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Formative Evaluation of Digital Health Interventions (3245) Mobile Health (mhealth) (3472) Demographics of Users, Social & Digital Divide (916) Use and User Demographics of mHealth (405) Usability of Apps and User Perceptions of mHealth (1190)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/80283, first published .
Awareness, Perceptions, Willingness, and Feasibility of mHealth Apps Among People Living With Epilepsy: Cross-Sectional Questionnaire Study

Awareness, Perceptions, Willingness, and Feasibility of mHealth Apps Among People Living With Epilepsy: Cross-Sectional Questionnaire Study

Awareness, Perceptions, Willingness, and Feasibility of mHealth Apps Among People Living With Epilepsy: Cross-Sectional Questionnaire Study

Authors of this article:

Bushra Batool Zahra1 Author Orcid Image ;   Muhammad Amir Hamza1 Author Orcid Image ;   Rehana Sarwat1 Author Orcid Image ;   Hina Batool Zahra1 Author Orcid Image ;   Ayesha Azam1 Author Orcid Image ;   Ali Ahmed2 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    Original Paper

    1Department of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Riphah International University, Islamabad, Pakistan

    2Division of Infectious Diseases and Global Public Health, School of Medicine, University of California San Diego, San Diego, CA, United States

    Corresponding Author:

    Ali Ahmed, PharmD, MPhil, PhD

    Division of Infectious Diseases and Global Public Health

    School of Medicine

    University of California San Diego

    9500 Gilman Drive, La Jolla

    San Diego, CA, 92093

    United States

    Phone: 1 6199539767

    Email: aliahmed@health.ucsd.edu


    Background: The rapid expansion of mobile health (mHealth) apps has transformed health care delivery worldwide. Despite their potential to improve epilepsy care, a substantial treatment gap remains, especially in low- and middle-income countries, due to limited resources, stigma, and low adoption of digital technologies. Although mHealth apps can bridge these disparities, their impact depends on acceptance and use by the target population.

    Objective: We aimed to assess the awareness, feasibility, willingness, perception, and factors influencing these behaviors for the usage of mHealth apps among people living with epilepsy in Pakistan.

    Methods: We conducted a cross-sectional analytical survey between March and July 2024 among people living with epilepsy attending the Pakistan Institute of Medical Sciences (PIMS). Participants completed a validated, self-administered questionnaire with 33 items across 5 domains. We recruited 406 participants through convenience sampling and analyzed the data using SPSS version 23.0 (IBM Corp). Through multivariable linear regression analysis, we explored factors associated with people living with epilepsy willingness to use mHealth apps. Correlation analysis was used to elucidate the association among awareness, perception, feasibility, and willingness.

    Results: Among 406 participants, 53.7% (n=218) were male, 64.5% (n=262) were married, and 89.2% (n=362) were identified as Muslim. Although 86.2% (n=350) of participants have heard about mHealth apps for epilepsy management, 78.1% (n=317) expressed negative perceptions of their use. More than half, 69% (n=280), reported concerns about the privacy of their medical information online, and 78.1% (n=317) were not comfortable using mHealth apps on smartphones or tablets. Multivariable linear regression analysis revealed that rural residents (P=.05), those with a college education (P<.001), and participants with a treatment duration of 2-3 years (P<.001) significantly influenced participants’ willingness. Correlation analysis showed a weak negative relationship between awareness and feasibility (ρ=–0.124; P=.01) and a weak positive relationship between awareness and willingness (ρ=0.013; P=.07).

    Conclusions: To expand mHealth use for epilepsy care in Pakistan, stakeholders must address concerns about digital literacy, data privacy, and trust. Collaborative efforts involving government, technologists, nongovernmental organizations, academia, and health care providers can improve education, enhance data security, and adapt mHealth tools to local needs, ultimately improving treatment access and outcomes for people living with epilepsy.

    JMIR Form Res 2025;9:e80283

    doi:10.2196/80283

    Keywords

    mobile health apps; epilepsy; feasibility; treatment compliance; digital tool; cross-sectional study 


    Epilepsy is one of the chronic neurological diseases that require special medical care due to susceptibility to experiencing epileptic seizures, resulting in neurological, cognitive, psychological, and social implications [1,2]. The global prevalence of active epilepsy, encompassing both idiopathic and secondary forms, is 658 per 100,000 individuals. The Global Burden of Disease Study 2021 Collaborators found that 80% of the bulk of the epilepsy burden was reported in low- and middle-income countries (LMICs) according to World Bank country income classifications [3]. In Pakistan, the prevalence of epilepsy is estimated to be 9.99/1000 according to a population-based survey in southern Pakistan [4], and recent epidemiology data is unavailable [5]. About 80% of people living with epilepsy live in areas with limited resources, encountering significant barriers to health care services [6]. This makes it exceedingly challenging for them to adhere to daily self-management practices for their condition, including taking medications as directed, keeping consistent sleep patterns, engaging in regular physical activity, and effectively managing stress [7].

    The evolution of mobile health (mHealth) apps has transformed them from basic information sources into advanced tools that can diagnose, monitor, and manage chronic neurological diseases [8,9]. mHealth apps, which are within the domain of eHealth and are defined as the usage of mobile and wireless communication technology to enhance health care delivery, outcomes, and research [10,11]. mHealth apps can provide substantial support to people living with epilepsy, encompassing medication reminders and alerts, educational resources, personalized feedback, cost management, medication education, and support [12-14].

    In recent years, numerous mobile phone apps have been developed and effectively used to enhance the self-management of individuals with diabetes mellitus, hypertension, and various other conditions [15]. Applications for self-management of epilepsy have been implemented in advanced nations like Australia and the United States [16]. Although Pakistan claims over 90% mobile network coverage, this statistic pertains mostly to basic signal availability and does not reflect smartphone accessibility [17]. National Information and Communication Technologies studies indicate smartphone ownership at approximately 50%-60%, with diminished adoption in rural regions [18]. Digital literacy barriers, such as literacy levels, gender gaps, and socioeconomic disparities, strongly influence whether mobile coverage translates into meaningful use of health applications [19]. These factors form critical preconditions for the feasibility of epilepsy-focused mHealth solutions. Moreover, the extent to which patients are willing to use mHealth apps also affects their widespread adoption and successful integration into current health care systems [20,21]. Shreds of evidence [22,23] have reported that the sustainability of mHealth apps depends on feasibility studies. In LMICs, there is insufficient study investigating public perceptions of mHealth apps [24].

    Several international studies have been conducted to evaluate the feasibility and acceptability of these mHealth apps. For example, in Uganda, Tumuhimbise and colleagues [25] found the use of mHealth apps feasible among health care workers to increase patient-based care to control tuberculosis. A pilot intervention study in British Columbia demonstrated the feasibility of the mHealth app among patients, which used automated SMS text messaging from a clinic's computer system [26]. An observational research in South Africa found epileptic-specific mHealth apps feasible among children with refractory epilepsy [27]. These studies collectively indicate the potential of mobile technologies to improve self-management. Despite promising international feasibility evidence, there is a lack of empirical evidence from Pakistan on perception, feasibility, and willingness to use epilepsy-focused mHealth apps. Nonetheless, these contexts are indicative of high- or middle-income environments with a stronger digital infrastructure, which restricts their applicability to Pakistan [28-30]. In Pakistan, the disparities in smartphone ownership, digital literacy, and cultural acceptance of health technologies indicate that feasibility has yet to be examined [8,31]. The absence of context-specific evidence highlights the significance of this study.

    We contextualize feasibility within broader access and literacy constraints, and mobile coverage alone is not sufficient. Therefore, this study sought to investigate the feasibility, awareness, and perceptions of mHealth apps among people living with epilepsy and to analyze the factors that may affect this population’s willingness to adopt mHealth apps for epilepsy management.


    Overview

    The study was reported by following the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist, adhering to the principles established by the Equator network [32]. The STROBE checklist has been attached in Multimedia Appendix 1.

    Study Design and Study Setting

    This hospital-based quantitative research follows a cross-sectional analytical study, which was conducted among people living with epilepsy from March to July 2024 using a self-administered questionnaire.

