Digital mental health intervention for well-being among low-distress workers in small enterprises on a secondary analysis of an RCT

Abstract

Enhancing the well-being of mentally healthy workers (i.e., those with lower psychological distress) is important to prevent mental health conditions, aligning with the trend of occupational positive psychology. No intervention study has investigated the effect of digital health intervention on well-being among workers with low levels of psychological distress in micro- and small-sized enterprises (MSEs). This secondary analysis examined the effectiveness of a fully automated text-based stress management program, “WellBe-LINE,” using the LINE app to improve the well-being of employees with low levels of psychological distress in MSEs. It used data from a previous randomized controlled trial (RCT) study. The previous RCT recruited adult Japanese full-time employees at an enterprise with fewer than 50 employees from registered members of a web survey company. From the RCT, only participants with low psychological distress (Kessler 6 score under 4) were employed for the present analyses. Participants in an intervention group received text messages once per week for 8 weeks. Well-being was assessed by the Mental Health Continuum-Short Form (MHC-SF) at baseline, post (2-month), and follow-up (6-month) surveys. A mixed model for repeated measures conditional growth model analysis was conducted using a group*time interaction as an intervention effect. 638 (62.5%) participants with low psychological distress were included in this secondary analysis from the 1,021 total participants. The intervention group showed a significant intervention effect post survey on improving well-being (Cohen’s d = 0.19 [95%CI 0.03–0.35], p = 0.016) and social well-being at both the post and follow-up survey with a small effect size (Cohen’s d = 0.26 [95%CI: 0.10–0.42], d = 0.17 [0.002–0.34]; respectively). No effect was observed on emotional and psychological well-being. This study suggested that fully automated universal mental health interventions with low intensity may benefit the healthy population. Further research is needed, focusing on the development and evaluation of programs aimed at promoting well-being and positive mental health among mentally healthy workers as part of primary mental health prevention.

Trial registration: The study protocol was registered in the UMIN clinical trial registration before starting recruitments: UMIN000050624 (registration date: March 18, 2023) https://center6.umin.ac.jp/cgi-bin/ctr/ctr_view_reg.cgi?recptno=R000057552.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-11206-4.

Introduction

The mental health of workers in micro- and small-sized enterprises (MSEs) is gaining increasing attention^1,2, emphasizing not only preventing mental health issues but also enhancing overall well-being^3,4 Under Japanese law, MSEs—defined as those with fewer than 50 employees—are not required to appoint occupational health professionals, resulting in limited workplace support for employee well-being. As the workforce continues to evolve, promoting well-being has become a significant concern, highlighting the need for interventions in MSEs that support both symptom management and the proactive improvement of workers’ performance^5,6.

A systematic review and meta-analysis reported psychological interventions (e.g., mindfulness, cognitive behavioral-based approach, and other psychological interventions) improved the well-being of workers⁷. Digital psychological intervention targeting symptom reduction also showed its effectiveness^8,9, but the need for interventions focused on well-being enhancement (not symptom reduction) has become more apparent¹⁰. Health promotion interventions to improve well-being are likely to focus on positive experiences at work and expand psychological capacity, otherwise paying attention to adverse mental health conditions¹⁰. Some randomized controlled trials reported the effectiveness of digital interventions to improve well-being in the workplace^11–15, but they have not yet been fully explored.

Workers without mental health conditions may be more likely to experience improvements in well-being compared to those with such conditions. Improving the well-being of people with depressive symptoms has proven more difficult than lessening the severity of symptoms¹⁶. In addition, traditional primary prevention interventions aimed at stress symptom reduction have often shown their effectiveness for workers with high stress levels⁸ but insufficient effects for those with low stress symptoms. For workers with low stress symptoms, improvements in well-being, positive mental health, and work-related outcomes are more critical than stress symptom reduction. Therefore, interventions designed to enhance this group’s well-being and positive mental health are necessary. Furthermore, while there is a possibility that workers with lower stress symptoms might experience more significant improvements in well-being compared to those with higher stress symptoms, this has not been thoroughly examined.

