Clinical effectiveness, feasibility, acceptability, and usability of mobile health applications for epilepsy: A systematic review

Evelyn Gotlieb; Shahab Marzoughi; Churl‐Su Kwon; Michael Harmon; Maren Kimura; Ashley Truesdale; Chloe Sweetnam; Céline Soudant; Margaret H Downes; Neil A Busis; Benjamin R Kummer; Nathalie Jetté

doi:10.1111/epi.18288

. 2025 Feb 13;66(5):1349–1373. doi: 10.1111/epi.18288

Clinical effectiveness, feasibility, acceptability, and usability of mobile health applications for epilepsy: A systematic review

Evelyn Gotlieb ¹, Shahab Marzoughi ², Churl‐Su Kwon ³, Michael Harmon ⁴, Maren Kimura ², Ashley Truesdale ², Chloe Sweetnam ⁵, Céline Soudant ⁶, Margaret H Downes ¹, Neil A Busis ⁷, Benjamin R Kummer ^4,^8,^9,^✉, Nathalie Jetté ^2,⁴

PMCID: PMC12097477 PMID: 39945400

Abstract

Mobile applications are widely used in epilepsy, although their impact on clinical effectiveness (CE) and their feasibility, acceptability, and usability (FAU) remain unclear. We conducted a systematic review investigating CE and FAU of epilepsy mobile applications using MEDLINE and Embase from database inception to June 21, 2024. We followed Preferred Reporting Items for Systematic Reviews and Meta‐Analyses reporting standards. The protocol was registered on PROSPERO (CRD42019134848). In duplicate, we determined study quality using the Newcastle–Ottawa Quality Assessment Scale (NOQAS) and the Joanna Briggs Critical Appraisal Checklist (to determine eligibility for inclusion), risk of bias using the Cochrane Risk of Bias tool, and usability study quality using the 15‐point Silva scale. We identified 8953 studies, of which 20 were included. Twelve (60.0%) addressed CE, nine (45.0%) acceptability, five (25.0%) usability, and eight (40.0%) feasibility. Five (25.0%) evaluated CE and FAU. Studies comprised prospective cohort (n = 9, 45.0%), pilot (n = 3, 15.0%), randomized controlled trial (n = 7, 35.0%), and pre/post (n = 1, 5.0%) designs. Most apps were used for self‐management or to enhance education or communication between patients and providers. Cohort studies demonstrated fair quality (median NOQAS score = 5, interquartile range [IQR] = 5.0–5.8), whereas of seven randomized controlled trials, four (57.1%) had some concern for bias. Usability studies demonstrated high quality (median Silva score = 10, IQR = 10–11). Apps were predominantly intended for patient use (n = 9, 75.0%). Symptom reporting and medication management were the most common app targets in both CE and FAU studies (n = 8, 66.7%; n = 9, 69.2%), although FAU studies more frequently used monitoring or tracking (n = 10, 76.9%) and reminder setting (n = 10, 76.9%) than CE apps (n = 7, 58.3%). Investigations of application use most commonly studied CE and patient‐facing apps. Additional high‐quality evidence is necessary to evaluate the CE and FAU of app use in epilepsy to work toward the standardization of FAU metrics and development of implementation guidelines.

Keywords: digital health, mHealth, self‐efficacy, self‐management, tracking

Key points.

Of 20 studies, 12 addressed CE, nine acceptability, five usability, eight feasibility, and five CE and FAU.
Apps were most frequently studied in patient users (n = 9 [75.0%] CE and n = 12 [92.3%] FAU studies).
The most common app target in both CE studies and FAU studies was symptom reporting (n = 8, 66.7%; n = 9, 69.2%).
Usability and randomized CE studies on epilepsy mobile apps showed higher quality than cohort studies, which were of fair quality.
It is essential to evaluate the clinical effectiveness, feasibility, acceptability, and usability of mobile apps developed for epilepsy.

