Background
Rationale
Technology-based ecological momentary assessments for depression
Ecological momentary interventions for depression
Previous systematic reviews
Objective
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Which are the technological characteristics (devices, sensors, biosensors) and the clinical features and outcomes (fields of application, sampling schemas, compliance, dropout rates, results obtained) of the available EMAs and EMIs for depression?
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How and why could the clinical practice benefit from the use of EMAs and EMIs?
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Building upon recent advances in machine learning for affective modeling and on the available examples of EMA and EMI studies adopting these techniques, what are the current gaps and future developments of EMAs and EMIs that could be tackled thanks to the combined use of sensors and biosensors data in addition to self-reports?
Methods
Eligibility criteria
Participant characteristics (P)
Study characteristics (I)
Ecological momentary assessment
Ecological momentary intervention
Comparators (C)
Outcome measures (O)
Ecological momentary assessment
Ecological momentary intervention
Type of studies
Searching and selection process
Preliminary search
Search strategy
Concept | Search terms |
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EMA–EMI |
EMA, ecological momentary assessment, EMI, ecological momentary intervention, mobile health, mHealth, mobile phone, smartphone, ecological momentary intervention, ESM, experience sampling method, ambulatory assessment, personal digital assistant, ambulatory monitoring, real time data capture, real time monitoring, real time interventions, computer assisted diary, electronic diary
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Major depressive disorder |
Depression, MDD, major depressive disorder, major depression, unipolar depression, emotion dysregulation, affective disorder, mood disorder, depress*, affective symptoms, depressive symptoms
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Selection process
Data extraction
Ecological momentary assessment
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General: authors, article title, type of publication, year of publication;
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Study characteristics: aim of the study, main variables of interest, type of electronic device, adoption of sensors and biosensors, sampling methods, assessment duration, and type of data analysis (i.e. use of machine learning techniques);
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Participants: number of participants, type of control group, inclusion/exclusion criteria, dropout rates;
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Outcome measures: unit of measurement, type of measurement, primary outcomes, compliance rates.
Ecological momentary intervention
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General: authors, article title, type of publication, year of publication;
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Study characteristics: aim of the study, type of electronic device, adoption of sensors and biosensors, sampling methods, duration and intensity of the treatment, type of data analysis (i.e., use of machine learning techniques);
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Participant: number of participants, control group, inclusion/exclusion criteria, dropout rates;
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Outcome measures: unit of measurement, type of measurement, successfulness of the intervention, compliance, participants’ satisfaction.
Quality assessment of the included studies
Ecological momentary assessment
Ecological momentary intervention
Data synthesis and result presentation
Key items | |
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EMA |
Author(s), Sample(s), Variable(s), Device(s), Sensor(s), Duration, Prompt(s) per day, Sampling Schema, Primary Outcome(s);
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EMI |
Author(s), Name of the intervention, Sample(s), Content of the Intervention, Duration, Sensor(s), Primary Outcome(s).
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