    The study was conducted at the Neurology department of the Pakistan Institute of Medical Sciences (PIMS), Islamabad [33]. The institute is the largest hospital in Islamabad, with a specialized 24-bed care unit for people living with epilepsy, equipped with the latest neurodiagnostic facilities. The institute is a well-known government hospital representing Pakistan due to its centrality. The Outpatient Department (OPD) serves 80-100 people living with epilepsy, providing free-of-cost treatment and diagnostic services [34].

    Study Population

    The study participants’ having sex (male and female), consisted of people living with epilepsy aged 18 years and older who were able to provide informed consent, had a clinical diagnosis, and had been undergoing treatment for more than 6 months. People living with epilepsy with limited verbal communication, aged more than 65 years, experiencing hearing or cognitive impairments, and with a treatment history of less than 3 months were excluded from the study. Furthermore, the study excluded people living with epilepsy who were unable to complete the questionnaire.

    Sample Size and Sampling Technique

    The online Rao soft calculator [35] was used to determine the sample size, incorporating a 5% margin of error, a 95% CI, and a 50% response rate. The calculation determined that a total sample size of 379 people was required. To secure a sufficient respondent pool, 427 people living with epilepsy were solicited, resulting in 406 being incorporated into the final analysis. A nonprobability sampling method, specifically convenience sampling, was used due to constraints in gathering data from a certain group of people living with epilepsy at specified times and locations.

    Questionnaire Development and Data Collection Procedure

    The data-gathering questionnaire was developed in English based on a relevant scientific literature review [36-41]. A preliminary evaluation was performed before commencing the data collection process to assess the content validity, face validity, and internal consistency of the questionnaire. Five pharmacy practice professionals, in conjunction with the head of the Neurology department at PIMS, conducted a comprehensive assessment of the questionnaire to ensure face and content validity. The feedback obtained was used to enhance the questionnaire, ensuring its conformity with the study’s aims.

    A pilot study with 28 participants was done to ascertain the clarity, comprehensibility, and alignment of the questions with the study’s aims. The pilot study results exhibited clarity, readability, and applicability. The feedback from the pilot study was integrated, resulting in the development of the final improved questionnaire before full implementation. Cronbach ⍺ value of the finalized questionnaire was determined to be 0.723. The pilot study was excluded from the final analysis.

    The self-administered questionnaire was comprised of 5 sections containing 33 questions. Throughout the development process, these items were allocated into 5 segments, with certain items dependent on the responses to prior items. The initial segment sought to gather data regarding the demographic attributes, educational background, and treatment records of the participants. The second section aimed to collect data regarding the people living with epilepsy awareness of mHealth apps. In the third section, the people living with epilepsy evaluated their perceptions of mHealth apps using a 5-point Likert scale. The fourth section sought to assess people living with epilepsy comfort and practicality with mHealth apps. The final fifth component aimed to collect data on the willingness, level of agreement, or degree of concern regarding the use of mHealth apps for disease management. BBZ also developed the Urdu-translated version of the questionnaire for Urdu-speaking participants by using the World Health Organization’s translation guidelines [42]. Pharmacy practice specialists and neurologists meticulously evaluated the translated versions for clarity and contextual appropriateness.

    For recruitment, BBZ was assigned a room within the neurology OPD by the department head in PIMS. After people living with epilepsy had completed their routine consultation with the attending neurologist, those meeting the predefined inclusion and exclusion criteria were approached by BBZ. The study objectives were explained, and informed written consent was obtained. The structured questionnaire was then administered. Each questionnaire lasted approximately 20-25 minutes. In total, 427 individuals were invited to participate, of whom 406 provided complete responses and were included in the final analysis. Twenty-one incomplete questionnaires were excluded, resulting in a study response rate of 95.1%.

    Statistical Analysis

    The questionnaire was checked for completeness, and data were entered and analyzed using SPSS (IBM Corp). Descriptive statistics were applied to estimate the frequencies and percentages. The categorical data were presented as percentages and frequencies, while means and SDs were computed for continuous variables. The Mann-Whitney U test and Kruskal-Wallis test, when appropriate, were used for unadjusted comparisons. To examine the relationship between awareness, perception, feasibility, and willingness, the Spearman rank correlation coefficient was used. The independent relationships between willingness scores and clinical, sociodemographic, and attitudinal characteristics were investigated using multivariable linear regression analysis. Age, gender, type of residence, education level, employment status, family income, duration of treatment, daily pill burden, prescribed antiepileptic medication, awareness, perception, and feasibility scores were all considered independent variables. Variables were selected based on their theoretical relevance and significant associations identified through univariate analysis. Model assumptions, such as linearity, residual normality, homoscedasticity, and multicollinearity, were assessed prior to analysis and were determined to be adequate. A 95% CI and a P value of .05 were considered statistically significant. Due to the cross-sectional design of the study, these analyses were interpreted as exploratory associations rather than causal inferences.

    Ethical Considerations

    The study was conducted in adherence with the guidelines outlined in the Declaration of Helsinki [43]. The Research Ethics Committee of Riphah Institute of Pharmaceutical Sciences, Riphah International University, Islamabad, Pakistan (reference number REC/RIPS/30), and Shaheed Zulfiqar Ali Bhutto University, Islamabad, Pakistan (reference number F.1-1/2015/ERB/SZABMU/1251), approved the research study. Participants were informed about the purpose of the research and its benefit to the population, and informed consent forms were obtained for each participant. The data confidentiality was also assured. No financial or nonfinancial compensation was provided to study participants.


    Sociodemographic and Clinical Characteristics of Participants

    The predominant study population consists of males (218/406, 53.7%), married individuals (262/406, 64.5%), and middle-aged individuals of 3-45 years (152/406, 30%) of age, with a majority adhering to the Muslim faith (362/406, 89.2%). The majority of the people living with epilepsy underwent treatment for 2-3 years (199/406, 49%) and experienced a daily pill burden of 1-5 pills (257/406, 63.3%), and the predominantly prescribed antiepileptic medicine was valproic acid (297/406, 73.2%). The comprehensive demographic and clinical attributes of the study population are delineated in Table 1.

    Table 1. Sociodemographic and clinical characteristics of the participants.
    CharacteristicsParticipants, n (%)
    Age range (years)

    		18-30122 (30)

    		31-45152 (37.4)

    		46-6085 (20.9)

    		≥6147 (11.6)
    Sex

    		Male218 (53.7)

    		Female188 (46.3)
    Religion

    		Muslim362 (89.2)

    		Non- Muslim44 (10.8)
    Marital status

    		Married262 (64.5)

    		Single121 (29.8)

    		Divorced10 (2.5)

    		Widowed13 (3.2)
    Type of residence

    		Rural139 (34.2)

    		Urban267 (65.8)
    Educational background

    		Uneducated47 (11.6)

    		Primary or secondary65 (16)

    		College226 (55.7)

    		Bachelors or postgraduation68 (16.7)
    Employment status

    		Employed206 (50.7)

    		Self-employed76 (18.7)

    		Retired12 (3)

    		Unemployed61 (15)

    		Housewife51 (12.6)
    Family income (US $)

    		≤177223 (54.9)

    		≥177167 (41.1)

    		≥35416 (3.9)
    Duration of treatment

    		>6 months125 (30.8)

    		2-3 years199 (49)

    		≥4 years82 (20.2)
    Daily pill burden

    		1-5257 (63.3)

    		6-10121 (29.8)

    		>1028 (6.9)
    Medicine prescribes (antiepileptics)

    		Valproic acid297 (73.2)

    		Levetiracetam44 (10.8)

    		Carbamazepine30 (7.4)

    		Clonazepam19 (4.7)

    		Lamotrigine16 (3.9)

    Correlation Among Awareness, Perception, Feasibility, and Willingness About mHealth Apps

    There is a very weak positive correlation between awareness and perception (ρ=0.012; P=.08). This suggests that as the awareness scores increase, perception scores decrease slightly. Awareness shows a negative correlation with feasibility (ρ=–0.124; P=.01), which indicates that increased awareness toward mHealth apps is associated with increased feasibility of using mHealth apps. Awareness is weakly and positively correlated with willingness (ρ=0.013; P=.07); however, the correlation is not statistically significant. Perception shows a positive but weak correlation with feasibility (ρ=0.189; P˂.001), and this correlation is statistically significant.