1. INTRODUCTION

mHealth (mobile health) is defined as technology that enables mobile devices to supplement medical and public health practices. ¹ The widespread availability of mobile devices and smartphone applications (apps) holds great promise for improving the health of patients in multiple dimensions. ² Driving much of this potential is the ubiquity of smartphones, which a significant proportion of the US ³ and global ⁴ population own, as well as mHealth programs, which are common in many countries. ¹ Mobile apps have been shown to positively impact care processes, especially when used to collect physiological or self‐reported data, facilitate communication with providers, provide reminders, or supply information about effective self‐management of conditions. ⁵ , ⁶ , ⁷ , ⁸ , ⁹ , ¹⁰

Epilepsy affects more than 50 million people worldwide, 80% of whom reside in low‐ or middle‐income countries with limited or no access to care. ¹¹ mHealth applications can support these vulnerable populations in receiving focused clinical care, communicating with their providers, and learning more about their condition. Apps can thus help address care access disparities, provide resources for self‐management of epilepsy including symptom and medication tracking, and create avenues for improving safety and communication with providers. ¹² , ¹³ , ¹⁴ Notably, the top users of smartphones by country include China, India, the United States, Indonesia, and Brazil, indicating global diversity in utilization. Importantly, however, penetration rates vary widely among top 10 users, emphasizing additional disparities in access within a country by socioeconomic status. ¹⁵

Although reviews of the use and benefits of mobile apps in epilepsy have been published, ¹³ , ¹⁴ , ¹⁶ , ¹⁷ , ¹⁸ the extent to which mobile apps impact meaningful clinical outcomes in people with epilepsy remains unclear. Standardized metrics to evaluate the successful implementation of these apps in patients with epilepsy are also yet to be developed. To address this gap, we conducted a systematic review of studies on mobile apps in epilepsy with a particular focus on studies investigating clinical effectiveness (CE) as well as feasibility, acceptability, and usability (FAU). The goal of this study was therefore to work toward a standardized evaluation framework of neurological apps, specifically the evidence base supporting use of mHealth apps in people with epilepsy.

2. MATERIALS AND METHODS

2.1. Literature search strategy and inclusion criteria

This systematic review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines. The protocol for this review was registered with PROSPERO (Prospective Register of Systematic Reviews; ID CRD42019134848) to study the CE impacts and FAU of mobile apps in six common neurological conditions (Alzheimer dementia, Parkinson disease, multiple sclerosis, migraine, cerebrovascular disease, and epilepsy). These disorders were chosen according to global prevalence data reported in the Global Burden of Disease Study. ¹⁹ This review focuses on epilepsy, the target population of our study. We included studies that focused on CE, the measure of an app's impact on qualitative (knowledge about epilepsy) or quantitative clinical outcomes (i.e., seizure frequency, medication adherence), as well as studies that examine FAU, that is, whether the app itself is a reasonable, accessible, and user‐friendly tool.

Aside from excluding studies that did not specifically address the outcomes of CE or FAU, we excluded gray literature reports, book chapters, reviews, study protocols, conference abstracts, and studies that did not include epilepsy as one of the studied conditions. In addition, we excluded studies that evaluated multiple apps simultaneously only if results could not be attributed to each distinct app, as this would limit our ability to evaluate each app. We also excluded studies in which apps were not the primary intervention or where the study consisted solely of app descriptions, implementations, or measurement validations. Studies in which a wearable technology was used were included if there was a mobile app component that was the primary focus of the study.

2.2. Search strategy and data extraction

Using a search strategy combining medical subject headings and free keywords (File S1), a medical librarian (C.So.) searched the Embase (Ovid) and MEDLINE ALL (Ovid) databases to generate a body of initial abstracts. The full search strategy was run on April 15, 2022, and updated on June 21, 2024 (see File S1). A total of 12 443 abstracts were imported and automatically deduplicated with Covidence software. Independently of seven reviewers (C.‐S.K., M.H., M.H.D., E.G., S.M., M.K., N.J.) who screened the body of included abstracts, two screened each study. Decision conflicts were resolved by discussion with a neurologist–informaticist or health services researcher who is also an epileptologist (B.R.K., N.J.). We then retrieved the full‐text version of 36 screened abstracts that focused on epilepsy, and of the seven available reviewers (B.R.K., C.Sw., M.H.D., E.G., S.M., M.K., N.J.), two reviewers for each study confirmed that the original inclusion criteria were met to generate a final list of included studies. Disagreements were again resolved in consultation with the same parties as previously.

