Browsing by Subject "behavior"

Now showing 1 - 5 of 5
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    A qualitative study of facilitators of medication adherence in systemic lupus erythematosus: Perspectives from rheumatology providers/staff and patients.
    (Lupus, 2024-01) Herndon, Shannon; Corneli, Amy; Dombeck, Carrie; Swezey, Teresa; Clowse, Megan Eb; Rogers, Jennifer L; Criscione-Schreiber, Lisa G; Sadun, Rebecca E; Doss, Jayanth; Eudy, Amanda M; Bosworth, Hayden B; Sun, Kai

    Objective

    Systemic lupus erythematosus (SLE) disproportionately affects patients from racial and ethnic minority groups. Medication adherence is lower among these patient populations, and nonadherence is associated with worse health outcomes. We aimed to identify factors that enable adherence to immunosuppressive medications among patients with SLE from racial and ethnic minority groups.

    Methods

    Using a qualitative descriptive study design, we conducted in-depth interviews with purposefully selected (1) patients with SLE from racial and ethnic minority groups who were taking immunosuppressants and (2) lupus providers and staff. We focused on adherence facilitators, asking patients to describe approaches supporting adherence and for overcoming common adherence challenges and providers and staff to describe actions they can take to foster patient adherence. We used applied thematic analysis and categorized themes using the Capability, Opportunity, Motivation, Behavior (COM-B) model.

    Results

    We interviewed 12 patients (4 adherent and 8 nonadherent based on medication possession ratio) and 12 providers and staff. Although each patient described a unique set of facilitators, patients most often described social support, physical well-being, reminders, and ability to acquire medications as facilitators. Providers also commonly mentioned reminders and easy medication access as facilitators as well as patient education/communication and empowerment.

    Conclusion

    Using an established behavioral change model, we categorized a breadth of adherence facilitators within each domain of the COM-B model while highlighting patients' individual approaches. Our findings suggest that an optimal adherence intervention may require a multi-modal and individually tailored approach including components from each behavioral domain-ensuring medication access (Capability) and utilizing reminders and social support (Opportunity), while coupled with internal motivation through improved communication and empowerment (Motivation).
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    Climatic and Resource Determinants of Forest Elephant Movements
    (Frontiers in Ecology and Evolution, 2020-04-17) Beirne, C; Meier, AC; Brumagin, G; Jasperse-Sjolander, L; Lewis, M; Masseloux, J; Myers, K; Fay, M; Okouyi, J; White, LJT; Poulsen, JR
    As a keystone megafaunal species, African forest elephants (Loxodonta cyclotis) influence the structure and composition of tropical forests. Determining the links between food resources, environmental conditions and elephant movement behavior is crucial to understanding their habitat requirements and their effects on the ecosystem, particularly in the face of poaching and global change. We investigate whether fruit abundance or climate most strongly influence forest elephant movement behavior at the landscape scale in Gabon. Trained teams of “elephant trackers” performed daily fruit availability and dietary composition surveys over a year within two relatively pristine and intact protected areas. With data from 100 in-depth field follows of 28 satellite-collared elephants and remotely sensed environmental layers, we use linear mixed-effects models to assess the effects of sites, seasons, focal elephant identification, elephant diet, and fruit availability on elephant movement behavior at monthly and 3-day time scales. At the month-level, rainfall, and to a lesser extent fruit availability, most strongly predicted the proportion of time elephants spent in long, directionally persistent movements. Thus, even elephants in moist tropical rainforests show seasonal behavioral phenotypes linked to rainfall. At the follow-level (2–4 day intervals), relative support for both rainfall and fruit availability decreased markedly, suggesting that at finer spatial scales forest elephants make foraging decisions largely based on other factors not directly assessed here. Focal elephant identity explained the majority of the variance in the data, and there was strong support for interindividual variation in behavioral responses to rainfall. Taken together, this highlights the importance of approaches which follow individuals through space and time. The links between climate, resource availability and movement behavior provide important insights into the behavioral ecology of forest elephants that can contribute to understanding their role as seed dispersers, improving management of populations, and informing development of solutions to human-elephant conflict.
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    Demographic, Clinical, and Psychosocial Predictors of Exercise Adherence: The STRRIDE Trials.
    (Translational journal of the American College of Sports Medicine, 2023-01) Collins, Katherine A; Huffman, Kim M; Wolever, Ruth Q; Smith, Patrick J; Ross, Leanna M; Siegler, Ilene C; Jakicic, John M; Costa, Paul T; Kraus, William E

    Purpose

    To identify baseline demographic, clinical, and psychosocial predictors of exercise intervention adherence in the Studies of a Targeted Risk Reduction Intervention through Defined Exercise (STRRIDE) trials.

