Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol

The screening of digital footprint for clinical purposes relies on the capacity of wearable technologies to collect data and extract relevant information’s for patient management. Artificial intelligence (AI) techniques allow processing of real-time observational information and continuously learning from data to build understanding. We designed a system able to get clinical sense from digital footprints based on the smartphone’s native sensors and advanced machine learning and signal processing techniques in order to identify suicide risk. Method/design: The Smartcrisis study is a cross-national comparative study. The study goal is to determine the relationship between suicide risk and changes in sleep quality and disturbed appetite. Outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) and the University Hospital of Nimes (France) will be proposed to participate to the study. Two smartphone applications and a wearable armband will be used to capture the data. In the intervention group, a smartphone application (MEmind) will allow for the ecological momentary assessment (EMA) data capture related with sleep, appetite and suicide ideations. Discussion: Some concerns regarding data security might be raised. Our system complies with the highest level of security regarding patients’ data. Several important ethical considerations related to EMA method must also be considered. EMA methods entails a non-negligible time commitment on behalf of the participants. EMA rely on daily, or sometimes more frequent, Smartphone notifications. Furthermore, recording participants’ daily experiences in a continuous manner is an integral part of EMA. This approach may be significantly more than asking a participant to complete a retrospective questionnaire but also more accurate in terms of symptoms monitoring. Overall, we believe that Smartcrises could participate to a paradigm shift from the traditional identification of risks factors to personalized prevention strategies tailored to characteristics for each patientThis study was partly funded by Fundación Jiménez Díaz Hospital, Instituto de Salud Carlos III (PI16/01852), Delegación del Gobierno para el Plan Nacional de Drogas (20151073), American Foundation for Suicide Prevention (AFSP) (LSRG-1-005-16), the Madrid Regional Government (B2017/BMD-3740 AGES-CM 2CM; Y2018/TCS-4705 PRACTICO-CM) and Structural Funds of the European Union. MINECO/FEDER (‘ADVENTURE’, id. TEC2015–69868-C2–1-R) and MCIU Explora Grant ‘aMBITION’ (id. TEC2017–92552-EXP), the French Embassy in Madrid, Spain, The foundation de l’avenir, and the Fondation de France. The work of D. Ramírez and A. Artés-Rodríguez has been partly supported by Ministerio de Economía of Spain under projects: OTOSIS (TEC2013–41718-R), AID (TEC2014–62194-EXP) and the COMONSENS Network (TEC2015–69648-REDC), by the Ministerio de Economía of Spain jointly with the European Commission (ERDF) under projects ADVENTURE (TEC2015– 69868-C2–1-R) and CAIMAN (TEC2017–86921-C2–2-R), and by the Comunidad de Madrid under project CASI-CAM-CM (S2013/ICE-2845). The work of P. Moreno-Muñoz has been supported by FPI grant BES-2016-07762

Diabetes Self-Management Using Mobile Apps: An Empirical Investigation Based On App Reviews And Through Value Sensitive Design Perspective

Smartphones have penetrated our everyday lives. Novel technologies facilitate self-management of chronic diseases such as diabetes. However, not all the patients are motivated to use technologies to manage their chronic conditions. Patients depend on certain human values to self-manage their conditions and these values are not implicated in the technologies they use. In this research in progress study we draw on value sensitive design methodological and theoretical approach to investigate human responses to self-management technology. We collect app reviews for a diabetes app and schematically code the review. Our findings contribute to designing technologies and systems that account for the human values of the patients-users

Discovering Design Principles for Health Behavioral Change Support Systems: A Text Mining Approach

Behavioral Change Support Systems (BCSSs) aim to change users’ behavior and lifestyle. These systems have been gaining popularity with the proliferation of wearable devices and recent advances in mobile technologies. In this article, we extend the existing literature by discovering design principles for health BCSSs based on a systematic analysis of users’ feedback. Using mobile diabetes applications as an example of Health BCSSs, we use topic modeling to discover design principles from online user reviews. We demonstrate the importance of the design principles through analyzing their existence in users’ complaints. Overall, the results highlight the necessity of going beyond the techno-centric approach used in current practice and incorporating the social and organizational features into persuasive systems design, as well as integrating with medical devices and other systems in their usage context

Discovering Design Principles for Health Behavioral Change Support Systems: A Text Mining Approach

Behavioral Change Support Systems (BCSSs) aim to change users’ behavior and lifestyle. These systems have been gaining popularity with the proliferation of wearable devices and recent advances in mobile technologies. In this article, we extend the existing literature by discovering design principles for health BCSSs based on a systematic analysis of users’ feedback. Using mobile diabetes applications as an example of Health BCSSs, we use topic modeling to discover design principles from online user reviews. We demonstrate the importance of the design principles through analyzing their existence in users’ complaints. Overall, the results highlight the necessity of going beyond the techno-centric approach used in current practice and incorporating the social and organizational features into persuasive systems design, as well as integrating with medical devices and other systems in their usage context

