Explore the vast range of titles available.

The purpose of this book is to introduce novice researchers to the tools of meta-analysis and meta-regression analysis and to summarize the state of the art for existing practitioners. Meta-regression analysis addresses the rising \"Tower of Babel\" that current economics and business research has become. Meta-analysis is the statistical analysis of previously published, or reported, research findings on a given hypothesis, empirical effect, phenomenon, or policy intervention. It is a systematic review of all the relevant scientific knowledge on a specific subject and is an essential part of the evidence-based practice movement in medicine, education and the social sciences. However, research in economics and business is often fundamentally different from what is found in the sciences and thereby requires different methods for its synthesis-meta-regression analysis. This book develops, summarizes, and applies these meta-analytic methods.

This paper presents a systematic literature review characterizing the methodological properties of usability studies conducted on educational and learning technologies in the past 20 years. PRISMA guidelines were followed to identify, select, and review relevant research and report results. Our rigorous review focused on (1) categories of educational and learning technologies that have been the focus of usability evaluation, (2) specific usability evaluation methods used, (3) outcome measures, and (4) research limitations. Findings revealed a diverse range of usability evaluation methods employed for different types of educational/learning technologies and the contexts in which those methods were used, with the majority of usability studies being performed on e-learning technologies within higher education contexts. Specific methods, instrumentation, and types of usability research found to be dominant in reviewed studies were further analyzed and classified, with findings suggesting inquiry methods using questionnaires were most prevalent. Prevalent outcome measures were also synthesized, with findings suggesting that the majority of usability research focuses on issues of technological usability, with very few studies considering pedagogical and socio-cultural aspects of usability. A number of limitations were found, including conceptual and procedural flaws, fundamental misunderstanding of usability evaluation methods, and inappropriate application of usability methods, suggesting potentially problematic and unreliable results. These findings are discussed in-depth, and implications for future research are provided.

\"Whether you are evaluating the effectiveness of a drug, a medical device, a behavioral intervention, a community mobilization, or even a new law, this is the book for you. Written in plain language, it simplifies the process of designing interventions, analyzing the data, and publishing the results. Because the choice of research design depends on the nature of the intervention, the book covers randomized and nonrandomized designs, prospective and retrospective studies, planned clinical trials and observational studies. In addition to reviewing standard statistical analysis, the book has easy-to-follow explanations of cutting edge techniques for evaluating interventions, including propensity score analysis, instrumental variable analysis, interrupted time series analysis and sensitivity analysis. All techniques are illustrated with up-to-date examples from medical and public health literature. This will be essential reading for a wide range of healthcare professionals involved in research as well as those more specifically interested in public health issues and epidemiology\"--Provided by publisher.

Abstract Rationale Bronchopulmonary dysplasia is the most common morbidity of prematurity, but the validity and utility of commonly used definitions have been questioned. Objectives To compare three commonly used definitions of bronchopulmonary dysplasia in a contemporary prospective, multicenter observational cohort of extremely preterm infants. Methods At 36 weeks postmenstrual age, the following definitions of bronchopulmonary dysplasia were applied to surviving infants with and without imputation: need for supplemental oxygen (Shennan definition), National Institutes of Health Workshop definition, and “physiologic” definition after a room-air challenge. Measurements and Main Results Of 765 survivors assessed at 36 weeks, bronchopulmonary dysplasia was diagnosed in 40.8, 58.6, and 32.0% of infants, respectively, with the Shennan, workshop and physiologic definitions. The number of unclassified infants was lowest with the workshop definition (2.1%) and highest with the physiologic definition (16.1%). After assigning infants discharged home in room air before 36 weeks as no bronchopulmonary dysplasia, the modified Shennan definition compared favorably to the workshop definition, with 2.9% unclassified infants. Newer management strategies with nasal cannula flows up to 4 L/min or more and 0.21 Fi  O2 at 36 weeks obscured classification of bronchopulmonary dysplasia status in 12.4% of infants. Conclusions Existing definitions of bronchopulmonary dysplasia differ with respect to ease of data collection and number of unclassifiable cases. Contemporary changes in management of infants, such as use of high-flow nasal cannula, limit application of existing definitions and may result in misclassification. A contemporary definition of bronchopulmonary dysplasia that correlates with respiratory morbidity in childhood is needed. Clinical trial registered with www.clinicaltrials.gov (NCT01435187).

