Journal of Behavioral Medicine最新文献

This study investigated associations between the Superwoman schema (socialized expectations to project strength and exhibit a determination to succeed, while at the same time helping others and suppressing one's emotions) and metabolic syndrome (MetS, a cluster of risk factors for diabetes, heart disease, and stroke detectable in childhood) across the period of adolescence. A sample of 256 Black adolescent girls (ages 14-19), all from lower-income households (≤ 2 × poverty threshold) was recruited for a cross-sectional study. Adolescents completed the Superwoman schema questionnaire, and MetS was measured using International Diabetes Federation criteria. Analyses posed a developmental question of whether associations varied by age across the period of adolescence. Age by Superwoman schema interactions were found, such that in younger adolescent girls, higher scores on the Superwoman schema questionnaire were associated with better cardiometabolic health (lower levels of MetS); however, by older adolescence, higher Superwoman schema scores were associated with worse cardiometabolic health (higher MetS). Psychologically, at older ages, a higher Superwoman schema score also was associated with experiencing greater conflict across life domains and with lower levels of perceived control. Overall these patterns suggest that a critical switch from the Superwoman schema being beneficial to being detrimental may occur some time during late adolescence. These findings suggest the importance of developing ways to cultivate and sustain the early beneficial aspects of a Superwoman schema as Black girls transition into adulthood.

Numerous dimensions of family involvement are important for chronic illness management. A recently developed and validated typology of diabetes-specific family functioning organizes these dimensions into four meaningful types (Collaborative & Helpful, Critically Involved, Satisfied with Low Involvement, and Want More Involvement). These types represent patterns of associations across dimensions of family involvement and synthesize these multiple dimensions of functioning into usable categories. The current study had two primary aims: first, to use qualitative data to enhance the quantitative understanding of types; and second, to describe qualitatively participants' own experiences with their family during a 9 month family-focused intervention (and 6 month follow-up) based on their diabetes-specific family functioning type at enrollment. Adults with type 2 diabetes (T2D) who participated in Family/Friend Activation to Motivate Self-care (FAMS), a family-focused self-care support intervention, were eligible. We recruited 77 participants across types to participate in semi-structured interviews at the completion of the follow-up. We found consistencies across types and differences between types. Regardless of type, harmful family involvement was described, but adults with T2D were hesitant to label it as such. Communication about diabetes and health increased during FAMS, but topics varied across types. Adults with T2D received more support from their families across time, though preference for emotional or instrumental support varied across types. This study qualitatively validated the typology tool paving the way for future use in intervention tailoring.

To investigate the link between HbA1c, a marker of glucose control, and positive emotional well-being (PEWB). Data were from a nationwide survey (N = 1933) which included an older, chronically ill subgroup (N = 905). Two aspects of PEWB were assessed via cross-sectional regression analyses predicting HbA1c from positive affect and life satisfaction individually, controlling for demographic variables. HbA1c was analyzed via blood-spot from a finger-prick. The mediating role of health behaviors (smoking, alcohol, BMI, and moderate exercise) were also examined. Higher positive affect and life satisfaction were significantly related to lower HbA1c in the overall and older, chronically ill samples controlling for demographics, as well as health behaviors and depression. Individuals with lower positive affect and lower life satisfaction were at increased odds of having clinically elevated HbA1c (> 6.5), indicative of diabetes, in both the overall sample (OR = 1.37; and OR = 1.13) and the chronically ill, older sample (OR = 1.59; and OR = 1.14). Two health behaviors emerged as mediators in the overall sample: BMI and exercise. These findings suggest that PEWB factors such as positive affect and life satisfaction are associated with HbA1c in both the general population and older, chronically ill individuals. Health factors such as BMI and moderate exercise mediate this relationship.

