What Digital Health Technology Types Are Used in Mental Health Prevention and Intervention? Review of Systematic Reviews for Systematization of Technologies

Overview

Journal J Occup Health

Publisher Oxford University Press

Specialty Occupational Medicine

Date 2024 Jan 23

PMID 38258936

Authors

Naomichi Tani

Hiroaki Fujihara

Kenji Ishii

Yoshiyuki Kamakura

Mafu Tsunemi

Chikae Yamaguchi

Hisashi Eguchi

Kotaro Imamura

Satoru Kanamori

Noriko Kojimahara

Takeshi Ebara

Affiliations

Soon will be listed here.

Abstract

Digital health technology has been widely applied to mental health interventions worldwide. Using digital phenotyping to identify an individual's mental health status has become particularly important. However, many technologies other than digital phenotyping are expected to become more prevalent in the future. The systematization of these technologies is necessary to accurately identify trends in mental health interventions. However, no consensus on the technical classification of digital health technologies for mental health interventions has emerged. Thus, we conducted a review of systematic review articles on the application of digital health technologies in mental health while attempting to systematize the technology using the Delphi method. To identify technologies used in digital phenotyping and other digital technologies, we included 4 systematic review articles that met the inclusion criteria, and an additional 8 review articles, using a snowballing approach, were incorporated into the comprehensive review. Based on the review results, experts from various disciplines participated in the Delphi process and agreed on the following 11 technical categories for mental health interventions: heart rate estimation, exercise or physical activity, sleep estimation, contactless heart rate/pulse wave estimation, voice and emotion analysis, self-care/cognitive behavioral therapy/mindfulness, dietary management, psychological safety, communication robots, avatar/metaverse devices, and brain wave devices. The categories we defined intentionally included technologies that are expected to become widely used in the future. Therefore, we believe these 11 categories are socially implementable and useful for mental health interventions.

Citing Articles

Ergonomic strategies for Digital Occupational Health: Preparing for the oncoming wave of technological innovation.

Tani N, Yamaguchi C, Tsunemi M, Fujihara H, Ishii K, Kamakura Y Environ Occup Health Pract. 2025; 6(1).

PMID: 40061365 PMC: 11841802. DOI: 10.1539/eohp.2023-0028-CT.

Virtual environments as a novel and promising approach in (neuro)diagnosis and (neuro)therapy: a perspective on the example of autism spectrum disorder.

Sokolowska E, Sokolowska B, Chrapusta S, Sulejczak D Front Neurosci. 2025; 18:1461142.

PMID: 39886337 PMC: 11780595. DOI: 10.3389/fnins.2024.1461142.

Is organizational intervention using Layered Voice Analysis effective in addressing operator mental health in call centers? A randomized controlled trial.

Tani N, Takao Y, Noro S, Fujihara H, Eguchi H, Sakai K J Occup Health. 2024; 66(1).

PMID: 39141838 PMC: 11460070. DOI: 10.1093/joccuh/uiae047.

References

Torous J, Bucci S, Bell I, Kessing L, Faurholt-Jepsen M, Whelan P . The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry. 2021; 20(3):318-335. PMC: 8429349. DOI: 10.1002/wps.20883. View

Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F . Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review. J Biomed Inform. 2022; 138:104278. DOI: 10.1016/j.jbi.2022.104278. View

Greenhalgh T, Peacock R . Effectiveness and efficiency of search methods in systematic reviews of complex evidence: audit of primary sources. BMJ. 2005; 331(7524):1064-5. PMC: 1283190. DOI: 10.1136/bmj.38636.593461.68. View

Martinez-Martin N, Greely H, Cho M . Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study. JMIR Mhealth Uhealth. 2021; 9(7):e27343. PMC: 8367187. DOI: 10.2196/27343. View

McIntyre R, Greenleaf W, Bulaj G, Taylor S, Mitsi G, Saliu D . Digital health technologies and major depressive disorder. CNS Spectr. 2023; 28(6):662-673. DOI: 10.1017/S1092852923002225. View

