‘Maintaining balance in life’—exploring older adults’ long-term engagement in self-managed digital fall prevention exercise

Background Accidental falls are one of the greatest threats to older adults’ health and well-being. The risk of falling can be significantly reduced with strength and balance interventions. However, there needs to be further knowledge into how older adults can be supported to achieve a maintained exercise behaviour. Therefore, the aim of this study was to explore factors that enabled older adults to maintain their exercise during a 1-year self-managed digital fall prevention exercise intervention. Methods This study used a grounded theory methodology. Semi-structured individual interviews were conducted by phone or conference call. Eighteen community-dwelling older adults aged 70 years or more participated. The participants had a self-reported exercise dose of 60 min or more per week during the last three months of participation in a 12-months intervention of self-managed digital fall prevention exercise, the Safe Step randomized controlled trial. Open, axial, and selective coding, along with constant comparative analysis, was used to analyze the data. Results The analysis resulted in a theoretical model. We found that the fall prevention exercise habits of adults were developed through three stages: Acting against threats to one’s own identity, Coordinating strategies to establish a routine, and Forming habits through cues and evaluation . The main category of Maintaining balance in life encases the participants transition through the three stages and reflects balance in both physical aspects and in between activities in daily life. The process of maintaining balance in life and desire to do so were mediated both by intrinsic person-dependent factors and the Safe Step application acting as an external mediator. Conclusion This study identified three stages of how older adults developed self-managed fall prevention exercise habits, supported by a digital application. The generated theoretical model can inform future interventions aiming to support long-term engagement in digitally supported and self-managed fall prevention interventions.