Micol Zweig

The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit

The feasibility of using mobile health applications to conduct observational clinical studies requires rigorous validation. Here, we report initial findings from the Asthma Mobile Health Study, a research study, including recruitment, consent, and enrollment, conducted entirely remotely by smartphone. We achieved secure bidirectional data flow between investigators and 7,593 participants from across the United States, including many with severe asthma. Our platform enabled prospective collection of longitudinal, multidimensional data (e.g., surveys, devices, geolocation, and air quality) in a subset of users over the 6-month study period. Consistent trending and correlation of interrelated variables support the quality of data obtained via this method. We detected increased reporting of asthma symptoms in regions affected by heat, pollen, and wildfires. Potential challenges with this technology include selection bias, low retention rates, reporting bias, and data security. These issues require attention to realize the full potential of mobile platforms in research and patient care.

Informed decision-making among students analyzing their personal genomes on a whole genome sequencing course: a longitudinal cohort study

BackgroundMultiple laboratories now offer clinical whole genome sequencing (WGS). We anticipate WGS becoming routinely used in research and clinical practice. Many institutions are exploring how best to educate geneticists and other professionals about WGS. Providing students in WGS courses with the option to analyze their own genome sequence is one strategy that might enhance students’ engagement and motivation to learn about personal genomics. However, if this option is presented to students, it is vital they make informed decisions, do not feel pressured into analyzing their own genomes by their course directors or peers, and feel free to analyze a third-party genome if they prefer. We therefore developed a 26-hour introductory genomics course in part to help students make informed decisions about whether to receive personal WGS data in a subsequent advanced genomics course. In the advanced course, they had the option to receive their own personal genome data, or an anonymous genome, at no financial cost to them. Our primary aims were to examine whether students made informed decisions regarding analyzing their personal genomes, and whether there was evidence that the introductory course enabled the students to make a more informed decision.MethodsThis was a longitudinal cohort study in which students (N = 19) completed questionnaires assessing their intentions, informed decision-making, attitudes and knowledge before (T1) and after (T2) the introductory course, and before the advanced course (T3). Informed decision-making was assessed using the Decisional Conflict Scale.ResultsAt the start of the introductory course (T1), most (17/19) students intended to receive their personal WGS data in the subsequent course, but many expressed conflict around this decision. Decisional conflict decreased after the introductory course (T2) indicating there was an increase in informed decision-making, and did not change before the advanced course (T3). This suggests that it was the introductory course content rather than simply time passing that had the effect. In the advanced course, all (19/19) students opted to receive their personal WGS data. No changes in technical knowledge of genomics were observed. Overall attitudes towards WGS were broadly positive.ConclusionsProviding students with intensive introductory education about WGS may help them make informed decisions about whether or not to work with their personal WGS data in an educational setting.

Development and preliminary evaluation of an online educational video about whole-genome sequencing for research participants, patients, and the general public

Background:As whole-genome sequencing (WGS) increases in availability, WGS educational aids are needed for research participants, patients, and the general public. Our aim was therefore to develop an accessible and scalable WGS educational aid.Methods:We engaged multiple stakeholders in an iterative process over a 1-year period culminating in the production of a novel 10-minute WGS educational animated video, “Whole Genome Sequencing and You” (https://goo.gl/HV8ezJ). We then presented the animated video to 281 online-survey respondents (the video-information group). There were also two comparison groups: a written-information group (n = 281) and a no-information group (n = 300).Results:In the video-information group, 79% reported the video was easy to understand, satisfaction scores were high (mean 4.00 on 1–5 scale, where 5 = high satisfaction), and knowledge increased significantly. There were significant differences in knowledge compared with the no-information group but few differences compared with the written-information group. Intention to receive personal results from WGS and decisional conflict in response to a hypothetical scenario did not differ between the three groups.Conclusions:The educational animated video, “Whole Genome Sequencing and You,” was well received by this sample of online-survey respondents. Further work is needed to evaluate its utility as an aid to informed decision making about WGS in other populations.Genet Med 18 5, 501–512.

How do students react to analyzing their own genomes in a whole-genome sequencing course?: outcomes of a longitudinal cohort study.

Purpose:Health-care professionals need to be trained to work with whole-genome sequencing (WGS) in their practice. Our aim was to explore how students responded to a novel genome analysis course that included the option to analyze their own genomes.Methods:This was an observational cohort study. Questionnaires were administered before (T3) and after the genome analysis course (T4), as well as 6 months later (T5). In-depth interviews were conducted at T5.Results:All students (n = 19) opted to analyze their own genomes. At T5, 12 of 15 students stated that analyzing their own genomes had been useful. Ten reported they had applied their knowledge in the workplace. Technical WGS knowledge increased (mean of 63.8% at T3, mean of 72.5% at T4; P = 0.005). In-depth interviews suggested that analyzing their own genomes may increase students’ motivation to learn and their understanding of the patient experience. Most (but not all) of the students reported low levels of WGS results–related distress and low levels of regret about their decision to analyze their own genomes.Conclusion:Giving students the option of analyzing their own genomes may increase motivation to learn, but some students may experience personal WGS results–related distress and regret. Additional evidence is required before considering incorporating optional personal genome analysis into medical education on a large scale.Genet Med 17 11, 866–874.

