Trial reportScientific reports2025
Digital mental health intervention for well-being among low-distress workers in small enterprises on a secondary analysis of an RCT.
Trial report in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Mobile internet use, physical exercise and depression levels: mediating mechanism and empirical evidence.Frontiers in public health · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
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 .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.