44 resultados para BEHAVIOR-CHANGE

em QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast


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Background: Financial incentives have been advocated by the UK and U.S. governments to encourage adoption of healthy lifestyles. However, evidence to support the use of incentives for changing physical activity (PA) behavior is sparse.
Purpose:To investigate the effectiveness of?nancial incentives to increase PA in adults in the workplace.
Design: Two-arm quasi-experimental design.
Setting/participants: Employees (n¼406) in a workplace setting in Belfast, Northern Ireland, UK.
Intervention: Using a loyalty card to collect points and earn rewards, participants (n¼199) in the Incentive Group monitored their PA levels and received ?nancial incentives (retail vouchers) for minutes of PA completed over the course of a 12-week intervention period. Participants (n¼207) in the comparison group used their loyalty card to self-monitor their PA levels but were not able to earn points or obtain incentives (No Incentive Group).
Main outcome measures:The primary outcome was minutes of PA objectively measured using a novel PA tracking system at baseline (April 2011); Week 6 (June 2011); and Week 12 (July 2011).
Other outcomes, including a self-report measure of PA, were collected at baseline, Week 12, and 6 months (October 2011). Data were analyzed in June 2012.
Results: No signi?cant differences between groups were found for primary or secondary outcomes at the 12-week and 6-month assessments. Participants in the Incentive Group recorded 17.52 minutes of PA/week (95% CI¼12.49, 22.56) compared to 16.63 minutes/week (95% CI¼11.76, 21.51) in the No Incentive Group at Week 12 (p¼0.59). At 6 months, participants in the Incentive Group recorded 26.18 minutes of PA/week (95% CI¼20.06, 32.29) compared to 24.00 minutes/week (95% CI¼17.45, 30.54) in the No Incentive Group (p¼0.45).
Conclusions: Financial incentives did not encourage participants to undertake more PA than selfmonitoring PA. This study contributes to the evidence base and has important implications for increasing participation in physical activity and fostering links with the business sector. (Am J Prev Med 2013;45(1):56–63) © 2013 American Journal of Preventive Medicine

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We investigated whether “hidden” (or unobserved) social networks were evident in a 2011 physical activity behavior change intervention in Belfast, Northern Ireland. Results showed evidence of unobserved social networks in the intervention and illustrated how the network evolved over short periods and affected behavior. Behavior change interventions should account for the interaction among participants (i.e., social networks) and how such interactions affect intervention outcome.

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BACKGROUND: Web-based programs are a potential medium for supporting weight loss because of their accessibility and wide reach. Research is warranted to determine the shorter- and longer-term effects of these programs in relation to weight loss and other health outcomes.

OBJECTIVE: The aim was to evaluate the effects of a Web-based component of a weight loss service (Imperative Health) in an overweight/obese population at risk of cardiovascular disease (CVD) using a randomized controlled design and a true control group.

METHODS: A total of 65 overweight/obese adults at high risk of CVD were randomly allocated to 1 of 2 groups. Group 1 (n=32) was provided with the Web-based program, which supported positive dietary and physical activity changes and assisted in managing weight. Group 2 continued with their usual self-care (n=33). Assessments were conducted face-to-face. The primary outcome was between-group change in weight at 3 months. Secondary outcomes included between-group change in anthropometric measurements, blood pressure, lipid measurements, physical activity, and energy intake at 3, 6, and 12 months. Interviews were conducted to explore participants' views of the Web-based program.

RESULTS: Retention rates for the intervention and control groups at 3 months were 78% (25/32) vs 97% (32/33), at 6 months were 66% (21/32) vs 94% (31/33), and at 12 months were 53% (17/32) vs 88% (29/33). Intention-to-treat analysis, using baseline observation carried forward imputation method, revealed that the intervention group lost more weight relative to the control group at 3 months (mean -3.41, 95% CI -4.70 to -2.13 kg vs mean -0.52, 95% CI -1.55 to 0.52 kg, P<.001), at 6 months (mean -3.47, 95% CI -4.95 to -1.98 kg vs mean -0.81, 95% CI -2.23 to 0.61 kg, P=.02), but not at 12 months (mean -2.38, 95% CI -3.48 to -0.97 kg vs mean -1.80, 95% CI -3.15 to -0.44 kg, P=.77). More intervention group participants lost ≥5% of their baseline body weight at 3 months (34%, 11/32 vs 3%, 1/33, P<.001) and 6 months (41%, 13/32 vs 18%, 6/33, P=.047), but not at 12 months (22%, 7/32 vs 21%, 7/33, P=.95) versus control group. The intervention group showed improvements in total cholesterol, triglycerides, and adopted more positive dietary and physical activity behaviors for up to 3 months verus control; however, these improvements were not sustained.

