2024 Volume 6 Issue 1 Article ID: 2023-0016-CT
The rapid progress of digital technologies, including both hardware and software, provides an opportunity in the field of occupational mental health services. Digitalization has inherent possibilities within the domains of prevention and health promotion, and these have been recognized by governments worldwide. For example, in Germany, the Act to Improve Healthcare Provision through Digitalisation and Innovation (Digital Healthcare Act, or DVG) was promulgated in 20191). Guidance has been issued in the United States about the content of digital health services2). In the United Kingdom (UK), the National Institute for Health and Care Excellence published an Evidence Standards Framework for Digital Health Technologies (DHTs) in 20183).
Workplaces are potentially ideal sites for delivering mental health prevention programs4) and increasing access to appropriate treatment5), providing benefits to both employees and employers6). In 2017 and 2018, multiple reviews concluded that digital interventions could significantly enhance employees’ psychological well-being and alleviate workplace stress7). Here, we report additional evidence from an analysis of data collected from a sample of employees in Japan.
Our research group (Developing Minds-compliant guideLines for General preventive intervention using digital Health Technologies for mental health [DeLiGHT]) has defined digital health technology services as “healthcare services aimed at primary prevention provided to the general workforce using information and communication technology (ICT) and digital technology”. This includes services that use technical algorithms (eg, application software [apps], communication robots, wearable devices, information provision through non-contact sensing devices, self-monitoring, and real-time feedback), as well as those that do not use technical algorithms (eg, online counseling that relies solely on internet-based methods of provision). The aim of digital mental health services is to prevent mental health issues, improve quality of life and functionality (eg, productivity and absenteeism), enhance positive mental health (eg, work engagement and well-being), and prevent suicide and abuse of substances, such as alcohol and drugs. In this research, it is important to note that the digital mental health services discussed do not encompass treatment.
A preliminary draft definition of digital health technology services for mental health was developed, drawing upon relevant guidelines. The initial definition underwent multiple rounds of extensive discussions among the authors, resulting in revisions and refinements. To establish a comprehensive and informed definition, a selected panel of researchers and practitioners representing three stakeholder groups — researchers, service providers, and occupational health professionals — was invited to review and provide feedback on the preliminary draft. This collaborative approach aimed to gather real-time insights and expertise on digital health technology services for mental health, ensuring the inclusion of perspectives from experts directly involved in the field. Their comments were meticulously examined, leading to the formulation of our final proposed definition.
To assess the prevalence of employees using digital technology services for mental health in occupational health settings in Japan, we conducted a cross-sectional online survey in February 2023. We collected information from 2,501 employees. The study received ethical approval from the Research Ethics Committee of the University of Occupational and Environmental Health, Japan (No. R4-069). We commissioned Rakuten Insight Inc. (Tokyo, Japan), an online survey company with 3 million registered members, to administer the survey. An invitation email was sent to 30,000 registered members as part of the sampling process. The survey defined digital health technology services for mental health as including the provision of mental health services, measures for suicide prevention, and interventions targeting the prevention of alcohol and substance abuse. These services are delivered to workers through a digital platform and may include information provision, digital counseling, self-monitoring, real-time feedback, and treatment services. The latter includes objective assessment and evaluation, triage of individuals based on the severity and urgency of their condition, and referral services. Peer support services may also be offered through multiple channels, such as smartphones (including voice calls), video conferencing, web-based platforms (including web chats), text messaging, mobile health applications (apps), robots, wearable devices, and non-contact sensing devices. The sampling plan was devised to ensure an equitable representation of two distinct groups: employees who have experienced digital mental health services and those who have not. The target sample size was set at 3,000, with 1,500 respondents expected from each group. Ultimately, the desired count of respondents without prior experience of using digital mental health services (n=1,500) was achieved. However, the number of respondents with prior experience fell short of the target (n=1,001). The demographic and occupational characteristics among employees who participated in this study are shown in Table 1. Among employees with experience using digital mental health services, 29.2% used them privately and 69.2% accessed them through their employers. It should be noted that our survey had limitations, including the cross-sectional study design, nonrepresentative sample of employees, and lack of information regarding duration and frequency of the use of services.
