Determinants of mobile health application utilization in Lebanon: a cross-sectional study of perceptions, barriers, and engagement.

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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Public Health . 2026 Apr 20;26:1764. doi: 10.1186/s12889-026-27427-9 Determinants of mobile health application utilization in Lebanon: a cross-sectional study of perceptions, barriers, and engagement Iqbal Fahs 1, 2, ✉ , Haya El Jabbawi 1 , Hala Mallah 1 , Nour Najm 1 , Mariam Alkhatib 1 , Jana Hayek 1 , Faten Hamed 1 , Seham Kanaan 1 , Mariam Dabbous 1, 3, 4 , Jihan Safwan 1, 3, 5, 6 , Fouad Sakr 1, 3 , Mohamad Rahal 1 Author information Article notes Copyright and License information 1 School of Pharmacy, Lebanese International University, Moussaitbeh, Salim Salam, Beirut, Lebanon 2 RITMES-E4 Research Unit (Epidemiological, Microbiological, Molecular, and Phylogenomic Surveillance of Infectious Diseases), Institute of Research for Development (IRD), University Hospital Institute Méditerranée-Infection, Aix-Marseille University, Marseille, France 3 INSPECT-LB INSPECT-LB (National Institute of Public Health, Clinical Epidemiology, and Toxicology -Lebanon), Beirut, Lebanon 4 Inserm U1094, IRD UMR270, Univ. Limoges, CHU Limoges, EpiMaCT - Epidemiology of Chronic Diseases in Tropical Zone, Institute of Epidemiology and Global Health – Michel Dumas, OmegaHealth, Limoges, France 5 Aging Research Team, Center for Epidemiology and Research in POPulation Health (CERPOP), University of Toulouse (formerly UPS – Toulouse III), Inserm, Toulouse, 31000 France 6 University of Toulouse (formerly UPS – Toulouse III), Toulouse, 31062 France ✉ Corresponding author. Received 2025 Dec 8; Accepted 2026 Apr 13; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13227778  PMID: 42010595 Abstract Background Mobile health (mHealth) applications (apps) are increasingly used to support health promotion and chronic disease management. However, evidence from Middle Eastern low-resource settings remains limited. This study assessed perceptions, usage patterns, barriers, and determinants of mHealth apps utilization among adults visiting community pharmacies in Lebanon. Methods A cross-sectional survey was conducted between February and September 2025 across selected community pharmacies in Lebanon’s six main districts. Face-to-face interviews collected sociodemographic data, perceptions, usage patterns, and barriers. Bivariate analyses identified factors associated with mHealth use, and multivariable logistic regression determined independent predictors. Results Among 514 participants (mean age 38.9 ± 8.6 years), 71.6% reported mHealth apps use. Perceptions among all participants were generally positive (mean score 47.3 ± 6.2/60). Users predominantly relied on lifestyle-related apps including step counters (73.8%), fitness (53.0%), and calorie trackers (50.9%). Only 20.9% of users reported sustained medication adherence. Nearly half of users encountered barriers such as poor internet access, low motivation, information overload, and privacy concerns. Higher mHealth apps use was independently associated with higher BMI (ORa = 2.123), urban residence (ORa = 2.273), working in a health-related profession (ORa = 1.420), having chronic conditions (ORa = 2.693), excellent perceived health knowledge (ORa = 1.223), greater medication use (ORa = 1.157), and more positive attitudes (ORa = 1.115). Lower odds of use were found among older adults and individuals residing in the Bekaa and North Lebanon regions. Conclusion mHealth apps are widely used and positively perceived among adults visiting community pharmacies in Lebanon but show modest impact on long-term adherence and remain hindered by usability, connectivity, and privacy barriers. Addressing national and regional digital inequities, strengthening clinical endorsement, and improving app quality are essential for maximizing mHealth value in resource-constrained areas. Keywords: MHealth, Apps, Mobile health applications, Digital health, Self-management, Community pharmacy, Health behavior, Lebanon, Technology adoption Introduction The rapid global spread of smartphones has reshaped the way people access health information and interact with healthcare systems [ 1 ]. According to the World Health Organization (WHO), mobile health applications (mHealth apps) refer to the use of portable and wireless technologies to enhance clinical practice and public health activities [ 2 ]. They are mobile phone-based applications designed to support health-related functions, including lifestyle and wellness tracking, medication reminders and adherence support, symptom monitoring, appointment management, access to health information, and self-management of acute or chronic conditions [ 3 ]. Such apps have seen an exponential increase, particularly following the COVID-19 pandemic [ 4 ]. mHealth apps offer opportunities to improve health outcomes. They can facilitate prevention and health promotion, support self-management of diseases, enhance medication adherence, foster health literacy by delivering structured and accessible information, and enhance patient engagement and accountability [ 5 – 8 ]. However, significant challenges hinder widespread mHealth adoption and sustained use including limited digital literacy, inadequate user interface design, unequal access to smartphones, and poor internet coverage [ 9 ]. Moreover, privacy and data security concerns, regulatory ambiguities and a lack of formal guidelines hinder integration into mainstream care [ 10 – 12 ]. In low- and middle-income countries (LMICs), where health systems often face resource limitations, mHealth technologies have been proposed as relatively affordable tools that can extend care to underserved communities [ 13 ]. Systematic reviews of mHealth in developing countries suggest that such technologies can contribute to patient education, health promotion, disease self-management, reductions in healthcare costs, and remote monitoring, yet evidence remains heterogeneous and frequently limited by small samples, short follow-up, and variable methodological quality [ 14 ]. Robust evidence on mHealth apps’ effectiveness, sustained engagement, and impact on health behaviors is relatively scarce, particularly outside high-income settings [ 14 ]. In the Arab world and broader Middle East and North Africa region, a systematic review of mHealth systems reported overall low utilization and identified multiple challenges related to usability, user interface design, quality of service, security, and cultural adaptation [ 15 ]. While some national and regional studies have begun to explore mHealth apps, they often focus on specific subpopulations or limited domains, and rarely combine use data with perceptions, health behavior changes, and barriers in a comprehensive framework [ 14 – 17 ]. In Lebanon, empirical data on mHealth adoption remain particularly scarce despite high mobile-phone penetration and escalating pressure on the health system. Since 2019, Lebanon has experienced a convergence of economic collapse, severe medication shortages, healthcare workforce migration, infrastructure challenges, and reduced access to physician-led services, collectively placing unprecedented strain on the delivery of essential healthcare [ 18 , 19 ]. However, while formal healthcare capacity has deteriorated, mobile phone ownership and use remain widespread, and digital platforms continue to serve as critical channels for communication and information access [ 20 ]. These conditions make Lebanon a compelling environment in which mHealth interventions could help bridge service gaps and mitigate limitations of the traditional healthcare infrastructure. Within this fragile health system landscape, community pharmacies have increasingly assumed a frontline role in healthcare delivery. Pharmacies often function as the first, and in some cases the only, point of contact for individuals seeking medical advice, medication access, or health-related guidance, particularly in the absence of timely physician care [ 21 ]. Their accessibility, walk-in availability, and provision of free medication counseling render community pharmacies critical primary-care touchpoints. Evidence from international and regional settings further demonstrates that community pharmacists play a substantial role in patient education, chronic disease support, self-care guidance, and triage of minor ailments [ 22 , 23 ]. These trusted relationships position community pharmacies as strategic environments for both supporting and observing engagement with mHealth apps. In this context, digital health tools, including mHealth apps, should be viewed not merely as technological innovations, but as potential adaptive mechanisms within a fragmented and resource-constrained health system. Examining mHealth utilization at the community pharmacy interface offers timely and context-specific insights into how digital tools are accessed, perceived, and operationalized under conditions of systemic strain. Such an approach enables assessment of mHealth use among individuals who remain engaged with formal healthcare access points, while also illuminating patterns of digital engagement that may inform pragmatic integration strategies. In addition, understanding how mHealth applications are currently used outside formal care pathways is a necessary first step for informing evidence-based digital health governance and integration strategies. Accordingly, this study aims to address a critical evidence gap by assessing mHealth apps utilization among adults visiting community pharmacies in Lebanon. Specifically, it evaluates the prevalence and patterns of mHealth use, types of applications installed, perceptions and perceived impact on health behaviors and management, experienced barriers, and factors associated with mHealth adoption. By focusing on adults visiting community pharmacies across all major Lebanese districts, this study provides practice-relevant evidence on real-world mHealth utilization at a key interface between the public and the health system. The findings can inform pharmacist-led digital health counseling, identify geographic and sociodemographic disparities in digital access relevant to national digital health planning, and support policymakers in prioritizing regulatory and governance frameworks addressing application quality, data protection, and patient safety. In addition, documenting the current disconnect between consumer mHealth use and formal healthcare delivery highlights opportunities for structured integration into care pathways and future policy discussions on reimbursement and professional oversight, particularly within fragile health system and low- and middle-income country contexts. Methods Study design and setting This research employed a cross-sectional design and was carried out over a seven-month period, from February through September 2025. The study targeted individuals visiting community pharmacies in Lebanon. Community pharmacies were selected because they represent one of the most accessible and consistently utilized healthcare access points in Lebanon, particularly amid reduced access to physician-led services during the ongoing health system crisis. To obtain a sampling frame, the list of all licensed pharmacies across the country’s six main districts (Beirut, Mount Lebanon, Bekaa, Nabatieh, South Lebanon, and North Lebanon) was requested from the Order of Pharmacists of Lebanon (OPL). From each district, three pharmacies were selected from all major Lebanese districts to ensure district-level geographic coverage and inclusion of both urban and rural environments. Data collection occurred on-site during scheduled field visits. Trained investigators approached pharmacy clients as they entered and invited them to take part in the study. Interviews were conducted in person using a structured questionnaire. All eligible participants were Lebanese residents aged 18 years or older, and recruitment followed a consecutive approach in each pharmacy. No fixed quota was assigned to individual pharmacies or districts. Participant numbers at each site were determined by client flow during scheduled data collection periods, rather than by proportional allocation based on pharmacy size or volume. Accordingly, the sampling strategy aimed to ensure standardized