Walking works wonders: a workplace health intervention evaluated over 24 months

Walking Works Wonders (WWW) is a workplace intervention designed to increase physical activity and reduce sedentary behaviour. WWW involves tailoring health information according to employees’ readiness for change. The approach recognises that when attempting to motivate behaviour change, success is greater when interventions align with recipients’ attitudes and beliefs. The impact of the tailored approach was investigated by comparing tailored interventions with standard conditions and control groups in a 24-month longitudinal study in 10 worksites across the UK. Employees who received either a standard or tailored intervention demonstrated significantly higher self-reported work ability and improved organizational commitment, job motivation, job satisfaction, and a reduction in intention to quit the organization. The tailored interventions significantly reduced BMI and waist circumference compared to standard and control conditions. Workplace health interventions designed to increase physical activity and reduce sedentary behaviour are likely to be more effective where the information is tailored to employees’ readiness to change

A randomised controlled trial and cost-effectiveness evaluation of "booster" interventions to sustain increases in physical activity in middle-aged adults in deprived urban neighbourhoods

Background: Systematic reviews have identified a range of brief interventions which increase physical activity in previously sedentary people. There is an absence of evidence about whether follow up beyond three months can maintain long term physical activity. This study assesses whether it is worth providing motivational interviews, three months after giving initial advice, to those who have become more active. Methods/Design: Study candidates (n = 1500) will initially be given an interactive DVD and receive two telephone follow ups at monthly intervals checking on receipt and use of the DVD. Only those that have increased their physical activity after three months (n = 600) will be randomised into the study. These participants will receive either a "mini booster" (n = 200), "full booster" (n = 200) or no booster (n = 200). The "mini booster" consists of two telephone calls one month apart to discuss physical activity and maintenance strategies. The "full booster" consists of a face-to-face meeting with the facilitator at the same intervals. The purpose of these booster sessions is to help the individual maintain their increase in physical activity. Differences in physical activity, quality of life and costs associated with the booster interventions, will be measured three and nine months from randomisation. The research will be conducted in 20 of the most deprived neighbourhoods in Sheffield, which have large, ethnically diverse populations, high levels of economic deprivation, low levels of physical activity, poorer health and shorter life expectancy. Participants will be recruited through general practices and community groups, as well as by postal invitation, to ensure the participation of minority ethnic groups and those with lower levels of literacy. Sheffield City Council and Primary Care Trust fund a range of facilities and activities to promote physical activity and variations in access to these between neighbourhoods will make it possible to examine whether the effectiveness of the intervention is modified by access to community facilities. A one-year integrated feasibility study will confirm that recruitment targets are achievable based on a 10% sample.Discussion: The choice of study population, study interventions, brief intervention preceding the study, and outcome measure are discussed

Cognitive and behavioral predictors of light therapy use

Objective: Although light therapy is effective in the treatment of seasonal affective disorder (SAD) and other mood disorders, only 53-79% of individuals with SAD meet remission criteria after light therapy. Perhaps more importantly, only 12-41% of individuals with SAD continue to use the treatment even after a previous winter of successful treatment. Method: Participants completed surveys regarding (1) social, cognitive, and behavioral variables used to evaluate treatment adherence for other health-related issues, expectations and credibility of light therapy, (2) a depression symptoms scale, and (3) self-reported light therapy use. Results: Individuals age 18 or older responded (n = 40), all reporting having been diagnosed with a mood disorder for which light therapy is indicated. Social support and self-efficacy scores were predictive of light therapy use (p's<.05). Conclusion: The findings suggest that testing social support and self-efficacy in a diagnosed patient population may identify factors related to the decision to use light therapy. Treatments that impact social support and self-efficacy may improve treatment response to light therapy in SAD. © 2012 Roecklein et al

Randomized Controlled Trial of a Computer-Based, Tailored Intervention to Increase Smoking Cessation Counseling by Primary Care Physicians

OBJECTIVE: The primary care visit represents an important venue for intervening with a large population of smokers. However, physician adherence to the Smoking Cessation Clinical Guideline (5As) remains low. We evaluated the effectiveness of a computer-tailored intervention designed to increase smoking cessation counseling by primary care physicians. METHODS: Physicians and their patients were randomized to either intervention or control conditions. In addition to brief smoking cessation training, intervention physicians and patients received a one-page report that characterized the patients’ smoking habit and history and offered tailored recommendations. Physician performance of the 5As was assessed via patient exit interviews. Quit rates and smoking behaviors were assessed 6 months postintervention via patient phone interviews. Intervention effects were tested in a sample of 70 physicians and 518 of their patients. Results were analyzed via generalized and mixed linear modeling controlling for clustering. MEASUREMENTS AND MAIN RESULTS: Intervention physicians exceeded controls on “Assess” (OR 5.06; 95% CI 3.22, 7.95), “Advise” (OR 2.79; 95% CI 1.70, 4.59), “Assist–set goals” (OR 4.31; 95% CI 2.59, 7.16), “Assist–provide written materials” (OR 5.14; 95% CI 2.60, 10.14), “Assist–provide referral” (OR 6.48; 95% CI 3.11, 13.49), “Assist–discuss medication” (OR 4.72;95% CI 2.90, 7.68), and “Arrange” (OR 8.14; 95% CI 3.98, 16.68), all p values being < 0.0001. Intervention patients were 1.77 (CI 0.94, 3.34,p = 0.078) times more likely than controls to be abstinent (12 versus 8%), a difference that approached, but did not reach statistical significance, and surpassed controls on number of days quit (18.4 versus 12.2, p < .05) but not on number of quit attempts. CONCLUSIONS: The use of a brief computer-tailored report improved physicians’ implementation of the 5As and had a modest effect on patients’ smoking behaviors 6 months postintervention

