Identifying the Leaders: Applying Diffusion of Innovation Theory to Use of a Public Bikeshare System in Vancouver, Canada

Public bike share programs are growing in popularity globally with increasing recognition of their potential and accrued benefits for mobility, health, and the environment. Any city planning to launch a program will be keenly interested in understanding who may use it, in order to enable strategic marketing that will facilitate quick uptake and adoption. We applied the Diffusion of Innovation Theory to data from a population-based telephone survey to characterize who is most likely to use a new public bike share program. The telephone survey of 901 Vancouver residents was conducted prior to the launch of Vancouver\u27s public bike share program. Results showed that a majority (n=614/901, 69.1%, 95% CI: 66.3%/72.7%) of respondents thought that public bike share was a good idea, however, only a quarter (n=217/901, 24.2%, 95% CI: 21.1%, 27.3%) said they would be either likely or very likely to use the program. Logistic regression identified characteristics associated with greater and lower likelihood of use. These characteristics were used to create an adoption curve that defines population segments anticipated to be the leaders in adopting the program. The theory was used to develop implementation recommendations to maximize program uptake including ensuring that the program has tangible advantages over driving or transit; is affordable and easy to try out; integrates with transit and car share opportunities; and appeals to social trends such as environmental responsibility. These results can assist planning and promotion in cities set to launch public bike share programs

Dropout from exercise trials among cancer survivors—An individual patient data meta-analysis from the POLARIS study

Introduction: The number of randomized controlled trials (RCTs) investigating the effects of exercise among cancer survivors has increased in recent years; however, participants dropping out of the trials are rarely described. The objective of the present study was to assess which combinations of participant and exercise program characteristics were associated with dropout from the exercise arms of RCTs among cancer survivors. Methods: This study used data collected in the Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS) study, an international database of RCTs investigating the effects of exercise among cancer survivors. Thirty-four exercise trials, with a total of 2467 patients without metastatic disease randomized to an exercise arm were included. Harmonized studies included a pre and a posttest, and participants were classified as dropouts when missing all assessments at the post-intervention test. Subgroups were identified with a conditional inference tree. Results: Overall, 9.6% of the participants dropped out. Five subgroups were identified in the conditional inference tree based on four significant associations with dropout. Most dropout was observed for participants with BMI &gt;28.4 kg/m2, performing supervised resistance or unsupervised mixed exercise (19.8% dropout) or had low-medium education and performed aerobic or supervised mixed exercise (13.5%). The lowest dropout was found for participants with BMI &gt;28.4 kg/m2 and high education performing aerobic or supervised mixed exercise (5.1%), and participants with BMI ≤28.4 kg/m2 exercising during (5.2%) or post (9.5%) treatment. Conclusions: There are several systematic differences between cancer survivors completing and dropping out from exercise trials, possibly affecting the external validity of exercise effects.</p

Moderators of Exercise Effects on Cancer-related Fatigue:A Meta-analysis of Individual Patient Data

PURPOSE: Fatigue is a common and potentially disabling symptom in patients with cancer. It can often be effectively reduced by exercise. Yet, effects of exercise interventions might differ across subgroups. We conducted a meta-analysis using individual patient data of randomized controlled trials (RCT) to investigate moderators of exercise intervention effects on cancer-related fatigue. METHODS: We used individual patient data from 31 exercise RCT worldwide, representing 4366 patients, of whom 3846 had complete fatigue data. We performed a one-step individual patient data meta-analysis, using linear mixed-effect models to analyze the effects of exercise interventions on fatigue (z score) and to identify demographic, clinical, intervention- and exercise-related moderators. Models were adjusted for baseline fatigue and included a random intercept on study level to account for clustering of patients within studies. We identified potential moderators by testing their interaction with group allocation, using a likelihood ratio test. RESULTS: Exercise interventions had statistically significant beneficial effects on fatigue (β = -0.17; 95% confidence interval [CI], -0.22 to -0.12). There was no evidence of moderation by demographic or clinical characteristics. Supervised exercise interventions had significantly larger effects on fatigue than unsupervised exercise interventions (βdifference = -0.18; 95% CI -0.28 to -0.08). Supervised interventions with a duration ≤12 wk showed larger effects on fatigue (β = -0.29; 95% CI, -0.39 to -0.20) than supervised interventions with a longer duration. CONCLUSIONS: In this individual patient data meta-analysis, we found statistically significant beneficial effects of exercise interventions on fatigue, irrespective of demographic and clinical characteristics. These findings support a role for exercise, preferably supervised exercise interventions, in clinical practice. Reasons for differential effects in duration require further exploration