recruitment procedures across sites. Survey development and measures The survey instrument was developed following an extensive review of the literature on mHealth apps and digital health [ 24 – 28 ]. The original questionnaire was drafted in English and translated into Arabic by a bilingual expert, followed by back-translation into English to ensure semantic equivalence and conceptual accuracy. Content validity was assessed through review by three academics with expertise in public health and digital health, who evaluated the instrument for clarity, item relevance, and coherence. An additional review by a digital health expert further confirmed content validity. The questionnaire was subsequently pilot-tested among 20 individuals from the target population to assess comprehensibility, clarity, and survey flow. Feedback from the pilot phase informed minor refinements to the final version, and pilot responses were not included in the main analysis. The questionnaire consisted of three main sections. The first section addressed socio-demographic characteristics, including age, weight, height, gender, area and place of residence (urban/rural), marital status, occupation, educational level, income, health-related background, current health status, and medication use. Perceived health knowledge was assessed using a single self-reported item in which participants were asked to rate their overall level of health-related knowledge by selecting one of three predefined response options: ‘poor,’ ‘moderate,’ or ‘excellent’. These categories were used directly in the analysis without applying any numerical scoring or transformation. The second section retrieved data regarding beliefs and attitudes toward mHealth apps using twelve five-point Likert-scale items (1 = strongly disagree to 5 = strongly agree). The perception scale assessed perceived usefulness, motivation for healthy behaviors, trust in app information, ease of use, willingness to recommend, and the advisory role of healthcare professionals. Total scores ranged between 12 to 60 with higher scores reflecting more positive attitudes. The perception scale showed excellent internal consistency with Cronbach’s alpha of 0.880. The third section examined the utilization of mHealth apps including prevalence, frequency of use, types of apps used, perceived impact, challenges, and barriers to effective utilization of mHealth apps. Ethical approval The study received ethical approval from the Ethical and Research Committee of the Lebanese International University (LIU) (Approval number: 2025ERC-108-LIUSOP). All procedures complied with the principles of the Declaration of Helsinki and its subsequent amendments. Participation was entirely voluntary, and no incentives were provided. Informed consent was obtained from each participant prior to enrollment. Respondents were assured that their identities would remain confidential and that no identifiable information would be collected. Sample size calculation The minimum sample size was determined using the CDC Epi-info software [ 29 ]. Assuming an expected frequency of 50%, a 5% margin of error, and a 95% confidence interval, the required sample size to achieve 80% statistical power was 384 participants. To improve precision and representation, the final sample included 514 respondents. Statistical analysis All data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 26. Descriptive statistics were used to summarize participant characteristics. Means and standard deviations were computed for continuous variables, whereas categorical variables were described using frequencies and percentages. Bivariate analyses were performed to examine factors associated with mHealth apps use. For categorical variables, the chi-square (χ 2 ) test or Fisher’s exact test (expected cell counts < 5) were applied to assess differences between users and nonusers. For continuous variables, normality was verified, and independent-samples t-test was used to compare mean scores between groups. Variables that demonstrated a significance level of p < 0.2 in bivariate analyses were included in a multivariable logistic regression model to identify independent factors of mHealth apps use among adults visiting community pharmacies in Lebanon [ 30 ]. This more inclusive threshold was selected to avoid the premature exclusion of potentially important confounding variables and to ensure a comprehensive assessment of factors associated with mHealth use [ 31 ]. Adjusted odds ratios (ORa) with 95% confidence intervals (95% CI) were calculated to assess the strength of associations. Internal consistency of the attitude scale was assessed using Cronbach’s alpha coefficient. All p -values were two-tailed, and statistical significance was set at p < 0.05. Results Socio-demographic characteristics A total of 514 participants were included in the study. The mean age was 38.95 ± 8.65 years, and the average BMI was 25.95 ± 5.33 kg/m 2 . Approximately two-thirds were female (66.5%), and 64.4% resided in urban areas. Participants were distributed across all six Lebanese districts, with the highest representation from Beirut (25.0%), followed by Mount Lebanon (20.6%), and South Lebanon (14.7%). Most respondents were single (71.4%), held a university-level degree (72.8%), and were employed (46.9%), while 34.2% were students. Regarding income, 66.0% reported a middle-income level. Nearly all participants owned a smartphone or tablet (97.1%). The demographic profile of participants reflects a subgroup of adults who are more digitally engaged and actively accessing community pharmacy services. Nearly one-third of respondents worked in a health-related profession (29.2%). More than half reported having at least one health condition (58.2%), and the most frequently reported conditions were women’s health disorders (12.1%), hypertension (8.2%), and mental health disorders (8.9%). Participants used, on average, 1.37 ± 1.19 medications regularly, and most rated their health knowledge as moderate (74.1%). Table 1 summarizes the sociodemographic characteristic of the studied individuals. Table 1. Sociodemographic characteristics of the participants ( n = 514) Characteristic Total n = 514 Age, years, mean ± SD 38.95 ± 8.65 BMI, kg/m 2 , mean ± SD 25.95 ± 5.33 Gender, n (%) Male 172 (33.5) Female 342 (66.5) Area, n (%) Rural 183 (35.6) Urban 331 (64.4) Residence, n (%) Beirut 129 (25.0) Mount Lebanon 106 (20.6) Bekaa 64 (12.4) North Lebanon 71 (13.5) Nabatieh 68 (13.8) South Lebanon 76 (14.7) Marital status, n (%) Single 367 (71.4) Married 137 (26.7) Widowed/Divorced 10 (1.9) Educational level, n (%) Secondary school & less 38 (7.4) University level 374 (72.8) Post-graduate studies 102 (19.8) Employment status, n (%) Student 176 (34.2) Employed 241 (46.9) Unemployed/Retired 67 (13.0) Housewife 30 (5.8) Income level, n (%) Low 135 (26.3) Middle 339 (66.0) High 40 (7.8) Own a smartphone or tablet, n (%) Yes 499 (97.1) No 15 (2.9) Work in a health-related profession, n (%) Yes 150 (29.2) No 364 (70.8) Have a health condition, n (%) 299 (58.2) Hypertension 42 (8.2) Diabetes Mellitus 33 (6.4) Respiratory disease 17 (3.3) Dyslipidemia 26 (5.1) Cardiovascular disease 21 (4.1) Osteoporosis 7 (1.4) Pain disorder 46 (8.9) Mental health disorder 37 (7.1) Women health disorder 62 (12.1) Other 8 (1.5) Number of medications used on a regular basis, mean ± SD 1.37 ± 1.19 Perceived knowledge of health, n (%) Poor 25 (4.9) Moderate 381 (74.1) Excellent 108 (21.0) Open in a new tab SD Standard deviation, BMI Body mass index Perception of mHealth apps Participants demonstrated overall positive perceptions toward mHealth apps with a relatively high mean attitude score of 47.25 ± 6.24 (range 34–60). A substantial majority agreed that mHealth tools improve medication adherence (83.2%), motivate the adoption of healthier habits (83.9%), educate users about health risks (82.7%), and enhance understanding of health conditions and treatments (70.2%). Moreover, 84.2% felt comfortable using mHealth apps to collect health-related information, and more than two-thirds (68.1%) preferred receiving prescription information through an app instead of a paper leaflet. Nearly three-quarters (73.5%) indicated they would recommend mHealth apps to others. Furthermore, the majority agreed that physicians (81.7%) and pharmacists (81.9%) should play an active role in recommending digital tools. However, perceptions of trust and reliability were more moderate where 57.2% considered app-based health information trustworthy, and 57.4% considered it reliable (Table 2 ). Table 2. Participants’ perception of mHealth apps ( n = 514) Statement n (%) Total n = 514 Strongly agree Agree Neutral Disagree Strongly disagree mHealth apps improve medication adherence 125 (24.3) 303 (58.9) 70 (13.6) 14 (2.7) 2 (0.4) mHealth apps motivate to adopt healthier habits 168 (32.7) 263 (51.2) 60 (11.7) 20 (3.9) 3 (0.6) mHealth apps are effective in educating people about health risks 153 (29.8) 272 (52.9) 79 (15.4) 6 (1.2) 4 (0.8) mHealth apps help me to understand health conditions and treatments better 108 (21.0) 253 (49.2) 131 (25.5) 17 (3.3) 5 (1.0) Using mHealth apps is a comfortable way for collecting health-related information 148 (28.8) 285 (55.4) 74 (14.4) 7 (1.4) - I prefer receiving my prescription medication information through a mHealth app instead of a paper leaflet 135 (26.3) 215 (41.8) 112 (21.8) 41 (8.0) 11 (2.1) It is easy to understand medical terms when using mHealth apps 120 (23.3) 242 (47.1) 120 (23.3) 32 (6.2) - mHealth apps provide trustworthy health information 90 (17.5) 204 (39.7) 196 (38.1) 21 (4.1) 3 (0.6) Information provided by mHealth apps is reliable 71 (13.8) 224 (43.6) 150 (29.2) 63 (12.3) 6 (1.2) I recommend mHealth apps to a friend or family member 106 (20.6) 272 (52.9) 124 (24.1) 12 (2.3) - Physicians should offer mHealth apps to help patients manage their medications 129 (25.1) 291 (56.6) 91 (17.7) 1 (0.2) 2 (0.4) Pharmacists should offer mHealth apps to help patients manage their medications 140 (27.2) 281 (54.7) 90 (17.5) 1 (0.2) 2 (0.4) Attitude Score Mean ± SD 47.25 ± 6.24 Range (12–60) 34–60 Open in a new tab SD Standard deviation Prevalence, patterns, and types of mHealth apps use Overall, 368 participants (71.6%) reported using mHealth apps, while 146 (28.4%) indicated that they had never used such apps (Table 3 ). Most users engaged with mHealth apps regularly, with 41.9% using them often (3–4 times per week). The majority of participants reported that they personally downloaded the mHealth apps (76.4%) and had more than three mHealth apps on their smartphones or tablets (60.1%). Social media was the most frequently cited source of knowledge about mHealth apps (39.7%), followed by family or friends’ recommendations (16.5%), self-directed web searches (14.1%), and healthcare providers (13.3%). Less common sources included community health organizations or programs (6.8%), schools or universities (4.1%), app store recommendations (3.3%), and traditional media such as television or radio (2.2%). In addition, only half of the users (50.5%) described these apps as easy to use. Table 3. mHealth apps utilization Characteristic Total n = 368 Frequency of use, n (%) Rare (< 3 times/month) 53 (14.4) Sometimes (1–2 times/week) 49 (13.3) Often (3–4 times/week) 154 (41.9) Usually (at least 5 times/week) 112 (30.4) mHealth apps download, n (%) By default (in smartphone) 87 (23.6) By myself 281 (76.4) Number of mHealth apps on smartphone/tablet, n (%) 1 62 (16.8) 2–3 85 (23.1) > 3 221 (60.1) Source of knowledge about mHealth apps, n (%) Healthcare providers 49 (13.3) Social media 146 (39.7) Self-web search 52 (14.1) Family member or Friend 61 (16.5) Television/Radio 8 (2.2) App store recommendations 12 (3.3) Schools or universities 15 (4.1) Community health organizations/programs 25 (6.8) Ever paid for a mHealth app, n (%) Yes 45 (12.2) No 323 (87.8) Ease of use of mHealth apps, n (%) Easy 186 (50.5) Neutral 107 (29.1) Difficult 75 (20.4) Open in a new tab Regarding the types of apps utilized among users (Fig. 1 ), lifestyle-related apps were the most frequently used, led by step counters (73.8%), fitness apps (53.0%), and calorie-tracking apps (50.9%). Conversely, pregnancy/parenting (5.3%), smoking cessation (4.1%), and medical appointment apps (2.3%) were the least utilized. Fig. 1. Open in a new tab Types of mHealth