This study aimed to examine whether WellBe-LINE, a low-intensity, automated, individualized psychoeducational program developed for real-world use among workers in MSEs with fewer than 50 employees, can improve the well-being of workers with low psychological distress. It investigated this through a secondary analysis of a randomized controlled trial¹⁷.

Methods

Trial design

This study was a secondary analysis of a randomized controlled trial (RCT). The study protocol was registered prior to initiating participant recruitment (UMIN000050624). The primary report has been published elsewhere¹⁷. The findings of this study were reported following the CONSORT guidelines¹⁸. The Research Ethics Review Board of the Graduate School of Medicine and Faculty of Medicine at the University of Tokyo approved all study procedures (No.2021190NI-(2)).

Participants and procedures

Participants were recruited from a panel of approximately 5 million individuals registered with a web survey firm in Japan (Cross Marketing Inc.). The inclusion criteria for this study were as follows: (1) Adults aged 18 years or older, (2) Full-time employees working in enterprises with fewer than 50 employees, and (3) Individuals who use the LINE app and agree to add the research account as a friend. There were no specific exclusion criteria at this time.

Eligible participants were provided with a URL that detailed the study and required them to consent by clicking an “Agree” button. After giving consent, they proceeded to a baseline questionnaire. Participants who answered the baseline survey were randomly assigned to either the intervention or control group. Participants were stratified based on their Kessler 6 (K6) scores (≤ 4 or ≥ 5) from the baseline survey as the stratification variable. An independent biostatistician generated a computer-based stratified permuted-block randomization list with a fixed block size of four. Allocation was then performed by a second researcher outside the study team, with all processes blinded to the investigators. The intervention group received an email with a QR code to start the program (i.e., adding WellBe-LINE as a friend on the LINE app). Control group participants were granted access to the program after the six-month follow-up period. Data were collected via online questionnaires at three time points: baseline, after the 8-week intervention (post-survey), and 6 months from baseline (follow-up survey). Each completed questionnaire was rewarded with a 500 JPY Amazon gift card.

In this secondary analysis, we employed only participants with low psychological distress (K6 ≤ 4). Based on an earlier study¹⁹, scores of 4 or less can be considered as at low risk for mood or anxiety disorders. In this study, participants with high distress (K6 ≥ 5) were excluded to focus on the intervention’s effectiveness in promoting well-being among workers with low levels of psychological distress.

Program completion was measured by self-report questionnaire in the post survey (8-week).

Intervention and control groups

The intervention group received WellBe-LINE, an automated, 8-week, text-based stress management program designed for employees of MSEs via the LINE app, incorporating evidence-based several psychological strategies —cognitive behavioral techniques (e.g., problem solving), acceptance and commitment therapy, self-compassion exercises, sleep hygiene advice, and encouragement of physical activity. The messages were tailored to 16 different scenarios based on gender, age, and psychological distress (K6 ≤ 4 or ≥ 5), offering content adapted to each user profile. One weekly message included a link to an informational website (https://wellbeing-kokoro.com/) containing more than 100 articles on mental health. The messages were concise, comprising attention-grabbing questions for participants, brief explanations, and links to the website (screenshot is available in Supplementary Fig. 1). This program was developed with embedded stakeholders (e.g., MSE employers and licensed social insurance consultants), feedback from 12 in-depth stakeholder interviews and quantitative data from 1,000 employees in MSEs. The algorithm for tailoring was developed based on preference patterns identified in survey responses from 1,000 MSE employees. Details of the intervention are available elsewhere^17,20.

After the six-month follow-up, control group participants were given access to the WellBe-LINE program. During the study, they could access other mental health resources or treatment as needed. Although they were not directly informed about the psychoeducational website, it remained publicly accessible.

Outcome measures

Psychological well-being

Psychological well-being was evaluated using the Mental Health Continuum-Short Form (MHC-SF)²¹, a widely recognized tool for assessing positive mental health. The MHC-SF comprises three factors: emotional, psychological, and social well-being. The Japanese version of the MHC-SF has demonstrated strong reliability and validity²².

