cannot fully capture the complexities of biological systems, they provide valuable insights into crucial underlying mechanisms, such as the effects of fluid-structure interactions (FSIs).
View Article and Find Full Text PDFJ Neurosci
September 2025
Institute of Psychology, Leiden University, the Netherlands.
Although phasic alertness generally benefits cognitive performance, it often increases the impact of distracting information, resulting in impaired decision-making and cognitive control. However, it is unclear why phasic alertness has these negative effects. Here, we present a novel, biologically-informed account, according to which phasic alertness generates a transient, evidence-independent input to the decision process.
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