Of the eight available reviewers (B.R.K., C.Sw., M.H.D., E.G., S.M., M.K., A.T., N.J.), two reviewers then extracted study characteristics for each included study into a web database, including neurological disorder, population descriptions, study designs, measured clinical outcomes (CE or FAU), and settings. In CE studies only, we recorded the specific effectiveness measure that was studied and whether it was assessed by a validated clinical scale or instrument. For each study, we also recorded the mobile operating systems and platforms used by the app, as well as the method of app development (commercial, institutional proprietary, or institutional–commercial joint venture), intended app users, and app functions. Additionally, we adapted the Mobile App Rating Scale (MARS), ²⁰ a tool used to evaluate the quality of mobile health apps, to characterize and record theoretical backgrounds and targets for each studied app (adapted table is provided in File S2). Studies could possess multiple targets and settings, app technical features, and adapted MARS characteristics.

2.3. Study quality and assessment of bias

Study quality in cohort studies was assessed using the 9‐point Newcastle–Ottawa Quality Assessment Scale (NOQAS). ²¹ An NOQAS score of at least 6 is typically indicative of good quality, a score of at least 5 suggests fair quality, and scores below 5 typically indicate poor quality. ²¹ In cases where the NOQAS score could not be properly utilized (e.g., no comparator group), the Joanna Briggs Institute (JBI) Critical Appraisal Tool for Cohort Studies was employed, and inclusion was based on consensus agreement. The risk of bias in randomized trials was evaluated using the revised Cochrane Collaboration Handbook Risk of Bias (RoB2) tool, ²² which categorizes risk of bias into high, low, or some concern for bias. The methodological quality of usability studies was investigated using the 15‐point validated scale, ²³ which reliably distinguishes between studies with high (mean score = 9.2, SD = .26) and low (mean = 6.9, SD = .8) methodological quality.

2.4. Statistical analyses

All included studies were tabulated, and their characteristics were analyzed with proportions. Descriptive statistics were used to summarize numeric results to determine centrality (mean, median) and variability (SD, interquartile range [IQR]). Missing data were not imputed or visually displayed outside of a table. Meta‐analyses, heterogeneity analyses, and sensitivity analyses to assess robustness were not conducted due to the small sample size precluding such analyses.

3. RESULTS

3.1. Included studies

We screened 8953 abstracts, of which 8432 were excluded based on prespecified criteria specified in File S1, resulting in 521 studies that fulfilled our inclusion criteria across six neurological disorders. Of those 521, 36 full‐text studies that focused on epilepsy required eligibility assessment. Of these studies, we excluded 16 for not meeting prespecified search criteria, primarily due to wrong publication status or type (n = 4) and wrong outcomes (n = 6). Of the remaining full‐text papers, 20 (55.6%) studies that focused on epilepsy were included (Figure 1).

Identification of studies via databases and registers. *From:* Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. https://doi.org/10.1136/bmj.n71. For more information, visit http://www.prisma‐statement.org/.

3.1.1. Study characteristics

Of 20 papers, we identified 12 (60.0%) CE ²⁴ , ²⁵ , ²⁶ , ²⁷ , ²⁸ , ²⁹ , ³⁰ , ³¹ , ³² , ³³ , ³⁴ , ³⁵ and 13 (65.0%) FAU ²⁴ , ²⁵ , ²⁷ , ³¹ , ³² , ³⁶ , ³⁷ , ³⁸ , ³⁹ , ⁴⁰ , ⁴¹ , ⁴² , ⁴³ studies, whereas five (25.0%) ²⁴ , ²⁵ , ²⁷ , ³¹ , ³² studies investigated both CE and FAU. These studies evaluated apps, summarized in Table 1, with a variety of primary use cases, for example, tools that deliver cognitive behavioral therapy, provide a seizure diary, assist with medication reminders, manage diet, track seizure triggers, provide educational materials, and help providers manage and communicate with patients.

TABLE 1.

Summary of included applications and purpose.