CONCLUSIONS: Although the intervention group had high attrition levels, this study provides evidence that this Web-based program can be used to initiate clinically relevant weight loss and lower CVD risk up to 3-6 months based on the proportion of intervention group participants losing ≥5% of their body weight versus control group. It also highlights a need for augmenting Web-based programs with further interventions, such as in-person support to enhance engagement and maintain these changes.


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Background: We sought to describe the theory used to design treatment adherence interventions, the content delivered, and the mode of delivery of these interventions in chronic respiratory disease. Methods: We included randomized controlled trials of adherence interventions (compared to another intervention or control) in adults with chronic respiratory disease (8 databases searched; inception until March 2015). Two reviewers screened and extracted data: post-intervention adherence (measured objectively); behavior change theory, content (grouped into psychological, education and self-management/supportive, telemonitoring, shared decision-making); and delivery. “Effective” studies were those with p < 0.05 for adherence rate between groups. We conducted a narrative synthesis and assessed risk of bias. Results: 12,488 articles screened; 46 included studies (n = 42,91% in OSA or asthma) testing 58 interventions (n = 27, 47% were effective). Nineteen (33%) interventions (15 studies) used 12 different behavior change theories. Use of theory (n = 11,41%) was more common amongst effective interventions. Interventions were mainly educational, self-management or supportive interventions (n = 27,47%). They were commonly delivered by a doctor (n = 20,23%), in face-to-face (n = 48,70%), one-to-one (n = 45,78%) outpatient settings (n = 46,79%) across 2–5 sessions (n = 26,45%) for 1–3 months (n = 26,45%). Doctors delivered a lower proportion (n = 7,18% vs n = 13,28%) and pharmacists (n = 6,15% vs n = 1,2%) a higher proportion of effective than ineffective interventions. Risk of bias was high in >1 domain (n = 43, 93%) in most studies. Conclusions: Behavior change theory was more commonly used to design effective interventions. Few adherence interventions have been developed using theory, representing a gap between intervention design recommendations and research practice.

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There is a growing emphasis on behavior change in intervention development programmes aimed at improving public health and healthcare professionals' practice. A number of frameworks and methodological tools have been established to assist researchers in developing interventions seeking to change healthcare professionals' behaviors. The key features of behavior change intervention design involve specifying the target group (i.e. healthcare professional or patient cohort), the target behavior and identifying mediators (i.e. barriers and facilitators) of behavior change. Once the target behavior is clearly specified and understood, specific behavior change techniques can then be used as the basis of the intervention to target identified mediators of behavior change. This commentary outlines the challenges for pharmacy practice-based researchers in targeting dispensing as a behavior when developing behavior change interventions aimed at pharmacists and proposes a definition of dispensing to consider in future research.

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BACKGROUND: The transtheoretical model has been successful in promoting health behavior change in general and clinical populations. However, there is little knowledge about the application of the transtheoretical model to explain physical activity behavior in individuals with non-cystic fibrosis bronchiectasis. The aim was to examine patterns of (1) physical activity and (2) mediators of behavior change (self-efficacy, decisional balance, and processes of change) across stages of change in individuals with non-cystic fibrosis bronchiectasis.

METHODS: Fifty-five subjects with non-cystic fibrosis bronchiectasis (mean age ± SD = 63 ± 10 y) had physical activity assessed over 7 d using an accelerometer. Each component of the transtheoretical model was assessed using validated questionnaires. Subjects were divided into groups depending on stage of change: Group 1 (pre-contemplation and contemplation; n = 10), Group 2 (preparation; n = 20), and Group 3 (action and maintenance; n = 25). Statistical analyses included one-way analysis of variance and Tukey-Kramer post hoc tests.

