Towards the Design of Evidence-based Mental Health Information Systems: A Preliminary Literature Review

Mental disorders belong to a significant and serious disease pattern with an increasing prevalence worldwide. Due to limited health personnel and financial resources, scalability of mental health services and tailored care of individuals are two key barriers for a successful treatment. Mental health information systems (MHIS) are expected to address these barriers and thus, it is of utmost importance to understand the underlying technological rules that guide the design of evidence-based MHIS interventions. However, up till now, there is no systematic literature review on the anatomy of MHIS that quantitatively tests the effects of MHIS service configurations on treatment success. We therefore conducted, as a very first step a preliminary review on MHIS in this research-in-progress. This review has not only the objective to present state-of-the-art on MHIS but also to propose a set of fine-grained evaluation criteria relevant for future work on the design of evidence-based MHIS interventions

The type II poly(A)-binding protein PABP-2 genetically interacts with the let-7 miRNA and elicits heterochronic phenotypes in Caenorhabditis elegans

The type II poly(A)-binding protein PABP2/PABPN1 functions in general mRNA metabolism by promoting poly(A) tail formation in mammals and flies. It also participates in poly(A) tail shortening of specific mRNAs in flies, and snoRNA biogenesis in yeast. We have identified Caenorhabditis elegans pabp-2 as a genetic interaction partner of the let-7 miRNA, a widely conserved regulator of animal stem cell fates. Depletion of PABP-2 by RNAi suppresses loss of let-7 activity, and, in let-7 wild-type animals, leads to precocious differentiation of seam cells. This is not due to an effect on let-7 biogenesis and activity, which remain unaltered. Rather, PABP-2 levels are developmentally regulated in a let-7-dependent manner. Moreover, using RNAi PABP-2 can be depleted by >80% without significantly impairing larval viability, mRNA levels or global translation. Thus, it unexpectedly appears that the bulk of PABP-2 is dispensable for general mRNA metabolism in the larva and may instead have more restricted, developmental functions. This observation may be relevant to our understanding of why the phenotypes associated with human PABP2 mutation in oculopharyngeal muscular dystrophy (OPMD) seem to selectively affect only muscle cells

The enzyme activities of Caf1 and Ccr4 are both required for deadenylation by the human Ccr4-Not nuclease module

In eukaryotic cells, the shortening and removal of the poly(A) tail (deadenylation) of cytoplasmic mRNA is a key event in regulated mRNA degradation. A major enzyme involved in deadenylation is the Ccr4-Not deadenylase complex, which can be recruited to its target mRNA by RNA-binding proteins or the miRNA repression complex. In addition to six non-catalytic components, the complex contains two enzymatic subunits with ribonuclease activity: Ccr4 and Caf1 (Pop2). In vertebrates, each deadenylase subunit is encoded by two paralogues: Caf1, which can interact with the anti-proliferative protein BTG2, is encoded by CNOT7 and CNOT8, while Ccr4 is encoded by the highly similar genes CNOT6 and CNOT6L. Currently, it is unclear whether the catalytic subunits work cooperatively, or whether the nuclease components have unique roles in deadenylation. We therefore developed a method to express and purify a minimal human BTG2-Caf1-Ccr4 nuclease sub-complex from bacterial cells. By using chemical inhibition and well-characterised inactivating amino acid substitutions, we demonstrate that the enzyme activities of Caf1 and Ccr4 are both required for deadenylation in vitro. These results indicate that Caf1 and Ccr4 cooperate in mRNA deadenylation and suggest that the enzyme activities of Caf1 and Ccr4 are regulated via allosteric interactions within the nuclease module

