scholarly journals Altering the availability of healthier vs. less healthy items in UK hospital vending machines: a multiple treatment reversal design

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Rachel Pechey ◽  
Holly Jenkins ◽  
Emma Cartwright ◽  
Theresa M. Marteau
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Sales Data ◽  
Snack Foods ◽  
Multiple Treatment ◽  
Vending Machines ◽  
Reversal Design ◽  
Condition C ◽  
The Impact ◽  
Condition B

Abstract Background Altering the availability of healthier or less-healthy products may increase healthier purchases, but evidence is currently limited. The current study aimed to investigate the impact of altering the absolute-and-relative availability of healthier and less-healthy products – i.e. simultaneously altering the number of options available and the proportion of healthier options – in hospital vending machines. Methods An adapted multiple treatment reversal design was used, altering products available in ten vending machines serving snack foods and/or cold drinks in one English hospital. Machines were randomised to one of two sequences for the seven 4-week study periods: ABCADEA or ADEABCA. In Condition A (study periods 1, 4 and 7) the proportions of healthier products were standardised across all machines, so that 25% of all snack slots and 75% of drink slots were healthier. In Condition B, 20% of vending machine slots were emptied by removing less-healthy products. In Condition C, the empty slots created in Condition B were filled with healthier products. Conditions D and E were operationalised in the same way as B and C, except healthier products were removed in D, and then less-healthy products added in E. Sales data were obtained from machine restocking records. Separate linear mixed models were conducted to examine the impact of altering availability on energy purchased (kcal) from (i) snacks or (ii) drinks each week, with random effects for vending machine. Results The energy purchased from drinks was reduced when the number of slots containing less-healthy drinks was decreased, compared to standardised levels (− 52.6%; 95%CI: − 69.3,-26.9). Findings were inconclusive for energy purchased from snacks when less-healthy snack slots were reduced (− 17.2%; 95%CI: − 47.4,30.5). Results for altering the number of slots for healthier drinks or snacks were similarly inconclusive, with no statistically significant impact on energy purchased. Conclusions Reducing the availability of less-healthy drinks could reduce the energy purchased from drinks in vending machines. Further studies are needed to establish whether any effects might be smaller for snacks, or found with higher baseline proportions of healthier options.

scholarly journals P075 Travel or cheers? Examining the drivers and mechanisms of home court advantage in the 2020/2021 NBA regular season

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A45-A45
Author(s):  
J Leota ◽  
D Hoffman ◽  
L Mascaro ◽  
M Czeisler ◽  
K Nash ◽  
...  
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Fixed Effects ◽  
Natural Experiment ◽  
National Basketball Association ◽  
Home Team ◽  
The Road ◽  
On The Road ◽  
The Impact ◽  
The Relationship

Abstract Introduction Home court advantage (HCA) in the National Basketball Association (NBA) is well-documented, yet the co-occurring drivers responsible for this advantage have proven difficult to examine in isolation. The Coronavirus disease (COVID-19) pandemic resulted in the elimination of crowds in ~50% of games during the 2020/2021 NBA season, whereas travel remained unchanged. Using this ‘natural experiment’, we investigated the impact of crowds and travel-related sleep and circadian disruption on NBA HCA. Methods 1080 games from the 2020/2021 NBA regular season were analyzed using mixed models (fixed effects: crowds, travel; random effects: team, opponent). Results In games with crowds, home teams won 58.65% of the time and outrebounded (M=2.28) and outscored (M=2.18) their opponents. In games without crowds, home teams won significantly less (50.60%, p = .01) and were outrebounded (M=-0.41, p < .001) and outscored (M=-0.13, p < .05) by their opponents. Further, the increase in home rebound margin fully mediated the relationship between crowds and home points margin (p < .001). No significant sleep or circadian effects were observed. Discussion Taken together, these results suggest that HCA in the 2020/2021 NBA season was predominately driven by the presence of crowds and their influence on the effort exerted by the home team to rebound the ball. Moreover, we speculate that the strict NBA COVID-19 policies may have mitigated the travel-related sleep and circadian effects on the road team. These findings are of considerable significance to a domain wherein marginal gains can have immense competitive, financial, and even historical consequences.

scholarly journals The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model

Stats ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 48-76
Author(s):  
Freddy Hernández ◽  
Viviana Giampaoli
Keyword(s):  
Weibull Distribution ◽  
Random Effects ◽  
Mixed Models ◽  
Fixed Effects ◽  
Mixed Model ◽  
Random Effect ◽  
Estimation Procedure ◽  
Weibull Regression ◽  
Two Parameters ◽  
The Impact

Mixed models are useful tools for analyzing clustered and longitudinal data. These models assume that random effects are normally distributed. However, this may be unrealistic or restrictive when representing information of the data. Several papers have been published to quantify the impacts of misspecification of the shape of the random effects in mixed models. Notably, these studies primarily concentrated their efforts on models with response variables that have normal, logistic and Poisson distributions, and the results were not conclusive. As such, we investigated the misspecification of the shape of the random effects in a Weibull regression mixed model with random intercepts in the two parameters of the Weibull distribution. Through an extensive simulation study considering six random effect distributions and assuming normality for the random effects in the estimation procedure, we found an impact of misspecification on the estimations of the fixed effects associated with the second parameter σ of the Weibull distribution. Additionally, the variance components of the model were also affected by the misspecification.

Facilitators and Barriers to Developing, Implementing, and Evaluating Healthy Vending Policies in Four Cities

2020 ◽  
pp. 152483991990049
Author(s):  
Sarah Green ◽  
Karen Glanz ◽  
Julie Bromberg
Keyword(s):  
Lessons Learned ◽  
Common Source ◽  
Sales Data ◽  
Vending Machines ◽  
Semistructured Interviews ◽  
Public Areas ◽  
Multiple Case ◽  
The Impact ◽  
Nutrition Standards

Vending machines are a common source of low-nutrient, energy-dense snacks, and beverages. Many cities are beginning to adopt healthy vending policies in public areas, but evidence regarding best practices for developing, implementing, and evaluating these healthy vending polices is limited. This study used a mixed-methods, multiple case study design to examine healthy vending policies and initiatives in four cities. Data were collected between August 2017 and December 2017. Research staff worked with a designated contact person to coordinate site visits to each city where observations of the vending machines were conducted. Semistructured interviews were conducted with multiple stakeholders from each site and documents, including policies, vendor contracts, and nutrition standards, were reviewed. The following elements were identified as being essential to a healthy vending policy or initiative: having a champion and support from leadership, internal and external partnerships, and clear communication. Conducting regular compliance checks of the vending machines and the ability to obtain sales data, especially pre– and post–healthy vending policy sales data, continues to be a challenge. Stakeholders across all cities reported that concerns about profit–loss from the vendor and city revenue and procurement departments are barriers to adopting healthy vending policies. More research and evaluation are needed, as results are mixed regarding the impact on overall revenue/profits. This study yielded a variety of resources and “lessons learned” from those who have developed and implemented healthy vending policies and initiatives. This information should be used by others looking to influence healthier snacking behaviors through vending machines.

The effect of random-effects misspecification on classification accuracy

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Riham El Saeiti ◽  
Marta García-Fiñana ◽  
David M. Hughes
Keyword(s):  
Normal Distribution ◽  
Random Effects ◽  
Mixed Models ◽  
Classification Accuracy ◽  
Multivariate Normal Distribution ◽  
Joint Distribution ◽  
Patient Specific ◽  
Model Parameters ◽  
Multivariate Normal ◽  
The Impact

Abstract Mixed models are a useful way of analysing longitudinal data. Random effects terms allow modelling of patient specific deviations from the overall trend over time. Correlation between repeated measurements are captured by specifying a joint distribution for all random effects in a model. Typically, this joint distribution is assumed to be a multivariate normal distribution. For Gaussian outcomes misspecification of the random effects distribution usually has little impact. However, when the outcome is discrete (e.g. counts or binary outcomes) generalised linear mixed models (GLMMs) are used to analyse longitudinal trends. Opinion is divided about how robust GLMMs are to misspecification of the random effects. Previous work explored the impact of random effects misspecification on the bias of model parameters in single outcome GLMMs. Accepting that these model parameters may be biased, we investigate whether this affects our ability to classify patients into clinical groups using a longitudinal discriminant analysis. We also consider multiple outcomes, which can significantly increase the dimensions of the random effects distribution when modelled simultaneously. We show that when there is severe departure from normality, more flexible mixture distributions can give better classification accuracy. However, in many cases, wrongly assuming a single multivariate normal distribution has little impact on classification accuracy.

Milk Vending Does Not Improve College Students’ Milk and Calcium Intakes

2017 ◽  
Vol 19 (2) ◽  
pp. 295-302 ◽  
Author(s):  
Angela M. Rose ◽  
Rachel A. Williams ◽  
Andrew S. Hanks ◽  
Julie A. Kennel ◽  
Carolyn Gunther
Keyword(s):  
College Students ◽  
Young Adulthood ◽  
Diet Quality ◽  
Life Stage ◽  
Public University ◽  
Effective Strategy ◽  
Sales Data ◽  
Vending Machines ◽  
Before And After ◽  
The Impact

In the transition from adolescence to young adulthood, overall diet quality decreases, including a reduction in both dairy and calcium consumption. The objective of this pilot study was to determine the impact of milk vending on milk and calcium intakes in college students. Participants were 124 college students living in dorms at a large public university (Fall 2012). Milk vending machines were installed in two campus dorms. Before and 2 months after installation, students were surveyed about milk and calcium intakes, as well as attitudes regarding milk vending. Sales data for the newly installed machines were also collected between the pre- and posttest surveys. Students reported similar milk and calcium consumption before and after the intervention. Mean calcium intakes were lower than the recommended dietary allowance for students in either life stage group (18 years old or 19 years and older). Milk vending sales data showed that during the study period, approximately nine bottles of milk were bought each day from the two dorms combined. Results from this study suggest that milk vending alone may not be an effective strategy for preventing the commonly observed decrease in milk and calcium intakes among college students.

scholarly journals Bayesian bivariate meta-analysis of correlated effects: Impact of the prior distributions on the between-study correlation, borrowing of strength, and joint inferences

2016 ◽  
Vol 27 (2) ◽  
pp. 428-450 ◽  
Author(s):  
Danielle L Burke ◽  
Sylwia Bujkiewicz ◽  
Richard D Riley
Keyword(s):  
Missing Data ◽  
Random Effects ◽  
Prior Distribution ◽  
Treatment Effectiveness ◽  
Meta Analysis ◽  
Prior Distributions ◽  
Treatment Groups ◽  
Multiple Treatment ◽  
Predictive Probabilities ◽  
The Impact

Multivariate random-effects meta-analysis allows the joint synthesis of correlated results from multiple studies, for example, for multiple outcomes or multiple treatment groups. In a Bayesian univariate meta-analysis of one endpoint, the importance of specifying a sensible prior distribution for the between-study variance is well understood. However, in multivariate meta-analysis, there is little guidance about the choice of prior distributions for the variances or, crucially, the between-study correlation, ρB; for the latter, researchers often use a Uniform(−1,1) distribution assuming it is vague. In this paper, an extensive simulation study and a real illustrative example is used to examine the impact of various (realistically) vague prior distributions for ρB and the between-study variances within a Bayesian bivariate random-effects meta-analysis of two correlated treatment effects. A range of diverse scenarios are considered, including complete and missing data, to examine the impact of the prior distributions on posterior results (for treatment effect and between-study correlation), amount of borrowing of strength, and joint predictive distributions of treatment effectiveness in new studies. Two key recommendations are identified to improve the robustness of multivariate meta-analysis results. First, the routine use of a Uniform(−1,1) prior distribution for ρB should be avoided, if possible, as it is not necessarily vague. Instead, researchers should identify a sensible prior distribution, for example, by restricting values to be positive or negative as indicated by prior knowledge. Second, it remains critical to use sensible (e.g. empirically based) prior distributions for the between-study variances, as an inappropriate choice can adversely impact the posterior distribution for ρB, which may then adversely affect inferences such as joint predictive probabilities. These recommendations are especially important with a small number of studies and missing data.

scholarly journals A goodness-of-fit test for the random-effects distribution in mixed models

2014 ◽  
Vol 26 (2) ◽  
pp. 970-983 ◽  
Author(s):  
Achmad Efendi ◽  
Reza Drikvandi ◽  
Geert Verbeke ◽  
Geert Molenberghs
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Goodness Of Fit ◽  
Real Data ◽  
Operating Characteristics ◽  
Goodness Of Fit Test ◽  
Data Analyses ◽  
Graphical Tool ◽  
Gradient Function ◽  
The Impact

In this paper, we develop a simple diagnostic test for the random-effects distribution in mixed models. The test is based on the gradient function, a graphical tool proposed by Verbeke and Molenberghs to check the impact of assumptions about the random-effects distribution in mixed models on inferences. Inference is conducted through the bootstrap. The proposed test is easy to implement and applicable in a general class of mixed models. The operating characteristics of the test are evaluated in a simulation study, and the method is further illustrated using two real data analyses.

scholarly journals Coenzyme Q10 supplementation for prophylaxis in adult patients with migraine—a meta-analysis

BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e039358
Author(s):  
Suhairul Sazali ◽  
Salziyan Badrin ◽  
Mohd Noor Norhayati ◽  
Nur Suhaila Idris
Keyword(s):  
Random Effects ◽  
Coenzyme Q10 ◽  
Migraine Headache ◽  
Meta Analysis ◽  
Adult Patients ◽  
Control Group ◽  
Cochrane Central Register ◽  
Statistical Heterogeneity ◽  
Coq10 Supplementation ◽  
The Impact

ObjectiveTo determine the effects of coenzyme Q10 (CoQ10) for reduction in the severity, frequency of migraine attacks and duration of headache in adult patients with migraine.DesignSystematic review and meta-analysis.Data sourcesCochrane Central Register of Controlled Trials, CENTRAL, MEDLINE, EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Psychological Information Database (PsycINFO) from inception till December 2019.Study selectionAll randomised control trials comparing CoQ10 with placebo or used as an adjunct treatment included in this meta-analysis. Cross-over designs and controlled clinical trials were excluded.Data synthesisHeterogeneity at face value by comparing populations, settings, interventions and outcomes were measured and statistical heterogeneity was assessed by means of the I2 statistic. The treatment effect for dichotomous outcomes were using risk ratios and risk difference, and for continuous outcomes, mean differences (MDs) or standardised mean difference; both with 95% CIs were used. Subgroup analyses were carried out for dosage of CoQ10 and if CoQ10 combined with another supplementation. Sensitivity analysis was used to investigate the impact risk of bias for sequence generation and allocation concealment of included studies.ResultsSix studies with a total of 371 participants were included in the meta-analysis. There is no statistically significant reduction in severity of migraine headache with CoQ10 supplementation. CoQ10 supplementation reduced the duration of headache attacks compared with the control group (MD: −0.19; 95% CI: −0.27 to −0.11; random effects; I2 statistic=0%; p<0.00001). CoQ10 usage reduced the frequency of migraine headache compared with the control group (MD: −1.52; 95% CI: −2.40 to −0.65; random effects; I2 statistic=0%; p<0.001).ConclusionCoQ10 appears to have beneficial effects in reducing duration and frequency of migraine attack.PROSPERO registration numberCRD42019126127.

373 Historical Use of Substances for Insomnia Impacts Current Beliefs about Hypnotic Medications

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A148-A149
Author(s):  
Jessica Dietch ◽  
Norah Simpson ◽  
Joshua Tutek ◽  
Isabelle Tully ◽  
Elizabeth Rangel ◽  
...  
Keyword(s):  
Prescription Medication ◽  
Stepped Care ◽  
Future Research ◽  
Prescription Medications ◽  
Over The Counter ◽  
Multiple Treatment ◽  
History Of ◽  
Study Participants ◽  
Beliefs About Medications ◽  
The Impact

Abstract Introduction The purpose of the current study was to examine the relationship between current beliefs about hypnotic medications and historical use of prescription hypnotic medications or non-prescription substances for sleep (i.e., over the counter [OTC] medications, alcohol, and cannabis). Methods Participants were 142 middle age and older adults with insomnia (M age = 62.9 [SD = 8.1]; 71.1% female) enrolled in the RCT of the Effectiveness of Stepped-Care Sleep Therapy In General Practice (RESTING) study. Participants reported on history of substances they have tried for insomnia and completed the Beliefs about Medications Questionnaire-Specific with two subscales assessing beliefs about 1) the necessity for hypnotics, and 2) concerns about potential adverse consequences of hypnotics. Participants were grouped based on whether they had used no substances for sleep (No Subs, 11.6%), only prescription medications (Rx Only, 9.5%), only non-prescription substances (NonRx Only, 26.6%), or both prescription and non-prescription substances (Both, 52.3%). Results Sixty-one percent of the sample had used prescription medication for sleep and 79% had used non-prescription substances (74% OTC medication, 23% alcohol, 34% cannabis). The greater number of historical substances endorsed, the stronger the beliefs about necessity of hypnotics, F(1,140)=23.3, p&lt;.001, but not about concerns. Substance groups differed significantly on necessity beliefs, F(3,1)=10.68, p&lt;.001; post-hocs revealed the Both group had stronger beliefs than the No and NonRx Only groups. Substance groups also differed significantly on the concerns subscale, F(3,1)=6.68, p&lt;.001; post-hocs revealed the NonRx Only group had stronger harm beliefs than the other three groups. Conclusion The majority of the sample had used both prescription and non-prescription substances to treat insomnia. Historical use of substances for treating insomnia was associated with current beliefs about hypnotics. Individuals who had used both prescription and non-prescription substances for sleep in the past had stronger beliefs about needing hypnotics to sleep at present, which may reflect a pattern of multiple treatment failures. Individuals who had only tried non-prescription substances for sleep may have specifically sought alternative substances due to concerns about using hypnotics. Future research should seek to understand the impact of treatment history on engagement in and benefit from non-medication-based treatment for insomnia. Support (if any) 1R01AG057500; 2T32MH019938-26A1

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