apps utilized by users ( n = 368) Impact of mHealth apps among users Among users, 58.4% reported overall satisfaction with the apps (Table 4 ). Most users perceived a favorable effect of mHealth apps on their health management (67.4%) and felt more empowered to manage their health (68.2%). Nearly three-quarters of users (72.3%) reported changing some of their health habits based on information obtained from mHealth apps. However, long-term medication adherence improvements were reported by only 20.9%. The majority (80.4%) planned to continue using mHealth apps. Almost half of users (46.2%) reported experiencing challenges or barriers while using mHealth apps, and 35.1% had stopped using at least one app due to difficulties. Table 4. Perceived impact of mHealth apps among users ( n = 368) Statement Total n = 368 Overall satisfaction with mHealth apps, n (%) Satisfied 215 (58.4) Neutral 89 (24.2) Dissatisfied 64 (17.4) Overall impact of mHealth apps on your health management, n (%) Positive 248 (67.4) Neutral 96 (26.1) Negative 24 (6.5) Feel empowered to manage your health since using mHealth apps, n (%) Yes 251 (68.2) No 117 (31.8) Long term medication adherence after using mHealth apps, n (%) Yes 77 (20.9) No 291 (79.1) Changed health habits because of something you learned through mHealth apps, n (%) Yes 266 (72.3) No 102 (27.7) Plan to continue using mHealth apps, n (%) Yes 296 (80.4) No 72 (19.6) Experienced challenges or barriers while using mHealth apps, n (%) Yes 170 (46.2) No 198 (53.8) Ever stopped using a mHealth app because it is challenging, n (%) Yes 129 (35.1) No 239 (64.9) Open in a new tab Perceived barriers to mHealth apps among users The main barriers to effective mHealth use among users are shown in Fig. 2 . The most frequently reported barriers were lack of internet access (47.9%), lack of motivation (46.3%), information overload (44.4%), and privacy concerns (42.2%). Difficulty understanding medical information within the apps was reported by 31.6% of users. Fig. 2. Open in a new tab Barriers for effective utilization of mHealth apps among users ( n = 368) Factors associated with mHealth apps use Bivariate analyses Bivariate analysis (Table 5 ) showed that mHealth users were younger on average than nonusers ( p = 0.021) and had higher BMI ( p < 0.001). mHealth use was more common among urban residents ( p < 0.001) and varied significantly across regions of residence ( p = 0.011). Educational level ( p < 0.001) and employment status ( p < 0.001) were also associated with mHealth use. Participants working in health-related professions were more likely to be mHealth users than nonusers ( p < 0.001). Having at least one health condition was associated with higher mHealth use ( p = 0.042). Significant associations were also observed for hypertension ( p = 0.012), diabetes mellitus ( p = 0.044), pain disorders ( p = 0.042), mental health disorders ( p = 0.028), and women’s health disorders ( p = 0.033). mHealth users reported a higher number of regularly used medications ( p = 0.001), higher perceived health knowledge ( p = 0.001), and higher attitude scores toward mHealth apps ( p = 0.003). Table 5. Bivariate analysis of variables affecting mHealth apps use among participants Variable Nonusers Total n = 146 Users Total n = 368 p -value Age, years, mean ± SD 41.21 ± 7.13 32.87 ± 8.25 0.021 BMI, Kg/m 2 , mean ± SD 23.9 ± 6.35 26.98 ± 7.38 < 0.001 Gender, n (%) 0.657 Male 51 (34.9) 121 (32.9) Female 95 (65.1) 247 (67.1) Area, n (%) < 0.001 Rural 81 (55.5) 102 (27.7) Urban 65 (44.5) 266 (72.3) Residence, n (%) 0.011 Beirut 30 (20.5) 99 (26.9) Mount Lebanon 26 (17.8) 80 (21.7) Bekaa 28 (19.2) 36 (9.8) North Lebanon 25 (17.1) 46 (12.5) Nabatieh 20 (13.7) 48 (13.0) South Lebanon 23 (15.7) 53 (14.4) Marital status, n (%) 0.272 Single 105 (71.9) 262 (71.2) Married 36 (24.7) 101 (27.4) Widowed/Divorced 5 (3.4) 5 (1.4) Educational level, n (%) < 0.001 Secondary school & less 11 (7.5) 27 (7.3) University level 124 (85.0) 250 (68.0) Post-graduate studies 11 (7.5) 91 (24.7) Employment status, n (%) < 0.001 Student 66 (45.2) 110 (29.9) Employed 54 (37.0) 187 (50.8) Unemployed/Retired 9 (6.2) 58 (15.8) Housewife 17 (11.6) 13 (3.5) Income level, n (%) 0.550 Low 42 (28.8) 93 (25.3) Middle 95 (65.1) 244 (66.3) High 9 (6.1) 31 (8.4) Work in a health-related profession, n (%) < 0.001 Yes 20 (13.7) 130 (35.3) No 126 (86.3) 238 (64.7) Have a health condition, n (%) 0.042 Yes 69 (47.3) 230 (62.5) No 77 (52.7) 138 (37.5) Hypertension, n (%) 0.012 Yes 10 (6.8) 32 (8.7) No 136 (93.2) 336 (91.3) Diabetes Mellitus, n (%) 0.044 Yes 7 (4.8) 26 (7.1) No 139 (95.2) 342 (92.9) Respiratory disease, n (%) 0.450 Yes 6 (4.1) 11 (3.0) No 140 (95.9) 357 (97.0) Dyslipidemia, n (%) 0.523 Yes 8 (5.5) 18 (4.9) No 138 (94.5) 350 (95.1) Cardiovascular disease, n (%) 0.052 Yes 4 (2.7) 17 (4.6) No 142 (97.3) 351 (95.4) Osteoporosis, n (%) 0.568 Yes 2 (1.4) 5 (1.4) No 144 (98.6) 363 (98.6) Pain, n (%) 0.042 Yes 11 (7.5) 35 (9.5) No 135 (92.5) 333 (90.5) Mental health disease, n (%) 0.028 Yes 8 (5.4) 29 (7.9) No 138 (94.6) 339 (92.1) Women health disease, n (%) 0.033 Yes 12 (8.2) 50 (13.6) No 134 (91.8) 318 (86.4) Number of medications used on a regular basis, mean ± SD 0.89 ± 1.41 2.09 ± 1.61 0.001 Perceived knowledge of health, n (%) 0.001 Poor 13 (8.9) 12 (3.3) Moderate 103 (70.6) 278 (75.5) Excellent 30 (20.5) 78 (21.2) Attitude Score, mean ± SD 43.96 ± 6.01 50.75 ± 6.27 0.003 Open in a new tab SD Standard deviation p -values are based on chi-square or Fisher’s exact tests for categorical variables and independent-samples t-tests for continuous variables; bold p-values denote statistical significance Multivariable analysis Multivariable logistic regression analysis (Table 6 ) identified several independent factors associated with mHealth apps use among adults visiting community pharmacies in Lebanon. Age demonstrated a significant inverse relationship with mHealth use (ORa = 0.437, p = 0.032), indicating lower odds of use with increasing age. Participants residing in Bekaa (ORa = 0.644, p = 0.042) and North Lebanon (ORa = 0.620, p = 0.009) had significantly lower odds of mHealth use relative to those living in Beirut. In contrast, higher BMI levels (ORa = 2.123, p = 0.003), and urban residence (ORa = 2.273, p = 0.002) were strongly associated with higher mHealth apps use. Additionally, working in a health-related profession (ORa = 1.420, p = 0.004), and having at least one health condition (ORa = 2.693, p = 0.024) significantly increased the likelihood of app use. Participants with hypertension (ORa = 1.526, p = 0.007), pain disorders (ORa = 1.388, p = 0.002), mental health disorders (ORa = 1.084, p = 0.040), and women’s health disorders (ORa = 2.325, p = 0.001) showed significantly greater odds of using mHealth apps. Perceived excellent health knowledge significantly increased the likelihood of mHealth adoption compared with poor health knowledge (ORa = 1.223, p = 0.003). Also, each increase in the number of regularly used medications was associated with higher mHealth use (ORa = 1.157, p = 0.032). Moreover, the attitude score demonstrated a positive association with mHealth apps use (ORa = 1.115, p = 0.001). Table 6. Regression analysis of variables affecting mHealth apps use Variable ORa p -value Confidence interval Lower Bound Upper Bound Age, years 0.437 0.032 0.293 0.689 BMI, kg/m 2 2.123 0.003 1.017 3.516 Area: Urban Vs. Rural 2.273 0.002 1.102 3.808 Residence: Bekaa Vs. Beirut 0.644 0.042 0.207 0.893 Residence: North Vs. Beirut 0.620 0.009 0.265 0.851 Work in a health-related profession 1.420 0.004 1.322 2.431 Have a health condition 2.693 0.024 1.474 4.206 Hypertension 1.526 0.007 1.182 3.595 Pain disorder 1.388 0.002 1.120 2.952 Mental health disorder 1.084 0.040 1.043 1.279 Women health disorder 2.325 0.001 1.138 3.641 Perceived knowledge of health: Excellent Vs. poor 1.223 0.003 1.099 2.429 Number of medications used on a regular basis 1.157 0.032 1.069 1.247 Attitude Score 1.115 0.001 1.032 1.416 Open in a new tab Reference categories: rural area, Beirut residence, non-health-related profession, no health condition, no specific disorder, poor perceived health knowledge ORa adjusted odds ratio; bold p-values denote statistical significance Discussion This study provides a comprehensive examination of mHealth apps perceptions, usage patterns, and determinants among individuals visiting community pharmacies in Lebanon. The widespread use of these apps observed in this population underscores the growing relevance of digital tools for self-management, particularly in countries where access to traditional healthcare services is constrained. Overall, the participant profile was characterized by higher levels of digital literacy, health awareness, and urban residence, indicating that the study population represents a relatively advantaged subgroup of adults more likely to engage with both community pharmacies and mobile health technologies. Rural residents, older adults, individuals with lower socioeconomic status, and marginalized populations, who may face structural barriers to both healthcare access and digital technology use, were underrepresented. Consequently, the findings should not be generalized to the Lebanese adult population as a whole. Importantly, this pattern also highlights an equity gap in digital health engagement, suggesting that current mHealth adoption disproportionately benefits individuals who are already digitally connected and engaged with formal healthcare services. These findings underscore the risk that unstructured expansion of mHealth interventions may exacerbate existing health inequities if vulnerable populations are not explicitly targeted. Across the entire sample, attitudes toward mHealth apps were favorable. Most participants agreed that apps could enhance adherence and health education. These findings are consistent with evidence from multiple settings showing that mHealth tools can strengthen self-monitoring and increase users’ sense of control over their health [ 14 ]. The strong support for physicians and pharmacists in recommending apps parallels findings from other surveys indicating that professional endorsement substantially increases trust, uptake, and long-term engagement with digital tools [ 32 , 33 ]. However, fewer than 60% of participants perceived app-based information as trustworthy or reliable. This reflects concerns frequently raised in the literature regarding the variable accuracy, insufficient regulation, and inconsistent quality of commercially available mHealth apps [ 34 ]. In LMICs, where formal regulatory frameworks for consumer-facing mHealth apps remain limited, these findings highlight the importance of clinician guidance and context-appropriate evaluation of app quality. The majority of respondents reported using at least one mHealth app. This prevalence is consistent with international data showing widespread penetration of health-related apps among smartphone owners [ 35 ]. Usage patterns in this study were dominated by lifestyle-oriented apps, particularly step counters, fitness apps, and calorie trackers along with prominence of women’s mHealth apps. This aligns with global and regional data showing that wellness and fitness, menstrual-cycle and fertility tracking apps account for a large share of downloads and active use [ 15 , 36 – 38 ]. Conversely, few participants reported using apps focused on smoking cessation, pregnancy and parenting, medical appointments, or comprehensive chronic disease management. This pattern echoes concerns raised in the literature that while the commercial app ecosystem is saturated with wellness products, tools that target complex clinical conditions or that integrate seamlessly into care pathways remain less visible, less user-friendly, or less trusted [ 39 , 40 ]. Given the substantial burden of chronic disease in the region, this relatively modest uptake of clinically oriented apps reflects an underutilized opportunity for targeted digital support among high-risk groups. Importantly, these usage patterns must be interpreted in light of the study population’s demographic profile. The predominance of lifestyle-oriented mHealth use reflects engagement among digitally literate, health-aware, and predominantly urban individuals, while vulnerable populations, including older adults, rural residents, individuals with lower socioeconomic status, and those managing complex chronic conditions, were underrepresented. These groups may face structural barriers to both digital access and sustained engagement with mHealth tools, further limiting the reach of clinically oriented applications. As a result, current mHealth ecosystems risk reinforcing existing health inequities if lifestyle-focused tools continue to dominate without targeted strategies to support vulnerable populations and high-risk patients. Evidence from culturally tailored and community-engaged mHealth interventions supports the importance of such targeted approaches. For example, hypertension-focused mHealth intervention studies have demonstrated that culturally adapted digital health tools developed in partnership with underserved communities can significantly improve engagement, self-management behaviors, and cardiovascular outcomes among high-risk populations [ 41 , 42 ]. These findings highlight that effective mHealth strategies must go beyond general wellness applications and incorporate cultural relevance, accessibility, and integration with care delivery. Addressing these gaps will require intentional digital health strategies that prioritize inclusion of vulnerable populations and the development of mHealth tools aligned with chronic disease care needs. Sources of information about mHealth in our study were dominated by social media and informal word-of-mouth. Similar patterns have been observed, where social networks act as primary discovery channels, while clinicians remain underused despite their potential to guide app use [ 43 ]. Importantly, only about one-fifth of users reported sustained improvements in medication adherence. Evidence demonstrates that digital adherence tools often show initial benefit that diminishes without meaningful integration into clinical follow-up or behavior-change support strategies [ 44 , 45 ]. This is particularly relevant in LMICs, where digital interventions are often expected to compensate for overstretched healthcare services. Barriers were widely reported where nearly half of users encountered difficulties, and more than one-third discontinued at least one app. High attrition is widely documented in digital health, often driven by poor usability, excessive notifications, and content that is not tailored to local needs or literacy levels [ 24 , 26 , 46 ]. Lebanon’s infrastructural instability including intermittent electricity, fluctuating internet connectivity, and variability in mobile data affordability, likely exacerbates these barriers. Privacy concerns were also prominent, reflecting global public skepticism regarding data protection in mHealth apps. Many commercial apps provide inadequate privacy disclosures or share user data with third parties without clear consent [ 47 , 48 ]. These issues reinforce the need for national and regional digital health governance policies addressing transparency, data security, and privacy standards. Multivariable analysis revealed several predictors of mHealth use among adults visiting community pharmacies in Lebanon. Younger age was independently associated with higher odds of app use, where younger populations typically demonstrate higher digital fluency and willingness to adopt new technologies [ 49 , 50 ]. Urban residence strongly predicted use, suggesting that structural and infrastructural disparities continue to influence digital engagement. Lower odds of use among residents of Bekaa and North Lebanon further reflect geographic inequities, likely shaped by differences in connectivity, digital literacy, and availability of health promotion initiatives. Conversely, participants in health-related professions showed greater mHealth use, likely reflecting higher health literacy, digital familiarity, and professional exposure to health information. This disparity highlights inequities in digital health engagement among non-health workers and suggests that healthcare professionals could act as key facilitators in promoting mHealth adoption through education, app recommendation, and guided use. Health-related need factors including having a chronic condition such as hypertension, pain disorders, mental health conditions, or women’s health issues were associated with greater use, aligning with evidence that individuals managing ongoing health challenges are more inclined to seek digital tools that support self-care [ 51 ]. In addition, higher medication burden, excellent perceived health knowledge, and more positive attitudes toward mHealth were also predictive of use. These findings support established behavioral theories, including the Technology Acceptance Model, which emphasizes perceived usefulness, ease of use, and individual readiness as key drivers of digital adoption [ 52 , 53 ]. Implications for practice and policy The generally positive attitudes toward mHealth apps and their widespread current use among adult pharmacy visitors suggest that digital tools have the potential to support health promotion, lifestyle modification, and selected aspects of self-management in Lebanon and comparable LMIC settings. However, the findings also indicate that mHealth apps are predominantly used as standalone consumer tools, underscoring the need for structured professional guidance and system-level integration to translate use into meaningful health outcomes. Community pharmacies represent a pragmatic and accessible entry point for such integration. Given their frequent patient contact and trusted role, pharmacists are well positioned to support appropriate mHealth use by guiding individuals toward high-quality, evidence-informed applications, particularly for medication adherence support, lifestyle counseling, and monitoring of common chronic conditions. Strengthening this role will require targeted capacity building, including equipping pharmacists and other frontline clinicians with practical skills to evaluate, recommend, and contextualize mHealth applications that are culturally and linguistically appropriate. Several policy-relevant priorities emerge from these findings. First, clinician-supported mHealth use should be reinforced through professional training and guidance frameworks that enable