Targeting exercise interventions to patients with cancer in need:An individual patient data meta-analysis

Background: Exercise effects in cancer patients often appear modest, possibly because interventions rarely target patients most in need. This study investigated the moderator effects of baseline values on the exercise outcomes of fatigue, aerobic fitness, muscle strength, quality of life (QoL), and self-reported physical function (PF) in cancer patients during and post-treatment. Methods: Individual patient data from 34 randomized exercise trials (n = 4519) were pooled. Linear mixed-effect models were used to study moderator effects of baseline values on exercise intervention outcomes and to determine whether these moderator effects differed by intervention timing (during vs post-treatment). All statistical tests were two-sided. Results: Moderator effects of baseline fatigue and PF were consistent across intervention timing, with greater effects in patients with worse fatigue (Pinteraction = .05) and worse PF (Pinteraction = .003). Moderator effects of baseline aerobic fitness, muscle strength, and QoL differed by intervention timing. During treatment, effects on aerobic fitness were greater for patients with better baseline aerobic fitness (Pinteraction = .002). Post-treatment, effects on upper (Pinteraction &lt; .001) and lower (Pinteraction = .01) body muscle strength and QoL (Pinteraction &lt; .001) were greater in patients with worse baseline values. Conclusion: Although exercise should be encouraged for most cancer patients during and post-treatments, targeting specific subgroups may be especially beneficial and cost effective. For fatigue and PF, interventions during and post-treatment should target patients with high fatigue and low PF. During treatment, patients experience benefit for muscle strength and QoL regardless of baseline values; however, only patients with low baseline values benefit post-treatment. For aerobic fitness, patients with low baseline values do not appear to benefit from exercise during treatment

Effects and moderators of exercise on quality of life and physical function in patients with cancer:An individual patient data meta-analysis of 34 RCTs

This individual patient data meta-analysis aimed to evaluate the effects of exercise on quality of life (QoL) and physical function (PF) in patients with cancer, and to identify moderator effects of demographic (age, sex, marital status, education), clinical (body mass index, cancer type, presence of metastasis), intervention-related (intervention timing, delivery mode and duration, and type of control group), and exercise-related (exercise frequency, intensity, type, time) characteristics. Relevant published and unpublished studies were identified in September 2012 via PubMed, EMBASE, PsycINFO, and CINAHL, reference checking and personal communications. Principle investigators of all 69 eligible trials were requested to share IPD from their study. IPD from 34 randomised controlled trials (n=4,519 patients) that evaluated the effects of exercise compared to a usual care, wait-list or attention control group on QoL and PF in adult patients with cancer were retrieved and pooled. Linear mixed-effect models were used to evaluate the effects of the exercise on post-intervention outcome values (z-score) adjusting for baseline values. Moderator effects were studies by testing interactions. Exercise significantly improved QoL (β=0.15, 95%CI=0.10;0.20) and PF (β=0.18,95%CI=0.13;0.23). The effects were not moderated by demographic, clinical or exercise characteristics. Effects on QoL (βdifference_in_effect=0.13, 95%CI=0.03;0.22) and PF (βdifference_in_effect=0.10, 95%CI=0.01;0.20) were significantly larger for supervised than unsupervised interventions. In conclusion, exercise, and particularly supervised exercise, effectively improves QoL and PF in patients with cancer with different demographic and clinical characteristics during and following treatment. Although effect sizes are small, there is consistent empirical evidence to support implementation of exercise as part of cancer care

Effects and moderators of exercise on muscle strength, muscle function and aerobic fitness in patients with cancer:A meta-analysis of individual patient data

