The role of mobile health in prevention, diagnosis, treatment and self-care of COVID-19 from the healthcare professionals’ perspectives

**Background:** The COVID-19 pandemic, first reported in Wuhan, China in December 2019, has caused over 204 million infections and 4 million deaths globally as of August 2021. In Iran, over 2.5 million infections and 74,000 deaths were confirmed by May 2021. Given the need for social distancing, information and communications technology (ICT) has become increasingly important for disease management. Mobile health (mHealth) interventions—including smartphone sensors, remote monitoring, telemedicine, and AI-based diagnostics—have been deployed worldwide. Understanding healthcare professionals' attitudes toward mHealth is critical for designing acceptable and effective interventions, as provider acceptance is a key determinant of technology adoption.

**Methods:** This cross-sectional survey study was conducted in Iran in 2020. The study population included physicians, nurses, allied medicine personnel (e.g., radiology and laboratory experts, operating room staff), and personnel with indirect patient contact (e.g., admissions, IT). Participants were recruited via social networks (WhatsApp, Telegram, LinkedIn). The sample size was calculated based on an estimated 60% knowledge ratio regarding mHealth, with power 1−β = 0.70 and α = 0.05, yielding a target of 100 participants plus 20% oversampling. The final sample included 120 complete responses (3 of 123 excluded for incomplete data). The 81-item questionnaire was developed by researchers, with face validity confirmed by five medical informatics specialists. Cronbach's alpha was 0.96. Questions covered demographics, mHealth familiarity, and the perceived contribution of mHealth functionalities to prevention (19 items), diagnosis (18 items), treatment (22 items), and self-care (16 items), rated on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). mHealth functionalities were categorized into four dimensions: innovative (e.g., image processing, wearables), monitoring and screening (e.g., patient monitoring, triage apps), remote services (e.g., tele-visit, tele-consultation), and education and decision-making (e.g., health education, decision support). Data were analyzed using descriptive statistics, ANOVA, Kruskal–Wallis tests, and linear regression.

**Key Results:** Among 120 participants, 59.8% were female, 49.2% were aged ≤30 years, 44.7% held bachelor's degrees, and 43.7% were nurses. Over 73% reported moderate or higher familiarity with mHealth. Overall, 87.4% of participants rated mHealth as moderately to most helpful for COVID-19 management (37% very helpful, 12.6% least helpful). Mean scores for mHealth's role were highest for self-care (mean = 3.96, SD = 0.51), followed by prevention (mean = 3.88, SD = 0.48), treatment (mean = 3.58, SD = 0.63), and diagnosis (mean = 3.57, SD = 0.60). The highest-rated specific functionality was patient decision aids for self-care (mean = 4.17, SD = 0.7); the lowest was computer games for treatment (mean = 3.06, SD = 1.067). Among functionality dimensions, monitoring and screening received the highest scores, and remote services the lowest. ANOVA showed significant differences across job groups for treatment (F = 2.658, P = 0.014), prevention (F = 2.677, P = 0.023), and diagnosis (F = 2.917, P = 0.006), with nurses consistently giving the highest scores and physicians the lowest. No significant difference was found for self-care (Kruskal–Wallis H = 2.808, P = 0.074). Significant differences across job groups were also found for remote services (F = 3.475, P = 0.007), innovative functionalities (F = 4.266, P = 0.023), and monitoring and screening (F value not fully reported, P = 0.03), but not for education and decision-making (P = 0.36). Linear regression showed that only job type was a significant predictor of attitudes; gender (P = 0.53), education (P = 0.41), age (P = 0.20), and mHealth familiarity (P = 0.13) were not significant.

**Clinical Implications:** Healthcare professionals in Iran perceive mHealth as most valuable for supporting patient self-care during COVID-19, suggesting that resource allocation and intervention design should prioritize self-care functionalities such as patient decision aids (e.g., reminders for medication, hand washing). Nurses showed the most positive attitudes across domains, while physicians were more cautious, possibly due to concerns about reimbursement models and professional replacement. The lack of association between demographic factors and attitudes suggests broad acceptability across age, gender, and education levels. These findings can guide health authorities in implementing targeted IT-based interventions. Limitations include the online-only recruitment method, low physician participation, lack of readiness assessment, and absence of open-ended questions. Future research should include larger, more diverse samples and qualitative methods to capture deeper insights.