Interaction and engagement with an anxiety management app: Analysis using large-Scale behavioral data

© Paul Matthews, Phil Topham, Praminda Caleb-Solly. Background: SAM (Self-help for Anxiety Management) is a mobile phone app that provides self-help for anxiety management. Launched in 2013, the app has achieved over one million downloads on the iOS and Android platform app stores. Key features of the app are anxiety monitoring, self-help techniques, and social support via a mobile forum (“the Social Cloud”). This paper presents unique insights into eMental health app usage patterns and explores user behaviors and usage of self-help techniques. Objective: The objective of our study was to investigate behavioral engagement and to establish discernible usage patterns of the app linked to the features of anxiety monitoring, ratings of self-help techniques, and social participation. Methods: We use data mining techniques on aggregate data obtained from 105,380 registered users of the app’s cloud services. Results: Engagement generally conformed to common mobile participation patterns with an inverted pyramid or “funnel” of engagement of increasing intensity. We further identified 4 distinct groups of behavioral engagement differentiated by levels of activity in anxiety monitoring and social feature usage. Anxiety levels among all monitoring users were markedly reduced in the first few days of usage with some bounce back effect thereafter. A small group of users demonstrated long-term anxiety reduction (using a robust measure), typically monitored for 12-110 days, with 10-30 discrete updates and showed low levels of social participation. Conclusions: The data supported our expectation of different usage patterns, given flexible user journeys, and varying commitment in an unstructured mobile phone usage setting. We nevertheless show an aggregate trend of reduction in self-reported anxiety across all minimally-engaged users, while noting that due to the anonymized dataset, we did not have information on users also enrolled in therapy or other intervention while using the app. We find several commonalities between these app-based behavioral patterns and traditional therapy engagement

Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept Trial

Background:Multiple chronic conditions (multimorbidity) are becomingmore prevalent among aging populations. Digital health technologies have thepotential to assist in the self-management of multimorbidity, improving theawareness and monitoring of health and well-being, supporting a betterunderstanding of the disease, and encouraging behavior change.Objective:The aim of this study was to analyze how 60 older adults(mean age 74, SD 6.4; range 65-92 years) with multimorbidity engaged withdigital symptom and well-being monitoring when using a digital health platformover a period of approximately 12 months. Methods:Principal component analysis and clustering analysis wereused to group participants based on their levels of engagement, and the dataanalysis focused on characteristics (eg, age, sex, and chronic healthconditions), engagement outcomes, and symptom outcomes of the differentclusters that were discovered.Results:Three clusters were identified: the typical user group, theleast engaged user group, and the highly engaged user group. Our findings showthat age, sex, and the types of chronic health conditions do not influenceengagement. The 3 primary factors influencing engagement were whether the samedevice was used to submit different health and well-being parameters, thenumber of manual operations required to take a reading, and the daily routineof the participants. The findings also indicate that higher levels ofengagement may improve the participants’ outcomes (eg, reduce symptomexacerbation and increase physical activity).Conclusions:The findings indicate potential factors that influence olderadult engagement with digital health technologies for home-based multimorbidityself-management. The least engaged user groups showed decreased health andwell-being outcomes related to multimorbidity self-management. Addressing thefactors highlighted in this study in the design and implementation ofhome-based digital health technologies may improve symptom management andphysical activity outcomes for older adults self-managing multimorbidity.</p

Commercial mHealth Apps and the Providers’ Responsibility for Hope

In this paper, we ask whether the providers of commercial mHealth apps for self-tracking create inflated or false hopes for vulnerable user groups and whether they should be held responsible for this. This question is relevant because hopes created by the providers determine the modalities of the apps’ use. Due to the created hopes, users who may be vulnerable to certain design features of the app can experience bad outcomes in various dimensions of their well-being. This adds to structural injustices sustaining or exacerbating the vulnerable position of such user groups. We define structural injustices as systemic disadvantages for certain social groups that may be sustained or exacerbated by unfair power relations. Inflated hopes can also exclude digitally disadvantaged users. Thus, the hopes created by the providers of commercial mHealth apps for self-tracking press the question of whether the deployment and use of mHealth apps meet the requirements for qualifying as a just public health endeavor

A framework for applying natural language processing in digital health interventions

BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. OBJECTIVE: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. METHODS: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. RESULTS: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. CONCLUSIONS: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts