Background: Smartphones allow for real-time monitoring of patients’ behavioral activities in a naturalistic setting. These data are suggested as markers for the mental state of patients with bipolar disorder (BD). Objective: We assessed the relations between data collected from smartphones and the clinically rated depressive and manic symptoms together with the corresponding affective states in patients with BD. Methods: BDmon, a dedicated mobile app, was developed and installed on patients’ smartphones to automatically collect the statistics about their phone calls and text messages as well as their self-assessments of sleep and mood. The final sample for the numerical analyses consisted of 51 eligible patients who participated in at least two psychiatric assessments and used the BDmon app (mean participation time, 208 [SD 132] days). In total, 196 psychiatric assessments were performed using the Hamilton Depression Rating Scale and the Young Mania Rating Scale. Generalized linear mixed-effects models were applied to quantify the strength of the relation between the daily statistics on the behavioral data collected automatically from smartphones and the affective symptoms and mood states in patients with BD. Results: Objective behavioral data collected from smartphones were found to be related with the BD states as follows: (1) depressed patients tended to make phone calls less frequently than euthymic patients (β=−.064, P=.01); (2) the number of incoming answered calls during depression was lower than that during euthymia (β=−.15, P=.01) and, concurrently, missed incoming calls were more frequent and increased as depressive symptoms intensified (β=4.431, P<.001; β=4.861, P<.001, respectively); (3) the fraction of outgoing calls was higher in manic states (β=2.73, P=.03); (4) the fraction of missed calls was higher in manic/mixed states as compared to that in the euthymic state (β=3.53, P=.01) and positively correlated to the severity of symptoms (β=2.991, P=.02); (5) the variability of the duration of the outgoing calls was higher in manic/mixed states (β=.0012, P=.045) and positively correlated to the severity of symptoms (β=.0017, P=.02); and (6) the number and length of the sent text messages was higher in manic/mixed states as compared to that in the euthymic state (β=.031, P=.01; β=.015, P=.01; respectively) and positively correlated to the severity of manic symptoms (β=.116, P<.001; β=.022, P<.001; respectively). We also observed that self-assessment of mood was lower in depressive (β=−1.452, P<.001) and higher in manic states (β=.509, P<.001). Conclusions: Smartphone-based behavioral parameters are valid markers for assessing the severity of affective symptoms and discriminating between mood states in patients with BD. This technology opens a way toward early detection of worsening of the mental state and thereby increases the patient’s chance of improving in the course of the illness.

Driving on an uneven road causes vibration of the vehicle. These vibrations affect the comfort of the road users but also convey information about the road profile. This study demonstrates the feasibility, accuracy, and repeatability of the accelerometer system for pavement evaluation that computes the international roughness index (IRI) using multiple smartphones to collect data. It uses quarter-car and half-car suspension models calibrated through rides on speed bumps or a section with a known profile. We report on a field test with eight runs over an 18.6-km route of urban asphalt roads. The correlation of our results with the reference laser profilometer IRI oscillates around 0.7 for a single measurement and reaches 0.85 in the case of using multiple sensors and repeated rides. We also demonstrate that inaccuracies in the spatial attribution of the road localization are a similarly important source of errors as the uncertainty related to mechanical models.

The paper discusses the genetic diversity of real-coded populations processed by an evolutionary algorithm (EA). Diversity is expressed as a variance or a covariance matrix of individuals contained in the population, in one-or multidimensional cases, respectively. We focus on the exploration stage of the optimization, therefore the fitness function is modeled as noise. We prove that the expected value of genetic diversity achieves a level proportional to the mutation covariance matrix. The proportionality coefficient depends solely on the EA parameters. Formulas are derived to predict the diversity for fitness proportionate, tournament and truncation selection, with and without arithmetic crossover and with Gaussian mutation. Experimental validation of the multidimensional case shows that prediction accuracy is satisfactory in a broad spectrum of settings of EA parameters.