    Methods

    A total of 947 adults with dyslipidemia or prediabetes were enrolled into an inactive control group or one of ten exercise interventions with doses of 10-23 kcal/kg/week, intensities of 40-80% of peak oxygen consumption, and training for 6-8-months. Two groups included resistance training. Mean percent aerobic and resistance adherence were calculated as the amount completed divided by the prescribed weekly minutes or total sets of exercise times 100, respectively. Thirty-eight clinical, demographic, and psychosocial measures were considered for three separate models: 1) clinical + demographic factors, 2) psychosocial factors, and 3) all measures. A backward bootstrapped variable selection algorithm and multiple regressions were performed for each model.

    Results

    In the clinical and demographic measures model (n=947), variables explained 16.7% of the variance in adherence (p<0.001); lesser fasting glucose explained the greatest amount of variance (partial R2 = 3.2%). In the psychosocial factors model (n=561), variables explained 19.3% of the variance in adherence (p<0.001); greater 36-Item Short Form Health Survey (SF-36) physical component score explained the greatest amount of variance (partial R2 = 8.7%). In the model with all clinical, demographic, and psychosocial measures (n=561), variables explained 22.1% of the variance (p<0.001); greater SF-36 physical component score explained the greatest amount of variance (partial R2 = 8.9%). SF-36 physical component score was the only variable to account for >5% of the variance in adherence in any of the models.

    Conclusions

    Baseline demographic, clinical, and psychosocial variables explain approximately 22% of the variance in exercise adherence. The limited variance explained suggests future research should investigate additional measures to better identify participants who are at risk for poor exercise intervention adherence.
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    Dynamics of electroencephalogram entropy and pitfalls of scaling detection
    (2010) Ignaccolo, M; Latka, M; Jernajczyk, W; Grigolini, P; West, BJ
    In recent studies a number of research groups have determined that human electroencephalograms (EEG) have scaling properties. In particular, a crossover between two regions with different scaling exponents has been reported. Herein we study the time evolution of diffusion entropy to elucidate the scaling of EEG time series. For a cohort of 20 awake healthy volunteers with closed eyes, we find that the diffusion entropy of EEG increments (obtained from EEG waveforms by differencing) exhibits three features: short-time growth, an alpha wave related oscillation whose amplitude gradually decays in time, and asymptotic saturation which is achieved after approximately 1 s. This analysis suggests a linear, stochastic Ornstein-Uhlenbeck Langevin equation with a quasiperiodic forcing (whose frequency and/or amplitude may vary in time) as the model for the underlying dynamics. This model captures the salient properties of EEG dynamics. In particular, both the experimental and simulated EEG time series exhibit short-time scaling which is broken by a strong periodic component, such as alpha waves. The saturation of EEG diffusion entropy precludes the existence of asymptotic scaling. We find that the crossover between two scaling regions seen in detrended fluctuation analysis (DFA) of EEG increments does not originate from the underlying dynamics but is merely an artifact of the algorithm. This artifact is rooted in the failure of the "trend plus signal" paradigm of DFA.
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    Hippocampal Transcriptomic and Proteomic Alterations in the BTBR Mouse Model of Autism Spectrum Disorder.
    (Front Physiol, 2015) Daimon, Caitlin M; Jasien, Joan M; Wood, William H; Zhang, Yongqing; Becker, Kevin G; Silverman, Jill L; Crawley, Jacqueline N; Martin, Bronwen; Maudsley, Stuart
    Autism spectrum disorders (ASD) are complex heterogeneous neurodevelopmental disorders of an unclear etiology, and no cure currently exists. Prior studies have demonstrated that the black and tan, brachyury (BTBR) T+ Itpr3tf/J mouse strain displays a behavioral phenotype with ASD-like features. BTBR T+ Itpr3tf/J mice (referred to simply as BTBR) display deficits in social functioning, lack of communication ability, and engagement in stereotyped behavior. Despite extensive behavioral phenotypic characterization, little is known about the genes and proteins responsible for the presentation of the ASD-like phenotype in the BTBR mouse model. In this study, we employed bioinformatics techniques to gain a wide-scale understanding of the transcriptomic and proteomic changes associated with the ASD-like phenotype in BTBR mice. We found a number of genes and proteins to be significantly altered in BTBR mice compared to C57BL/6J (B6) control mice controls such as BDNF, Shank3, and ERK1, which are highly relevant to prior investigations of ASD. Furthermore, we identified distinct functional pathways altered in BTBR mice compared to B6 controls that have been previously shown to be altered in both mouse models of ASD, some human clinical populations, and have been suggested as a possible etiological mechanism of ASD, including "axon guidance" and "regulation of actin cytoskeleton." In addition, our wide-scale bioinformatics approach also discovered several previously unidentified genes and proteins associated with the ASD phenotype in BTBR mice, such as Caskin1, suggesting that bioinformatics could be an avenue by which novel therapeutic targets for ASD are uncovered. As a result, we believe that informed use of synergistic bioinformatics applications represents an invaluable tool for elucidating the etiology of complex disorders like ASD.