    Awareness About the mHealth Apps

    The majority of the participants, 86.2% (350/406), reported that they have heard about mHealth apps used for epilepsy management. Similarly, 86.2% (350/406) of participants were aware of the potential benefits of mHealth apps. About 72.9% (296/406) of respondents used mHealth apps and found them helpful. In contrast, 57.1% (232/406) of participants have not entered information into mobile apps related to disease and medication, highlighting a deficiency in the use of technology for managing chronic diseases (Table 2).

    Table 2. Awareness about the mobile health apps.
    StatementsYes, n (%)No, n (%)I don’t know, n (%)
    Have you ever heard of mobile health apps for epilepsy management?350 (86.2)49 (12.1)7 (1.7)
    Are you familiar with specific mobile apps for tracking seizures, medications, or other aspects of epilepsy management?343 (84.5)39 (9.6)24 (5.9)
    Have you ever used a mobile health app to monitor your epilepsy symptoms, medication, or overall health?313 (77.1)58 (14.3)35 (8.6)
    If you have used any mobile health apps for epilepsy, did you find the app features helpful?296 (72.9)92 (22.7)18 (4.4)
    Are you aware of the potential benefits of using mobile health apps for medication adherence?350 (86.2)36 (8.9)20 (4.9)
    Have you ever entered information into a mobile appn about your disease, medications, and diet?133 (32.8)232 (57.1)41 (10.1)

    Perception, Feasibility, and Willingness About mHealth Apps

    Overall, 69.2% (282/406) of participants were not open to using mHealth apps as an additional tool for epilepsy management. More than half of the participants (317/406, 78.1%) perceived that mHealth apps are not a valuable addition to their epilepsy management toolkit. In total, 75.9% (308/406) of the participants reported that mHealth apps can make medical care cheaper and faster. Meanwhile, 77.3% (314/406) reported that mHealth apps can save them from transportation costs, offering them significant financial relief. Around 69% (280/406) of participants had privacy concerns regarding medical information security offered by mHealth apps.

    A total of 82.8% (336/406) of respondents indicated challenges in the usability of mHealth apps, and due to this reason, more than half participants (317/406, 78.1%) were not comfortable while using the mHealth apps on smartphones or tablets. Similarly, 79.3% (322/406) of participants reported that they did not find the app feasible or convenient for managing their disease. Overall, 36.7% (149/406) were not interested, and 73.4% (298/406) did not prefer mHealth apps for epilepsy management. Detailed perception, feasibility, and willingness about mHealth apps can be seen in Tables 3-5.

    Table 3. Perception response of participants toward mobile health apps.
    Statement and responseStrongly disagree, n (%)

    		Disagree, n (%)

    		Neutral, n (%)

    		Agree, n (%)

    		Strongly agree, n (%)

    Are you open to using mobile health apps as an additional tool for managing epilepsy?281 (69.2)59 (14.5)15 (3.7)28 (6.9)23 (5.7)
    Do you think a mobile health app is a valuable addition to your epilepsy management toolkit?317 (78.1)14 (3.4)35 (8.6)31 (7.6)9 (2.2)
    Do you think mobile health apps make medical care cheaper and faster?2 (0.5)34 (8.4)308 (75.9)36 (8.9)26 (6.4)
    Do you think mobile health apps save money and transportation costs?3 (0.7)43 (10.6)24 (5.9)314 (77.3)22 (5.4)
    Do you think mobile health apps can improve and increase antiepileptic medication adherence?18 (4.4)294 (72.4)44 (10.8)39 (9.6)11 (2.7)
    Do you think mobile health apps increase communication with healthcare providers?16 (3.9)310 (76.4)42 (10.3)25 (6.2)13 (3.2)
    Do you think using mobile health apps to access health care offers great security?25 (6.2)25 (6.2)280 (69.0)68 (16.7)8 (2)
    Do you think it is an important tool that can be used to complement treatment strategies?16 (3.9)286 (70.4)44 (10.8)50 (12.3)10 (2.5)
    Table 4. Feasibility response of participants toward mobile health apps.
    Statement and responseParticipants, n (%)
    Howcomfortable are you in using smartphones or tablets to manage your epilepsy?

    		Very uncomfortable2 (0.5)

    		Uncomfortable317 (78.1)

    		Neutral53 (13.1)

    		Comfortable18 (4.4)

    		Very comfortable16 (3.9)
    Do you think it is easy to handle a mobile health app?

    		Strongly disagree2 (0.5)

    		Disagree5 (1.2)

    		Neutral336 (82.8)

    		Agree62 (15.3)

    		Strongly agree1 (0.2)
    Do you think it is convenient to use and share medical information on mobile health apps?

    		Strongly disagree2 (0.5)

    		Disagree26 (6.4)

    		Neutral322 (79.3)

    		Agree53 (13.1)

    		Strongly agree3 (0.7)
    Would you prefer the mobile app to have medication reminders and tracking features?

    		Strongly do not prefer298 (73.4)

    		Do not prefer44 (10.8)

    		Neutral27 (6.7)

    		Prefer17 (4.2)

    		Strongly prefer

    		20 (4.9)
    Table 5. Willingness response of participants toward mobile health apps.
    Statement and responseParticipants, n (%)
    I feel positive about using mobile health apps to manage my epilepsy condition

    		Strongly disagree

    		11 (2.7)

    		Disagree

    		32 (7.9)

    		Neutral

    		117 (28.8)

    		Agree

    		142 (35)

    		Strongly agree

    		104 (25.6)
    I am interested in using mobile health apps to monitor medication adherence

    		Not interested at all

    		8 (2)

    		Not very interested

    		47 (11.6)

    		Neutral

    		114 (28.1)

    		Somewhat interested

    		149 (36.7)

    		Very interested

    		88 (21.7)
    I am worried about my privacy and confidentiality if my health information is online

    		Not worried at all

    		17 (4.2)

    		Not very worried

    		40 (9.9)

    		Neutral

    		122 (30)

    		Worried

    		146 (36)

    		Very worried

    		81 (20)
    I prefer digital health tools over conventional hospital visits

    		Strongly do not prefer19 (4.7)

    		Do not prefer48 (11.8)

    		Neutral94 (23.2)

    		Prefer149 (36.7)

    		Strongly prefer

    		96 (23.6)

    Impact of Sociodemographic and Clinical Characteristics on Awareness, Perception, Feasibility, and Willingness About mHealth Apps

    The median level of appropriate awareness among people living with epilepsy possessing a college education exceeded that of people living with epilepsy with primary or secondary education or those with bachelor’s or postgraduate degrees (P˂.001). The awareness of mHealth apps among people living with epilepsy was higher for the self-employed compared to retirees, the unemployed, employees, and housewives (P˂.001). Statistically significant differences were also found between awareness of mHealth apps and duration of treatment, which was higher in people living with epilepsy whose duration of treatment was ≤1 year as compared to 2-3 years or ≥4 years (P˂.001). The perception score was higher in people living with epilepsy aged 31-45 years as compared with those aged 18-30 years, 46-60 years, and ≥61 years (P=.04). Furthermore, the perception score was elevated in people living with epilepsy whose family income was ≥$354 compared to those with a family income of ≥$177 and ≤$177(P=.01). People living with epilepsy having a daily 6-10 pill burden have greater perception scores than those with a daily pill burden of 1-5 pills and ˃10 pills (P˂.001).

    The feasibility score for using mHealth apps among people living with epilepsy with bachelor’s or postgraduate degrees surpassed that of people living with epilepsy with primary or secondary education, as well as those with college degrees and those who are uneducated (P˂.001). The feasibility score was also greater in people living with epilepsy having a family income of ≤$177 as compared to a family income of ≥$177 and ≥$354 (P≥.99). Moreover, people living with epilepsy having a treatment duration of ≥4 years have a higher feasibility score than those with ˂1 year and 2-3 years (P˂.001). The willingness score was greater among participants aged 31-45 years as compared to those aged 18-30 years, 46-60 years, and ≥61 years (P=.03). People living with epilepsy following the Muslim religion has a higher willingness toward mHealth apps as compared to non-Muslims (P˂.001). People living with epilepsy from urban residential areas exhibit a higher willingness score compared to those from rural locations (P≥.99). Comprehensive findings about the impact of demographic and clinical variables on the awareness, perception, feasibility, and willingness to adopt mHealth apps are provided in Table 6.

