Mobile health is a rapidly developing field in which behavioral treatments are delivered to individuals via wearables or smartphones to facilitate health-related behavior change. Micro-randomized trials (MRT) are an experimental design for developing mobile health interventions. In an MRT the treatments are randomized numerous times for each individual over course of the trial. Along with assessing treatment effects, behavioral scientists aim to understand between-person heterogeneity in the treatment effect. A natural approach is the familiar linear mixed model. However, directly applying linear mixed models is problematic because potential moderators of the treatment effect are frequently endogenous---that is, may depend on prior treatment. We discuss model interpretation and biases that arise in the absence of additional assumptions when endogenous covariates are included in a linear mixed model. In particular, when there are endogenous covariates, the coefficients no longer have the customary marginal interpretation. However, these coefficients still have a conditional-on-the-random-effect interpretation. We provide an additional assumption that, if true, allows scientists to use standard software to fit linear mixed model with endogenous covariates, and person-specific predictions of effects can be provided. As an illustration, we assess the effect of activity suggestion in the HeartSteps MRT and analyze the between-person treatment effect heterogeneity.

Micro-randomized trials (MRTs) are a novel experimental design for developing mobile health interventions. Participants are repeatedly randomized in an MRT, resulting in longitudinal data with time-varying treatments. Causal excursion effects are the main quantities of interest in MRT primary and secondary analyses. We consider MRTs where the proximal outcome is binary and the randomization probability is constant or time-varying but not data-dependent. We develop a sample size formula for detecting a nonzero marginal excursion effect. We prove that the formula guarantees power under a set of working assumptions. We demonstrate via simulation that violations of certain working assumptions do not affect the power, and for those that do, we point out the direction in which the power changes. We then propose practical guidelines for using the sample size formula. As an illustration, the formula is used to size an MRT on interventions for excessive drinking. The sample size calculator is implemented in R package MRTSampleSizeBinary and an interactive R Shiny app. This work can be used in trial planning for a wide range of MRTs with binary proximal outcomes.

Advances in wearables and digital technology now make it possible to deliver behavioral mobile health interventions to individuals in their everyday life. The micro-randomized trial (MRT) is increasingly used to provide data to inform the construction of these interventions. In an MRT, each individual is repeatedly randomized among multiple intervention options, often hundreds or even thousands of times, over the course of the trial. This work is motivated by multiple MRTs that have been conducted, or are currently in the field, in which the primary outcome is a longitudinal binary outcome. The primary aim of such MRTs is to examine whether a particular time-varying intervention has an effect on the longitudinal binary outcome, often marginally over all but a small subset of the individual's data. We propose the definition of causal excursion effect that can be used in such primary aim analysis for MRTs with binary outcomes. Under rather restrictive assumptions one can, based on existing literature, derive a semiparametric, locally efficient estimator of the causal effect. We, starting from this estimator, develop an estimator that can be used as the basis of a primary aim analysis under more plausible assumptions. Simulation studies are conducted to compare the estimators. We illustrate the developed methods using data from the MRT, BariFit. In BariFit, the goal is to support weight maintenance for individuals who received bariatric surgery.

Objective

The aim of this study is to examine validity, reliability, and responsiveness to change of Patient-Reported Outcomes Measurement Information System Self-Efficacy for Managing Chronic Conditions in persons with systemic sclerosis.

Methods

We conducted a post hoc analysis of the Patient-Reported Outcomes Measurement Information System Self-Efficacy measure and other quality-of-life measures from systemic sclerosis participants from a 16-week randomized control trial. The trial compared an Internet-based self-management program to a control condition where participants were provided an educational book. All participants completed outcome measures at baseline and following the 16-week trial period.

Results

The mean age of participants was 53.7 years, 91% were female and systemic sclerosis subtype included 44.9% limited/sine and 43.1% diffuse; mean disease duration was 9.0 years. All self-efficacy subscales (Managing Emotions, Symptoms, Daily Activities, Social Interactions, and Medications/Treatment) demonstrated good internal consistency (.92-.96). All subscales showed statistically significant correlations with other validated measures of depressive symptoms and quality of life (.20-.86) but were not associated with satisfaction nor with appearance. The subscales appropriately discriminated between those with and without depressive symptoms and demonstrated responsiveness to change over the 16-week period for those who had a corresponding increase in reported quality of life.

Conclusion

The Patient-Reported Outcomes Measurement Information System Self-Efficacy measure is valid, reliable, and responsive to change for persons with systemic sclerosis.

Objective

This study tested the effect of beginning treatment with a speech-generating device (SGD) in the context of a blended, adaptive treatment design for improving spontaneous, communicative utterances in school-aged, minimally verbal children with autism.

