Electronic Health Risk Behavior Screening With Integrated Feedback Among Adolescents in Primary Care: Randomized Controlled Trial.

BACKGROUND: Health risk behaviors are the most common sources of morbidity among adolescents. Adolescent health guidelines (Guidelines for Preventive Services by the AMA and Bright Futures by the Maternal Child Health Bureau) recommend screening and counseling, but the implementation is inconsistent. OBJECTIVE: This study aims to test the efficacy of electronic risk behavior screening with integrated patient-facing feedback on the delivery of adolescent-reported clinician counseling and risk behaviors over time. METHODS: This was a randomized controlled trial comparing an electronic tool to usual care in five pediatric clinics in the Pacific Northwest. A total of 300 participants aged 13-18 years who attended a well-care visit between September 30, 2016, and January 12, 2018, were included. Adolescents were randomized after consent by employing a 1:1 balanced age, sex, and clinic stratified schema with 150 adolescents in the intervention group and 150 in the control group. Intervention adolescents received electronic screening with integrated feedback, and the clinicians received a summary report of the results. Control adolescents received usual care. Outcomes, assessed via online survey methods, included adolescent-reported receipt of counseling during the visit (measured a day after the visit) and health risk behavior change (measured at 3 and 6 months after the visit). RESULTS: Of the original 300 participants, 94% (n=282), 94.3% (n=283), and 94.6% (n=284) completed follow-up surveys at 1 day, 3 months, and 6 months, respectively, with similar levels of attrition across study arms. The mean risk behavior score at baseline was 2.86 (SD 2.33) for intervention adolescents and 3.10 (SD 2.52) for control adolescents (score potential range 0-21). After adjusting for age, gender, and random effect of the clinic, intervention adolescents were 36% more likely to report having received counseling for endorsed risk behaviors than control adolescents (adjusted rate ratio 1.36, 95% CI 1.04 to 1.78) 1 day after the well-care visit. Both the intervention and control groups reported decreased risk behaviors at the 3- and 6-month follow-up assessments, with no significant group differences in risk behavior scores at either time point (3-month group difference: ß=-.15, 95% CI -0.57 to -0.01, P=.05; 6-month group difference: ß=-.12, 95% CI -0.29 to 0.52, P=.57). CONCLUSIONS: Although electronic health screening with integrated feedback improves the delivery of counseling by clinicians, the impact on risk behaviors is modest and, in this study, not significantly different from usual care. More research is needed to identify effective strategies to reduce risk in the context of well-care. TRIAL REGISTRATION: ClinicalTrials.gov NCT02882919; https://clinicaltrials.gov/ct2/show/NCT02882919.

The Biobehavioral Family Model: Close relationships and allostatic load.

RATIONALE: This study tested the inclusion of allostatic load as an expansion of the biobehavioral reactivity measurement in the Biobehavioral Family Model (BBFM). The BBFM is a biopsychosocial approach to health which proposes biobehavioral reactivity (anxiety and depression) mediates the relationship between family emotional climate and disease activity. METHODS: Data for this study included a subsample of n = 1255 single and married, English-speaking adult participants (57% female, M age = 56 years) from the National Survey of Midlife Development in the United States (MIDUS II), a nationally representative epidemiological study of health and aging in the United States. Participants completed self-reported measures of family and marital functioning, anxiety and depression (biobehavioral reactivity), number of chronic health conditions, number of prescribed medications, and a biological protocol in which the following indices were obtained: cardiovascular functioning, sympathetic and parasympathetic nervous system activity, hypothalamic pituitary adrenal axis activity, inflammation, lipid/fat metabolism, and glucose metabolism. RESULTS: Structural equation modeling indicated good fit of the data to the hypothesized family model (χ (2) = 125.13 p = .00, SRMR = .03, CFI = .96, TLI = .94, RMSEA = .04) and hypothesized couple model (χ(2) = 132.67, p = .00, SRMR = .04, CFI = .95, TLI = .93, RMSEA = .04). Negative family interactions predicted biobehavioral reactivity for anxiety and depression and allostatic load; however couple interactions predicted only depression and anxiety measures of biobehavioral reactivity. CONCLUSION: Findings suggest the importance of incorporating physiological data in measuring biobehavioral reactivity as a predicting factor in the overall BBFM model.