Euthymia

Euthymia, introduced by Fava in 2016, is a broad construct for well-being, representing psychological flexibility, a cohesive life perspective, and stress resilience (including resistance to anxiety and frustration)^23,24. The Euthymia Scale (ES)²³ contains ten items, with each answer being either False (0) or True (1), resulting in a total score between 0 and 10; higher scores indicate a more euthymic state. The Japanese ES version has shown good concurrent validity and is sensitive as a clinimetric measure^25–27. At baseline survey of our study, the Euthymia scale demonstrated acceptable internal consistency, with a Cronbach’s α of 0.732.

Work engagement

Work engagement was measured using the 9-item short form of the Utrecht Work Engagement Scale (UWES-9)²⁸. The UWES-9 includes three subscales—vigor, dedication, and absorption—each consisting of three items. It uses a self-reported 7-point scale ranging from “never” (0) to “every day” (6). The mean scores of the three subscales and the total score are computed by adding the scores and dividing the sum by the number of items in each subscale. Hence, the UWES’s three subscale scores and a total score range from 0 to 6. The Japanese version of the UWES-9 has proven to be highly reliable and valid²⁹. At baseline survey of our study, the UWES-9 demonstrated acceptable internal consistency, with a Cronbach’s α of 0.955.

Demographic characteristics

The baseline survey collected information on demographics such as age, gender, marital status, education level, occupation, and work style.

Sample size

The sample size was initially determined by our primary outcome (reduction of psychological distress). Aiming for a small effect size (0.20) in psychological distress (K6) with an alpha of 0.05 and a power of 0.90, the required sample size was 527 per group. There have been no previous reports of intervention studies exclusively targeting workers with low psychological distress. An earlier systematic review to examine the effects of universal interventions (i.e., with no set psychological inclusion criteria) of an online program on improving well-being among workers yielded a Hedges g = 0.25 at the post-intervention period⁹. An estimated post hoc power (1-beta) was 0.88 if the effect size was 0.25, assuming that alpha was less than 0.05 (two-tailed) and each of the 319 participants participated, using the G*Power 3.1 program^30,31.

Statistical analyses

In this secondary analysis, we employed only participants with low psychological distress (K6 ≤ 4). We utilized a mixed model with repeated measures and an unstructured covariance matrix to analyze interactions between group (intervention vs. control) and time (baseline, 2-month post-survey, and 6-month follow-up survey). Maximum likelihood estimation was employed to handle missing data, with primary analyses adhering to the intention-to-treat (ITT) approach. Cohen’s d effect sizes were calculated for each follow-up.

Result

Of the total of 1,021 participants (510 in an intervention group and 511 in a control group), participants with low psychological distress (K6 ≤ 4) were 638 (62.5%); an intervention group (n = 319) and a control group (n = 319). The follow-up rate in the intervention group was 90.9% at 2 months and 82.8% at 6 months; the control group was 94.4% and 87.5%, respectively. Program completion rate in the intervention group was 59.6%. The characteristics of those participants with low psychological distress are presented in Table 1. The majority of participants were over 50 years old, male, and married.

Table 1.

Participants’ characteristics in the intervention and control groups of those with low psychological distress (N = 638).

	Intervention (n = 319)	Control (n = 319)
	n (%)	n (%)
Age (years), mean (SD), [range]	50.7 (10.2) [26–73]	51.3 (10.9) [25–80]
Less than 30 years old	8 (2.5)	7 (2.2)
30–39	44 (13.8)	40 (12.5)
40–49	85 (26.6)	99 (31.0)
50 or more	182 (57.1)	173 (54.2)
Sex
Men	230 (72.1)	226 (70.8)
Women	89 (27.9)	93 (29.2)
Marital status
Single	74 (23.2)	82 (25.7)
Married	213 (66.8)	204 (63.9)
Divorced/widowed	32 (10.0)	33 (10.3)
Educational attainment
High school	78 (24.5)	71 (22.3)
Some college	61 (19.1)	58 (18.2)
Undergraduate	167 (52.4)	170 (53.3)
Graduate or over	13 (4.1)	20 (6.3)
Occupation
Manager	87 (27.3)	89 (27.9)
Professional	66 (20.7)	62 (19.4)
Clerical	82 (25.7)	93 (29.2)
Production	39 (12.2)	37 (11.6)
Sales	19 (6.0)	12 (3.8)
Others	26 (8.2)	26 (8.2)

SD: standard deviation.