healthcare providers to assess app quality, usability, and safety. Second, equity-oriented digital health strategies are essential to address the observed disparities in mHealth engagement. Tailored approaches, such as simplified interfaces, multilingual content, offline functionality, and community-based digital literacy support, are needed to ensure that older adults, rural residents, and individuals with limited digital literacy are not further marginalized. Third, the limited role of mHealth applications in structured chronic disease care observed in this study highlights the importance of integration into existing care pathways. Embedding app-based adherence reminders, symptom tracking, or self-monitoring tools within routine pharmacy or primary care follow-up may enhance sustained engagement and reinforce continuity of care, particularly in settings where physician access is constrained. Finally, the findings underscore the importance of governance and regulatory oversight. Establishing or strengthening national and regional standards for mHealth application quality, data protection, transparency, and evaluation may help address user concerns regarding privacy and trust. While formal reimbursement mechanisms for mHealth applications remain limited in Lebanon, documenting current utilization patterns provides an essential evidence base to inform future discussions on value-based integration and professional oversight of digital health tools within routine care delivery. Strengths and limitations This study has several strengths, including a relatively large sample size, recruitment from community pharmacies across all major districts, and the use of a structured instrument with strong internal consistency. These features enhance the internal validity and provide a broad snapshot of mHealth perceptions and behaviors among adults visiting community pharmacies in Lebanon. Nonetheless, some limitations must be acknowledged. The cross-sectional design precludes causal inference regarding the relationships between determinants and mHealth use. All measures were self-reported and thus vulnerable to recall and social desirability bias. Recruitment through community pharmacies may over-represent individuals who are more health-conscious, have chronic conditions, or are more engaged with the health system, potentially inflating estimates of mHealth use and positive attitudes. In addition, recruitment through community pharmacies presents a major structural selection bias. While this setting captures individuals engaged with formal healthcare access points, it systematically excludes marginalized populations and those disconnected from pharmacy-based care. As such, the study does not provide population-level estimates of mHealth adoption but offers context-specific insights into mHealth use at a critical healthcare interface. The use of multivariable regression does not mitigate the structural selection bias introduced by the pharmacy-based sampling strategy. Consequently, analytical findings cannot compensate for limited representativeness and should be interpreted within the defined study population. Moreover, the external validity of this study is limited by the demographic profile of the study population, which was predominantly urban, digitally literate, and health-aware. As a result, the findings may not be applicable to rural populations, older adults, lower-income individuals, or marginalized groups who face structural barriers to digital health access. Finally, the study did not capture detailed information on specific apps, app quality, or objective usage metrics, which would be valuable for linking particular design features to reported outcomes. Conclusion mHealth apps are widely used and generally well regarded among adults visiting community pharmacies in Lebanon, particularly as tools for lifestyle management and health information seeking. However, limited perceived impact on long-term medication adherence, significant barriers related to connectivity, motivation, and privacy, and clear sociodemographic and geographic disparities in use indicate that the promise of mHealth is not yet fully realized. Context-sensitive strategies that enhance app quality, strengthen clinician involvement, and address digital inequities will be essential to harness mHealth as a meaningful complement to conventional care in Lebanon and similar resource-constrained settings. Abbreviations APP Application BMI Body mass index Epi Info Centers for Disease Control and Prevention Epidemiological Software LMIC Low- and Middle-Income Country mHealth Mobile Health ORa Adjusted odds ratio SD Standard deviation SPSS Statistical Package for Social Sciences WHO World Health Organization Authors’ contributions IF conceptualized the study, had the responsibility of the project administration, planned the methodology, and carried out the statistical analyses. IF, MD, JS, FS and MR supervised the project. HEJ, HM, and NN collected data. IF, MAK, JH, FH, and SK wrote the manuscript text. All authors reviewed and edited the text, and approved the final manuscript. Funding This research received no external funding. Data availability The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Requests for access to the data should be directed to Dr. Iqbal Fahs, School of Pharmacy, Lebanese International University, Beirut, Lebanon (Email: iqbal.fahs@liu.edu.lb). Declarations Ethics approval and consent to participate This research was conducted in full accordance with the principles outlined in the Declaration of Helsinki. The study protocol was reviewed and approved by the Ethical and Research Committee at the Lebanese International University (LIU) under approval number 2025ERC-108-LIUSOP. Informed consent was obtained from all participants prior to study enrollment, and survey access was granted only after participants confirmed their willingness to participate. The study was observational and cross-sectional in nature and did not involve any invasive procedures or interventions. Consent for publication Not applicable. Competing interests The authors declare no competing interests. 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