Mobile Mental Health Information System for People with Depression

Mental disorders belong to a significant and serious disease pattern with an increasing prevalence worldwide. Due to limited health personnel and financial resources, scalability of mental health services and tailored care of individuals are two key barriers for successful treatment. Scalable mental health information systems (MHISs) are expected to address these barriers. Thus, from an information system design perspective, it is of utmost importance to understand the underlying mechanisms that determine the composition of system components of evidence-based MHIS interventions. While existing research on MHIS postulates a variety of components that show an impact on utilization of mental health information systems and treatment outcomes, there largely remains uncertainty with regard to which technical design choices guide the development of successful MHIS. In investigating this question, the present thesis presents a comprehensive overview of the state of the art of technology-mediated MHIS for patients with depression in the form of a systematic review and meta-analysis and identifies and discusses relevant system components by means of Oinas- Kukkonens' Persuasive System Design framework for future work on the design of evidence-based MHIS interventions. In total, 6,387 studies were screened of which 45 were included for analysis. Results suggest, that technology-mediated MHISs for the treatment of depression have a consistent positive overall effect compared to controls. Adding to the debate on which system components should be considered for future MHIS design, a new set of system components was identified and qualitatively analysed with respect to its therapeutic gain. A particularly promising subset of MHISs relates to those delivering support via smartphones. Smartphones are ubiquitous and have a large complement of sensors that can potentially be useful in monitoring behavioural patterns and providing context-based interventions. This leads to the exploration of the question whether mobile sensors can be utilized for context-aware just-in-time interventions for people suffering from depression. In an explorative approach, a smartphone app, Mobile Sensing and Support (MOSS), is designed from the ground up. In a first proof of concept, the developed app is tested in a nonrandomized, uncontrolled single-arm clinical trial, with respect to implementation feasibility, acceptability and, to a limited degree, efficacy. The app utilizes a novel intervention recommendation system, extending the concept of a just-in-time adaptive intervention by utilizing a holistic view on the users’ behavioural context derived from mobile sensor data. Over 80 interactive micro interventions stemming from cognitive behavioural therapy (CBT) are implemented for administration. Depression symptom progression is tracked using the patient health questionnaire (PHQ-9) to conduct non-parametric statistical hypothesis tests to compare scores before and after intervention. Correlation analysis is used to test the relationship between adherence and change in PHQ-9. Results from the clinical trial suggest, that the users’ context derived from smartphone sensor data was successfully utilised to deliver context-sensitive and personalized interventions to people with symptoms of depression. Users with a clinically relevant PHQ-9 level at baseline who used the app for an extended period of time (t≥6 weeks, n=12) showed significant reduction in self- reported symptom severity. Further, a trend towards a relationship between adherence and reduction in symptoms was observed. Modelling a users’ behaviour and context using sensor data raises the question whether the same data could be utilized to passively predict depression. If behavioural patterns are indicative of a person’s mental health state, this would open up a range of opportunities for unobtrusive mental health screening, potentially able to alert a user if a critical mental state is reached. The present work explores this question by constructing classification models from the field of machine learning on a set of characteristics extracted from mobile sensors such as accelerometer, Wi-Fi, and global positioning systems (GPS). Nonlinear binary classification models trained on 127 extracted characteristics resulted in an accuracy performance of 60%, demonstrating superiority compared to a random classification model. To summarize, the present thesis makes a significant contribution to the body of literature at the intersection of clinical psychology, information systems, and applied computer science and introduces a range of novelties. It provides the most complete systematic literature review on this topic up to the present day. Further, and as a first, it presents a proof of concept of a sensor-based, context-aware mental health information system for people with depression. Finally, although with low overall accuracy and for now unsuitable in practical terms, it presents the first objective measurement of clinically relevant depression levels solely based on mobile sensor data

stressOUT: Design, Implementation and Evaluation of a Mouse-based Stress Management Service

Work-related stress has the potential to increase the risk of chronic stress, major depression and other non-communicable diseases. Organizational stress monitoring usually applies long-term self-report instruments that are designed in a retrospective manner, and thus, is obtrusive, time-consuming and, most important, fails to detect and predict short-term episodes of stress. To ad- dress this shortcoming, we apply design science research with the goal to design, implement and evaluate a stress management service for knowledge workers (stressOUT) that senses the degree of work-related stress solely based on mouse movements. Using stress theory as justificatory knowledge, we implemented stressOUT that tracks mouse movements and perceived stress levels randomly twice a day with the goal to learn features of mouse movements that are related to stress perceptions. Results of a first longitudinal field study indicate that mouse cursor speed is negatively related to perceived stress. Future work is discussed.ISSN:2194-162

Toward the Design of Evidence-Based Mental Health Information Systems for People With Depression: A Systematic Literature Review and Meta-Analysis