The mean scores of outcomes in each group are shown in Table 2. The total well-being scores significantly improved in the intervention group compared to the control group (Cohen’s d = 0.19 [95%CI: 0.03–0.35]) at the 2-month post-survey. The scores of social well-being also significantly improved in the intervention group compared to the control group at both the 2-month post-survey and the 6-month follow-up survey (d = 0.26 [0.10–0.42] and 0.17 [0.002–0.34], respectively).

Table 2.

The mean of outcome variables at baseline, 2 months and follow-up.

Outcome variables	Intervention (n = 319)		Control (n = 319)		Intervention vs. Control Effect size
	n (%)	Mean (SD)	n (%)	Mean (SD)	Cohen’s d (95% CI)
Well-being (MHC-SF total)
Baseline	319	31.85 (14.84)	319	34.29 (15.47)	n/a
2-month (post)	290	32.46 (15.16)	301	33.08 (14.98)	0.19 (0.03–0.35)
6-month (follow-up)	264	32.77 (15.09)	279	33.61 (15.78)	0.09 (−0.08–0.25)
Emotional well-being (MHC-SF)
Baseline	319	7.40 (3.59)	319	7.77 (3.67)	n/a
2-month (post)	290	7.40 (3.57)	301	7.57 (3.65)	0.08 (−0.08–0.24)
6-month (follow-up)	264	7.50 (3.61)	279	7.81 (3.65)	−0.05 (−0.22–0.12)
Social well-being (MHC-SF)
Baseline	319	10.27 (5.89)	319	11.34 (6.04)	n/a
2-month (post)	290	10.67 (6.01)	301	10.67 (5.77)	0.26 (0.10–0.42)
6-month (follow-up)	264	10.78 (6.08)	279	10.90 (6.11)	0.17 (0.002–0.34)
Psychological well-being (MHC-SF)
Baseline	319	14.18 (6.68)	319	15.18 (7.03)	n/a
2-month (post)	290	14.38 (6.87)	301	14.83 (6.86)	0.11 (−0.05–0.27)
6-month (follow-up)	264	14.49 (6.78)	279	14.89 (7.19)	0.04 (−0.12–0.21)
Euthymia (ES-J)
Baseline	319	8.20 (2.07)	319	8.39 (1.92)	n/a
2-month (post)	290	7.97 (2.26)	301	8.24 (2.15)	−0.02 (−0.18–0.14)
6-month (follow-up)	264	8.24 (1.97)	279	8.28 (2.18)	0.07 (−0.10–0.24)
Work engagement (UWES)
Baseline	319	2.86 (1.11)	319	2.99 (1.17)	n/a
2-month (post)	290	2.81 (1.08)	301	2.90 (1.13)	0.07 (−0.09–0.24)
6-month (follow-up)	264	2.82 (1.17)	279	2.92 (1.24)	0.07 (−0.10–0.24)

CI: confidence interval. ES-J: Japanese version of the Euthymia scale. MHC-SF: Mental Health Continuum-Short Form. SD: standard deviation. n/a: not applicable. UWES: Utrecht Work Engagement Scale.

Cohen’s d values were calculated among participants who answered each follow-up survey.

Table 3 shows the estimated effects of the intervention on well-being based on mixed model analysis. In ITT analyses, the scores of social well-being measured by MHC-SF significantly improved in the intervention group (pooled effect = 0.44, p = 0.021). Using the ANOVA model, a significant estimated effect was seen for improving total well-being scores at the 2-month post-survey (p = 0.016) and for improving social well-being at both the 2-month post-survey and 6-month survey (p = 0.001, p = 0.027; respectively). There was no significant estimated effect on emotional well-being, psychological well-being, euthymia, or work engagement.

Table 3.

Effects of the WellBe-LINE program on outcomes using intention to treat (ITT) analysis (N = 638).

