Dataset Information

Effect of Electronic Screening With Personalized Feedback on Adolescent Health Risk Behaviors in a Primary Care Setting: A Randomized Clinical Trial.

ABSTRACT: Importance:Health risk behaviors are a leading cause of morbidity during adolescence. Screening and counseling for health risk behaviors are recommended but infrequently performed. Objective:To test the effect of an electronic screening and feedback tool on clinician counseling and adolescent-reported health risk behaviors. Design, Setting, and Participants:A randomized clinical trial compared electronic screening and feedback on an intention-to-treat basis with usual care among 300 youths 13 to 18 years of age at 5 pediatric clinics in the Pacific Northwest. Outcomes were assessed via electronic survey at 1 day and 3 months after the initial visit. Study data collection occurred from March 13, 2015, to November 29, 2016, and statistical analysis was conducted between February 6, 2017, and June 20, 2018. Interventions:Youths in the intervention group (n?=?147) received electronic screening and personalized feedback with clinician clinical decision support. Youths in the control group (n?=?153) received standard screening and counseling as provided by their clinic. Main Outcomes and Measures:Youths' report of receipt of counseling during the visit and risk behaviors at 3 months. Results:In the final study sample of 300 youths (intervention group, 75 girls and 72 boys; mean [SD] age, 14.5 [1.4 years]; and control group, 80 girls and 73 boys; mean [SD] age, 14.5 [1.4] years), 234 (78.0%) were aged 13 to 15 years. After adjusting for age, sex, and random effect of clinic, youths in the intervention group were more likely to receive counseling for each of their reported risk behaviors than were youths in the control group (adjusted rate ratio, 1.32; 95% CI, 1.07-1.63). Youths in the intervention group had a significantly greater reduction (??=?-0.48; 95% CI, -0.89 to -0.02; P?=?.02) in their risk behavior scores at 3 months when compared with youths in the control group. Conclusions and Relevance:Electronic screening of health risk behavior with clinical decision support and motivational feedback to teens can improve care delivery and outcomes. Trial Registration:ClinicalTrials.gov identifier: NCT02360410.

SUBMITTER: Richardson LP

PROVIDER: S-EPMC6512281 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Effect of Electronic Screening With Personalized Feedback on Adolescent Health Risk Behaviors in a Primary Care Setting: A Randomized Clinical Trial.

Richardson Laura P LP Zhou Chuan C Gersh Elon E Spielvogle Heather H Taylor James A JA McCarty Carolyn A CA

JAMA network open 20190503 5

<h4>Importance</h4>Health risk behaviors are a leading cause of morbidity during adolescence. Screening and counseling for health risk behaviors are recommended but infrequently performed.<h4>Objective</h4>To test the effect of an electronic screening and feedback tool on clinician counseling and adolescent-reported health risk behaviors.<h4>Design, setting, and participants</h4>A randomized clinical trial compared electronic screening and feedback on an intention-to-treat basis with usual care ...[more]

PMID: 31074815

Similar Datasets

Project description:BackgroundHealth 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.ObjectiveThis 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.MethodsThis 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).ResultsOf 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: β=-.15, 95% CI -0.57 to -0.01, P=.05; 6-month group difference: β=-.12, 95% CI -0.29 to 0.52, P=.57).ConclusionsAlthough 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 registrationClinicalTrials.gov NCT02882919; https://clinicaltrials.gov/ct2/show/NCT02882919.

Project description:Electronic health screening tools for primary care present an opportunity to go beyond data collection to provide education and feedback to adolescents in order to motivate behavior change. However, there is limited research to guide feedback message development.The aim of this study was to explore youth perceptions of and preferences for receiving personalized feedback for multiple health risk behaviors and reinforcement for health promoting behaviors from an electronic health screening tool for primary care settings, using qualitative methodology.In total, 31 adolescents aged 13-18 years completed the screening tool, received the electronic feedback, and subsequently participated in individual, semistructured, qualitative interviews lasting approximately 60 min. Participants were queried about their overall impressions of the tool, perceptions regarding various types of feedback messages, and additional features that would help motivate health behavior change. Using thematic analysis, interview transcripts were coded to identify common themes expressed across participants.Overall, the tool was well-received by participants who perceived it as a way to enhance-but not replace-their interactions with providers. They appreciated receiving nonjudgmental feedback from the tool and responded positively to information regarding the consequences of behaviors, comparisons with peer norms and health guidelines, tips for behavior change, and reinforcement of healthy choices. A small but noteworthy minority of participants dismissed the peer norms as not real or relevant and national guidelines as not valid or reasonable. When prompted for possible adaptations to the tool, adolescents expressed interest in receiving follow-up information, setting health-related goals, tracking their behaviors over time, and communicating with providers electronically between appointments.Adolescents in this qualitative study desired feedback that validates their healthy behavior choices and supports them as independent decision makers by neutrally presenting health information, facilitating goal setting, and offering ongoing technological supports.

