A systematic review of the psychometric properties, usability and clinical impacts of mobile mood-monitoring applications in young people

Background: Mobile mood-monitoring applications are increasingly used by mental health providers, widely advocated within research, and a potentially effective method to engage young people. However, little is known about their efficacy and usability in young populations. Method: A systematic review addressing 3 research questions focused on young people: 1) what are the psychometric properties of mobile mood-monitoring applications; 2) what is their usability; and 3) what are their positive and negative clinical impacts? Findings were synthesised narratively, study quality assessed, and compared with evidence from adult studies. Results: We reviewed 25 articles. Studies on the psychometric properties of mobile mood-monitoring applications were sparse, but indicate questionable to excellent internal consistency, moderate concurrent validity, and good usability. Participation rates ranged from 30-99% across studies, and appeared to be affected by methodological factors (e.g., payments) and individual characteristics (e.g., IQ score). Mobile mood-monitoring applications are positively perceived by youth, may reduce depressive symptoms by increasing emotional awareness, and could aid in the detection of mental health and substance use problems. There was very limited evidence on potential negative impacts. Conclusions: Evidence for the use of mood-monitoring applications in youth is promising but limited due to a lack of high quality studies. Future work should explicate the effects of mobile mood-monitoring applications on effective self-regulation, clinical outcomes across disorders, and young people's engagement with mental health services. Potential negative impacts in this population should also be investigated, as the adult literature suggests that application use could potentially increase negativity and depression symptoms

Daily longitudinal self-monitoring of mood variability in bipolar disorder and borderline personality disorder

Background Traditionally, assessment of psychiatric symptoms has been relying on their retrospective report to a trained interviewer. The emergence of smartphones facilitates passive sensor-based monitoring and active real-time monitoring through time-stamped prompts; however there are few validated self-report measures designed for this purpose. Methods We introduce a novel, compact questionnaire, Mood Zoom (MZ), embedded in a customized smartphone application. MZ asks participants to rate anxiety, elation, sadness, anger, irritability and energy on a 7-point Likert scale. For comparison, we used four standard clinical questionnaires administered to participants weekly to quantify mania (ASRM), depression (QIDS), anxiety (GAD-7), and quality of life (EQ-5D). We monitored 48 Bipolar Disorder (BD), 31 Borderline Personality Disorder (BPD) and 51 Healthy Control (HC) participants to study longitudinal (median±iqr: 313±194 days) variation and differences of mood traits by exploring the data using diverse time-series tools. Results MZ correlated well (|R| &gt; 0.5, p &lt; 0.0001) with QIDS, GAD-7, and EQ-5D. We found statistically strong (|R| &gt; 0.3, p &lt; 0.0001) differences in variability in all questionnaires for the three cohorts. Compared to HC, BD and BPD participants exhibit different trends and variability, and on average had higher self-reported scores in mania, depression, and anxiety, and lower quality of life. In particular, analysis of MZ variability can differentiate BD and BPD which was not hitherto possible using the weekly questionnaires. Limitations All reported scores rely on self-assessment; there is a lack of ongoing clinical assessment by experts to validate the findings. Conclusions MZ could be used for efficient, long-term, effective daily monitoring of mood instability in clinical psychiatric practice.</p

Trabecular bone analysis in CT and X-ray images of the proximal femur for the assessment of local bone quality

Currently, conventional X-ray and CT images as well as invasive methods performed during the surgical intervention are used to judge the local quality of a fractured proximal femur. However, these approaches are either dependent on the surgeon's experience or cannot assist diagnostic and planning tasks preoperatively. Therefore, in this work a method for the individual analysis of local bone quality in the proximal femur based on model-based analysis of CT- and X-ray images of femur specimen will be proposed. A combined representation of shape and spatial intensity distribution of an object and different statistical approaches for dimensionality reduction are used to create a statistical appearance model in order to assess the local bone quality in CT and X-ray images. The developed algorithms are tested and evaluated on 28 femur specimen. It will be shown that the tools and algorithms presented herein are highly adequate to automatically and objectively predict bone mineral density values as well as a biomechanical parameter of the bone that can be measured intraoperatively

