Toi Même, a Mobile Health Platform for Measuring Bipolar Illness Activity: Protocol for a Feasibility Study

App-based self-trainings targeting strain recovery and their effect on concentration

During the COVID-19 pandemic, many knowledge workers reported concentration problems. This can be seen as critical as concentration is an important indicator for both cognitive wellbeing and occupational success. Drawing on the load theory of selective attention, we argue that concentration problems can be caused by the strain workers experienced during the pandemic. Consequently, by associating impaired concentration with strain, we hypothesize that strengthening strain recovery is a method that potentially supports concentration in stressful times. We developed the smartphone app "swoliba" containing self-training exercises targeting recovery experiences and tested the benefit of this app with two intervention groups and one waitlist-control group. Participants of the intervention groups were asked to carry out the exercises accompanied by surveys throughout a period of 4 weeks in 2020/2021. Results show that participants in the intervention groups reported higher concentration levels and lower strain levels than those in the control group, and this beneficial effect on concentration is partially mediated via lower strain levels. We conclude that self-training apps can be an effective tool for recovery interventions reducing strain but also supporting concentration. Using two different intervention conditions, we can reliably demonstrate the beneficial effect of our swoliba training program.

Developing “MinDag” – an app to capture symptom variation and illness mechanisms in bipolar disorder

Introduction

The illness course of bipolar disorder (BD) is highly heterogeneous with substantial variation between individuals with the same BD subtype and within individuals over time. This heterogeneity is not well-delineated and hampers the development of more targeted treatment. Furthermore, although lifestyle-related behaviors are believed to play a role in the illness course, such mechanisms are poorly understood. To address some of these knowledge gaps, we aimed to develop an app for collection of multi-dimensional longitudinal data on BD-relevant symptoms and lifestyle-related behaviors.

Methods

An app named MinDag was developed at the Norwegian Center for Mental Disorders Research in Oslo, Norway. The app was designed to tap into selected areas: mood, sleep, functioning/activities (social, occupational, physical exercise, leisure), substance use, emotional reactivity, and psychotic experiences. Ethical, security and usability issues were highly prioritized throughout the development and for the final app solution. We conducted beta- and pilot testing to eliminate technical problems and enhance usability and acceptability.

Results

The final version of MinDag comprises six modules; three which are presented for the user once daily (the Sleep module in the morning and the Mood and Functoning/Activities modules in the evening) and three which are presented once weekly (Substance Use, Emotional Reactivity, and Psychotic Experiences modules). In general, MinDag was well received in both in the beta-testing and the pilot study, and the participants provided valuable feedback that was taken into account in the final development. MinDag is now in use as part of the research protocol at the NORMENT center and in a specialized treatment unit for BD at Oslo University Hospital in Norway.

Discussion

We believe that MinDag will generate unique longitudinal data well suited for capturing the heterogeneity of BD and clarifying important unresolved issues such as how life-style related behavior may influence BD symptoms. Also, the experiences and knowledge derived from the development of MinDag may contribute to improving the security, acceptability, and benefit of digital tools in mental health.

Effectiveness of mobile applications for patients with severe mental illness: A meta-analysis of randomized controlled trials

BACKGROUND

A systematic review and meta-analysis was conducted to evaluate the effectiveness of mobile applications used by patients diagnosed with mental disorders.

METHODS

An electronic literature search in five databases including PubMed, Embase, the Cochrane Library, CINAHL, and PsychInfo was conducted. The keywords used were "mental disorder," "mental illness," "mobile phone," "smartphone," "mHealth," "application," and "app". The search was restricted to randomized controlled trials (RCTs) written in English and Korean.

RESULTS

Fourteen RCTs, involving 1307 patients diagnosed with depression, schizophrenia, and bipolar disorder were included in the analysis. The included studies were published between 2012 and 2020 and used mobile applications. The risk of bias tool was used to assess methodological quality and the overall risk of bias of the included studies was moderate. The pooled data favored mobile application interventions in reducing the disease-related symptoms of depression (standardized mean difference [SMD] = -0.255, 95% CI: -0.370 to -0.141), mania symptoms (SMD = -0.279, 95% CI: -0.456 to -0.102), and positive (SMD = -0.205, 95% CI: -0.388 to -0.022) and negative psychotic symptoms (SMD = -0.406, 95% CI: -0.791 to -0.020). In subgroup analysis, the incorporation of feedback, notification, and data tracking features in the mobile application intervention produced better outcomes.

CONCLUSION

This review provided evidence that mobile applications could well-assist patients diagnosed with mental disorders. Greater benefits could be achieved by well-designed interventions incorporating strategies with thoughtful consideration of the disease characteristics. Mobile applications present the potential to be effective supplements to clinical treatment.

Exploring machine learning to predict depressive relapses of bipolar disorder patients

BACKGROUND

Bipolar disorder (BD) is a chronic mood disorder characterized by recurrent episodes of mania or hypomania and depression, expressed by changes in energy levels and behavior. However, most of relapse studies use evidence-based approaches with statistical methods. With the advance of the precision medicine this study aims to use machine learning (ML) approaches as a possible predictor in depressive relapses in BD.

