Table 2.

Predictive models used in e-mental health.

Model type	Studya	Data	Prediction	Method	Comment
1	Ankarali et al. (2007)	Social-demographic data	Postpartum depression	Classification tree, logistic regression	Estimation of risk for postpartum depression in women.
1	Van der Werf et al. (2006)	Time-to-event data	Estimation of recovery probability	Sequential-phase model	Models transitions from non-depressed to depressed states in the general population. Identifies factors that lead to depression.
1	Gittelman et al. (2015)	Facebook likes	Life expectancy is estimated from Facebook likes	Principal Component Analysis, linear regression	Facebook likes indicate habits and activities, which are used to predict life expectancy.
1	Pestian and Nasrallah (2010)	Suicide notes	Possible suicide	Support vector machine	The genuinely of suicide notes is estimated.
1	Burns et al. (2011)	EMA	EMA ratings are inferred from the collected smartphone data	Various types of regression trees	Provides EMI to the user in stressful situations.
1	Saeb et al. (2015)	GPS movement data	Correlation between GPS and phone usage	K-means for location clustering, and elastic net regularization for prediction of depressive symptoms	The relationship among mobile phone GPS sensor features, EMA ratings, and the PHQ-9 scores is analyzed.
1	Kim et al. (2015)	Activity measures	Estimation of mood	Hierarchical model	Mood is inferred from physical activity.
1	Doryab et al. (2014)	Noise level, movement and location data, light intensity, phone usage	Correlation between depression and phone usage and sleep behavior	Tertius algorithm (see Flach and Lachiche, 2001)	Preliminary study on detection of behavior change in people with depression.
1	Mestry et al. (2015)	Smartphone measures	Estimation of mental state	Correlation	Smartphone data is analyzed for correlation with the mental state such as depression, anxiety or stress.
1	Demirci et al. (2015)	Pittsburgh Sleep Quality Index, Beck Depression Inventory, Beck Anxiety Inventory	Sleep quality, depression and anxiety score	Correlation	Smartphone use correlates with sleep quality and symptoms of anxiety and depression.
1	Ma et al. (2012)	Smartphone measures	Estimation of mood	Factor graph	Mood is inferred from data recorded by the personal smartphone.
1	Likamwa et al. (2013)	Smartphone measures	Estimation of mood	Regression model	Mood is inferred from data recorded by the personal smartphone.
1	Panagiotakopoulos et al. (2010)	EMA rating and contextual Information	Mental state	Bayesian network	Mental states such as depression, anxiety and stress are estimated from contextual data.
1	Lu et al. (2012)	Voice samples	Stress estimation from voice samples	Mixture of hidden Markov models	Stress level can be estimated from voice-based features.
1	Chang et al. (2011)	Speech samples	Emotion recognition	Support vector machine	Library that runs on a smartphone and estimates emotion from speech samples.
1	Van der Sluis et al. (2012)	Speech samples	Stress estimation	Regression	Stress level of post-traumatic stress disorder patients is estimated from voice samples.
1	Asselbergs et al. (2016)	Smartphone measures	Estimation of mood	Regression model	Failed replication of Likamwa et al. (2013)
2	Daugherty et al. (2009)	Mood measures	Estimation of mood swing cycles and treatment influence	Dynamic model	Simulation of treatment and coupling behavior for bipolar individuals.
2	Both et al. (2010), Both and Hoogendoorn (2011), Both, Hoogendoorn and Klein (2012)	EMA	Simulates client's symptom trajectory	Dynamic model	Allows predictions about the client's recovery curve and simulates the influence of various therapy forms.
2	Touboul et al. (2010)	EMA	Estimation of recovery curve and relapse risk	Dynamic model	The identification of underlying model parameters allow client specific predictions.
2	Demic and Cheng (2014)	EMA, clinical data	Prediction of depressive episodes and recovery chance	Finite-state machine, dynamical system	Modeling of occurrence of depressive episodes, and influence of treatment.
2	Noble (2014)	Questionnaires	Scheduling of face-to-face interventions	Control-theoretic model	Control-theoretic scheduling of psychotherapy based on client individual data.
2 (4)	Patten (2005)	Depression score	Estimated recovery curves	Markov model	Prevalence and recovery from major depressive episodes are estimated with a Markov model
2	Becker et al. (2016)	Phone usage data, EMA data	Mood of the next day	Lasso Regression, Support Vector Machines, linear regression, Bayesian Hierarchical Regression	Try to predict the mood level of the next day based on reported EMA data and phone usage data
