Background: Researchers are working to identify dynamic factors involved in the shift from behavioral initiation to maintenance-factors which may depend on behavioral complexity. We test hypotheses regarding changes in factors involved in behavioral initiation and maintenance and their relationships to behavioral frequency over time, for a simple (taking a supplement) vs. complex (exercise) behavior.
Methods: Data are secondary analyses from a larger RCT, in which young adult women, new to both behaviors, were randomly assigned to take daily calcium ( = 161) or to go for a daily, brisk walk ( = 171), for 4-weeks. Factors (intentions, self-efficacy, intrinsic motivation, self-identity, habit strength) were measured weekly. Multi-level modeling evaluated their change over time. Bivariate correlations and multiple regression determined the relationships between factors and the subsequent-week behavioral frequency (self-report and objective).
Finding: Results were partly in-line with expectations, in that individuals' intentions and self-efficacy predicted initial behavioral engagement for both behaviors, and habit strength increased for both behaviors, becoming a significant predictor of behavioral frequency in later weeks of the study in some analyses. However, results depended on whether the outcome was self-reported or objectively measured and whether analyses were bivariate or multivariate (regression).
Discussion: The factors theorized to play a role in behavioral maintenance (intrinsic motivation, self-identity, and habit strength) started to develop, but only habit strength predicted behavioral frequency by study-end, for both behaviors. Differences in initiation and maintenance between behaviors of differing complexity may not be as stark as theorized, but longer follow-up times are required to evaluate maintenance factors.
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http://dx.doi.org/10.3389/fpsyg.2022.962150 | DOI Listing |
Infect Dis Ther
December 2024
Department of Respiratory Medicine, The First Affiliated Hospital of Wannan Medical College, No 2 Zheshan West Road, Wuhu, 241000, Anhui, China.
Introduction: Stenotrophomonas maltophilia is an opportunistic pathogen associated with various nosocomial infections and is known for its intrinsic multidrug resistance. This study aims to provide a comprehensive overview of the epidemiology and resistance patterns of S. maltophilia in China from 2014 to 2021.
View Article and Find Full Text PDFJ Med Ultrason (2001)
December 2024
Department of Internal Medicine, Kuma Hospital, Kobe, Hyogo, 650-0011, Japan.
Purpose: Parathyroid lipoadenomas are difficult to recognize preoperatively; hence, they may remain undetected. Difficulty in recognition is thought to be due to the adipocytes present in the tumor. This study aimed to clarify the impact of adipocytes as a component of parathyroid adenomas on ultrasound evaluation.
View Article and Find Full Text PDFJ Biomol Struct Dyn
December 2024
School of Physical Sciences, Swami Ramanand Teerth Marathwada University, Nanded, Maharashtra, India.
The dielectric behavior of Asparagine (CHNO) in water over the frequency range of 10 MHz to 30 GHz in the temperature region of 278.15-303.15 K in a step of 5 K has been carried out using time domain reflectometry (TDR) at various concentrations of asparagine.
View Article and Find Full Text PDFStress
December 2025
Technology Transfer and Innovation-Support Office, North-West University, Potchefstroom, South Africa.
Background: Self-reported mental stress is not consistently recognized as a risk factor for stroke. This prompted development of a novel algorithm for stress-phenotype indices to quantify chronic stress prevalence in relation to a modified stroke risk score in a South African cohort. The algorithm is based on biomarkers adrenocorticotrophic hormone, high-density lipoprotein cholesterol, high-sensitive cardiac-troponin-T, and diastolic blood pressure which exemplifies the stress-ischemic-phenotype index.
View Article and Find Full Text PDFJ Am Med Inform Assoc
December 2024
AI for Health Institute, Washington University in St Louis, St Louis, MO 63130, United States.
Objective: Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning.
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