METHOD

Four accepted and well used ML algorithms (Support Vector Machines, Random Forests, Naïve Bayes, and Multilayer Perceptron) were applied to the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) dataset in a cohort of 800 patients (507 patients presented depressive relapse and 293 did not), who became euthymic during the study and were followed for one year.

RESULTS

The ML algorithms presented reasonable performance in the prediction task, ranging from 61 to 80% in the F-measure. The Random Forest algorithm obtained a higher average of performance (Relapse Group 68%; No Relapse Group 74%). The three most important mood symptoms observed in the relapse visit (Random Forest) were: interest; depression mood and energy.

LIMITATIONS

Social and psychological parameters such as marital status, social support system, personality traits, might be an important predictor in depressive relapses, although we did not compute this data in our study.

CONCLUSIONS

Our findings indicate that applying precision medicine models by means of machine learning in BD studies could be feasible as a sensible approach to better support medical decision-making in the BD treatment and prevention of future relapses.

Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring

Collecting and analyzing data from sensors embedded in the context of daily life has been widely employed for the monitoring of mental health. Variations in parameters such as movement, sleep duration, heart rate, electrocardiogram, skin temperature, etc., are often associated with psychiatric disorders. Namely, accelerometer data, microphone, and call logs can be utilized to identify voice features and social activities indicative of depressive symptoms, and physiological factors such as heart rate and skin conductance can be used to detect stress and anxiety disorders. Therefore, a wide range of devices comprising a variety of sensors have been developed to capture these physiological and behavioral data and translate them into phenotypes and states related to mental health. Such systems aim to identify behaviors that are the consequence of an underlying physiological alteration, and hence, the raw sensor data are captured and converted into features that are used to define behavioral markers, often through machine learning. However, due to the complexity of passive data, these relationships are not simple and need to be well-established. Furthermore, parameters such as intrapersonal and interpersonal differences need to be considered when interpreting the data. Altogether, combining practical mobile and wearable systems with the right data analysis algorithms can provide a useful tool for the monitoring and management of mental disorders. The current review aims to comprehensively present and critically discuss all available smartphone-based, wearable, and environmental sensors for detecting such parameters in relation to the treatment and/or management of the most common mental health conditions.

Conceptual Ambiguity Surrounding Gamification and Serious Games in Health Care: Literature Review and Development of Game-Based Intervention Reporting Guidelines (GAMING)

BACKGROUND

In health care, the use of game-based interventions to increase motivation, engagement, and overall sustainability of health behaviors is steadily becoming more common. The most prevalent types of game-based interventions in health care research are gamification and serious games. Various researchers have discussed substantial conceptual differences between these 2 concepts, supported by empirical studies showing differences in the effects on specific health behaviors. However, researchers also frequently report cases in which terms related to these 2 concepts are used ambiguously or even interchangeably. It remains unclear to what extent existing health care research explicitly distinguishes between gamification and serious games and whether it draws on existing conceptual considerations to do so.

OBJECTIVE

This study aims to address this lack of knowledge by capturing the current state of conceptualizations of gamification and serious games in health care research. Furthermore, we aim to provide tools for researchers to disambiguate the reporting of game-based interventions.

METHODS

We used a 2-step research approach. First, we conducted a systematic literature review of 206 studies, published in the Journal of Medical Internet Research and its sister journals, containing terms related to gamification, serious games, or both. We analyzed their conceptualizations of gamification and serious games, as well as the distinctions between the two concepts. Second, based on the literature review findings, we developed a set of guidelines for researchers reporting on game-based interventions and evaluated them with a group of 9 experts from the field.

RESULTS

Our results show that less than half of the concept mentions are accompanied by an explicit definition. To distinguish between the 2 concepts, we identified four common approaches: implicit distinction, synonymous use of terms, serious games as a type of gamified system, and distinction based on the full game dimension. Our Game-Based Intervention Reporting Guidelines (GAMING) consist of 25 items grouped into four topics: conceptual focus, contribution, mindfulness about related concepts, and individual concept definitions.

CONCLUSIONS

Conceptualizations of gamification and serious games in health care literature are strongly heterogeneous, leading to conceptual ambiguity. Following the GAMING can support authors in rigorous reporting on study results of game-based interventions.

Digital Phenotyping in Bipolar Disorder: Which Integration with Clinical Endophenotypes and Biomarkers?

Bipolar disorder (BD) is a complex neurobiological disorder characterized by a pathologic mood swing. Digital phenotyping, defined as the 'moment-by-moment quantification of the individual-level human phenotype in its own environment', represents a new approach aimed at measuring the human behavior and may theoretically enhance clinicians' capability in early identification, diagnosis, and management of any mental health conditions, including BD. Moreover, a digital phenotyping approach may easily introduce and allow clinicians to perform a more personalized and patient-tailored diagnostic and therapeutic approach, in line with the framework of precision psychiatry. The aim of the present paper is to investigate the role of digital phenotyping in BD. Despite scarce literature published so far, extremely heterogeneous methodological strategies, and limitations, digital phenotyping may represent a grounding research and clinical field in BD, by owning the potentialities to quickly identify, diagnose, longitudinally monitor, and evaluating clinical response and remission to psychotropic drugs. Finally, digital phenotyping might potentially constitute a possible predictive marker for mood disorders.