Project description:ImportanceGuidelines recommend shared decision-making prior to initiating lung cancer screening (LCS). However, evidence is lacking on how to best implement shared decision-making in clinical practice.ObjectiveTo evaluate the impact of an LCS Decision Tool (LCSDecTool) on the quality of decision-making and LCS uptake.Design, setting, and participantsThis randomized clinical trial enrolled participants at Veteran Affairs Medical Centers in Philadelphia, Pennsylvania; Milwaukee, Wisconsin; and West Haven, Connecticut, from March 18, 2019, to September 29, 2021, with follow-up through July 18, 2022. Individuals aged 55 to 80 years with a smoking history of at least 30 pack-years who were current smokers or had quit within the past 15 years were eligible to participate. Individuals with LCS within 15 months were excluded. Of 1047 individuals who were sent a recruitment letter or had referred themselves, 140 were enrolled.InterventionA web-based patient- and clinician-facing LCS decision support tool vs an attention control intervention.Main outcome and measuresThe primary outcome was decisional conflict at 1 month. Secondary outcomes included decisional conflict immediately after intervention and 3 months after intervention, knowledge, decisional regret, and anxiety immediately after intervention and 1 and 3 months after intervention and LCS by 6 months.ResultsOf 140 enrolled participants (median age, 64.0 [IQR, 61.0-69.0] years), 129 (92.1%) were men and 11 (7.9%) were women. Of 137 participants with data available, 75 (53.6%) were African American or Black and 62 (44.3%) were White; 4 participants (2.9%) also reported Hispanic or Latino ethnicity. Mean decisional conflict score at 1 month did not differ between the LCSDecTool and control groups (25.7 [95% CI, 21.4-30.1] vs 29.9 [95% CI, 25.6-34.2], respectively; P = .18). Mean LCS knowledge score was greater in the LCSDecTool group immediately after intervention (7.0 [95% CI, 6.3-7.7] vs 4.9 [95% CI, 4.3-5.5]; P < .001) and remained higher at 1 month (6.3 [95% CI, 5.7-6.8] vs 5.2 [95% CI, 4.5-5.8]; P = .03) and 3 months (6.2 [95% CI, 5.6-6.8] vs 5.1 [95% CI, 4.4-5.8]; P = .01). Uptake of LCS was greater in the LCSDecTool group at 6 months (26 of 69 [37.7%] vs 15 of 71 [21.1%]; P = .04).Conclusions and relevanceIn this randomized clinical trial of an LCSDecTool compared with attention control, no effect on decisional conflict occurred at 1 month. The LCSDecTool used in the primary care setting did not yield a significant difference in decisional conflict. The intervention led to greater knowledge and LCS uptake. These findings can inform future implementation strategies and research in LCS shared decision-making.Trial registrationClinicalTrials.gov Identifier: NCT02899754.

Project description:INTRODUCTION:Intensive behavioral counseling is effective in preventing type 2 diabetes, and insurance coverage for such interventions is increasing. Although primary care provider referrals are not required for entry to the Centers for Disease Control and Prevention (CDC)-recognized National Diabetes Prevention Program lifestyle change program, referral rates remain suboptimal. This study aims to assess the association between primary care provider behaviors regarding prediabetes screening, testing, and referral and awareness of the CDC-recognized lifestyle change program and the Prevent Diabetes STAT: Screen, Test, and Act Today™ toolkit. Awareness of the lifestyle change program and the STAT toolkit, use of electronic health records, and the ratio of lifestyle change program classes to primary care physicians were hypothesized to be positively associated with primary care provider prediabetes screening, testing, and referral behaviors. METHODS:Responses from primary care providers (n=1,256) who completed the 2016 DocStyles cross-sectional web-based survey were analyzed in 2017 to measure self-reported prediabetes screening, testing, and referral behaviors. Multivariate logistic regression was used to estimate the effects of primary care provider awareness and practice characteristics on these behaviors, controlling for provider characteristics. RESULTS:Overall, 38% of primary care providers were aware of the CDC-recognized lifestyle change program, and 19% were aware of the STAT toolkit; 27% screened patients for prediabetes using a risk test; 97% ordered recommended blood tests; and 23% made referrals. Awareness of the lifestyle change program and the STAT toolkit was positively associated with screening and referring patients. Primary care providers who used electronic health records were more likely to screen, test, and refer. Referring was more likely in areas with more lifestyle change program classes. CONCLUSIONS:This study highlights the importance of increasing primary care provider awareness of and referrals to the CDC-recognized lifestyle change program.

