AI-Powered Micro-Breaks at Work
- Conditions
- Musculoskeletal DiscomfortWork-related Vigor
- Registration Number
- NCT07209553
- Lead Sponsor
- West University of Timisoara
- Brief Summary
This study evaluates the feasibility of an online intervention based on artificial intelligence-the Movebite app integrated into the Slack platform-aimed at promoting engagement in micro-breaks involving physical activity, with the goal of enhancing workplace well-being and reducing musculoskeletal discomfort among remote workers.
- Detailed Description
Remote and hybrid work have intensified sedentary behavior, increasing the risk of professional burnout and negatively affecting employee health. This study investigates the feasibility and accessibility of the Movebite app, an AI-based tool integrated into Slack, designed to encourage active micro-breaks. Through a one-arm, pre-test post-test design, we evaluate the feasibility and usability of the AI-based virtual coach and its relevance for well-being of employees working from home.
This research contributes to the development and promotion of empirically grounded solutions in the field of occupational health psychology, testing an innovative technological approach to reduce the negative impact of sedentary work.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 16
- Employees between the ages of 18-60 years' old
- Full-time working entirely from home (home office)
- Have a PC or laptop and basic digital competencies
- Proficient in English language
- Other work arrangements such as shift-work, part-time work
- Unable to access the internet/computer/install Slack app (Yes/No questions)
- No proficiency for English
- Not working mainly from a desk (e.g., fieldwork)
- Current health issues (current pregnancy, any neurological, vascular, or acute musculoskeletal condition or any disease or symptom)
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- SINGLE_GROUP
- Primary Outcome Measures
Name Time Method Positive and negative affect Change from baseline to post-test (1 week; 5 workdays) Affect will be measured using the Positive Affect subscale from the Positive and Negative Affect Schedule (PANAS; Watson, Clark, \& Tellegen, 1988). This scale assesses the extent to which individuals have experienced a range of positive emotions (e.g., enthusiastic, inspired, alert) over the past week. Participants rate each item on a 5-point scale, from "Very slightly or not at all" to "Extremely." Higher scores reflect a higher level of positive emotional experience.
Satisfaction with the intervention Post-intervention (1 week). Seven additional items are open-ended and will be analyzed qualitatively. These items allow participants to elaborate on what aspects of the program they found most or least valuable, describe their experience with communication tools within the intervention, and offer suggestions for improvement.
System usability Post-intervention (1 week) We will use a 10 item questionnaire (Bangor et al., 2009) designed to measure participants' satisfaction with Mobi, the AI health coach. The total score of System Usability Scale is 0 and the highest one is 100. A higher score means a better outcome.
Treatment adherence Post-intervention only (1 week) Dropout rate and frequency of app usage.
Vigor Change from baseline to post-test (1 week; 5 workdays) Vigor refers to a positive affective state experienced at work. It will be assessed using five items from the Physical Strength subscale of the instrument developed by Shirom (2003). A higher score means a better outcome (i.e., increased employee vigor).
Focus Change from baseline to post-test (1 week; 5 workdays). Focus refers to an employee's capacity to maintain attention and mental clarity while working. It will be assessed using five items from the Cognitive Liveliness subscale of the instrument developed by Shirom (2003). A higher score indicates a better outcome (i.e., increased cognitive focus at work).
Physical (dis)comfort Change from baseline to post-test (1 week; 5 workdays) Physical discomfort (musculoskeletal pain) will be measured using a single-item instrument based on the Visual Analog Scale (VAS) (Häfeli \& Elfering, 2006). The VAS is a widely used and validated method for assessing subjective physical symptoms such as pain intensity. Participants will indicate their level of discomfort by marking a point along a continuum, reflecting their experience over the past week. Higher scores indicate greater musculoskeletal discomfort.
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
West University of Timisoara
🇷🇴Timișoara, Timiș County, Romania
West University of Timisoara🇷🇴Timișoara, Timiș County, Romania