Preparing the next generation of genomicists: a laboratory-style course in medical genomics

The growing gap between the demand for genome sequencing and the supply of trained genomics professionals is creating an acute need to develop more effective genomics education. In response we developed “Practical Analysis of Your Personal Genome”, a novel laboratory-style medical genomics course in which students have the opportunity to obtain and analyze their own whole genome. This report describes our motivations for and the content of a “practical” genomics course that incorporates personal genome sequencing and the lessons we learned during the first three iterations of this course.

Identification of a novel RASD1 somatic mutation in a USP8-mutated corticotroph adenoma

Cushings disease (CD) is caused by pituitary corticotroph adenomas that secrete excess adrenocorticotropic hormone (ACTH). In these tumors, somatic mutations in the gene USP8 have been identified as recurrent and pathogenic and are the sole known molecular driver for CD. Although other somatic mutations were reported in these studies, their contribution to the pathogenesis of CD remains unexplored. No molecular drivers have been established for a large proportion of CD cases and tumor heterogeneity has not yet been investigated using genomics methods. Also, even in USP8-mutant tumors, a possibility may exist of additional contributing mutations, following a paradigm from other neoplasm types where multiple somatic alterations contribute to neoplastic transformation. The current study utilizes whole-exome discovery sequencing on the Illumina platform, followed by targeted amplicon-validation sequencing on the Pacific Biosciences platform, to interrogate the somatic mutation landscape in a corticotroph adenoma resected from a CD patient. In this USP8-mutated tumor, we identified an interesting somatic mutation in the gene RASD1, which is a component of the corticotropin-releasing hormone receptor signaling system. This finding may provide insight into a novel mechanism involving loss of feedback control to the corticotropin-releasing hormone receptor and subsequent deregulation of ACTH production in corticotroph tumors.

The asthma mobile health study, smartphone data collected using ResearchKit

Widespread adoption of smart mobile platforms coupled with a growing ecosystem of sensors including passive location tracking and the ability to leverage external data sources create an opportunity to generate an unprecedented depth of data on individuals. Mobile health technologies could be utilized for chronic disease management as well as research to advance our understanding of common diseases, such as asthma. We conducted a prospective observational asthma study to assess the feasibility of this type of approach, clinical characteristics of cohorts recruited via a mobile platform, the validity of data collected, user retention patterns, and user data sharing preferences. We describe data and descriptive statistics from the Asthma Mobile Health Study, whereby participants engaged with an iPhone application built using Apples ResearchKit framework. Data from 6346 U.S. participants, who agreed to share their data broadly, have been made available for further research. These resources have the potential to enable the research community to work collaboratively towards improving our understanding of asthma as well as mobile health research best practices.

METHODS FOR CLUSTERING TIME SERIES DATA ACQUIRED FROM MOBILE HEALTH APPS.

In our recent Asthma Mobile Health Study (AMHS), thousands of asthma patients across the country contributed medical data through the iPhone Asthma Health App on a daily basis for an extended period of time. The collected data included daily self-reported asthma symptoms, symptom triggers, and real time geographic location information. The AMHS is just one of many studies occurring in the context of now many thousands of mobile health apps aimed at improving wellness and better managing chronic disease conditions, leveraging the passive and active collection of data from mobile, handheld smart devices. The ability to identify patient groups or patterns of symptoms that might predict adverse outcomes such as asthma exacerbations or hospitalizations from these types of large, prospectively collected data sets, would be of significant general interest. However, conventional clustering methods cannot be applied to these types of longitudinally collected data, especially survey data actively collected from app users, given heterogeneous patterns of missing values due to: 1) varying survey response rates among different users, 2) varying survey response rates over time of each user, and 3) non-overlapping periods of enrollment among different users. To handle such complicated missing data structure, we proposed a probability imputation model to infer missing data. We also employed a consensus clustering strategy in tandem with the multiple imputation procedure. Through simulation studies under a range of scenarios reflecting real data conditions, we identified favorable performance of the proposed method over other strategies that impute the missing value through low-rank matrix completion. When applying the proposed new method to study asthma triggers and symptoms collected as part of the AMHS, we identified several patient groups with distinct phenotype patterns. Further validation of the methods described in this paper might be used to identify clinically important patterns in large data sets with complicated missing data structure, improving the ability to use such data sets to identify at-risk populations for potential intervention.