RESULTS: Physical activity variables were significantly (P < .05) higher in Group 3 (action and maintenance) compared with Group 2 (preparation) and Group 1 (pre-contemplation and contemplation). For self-efficacy, there were no significant differences between groups for mean scores (P = .14). Decisional balance cons (barriers to being physically active) were significantly lower in Group 3 versus Group 2 (P = .032). For processes of change, substituting alternatives (substituting inactive options for active options) was significantly higher in Group 3 versus Group 1 (P = .01), and enlisting social support (seeking out social support to increase and maintain physical activity) was significantly lower in Group 3 versus Group 2 (P = .038).

CONCLUSIONS: The pattern of physical activity across stages of change is consistent with the theoretical predictions of the transtheoretical model. Constructs of the transtheoretical model that appear to be important at different stages of change include decisional balance cons, substituting alternatives, and enlisting social support. This study provides support to explore transtheoretical model-based physical activity interventions in individuals with non-cystic fibrosis bronchiectasis.

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Objective: To apply the UK Medical Research Council (MRC) framework for development and evaluation of trials of complex interventions to a primary healthcare intervention to promote secondary prevention of coronary heart disease. Study Design: Case report of intervention development. Methods: First, literature relating to secondary prevention and lifestyle change was reviewed. Second, a preliminary intervention was modeled, based on literature findings and focus group interviews with patients (n = 23) and staff (n = 29) from 4 general practices. Participants’ experiences of and attitudes toward key intervention components were explored. Third, the preliminary intervention was pilot-tested in 4 general practices. After delivery of the pilot intervention, practitioners evaluated the training sessions, and qualitative data relating to experiences of the intervention were collected using semistructured interviews with staff (n = 10) and patient focus groups (n = 17). Results: Literature review identified 3 intervention components: a structured recall system, practitioner training, and patient information. Initial qualitative data identified variations in recall system design, training requirements (medication prescribing, facilitating behavior change), and information appropriate to the prospective study participants. Identifying detailed structures within intervention components clarified how the intervention could be tailored to individual practice, practitioner, and patient needs while preserving the theoretical functions of the components. Findings from the pilot phase informed further modeling of the intervention, reducing administrative time, increasing practical content of training, and omitting unhelpful patient information. Conclusion: Application of the MRC framework helped to determine the feasibility and development of a complex intervention for primary care research.

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Aim. This paper is a report of a study to describe how treatment fidelity is being enhanced and monitored, using a model from the National Institutes of Health Behavior Change Consortium. Background. The objective of treatment fidelity is to minimize errors in interpreting research trial outcomes, and to ascribe those outcomes directly to the intervention at hand. Treatment fidelity procedures are included in trials of complex interventions to account for inferences made from study outcomes. Monitoring treatment fidelity can help improve study design, maximize reliability of results, increase statistical power, determine whether theory-based interventions are responsible for observed changes, and inform the research dissemination process. Methods. Treatment fidelity recommendations from the Behavior Change Consortium were applied to the SPHERE study (Secondary Prevention of Heart DiseasE in GeneRal PracticE), a randomized controlled trial of a complex intervention. Procedures to enhance and monitor intervention implementation included standardizing training sessions, observing intervention consultations, structuring patient recall systems, and using written practice and patient care plans. The research nurse plays an important role in monitoring intervention implementation. Findings. Several methods of applying treatment fidelity procedures to monitoring interventions are possible. The procedure used may be determined by availability of appropriate personnel, fiscal constraints, or time limits. Complex interventions are not straightforward and necessitate a monitoring process at trial stage. Conclusion. The Behavior Change Consortium’s model of treatment fidelity is useful for structuring a system to monitor the implementation of a complex intervention, and helps to increase the reliability and validity of evaluation findings.