Title or name of mobile application	Description	Study location	First author (year)
CBT‐I mobile app	CBT‐I therapy delivered weekly for 6 weeks to improve insomnia.	Islamic Republic of Iran	Ahorsu (2020) ²⁹
Seer Medical App	Seizure diary, seizure questionnaire completion.	United Kingdom	Biondi (2024) ⁴¹
Epilepsy Care	Aimed to be child‐friendly and improve epilepsy self‐management. Its features include epilepsy and antiseizure medication information, a seizure diary, medication reminders, and emergency contacts for medical personnel.	Thailand	Chinsuwan (2024) ³²
Brain4U	Seizure diary, medication reminders with adherence data, comorbidity self‐screening tools, personal dashboard, educational material.	South Korea	Choi (2021) ³⁹
KetApp	Ketogenic diet management app for pediatric patients with refractory epilepsy.	Italy	Costa (2021) ⁴⁰
EpiWatch	A paired mobile iPhone app and Apple Watch system aimed to assess characteristics of seizure triggers.	United States	Ge (2022) ³⁶
Constant Therapy	Provide cognitive rehabilitation therapies.	United States	Glynn (2016) ³⁷
EpApp	Provide education and facilitate epilepsy management.	Australia	Le Marne (2018) ²⁷
Unspecified	Delivery of menu plan, seizure e‐diary.	China	Li (2024) ³³
Helpilepsy	Contains an e‐diary where patients can register seizures, set reminders to take their antiseizure medication, and record daily their mood and sleep quality.	Germany	Michaelis (2024) ⁴²
AIMEO	Medication reminder system, seizure diary, lifestyle checklist, health education, physician's portal, and real‐time progress tracker.	India	Mirpuri (2021) ³¹
mHealth social norms intervention	Automated digital reminders to take antiseizure medications, individualized antiseizure medication adherence feedback reports.	United States	Modi (2023) ⁴³
MyCareCentric Epilepsy (patient) CareFlow Connect (provider)	Patient portal, integrated electronic health record, social networking, wearable/sensor integration and transmission, clinical communication to care team.	United Kingdom	Page (2019) ²⁵
PAUSE to Learn Your Epilepsy	Personalized self‐management education intervention.	United States	Pandey (2020) ²⁸
Epilepsy Management Aid	Nonphysician health care worker communication with epilepsy specialist for diagnosis.	Pakistan	Patterson (2022) ²⁴
Working Memory Intervention mobile application and activity booklet	An application designed to improve working memory for children with epilepsy.	India	Phakey (2021) ²⁶
Mobile Epilepsy Education Package	Epilepsy education package section with 4 weekly training modules; parental follow‐up/monitoring section containing child's profile, diagnosis/test results/treatment/seizure events, treatment/examination checkup reminders, ability to message researchers.	Turkey	Sayik (2023) ³⁴
MINDSET	Bilingual decision support tool (available in both English and Spanish) designed to assist people with epilepsy and health care providers to quickly assess epilepsy self‐management needs, identify strategies for improvement, and monitor progress during clinic visits.	United States	Sepulveda (2024) ³⁵
Epilepsy Management App Built on WeChat	Medication calendar, online educational forums and blogs, online seizure reporting, online consultations, online questionnaires.	China	Si (2020) ³⁰
Brain4U	Seizure diary, medication (reminder), education, image/video upload, emotion management integrated with hospital electronic health record system.	South Korea	Yoo (2020) ³⁸

Open in a new tab

Abbreviation: CBT‐I, cognitive behavioral therapy for insomnia.

Study characteristics are tabulated by study group in Table 2. Studies are listed according to selected characteristics in Tables 3 and 4 for CE and FAU, respectively. The median study period length and population size were 8.0 months (2.3–12.0) and 66.5 (37.0–143.25) participants, respectively. The academic medical center clinic (n = 9, 45.0%) was the most frequent study setting, and study designs comprised prospective cohort (n = 9, 45.0%), pilot studies (n = 3, 15.0%), randomized trials (n = 7, 35.0%), and pre/post studies (n = 1, 5.0%). Studied apps were most commonly deployed on both iOS and Android platforms on both tablets and smartphones when specified (n = 5, 25.0%).

TABLE 2.

Study characteristics, stratified by study type.

