Utilization and familiarity of orthodontic mobile applications among orthodontists and trainees: a cross‑sectional survey with implications for Low‑ and Middle‑Income Countries (LMICs)

Background: Mobile applications are increasingly integrated into orthodontic practice worldwide, yet their adoption among clinicians in emerging markets remains underexplored. This study investigates awareness, utilization, and willingness to recommend evidence-based orthodontic applications among orthodontists and trainees in Pakistan, providing insights relevant to global digital health strategies. Materials and methods: The sample comprised of 53 participants, including orthodontists and orthodontic trainees. A validated online questionnaire was distributed across various institutions in Pakistan via Google forms from August to September 2024. Descriptive statistics were presented using median and frequencies, while binomial logistic regression was employed to evaluate the influence of demographic variables on the awareness, adoption and recommendation of mobile applications and professional social media applications. Results: The study included 34 orthodontic trainees and 19 orthodontists. The orthodontists had a mean age of 33 years, while the trainees had a mean age of 28 years. Only 7% of participants reported incorporating orthodontic applications, while 30% engaged with social media platforms for professional activities. WebCeph and Instagram emerged as the most frequently utilized applications and 3D treatment simulation was the most desired feature. Binomial logistic regression analysis revealed a significant positive association between orthodontic experience and the use of mobile applications and social media. Conclusions: The findings highlight a substantial gap in orthodontic mobile application adoption in emerging markets, contrasting with higher utilization rates reported in developed countries. These insights underscore the need for globally inclusive strategies to promote digital orthodontics, particularly in resource-limited settings.