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Objective: The purpose of this study was to show the association between changes in clinician self-efficacy and readiness to change and implementation of an asthma management program (Easy Breathing). Methods: A 36 month randomized, controlled trial was conducted involving 24 pediatric practices (88 clinicians). Randomized clinicians received interventions designed to enhance clinician self-efficacy and readiness to change which were measured at baseline and 3 years. Interventions consisted of an educational toolbox, seminars, teleconferences, mini-fellowships, opinion leader visits, clinician-specific feedback, and pay for performance. The primary outcome was program utilization (number of children enrolled in Easy Breathing/year); secondary outcomes included development of a written treatment plan and severity-appropriate therapy. Results: At baseline, clinicians enrolled 149 ± 147 (mean ± SD) children/clinician/year; 84% of children had a written treatment plan and 77% of plans used severity-appropriate therapy. At baseline, higher self-efficacy scores were associated with greater program utilization (relative rate [RR], 1.34; 95% confidence interval [CI], 1.04-1.72; P =.04) but not treatment plan development (RR, 0.63; 95% CI, 0.29-1.35; P =.23) or anti-inflammatory use (RR, 1.76; 95% CI, 0.92-3.35; P =.09). Intervention clinicians participated in 17 interventions over 36 months. At study end, self-efficacy scores increased in intervention clinicians compared to control clinicians (P =.01) and more clinicians were in an action stage of change (P =.001) but these changes were not associated with changes in primary or secondary outcomes. Conclusions: Self-efficacy scores correlated with program use at baseline and increased in the intervention arm, but these increases were not associated with greater program-related activities. Self-efficacy may be necessary but not sufficient for behavior change. Copyright © 2012 by Academic Pediatric Association.

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Social and psychological interventions are often complex. Understanding randomized controlled trials (RCTs) of these complex interventions requires a detailed description of the interventions tested and the methods used to evaluate them; however, RCT reports often omit, or inadequately report, this information. Incomplete and inaccurate reporting hinders the optimal use of research, wastes resources, and fails to meet ethical obligations to research participants and consumers. In this article, we explain how reporting guidelines have improved the quality of reports in medicine and describe the ongoing development of a new reporting guideline for RCTs: Consolidated Standards of Reporting Trials-SPI (an extension for social and psychological interventions). We invite readers to participate in the project by visiting our website, in order to help us reach the best-informed consensus on these guidelines (http://tinyurl.com/CONSORT-study).

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Evidence is mounting on the association between the built environment and physical activity (PA) with a call for intervention research. A broader approach which recognizes the role of supportive environments that can make healthy choices easier is required. A systematic review was undertaken to assess the effectiveness of interventions to encourage PA in urban green space. Five databases were searched independently by two reviewers using search terms relating to 'physical activity', 'urban green space' and 'intervention' in July 2014. Eligibility criteria included: (i) intervention to encourage PA in urban green space which involved either a physical change to the urban green space or a PA intervention to promote use of urban green space or a combination of both; and (ii) primary outcome of PA. Of the 2405 studies identified, 12 were included. There was some evidence (4/9 studies showed positive effect) to support built environment only interventions for encouraging use and increasing PA in urban green space. There was more promising evidence (3/3 studies showed positive effect) to support PAprograms or PA programs combined with a physical change to the built environment, for increasing urban green space use and PAof users. Recommendations for future research include the need for longer term follow-up post-intervention, adequate control groups, sufficiently powered studies, and consideration of the social environment, which was identified as a significantly under-utilized resource in this area. Interventions that involve the use of PA programs combined with a physical change to the built environment are likely to have a positive effect on PA. Robust evaluations of such interventions are urgently required. The findings provide a platform to inform the design, implementation and evaluation of future urban green space and PAintervention research.

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This research explored the influence of children’s perceptions of a pro-social behavior after-school program on actual change in the children’s behavioral outcomes over the program’s duration. Children’s perceptions of three program processes were collected as well as self-reported pro-social and anti-social behavior before and after the program. Statistical models showed that: Positive perceptions of the program facilitators’ dispositions significantly predicted reductions in anti-social behavior; and positive perceptions with the program activities significantly predicted gains in pro-social behavior. The children’s perceptions of their peers’ behavior in the sessions were not found to a significant predictor of behavioral change. The two significant perceptual indicators predicted a small percentage of the change in the behavioral outcomes. However, as after-school social learning programs have a research history of problematic implementation children’s perceptions should be considered in future program design, evaluation and monitoring.

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The paper Bereavement: A behavioural process (Dillenburger & Keenan, 1994) was first published within a vacuum of behaviour analytic thinking or research in this field. The paper was meant to be a first step in stimulating others to contribute to the understanding of one of the most complex, yet most universal, human behavioural processes. The only behaviour analyst addressing the issues directly was Calkin (1990). Recently, after reading our original 1994 paper, Beth Sulzer-Azaroff suggested that we should solicit comments directly from the behaviour analytic community. This we did with the help of Erik Arntzen and now the reprint and the commentaries in this edition of the European Journal of Behaviour Analysis (EJBA) fully embrace and extend the contribution of behaviour analysis to the understanding of the behavioural process that is bereavement.