Living with a chronic health condition (CHC) can negatively impact quality of life (QoL) through a complex interaction of mental health challenges, functional limitations, and disease management demands. Acceptance and Commitment Therapy (ACT) is a transdiagnostic approach that shows promise in addressing shared challenges across various CHCs by fostering psychological flexibility (PF). PF has been associated with improved QoL and functional outcomes and reduced psychological symptoms in individuals living with specific CHCs; yet its mediating role in these outcomes remains underexplored. This secondary analysis examined whether changes in PF mediated improvements in QoL, psychological symptoms, and functional impairment among individuals with various CHCs. Participants (n = 100) were randomized to a six-session self-guided, online ACT program or a waitlist control group. Outcomes were assessed at baseline, post-treatment, and four-week follow-up. Results revealed that increases in PF significantly mediated improvements in QoL, with indirect effects indicating that higher PF at post-treatment predicted better QoL at follow-up. The ACT group demonstrated significant reductions in functional impairment at follow-up compared to the waitlist group, though this effect was not mediated by changes in PF. Improvements in psychological symptoms were not statistically significant and were not mediated by PF. These findings suggest that ACT effectively enhances PF, which subsequently improves QoL in individuals with CHCs. This supports the transdiagnostic applicability of ACT for improving mental health and QoL across diverse chronic conditions. Future research should explore additional mechanisms underlying ACT's effects and investigate ways to optimize its impact on functional and psychological outcomes within CHCs.

We used a nationally representative dataset to examine the associations between self-reported and actigraphy-measured sleep duration and a continuous metabolic syndrome (MetS) severity score in the United States population. We analyzed cross-sectional data from the national and nutrition examination survey (NHANES) 2011-2014 cycles. Our sample included adults (≥ 20 years) with complete data on sleep, sociodemographic, and MetS-related variables (N = 3245). A composite MetS severity score (MetSSS) was created using measures of waist circumference, fasting glucose, blood pressure, HDL cholesterol, and triglycerides. Actigraphy-measured sleep duration was obtained from accelerometer data collected across 7 days. Self-reported sleep duration was obtained using a one-time single question. The association between sleep duration and MetSSS was examined using linear regression models adjusting for relevant confounders. Self-reported short sleep duration (but not long), and actigraphy-measured long sleep duration (but not short) predicted greater MetSSS (b = 0.21, p < .001 and b = 0.33, p < .001, respectively), when adjusting for sociodemographic factors (age, sex, race/ethnicity, education, income, food insecurity, and health insurance status). However, after covarying for depression, sedentary behavior, sleep disturbances, and diet, only actigraphy measured long sleep duration remained significantly associated with MetS. Both short and long sleep duration may increase the severity of metabolic syndrome. However, the association between short sleep duration and metabolic syndrome may be confounded by other variables (e.g., health behaviors, sleep quality, mood). Research and clinical implications of these results are discussed.

Objective: Behavioral weight loss programs (BWL) prescribe moderate-to-vigorous physical activity (MVPA) to promote weight control and overall health. However, adherence to the MVPA prescription is low. One factor that may be associated with poor MVPA adherence, both theoretically and as demonstrated by between-subject studies, is suboptimal sleep. Nevertheless, no study to date has examined the within-subject relations between sleep and MVPA among BWL participants, which could better account for between-subject third variables that may confound the relation between sleep and MVPA (e.g., socioeconomic status). This secondary analysis is the first to investigate the within-subject, prospective relations between sleep duration (controlling for time in bed) and sleep quality (operationalized as sleep efficiency, approximately minutes asleep divided by time in bed) with next-day MVPA among BWL participants.

Method: Participants (N = 104) were adults with overweight/obesity engaging in a year-long BWL program. Sleep variables and MVPA bouts were measured using accelerometers at mid-treatment. Predictor variables were standardized, and analyses were conducted using multilevel models, controlling for weekday/weekend status, condition, gender, and body mass index (BMI).

Results: Results indicated within-subject sleep duration (b = 15.48, SE = 6.30, p =.014), but not sleep efficiency (b = 2.16, SE = 1.22, p =.08), was positively associated with next-day minutes of MVPA.

Conclusion: Findings may support modifications of BWL programs to include sleep hygiene education or strategies from cognitive behavioral therapy for insomnia to target sleep duration, which in turn may improve MVPA engagement.