Effects of a Tailored Follow-Up Intervention on Health Behaviors, Beliefs, and Attitudes

Background: The high rates of relapse that tend to occur after short-term behavioral interventions indicate the need for maintenance programs that promote long-term adherence to new behavior patterns. Computer-tailored health messages that are mailed to participants or given in brief telephone calls offer an innovative and time-efficient alternative to ongoing face-to-face contact with healthcare providers. Methods: Following a 1-year behavior change program, 22 North Carolina health departments were randomly assigned to a follow-up intervention or control condition. Data were collected from 1999 to 2001 by telephone-administered surveys at preintervention and postintervention for 511 low-income, midlife adult women enrolled in the Well-Integrated Screening and Evaluation for Women Across the Nation (WISEWOMAN) program at local North Carolina health departments. During the year after the behavior change program, intervention participants were mailed six sets of computer-tailored health messages and received two computer-tailored telephone counseling sessions. Main outcomes of dietary and physical activity behaviors, beliefs, and attitudes were measured. Results: Intervention participants were more likely to move forward into more advanced stages of physical activity change (p = 0.02); control participants were more likely to increase their level of dietary social support at follow-up (p = 0.05). Both groups maintained low levels of reported saturated fat and cholesterol intake at follow-up. No changes were seen in physical activity in either group. Conclusions: Mailed computer-tailored health messages and telephone counseling calls favorably modified forward physical activity stage movement but did not appreciably affect any other psychosocial or behavioral outcomes

Behavioral determinants as predictors of return to work after long-term sickness absence: an application of the theory of planned behavior

Background The aim of this prospective, longitudinal cohort study was to analyze the association between the three behavioral determinants of the theory of planned behavior (TPB) model-attitude, subjective norm and self-efficacy-and the time to return-to-work (RTW) in employees on long-term sick leave. Methods The study was based on a sample of 926 employees on sickness absence (maximum duration of 12 weeks). The employees filled out a baseline questionnaire and were subsequently followed until the tenth month after listing sick. The TPB-determinants were measured at baseline. Work attitude was measured with a Dutch language version of the Work Involvement Scale. Subjective norm was measured with a self-structured scale reflecting a person's perception of social support and social pressure. Self-efficacy was measured with the three subscales of a standardised Dutch version of the general self-efficacy scale (ALCOS): willingness to expend effort in completing the behavior, persistence in the face of adversity, and willingness to initiate behavior. Cox proportional hazards regression analyses were used to identify behavioral determinants of the time to RTW. Results Median time to RTW was 160 days. In the univariate analysis, all potential prognostic factors were significantly associated (P < 0.15) with time to RTW: work attitude, social support, and the three subscales of self-efficacy. The final multivariate model with time to RTW as the predicted outcome included work attitude, social support and willingness to expend effort in completing the behavior as significant predictive factors. Conclusions This prospective, longitudinal cohort-study showed that work attitude, social support and willingness to expend effort in completing the behavior are significantly associated with a shorter time to RTW in employees on long-term sickness absence. This provides suggestive evidence for the relevance of behavioral characteristics in the prediction of duration of sickness absence. It may be a promising approach to address the behavioral determinants in the development of interventions focusing on RTW in employees on long-term sick leave

Mobile phone apps for behavioral interventions for at-risk drinkers in Australia: literature Review

Background: The mobile technology era has ushered in the use of mobile phone apps for behavioral intervention for at-risk drinkers. Objective: Our objective was to review recent research relevant to mobile phone apps that can be used for behavioral intervention for at-risk drinkers in Australia. Methods: The inclusion criteria for this review were articles published in peer-reviewed journals from 2001 to 2017 with use of the search terms "smartphone application," "alcohol," "substance," "behavioural intervention," "electronic health," and "mobile health." Results: In total, we identified 103 abstracts, screened 90 articles, and assessed 50 full-text articles that fit the inclusion criteria for eligibility. We included 19 articles in this review. Conclusions: This review highlighted the paucity of evidence-based and empirically validated research into effective mobile phone apps that can be used for behavioral interventions with at-risk drinkers in Australia