Study characteristic	Study type
Study characteristic	All studies, N = 20	Clinical effectiveness, n = 12, 60.0%	Feasibility, usability, acceptability, n = 13, 65.0%
Median study length, months (IQR)	8.0 (2.3–12.0)	10.0 (2.3–12.0)	5.0 (2.65–12.0)
Median study population size, n (IQR)	66.5 (37.0–143.25)	82 (52.5–239)	59 (24.0–128.5)
Study design, n (%)
Randomized trial	7 (35.0%)	5 (41.7%)	4 (30.8%)
Prospective cohort	9 (45.0%)	5 (41.7%)	5 (38.5%)
Pre/post	1 (5.0%)	1 (8.3%)	1 (7.7%)
Pilot	3 (15.0%)	1 (8.3%)	3 (23.1%)
Recruitment setting, n (%)
Academic medical center clinic	9 (45.0%)	6 (50.0%)	5 (38.5%)
Academic medical center hospital	7 (35.0%)	3 (25.0%)	5 (38.5%)
Community clinic	2 (10.0%)	2 (16.7%)	1 (7.7%)
Community hospital	1 (5.0%)	1 (8.3%)	1 (7.7%)
Other	1 (5.0%)	0 (.0%)	1 (7.7%)
Target user audience, n (%)
Patients	17 (85.0%)	9 (75.0%)	12 (92.3%)
Caregivers	10 (50.0%)	5 (41.7%)	7 (53.8%)
Providers	5 (25.0%)	4 (33.3%)	4 (30.8%)
Patients and providers	4 (20.0%)	3 (25.0%)	3 (23.1%)
Patients and caregivers	8 (40.0%)	3 (25.0%)	7 (53.8%)
MARS app target, n (%)
Symptom reporting	13 (65.0%)	8 (66.7%)	9 (69.2%)
Medication management	11 (55.0%)	7 (58.3%)	7 (53.8%)
Physical health	10 (50.0%)	6 (50.0%)	6 (46.2%)
Symptom management	11 (55.0%)	6 (50.0%)	8 (61.5%)
Care coordination	5 (25.0%)	5 (41.7%)	3 (23.1%)
Behavior change	5 (25.0%)	4 (33.3%)	1 (7.7%)
Secure messaging	3 (15.0%)	3 (25.0%)	2 (15.4%)
Increase happiness or well‐being	3 (15.0%)	2 (16.7%)	2 (15.4%)
Mindfulness, meditation, or relaxation	2 (10.0%)	2 (16.7%)	1 (7.7%)
Quality improvement	4 (20.0%)	2 (16.7%)	4 (30.8%)
Enhancement of provider assessments	5 (25.0%)	2 (16.7%)	5 (38.5%)
Address depression	3 (20.0%)	2 (16.7%)	1 (7.7%)
Manage anxiety or stress	3 (20.0%)	2 (16.7%)	1 (7.7%)
Validation of measurement	1 (5.0%)	1 (8.3%)	1 (7.7%)
Diagnostic accuracy	1 (5.0%)	1 (8.3%)	1 (7.7%)
Goal setting	1 (5.0%)	0 (.0%)	1 (7.7%)
Entertainment	1 (5.0%)	0 (.0%)	1 (7.7%)
Other	4 (20.0%)	3 (25.0%)	2 (15.4%)
MARS theoretical background, n (%)
Information or education	11 (55.0%)	7 (58.3%)	7 (53.8%)
Monitoring or tracking	14 (70.0%)	7 (58.3%)	10 (76.9%)
Advice, tips, strategies, or skills training	4 (20.0%)	3 (25.0%)	3 (23.1%)
Relaxation	2 (10.0%)	2 (16.7%)	1 (7.7%)
Assessment	1 (5.0%)	1 (8.3%)	1 (7.7%)
CBT: behavioral [positive events]	1 (5.0%)	1 (8.3%)	0 (.0%)
CBT: cognitive [thought challenging]	1 (5.0%)	1 (8.3%)	0 (.0%)
Mindfulness/meditation	1 (5.0%)	1 (8.3%)	0 (.0%)
Feedback	2 (10.0%)	0 (.0%)	2 (15.4%)
Goal setting	1 (5.0%)	0 (.0%)	1 (7.7%)
Other	1 (5.0%)	0 (.0%)	1 (7.7%)
App technical features, n (%)
Sending of reminders	12 (60.0%)	7 (58.3%)	10 (76.9%)
Internet connection required	8 (40.0%)	6 (50.0%)	4 (30.8%)
Allows sharing results with care team	5 (25.0%)	4 (33.3%)	3 (23.1%)
Allows concurrent patient/provider use	6 (30.0%)	3 (25.0%)	4 (30.8%)
Requires login	6 (30.0%)	3 (25.0%)	4 (30.8%)
Allows password protection	3 (15.0%)	2 (16.7%)	2 (15.4%)
Presence of app community	1 (5.0%)	1 (8.3%)	1 (7.7%)
Associated wearable device	3 (15.0%)	1 (8.3%)	3 (23.1%)
Allows sharing on social media	0 (.0%)	0 (.0%)	0 (.0%)
Tablet required	1 (5.0%)	0 (.0%)	1 (7.7%)
Other	3 (15.0%)	2 (16.7%)	3 (23.1%)
App OS and device platforms, n (%)
iPhone, iPad, and Android phone [or tablet]	5 (25.0%)	4 (33.3%)	5 (38.5%)
Android mobile phone [or tablet]	4 (20.0%)	3 (25.0%)	1 (7.7%)
iPhone only	1 (5.0%)	0 (.0%)	1 (7.7%)
iPad tablet only	1 (5.0%)	0 (.0%)	1 (7.7%)
Unspecified	9 (45.0%)	5 (41.7%)	6 (46.2%)
App development method, n (%)
Institutional proprietary	5 (20.0%)	3 (25.0%)	3 (23.1%)
Commercial	3 (10.0%)	1 (8.3%)	3 (23.1%)
Joint venture	1 (5.0%)	1 (8.3%)	1 (7.7%)
Unspecified	11 (55.0%)	7 (58.3%)	6 (46.2%)

