SBM's two journals, Annals of Behavioral Medicine and Translational Behavioral Medicine: Practice, Policy, Research (TBM), continuously publish online articles, many of which become available before issues are printed. Three recently published Annals and TBM online articles are listed below.
SBM members who have paid their 2016 membership dues are able to access the full text of all Annals and TBM online articles via the SBM website by following the steps below.
To check if you are a current SBM member, or if you are having trouble accessing the journals online, please contact the SBM national office at info@sbm.org or (414) 918-3156.
Annals of Behavioral Medicine
Coordination of Self- and Parental-Regulation Surrounding Type I Diabetes Management in Late Adolescence
Jonathan E. Butner, Cynthia A. Berg, A. K. Munion, Sara L. Turner, Amy Hughes-Lansing, Joel B. Winnick, Deborah J. Wiebe
Background
Type 1 diabetes management involves self- and social-regulation, with past research examining components through individual differences unable to capture daily processes.
Purpose
Dynamical systems modeling was used to examine the coordinative structure of self- and social-regulation (operationalized as parental-regulation) related to daily diabetes management during late adolescence.
Methods
Two hundred and thirty-six late adolescents with type 1 diabetes (M age = 17.77 years, SD = .39) completed a 14-day diary reporting aspects of self- (e.g., adherence behaviors, cognitive self-regulation failures, and positive and negative affect) and parental-regulation (disclosure to parents, knowledge parents have, and help parents provide).
Results
Self-regulation functioned as one coordinative structure that was separate from parental-regulation, where mothers and fathers were coordinated separately from each other. Mothers’ perceived helpfulness served as a driver of returning adolescents back to homeostasis.
Conclusions
The results illustrate a dynamic process whereby numerous facets of self- and social-regulation are coordinated in order to return diabetes management to a stable state.
High-Frequency Heart Rate Variability Reactivity and Trait Worry Interact to Predict the Development of Sleep Disturbances in Response to a Naturalistic Stressor
Sasha MacNeil, Sonya S. Deschênes, Warren Caldwell, Melanie Brouillard, Thien-Thanh Dang-Vu, Jean-Philippe Gouin
Background
High-frequency heart rate variability (HF-HRV) reactivity was proposed as a vulnerability factor for stress-induced sleep disturbances. Its effect may be amplified among individuals with high trait worry or sleep reactivity.
Purpose
This study evaluated whether HF-HRV reactivity to a worry induction, sleep reactivity, and trait worry predict increases in sleep disturbances in response to academic stress, a naturalistic stressor.
Method
A longitudinal study following 102 undergraduate students during an academic semester with well-defined periods of lower and higher academic stress was conducted. HF-HRV reactivity to a worry induction, trait worry using the Penn State Worry Questionnaire, and sleep reactivity using the Ford Insomnia Stress Reactivity Test were measured during the low stress period. Sleep disturbances using the Pittsburgh Sleep Quality Index were assessed twice during the lower stress period and three times during the higher stress period.
Results
Greater reductions in HF-HRV in response to the worry induction predicted increases in sleep disturbances from the lower to the higher academic stress period. Trait worry moderated this association: individuals with both higher trait worry and greater HF-HRV reactivity to worry had larger increases in stress-related sleep disturbances over time, compared to participants with lower trait worry and HF-HRV reactivity. A similar, but marginally significant effect was found for sleep reactivity.
Conclusion
This study supports the role of HF-HRV reactivity as a vulnerability factor for stress-induced sleep disturbances. The combination of high trait worry and high HF-HRV reactivity to worry might identify a subgroup of individuals most vulnerable to stress-related sleep disturbances.
Translational Behavioral Medicine
Engaging multilevel stakeholders in an implementation trial of evidence-based quality improvement in VA women’s health primary care
The Veterans Health Administration (VHA) has undertaken primary care transformation based on patient-centered medical home (PCMH) tenets. VHA PCMH models are designed for the predominantly male Veteran population, and require tailoring to meet women Veterans’ needs. We used evidence-based quality improvement (EBQI), a stakeholder-driven implementation strategy, in a cluster randomized controlled trial across 12 sites (eight EBQI, four control) that are members of a Practice-Based Research Network. EBQI involves engaging multilevel, inter-professional leaders and staff as stakeholders in reviewing evidence and setting QI priorities. The goal of this analysis was to examine processes of engaging stakeholders in early implementation of EBQI to tailor VHA’s medical home for women. Four inter-professional regional stakeholder planning meetings were conducted; these meetings engaged stakeholders by providing regional data about gender disparities in Veterans’ care experiences. Subsequent to each meeting, qualitative interviews were conducted with 87 key stakeholders (leaders and staff). Stakeholders were asked to describe QI efforts and the use of data to change aspects of care, including women’s health care. Interview transcripts were summarized and coded using a hybrid deductive/inductive analytic approach. The presentation of regional-level data about gender disparities resulted in heightened awareness and stakeholder buy-in and decision-making related to women’s health-focused QI. Interviews revealed that stakeholders were familiar with QI, with regional and facility leaders aware of inter-disciplinary committees and efforts to foster organizational change, including PCMH transformation. These efforts did not typically focus on women’s health, though some informal efforts had been undertaken. Barriers to engaging in QI included lack of communication across clinical service lines, fluidity in staffing, and lack of protected time. Inter-professional, multilevel stakeholders need to be engaged in implementation early, with data and discussion that convey the importance and relevance of a new initiative. Stakeholder perspectives on institutional norms (e.g., gender norms) and readiness for population-specific QI are useful drivers of clinical initiatives designed to transform care for clinical subpopulations.
A 6-year update of the health policy and advocacy priorities of the Society of Behavioral Medicine
Active involved community partnerships: co-creating implementation infrastructure for getting to and sustaining social impact
Active involved community partnerships (AICPs) are essential to co-create implementation infrastructure and translate evidence into real-world practice. Across varied forms, AICPs cultivate community and tribal members as agents of change, blending research and organizational knowledge with relationships, context, culture, and local wisdom. Unlike selective engagement, AICPs enable active involvement of partners in the ongoing process of implementation and sustainability. This includes defining the problem, developing solutions, detecting practice changes, aligning organizational supports, and nurturing shared responsibility, accountability, and ownership for implementation. This paper builds on previously established active implementation and scaling functions by outlining key AICP functions to close the research-practice gap. Part of a federal initiative, California Partners for Permanency (CAPP) integrated AICP functions for implementation and system change to reduce disproportionality and disparities in long-term foster care. This paper outlines their experience defining and embedding five AICP functions: (1) relationship-building; (2) addressing system barriers; (3) establishing culturally relevant supports and services; (4) meaningful involvement in implementation; and (5) ongoing communication and feedback for continuous improvement. Planning for social impact requires the integration of AICP with other active implementation and scaling functions. Through concrete examples, authors bring multilevel AICP roles to life and discuss implications for implementation research and practice.