Method

A total of 61 minimally verbal children with autism, aged 5 to 8 years, were randomized to a blended developmental/behavioral intervention (JASP+EMT) with or without the augmentation of a SGD for 6 months with a 3-month follow-up. The intervention consisted of 2 stages. In stage 1, all children received 2 sessions per week for 3 months. Stage 2 intervention was adapted (by increased sessions or adding the SGD) based on the child's early response. The primary outcome was the total number of spontaneous communicative utterances; secondary measures were the total number of novel words and total comments from a natural language sample.

Results

Primary aim results found improvements in spontaneous communicative utterances, novel words, and comments that all favored the blended behavioral intervention that began by including an SGD (JASP+EMT+SGD) as opposed to spoken words alone (JASP+EMT). Secondary aim results suggest that the adaptive intervention beginning with JASP+EMT+SGD and intensifying JASP+EMT+SGD for children who were slow responders led to better posttreatment outcomes.

Conclusion

Minimally verbal school-aged children can make significant and rapid gains in spoken spontaneous language with a novel, blended intervention that focuses on joint engagement and play skills and incorporates an SGD. Future studies should further explore the tailoring design used in this study to better understand children's response to treatment. Clinical trial registration information-Developmental and Augmented Intervention for Facilitating Expressive Language (CCNIA); http://clinicaltrials.gov/; NCT01013545.

Although there is much excitement surrounding the use of mobile and wearable technology for the purposes of delivering interventions as people go through their day-to-day lives, data analysis methods for constructing and optimizing digital interventions lag behind. Here, we elucidate data analysis methods for primary and secondary analyses of micro-randomized trials (MRTs), an experimental design to optimize digital just-in-time adaptive interventions. We provide a definition of causal "excursion" effects suitable for use in digital intervention development. We introduce the weighted and centered least-squares (WCLS) estimator which provides consistent causal excursion effect estimators for digital interventions from MRT data. We describe how the WCLS estimator along with associated test statistics can be obtained using standard statistical software such as SAS or R. Throughout we use HeartSteps, an MRT designed to increase physical activity among sedentary individuals, to illustrate potential primary and secondary analyses.

Just-in-time adaptive interventions (JITAIs) are time-varying adaptive interventions that use frequent opportunities for the intervention to be adapted-weekly, daily, or even many times a day. The microrandomized trial (MRT) has emerged for use in informing the construction of JITAIs. MRTs can be used to address research questions about whether and under what circumstances JITAI components are effective, with the ultimate objective of developing effective and efficient JITAI. The purpose of this article is to clarify why, when, and how to use MRTs; to highlight elements that must be considered when designing and implementing an MRT; and to review primary and secondary analyses methods for MRTs. We briefly review key elements of JITAIs and discuss a variety of considerations that go into planning and designing an MRT. We provide a definition of causal excursion effects suitable for use in primary and secondary analyses of MRT data to inform JITAI development. We review the weighted and centered least-squares (WCLS) estimator which provides consistent causal excursion effect estimators from MRT data. We describe how the WCLS estimator along with associated test statistics can be obtained using standard statistical software such as R (R Core Team, 2019). Throughout we illustrate the MRT design and analyses using the HeartSteps MRT, for developing a JITAI to increase physical activity among sedentary individuals. We supplement the HeartSteps MRT with two other MRTs, SARA and BariFit, each of which highlights different research questions that can be addressed using the MRT and experimental design considerations that might arise. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, discuss future possibilities, and set a grand goal for the emerging field of mHealth research.

High maternal weight is associated with detrimental outcomes in offspring, including increased susceptibility to neurological disorders such as anxiety, depression and communicative disorders. Despite widespread acknowledgement of sex biases in the development of these disorders, few studies have investigated potential sex-biased mechanisms underlying disorder susceptibility. Here, we show that a maternal high-fat diet causes endotoxin accumulation in fetal tissue, and subsequent perinatal inflammation contributes to sex-specific behavioural outcomes in offspring. In male offspring exposed to a maternal high-fat diet, increased macrophage Toll-like receptor 4 signalling results in excess microglial phagocytosis of serotonin (5-HT) neurons in the developing dorsal raphe nucleus, decreasing 5-HT bioavailability in the fetal and adult brains. Bulk sequencing from a large cohort of matched first-trimester human samples reveals sex-specific transcriptome-wide changes in placental and brain tissue in response to maternal triglyceride accumulation (a proxy for dietary fat content). Further, fetal brain 5-HT levels decrease as placental triglycerides increase in male mice and male human samples. These findings uncover a microglia-dependent mechanism through which maternal diet can impact offspring susceptibility for neuropsychiatric disorder development in a sex-specific manner.