Young adult melanoma survivors and their close family (first degree relatives/FDRs) are at increased risk for developing a melanoma, but little is known about engagement in and correlates of their clinical skin exam (CSE) and skin self-examination (SSE) behaviors. Five hundred and seventy-four YA survivors and their FDRs completed an online survey assessing engagement in CSE and SSE, as well as measures of background factors, cognitive and psychosocial factors, CSE and SSE planning, and family influences. Approximately 90% of YAs had a CSE and 90% performed SSE in the last year, but engagement in CSE among FDRs was lower (63.2%, CSE; 64.9%, SSE). For CSE, females, a physician recommendation, fewer barriers, and more planning were associated with CSE. Family influences were not associated with CSE. For SSE, a physician recommendation and greater self-efficacy were associated with engagement, and more comprehensive SSE was associated with a physician recommendation, lower education, greater self-efficacy, and more planning. Stronger family normative influences were associated with more comprehensive SSEs among males. Findings suggest FDRs may benefit from interventions to improve CSE and SSE and indicate that physician recommendation may be a key intervention target to foster CSE and SSE.

Latent profile analysis (LPA) is in the finite mixture model analysis family and identifies subgroups by participants' responses to continuous variables (i.e., indicators); participants' probable membership in each subgroup is based on the similarity between the subgroup's prototypical responses and the person's unique responses. Compared to latent class analysis (LCA) with categorical data, LPA is a better fit for many variables and theories in behavioral medicine, because LPA can have continuous item, sub-scale, or scale scores as indicators, which can enable identifying and examining subgroups defined by responses representing complex, multidimensional concepts (e.g., orientations, motivations, well-being, ill-being, physical activity engagement) and biomarkers of diseases and disorders. Recently, the use of LPA has increased and as it continues to evolve, it is important researchers know best practice recommendations and explanations for both conducting as well as reading/reviewing LPA models. With this paper we: 1) discuss the strengths and weaknesses of LPA and the questions it is most appropriate to answer, 2) introduce LPA conceptually, 3) illustrate an LPA conducted with exercise psychology variables following current best practice recommendations, and 4) juxtapose resulting models from the LPA approach to a typical approach with the same data. We also share the data and syntax files used to conduct the basic steps of the LPA analyses as supplemental appendix files in addition to including the tables and figures for reporting LPA results following best practices.

Estimating statistical power is essential for designing behavioral medicine studies efficiently and conserving finite resources. Sometimes behavioral medicine researchers are interested in calculating power for 1-sided z-tests of individual parameters (e.g., slopes) in complex models such as multilevel structural equation models or multilevel mixture regression models. For such models, calculating power for 1-sided z-tests is cumbersome because: (a) online z-test power calculator tools are inapplicable, (b) commonly-used power analysis software provides power only for 2-sided z-tests and does not allow changing alpha, and (c) published power tables typically provide power results only for 2-sided z-tests. Hence, here we introduce straightforward and resource-efficient conversion formulas to estimate the power of 1-sided z-tests of individual parameters in any model by using direct power conversions from the corresponding 2-sided tests. We then implement these conversion formulas in accessible R and Excel software. This brief report thus provides behavioral medicine researchers with a convenient and practical solution for power calculation that minimizes the time, financial, and computational resources typically needed for power estimation.

Randomized pretest, posttest, follow-up (RPPF) designs are widely used in longitudinal behavioral intervention research to evaluate the efficacy of treatments over time. These designs typically involve random assignment of participants to treatment and control conditions, with assessments conducted at baseline, immediately post-intervention, and during the follow-up period. Researchers primarily focus on determining whether the intervention is more effective than the control condition at post-treatment and whether these effects are sustained or change over time. This paper presents Latent Change Models (LCMs) as a practical approach for analyzing randomized pretest-posttest-follow-up (RPPF) trials, directly estimating discrete changes between timepoints and intervention-control group differences. The utility of LCMs is demonstrated through an application to the STAR (Supporting Treatment Adherence Regimens) trial, a pediatric randomized behavioral clinical trial aimed at improving adherence to anti-seizure medications (ASMs) among children with new-onset epilepsy. The results of the trial analyzed via an LCM are contrasted with the results as analyzed by an ANCOVA, a longitudinal linear mixed-effects model, and a latent growth curve model. The tutorial and application to the STAR trial demonstrate that LCMs offer notable strengths, including the ability to estimate discrete changes over time, control for baseline variability in the outcome, and incorporate all longitudinal data within a single, parsimonious model. These models provide an accurate and nuanced understanding of intervention effects in RPPF designs, with implications for clinical intervention research.