Health Psychology and Behavioral Medicine最新文献

Background: Adolescents' risk-taking behaviors can have profound impacts on their future health. Few studies have established a relationship between multiple social environmental factors and adolescent risk behaviors. We used structural equation modeling to examine the role of parental monitoring and environmental risks on adolescents' behavioral intentions and risk behaviors.

Methods: Data were collected through the baseline survey of a national implementation project among 2205 Grade 6 students in 24 government schools in The Bahamas in 2019. Structural equation modeling examined relations among parental monitoring, environmental risk factors, behavioral intentions, and risk behaviors.

Results: Students had engaged in various delinquent, substance use, and sexual risks. In the structural equation model, parental monitoring demonstrated direct negative (protective) effects on behavioral intentions and risk behaviors, whereas environmental risk factors had a direct positive effect on adolescent behavioral intentions and risk behaviors. The model had an R² value of 0.57 for adolescent risk behaviors.

Conclusion: Parental monitoring and environmental risk factors had strong influences on risk-taking behaviors of early adolescents. Future adolescent health behavior interventions should consider offering additional prevention resources to early adolescents who are exposed to multiple environmental risk factors.

Background: No study has investigated the cancer-related fatigue (CRF) among Palestinian breast cancer survivors. Our purpose is to assess, compare, and correlate CRF in breast cancer survivors undergoing radiotherapy (RT) with study variables.

Methods: CRF in breast cancer survivors was assessed using Functional Assessment of Chronic Illness Therapy - Fatigue Scale (FACIT-F) (version 4). The sample consisted of 148 breast cancer survivors undergoing RT. Data was collected between 1 May 2021 and 1 September 2021. The means and standard deviations of the questionnaire using one-way ANOVA, and Pearson correlation coefficient were reported.

Results: Respondents ages ranged from 20 to >65 years old and was divided into four groups: (20-35, 36-50, 51-65, and >65 years, respectively). The total fatigue mean was 2.88 and the SD was 0.84, indicating an intermediate fatigue level among breast cancer survivors. Study survivors with higher education were more likely to be fatigued (F =7.68, P-value =0.001). Divorced survivors were more prone to fatigue compared to married survivors (F =5.83, P-value= 0.001). Finally, survivors who do not have children were more vulnerable to exhaustion compared to those with children (F =7.35, P-value =0.001). Also, younger survivors were more prone to fatigue, compared to older survivors (F =5.29, P-value = 0.002). Results also showed a positive relationship between each of the variables; the number of children (R =0.221, P-value =0.007), age (R =0.311, P-value =0.000), and duration of treatment (R =0.290, P-value =0.000), which means that the greater the number of children, the younger the age, or the longer the duration of treatment, the more fatigue is reported in breast cancer survivors.

Conclusions: Fatigue is frequently observed in breast cancer survivors undergoing RT. It can limit RT treatment continuity. Therefore, early detection of fatigue can help survivors adhere to RT treatment and achieve better clinical results.

Background: Many adults with type 1 (T1D) and type 2 diabetes (T2D) have inadequate sleep increasing their risk of hyperglycemia and developing complications. The objective was to identify psychosocial determinants of healthy sleep habits (HSH) among adults with T1D and T2D.

Methods: The two HSH were: avoiding screen use in bed and having sleep regularity. Adults (≥18 years) with T1D and T2D were invited to complete an anonymous online survey. The questionnaires were based on the Reasoned Action Approach and formative qualitative research previously conducted in 56 adults with T1D and T2D. Habit was included as an additional variable for screen use in bed.

Results: In total, 320 adults with diabetes (T1D: 39%; T2D: 61%) completed the questionnaires (screen use in bed: 174; sleep timing: 146). Close to 75% of participants reported screen use in bed and close to 90% reported sleep timing variability in the last month. Perceived behavioral control (PBC) to avoid screen use in bed (β = -0.4486, p < 0.0001), habit of using screens in bed (β = 0.4002; p < 0.0001), and age (β = -0.0202; p = 0.0086) were determinants of screen use in bed, and this model explained 71% of the variance. PBC for sleep regularity (β = -0.2909; p = 0.0004) and being female (β = 0.5057; p = 0.0069) were determinants of sleep timing variability, and this model explained 28% of the variance. The most important beliefs associated with each HSH were identified to obtain information to design targeted interventions.

Conclusions: Few adults with diabetes have HSH. Screen use in bed was strongly influenced by habit and the results suggest that both HSH are not easy to adopt among adults with diabetes. Younger adults with diabetes should be prioritized for screen use in bed, while females with diabetes should be prioritized for sleep timing variability. Adults with diabetes should have access to behavior change interventions to encourage them to adopt HSH.