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Possibilities to improve online mental health treatment: Recommendations for future research and developments

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Standard

Possibilities to improve online mental health treatment: Recommendations for future research and developments. / Becker, Dennis.
Advances in Information and Communication Networks: Proceedings of the 2018 Future of Information and Communication Conference FICC, Vol. 1. ed. / Kohei Arai; Supriya Kapoor; Rahul Bhatia. Springer, 2019. p. 91-112 (Advances in Intelligent Systems and Computing; Vol. 886).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Harvard

Becker, D 2019, Possibilities to improve online mental health treatment: Recommendations for future research and developments. in K Arai, S Kapoor & R Bhatia (eds), Advances in Information and Communication Networks: Proceedings of the 2018 Future of Information and Communication Conference FICC, Vol. 1. Advances in Intelligent Systems and Computing, vol. 886, Springer, pp. 91-112, Future of Information and Communication Conference - FICC 2018, Singapore, Singapore, 05.04.18. https://doi.org/10.1007/978-3-030-03402-3_8

APA

Becker, D. (2019). Possibilities to improve online mental health treatment: Recommendations for future research and developments. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Advances in Information and Communication Networks: Proceedings of the 2018 Future of Information and Communication Conference FICC, Vol. 1 (pp. 91-112). (Advances in Intelligent Systems and Computing; Vol. 886). Springer. https://doi.org/10.1007/978-3-030-03402-3_8

Vancouver

Becker D. Possibilities to improve online mental health treatment: Recommendations for future research and developments. In Arai K, Kapoor S, Bhatia R, editors, Advances in Information and Communication Networks: Proceedings of the 2018 Future of Information and Communication Conference FICC, Vol. 1. Springer. 2019. p. 91-112. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-03402-3_8

Bibtex

@inbook{0b96acec863b4548acc4ec7bc9817c80,
title = "Possibilities to improve online mental health treatment: Recommendations for future research and developments",
abstract = "Online mental health treatment has the potential to meet the increasing demand for mental health treatment. But low adherence to the treatment remains a problem that endangers treatment outcomes and their cost-effectiveness. This literature review compares predictors of adherence and outcome for clinical and online treatment of mental disorders to identify ways to improve the efficacy of online treatment and increase clients{\textquoteright} adherence. Personalization of treatment and client improvement tracking appears to provide the most potential to improve clients{\textquoteright} outcome and increase the cost-effectiveness of online treatment. Overall, it was noticed that decision support tools to improve online treatment are commonly not utilized and that their influence on treatment is unknown. However, integration of statistical methods into online treatment and research of their influence on the client has begun. Decision support systems derived from predictors of adherence might be required for personalization of online treatments and to improve outcome and cost-effectiveness to ease the burden of mental disorders.",
keywords = "Business informatics, e-mental-health, Online treatment, Outcome prediction",
author = "Dennis Becker",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-03402-3_8",
language = "English",
isbn = "978-3-030-03401-6",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer",
pages = "91--112",
editor = "Kohei Arai and Supriya Kapoor and Rahul Bhatia",
booktitle = "Advances in Information and Communication Networks",
address = "Germany",
note = "Future of Information and Communication Conference - FICC 2018, FICC 2018 ; Conference date: 05-04-2018 Through 06-04-2018",

}

RIS

TY - CHAP

T1 - Possibilities to improve online mental health treatment

T2 - Future of Information and Communication Conference - FICC 2018

AU - Becker, Dennis

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Online mental health treatment has the potential to meet the increasing demand for mental health treatment. But low adherence to the treatment remains a problem that endangers treatment outcomes and their cost-effectiveness. This literature review compares predictors of adherence and outcome for clinical and online treatment of mental disorders to identify ways to improve the efficacy of online treatment and increase clients’ adherence. Personalization of treatment and client improvement tracking appears to provide the most potential to improve clients’ outcome and increase the cost-effectiveness of online treatment. Overall, it was noticed that decision support tools to improve online treatment are commonly not utilized and that their influence on treatment is unknown. However, integration of statistical methods into online treatment and research of their influence on the client has begun. Decision support systems derived from predictors of adherence might be required for personalization of online treatments and to improve outcome and cost-effectiveness to ease the burden of mental disorders.

AB - Online mental health treatment has the potential to meet the increasing demand for mental health treatment. But low adherence to the treatment remains a problem that endangers treatment outcomes and their cost-effectiveness. This literature review compares predictors of adherence and outcome for clinical and online treatment of mental disorders to identify ways to improve the efficacy of online treatment and increase clients’ adherence. Personalization of treatment and client improvement tracking appears to provide the most potential to improve clients’ outcome and increase the cost-effectiveness of online treatment. Overall, it was noticed that decision support tools to improve online treatment are commonly not utilized and that their influence on treatment is unknown. However, integration of statistical methods into online treatment and research of their influence on the client has begun. Decision support systems derived from predictors of adherence might be required for personalization of online treatments and to improve outcome and cost-effectiveness to ease the burden of mental disorders.

KW - Business informatics

KW - e-mental-health

KW - Online treatment

KW - Outcome prediction

UR - http://www.scopus.com/inward/record.url?scp=85058538452&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-03402-3_8

DO - 10.1007/978-3-030-03402-3_8

M3 - Article in conference proceedings

AN - SCOPUS:85058538452

SN - 978-3-030-03401-6

T3 - Advances in Intelligent Systems and Computing

SP - 91

EP - 112

BT - Advances in Information and Communication Networks

A2 - Arai, Kohei

A2 - Kapoor, Supriya

A2 - Bhatia, Rahul

PB - Springer

Y2 - 5 April 2018 through 6 April 2018

ER -

Other publications by the same author(s)

Evaluation of a temporal causal model for predicting the mood of clients in an online therapy

Becker, D., Bremer, V., Funk, B., Hoogendoorn, M., Rocha, A. & Riper, H., 11.02.2020, In: BMJ mental health. 23, 1, p. 27-33 7 p.

Research output: Journal contributions › Journal articles › Research › peer-review

A two-step approach for the prediction of mood levels based on diary data

Bremer, V., Becker, D., Genz, T., Funk, B. & Lehr, D., 2019, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings: European Conference, ECML PKDD 2018, Dublin, Ireland. Brefeld, U., Curry, E., Daly, E., MacNamee, B., Marascu, A., Pinelli, F., Berlingerio, M. & Hurley, N. (eds.). Cham: Springer International Publishing AG, p. 626-629 4 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11053 LNAI).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis

Bremer, V., Becker, D., Kolovos, S., Funk, B., van Breda, W., Hoogendoorn, M. & Riper, H., 21.08.2018, In: Journal of Medical Internet Research. 20, 8, 11 p., e10275.

Research output: Journal contributions › Journal articles › Research › peer-review

Predictive modeling in e-mental health: A common language framework

Becker, D., van Breda, W., Funk, B., Hoogendoorn, M., Ruwaard, J. & Riper, H., 01.06.2018, In: Internet Interventions. 12, Juni 2018, p. 57-67 11 p.

Research output: Journal contributions › Scientific review articles › Research

Predicting the Individual Mood Level based on Diary Data

Bremer, V., Becker, D., Funk, B. & Lehr, D., 06.2017, Proceedings of the 25th European Conference on Information Systems, ECIS 2017. AIS eLibrary, p. 1161-1177 17 p. (Proceedings of the 25th European Conference on Information Systems, ECIS 2017).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

DOI