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Development of a Blended Physical Activity Intervention for Office Employees Using Intervention Mapping: Intervention Development Study

  • Yan Sun
  • , Alison YT Ou
  • , Tony Lei Shi
  • , Yang Gao*
  • , Martin S Hagger
  • , Zhennan Xiong
  • , Xiang-Yan Chen
  • , Shirley SM Fong
  • , Linda L.D. Zhong
  • , Yao Jie Xie
  • , Julien S. Baker
  • *Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

Background: Insufficient engagement in moderate to vigorous physical activity (MVPA) is a significant risk factor for major noncommunicable diseases, including cardiovascular diseases (eg, coronary heart disease and stroke), type 2 diabetes, and several cancers. Physical inactivity accounts for an estimated 3.2 million deaths annually. Office employees, due to their sedentary and desk-based work patterns, are particularly vulnerable to low MVPA levels, which negatively affect health and work productivity. The COVID-19 pandemic exacerbated these issues, further reducing MVPA levels due to lockdowns and work-from-home policies. Although numerous interventions have aimed to promote MVPA, many lack theoretical grounding, stakeholder involvement, or systematic development frameworks. A theory- and evidence-informed approach is warranted to target modifiable determinants and mechanisms, improve coherence and replicability, and enhance effectiveness and scalability.

Objective: This study aimed to design an evidence-based blended intervention to improve MVPA, health, and productivity among office employees, guided by the intervention mapping (IM) framework.

Methods: Following the first 4 steps of the IM framework, we developed a 12-week blended intervention and created a website named “Smartly Active for Health and Life.” Step 1 involved a needs assessment through a literature review and focus group discussions (n=12 office employees) to identify barriers, facilitators, and determinants of MVPA. Step 2 defined intervention outcomes, performance objectives, and change objectives based on the social ecological model. Step 3 integrated theory- and evidence-based behavior change techniques with delivery methods, resulting in a 3-group randomized controlled trial design: (1) a blended intervention group (web-based sessions and e-workshops), (2) a web-only intervention group, and (3) an active control group. Step 4 developed and refined intervention materials and protocols through a pilot study (n=18). Key behavior change techniques included problem-solving, goal setting, self-monitoring, and habit formation.

Results: The pilot study demonstrated the intervention’s acceptability, feasibility, and adaptability. Participants reported increased motivation, improved knowledge of MVPA benefits, and greater confidence in goal setting and self-regulation. Feedback led to refinements such as improved website navigation and interactive content. The intervention is expected to enhance MVPA levels, health, and work productivity by addressing both motivational and volitional phases of behavior change while supporting habitual behaviors.

Conclusions: The IM framework offered a systematic and iterative approach for developing a blended MVPA intervention tailored to the needs of office employees. This study highlights the importance of theory-driven and stakeholder-informed approaches in designing scalable and sustainable health promotion programs. The intervention has the potential to address the critical public health challenge of physical inactivity in office settings and serves as a model for future behavior change interventions.
Original languageEnglish
Article numbere87328
JournalJMIR Human Factors
Volume13
DOIs
Publication statusPublished - 14 Jul 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

User-Defined Keywords

  • behavior change
  • intervention design
  • intervention development
  • intervention mapping
  • physical activity
  • theory and evidence-based

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