Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static "snapshots" of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing "gold standard" measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a "knowledge commons," which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.

The analysis of human behavior patterns is increasingly used for several research fields. The individualized modeling of behavior using classical techniques requires too much time and resources to be effective. A possible solution would be the use of pattern recognition techniques to automatically infer models to allow experts to understand individual behavior. However, traditional pattern recognition algorithms infer models that are not readily understood by human experts. This limits the capacity to benefit from the inferred models. Process mining technologies can infer models as workflows, specifically designed to be understood by experts, enabling them to detect specific behavior patterns in users. In this paper, the eMotiva process mining algorithms are presented. These algorithms filter, infer and visualize workflows. The workflows are inferred from the samples produced by an indoor location system that stores the location of a resident in a nursing home. The visualization tool is able to compare and highlight behavior patterns in order to facilitate expert understanding of human behavior. This tool was tested with nine real users that were monitored for a 25-week period. The results achieved suggest that the behavior of users is continuously evolving and changing and that this change can be measured, allowing for behavioral change detection.

Background Remote care services and patient empowerment have boosted mobile health (mHealth). A study of user needs related to mHealth for pediatric cystic fibrosis (PCF) identified the set of preferred features mobile apps should support; however, the potential use of PCF apps and their suitability to fit into PCF clinical management remains unexplored. Objective We examine whether PCF holds potential for the implementation of mHealth care. Methods The study is based on a literature review and qualitative analysis of content and was conducted in two parts: (1) we reviewed scientific and gray literature to explore how European countries manage PCF and conducted a qualitative study of 6 PCF units and (2) we performed a systematic review of apps available in the myhealthapps.net repository searching for cystic fibrosis (CF) management and nutrition apps, which we analyzed for characteristics, business models, number of downloads, and usability. Results European CF routine care guidelines are acknowledged in most European countries, and treatments are fully covered in almost all countries. The majority of teams in CF units are interdisciplinary. With respect to the systematic review of apps, we reviewed 12 apps for CF management and 9 for general nutrition management in the myhealthapps.net directory. All analyzed apps provided functionalities for recording aspects related to the disease and nutrition such as medication, meals, measurements, reminders, and educational material. None of the apps reviewed in this study supported pancreatic enzyme replacement therapy. CF apps proved to be less appealing and usable than nutrition apps (2.66 [SD 1.15] vs 4.01 [SD 0.90]; P <.001, z -value: –2.6). User needs detected in previous research are partially matched by current apps for CF management. Conclusions The health care context for PCF is a unique opportunity for the adoption of mHealth. Well-established clinical guidelines, heterogeneous clinical teams, and coverage by national health care systems provide a suitable scenario for the use of mHealth solutions. However, available apps for CF self-management do not cover essential aspects such as nutrition and education. To increase the adoption of mHealth for CF self-management, new apps should include these features. International Registered Report Identifier (IRRID) RR2-10.1136/bmjopen-2016-014931

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The widespread adoption of real-time location systems is boosting the development of software applications to track persons and assets in hospitals. Among the vast amount of applications, real-time location systems in operating rooms have the advantage of grounding advanced data analysis techniques to improve surgical processes, such as process mining. However, such applications still find entrance barriers in the clinical context. In this paper, we aim to evaluate the preferred features of a process mining-based dashboard deployed in the operating rooms of a hospital equipped with a real-time location system. The dashboard allows to discover and enhance flows of patients based on the location data of patients undergoing an intervention. Analytic hierarchy process was applied to quantify the prioritization of the dashboard features (filtering data, enhancement, node selection, statistics, etc.), distinguishing the priorities that each of the different roles in the operating room service assigned to each feature. The staff in the operating rooms (n = 10) was classified into three groups: Technical, clinical, and managerial staff according to their responsibilities. Results showed different weights for the features in the process mining dashboard for each group, suggesting that a flexible process mining dashboard is needed to boost its potential in the management of clinical interventions in operating rooms. This paper is an extension of a communication presented in the Process-Oriented Data Science for Health Workshop in the Business Process Management Conference 2018.

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BackgroundResearch in type 1 diabetes management has increased exponentially since the irruption of mobile health apps for its remote and self-management. Despite this fact, the features affect in the disease management and patient empowerment are adopted by app makers and provided to the general population remain unexplored.ObjectiveTo study the gap between literature and available apps for type 1 diabetes self-management and patient empowerment and to discover the features that an ideal app should provide to people with diabetes.MethodsThe methodology comprises systematic reviews in the scientific literature and app marketplaces. We included articles describing interventions that demonstrated an effect on diabetes management with particular clinical endpoints through the use of mobile technologies. The features of these apps were gathered in a taxonomy of what an ideal app should look like to then assess which of these features are available in the market.ResultsThe literature search resulted in 231 matches. Of these, 55 met the inclusion criteria. A taxonomy featuring 3 levels of characteristics was designed based on 5 papers which were selected for the synthesis. Level 1 includes 10 general features (Personalization, Family support, Agenda, Data record, Insulin bolus calculator, Data management, Interaction, Tips and support, Reminders, and Rewards) Level 2 and Level 3 included features providing a descriptive detail of Level 1 features. Eighty apps matching the inclusion criteria were analyzed. None of the assessed apps fulfilled the features of the taxonomy of an ideal app. Personalization (70/80, 87.5%) and Data record (64/80, 80.0%) were the 2 top prevalent features, whereas Agenda (5/80, 6.3%) and Rewards (3/80, 3.8%) where the less predominant. The operating system was not associated with the number of features (P=.42, F=.81) nor the type of feature (P=.20, χ2=11.7). Apps were classified according to the number of level 1 features and sorted into quartiles. First quartile apps had a regular distribution of the ten features in the taxonomy whereas the other 3 quartiles had an irregular distribution.ConclusionsThere are significant gaps between research and the market in mobile health for type 1 diabetes management. While the literature focuses on aspects related to gamification, rewarding, and social communities, the available apps are focused on disease management aspects such as data record and appointments. Personalized and tailored empowerment features should be included in commercial apps for large-scale assessment of potential in the self-management of the disease.

A system based on mobile phones with built-in cameras has been developed for the postoperative management of patients sent home after day surgery. The system allows patients to send pictures and pulse oximetry measurements to a hospital server in a few seconds. Health professionals can then see what patients are describing and make a more objective estimate of the patients' status. Over a five-month period, a total of 49 patients used the system. A total of 222 images were sent, the average image delivery time being 29 s (SD 11). In nine cases (18%), the availability of images modified the treatment, and a visit to an emergency unit was avoided in eight of them who had blood-stained dressings and normal haematomas. The patients who had their treatment modified sent more images (an average of 5.4/patient) than those for whom images confirmed the correct treatment (3.1/patient). Each telephone call lasted for a mean duration of 18 min (range 8-34). The patient satisfaction data showed that all the aspects studied were evaluated in a very positive way, with 96% of the patients completely satisfied with the attention received by the mobile health application.