    Table 6. Comparison of sociodemographic and clinical characteristics with awareness, perception, feasibility, and willingness of participants toward mobile health apps.
    CharacteristicsParticipants, n (%)AwarenessPerceptionFeasibilityWillingness


    		Median (IQR)P valueMedian (IQR)P valueMedian (IQR)P valueMedian (IQR)P value
    Age (years).04
    		.002
    		.002
    		.03

    		18-30122 (30)5 (3-5)
    		18 (18-19.25)
    		9 (9-11)
    		15 (12-17)

    		31-45152 (37.4)5 (4-6)
    		19 (18-23)
    		9 (9-9)
    		15 (12-18)

    		46-6085 (20.9)5 (3-5)
    		18 (18-20)
    		9 (9-10)
    		15 (14-17)

    		≥6147 (11.6)5 (4-6)
    		18 (18-20)
    		9 (9-11)
    		13 (11-16)
    Sex.65
    		<.001
    		.18
    		.80

    		Male218 (53.7)5 (4-6)
    		19 (18-22)
    		9 (9-10)
    		1 5 (12-17)

    		Female188 (46.3)5 (3-5)
    		18 (18-19.75)
    		9 (9-10)
    		15 (12-17)
    Religion.05
    		.09
    		.56
    		<.001

    		Muslim362 (89.2)5 (4-5)
    		18 (18-21)
    		9 (9-10)
    		15 (12-17)

    		Non-Muslim44 (10.8)4 (2.25-5)
    		18 (17.25-19)
    		9 (9-11)
    		12.50 (10-15)
    Marital status.09
    		.03
    		.12
    		.10

    		Married262 (64.5)5 (3-5)
    		19 (18-21)
    		9 (9-10)
    		15 (12-17)

    		Single121 (29.8)5 (4-5)
    		18 (18-20)
    		9 (9-10)
    		15 (12-17)

    		Divorced10 (2.5)5 (2.75-5)
    		18 (17.75-24.50)
    		10.50 (9-15.25)
    		13.50 (9.50-14)

    		Widowed13 (3.2)5 (5-6)
    		18 (17-18)
    		9 (9-10)
    		13 (12-14)
    Type of residence.06
    		.39
    		.44
    		.006

    		Rural139 (34.2)5 (4-6)
    		19 (18-20)
    		9 (9-10)
    		14 (12-16)

    		Urban267 (65.8)5 (3-5)
    		18 (18-20)
    		9 (9-10)
    		15 (12-18)
    Educational background.001
    		.17
    		<.001
    		<.001

    		Uneducated47 (11.6)5 (3-5)
    		19 (18-21)
    		9 (9-10)
    		15 (12-16)

    		Primary or Secondary65 (16)4 (3-5)
    		18 (18-20)
    		10 (9-12.50)
    		14 (11-16)

    		College226 (55.7)5 (4-6)
    		18.50 (18-21)
    		9 (9-9)
    		16 (14-19)

    		Bachelors or postgraduation68 (16.7)5 (3.25-6)
    		18 (18-19)
    		9 (9-14.75)
    		12 (10-15)
    Employment status<.001
    		.001
    		.74
    		.01

    		Employed206 (50.7)5 (3-5)
    		18 (18-20)
    		9 (9-10)
    		16 (13-18)

    		Self-employed76 (18.7)5 (5-6)
    		19 (18-21.75)
    		9 (9-10)
    		13.50 (11-16)

    		Retired12 (3)5 (4-6)
    		18 (16.25-24.75)
    		9.50 (9-13)
    		13.50 (12- 15.75)

    		Unemployed61 (15)4 (3-5)
    		19 (18-23)
    		9 (9-11.50)
    		15 (12-17)

    		Housewife51 (12.6)4 (3-5)
    		18 (18-18)
    		9 (9-9)
    		15 (13-17)
    Family income (US dollars).19
    		.02
    		.002
    		.002

    		≤$177223 (54.9)5 (3-5)
    		19 (18-22)
    		9 (9-11)
    		15 (13-17)

    		≥$177167 (41.1)5 (3-5)
    		18 (18-20)
    		9 (9-9)
    		14 (12-16)

    		≥$35416 (3.9)5 (5-6)
    		18 (18-23.50)
    		9 (9-9)
    		16.50 (15.25-19)
    Duration of treatment (years).001
    		.45
    		<.001
    		<.001

    		≤1125 (30.8)5 (4-6)
    		18 (18-21)
    		9 (9-9)
    		15 (13-17)

    		2-3199 (49)5 (3-5)
    		18 (18-20)
    		9 (9-10)
    		16 (13-19)

    		≥482 (20.2)4.50 (3-5)
    		18.50 (17-20.25)
    		10.50 (9-13.25)
    		12 (10-14)
    Daily pill burden<.001
    		<.001
    		.41
    		.95

    		1-5257 (63.3)5 (3-5)
    		18 (18-20)
    		9 (9-10)
    		15 (12-17)

    		6-10121 (29.8)5 (4-6)
    		19 (18-24.50)
    		9 (9-10.50)
    		15 (12-17)

    		>1028 (6.9)4 (3-5)
    		19 (17.25-21.75)
    		9 (9-10.75)
    		14.50 (12-19)
    Medicine prescribes (antiepileptic).10
    		.22
    		<.001
    		<.001

    		Valproic acid297 (73.2)5 (4-6)
    		18 (18-20)
    		9 (9-10)
    		15 (13-18)

    		Levetiracetam44 (10.8)4 (3-5)
    		18 (18-22)
    		9 (9-10)
    		15 (12-17)

    		Carbamazepine30 (7.4)4 (3.75-5)
    		18 (18-19)
    		9 (9-10)
    		12 (11-14.25)

    		Clonazepam19 (4.7)4 (3-5)
    		19 (16-23)
    		11 (10-16)
    		12 (10-14)

    		Lamotrigine16 (3.9)5 (3.25-6)
    		18 (18-19)
    		9 (9-11)
    		13 (9.25-14.75)

    Factors Associated With Participants’ Willingness to Use mHealth Apps

    Multivariable linear regression analysis showed that the willingness to use mHealth apps differed among participants with varying educational backgrounds, residences, and family incomes. Rural resident participants have lower willingness than urban residents, with a P value of .05, which is borderline statistically significant. Participants having a college educational background have a significantly higher willingness (P˂.001) as compared to a bachelor’s or postgraduate degree. There was no statistically significant effect for uneducated or primary or secondary educated individuals. People living with epilepsy whose family income is ≥$177 show significantly lower willingness (P=.01). People living with epilepsy having a treatment duration of ≤1 year (P=.001) and 2-3 years (P˂.001) show significantly higher willingness as compared to those on treatment for ≥4 years. Other demographic and clinical factors, and awareness, perception, and feasibility scores do not show statistically significant influence. The findings from the multivariable linear regression analysis are summarized in Table 7.

    Table 7. Multivariable linear regression analysis of the factors influencing the willingness of participants to use mobile health apps.
    CharacteristicsParticipants, n (%)Unstandardized coefficients, β (95% CI)Standardized coefficients (β)P value
    Age range (years)

    		18-30122 (30)0.446 (–0.81 to 1.70)0.058.49

    		31-45152 (37.4)–0.361 (–1.62 to 0.89)–0.049.57

    		46-6085 (20.9)0.738 (–0.58 to 2.06)0.085.28

    		≥6147 (11.6)Reference—a—
    Sex

    		Male218 (53.7)–0.025 (–0.75 to 0.70)–0.004.95

    		Female188 (46.3)Reference——
    Religion

    		Muslim362 (89.2)0.275 (–0.83 to 1.38)0.024.63

    		Non-Muslim44 (10.8)Reference——
    Marital status

    		Married262 (64.5)0.521 (–1.61 to 2.65)0.070.63

    		Single121 (29.8)0.155 (–2.02 to 2.34)0.020.89

    		Divorced10 (2.5)–0.374 (–3.22 to 2.47)–0.016.80

    		Widowed13 (3.2)Reference——
    Type of residence

    		Rural139 (34.2)–0.729 (–1.47 to 0.01)–0.098.05

    		Urban267 (65.8)Reference——
    Educational background

    		Uneducated47 (11.6)0.889 (–0.38 to 2.16)0.080.17

    		Primary or Secondary65 (16)0.530 (–0.69 to 1.75)0.055.39

    		College226 (55.7)2.27 (1.23-3.30)0.319<.001

    		Bachelor’s or postgraduation68 (16.7)Reference——
    Employment status

    		Employed206 (50.7)0.678 (–0.56 to 1.91)0.096.28

    		Self-employed76 (18.7)–0.426 (–1.76 to 0.91)–0.047.53

    		Retired12 (3)–0.143 (–2.44 to 2.15)–0.007.90

    		Unemployed61 (15)–0.008 (–1.42 to 1.41)–0.001.99

    		Housewife51 (12.6)Reference——
    Family income (US dollars)