To optimally target exercise interventions for patients with cancer, it is important to identify which patients benefit from which interventions. Design We conducted an individual patient data meta-analysis to investigate demographic, clinical, intervention-related and exercise-related moderators of exercise intervention effects on physical fitness in patients with cancer. Data sources We identified relevant studies via systematic searches in electronic databases (PubMed, Embase, PsycINFO and CINAHL). Eligibility criteria We analysed data from 28 randomised controlled trials investigating the effects of exercise on upper body muscle strength (UBMS) and lower body muscle strength (LBMS), lower body muscle function (LBMF) and aerobic fitness in adult patients with cancer. Results Exercise significantly improved UBMS (β=0.20, 95% Confidence Interval (CI) 0.14 to 0.26), LBMS (β=0.29, 95% CI 0.23 to 0.35), LBMF (β=0.16, 95% CI 0.08 to 0.24) and aerobic fitness (β=0.28, 95% CI 0.23 to 0.34), with larger effects for supervised interventions. Exercise effects on UBMS were larger during treatment, when supervised interventions included ≥3 sessions per week, when resistance exercises were included and when session duration was >60 min. Exercise effects on LBMS were larger for patients who were living alone, for supervised interventions including resistance exercise and when session duration was >60 min. Exercise effects on aerobic fitness were larger for younger patients and when supervised interventions included aerobic exercise. Conclusion Exercise interventions during and following cancer treatment had small effects on UBMS, LBMS, LBMF and aerobic fitness. Demographic, intervention-related and exercise-related characteristics including age, marital status, intervention timing, delivery mode and frequency and type and time of exercise sessions moderated the exercise effect on UBMS, LBMS and aerobic fitness.Sin financiación12.022 JCR (2019) Q1, 1/85 Sport Sciences3.712 SJR (2019) Q1, 48/2754 Medicine (miscellaneous), 1/284 Orthopedics and Sports Medicine, 1/207 Physical Therapy, Sports Therapy and Rehabilitation, 2/125 Sports ScienceNo data IDR 2019UE

Selecting teachers and prospective teachers : a meta-analysis

The purpose of this review article was to examine the methods used for the selection of teachers for employment and prospective teachers entering initial teacher education (ITE) programs, and to assess the predictive validity of these methods. We reviewed 32 studies reporting selection methods administered in high-stakes conditions and that included an external (not self-reported) teacher effectiveness outcome measure. The overall effect size was small but significant (r = 0.12, p <.001). Moderator analyses showed that academic and non-academic predictors were both significantly associated with teacher effectiveness measures and that effect sizes were small (but significant) for selection into employment and ITE. We conclude the review by proposing a research agenda that has the potential to enhance educational outcomes by improving the selection of prospective teachers

Notes for genera: basal clades of Fungi (including Aphelidiomycota, Basidiobolomycota, Blastocladiomycota, Calcarisporiellomycota, Caulochytriomycota, Chytridiomycota, Entomophthoromycota, Glomeromycota, Kickxellomycota, Monoblepharomycota, Mortierellomycota, Mucoromycota, Neocallimastigomycota, Olpidiomycota, Rozellomycota and Zoopagomycota)

Compared to the higher fungi (Dikarya), taxonomic and evolutionary studies on the basal clades of fungi are fewer in number. Thus, the generic boundaries and higher ranks in the basal clades of fungi are poorly known. Recent DNA based taxonomic studies have provided reliable and accurate information. It is therefore necessary to compile all available information since basal clades genera lack updated checklists or outlines. Recently, Tedersoo et al. (MycoKeys 13:1--20, 2016) accepted Aphelidiomycota and Rozellomycota in Fungal clade. Thus, we regard both these phyla as members in Kingdom Fungi. We accept 16 phyla in basal clades viz. Aphelidiomycota, Basidiobolomycota, Blastocladiomycota, Calcarisporiellomycota, Caulochytriomycota, Chytridiomycota, Entomophthoromycota, Glomeromycota, Kickxellomycota, Monoblepharomycota, Mortierellomycota, Mucoromycota, Neocallimastigomycota, Olpidiomycota, Rozellomycota and Zoopagomycota. Thus, 611 genera in 153 families, 43 orders and 18 classes are provided with details of classification, synonyms, life modes, distribution, recent literature and genomic data. Moreover, Catenariaceae Couch is proposed to be conserved, Cladochytriales Mozl.-Standr. is emended and the family Nephridiophagaceae is introduced