Lee K, Lee T, Yefimova M, Kumar S, Puga F, Azuero A . Using digital phenotyping to understand health-related outcomes: A scoping review. Int J Med Inform. 2023; 174:105061. DOI: 10.1016/j.ijmedinf.2023.105061. View

Baglioni C, Battagliese G, Feige B, Spiegelhalder K, Nissen C, Voderholzer U . Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies. J Affect Disord. 2011; 135(1-3):10-9. DOI: 10.1016/j.jad.2011.01.011. View

Wienert J, Jahnel T, Maass L . What are Digital Public Health Interventions? First Steps Toward a Definition and an Intervention Classification Framework. J Med Internet Res. 2022; 24(6):e31921. PMC: 9277526. DOI: 10.2196/31921. View

Onnela J, Rauch S . Harnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health. Neuropsychopharmacology. 2016; 41(7):1691-6. PMC: 4869063. DOI: 10.1038/npp.2016.7. View

10.

Saccaro L, Amatori G, Cappelli A, Mazziotti R, DellOsso L, Rutigliano G . Portable technologies for digital phenotyping of bipolar disorder: A systematic review. J Affect Disord. 2021; 295:323-338. DOI: 10.1016/j.jad.2021.08.052. View

11.

Tremain H, McEnery C, Fletcher K, Murray G . The Therapeutic Alliance in Digital Mental Health Interventions for Serious Mental Illnesses: Narrative Review. JMIR Ment Health. 2020; 7(8):e17204. PMC: 7442952. DOI: 10.2196/17204. View

12.

Hohmann E, Brand J, Rossi M, Lubowitz J . Expert Opinion Is Necessary: Delphi Panel Methodology Facilitates a Scientific Approach to Consensus. Arthroscopy. 2018; 34(2):349-351. DOI: 10.1016/j.arthro.2017.11.022. View

13.

Harvey S, Overland S, Hatch S, Wessely S, Mykletun A, Hotopf M . Exercise and the Prevention of Depression: Results of the HUNT Cohort Study. Am J Psychiatry. 2017; 175(1):28-36. DOI: 10.1176/appi.ajp.2017.16111223. View

14.

Kane H, Baumgart J, El-Hage W, Deloyer J, Maes C, Lebas M . Opportunities and Challenges for Professionals in Psychiatry and Mental Health Care Using Digital Technologies During the COVID-19 Pandemic: Systematic Review. JMIR Hum Factors. 2021; 9(1):e30359. PMC: 8820762. DOI: 10.2196/30359. View

15.

Moher D, Liberati A, Tetzlaff J, Altman D . Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009; 6(7):e1000097. PMC: 2707599. DOI: 10.1371/journal.pmed.1000097. View

16.

Balcombe L, De Leo D . Evaluation of the Use of Digital Mental Health Platforms and Interventions: Scoping Review. Int J Environ Res Public Health. 2023; 20(1). PMC: 9819791. DOI: 10.3390/ijerph20010362. View

17.

Kamath J, Leon Barriera R, Jain N, Keisari E, Wang B . Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives. World J Psychiatry. 2022; 12(3):393-409. PMC: 8968499. DOI: 10.5498/wjp.v12.i3.393. View

18.

Mouchabac S, Conejero I, Lakhlifi C, Msellek I, Malandain L, Adrien V . Improving clinical decision-making in psychiatry: implementation of digital phenotyping could mitigate the influence of patient's and practitioner's individual cognitive biases. Dialogues Clin Neurosci. 2022; 23(1):52-61. PMC: 9286737. DOI: 10.1080/19585969.2022.2042165. View

19.

Torous J, Kiang M, Lorme J, Onnela J . New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research. JMIR Ment Health. 2016; 3(2):e16. PMC: 4873624. DOI: 10.2196/mental.5165. View

20.

Serou N, Sahota L, Husband A, Forrest S, Slight R, Slight S . Learning from safety incidents in high-reliability organizations: a systematic review of learning tools that could be adapted and used in healthcare. Int J Qual Health Care. 2021; 33(1). PMC: 8271183. DOI: 10.1093/intqhc/mzab046. View