# There is an app for that! The current state of mobile applications (apps) for DSM-5 obsessive-compulsive disorder, posttraumatic stress disorder, anxiety and mood disorders Michael Van Ameringen MD, FRCPC1,2,4 Jasmine Turna BSc, PhD(c)2,3 Zahra Khalesi BSc2 Katrina Pullia BSc2 Beth Patterson BScN, MSc1,2 [[HOPE-S Project Index]] #OCD #PTSD #anxiety #mood_regulation #EMI #EMA --- # Takeway The use of passive data, and self-reported mood correlated to symptoms severity. Generally the question they ask are the same ![[Daily electronic Self-monitoring of subjective and objective symptoms in bipolar disorder - the MONARCA trial#What EMA do they ask]] --- Bipolar Disorder # MONARCA [[Daily electronic Self-monitoring of subjective and objective symptoms in bipolar disorder - the MONARCA trial]] MONARCA (MONitoring, treAtment, and pRediCtion of bipolAr Disorder Episodes) is a self-monitoring app assessing illness activity (mood, sleep length, activity level, medicine intake). ==MONARCA collects passive data related to behavioral activities (e.g., the number and duration of incoming and outgoing of phone calls and text messages (social activities); accelerometer data (physical activity); the amount of movement between cell tower IDs (mobility); and phone usage) (Bardram et al., 2013).== Studies using MONARCA in BD patients have shown that ==passive data correlates with self-report data, and severity of clinically rated depressive and manic symptoms, as rated by the Hamilton Depression Rating Scale-17 items (HDRS-17) and the Young Mania Rating Scale (YMRS).== Following 3 months of use, ==significant correlations have been noted between self-reported mood and HDRS-17 (B = −0.051, P < .001); decreased movement was also correlated with a higher score on the HDRS-17 (B = −0.48, P = .020) (Faurholt-Jepsen et al., 2014).== A later 6-month trial with 61 BD patients (Faurholt- Jepsen, Frost et al., 2015) revealed that ==self-reported mood was correlated with HDRS-17 (B = −0.058, P < .001) as was decreased activity level (B = −0.042, P < .001) and increased self-monitored stress level (B = 0.047, P > .001).== An increase in ==self-reported mood was correlated with YMRS scores (B = −0.039, P < .001) as was increased activity level (B = 0.047, P < .001) and decreased sleep length (B = 0.047, P = .026).== The results indicated that active and passive data collected using MONARCA correlated with clinician-rated depressive and manic symptoms (Faurholt-Jepsen, Frost et al., 2015). ==MONARCA has also been effectively able to predict affective states using voice features extracted from naturalistic app use (Faurholt-Jepsen, Busk et al., 2016). However, daily self-monitoring does not reduce BD symptoms over a 6-month period (Faurholt-Jepsen, Frost et al., 2015).== ![[Screenshot 2022-03-14 at 11.40.15 AM.png]]![[Screenshot 2022-03-14 at 11.40.24 AM.png]] # SIMBA SIMBA (Social Information Monitoring for Patients with Bipolar Affective Disorder) has been evaluated in 133 BD patients (Beiwinkel et al., 2016). The app was used for 12 months and clinical assessments were completed at 8-week intervals using the German YMRS and the German HAM-D. ==Participants rated their mood and energy on a 10-point scale and passive data including distance travelled, location changes, and device activity were simultaneously recorded and paired to self-reports. Lower self-reported mood, decreased social communication, and a decline in physical activity predicted higher overall levels of depressive symptoms ==(B = −0.56, P < .001; B = −0.28, P < .001; B = −0.11, P = .03). ==Higher levels of manic symptoms were predicted by decreased smartphone activity (B = −0.17, P < .001), less distance travelled (B = −0.37, P < .001), and higher social communication (B = 0.48, P = .03) (Beiwinkel et al., 2016).== Passive data have also been used to predict state changes (e.g., normal to manic or depressed to normal) in BD. In a 10-month trial, 10 BD patients used a smartphone which collected passive information, paired with clinician-rated HAM-D and YMRS conducted every 3 weeks (Grunerbl et al., 2015). The app detected state changes with 76% recognition accuracy and 97% perfect recall and precision. The state change recognition function of the app also alerted the primary physician and scheduled appointments when appropriate (Grunerbl et al., 2015). A relationship between self-reported social rhythm metric (SRM) data and passive data from smartphones has also been reported (Abdullah et al., 2016). Location, distance traveled, conversation fre- quency, and nonstationary duration as inputs, were able to predict sta- ble (SRM score ≥ 3.5) and unstable (SRM score < 3.5) states with high accuracy (precision: 0.85 and recall: 0.86), suggesting that automated sensing can be used to infer a SRM score Abdullah et al., 2016.