| Prior experience of using digital mental health services | |||||
|---|---|---|---|---|---|
| Yes (n=1,001) | No (n=1,500) | p-value | |||
| Age, years, mean (SD) | 44.4 | (11.1) | 46.4 | (10.7) | <0.001a |
| Range, minimum–maximum | 20–65 | 20–65 | |||
| Sex | |||||
| Men | 681 | (68.0) | 928 | (61.9) | 0.002b |
| Women | 320 | (32.0) | 572 | (38.1) | |
| Educational attainment, N (%) | |||||
| Junior high school | 7 | (0.7) | 13 | (0.9) | 0.002b |
| High school | 139 | (13.9) | 278 | (18.5) | |
| Junior college/technical school | 185 | (18.5) | 306 | (20.4) | |
| University | 557 | (55.6) | 782 | (52.1) | |
| Graduate school | 113 | (11.3) | 121 | (8.1) | |
| Employment status, N (%) | |||||
| Executive | 156 | (15.6) | 140 | (9.3) | <0.001b |
| Fulltime employee | 718 | (71.7) | 1,056 | (70.4) | |
| Parttime employee | 113 | (11.3) | 279 | (18.6) | |
| Self-employed person | 14 | (1.4) | 25 | (1.7) | |
| Occupation, N (%) | |||||
| Manager | 215 | (21.5) | 241 | (16.1) | <0.001b |
| Non-manual worker | 67 | (62.6) | 944 | (62.9) | |
| Manual worker | 85 | (8.5) | 182 | (12.1) | |
| Other | 74 | (7.4) | 133 | (8.9) | |
| Industry, N (%) | |||||
| Agriculture and forestry | 14 | (1.4) | 7 | (0.5) | 0.001b |
| Fisheries | 1 | (0.1) | 1 | (0.1) | |
| Mining | 6 | (0.6) | 1 | (0.1) | |
| Construction | 49 | (4.9) | 68 | (4.5) | |
| Manufacturing | 198 | (19.8) | 270 | (18.0) | |
| Electricity and gas | 15 | (1.5) | 11 | (0.7) | |
| Information | 77 | (7.7) | 100 | (6.7) | |
| Transport | 34 | (3.4) | 74 | (4.9) | |
| Wholesale and retail | 103 | (10.3) | 159 | (10.6) | |
| Finance | 78 | (7.8) | 83 | (5.5) | |
| Real estate and rental | 23 | (2.3) | 41 | (2.7) | |
| Research and professional services | 30 | (3.0) | 26 | (1.7) | |
| Accommodations and dining services | 16 | (1.6) | 54 | (3.6) | |
| Amusement services | 15 | (1.5) | 32 | (2.1) | |
| Education | 61 | (6.1) | 95 | (6.3) | |
| Medical and welfare | 114 | (11.4) | 204 | (13.6) | |
| Compound services | 12 | (1.2) | 19 | (1.3) | |
| Government | 98 | (9.8) | 152 | (10.1) | |
| Other services | 57 | (5.7) | 103 | (6.9) | |
| Company size, N (%) | |||||
| 1–4 | 29 | (2.9) | 76 | (5.1) | <0.001b |
| 5–29 | 60 | (6.0) | 216 | (14.4) | |
| 30–99 | 117 | (11.7) | 204 | (13.6) | |
| 100–299 | 160 | (16.0) | 238 | (15.9) | |
| 300–499 | 82 | (8.2) | 101 | (6.7) | |
| 500–999 | 113 | (11.3) | 135 | (9.0) | |
| 1,000–4,999 | 187 | (18.7) | 218 | (14.5) | |
| 5,000 and more | 208 | (20.8) | 252 | (16.8) | |
| Public | 45 | (4.5) | 60 | (4.0) | |
SD, standard deviation.
Japan has lagged behind in the development of guidelines for digital health technology services in the realm of mental health. Even in countries where guidelines related to these services have already been issued, not all digital services for mental health have demonstrated population-level health impact through randomized clinical trials or efficacy in quasi-experimental real-world circumstances. Within occupational health settings, the objective of digital health technology services for mental health is to provide employees with valuable prevention tools precisely when they are needed. Our findings indicate that in Japan, companies may play a crucial role in facilitating the adoption of these services among employees. In the UK, for instance, the framework designed for digital health technology services that are commissioned within the UK health and care system was established in 20183). This framework provides standards of effectiveness and evidence to assess the economic impact of these services. Frameworks for services will vary depending on the business model of the technology, social demand, national healthcare services, and social culture in each country. It is, therefore, important to establish guidelines to evaluate the effectiveness of digital health technology services for mental health to fit local needs in Japan.
The authors appreciate the help of Dr. Naoto Fukutani (BackTech Inc.), Dr. Yoshihiro Nakamura (wellday Inc.), Mr. Shinichiro Ogawa (Awarefy Inc.), Mr. Daiki Takegawa (emol Inc.), Dr. Takeshi Araki (Advantage Risk Management Co., Ltd.), Assistant Professor Shiho Koizumi (Kyoto University), Dr. Kazumichi Yamamoto (Institute for Airway Disease), Ms. Hikari N. Takashina (Chiba University), Associate Professor Mitsuo Uchida (Gunma University), Dr. Kazuhiro Watanabe (Kitasato University), Associate Professor Akiomi Inoue (University of Occupational and Environmental Health, Japan), Associate Professor Reiko Kuroda (the University of Tokyo), Professor Yuko Morikagi (Yamagata University), Dr. Hiroaki Kobayashi (Sumitomo Corporation) and Dr. Keita Kiuchi (National Institute of Occupational Safety and Health, Japan). We also thank Melissa Leffler, MBA, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.
This work was supported by AMED under Grant Number JP22rea522006. The funder had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.
The authors contributed to this study as follows: HE designed the study and carried out the data collection, HE performed the statistical analysis, HE and TE contributed to the interpretation of results, and HE, NK, SK, KI, NT and TE drafted the manuscript. All authors reviewed and approved the final version of the paper.
