A growing number of investigators have commented on the lack of models to inform the design of behavioral intervention technologies (BITs). BITs, which include a subset of mHealth and eHealth interventions, employ a broad range of technologies, such as mobile phones, the Web, and sensors, to support users in changing behaviors and cognitions related to health, mental health, and wellness. We propose a model that conceptually defines BITs, from the clinical aim to the technological delivery framework. The BIT model defines both the conceptual and technological architecture of a BIT. Conceptually, a BIT model should answer the questions why, what, how (conceptual and technical), and when. While BITs generally have a larger treatment goal, such goals generally consist of smaller intervention aims (the "why") such as promotion or reduction of specific behaviors, and behavior change strategies (the conceptual "how"), such as education, goal setting, and monitoring. Behavior change strategies are instantiated with specific intervention components or “elements” (the "what"). The characteristics of intervention elements may be further defined or modified (the technical "how") to meet the needs, capabilities, and preferences of a user. Finally, many BITs require specification of a workflow that defines when an intervention component will be delivered. The BIT model includes a technological framework (BIT-Tech) that can integrate and implement the intervention elements, characteristics, and workflow to deliver the entire BIT to users over time. This implementation may be either predefined or include adaptive systems that can tailor the intervention based on data from the user and the user’s environment. The BIT model provides a step towards formalizing the translation of developer aims into intervention components, larger treatments, and methods of delivery in a manner that supports research and communication between investigators on how to design, develop, and deploy BITs.

Stress may rise for physicians with a moderate number of EMR functions. Time pressure was associated with poor physician outcomes mainly in the high EMR cluster. Work redesign may address these stressors.

Objective The purpose of this paper is to describe the use of video-based observation research methods in primary care environment and highlight important methodological considerations and provide practical guidance for primary care and human factors researchers conducting video studies to understand patient-clinician interaction in primary care settings. Methods We reviewed studies in the literature which used video methods in health care research and, we also used our own experience based on the video studies we conducted in primary care settings. Results This paper highlighted the benefits of using video techniques such as multi-channel recording and video coding and compared “unmanned” video recording with the traditional observation method in primary care research. We proposed a list, which can be followed step by step to conduct an effective video study in a primary care setting for a given problem. This paper also described obstacles researchers should anticipate when using video recording methods in future studies. Conclusion With the new technological improvements, video-based observation research is becoming a promising method in primary care and HFE research. Video recording has been under-utilized as a data collection tool because of confidentiality and privacy issues. However, it has many benefits as opposed to traditional observations, and recent studies using video recording methods have introduced new research areas and approaches.

Depression is common in primary care settings, but barriers prevent many primary care patients from initiating treatment. Smartphone apps stand as a possible means to overcome such barriers. However, there is limited evidence to understand the use and efficacy of these apps. The purpose of the current study was to pilot an evaluation of the usage and efficacy of apps for depression based upon behavioral or cognitive intervention skills, compared to a wait-list control. Thirty adults with depression were randomized to the use of either a behavioral app (Boost Me) or a cognitive app (Thought Challenger) or to a wait-list control. Boost Me and Thought Challenger participants received 6 weeks of the respective intervention along with weekly coaching sessions, with a 4-week follow-up period; wait-list control participants received no interventions for 10 weeks. A repeated-measures analysis of variance was conducted to examine depression over time and across treatment groups; t tests compared app usage across groups. Depression scores changed significantly over time (p < .001), with group differences occurring between Thought Challenger and wait-list control participants (p = .03). Boost Me was used significantly more than was Thought Challenger (p = .02); however, there was no evidence to suggest correlations between usage and changes in depression (ps > .05). The present study provides initial support that intervention strategies for depression delivered via apps with human support can impact symptoms and may promote continued use over 6 weeks. This pilot also demonstrates the feasibility of future research regarding the delivery of behavioral and cognitive intervention strategies via apps.

Background Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM).Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms.Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility.Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.