Project description:Audit and feedback to physicians is a commonly used quality improvement strategy, but its optimal design is unknown. This trial tested the effects of a theory-informed worksheet to facilitate goal setting and action planning, appended to feedback reports on chronic disease management, compared to feedback reports provided without these worksheets.A two-arm pragmatic cluster randomized trial was conducted, with allocation at the level of primary care clinics. Participants were family physicians who contributed data from their electronic medical records. The 'usual feedback' arm received feedback every six months for two years regarding the proportion of their patients meeting quality targets for diabetes and/or ischemic heart disease. The intervention arm received these same reports plus a worksheet designed to facilitate goal setting and action plan development in response to the feedback reports. Blood pressure (BP) and low-density lipoprotein cholesterol (LDL) values were compared after two years as the primary outcomes. Process outcomes measured the proportion of guideline-recommended actions (e.g., testing and prescribing) conducted within the appropriate timeframe. Intention-to-treat analysis was performed.Outcomes were similar across groups at baseline. Final analysis included 20 physicians from seven clinics and 1,832 patients in the intervention arm (15% loss to follow up) and 29 physicians from seven clinics and 2,223 patients in the usual feedback arm (10% loss to follow up). Ten of 20 physicians completed the worksheet at least once during the study. Mean BP was 128/72 in the feedback plus worksheet arm and 128/73 in the feedback alone arm, while LDL was 2.1 and 2.0, respectively. Thus, no significant differences were observed across groups in the primary outcomes, but mean haemoglobin A1c was lower in the feedback plus worksheet arm (7.2% versus 7.4%, p<0.001). Improvements in both arms were noted over time for one-half of the process outcomes.Appending a theory-informed goal setting and action planning worksheet to an externally produced audit and feedback intervention did not lead to improvements in patient outcomes. The results may be explained in part by passive dissemination of the worksheet leading to inadequate engagement with the intervention.ClinicalTrials.gov NCT00996645.

Project description:BACKGROUND:Audit and feedback to physicians is commonly used alone or as part of multifaceted interventions. While it can play an important role in quality improvement, the optimal design of audit and feedback is unknown. This study explores how feedback can be improved to increase acceptability and usability in primary care. The trial seeks to determine whether a theory-informed worksheet appended to feedback reports can help family physicians improve quality of care for their patients with diabetes and/or ischemic heart disease. METHODS:Two-arm cluster trial was conducted with participating primary care practices allocated using minimization to simple feedback or enhanced feedback group. The simple feedback group receives performance feedback reports every six months for two years regarding the proportion of their patients with diabetes and/or ischemic heart disease who are meeting quality targets. The enhanced feedback group receives these same reports as well as a theory-informed worksheet designed to facilitate goal setting and action plan development in response to the feedback reports. Participants are family physicians from across Ontario who use electronic medical records; data for rostered patients are used to produce the feedback reports and for analysis. OUTCOMES:The primary disease outcomes are the blood pressure (BP), and low-density lipoprotein cholesterol (LDL) levels. The primary process measure is a composite score indicating the number of recommended activities (e.g., tests and prescriptions) conducted by the family physicians for their patients with diabetes and/or ischemic heart disease within the appropriate timeframe. Secondary outcomes are the proportion of patients whose results meet targets for glucose, LDL, and BP as well as the percent of patients receiving relevant prescriptions. A qualitative process evaluation using semi-structured interviews will explore perceived barriers to behaviour change in response to feedback reports and preferences with regard to feedback design. ANALYSIS:Intention-to-treat approach will be used to analyze the trial. Analysis will be performed on patient-level variables using generalized estimating equation models to adjust for covariates and account for the clustered nature of the data. The trial is powered to show small, but clinically important differences of 7 mmHG in systolic BP and 0.32 mmol/L in LDL. TRIAL REGISTRATION:ClinicalTrials.gov NCT00996645.

Dataset Information

Effect of Electronic Screening With Personalized Feedback on Adolescent Health Risk Behaviors in a Primary Care Setting: A Randomized Clinical Trial.

Publications

Effect of Electronic Screening With Personalized Feedback on Adolescent Health Risk Behaviors in a Primary Care Setting: A Randomized Clinical Trial.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets