MIXREGLS is a program which provides estimates for a mixed-effects location scale model assuming a (conditionally) normally-distributed dependent variable. This model can be used for analysis of data in which subjects may be measured at many observations and interest is in modeling the mean and variance structure. In terms of the variance structure, covariates can by specified to have effects on both the between-subject and within-subject variances. Another use is for clustered data in which subjects are nested within clusters (e.g., clinics, hospitals, schools, etc.) and interest is in modeling the between-cluster and within-cluster variances in terms of covariates. MIXREGLS was written in Fortran and uses maximum likelihood estimation, utilizing both the EM algorithm and a Newton-Raphson solution. Estimation of the random effects is accomplished using empirical Bayes methods. Examples illustrating stand-alone usage and features of MIXREGLS are provided, as well as use via the SAS and R software packages.

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The use of intensive sampling methods, such as ecological momentary assessment (EMA), is increasingly prominent in medical research. However, inferences from such data are often limited to the subject-specific mean of the outcome and between-subject variance (i.e., random intercept), despite the capability to examine within-subject variance (i.e., random scale) and associations between covariates and subject-specific mean (i.e., random slope). MixWILD (Mixed model analysis With Intensive Longitudinal Data) is statistical software that tests the effects of subject-level parameters (variance and slope) of time-varying variables, specifically in the context of studies using intensive sampling methods, such as ecological momentary assessment. MixWILD combines estimation of a stage 1 mixed-effects location-scale (MELS) model, including estimation of the subject-specific random effects, with a subsequent stage 2 linear or binary/ordinal logistic regression in which values sampled from each subject's random effect distributions can be used as regressors (and then the results are aggregated across replications). Computations within MixWILD were written in FORTRAN and use maximum likelihood estimation, utilizing both the expectation-maximization (EM) algorithm and a Newton-Raphson solution. The mean and variance of each individual's random effects used in the sampling are estimated using empirical Bayes equations. This manuscript details the underlying procedures and provides examples illustrating standalone usage and features of MixWILD and its GUI. MixWILD is generalizable to a variety of data collection strategies (i.e., EMA, sensors) as a robust and reproducible method to test predictors of variability in level 1 outcomes and the associations between subject-level parameters (variances and slopes) and level 2 outcomes.

More attention to SQoL as an outcome may strengthen interventions aimed at preventing HIV and STIs and improving sexual health holistically. Mehta SD, Nordgren RK, Agingu W, et al. Sexual Quality of Life and Association With HIV and Sexually Transmitted Infections Among a Cohort of Heterosexual Couples in Kenya. J Sex Med 2018;15:1446-1455.

Ecological Momentary Assessment data present some new modeling opportunities. Typically, there are sufficient data to explicitly model the within-subject (WS) variance, and in many applications, it is of interest to allow the WS variance to depend on covariates as well as random subject effects. We describe a model that allows multiple random effects per subject in the mean model (eg, random location intercept and slopes), as well as random scale in the error variance model. We present an example of the use of this model on a real dataset and a simulation study that shows the benefit of this model, relative to simpler approaches.

Objectives-Higher levels of positive affect and feelings of energy and vitality are associated with greater physical activity (PA) and lower sedentary time (ST). However, whether fluctuations in these feelings contribute to the regulation of these behaviors is unclear. This study examined the extent to which within-person variability in positive affect and feeling energetic predicted participants' overall levels of PA and ST. Design-This analysis combined data from four ecological momentary assessment (EMA) studies (age range : 8-73 years) with ambulatory monitoring via waist-worn accelerometry (N=661). Methods-Positive affect and energy were assessed through EMA several times per day across 4-7 days. Accelerometer data was used to create the following behavioral outcomes: (1) meeting MVPA guidelines (children: 60 minutes/day, adults: 30 minutes/day) and (2) minutes of ST per hour of accelerometer wear. A two-stage analytic approach was used to test the study aim. In the first stage, Mixed-Effects Location Scale Modeling decomposed mean levels and variability in positive affect and energy. In the second stage, a linear or logistic regression (depending on whether the outcome was continuous or dichotomous, respectively) was tested to investigate associations between subject-level mean and variability in EMA ratings and the behavioral outcome.

Public health and social science increasingly use Twitter for behavioral and marketing surveillance. However, few studies provide sufficient detail about Twitter data collection to allow either direct comparisons between studies or to support replication. The three primary application programming interfaces (API) of Twitter data sources are Streaming, Search, and Firehose. To date, no clear guidance exists about the advantages and limitations of each API, or about the comparability of the amount, content, and user accounts of retrieved tweets from each API. Such information is crucial to the validity, interpretation, and replicability of research findings. This study examines whether tweets collected using the same search filters over the same time period, but calling different APIs, would retrieve comparable datasets. We collected tweets about anti-smoking, e-cigarettes, and tobacco using the aforementioned APIs. The retrieved tweets largely overlapped between three APIs, but each also retrieved unique tweets, and the extent of overlap varied over time and by topic, resulting in different trends and potentially supporting diverging inferences. Researchers need to understand how different data sources can influence both the amount, content, and user accounts of data they retrieve from social media, in order to assess the implications of their choice of data source.

Objectives: Coordinated Oral health Promotion (CO-OP) Chicago is a cluster randomized controlled trial testing the efficacy of a community health worker (CHW) intervention to improve tooth brushing in low-income children. Methods:Four hundred twenty children under 3 years old (mean 21.5 months) were recruited from 20 sites in or near Chicago, IL. Children were identified mainly as Black race (41.9%) or Hispanic ethnicity (53.8%) and most (85.2%) had Medicaid. Intervention families were offered four CHW home visits over 1 year. Brushing frequency was selfreported. Plaque score was determined from images collected in homes using disclosing solution. Analyses used GEE logistic models with variable selection at p < .05.Results: At enrolment, 45.0% of families reported twice a day or more child brushing frequency, and child plaque scores were poor (mean of 1.9, SD: 0.6). Data were obtained from 87.1% of children at 6 months and 86.2% at 12 months. In the CHW intervention arm (10 sites, N = 211), 23.7% received 4 visits, 12.8% 3 visits, 21.3% 2 visits, 23.2% 1 visit and 19% no visits from CHWs. No intervention effect was seen for brushing frequency or plaque score. Child brushing frequency improvement over time was associated with a range of child and caregiver factors. The only factor associated with a change in plaque score over time was parent involvement in brushing.Conclusions: Oral-health-specific CHW services were not associated with improved brushing behaviours in these young children. However, caregiver involvement with brushing supported more quality brushing. More robust interventions are needed to support families during this critical developmental period.