The benefit of activity recognition for mobile phone based nursing documentation: A Wizard-of-Oz study

In a Wizard-of-Oz experiment we investigate to what degree automatic activity recognition could support the use of prioritized lists for nursing documentation on hand held mobile devices. The study involved 15 nurses, 60 patients records and over 250 documented processes at a geriatric care ward. Based on time effort, interaction complexity, error rate and subjective system perception our wizard of oz study shows that activity recognition is a key factor in the usability and acceptance of the system. We also study the impact of varying simulated activity recognition errors and demonstrate that error rates of up to 25% do not aversely affect the documentation process

Correlation of significant places with self-reported state of patients with bipolar disorder

Capabilities of smartphones can be utilised to monitor a range of aspects of users' behaviour. This has potential to affect a number of areas where users' behaviour is considered relevant information. Most notably, healthcare in general and mental health in particular are excellent candidates to utilise capabilities of smartphones, since mental disorders typically have a strong behaviour component. This is especially true for bipolar disorder, where mobility and activity of the patients is considered an indicator of a bipolar episode (depressive or manic). In this work we report on results of using capabilities of smartphones to monitor mobility of the patients, monitored over the period of 12 weeks. Through the continuous discovery of Wi-Fi access points we have inferred significant places (where the patient spent majority of the time) for each patient and investigate correlation of these places with patients' self-reported state. The results show that for majority of patients there exists negative correlation between time spent in clinic and their self-assessment score, while there is a positive correlation between self-assessment scores and time spent outside the home or clinic

Smartphone Based Recognition of States and State Changes in Bipolar Disorder Patients

none9siToday's health care is difficult to imagine without the possibility to objectively measure various physiological parameters related to patients' symptoms (from temperature through blood pressure to complex tomographic procedures). Psychiatric care remains a notable exception that heavily relies on patient interviews and self-assessment. This is due to the fact that mental illnesses manifest themselves mainly in the way patients behave throughout their daily life and, until recently there were no “behavior measurement devices.” This is now changing with the progress in wearable activity recognition and sensor enabled smartphones. In this paper, we introduce a system, which, based on smartphone-sensing is able to recognize depressive and manic states and detect state changes of patients suffering from bipolar disorder. Drawing upon a real-life dataset of ten patients, recorded over a time period of 12 weeks (in total over 800 days of data tracing 17 state changes) by four different sensing modalities, we could extract features corresponding to all disease-relevant aspects in behavior. Using these features, we gain recognition accuracies of 76% by fusing all sensor modalities and state change detection precision and recall of over 97%. This paper furthermore outlines the applicability of this system in the physician-patient relations in order to facilitate the life and treatment of bipolar patients.Grunerbl, A.; Muaremi, A.; Osmani, Venet; Bahle, G.; Oehler, S.; Troester, G.; Mayora Ibarra, Oscar Arturo; Haring, C.; Lukowicz, P.A., Grunerbl; A., Muaremi; Osmani, Venet; G., Bahle; S., Oehler; G., Troester; Mayora Ibarra, Oscar Arturo; C., Haring; P., Lukowic

Stress Modelling Using Transfer Learning in Presence of Scarce Data

Stress at work is a significant occupational health concern nowadays. Thus, researchers are looking to find comprehensive approaches for improving wellness interventions relevant to stress. Recent studies have been conducted for inferring stress in labour settings; they model stress behaviour based on non-obtrusive data obtained from smartphones. However, if the data for a subject is scarce, a good model cannot be obtained. We propose an approach based on transfer learning for building a model of a subject with scarce data. It is based on the comparison of decision trees to select the closest subject for knowledge transfer. We present an study carried out on 30 employees within two organisations. The results show that the in the case of identifying a “similar” subject, the classification accuracy is improved via transfer learning