Open in a new tab

Note: n = 5 studies evaluated CE and FAU concurrently. Proportions of characteristics may not always add up to 100%, as each study could possess multiple characteristics that were not mutually exclusive.

Abbreviations: CBT, cognitive behavioral therapy; IQR, interquartile range; MARS, Mobile App Rating Scale; OS, operating system.

TABLE 3.

Clinical effectiveness studies of mobile apps in epilepsy.

First author (year) country of origin	Design	App implementations	Study n/control n	Follow‐up time, months	App name	Development method	OS in study	Target audience	Description of app function	Validated scale	Technical features	Main study conclusions
Sayik (2023) ³⁴ Turkey	Randomized controlled trial	CE	30/30	NA	MEEP	Unspecified	Android mobile phone only	Caregiver	Epilepsy education package section with 4 weekly training modules; parental follow‐up/monitoring section containing child's profile, diagnosis/test results/treatment/seizure events, treatment/examination checkup reminders, ability to message researchers	Epilepsy Knowledge Scale for Parents and Parental Anxiety Over Seizures Scale	Sends reminders	Mothers of children with epilepsy had significantly improved knowledge about epilepsy and significantly reduced anxiety over seizures compared to baseline 3 weeks after concluding their use of MEEP. Knowledge about epilepsy of mothers who used MEEP was significantly increased compared to mothers who underwent normal hospital procedure for providing training and information.
Sepulveda (2024) ³⁵ United States	Prospective cohort study	CE	46/NA	NA	MINDSET	Institutional	Unspecified	Patient	Bilingual decision support tool (available in both English and Spanish) designed to assist PWE and HCPs to quickly assess ESM needs, identify strategies for improvement, and monitor progress during clinic visits	NA	Requires login, needs internet/cellular network to access	The participants who used MINDSET as a self‐management intervention reported an overall positive experience. Qualitative data in this study show that MINDSET is a valuable ESM tool for Hispanic patients with epilepsy.
Li (2024) ³³ China	Prospective cohort study	CE	73/NA	3, 6, and 12	Unspecified	Unspecified	Unspecified	Caregiver	Delivery of menu plan, seizure e‐diary	NA	Allows sharing of results with care team	The use of the smartphone application can improve the retention rate and reduce the occurrence of side effects in patients undergoing ketogenic diet for epilepsy management.
Chinsuwan (2024) ³² Thailand	Randomized controlled trial	CE F	85/73	6	Epilepsy Care	Unspecified	Unspecified	Patient	Features included epilepsy and antiseizure medication information, a seizure diary, medication reminders, and emergency contacts for medical personnel	Pediatric Epilepsy Medication Self‐Management Questionnaire	Sends reminders	Application “Epilepsy Care” may serve as an effective adjunctive therapy to enhance self‐management and seizure control in children and adolescents with epilepsy.
Le Marne (2018) ²⁷ Australia	Prospective cohort study	CE U A	36/NA	2.3 (mean)	EpApp	Institutional proprietary	iPhone, iPad, and Android phone/tablet (all devices/platforms)	Patient, caregiver	Provide education and facilitate epilepsy management	Adolescent Knowledge of Epilepsy questionnaire (general epilepsy); Seizure Self‐Efficacy Scale for Children and Adolescents with Epilepsy and Child Attitude Towards Illness Scale for psychosocial outcomes	Sends reminders, needs internet connection	Both self and general epilepsy knowledge significantly improved from baseline to follow‐up; no significant improvement from baseline to follow‐up on measures of attitude toward illness or seizure self‐efficacy; compared to baseline counts (M = 6.64), post hoc comparisons revealed fewer medication reminders were required during intervention (M = 2.93, p = .002) and follow‐up (M = 3.54, p = .030); no significant effect on seizure burden. Most patients and parents felt that the app was moderately usable and found the information/design useful and acceptable (using Mobile Application Rating Scale).
