Ecological Momentary Assessment with smartphones for measuring mental health problems in adolescents

AIM To analyze the viability of Ecological Momentary Assessment(EMA) for measuring the mental states associated with psychopathological problems in adolescents.METHODS In a sample of 110 adolescents,a sociodemographic data survey and an EMA Smartphone application over a oneweek period(five times each day),was developed to explore symptom profiles,everyday problems,coping strategies,and the contexts in which the events take place.RESULTS The positive response was 68.6%.Over 2250 prompts about mental states were recorded.In 53% of situations the smartphone was answered at home,25.5% of casesthey were with their parents or with peers(20.3%).Associations were found with attention,affective and anxiety problems(P < 0.001) in the participants who took longer to respond to the EMA app.Anxious and depressive states were highly interrelated(rho = 0.51,P < 0.001),as well as oppositional defiant problems and conduct problems(rho = 0.56,P < 0.001).Only in 6.2% of the situations the subjects perceived they had problems,mainly associated with inter-relational aspects with family,peers,boyfriends or girlfriends(31.2%).We also found moderate-high reliability on scales of satisfaction level on the context,on positive emotionality,and on the discomfort index associated with mental health problems.CONCLUSION EMA methodology using smartphones is a useful tool for understanding adolescents' daily dynamics.It achieved moderate-high reliability and accurately identified psychopathological manifestations experienced by community adolescents in their natural context.

TRANSMISSION MOMENTARY EFFICIENCY BASED ON THE D'ALEMBERT-LAGRANGE EQUATION FOR INVOLUTES GEARS

The D'Alembert-Lagrange equation is introduced and used to derive theformulas of momentary efficiency for external gearing of standard involutes spur gears. The gearingswith correct and increased center distance are discussed. The momentary efficiency formula iscalculated and analyzed using software Matlab. The derived formula of momentary efficiency is alsocompared with the traditional formula.

Identifying Distinct Quitting Trajectories after an Unassisted Smoking Cessation Attempt: An Ecological Momentary Assessment Study

Objectives: This study aimed at identifying distinct quitting trajectories over 29 days after an unassisted smoking ces- sation attempt by ecological momentary assessment (EMA). In order to validate these trajectories we tested if they predict smoking frequency up to six months later. Methods: EMA via mobile phones was used to collect real time data on smoking (yes/no) after an unassisted quit attempt over 29 days. Smoking frequency one, three and six months after the quit attempt was assessed with online questionnaires. Latent class growth modeling was used to analyze the data of 230 self-quitters. Results: Four different quitting trajectories emerged: quitter (43.9%), late quitter (11.3%), returner (17%) and persistent smoker (27.8%). The quitting trajectories predicted smoking frequency one, three and six months after the quit attempt (all p < 0.001). Conclusions: Outcome after a smoking cessation attempt is better described by four distinct trajectories instead of a binary variable for abstinence or relapse. In line with the relapse model by Marlatt and Gordon, late quitter may have learned how to cope with lapses during one month after the quitting attempt. This group would have been allocated to the relapse group in traditional outcome studies.

Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives

Depression is a serious medical condition and is a leading cause of disability worldwide.Current depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations,lack of objective assessments,and assessments that rely on patients'perceptions,memory,and recall.Digital phenotyping(DP),especially assessments conducted using mobile health technologies,has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable endophenotypes.DP includes two primary sources of digital data generated using ecological momentary assessments(EMA),assessments conducted in real-time,in subjects'natural environment.This includes active EMA,data that require active input by the subject,and passive EMA or passive sensing,data passively and automatically collected from subjects'personal digital devices.The raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients'clinical status.Preliminary investigations have also shown that linguistic and behavioral clues from social media data and data extracted from the electronic medical records can be used to predict depression status.These other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed patients.Success of DP endeavors depends on critical contributions from both psychiatric and engineering disciplines.The current review integrates important perspectives from both disciplines and discusses parameters for successful interdisciplinary collaborations.A clinically-relevant model for incorporating DP in clinical setting is presented.This model,based on investigations conducted by our group,delineates development of a depression prediction system and its integration in clinical setting to enhance depression diagnostics and inform the clinical decision making process.Benefits,challenges,and opportunities pertaining to clinical integration of DP of depression diagnostics are discussed from interdisciplinary perspectives.

Structural Earthquake Resistance Design Using Energy Method

A summary of status of researches in the field of structural earthquake resistance design on energy concept is presented in three parts: earthquake input, demands on the structure and supplied capacity of the structure. A new approach is proposed for analysis of the seismic response and damage criteria based on the momentary input energy.