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Background: Excessive gestational weight gain (GWG) contributes to the development of obesity in mother and child. Internet-based interventions have the potential for delivering innovative and interactive options for prevention of excessive GWG to large numbers of people.
Objective: The objective of this study was to create a novel measure of Internet-based intervention usage patterns and examine whether usage of an Internet-based intervention is associated with reduced risk of excessive GWG.
Methods: The website featured blogs, local resources, articles, frequently asked questions (FAQs), and events that were available to women in both the intervention and control arm. Weekly reminders to use the website and to highlight new content were emailed to participants in both arms. Only intervention arm participants had access to the weight gain tracker and diet and physical activity goal-setting tools. A total of 1335 (898 intervention and 437 control) relatively diverse and healthy pregnant women were randomly assigned to the intervention arm or control arm. Usage patterns were examined for both intervention and control arm participants using latent class analysis. Regression analyses were used to estimate the association between usage patterns and three GWG outcomes: excessive total GWG, excessive GWG rate, and GWG.
Results: Five usage patterns best characterized the usage of the intervention by intervention arm participants. Three usage patterns best characterized control arm participants' usage. Control arm usage patterns were not associated with excessive GWG, whereas intervention arm usage patterns were associated with excessive GWG.
Conclusions: The control and intervention arm usage pattern characterization is a unique methodological contribution to process evaluations for self-directed Internet-based interventions. In the intervention arm some usage patterns were associated with GWG outcomes.
Clinicaltrial: ClinicalTrials.gov; Clinical Trials Number: NCT01331564; https://clinicaltrials.gov/ct2/show/NCT01331564 (Archived by WebCite at http://www.webcitation/6nI9LuX9w).
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http://dx.doi.org/10.2196/jmir.6644 | DOI Listing |
Eur J Clin Microbiol Infect Dis
September 2025
School of Bioengineering and Biosciences, Department of Biochemistry, Lovely Professional University, Punjab, 144411, India.
Purpose: This study investigates codon usage and amino acid usage bias in the genus Acinetobacter to uncover the evolutionary forces shaping these patterns and their implications for pathogenicity and biotechnology.
Methods: Codon usage patterns were examined in representative genomes of the genus Acinetobacter using standard codon bias indices, including GC content, relative synonymous codon usage (RSCU), effective number of codons (ENC), and codon adaptation index (CAI). Neutrality and parity plots were employed to evaluate the relative influence of mutational pressure and natural selection on codon preferences.
Background: New psychoactive substances (NPS) represent a global problem, especially among young people. In Central Asia, while the trafficking in NPS continues to grow, there remains a lack of data on the social, health and psychological consequences of their use.
Aim: To investigate the motives behind the NPS use among young people in the Republic of Uzbekistan, as well as the medical and social characteristics of this group.
Int J Nephrol Renovasc Dis
September 2025
Department of Nephrology, Bhumirajanagarindra Kidney Institute, Bangkok, Thailand.
Purpose: Unhealthy behaviors can accelerate the progression of chronic kidney disease (CKD). This study aimed to evaluate the effectiveness of a community-based integrated care program in modifying key unhealthy behaviors among CKD patients in rural Thailand and to assess the impact of these behaviors on the rate of kidney function decline.
Patients And Methods: This is a post-hoc analysis of the ESCORT-2 trial, which is a 3-year prospective cohort study that enrolled 914 patients with CKD stages 3-4 in rural Thailand.
Patterns (N Y)
July 2025
L3S Research Center, Leibniz University Hannover, Hannover, Germany.
OpenML is an open-source platform that democratizes machine-learning evaluation by enabling anyone to share datasets in uniform standards, define precise machine-learning tasks, and automatically share detailed workflows and model evaluations. More than just a platform, OpenML fosters a collaborative ecosystem where scientists create new tools, launch initiatives, and establish standards to advance machine learning. Over the past decade, OpenML has inspired over 1,500 publications across diverse fields, from scientists releasing new datasets and benchmarking new models to educators teaching reproducible science.
View Article and Find Full Text PDFAlpha Psychiatry
August 2025
College of Literature and Media, Wenzhou University of Technology, 325027 Wenzhou, Zhejiang, China.
Objective: Problematic internet use (PIU) is a general behavioral addiction and encompasses various syndromes. Previous research found that traumatic events may potentially influence or alter the propensity for PIU. This study aimed to explore the mediating role of fear of missing out (FOMO) and rumination in the influence of post-traumatic stress disorder (PTSD) on PIU among Wenchuan earthquake survivors.
View Article and Find Full Text PDF