Digital illness monitoring with MinDag
Digital monitoring of illness fluctuation and complex interplays in psychotic disorders
Even with adequate treatment, there are large variations in the course and symptomatology of psychotic disorders between and within individuals. To capture this variation and predict new illness episodes is challenging with traditional assessment tools and methods, which are often prone to recall bias and generalizations. Furthermore, symptom changes are likely to be triggered or worsened by behavioral and psychological factors. In addition, to identify which symptoms that have primacy to others are of importance in order to develop more targeted treatment interventions.
Disentangling complex relationships between symptoms and other behavior and psychological factors requires fine-grained longitudinal data. To meet these challenges, we developed a smartphone application in which individuals with bipolar and psychotic disorders can track symptoms and symptom-relevant factors over time.
An inter-disciplinary team of clinical researchers, service-users, technicians, and designers has collaborated to develop the app - MinDag (My Day) - for illness monitoring. A pilot trial included focus groups and surveys. MinDag has been revised according to user feedback addressing technical issues, design, content and motivational features. Participants in the MinDag study will take part in extensive baseline characterization including clinical, neuropsychological, biochemical (incl. genotyping) measures, and in some cases brain imaging. Participants will also be asked to wear actigraphs (MotionWatch 81) in order to collect objective data on activity patterns, sleep and light exposure. Clinician rated symptom assessments will be conducted during the study to investigate validity of app based self-report.
The app includes the following modules: Daily registration of Sleep (corresponding with the Consensus Sleep Diary2, Mood3, Functioning (work/school, social activities, physical activity), and weekly registration of Substance use and - craving, Psychotic experiences (based on ClinTouch4), and Emotional reactivity (Multidimentional Assessment of Thymic States3). Some of the modules are based on previously validated self-report scales, while some were developed by in-house clinical researchers with relevant expertise.
Data collection with MinDag started in August 2019. We expect to recruit 30-50 individuals per year for 4 years for up to six months of app use. App data will be monitored for safety purposes, and participants will receive feedback based on their registrations in follow-up appointments with clinical researchers. App data will be analyzed in relation to data from the extensive baseline assessments. With MinDag we aim to gain new, important knowledge regarding sub-phenotypes, the course of psychotic disorders, and the interplay between symptoms and relevant behavioral and psychological dimensions.
1. CamNtech. MotionWatch 8. CamNtech . Accessed 07.03.19, 2019.
2. Carney CE, Buysse DJ, Ancoli-Israel S, et al. The consensus sleep diary: standardizing prospective sleep self-monitoring. Sleep. 2012;35(2):287-302.
3. Henry C, M'Bailara K, Mathieu F, Poinsot R, Falissard B. Construction and validation of a dimensional scale exploring mood disorders: MAThyS (Multidimensional Assessment of Thymic States). BMC Psychiatry. 2008;8:82.
4. Palmier-Claus JE, Ainsworth J, Machin M, et al. The feasibility and validity of ambulatory self-report of psychotic symptoms using a smartphone software application. BMC Psychiatry. 2012;12:172.