	Effect	95% CI	SE	t value	p value
Well-being (MHC-SF total)
2-month (post) (a)	1.85	0.35–3.36	0.77	2.42	0.016*
6-month (follow-up) (a)	1.00	−0.63–2.62	0.83	1.21	0.229
Pooled (b)	0.53	−0.28–1.35	0.42	1.28	0.200
Emotional well-being (MHC-SF)
2-month (post) (a)	0.21	−0.2–0.61	0.21	1.00	0.318
6-month (follow-up) (a)	−0.11	−0.54–0.33	0.22	−0.48	0.631
Pooled (b)	−0.05	−0.26–0.17	0.11	−0.42	0.672
Social well-being (MHC-SF)
2-month (post) (a)	1.11	0.43–1.78	0.34	3.23	0.001*
6-month (follow-up) (a)	0.84	0.10–1.58	0.38	2.22	0.027*
Pooled (b)	0.44	0.07–0.81	0.19	2.31	0.021*
Psychological well-being (MHC-SF)
2-month (post) (a)	0.54	−0.22–1.31	0.39	1.40	0.163
6-month (follow-up) (a)	0.29	−0.50–1.08	0.40	0.72	0.474
Pooled (b)	0.15	−0.24–0.55	0.20	0.76	0.449
Euthymia (ES-J)
2-month (post) (a)	−0.04	−0.29–0.20	0.13	−0.35	0.728
6-month (follow-up) (a)	0.12	−0.16–0.39	0.14	0.82	0.414
Pooled (b)	0.05	−0.09–0.19	0.07	0.74	0.458
Work engagement (UWES)
2-month (post) (a)	0.04	−0.05–0.14	0.05	0.89	0.374
6-month (follow-up) (a)	0.04	−0.08–0.15	0.06	0.61	0.543
Pooled (b)	0.02	−0.04–0.08	0.03	0.63	0.532

CI: confidence interval. SE: standardized error.

ES-J: Japanese version of the Euthymia scale. MHC-SF: Mental Health Continuum-Short Form. SD: standard deviation. n/a: not applicable. UWES: Utrecht Work Engagement Scale.

^(a) A mixed model with repeated measures of analysis of variance was conducted.

^(b) A mixed model mixed with repeated measures of conditional growth model analysis was conducted.

*p < 0.05.

Discussion

This study examined the effectiveness of “WellBe-LINE,” a low-intensity, automated, individualized psychoeducational program developed for real-world use among workers with low psychological distress in MSEs. There was no significant effect on emotional well-being, psychological well-being, euthymia, or work engagement at 2-month (post) and 6-month (follow-up). But it had modest improvements in overall well-being and social well-being following an intervention compared to a control group. The total well-being scores improved slightly at the post survey period (after the 8-week intervention) in the intervention group, with a small effect size. Among the three well-being subscales, social well-being showed consistent improvement at both 2 and 6 month. These effect sizes indicate small in well-being, particularly social well-being, although other outcomes including emotional and psychological well-being, euthymia, and work engagement did not show notable changes.

The intervention improved the total scores of well-being post-intervention. This result aligned with previous studies reporting the effectiveness of workplace interventions in enhancing well-being^11–14. Our study added findings that online self-help intervention could benefit mentally healthy employees in MSEs who face low-resource and disadvantaged settings, such as lack of resources (time, money) and employers’ low engagement^32–39. Our study also found that the intervention showed long-term effectiveness on social well-being. Social well-being encompassed the concepts of social integration, social contribution, social coherence, social actualization, and social acceptance^21,40. Social well-being is developed by social involvement (e.g., social engagement)²¹. Previous studies reporting social well-being improvement used positive psychology interventions^41,42 and group psychosocial interventions⁴³. The information our semi-tailored program provided for people without distress included how to improve motivation, work-life balance, and positive feelings²⁰. While the intervention was self-help without face-to-face or group sessions, its content may promote social (or workplace) participation and lead to effectiveness in improving social well-being. Otherwise, it may be easy for mentally healthy workers to be stimulated to take pro-social behaviors by interventions. The content mainly developed to improve well-being showed effectiveness accordingly. Yet, caution is warranted to avoid overestimating the results since all baseline outcome measures were more favorable in the control group than in the intervention group, the slight decline in outcomes in the control group and the slight increase (improvement) in the intervention group at follow-up may have contributed to the observed significant differences.