Background: Existing research postulates a variety of components that show an impact on utilization of technology-mediated mental health information systems (MHIS) and treatment outcome. Although researchers assessed the effect of isolated design elements on the results of Web-based interventions and the associations between symptom reduction and use of components across computer and mobile phone platforms, there remains uncertainty with regard to which components of technology-mediated interventions for mental health exert the greatest therapeutic gain. Until now, no studies have presented results on the therapeutic benefit associated with specific service components of technology-mediated MHIS for depression. Objective: This systematic review aims at identifying components of technology-mediated MHIS for patients with depression. Consequently, all randomized controlled trials comparing technology-mediated treatments for depression to either waiting-list control, treatment as usual, or any other form of treatment for depression were reviewed. Updating prior reviews, this study aims to (1) assess the effectiveness of technology-supported interventions for the treatment of depression and (2) add to the debate on what components in technology-mediated MHIS for the treatment of depression should be standard of care. Methods: Systematic searches in MEDLINE, PsycINFO, and the Cochrane Library were conducted. Effect sizes for each comparison between a technology-enabled intervention and a control condition were computed using the standard mean difference (SMD). Chi-square tests were used to test for heterogeneity. Using subgroup analysis, potential sources of heterogeneity were analyzed. Publication bias was examined using visual inspection of funnel plots and Begg’s test. Qualitative data analysis was also used. In an explorative approach, a list of relevant components was extracted from the body of literature by consensus between two researchers. Results: Of 6387 studies initially identified, 45 met all inclusion criteria. Programs analyzed showed a significant trend toward reduced depressive symptoms (SMD –0.58, 95% CI –0.71 to –0.45, P<.001). Heterogeneity was large (I2≥76). A total of 15 components were identified. Conclusions: Technology-mediated MHIS for the treatment of depression has a consistent positive overall effect compared to controls. A total of 15 components have been identified. Further studies are needed to quantify the impact of individual components on treatment effects and to identify further components that are relevant for the design of future technology-mediated interventions for the treatment of depression and other mental disorders.ISSN:1438-887

MOSS: Mobile Sensing and Support mit einer App depressive Verstimmungen erkennen und Betroffenen helfen

Major depression is regarded as a significant and serious disease with an increasing prevalence worldwide. However, not all individuals with depressive pressive symptoms seek help for their problems. These untreated "hidden" individuals with depressive symptoms require the design and dissemination of evidence-based, /ow-cost and scalable mental health interventions. Such interventions provided by mobile applications are promising as they have the potential to support people in their everyday life. However, as of today it is unclear how to design mental health applications that are effective and motivating yet non-intrusive. In addressing this problem, the MOSS application is a recent endeavor of a Swiss project team from Universitiitsspital Zurich, ETH Zurich, University of St. Gallen and makora AG, to support people with depressive symptoms. In particular, evidence-based micro-interventions are recommended and triggered by individual characteristics that are derived from self-reports, smartphone interactions and sensor data. After one year of development, the study team now conducts a first empirical study and thus, recruits people affected by depressive symptoms to improve not only the application as such but with it, the delivery of mental health interventions in the long run. Depressionen zeichnen sich durch eine weltweit steigende Prävalenz aus. Gleichzeitig nimmt nur eine Minderheit der Betroffenen zeitnah Hilfe in Anspruch. Mit Hilfe moderner Informationstechniken könnte ein Teil der unbehandelten Betroffenen erreicht werden. Eine potentielle Möglichkeit sind mobile Applikationen, welche Betroffenen zeitnah und zielgerichtet helfen. Wie genau solche Applikationen aufgebaut sein müssen, um Betroffene anzusprechen und zu unterstützen, ist bislang jedoch kaum untersucht. Das Entwicklerteam des Universitätsspitals Zürich, der Eidgenössischen Technischen Hochschule Zürich, der Universität St. Gallen und der makora AG, hat deswegen eine neue Applikation entwickelt, um Menschen mit depressiven Symptomen zu unterstützen. Die Applikation soll dabei in einem ersten Schritt lernen, depressive Symptome sicher zu erkennen. Durch aus evidenzbasierten Ansätzen abgeleitete Interventionen, welche durch die Erfassung von Sensordaten, Eigenangaben und der Interaktion mit dem Mobiltelephon, individuell auf die Betroffenen zugeschnitten sind, soll diesen dann in einem zweiten Schritt zielgerichtet geholfen werden