    		≤177223 (54.9)–1.648 (–3.36 to 0.07)–0.232.06

    		≥177167 (41.1)–2.186 (–3.92 to –0.44)–0.304.01

    		≥35416 (3.9)Reference——
    Duration of treatment

    		≤1 year125 (30.8)1.964 (0.83-3.09)0.257.001

    		2-3 years199 (49)2.203 (1.20-3.19)0.311<.001

    		≥4 years82 (20.2)Reference——
    Daily pill burden

    		1-5257 (63.3)0.023 (–1.34 to 1.38)0.003.97

    		6-10121 (29.8)0.260 (–1.16 to 1.68)0.034.72

    		>1028 (6.9)Reference——
    Medicine prescribes (antiepileptics)

    		Valproic acid297 (73.2)1.381 (–0.35 to 3.11)0.173.12

    		Levetiracetam44 (10.8)1.778 (–0.14 to 3.70)0.156.07

    		Carbamazepine30 (7.4)–0.265 (–2.27 to 1.74)–0.020.80

    		Clonazepam19 (4.7)0.616 (–1.54 to 2.77)0.037.58

    		Lamotrigine16 (3.9)Reference——
    Awareness score

    		Continuous406 (100)–0.058 (–0.31 to 0.20)–0.022.66
    Perception score

    		Continuous406 (100)–0.041 (–0.11 to 0.03)–0.053.29
    Feasibility score

    		Continuous406 (100)–0.039 (–0.20 to 0.12)–0.025.63

    aNot applicable.


    Principal Findings

    This study found that while most participants have heard about mHealth apps used for epilepsy management, the majority expressed concerns about privacy and security associated with these apps. More than half of the participants felt uncomfortable while using them due to challenges faced in the usability of mHealth apps. Similarly, the majority of participants reported that mHealth apps were not a valuable addition to their epilepsy management toolkit. In contrast, the study findings indicate that middle-aged urban residents with higher educational attainment and extended durations of epilepsy treatment exhibit a greater self-reported willingness to embrace mHealth apps as a digital health tool. This study highlights that awareness of mHealth apps and trial use do not necessarily translate into active, disease-focused usage.

    The study findings indicated that participants with a high level of education were more likely to use mHealth apps for epilepsy management. Our findings are in line with the results of previous studies [44,45]. A considerable number of individuals with lower levels of education face significant health care needs; yet, they frequently struggle to comprehend the information provided on mHealth apps [46]. Ongoing endeavors to guarantee that mHealth apps are user-friendly and that health and treatment information is accessible, comprehensible, and retrievable are essential.

    Participants living in rural regions exhibit a reduced willingness to engage with mHealth apps. This result supports earlier findings [47,48] indicating that the urban population exhibited a greater level of agreement regarding the use of mHealth apps. In resource-limited settings, traditional forms of health care provision are used more than advanced health care services [49]. The implementation of mHealth apps is difficult in LMICs due to the perpetuation of digital divides [50]. Therefore, by increasing the accessible treatment facilities and addressing the digital divides, we may assist the rural population affected by epilepsy in benefiting from these apps.

    In total, 50% of the participants in the study expressed apprehension about sharing their medical information online, as this heightens their privacy concerns over the usage of mHealth apps that monitor their symptoms and facilitate illness treatment, which is compatible with the study conducted in Germany [51]. The transmission of patient information via digital tools presents legal concerns. Encryption must be adequate to comply with the Health Insurance Portability and Accountability Act (HIPAA) standards, ensuring patient confidentiality and the security of personal data [52]. Awareness about data privacy and the availability of medical information of people living with epilepsy should be provided to people living with epilepsy visiting hospitals for follow-ups, and privacy-preserving edge federated learning should be incorporated in mobile health systems [53].

    In the study, we found that people living with epilepsy have a negative perception regarding the use of mHealth apps for epilepsy management. The negative perception surrounding the digitalization of the health care system arises from various cultural, systematic, and technological barriers, including stigma and inadequate digital health literacy [54]. Previous research [55] indicates the disparity between the creation and implementation of mHealth apps and the patients for whom they are designed. We recommend that the government and health care institutions take proactive steps to establish a digital literacy program aimed at educating people living with epilepsy and their caregivers on how to effectively access and navigate the mHealth apps, ultimately leading to improved treatment outcomes.

    A recently published systematic review [8] indicates that people living with epilepsy are generally cognizant of mHealth apps, but there is a deficiency in comprehension of the app features and benefits that could facilitate timely disease management. This lack of understanding may impede the usage of these apps and exacerbate the atypical concerns of people living with epilepsy, which is consistent with previous research findings [56-58]. Awareness and practical implementation of mHealth app features for epilepsy management should be initiated in government hospitals to alleviate the disease burden on the impoverished population affected by epilepsy in Pakistan.

    We also found that occupation correlates with the viability of using mHealth apps among people living with epilepsy. This study indicated that self-employed and employed participants were inclined to use mHealth apps. This is attributed to the high educational and socioeconomic status that facilitates their access to mHealth apps [59]. In contrast, the unemployed participants demonstrated a lower feasibility for using the mHealth app. This finding was similar to a multiethnic study [60] in the United States, which indicates that individuals from lower socioeconomic backgrounds had limited access to advanced treatment options.

    A significant portion of the participants expressed discomfort and demonstrated a decreased preference for mHealth apps that include medication reminders and tracking functionalities. Previous studies [61,62] have demonstrated the significance of using mHealth apps in the management of chronic diseases. A key difficulty for 50%-60% of chronically ill persons is medication adherence [63]. mHealth apps appear to be a promising strategy for addressing issues related to medication nonadherence and enhancing treatment outcomes [64]. To improve medication adherence and management in people living with epilepsy, it is crucial to offer guidance by health care providers on mHealth apps for those receiving care in hospitals with suboptimal treatment outcomes for epilepsy.

    Our findings may further substantiate the necessity for rules and frameworks for the creation and categorization of epilepsy apps within the existing epilepsy app ecosystem for less formally educated individuals [65,66]. Moreover, studies reported [67,68] challenges in conveying referrals to health care facilities due to limited knowledge and obstacles encountered by the uneducated people living with epilepsy. The regular enhancement of digital literacy skills and education for uneducated people living with epilepsy in need is essential to optimize the efficacy of an mHealth intervention.

    In this study, people living with epilepsy prefer the mHealth apps to the conventional hospital visits, as the majority of participants belong to rural areas. Frequent hospital visits can incur significant expenses, especially in rural regions, owing to transportation expenditures [69]. A comprehensive systematic literature review [70] substantiates that mHealth apps demonstrate significant potential in assisting individuals and health care systems amid the rising prevalence and financial burdens of chronic diseases globally by providing informational support and an easier way to contact health care providers [71]. Consequently, the incorporation of mHealth apps may assist people living with epilepsy in managing their condition by alleviating financial strains and treatment burden.

    Our findings indicate a notable level of awareness regarding mHealth apps in Pakistan when compared to various other LMICs. However, engagement with functions tailored to specific diseases remains limited. This aligns with global observations, as documented in the systematic review by Gotlieb et al [8]. The analysis emphasizes that, although feasibility and acceptability appear encouraging, genuine adoption and sustained use are dependent upon factors such as usability, literacy, and support from the health system. This highlights the necessity for strategies that are specifically tailored to the context of Pakistan. The comparison of the study data with previously published research has been elaborated in Table 8.

    Table 8. Comparison of the study data with previously published studies targeting mobile health apps.
    StudyCountry and settingPopulationSample SizeKey variables measuredAwareness and use of mHealth appsActive engagementKey findings or predictors
    Our studyPakistan, [urban+rural] clinicAdults with epilepsy on treatment406
    • Awareness
    • Perception
    • Feasibility
    • Willingnes
    • Treatment
    • Duration
    • Education
    		77.1% reported having used mHealth apps42.9% input disease or medication dataHigher self-reported willingness among middle-aged, urban, higher-educated participants
    Patterson et al [72], 2022Pakistan and United KingdomPatients with epilepsy referred via health workers59
    • Diagnostic accuracy
    • Referral timing
    		Not applicable (usage by health workers)Not applicableHigh appropriateness of referrals; timeliness improved
    Alzamanan et al [73], 2024MalaysiaExpert panel & literature review-
    • Domains or features of self-management apps
    		Not applicableFramework focus, not direct patient useIdentified key domains; usability rated highly among experts
    Gotlieb et al [8], 2025Multiple countriesMixed epilepsy populations20 studies in review
    • Effectiveness
    • Feasibility
    • Usability
    		Varied installation and uptakeMixed engagement with disease-specific functionsBarriers: usability, literacy, infrastructure; predictors include usefulness, interface quality

    Limitations

    The study presents multiple limitations. First, we used convenience sampling, which results in limited generalizability. Second, data were collected from PIMS, primarily frequented by people living with epilepsy with low educational backgrounds resulting from illiteracy; the participants exhibited a lack of focus on the study’s objectives. Third, in the questionnaire, certain questions were addressed on behalf of a people living with epilepsy. As a result, the responses may be influenced by the perspective of the person conducting the survey on behalf of a patient, leading to self-reported data. Fourth, this study is deficient in qualitative insights and factor analytic approaches. Moreover, the assessment of willingness relied on self-reporting instead of direct behavioral measures. While face and content validity were established, no formal psychometric validation (eg, factor analysis) of the feasibility, perception, and willingness scales was performed. Due to the execution of several subgroup comparisons without formal adjustment, there exists a risk of increased type I error; hence, these findings should be interpreted as exploratory and hypothesis-generating rather than confirmatory.

    Future Implications

    In the future, policymakers, health care providers, and app developers need to work together in co-designing and implementing evidence-based strategies for the use of mHealth apps, while also ensuring the protection of privacy within these apps. Addressing public health concerns necessitates the implementation of digital literacy sessions and training programs. Future research may concentrate on longitudinal and interventional studies, examining the functionality and efficacy of these mHealth apps with larger sample sizes, alongside user observations and perspectives, particularly highlighting the dynamics of user-platform interaction, to evaluate the causal pathways of mHealth usage and the establishment of trustworthiness. Moreover, it is recommended to conduct psychometric validation of the feasibility, perception, and willingness scales using factor analytic methods. Objective behavioral metrics, such as app usage logs, need to be incorporated to validate self-reported willingness. Probability sampling across multiple centers should be used to improve representativeness.

    Conclusions

    This study demonstrates that urban residents with higher socioeconomic status were more likely to use mHealth apps, although they raised concerns about privacy protection. Stakeholders should implement a robust privacy policy in these apps. Future research may focus on addressing the digital divides, along with cultural, systemic, and technological challenges, as informed by the identified impacting factors. To improve the comfort of people living with epilepsy with mHealth apps, it is crucial to implement training programs and digital literacy sessions for both users and health care providers, focused on enhancing the functionality and effectiveness of mHealth apps.

    Acknowledgments

    We thank the head of the neurology department, Dr. Mazhar Badshah, for his valuable suggestions in the development of the questionnaire, the administration of PIMS for their assistance, and people living with epilepsy, who cooperated with us during our research work there.

    Funding

    No external financial support or grants were received from any public, commercial, or not-for-profit entities for the research, authorship, or publication of this article.

    Data Availability

    The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

    Authors' Contributions

    AA and BBZ contributed to conceptualization, investigation, and methodology. BBZ performed data collection and writing–original draft. BBZ and MAH handled the data curation and formal analysis. BBZ, AA, and MAH managed software. AA provided supervision. AA, MAH, RS, HBZ, and AA writing–review and editing.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    STROBE checklist.