There is mixed evidence to support current ambitions for mobile health (mHealth) apps to improve chronic health and well-being. One proposed explanation for this variable effect is that users do not engage with apps as intended. The application of analytics, defined as the use of data to generate new insights, is an emerging approach to study and interpret engagement with mHealth interventions. This study aimed to consolidate how analytic indicators of engagement have previously been applied across clinical and technological contexts, to inform how they might be optimally applied in future evaluations. We conducted a scoping review to catalog the range of analytic indicators being used in evaluations of consumer mHealth apps for chronic conditions. We categorized studies according to app structure and application of engagement data and calculated descriptive data for each category. Chi-square and Fisher exact tests of independence were applied to calculate differences between coded variables. A total of 41 studies met our inclusion criteria. The average mHealth evaluation included for review was a two-group pretest-posttest randomized controlled trial of a hybrid-structured app for mental health self-management, had 103 participants, lasted 5 months, did not provide access to health care provider services, measured 3 analytic indicators of engagement, segmented users based on engagement data, applied engagement data for descriptive analyses, and did not report on attrition. Across the reviewed studies, engagement was measured using the following 7 analytic indicators: the number of measures recorded (76%, 31/41), the frequency of interactions logged (73%, 30/41), the number of features accessed (49%, 20/41), the number of log-ins or sessions logged (46%, 19/41), the number of modules or lessons started or completed (29%, 12/41), time spent engaging with the app (27%, 11/41), and the number or content of pages accessed (17%, 7/41). Engagement with unstructured apps was mostly measured by the number of features accessed (8/10, P=.04), and engagement with hybrid apps was mostly measured by the number of measures recorded (21/24, P=.03). A total of 24 studies presented, described, or summarized the data generated from applying analytic indicators to measure engagement. The remaining 17 studies used or planned to use these data to infer a relationship between engagement patterns and intended outcomes. Although researchers measured on average 3 indicators in a single study, the majority reported findings descriptively and did not further investigate how engagement with an app contributed to its impact on health and well-being. Researchers are gaining nuanced insights into engagement but are not yet characterizing effective engagement for improved outcomes. Raising the standard of mHealth app efficacy through measuring analytic indicators of engagement may enable greater confidence in the causal impact of apps on improved chronic health and well-being.

Reducing sitting time as well as increasing physical activity in inactive people is beneficial for their health. This paper investigates the effectiveness of the European Fans in Training (EuroFIT) programme to improve physical activity and sedentary time in male football fans, delivered through the professional football setting. A total of 1,113 men aged 30-65 with self-reported body mass index (BMI) ≥27 kg/m2 took part in a randomised controlled trial in 15 professional football clubs in England, the Netherlands, Norway, and Portugal. Recruitment was between September 19, 2015, and February 2, 2016. Participants consented to study procedures and provided usable activity monitor baseline data. They were randomised, stratified by club, to either the EuroFIT intervention or a 12-month waiting list comparison group. Follow-up measurement was post-programme and 12 months after baseline. EuroFIT is a 12-week, group-based programme delivered by coaches in football club stadia in 12 weekly 90-minute sessions. Weekly sessions aimed to improve physical activity, sedentary time, and diet and maintain changes long term. A pocket-worn device (SitFIT) allowed self-monitoring of sedentary time and daily steps, and a game-based app (MatchFIT) encouraged between-session social support. Primary outcome (objectively measured sedentary time and physical activity) measurements were obtained for 83% and 85% of intervention and comparison participants. Intention-to-treat analyses showed a baseline-adjusted mean difference in sedentary time at 12 months of -1.6 minutes/day (97.5% confidence interval [CI], -14.3-11.0; p = 0.77) and in step counts of 678 steps/day (97.5% CI, 309-1.048; p < 0.001) in favor of the intervention. There were significant improvements in diet, weight, well-being, self-esteem, vitality, and biomarkers of cardiometabolic health in favor of the intervention group, but not in quality of life. There was a 0.95 probability of EuroFIT being cost-effective compared with the comparison group if society is willing to pay £1.50 per extra step/day, a maximum probability of 0.61 if society is willing to pay £1,800 per minute less sedentary time/day, and 0.13 probability if society is willing to pay £30,000 per quality-adjusted life-year (QALY). It was not possible to blind participants to group allocation. Men attracted to the programme already had quite high levels of physical activity at baseline (8,372 steps/day), which may have limited room for improvement. Although participants came from across the socioeconomic spectrum, a majority were well educated and in paid work. There was an increase in recent injuries and in upper and lower joint pain scores post-programme. In addition, although the five-level EuroQoL questionnaire (EQ-5D-5L) is now the preferred measure for cost-effectiveness analyses across Europe, baseline scores were high (0.93), suggesting a ceiling effect for QALYs. Participation in EuroFIT led to improvements in physical activity, diet, body weight, and biomarkers of cardiometabolic health, but not in sedentary time at 12 months. Within-trial analysis suggests it is not cost-effective in the short term for QALYs due to a ceiling effect in quality of life. Nevertheless, decision-makers may consider the incremental cost for increase in steps worth the investment. International Standard Randomised Controlled Trials, ISRCTN-81935608.