No significant effectiveness was shown in emotional and psychological well-being. This result was inconsistent with a previous report that internet-based Acceptance and Commitment Therapy (iACT) program improved psychological well-being¹⁵. Meta-analyses of positive psychology and digital interventions suggest that improvements in emotional well-being (e.g., life satisfaction, positive affect) and deeper psychological functioning (e.g., purpose in life, resilience) typically require more intensive or longer-duration programs, often including interactive exercises, personalized feedback, or guided practice^44,45. Thus, emotional and psychological well-being may not be fully addressed by brief psychoeducational texts alone.

No significant effectiveness was shown in work engagement and euthymia. This finding was inconsistent with the previous review, which reported that psychological interventions to enhance personal resources can effectively improve work engagement^46,47. Our content included a topic about work engagement (e.g., focusing on self-perceived positive attributes and strengths). Still, we did not provide most participants in this study (i.e., aged 50 or older) with information about increasing work engagement through semi-tailored messages because our previous survey showed that individuals aged 50 and above did not favor this content. This may affect the non-significant effectiveness on work engagement. Regarding euthymia, the present finding was inconsistent with a previous RCT in which the authors conducted fully automated web-based acceptance and commitment therapy for female workers and reported a favorable effect on euthymia¹⁵. One possible reason is that the intervention effect on euthymia may be likely to be observed primarily in people with high distress because the study’s participants included 75% with high distress. Considering that euthymia is a concept for a clinical target of depression treatment²⁴, euthymia may not be sensitive in measuring the well-being of healthy workers.

Limitations

This study has several limitations. First, participants of this RCT were recruited from an online survey panel, predominantly male, married, office-based, and with undergraduate degrees. In this study, the participants without high psychological distress were primarily older male workers. This may limit generalizability, as other samples from MSEs could yield different results. Second, outcomes were self-reported and subject to bias from personal perceptions or workplace conditions. Without blinding, social preferences may influence responses. Learning progress (i.e., completion) was assessed via self-report questionnaires due to limitations in the program system’s functionality. Obtaining more detailed usage data could boost actual engagement with the program and thereby enhance its effectiveness. Third, because data on other potential determinants of well-being were unavailable, we cannot rule out that additional factors contributed to the observed improvement in social well-being. Fourth, we did not send any reminders or tailored messages to encourage end-users in the intervention group to engage with the program. The absence of such proactive, personalized prompts may have contributed to the low completion rate and limited effectiveness observed in this study. Finally, industry data for participants were unavailable, complicating the assessment of sample representativeness. Contamination could also occur if participants worked in the same company, which is quite unlikely.

Conclusion

Although euthymia and work engagement did not show significant effectiveness, this study partially showed that universal low-intensity mental health interventions benefited a healthy population for improving social well-being. The WellBe-LINE program may serve as a practical intervention to enhance social well-being among workers in MSEs, especially those with low psychological stress. Occupational health practitioners should consider incorporating these low-intensity, automated programs into workplace well‐being initiatives alongside existing interventions prioritized for workers with high levels of distress.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Author contributions

KI was in charge of this study protocol, supervised the process, and provided his expert opinion. AT provided expert suggestions regarding the study concept as a supervisor. NS and KI organized the study design. SO and US supported the study management. NS and KI wrote the first draft of the manuscript, and all the other authors critically revised the manuscript. All the authors approved the final version of the manuscript.

Funding

This work was supported by a work-related diseases clinical research grant 2020 (200401-01) and JSPS KAKENHI Grant Number JP22K03168.

Data availability

The data related to the present study are available from the corresponding author, KI, upon reasonable request.

Declarations

Competing interests

NS reports personal fees from Medilio Inc., outside the submitted work. KI is employed at the Department of Digital Mental Health, an endowment department supported by an unrestricted grant from 15 enterprises (https://dmh.m.u-tokyo.ac.jp/c) outside the submitted work. Others declared no competing interests related to this work.

Ethics Approval

Ethics approval and consent to participate: This study protocol was approved by the Research Ethics Committee of the Graduate School of Medicine/Faculty of Medicine, The University of Tokyo, No.2021190NI-(2). This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki.

Informed consent

Online informed consent was obtained from all participants, who were given full disclosure and explanation of the purpose and procedures of this study. The panelists had the option not to respond to any part of the questionnaire and to discontinue participation at any point.