    PDF File (Adobe PDF File), 158 KB
    1. Sezgin E, Noritz G, Lin S, Huang Y. Feasibility of a voice-enabled medical diary app (SpeakHealth) for caregivers of children with special health care needs and health care providers: mixed methods study. JMIR Formative Research. 2021;5(5):e25503. [FREE Full text] [CrossRef] [Medline]
    2. Catalán-Aguilar J, González-Bono E, Cano-López I. Perceived stress in adults with epilepsy: a systematic review. Neuroscience and Biobehavioral Reviews. 2025;170:106065. [FREE Full text] [CrossRef] [Medline]
    3. Huang J, Man Y, Shi Z, Fu X, Shi W, Liang X. Global, regional, and national burden of maternal disorders, 1990-2021: a systematic analysis from the global burden of disease study 2021. BMC Public Health. 2025;25(1):2576. [FREE Full text] [CrossRef] [Medline]
    4. Aziz H, Ali SM, Frances P, Khan MI, Hasan KZ. Epilepsy in Pakistan: a population-based epidemiologic study. Epilepsia. 1994;35(5):950-958. [CrossRef] [Medline]
    5. Tanveer M, Tahir A, Iqbal M, Aslam F, Ahmed A. Health-related quality of life and medication adherence of people living with epilepsy in Pakistan: a cross-sectional study. Brain Behavior. 2023;13(9):e3127. [FREE Full text] [CrossRef] [Medline]
    6. Ali A, Clarke DF. Digital measures in epilepsy in low-resourced environments. Expert Rev Pharmacoeconomic Outcomes Research. 2024;24(6):705-712. [CrossRef] [Medline]
    7. Moalong K, Espiritu A, Fernandez M, Jamora R. Treatment gaps and challenges in epilepsy care in the Philippines. Epilepsy Behavior. 2021;115:107491. [CrossRef] [Medline]
    8. Gotlieb E, Marzoughi S, Kwon C, Harmon M, Kimura M, Truesdale A, et al. Clinical effectiveness, feasibility, acceptability, and usability of mobile health applications for epilepsy: a systematic review. Epilepsia. 2025;66(5):1349-1373. [CrossRef] [Medline]
    9. Fatehi V, Salahzadeh Z, Mohammadzadeh Z. Mapping and analyzing the application of digital health for stroke rehabilitation: scientometric analysis. Disability and Rehabilitation: Assistive Technology. 2025;20(2):321-330. [CrossRef] [Medline]
    10. Bernard-Willis Y, Oliveira ED, Lakhan SE. An overview of digital health in the transition of pediatric to adult epilepsy care. Journal of Pediatric Epilepsy. 2020;09(04):106-113. [CrossRef]
    11. Sánchez Rodríguez M, Collado Vázquez S, Martín Casas P, Cano de la Cuerda R. Neurorehabilitation and apps: a systematic review of mobile applications. Neurología (English Edition). 2018;33(5):313-326. [CrossRef]
    12. Nevin SM, Wakefield CE, Schilstra CE, McGill BC, Bye A, Palmer EE. The information needs of parents of children with early-onset epilepsy: a systematic review. Epilepsy Behavior. 2020;112:107382. [CrossRef] [Medline]
    13. Choi S, Lim K, Baek H, Yoo S, Cho A, Kim H, et al. Impact of mobile health application on data collection and self-management of epilepsy. Epilepsy Behavior. 2021;119:107982. [CrossRef] [Medline]
    14. Feldman AG, Beaty BL, Moore SL, Bull S, Wilson K, Atkinson KM, et al. Feasibility, acceptability, and effectiveness of a smartphone app to increase pretransplant vaccine rates: usability study. JMIR Formative Research. 2025;9:e68855. [FREE Full text] [CrossRef] [Medline]
    15. Jiang J, Zhu Q, Zheng Y, Zhu Y, Li Y, Huo Y. Perceptions and acceptance of mhealth in patients with cardiovascular diseases: a cross-sectional study. JMIR Mhealth Uhealth. 2019;7(2):e10117. [FREE Full text] [CrossRef] [Medline]
    16. Liu X, Wang R, Zhou D, Hong Z. Feasibility and acceptability of smartphone applications for seizure self-management in China: questionnaire study among people with epilepsy. Epilepsy Behavior. 2016;55:57-61. [CrossRef] [Medline]
    17. Khan UI, Shah S, Qureshi A, Viswanathan S, Merchant AT, Virani SS, et al. Burden of cardiometabolic diseases and depression in a low-income, urban community in Pakistan: a cross-sectional survey. BMC Public Health. 2025;25(1):757. [FREE Full text] [CrossRef] [Medline]
    18. Abid M, Chaudhry M. Socioeconomic determinants of digital technology adoption: a case study of south Punjab, Pakistan. Pakistan Journal of Social Sciences (PJSS). 2024;44(2).
    19. Kazi AM, Ahsan N, Jamal S, Khan A, Mughis W, Allana R, et al. Characteristics of mobile phone access and usage among caregivers in Pakistan - A mHealth survey of urban and rural population. International Journal of Medical Informatics. 2021;156:104600. [CrossRef] [Medline]
    20. van Velsen L, Flierman I, Tabak M. The formation of patient trust and its transference to online health services: the case of a Dutch online patient portal for rehabilitation care. BMC Medical Informatics and Decision Making. 2021;21(1):188. [FREE Full text] [CrossRef] [Medline]
    21. Ncube B, Mars M, Scott RE. Perceptions and attitudes of patients and healthcare workers towards the use of telemedicine in Botswana: an exploratory study. PLoS One. 2023;18(2):e0281754. [FREE Full text] [CrossRef] [Medline]
    22. van Leersum CM, Bults M, Siebrand E, Olthuis TJJ, Bekhuis REM, Konijnendijk AAJ, et al. Exploring opportunities and challenges for the spread, scale-up, and sustainability of mhealth apps for self-management of patients with type 2 diabetes mellitus in the Netherlands: citizen science approach. JMIR Diabetes. 2024;9:e56917. [FREE Full text] [CrossRef] [Medline]
    23. Kaboré SS, Ngangue P, Soubeiga D, Barro A, Pilabré AH, Bationo N, et al. Barriers and facilitators for the sustainability of digital health interventions in low and middle-income countries: a systematic review. Frontiers Digit Health. 2022;4:1014375. [FREE Full text] [CrossRef] [Medline]
    24. Gholamzadeh M, Abtahi H, Safdari R, Haghshenas E, Gharabaghi M, Rahimi B. Attitudes, awareness, and willingness of iranians toward using telemedicine in post-COVID era: a cross-sectional study. Health Science Reports. 2025;8(1):e70321. [FREE Full text] [CrossRef] [Medline]
    25. Tumuhimbise W, Atwine D, Kaggwa F, Musiimenta A. Acceptability and feasibility of a mobile health application for enhancing public private mix for TB care among healthcare Workers in Southwestern Uganda. BMC Digital Health. 2023;1(1):9. [FREE Full text] [CrossRef] [Medline]
    26. van der Kop ML, Memetovic J, Smillie K, Coleman J, Hajek J, Van Borek N, et al. Use of the WelTel mobile health intervention at a tuberculosis clinic in British Columbia: a pilot study. Journal of Mobile Technology in Medicine. 2013;2(3):7-14. [CrossRef]
    27. Davies E, Fieggen K, Wilmshurst J, Anyanwu O, Burman R, Komarzynski S. Demonstrating the feasibility of digital health to support pediatric patients in South Africa. Epilepsia Open. 2021;6(4):653-662. [FREE Full text] [CrossRef] [Medline]
    28. Bruno E, Simblett S, Lang A, Biondi A, Odoi C, Schulze-Bonhage A, et al. RADAR-CNS Consortium. Wearable technology in epilepsy: the views of patients, caregivers, and healthcare professionals. Epilepsy Behavior. 2018;85:141-149. [CrossRef] [Medline]
    29. Peterson CL, Coleman H, Walker C. Needs for aids and equipment for the management of epilepsy in an Australian cohort. Epilepsy Behavior. 2020;108:107062. [CrossRef] [Medline]
    30. Esmaeili B, Vieluf S, Dworetzky BA, Reinsberger C. The potential of wearable devices and mobile health applications in the evaluation and treatment of epilepsy. Neurologic Clinics. 2022;40(4):729-739. [CrossRef] [Medline]
    31. Latif S, Rana R, Qadir J, Ali A, Imran MA, Younis MS. Mobile health in the developing world: review of literature and lessons from a case study. IEEE Access. 2017;5:11540-11556. [CrossRef]
    32. Cuschieri S. The STROBE guidelines. Saudi Journal of Anesthesia. 2019;13(Suppl 1):S31-S34. [FREE Full text] [CrossRef] [Medline]
    33. Ahmed A, Saqlain M, Umair MM, Hashmi FK, Saeed H, Amer M, et al. Stigma, social support, illicit drug use, and other predictors of anxiety and depression among HIV/AIDS patients in Pakistan: a cross-sectional study. Frontiers in Public Health. 2021;9:745545. [FREE Full text] [CrossRef] [Medline]
    34. Ahmed A, Dujaili JA, Jabeen M, Umair MM, Chuah L, Hashmi FK, et al. Barriers and enablers for adherence to antiretroviral therapy among people living with HIV/AIDS in the Era of COVID-19: a qualitative study from Pakistan. Frontiers in Pharmacology. 2021;12:807446. [FREE Full text] [CrossRef] [Medline]
    35. Liu H, Li VL, Liu Q, Liu Y, Su C, Wong H, et al. Lac-Phe induces hypophagia by inhibiting AgRP neurons in mice. Nature Metabolism. Oct 2025;7(10):2004-2017. [CrossRef] [Medline]
    36. Houta S, Bader A, Effert J, Esser B, Henze J, Spaic A, et al. Digital health applications in the self-management of epilepsy-A survey on patients' perspective. Epilepsia Open. 2023;8(4):1288-1299. [FREE Full text] [CrossRef] [Medline]
    37. Belachew EA, Getachew D, Netere AK, Gizachew E, Sendekie AK. Perception, willingness, and practices of telemedicine in patients with chronic diseases: implication of digital health in patients' perspective at a tertiary care hospital in Ethiopia. Frontiers in Public Health. 2023;11:1234436. [FREE Full text] [CrossRef] [Medline]
    38. Hofstede J, de Bie J, van Wijngaarden B, Heijmans M. Knowledge, use and attitude toward eHealth among patients with chronic lung diseases. International Journal of Medical Informatics. 2014;83(12):967-974. [CrossRef] [Medline]
    39. Tilahun M, Habte N, Mekonnen K, Srahbzu M, Ayelegne D. Nonadherence to antiepileptic medications and its determinants among epileptic patients at the university of Gondar Referral Hospital, Gondar, ethiopia, 2019: an institutional-based cross-sectional study. Neurology Research International. 2020;2020:8886828. [FREE Full text] [CrossRef] [Medline]
    40. Patel S, Akhtar A, Malins S, Wright N, Rowley E, Young E, et al. The acceptability and usability of digital health interventions for adults with depression, anxiety, and somatoform disorders: qualitative systematic review and meta-synthesis. Journal of Medical Internet Research. 2020;22(7):e16228. [FREE Full text] [CrossRef] [Medline]
    41. Simblett SK, Bruno E, Siddi S, Matcham F, Giuliano L, López JH, et al. RADAR-CNS Consortium. Patient perspectives on the acceptability of mHealth technology for remote measurement and management of epilepsy: a qualitative analysis. Epilepsy Behavior. 2019;97:123-129. [CrossRef] [Medline]
    42. Process of translation and adaptation of instruments. Organization WH. World Health Organization, Geneva; 2009. URL: http://www who int/substance_abuse/research_tools/translation/en/ [accessed 2025-11-07]
    43. Ashcroft R. The declaration of Helsinki. In: The Oxford textbook of clinical research ethics. England. Oxford Academic; 2008:141-148.
    44. Jembai JVJ, Wong YLC, Bakhtiar NAMA, Lazim SNM, Ling HS, Kuan PX, et al. Mobile health applications: awareness, attitudes, and practices among medical students in Malaysia. BMC Medical Education. 2022;22(1):544. [FREE Full text] [CrossRef] [Medline]
    45. Slater K, Taylor R, Collins CE, Hutchesson M. Awareness of cardiovascular disease risk and care received among Australian women with a history of hypertensive disorders of pregnancy: a cross-sectional survey. BMC Pregnancy Childbirth. 2025;25(1):15. [FREE Full text] [CrossRef] [Medline]
    46. Giebel GD, Speckemeier C, Abels C, Plescher F, Börchers K, Wasem J, et al. Problems and barriers related to the use of digital health applications: scoping review. Journal of Medical Internet Research. 2023;25:e43808. [FREE Full text] [CrossRef] [Medline]
    47. Patel S, Sreelal B, Kalyani S, Joy S, Pravalika T, Ramya T, et al. Digital health literacy among the ageing population: a comparative cross-sectional study between rural and urban Telangana, India. Medical Journal Armed Forces India. 2024;80(3):313-319. [CrossRef] [Medline]
    48. Cao L, Chongsuvivatwong V, McNeil EB. The sociodemographic digital divide in mobile health app use among clients at outpatient departments in inner Mongolia, China: cross-sectional survey study. JMIR Human Factors. 2022;9(2):e36962. [FREE Full text] [CrossRef] [Medline]
    49. Osei E, Mashamba-Thompson TP. Mobile health applications for disease screening and treatment support in low-and middle-income countries: a narrative review. Heliyon. 2021;7(3):e06639. [FREE Full text] [CrossRef] [Medline]
    50. Mbunge E, Batani J, Gaobotse G, Muchemwa B. Virtual healthcare services and digital health technologies deployed during coronavirus disease 2019 (COVID-19) pandemic in South Africa: a systematic review. Global Health Journal. 2022;6(2):102-113. [FREE Full text] [CrossRef] [Medline]
    51. Zöllner JP, Noda AH, McCoy J, Schulz J, Tsalouchidou P, Langenbruch L, et al. Use of health-related apps and telehealth in adults with epilepsy in germany: a multicenter cohort study. Telemedicine and e‑Health. 2023;29(4):540-550. [CrossRef] [Medline]
    52. Patel UK, Malik P, DeMasi M, Lunagariya A, Jani VB. Multidisciplinary approach and outcomes of tele-neurology: a review. Cureus. 2019;11(4):e4410. [FREE Full text] [CrossRef] [Medline]
    53. Aminifar A, Shokri M, Aminifar A. Privacy-preserving edge federated learning for intelligent mobile-health systems. Future Generation Computer Systems. 2024;161:625-637. [FREE Full text] [CrossRef]
    54. Shegog R, Braverman L, Hixson J. Digital and technological opportunities in epilepsy: toward a digital ecosystem for enhanced epilepsy management. Epilepsy Behavior. 2020;102:106663. [CrossRef] [Medline]
    55. Madanian S, Nakarada-Kordic I, Reay S, Chetty T. Patients' perspectives on digital health tools. PEC Innovation. 2023;2:100171. [FREE Full text] [CrossRef] [Medline]
    56. Yoo S, Lim K, Baek H, Jang S, Hwang G, Kim H, et al. Developing a mobile epilepsy management application integrated with an electronic health record for effective seizure management. International Journal of Medical Informatics. 2020;134:104051. [CrossRef] [Medline]
    57. Ernawati I, Yasin NM, Setyopranoto I, Ikawati Z. Effect of mobile health applications on improving self-management knowledge and seizure control in epilepsy patients: a scoping review. Healthcare Informatics Research. 2024;30(2):127-139. [FREE Full text] [CrossRef] [Medline]
    58. Verma A, Pathak P, Mishra A, Upadhya S. Factors linked with perceived stigma amid people with Epilepsy -across sectional study. Epilepsy Research. 2024;205:107428. [CrossRef] [Medline]
    59. Batada I. Impact of Digital Marketing on Consumer Buying Perspectives in the Pakistan Market. J Entrep Manag Innov. Jun 30, 2023;5(3):319-345. [CrossRef]
    60. Patel RJS, Ding J, Marvel FA, Shan R, Plante TB, Blaha MJ, et al. Associations of demographic, socioeconomic, and cognitive characteristics with mobile health access: MESA (Multi-Ethnic Study of Atherosclerosis). Journal of the American Heart Association. 2022;11(17):e024885. [FREE Full text] [CrossRef] [Medline]
    61. Ridho A, Alfian SD, van Boven JFM, Levita J, Yalcin EA, Le L, et al. Digital health technologies to improve medication adherence and treatment outcomes in patients with tuberculosis: systematic review of randomized controlled trials. Journal of Medical Internet Research. 2022;24(2):e33062. [FREE Full text] [CrossRef] [Medline]
    62. Quisel T, Foschini L, Zbikowski SM, Juusola JL. The association between medication adherence for chronic conditions and digital health activity tracking: retrospective analysis. Journal of Medical Internet Research. 2019;21(3):e11486. [FREE Full text] [CrossRef] [Medline]
    63. Conway CM, Kelechi TJ. Digital health for medication adherence in adult diabetes or hypertension: an integrative review. JMIR Diabetes. 2017;2(2):e20. [FREE Full text] [CrossRef] [Medline]
    64. Du S, Zhao Y. Enhancing digital health interventions for medication adherence: considerations for broader applicability and long-term impact. Journal of Medical Internet Research. 2025;27:e69204. [FREE Full text] [CrossRef] [Medline]
    65. Torous J, Andersson G, Bertagnoli A, Christensen H, Cuijpers P, Firth J, et al. Towards a consensus around standards for smartphone apps and digital mental health. World Psychiatry. 2019;18(1):97-98. [FREE Full text] [CrossRef] [Medline]
    66. Huckvale K, Nicholas J, Torous J, Larsen ME. Smartphone apps for the treatment of mental health conditions: status and considerations. Current Opinion in Psychology. 2020;36:65-70. [FREE Full text] [CrossRef] [Medline]
    67. Ezeala-Adikaibe BA, Ekochin CF, Ezeme MS, Mbadiwe CN, Okwara CC, Nwobodo MU. Knowledge, attitude, and practice of epilepsy in a rural community in Enugu, Southeast Nigeria. International Journal of Medicine & Health Development. 2024;29(2):77-87. [CrossRef]
    68. Makhado L, Maphula A, Ngomba R, Musekwa O, Makhado T, Nemathaga M, et al. Epilepsy in rural South Africa: patient experiences and healthcare challenges. Epilepsia Open. 2024;9(4):1565-1574. [FREE Full text] [CrossRef] [Medline]
    69. Phimha S, Sirikarn P, Tiamkao S. Economic burden and determinants among hospitalized patients with epilepsy in Thailand. BMC Health Service Research. 2025;25(1):413. [FREE Full text] [CrossRef] [Medline]
    70. Jakob R, Harperink S, Rudolf AM, Fleisch E, Haug S, Mair JL, et al. Factors influencing adherence to mhealth apps for prevention or management of noncommunicable diseases: systematic review. Journal of Medical Internet Research. 2022;24(5):e35371. [FREE Full text] [CrossRef] [Medline]
    71. Polus M, Keikhosrokiani P, Uusimaa J, Komulainen-Ebrahim J, Annunen J, Khan S. The role of digital care pathway for epilepsy on patients’ treatment burden: clinicians’ perspective. 2024. Presented at: First Nordic Conference​, NCDHWS 2024; 2024 May 7–8:267-268; Oulu, Finland. [CrossRef]
    72. Patterson V, Larik AB, Soomro MZ, Coates S. Validation of an epilepsy management smartphone application in Pakistan. Acta Epileptol. 2022;4:12. [FREE Full text] [CrossRef] [Medline]
    73. Alzamanan MZ, Lim KS, Ismail MA, Ghani NA. Development of an epilepsy self-management mobile health app framework: content validity study results. PLoS One. 2024;19(6):e0302844. [FREE Full text] [CrossRef] [Medline]

    ‎
    HIPAA: Health Insurance Portability and Accountability Act
    LMIC: low- and middle-income country
    mHealth: mobile health
    OPD: Outpatient department
    PIMS: Pakistan Institute of Medical Sciences
    STROBE: Strengthening the Reporting of Observational Studies in Epidemiology

    Edited by A Stone, A Mavragani; submitted 07.Jul.2025; peer-reviewed by MD Butt, C Ochoa-Zezzatti; comments to author 08.Sep.2025; revised version received 19.Oct.2025; accepted 23.Oct.2025; published 10.Dec.2025.

    Copyright

    ©Bushra Batool Zahra, Muhammad Amir Hamza, Rehana Sarwat, Hina Batool Zahra, Ayesha Azam, Ali Ahmed. Originally published in JMIR Formative Research (https://formative.jmir.org), 10.Dec.2025.

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