We describe the participation of the University of Amsterdams ILPS group in the Total Recall track at TREC 2015. Based on the provided Baseline Model Implemention (”BMI”) we set out to provide two more baselines we can compare to in future work. The two methods are bootstrapped by a synthetic document based on the query, use TF/IDF features, and sample with dynamic batch sizes which depend on the percentage of predicted relevant documents. We sample at least 1 percent of the corpus and stop sampling if a batch contains no relevant documents. The methods differ in the classifier used, i.e. Logistic Regression and Random Forest.
MULTIFILE
BACKGROUND: The number of mobile apps that support smoking cessation is growing, indicating the potential of the mobile phone as a means to support cessation. Knowledge about the potential end users for cessation apps results in suggestions to target potential user groups in a dissemination strategy, leading to a possible increase in the satisfaction and adherence of cessation apps.OBJECTIVE: This study aimed to characterize potential end users for a specific mobile health (mHealth) smoking cessation app.METHODS: A quantitative study was conducted among 955 Dutch smokers and ex-smokers. The respondents were primarily recruited from addiction care facilities and hospitals through Web-based media via websites and forums. The respondents were surveyed on their demographics, smoking behavior, and personal innovativeness. The intention to use and the attitude toward a cessation app were determined on a 5-point Likert scale. To study the association between the characteristics and intention to use and attitude, univariate and multivariate ordinal logistic regression analyses were performed.RESULTS: The multivariate ordinal logistic regression showed that the number of previous quit attempts (odds ratio [OR] 4.1, 95% CI 2.4-7.0, and OR 3.5, 95% CI 2.0-5.9) and the score on the Fagerstrom Test of Nicotine Dependence (OR 0.8, 95% CI 0.8-0.9, and OR 0.8, 95% CI 0.8-0.9) positively correlates with the intention to use a cessation app and the attitude toward cessation apps, respectively. Personal innovativeness also positively correlates with the intention to use (OR 0.3, 95% CI 0.2-0.4) and the attitude towards (OR 0.2, 95% CI 0.1-0.4) a cessation app. No associations between demographics and the intention to use or the attitude toward using a cessation app were observed.CONCLUSIONS: This study is among the first to show that demographic characteristics such as age and level of education are not associated with the intention to use and the attitude toward using a cessation app when characteristics related specifically to the app, such as nicotine dependency and the number of quit attempts, are present in a multivariate regression model. This study shows that the use of mHealth apps depends on characteristics related to the content of the app rather than general user characteristics.
Objectives To identify factors associated with kinesiophobia (fear of movement) after cardiac hospitalisation and to assess the impact of kinesiophobia on cardiac rehabilitation (CR) initiation.Design Prospective cohort study.Setting Academic Medical Centre, Department of Cardiology.Participants We performed a prospective cohort study in cardiac patients recruited at hospital discharge. In total, 149 patients (78.5% male) with a median age of 65 years were included, of which 82 (59%) were referred for CR.Primary and secondary outcome measures We assessed kinesiophobia with the Tampa Scale for Kinesiophobia (TSK). For this study, the total score was used (range 13–52). We assessed baseline factors (demographics, cardiac disease history, questionnaire data on anxiety, biopsychosocial complexity and self-efficacy) associated with kinesiophobia using linear regression with backward elimination. For linear regression, the standardised beta (β) was reported. Prospectively, the impact of kinesiophobia on probability of CR initiation, in the first 3 months after hospital discharge (subsample referred for CR), was assessed with logistic regression. For logistic regression, the OR was reported.Results Moderate and severe levels of kinesiophobia were found in 22.8%. In the total sample, kinesiophobia was associated with cardiac anxiety (β=0.33, 95% CI: 0.19 to 0.48), social complexity (β=0.23, 95% CI: 0.06 to 0.39) and higher education (β=−0.18, 95% CI: −0.34 to −0.02). In those referred for CR, kinesiophobia was negatively associated with self-efficacy (β=−0.29, 95% CI: −0.47 to −0.12) and positively with cardiac anxiety (β=0.43, 95% CI: 0.24 to 0.62). Kinesiophobia decreased the probability of CR initiation (ORRange13–52 points=0.92, 95% CI: 0.85 to 0.99).Conclusion In patients hospitalised for cardiovascular disease, kinesiophobia is associated with cardiac anxiety, social complexity, educational level and self-efficacy. Kinesiophobia decreased the likelihood of CR initiation with 8% per point on the TSK.
