Background:

In primary care, physicians are uniquely positioned to influence lifestyle behaviours through brief counselling. Becoming physically active can be encouraged by behavioural change which is difficult to achieve without ongoing support and personal motivation. Individuals tend to be more active and consistent when they can observe and compare their behaviour to that of peers, especially within a supportive group. Recent technological advancements have made mobile pedometer applications widely accessible. However, studies have shown that these tools alone often fail to maintain long-term engagement without social or professional reinforcement.By targeting a young, healthy population at a stage where habits can be formed and sustained long-term, this project aligns with the goals of preventive medicine in primary care.

Research questions:

Do active follow-up with digital self-monitoring (via pedometer app) make a difference in physical activity levels and habits in primary care patients?

Method:

This is a single-blind, prospective, two-arm intervention study to be conducted in a primary care outpatient clinic. Seventy healthy adults volunteers aged 18–35 who present to the clinic for any reason will be randomly assigned to either the intervention or control group using simple randomization. All participants will complete the IPAQ-SF at baseline, Week 4, and Week 10. To maintain blinding, data collection will be conducted by an independent researcher. A priori power analysis (G*Power, d = 0.7, α = 0.05, power = 80%) indicated a sample size of 66; 70 will be recruited to allow for dropouts. The intervention group will download a pedometer app (“Step-Up”), join a shared virtual group, and receive weekly motivational messages from a physician.The control group will receive standard walking advice. Descriptive statistics and chi-square tests will be used for categorical variables. Between-group differences will be analyzed using the Wilcoxon test. A p-value <0.05 will be considered statistically significant.

Results:

Conclusions:

Points for discussion:

Could AI-supported mobile applications enhance the effectiveness of physical activity interventions by delivering personalised feedback and adaptive motivation strategies?

What strategies can be employed to sustain increased physical activity after the initial intervention ends?

What are the main barriers to maintaining user engagement beyond the active intervention period in mobile health solutions?