The mobile sleep lab app: An open-source framework for mobile sleep assessment based on consumer-grade wearable devices.

BACKGROUND: Sleep disorders have a prevalence of up to 50% and are commonly diagnosed using polysomnography. However, polysomnography requires trained staff and specific equipment in a laboratory setting, which are expensive and limited resources are available. Mobile and wearable devices such as fitness wristbands can perform limited sleep monitoring but are not evaluated well. Here, the development and evaluation of a mobile application to record and synchronize data from consumer-grade sensors suitable for sleep monitoring is presented and evaluated for data collection capability in a clinical trial. METHODS: Wearable and ambient consumer-grade sensors were selected to mimic the functionalities of clinical sleep laboratories. Then, a modular application was developed for recording, processing and visualizing the sensor data. A validation was performed in three phases: (1) sensor functionalities were evaluated, (2) self-experiments were performed in full-night experiments, and (3) the application was tested for usability in a clinical trial on primary snoring. RESULTS: The evaluation of the sensors indicated their suitability for assessing basic sleep characteristics. Additionally, the application successfully recorded full-night sleep. The collected data was of sufficient quality to detect and measure body movements, cardiac activity, snoring and brightness. The ongoing clinical trial phase showed the successful deployment of the application by medical professionals. CONCLUSION: The proposed software demonstrated a strong potential for medical usage. With low costs, it can be proposed for screening, long-term monitoring or in resource-austere environments. However, further validations are needed, in particular the comparison to a clinical sleep laboratory.

Psychologist in a Pocket: Lexicon Development and Content Validation of a Mobile-Based App for Depression Screening.

BACKGROUND: Language reflects the state of one's mental health and personal characteristics. It also reveals preoccupations with a particular schema, thus possibly providing insights into psychological conditions. Using text or lexical analysis in exploring depression, negative schemas and self-focusing tendencies may be depicted. As mobile technology has become highly integrated in daily routine, mobile devices have the capacity for ecological momentary assessment (EMA), specifically the experience sampling method (ESM), where behavior is captured in real-time or closer in time to experience in one's natural environment. Extending mobile technology to psychological health could augment initial clinical assessment, particularly of mood disturbances, such as depression and analyze daily activities, such as language use in communication. Here, we present the process of lexicon generation and development and the initial validation of Psychologist in a Pocket (PiaP), a mobile app designed to screen signs of depression through text analysis. OBJECTIVE: The main objectives of the study are (1) to generate and develop a depressive lexicon that can be used for screening text-input in mobile apps to be used in the PiaP; and (2) to conduct content validation as initial validation. METHODS: The first phase of our research focused on lexicon development. Words related to depression and its symptoms based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and in the ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines classification systems were gathered from focus group discussions with Filipino college students, interviews with mental health professionals, and the review of established scales for depression and other related constructs. RESULTS: The lexicon development phase yielded a database consisting of 13 categories based on the criteria depressive symptoms in the DSM-5 and ICD-10. For the draft of the depression lexicon for PiaP, we were able to gather 1762 main keywords and 9655 derivatives of main keywords. In addition, we compiled 823,869 spelling variations. Keywords included negatively-valenced words like "sad", "unworthy", or "tired" which are almost always accompanied by personal pronouns, such as "I", "I'm" or "my" and in Filipino, "ako" or "ko". For the content validation, only keywords with CVR equal to or more than 0.75 were included in the depression lexicon test-run version. The mean of all CVRs yielded a high overall CVI of 0.90. A total of 1498 main keywords, 8911 derivatives of main keywords, and 783,140 spelling variations, with a total of 793, 553 keywords now comprise the test-run version. CONCLUSIONS: The generation of the depression lexicon is relatively exhaustive. The breadth of keywords used in text analysis incorporates the characteristic expressions of depression and its related constructs by a particular culture and age group. A content-validated mobile health app, PiaP may help augment a more effective and early detection of depressive symptoms.

Psychologist in a pocket: towards depression screening on mobile phones.

Depression is the most prevalent clinical disorder and one of the main causes of disability. This makes early detection of depressive symptoms critical in its prevention and management. This paper presents and discusses the development of Psychologist in a Pocket (PiaP), a mental mHealth application for Android which screens and monitors for these symptoms, and-given the explicit permission of the user-alerts a trusted contact such as the mental health professional or a close friend, if it detects symptoms. All text inputted electronically-such as short message services, emails, social network posts-is analyzed based on keywords related to depression based on DSM-5 and ICD criteria as well as Beck's Cognitive Theory of Depression and the Self-Focus Model. Data evaluation and collection happen in the background, on-device, without requiring any user involvement. Currently, the application is in an early prototype phase entering initial clinical validation.

Towards Bringing EEG Research and Diagnostics out of the Lab.

Bringing brain research tools like EEG devices out of the lab into the pockets of practitioners and researchers may fundamentally change the way we perform diagnostics and research. While most of the current techniques are limited to research clinics and require excessive set-up, new consumer EEG devices connected to standard, off-the-shelf mobile devices allow us to lift these limitations. This allows neuropsychological assessment and research in mobile settings, possibly even in remote areas with limited accessibility and infrastructure, thus bringing the equipment to the patient, instead of bringing the patient to the equipment. We are developing an Android based mobile framework to perform EEG studies. By connecting a mobile consumer EEG headset directly to an unmodified mobile device, presenting auditory and visual stimuli, as well as user interaction, we create a self-contained experimental platform. We complement this platform by a toolkit for immediate evaluation of the recorded data directly on the device, even without Internet connectivity. Initial results from the replication of two Event Related Potentials studies indicate the feasibility of the approach.

Ratpack: wearable sensor networks for animal observation.

The goal of our project is to describe the behavior of rats. For this purpose we are using wireless sensor networks, monitoring various quantities that yield important information to complement current knowledge on the behavioral repertoire of rats. So far, on the sensing and processing side we have developed innovative, minimalist approaches pointing in two directions: vocalization analysis and movement tracking. On the data collection and routing side we have adapted to the known burrowing habits of rats by developing new methods for synchronization and data aggregation under the paradigm of sporadic connectivity in a sparse, dynamic network.