An infinite number of fitness apps are available on various app stores. However, hardly any of them are fitted to the needs and requirements of care-dependent people. This paper investigates the effectiveness of a customised fitness-app prototype for increasing physical activity in home care service users. Home care service users from Austria and Italy were randomly assigned to two groups. In total, 216 participants were involved in the field trial, 104 received a tablet with the fitness app and an activity tracker (treatment group), 112 did not (control group). Regularity of physical activity, frequency of fitness exercises and walking behaviour were self-reported by participants at baseline, after 4 months and after 8 months. In addition, the frequency of using the prototype was assessed based on the fitness app's logged usage data. We estimated multilevel mixed-effects ordered logistic models to examine the effects of the intervention. After 4 months, the intervention increased the home care users' probability of agreeing strongly with being physically active on a regular basis by 28 percentage points (p < 0.001; 95% CI: 0.20, 0.36) and their probability of reporting to exercise more than once a week by 45 percentage points (p < 0.001; 95% CI: 0.32, 0.57). Walking behaviour was not affected on group-level but improved for frequent users of the activity tracker. Frequent and regular users of the fitness app benefited most and effects persisted until the end of the 8 months controlled trial. Tailoring a fitness-app prototype to the needs of care-dependent people has the potential to support people with functional limitations to engage in a more active lifestyle. Future research is encouraged to seek further insights into how new technologies can support physical activities in people with long-term care needs.
Keyphrases
- physical activity
- body composition
- healthcare
- randomized controlled trial
- mental health
- body mass index
- systematic review
- study protocol
- sleep quality
- type diabetes
- palliative care
- resistance training
- depressive symptoms
- pain management
- emergency department
- chronic pain
- current status
- artificial intelligence
- deep learning
- adverse drug
- lower limb
- machine learning
- phase ii
- quality improvement
- drug induced
- data analysis