Mobile sensing and support for people with depression: a pilot trial in the wild

BACKGROUND: Depression is a burdensome, recurring mental health disorder with high prevalence. Even in developed countries, patients have to wait for several months to receive treatment. In many parts of the world there is only one mental health professional for over 200 people. Smartphones are ubiquitous and have a large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms and providing context-sensitive intervention support. OBJECTIVE: The objective of this study is 2-fold, first to explore the detection of daily-life behavior based on sensor information to identify subjects with a clinically meaningful depression level, second to explore the potential of context sensitive intervention delivery to provide in-situ support for people with depressive symptoms. METHODS: A total of 126 adults (age 20-57) were recruited to use the smartphone app Mobile Sensing and Support (MOSS), collecting context-sensitive sensor information and providing just-in-time interventions derived from cognitive behavior therapy. Real-time learning-systems were deployed to adapt to each subject's preferences to optimize recommendations with respect to time, location, and personal preference. Biweekly, participants were asked to complete a self-reported depression survey (PHQ-9) to track symptom progression. Wilcoxon tests were conducted to compare scores before and after intervention. Correlation analysis was used to test the relationship between adherence and change in PHQ-9. One hundred twenty features were constructed based on smartphone usage and sensors including accelerometer, Wifi, and global positioning systems (GPS). Machine-learning models used these features to infer behavior and context for PHQ-9 level prediction and tailored intervention delivery. RESULTS: A total of 36 subjects used MOSS for ≥2 weeks. For subjects with clinical depression (PHQ-9≥11) at baseline and adherence ≥8 weeks (n=12), a significant drop in PHQ-9 was observed (P=.01). This group showed a negative trend between adherence and change in PHQ-9 scores (rho=-.498, P=.099). Binary classification performance for biweekly PHQ-9 samples (n=143), with a cutoff of PHQ-9≥11, based on Random Forest and Support Vector Machine leave-one-out cross validation resulted in 60.1% and 59.1% accuracy, respectively. CONCLUSIONS: Proxies for social and physical behavior derived from smartphone sensor data was successfully deployed to deliver context-sensitive and personalized interventions to people with depressive symptoms. Subjects who used the app for an extended period of time showed significant reduction in self-reported symptom severity. Nonlinear classification models trained on features extracted from smartphone sensor data including Wifi, accelerometer, GPS, and phone use, demonstrated a proof of concept for the detection of depression superior to random classification. While findings of effectiveness must be reproduced in a RCT to proof causation, they pave the way for a new generation of digital health interventions leveraging smartphone sensors to provide context sensitive information for in-situ support and unobtrusive monitoring of critical mental health states

Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild

Background: Depression is a burdensome, recurring mental health disorder with high prevalence. Even in developed countries, patients have to wait for several months to receive treatment. In many parts of the world there is only one mental health professional for over 200 people. Smartphones are ubiquitous and have a large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms and providing context-sensitive intervention support. Objective: The objective of this study is 2-fold, first to explore the detection of daily-life behavior based on sensor information to identify subjects with a clinically meaningful depression level, second to explore the potential of context sensitive intervention delivery to provide in-situ support for people with depressive symptoms. Methods: A total of 126 adults (age 20-57) were recruited to use the smartphone app Mobile Sensing and Support (MOSS), collecting context-sensitive sensor information and providing just-in-time interventions derived from cognitive behavior therapy. Real-time learning-systems were deployed to adapt to each subject’s preferences to optimize recommendations with respect to time, location, and personal preference. Biweekly, participants were asked to complete a self-reported depression survey (PHQ-9) to track symptom progression. Wilcoxon tests were conducted to compare scores before and after intervention. Correlation analysis was used to test the relationship between adherence and change in PHQ-9. One hundred twenty features were constructed based on smartphone usage and sensors including accelerometer, Wifi, and global positioning systems (GPS). Machine-learning models used these features to infer behavior and context for PHQ-9 level prediction and tailored intervention delivery. Results: A total of 36 subjects used MOSS for ≥2 weeks. For subjects with clinical depression (PHQ-9≥11) at baseline and adherence ≥8 weeks (n=12), a significant drop in PHQ-9 was observed (P=.01). This group showed a negative trend between adherence and change in PHQ-9 scores (rho=−.498, P=.099). Binary classification performance for biweekly PHQ-9 samples (n=143), with a cutoff of PHQ-9≥11, based on Random Forest and Support Vector Machine leave-one-out cross validation resulted in 60.1% and 59.1% accuracy, respectively. Conclusions: Proxies for social and physical behavior derived from smartphone sensor data was successfully deployed to deliver context-sensitive and personalized interventions to people with depressive symptoms. Subjects who used the app for an extended period of time showed significant reduction in self-reported symptom severity. Nonlinear classification models trained on features extracted from smartphone sensor data including Wifi, accelerometer, GPS, and phone use, demonstrated a proof of concept for the detection of depression superior to random classification. While findings of effectiveness must be reproduced in a RCT to proof causation, they pave the way for a new generation of digital health interventions leveraging smartphone sensors to provide context sensitive information for in-situ support and unobtrusive monitoring of critical mental health states.ISSN:2291-522