2	van Breda et al. (2016)	EMA data	Mood of the next day	Linear regression with a bagging approach	Predict the mood of the next day by means of EMA data collected during previous days. Optimize the historical period used for predictions.
2	Bremer et al. (2017)	Diary data	Current mood	Text mining and Bayesian regression	Clients' activity diary data is used to infer the current mood.
2	Osmani et al. (2013)	Smartphone measures	Depressive state	Correlation	Smartphone measured of the activity are correlated to the depressive state of bipolar individuals.
3	Karyotaki et al. (2015)	Demographics	Estimation of drop-out risk factors	Hierarchical Poisson regression modeling	Individual Patient Data Meta-Analysis: raw data from various trials were analyzed to identify drop-out risk factors for web-based interventions.
3	Kegel and Flückiger (2014)	Self-esteem, mastery, clarification, global Alliance	Treatment dropout	Hierarchical regression	Clients with lower levels of self-esteem, fewer clarifying experiences, and absence of therapeutic alliance are more likely to dropout.
3	Meulenbeek et al. (2015)	Socio-demographic, personal, and illness-related variables	Treatment dropout	Logistic regression	Dropout estimation for clients with mild panic disorder.
3	Proudfoot et al. (2013)	Perceived self-efficacy questionnaire	Symptom improvement	Correlation	Perceived self-efficacy reported at the beginning of web-based treatment indicates outcome.
3	Van et al. (2008)	Hamilton Rating Scale for Depression	Treatment failure	Logistic regression	Early improvement can be used to predict therapy outcome.
3	Priebe et al. (2011)	Therapeutic relationship	Treatment outcome	χ2 Analysis	Outcome of 9 studies was compared to estimate the predictive capability of therapeutic relationship.
3	Donkin et al. (2013)	Application usage data	Completion of interventions	Logistic regression	Usage data and its influence on the outcome.
3	Van Gemert-Pijnen et al. (2014)	Login frequency	Prediction of outcome after therapy	Linear regression model	Client login frequency was correlated with improvement after the therapy.
3	Whitton et al. (2015)	Collected data about used program features	Outcome prediction	Correlation	Usage of (some) program features was correlated with treatment outcome.
3	Bennett et al. (2011)	Session based outcome questionnaire	Treatment outcome	Various methods	Treatment outcome estimation based on session based questionnaires.
3	Perlis (2013)	Socio-demographics, self-reported clinical data	Treatment resistance	Naïve Bayes, logistic regression, support vector machine, random forest	Treatment resistance is predicted based on self-reported data.
3	Hoogendoorn et al. (2017)	Socio-demographics, emails sent by patient	Treatment success	Logistic regression, decision tree, random forest	Treatment success is predicted based on the text contained in the emails sent by the patient to the therapist.
4	Kessing (1999)	ICD-10 Depression rating	Relapse risk, suicide risk	Cox-regression	The risk of relapse is significantly related to the severity of baseline and post-treatment depression.
4	Busch et al. (2012)	Demographics, medication, clinical data	Predict one year follow up outcome	Hierarchical logistic regression	The outcome of bipolar clients at one year follow up is predicted using clinical data.
4	Farren and McElroy (2010)	Demographics, previous drinking characteristics, comorbidity	Alcoholic relapse risk	Logistic regression	Relapse after 3 or 6 months of clients with alcohol-dependence and depression or bipolar disorder.
4	Farren et al. (2013)	Demographics, previous drinking characteristics, comorbidity	Alcoholic relapse risk	Logistic regression	Longitudinal outcome after 2 years of clients with alcohol-dependence and depression or bipolar disorder.
4	Pedersen and Hesse (2009)	Demographics, previous drinking characteristics	Alcoholic relapse risk	Logistic regression	Based on demographics and previous drinking behavior the alcoholic relapse risk is predicted.
4	van Voorhees et al. (2008)	Mood, social and cognitive vulnerability	relapse risk	Regression trees	Estimation of depression relapse risk.
4	Gustafson et al. (2014)	GPS position	Trigger EMI	Previously entered locations	EMI is triggered in locations where alcohol was obtained in the past.
4	Chih et al. (2014)	Weekly assessed EMA ratings	Predict relapse risk in coming week	Bayesian network model	Bases on weekly surveys, the relapse risk in the coming week of previously alcohol-dependent clients is predicted.
4	Aziz et al. (2009)	Ambient measures	Triggers EMI, notifies family members or supervisors	Temporal trace langue rules	A support agent that triggers EMI or notifies medical staff based on monitoring techniques designed to identify risk of relapse.

^aStudies are listed in the order in which they were presented in the main text.