Page (2019) ²⁵ United Kingdom	Pre/post study	CE F A	377/NA	12	MyCareCentric Epilepsy Patient App; CareFlow Connect	Commercial	iPhone, iPad, and Android phone/tablet (all devices/platforms)	Provider, patient	Patient portal, integrated EHR, social networking, wearable/sensor integration and transmission, clinical communication to care team	NA	Has app community, allows password protection, requires login, needs internet connection, has an associated wearable device, allows sharing of results with care team, integrates with patient's EHR	The interval between seizure occurrence and notification of the specialist team reduced by 24–48 h compared to similar notifications arising through advice line. Compared to year prior, there was a 30% reduction in admissions for patients with epilepsy and 10% reduction in length of stay. Patients using the technology report an increased feeling of empowerment. Rate of seizure reporting increased by at most 50%.
Patterson (2022) ²⁴ Pakistan	Pilot study	CE F MV	59/NA	NA	Epilepsy Management Aid	Unspecified	iPhone, iPad, and Android phone/tablet (all devices/platforms)	Provider	Nonphysician health care worker communication with epilepsy specialist for diagnosis	NA	Sends reminders	The epilepsy management app improves access to timely and efficient care for people with epilepsy. The app had a sensitivity and specificity of 97% in diagnosing epileptic episodes and misdiagnosed these episodes in 6% of cases that were assessed face‐to‐face. 70% of patients reported feeling better after the treatment recommended by the app.
Ahorsu (2020) ²⁹ Iran	Randomized controlled trial	CE	160/160	1	CBT‐I mobile app	Institutional proprietary	Android mobile phone OR tablet	Patient	CBT‐I therapy delivered weekly for 6 weeks to improve insomnia	Pittsburgh Sleep Quality Index, Insomnia Severity Index, sleep hygiene scale (Todd and Mullan), behavioral intention scale (Kor and Mullan), Self‐Report Behavioral Automaticity Index, Likert scale, Quality of Life in Epilepsy‐31 Inventory	Allows password protection, requires login, sends reminders, needs internet connection	CBT‐I app improved sleep quality, insomnia severity, sleep hygiene behavior, and sleep onset latency when compared to patients receiving patient education only.
Si (2020) ³⁰ China	Randomized controlled trial	CE	190/190	6	Epilepsy Management App Built on WeChat	Unspecified	iPhone, iPad, and Android phone/tablet (all devices/platforms)	Patient	Medication calendar, online educational forums and blogs, online seizure reporting, online consultations, online questionnaires	C‐ESMS	Needs internet connection	C‐ESMS scores of app users were higher than those of non‐app‐users at the 6‐month follow‐up date overall and on information, lifestyle, medication, and safety management subscales. Seizure management subscales were not different between the two groups. Proportion of patients who were seizure‐free at 6 months follow‐up was larger in the app group versus control.
Mirpuri (2021) ³¹ India	Randomized controlled trial	CE U A	48/48	NA	Epilepsy medication adherence application	Commercial	Unspecified	Provide, patient, caregiver	Medication reminder system, seizure diary, lifestyle checklist, health education, physician's portal, and real‐time progress tracker	Morisky Green Levine Scale, Epilepsy Self‐Efficacy Scale	Sends reminders, allows sharing results with care team, allows both patients and caregivers to use simultaneously	In the mobile application group, there was a 60% increase in the proportion of medication adherence, compared to no significant change in medication adherence in the control group. Self‐efficacy increased in users of the mobile application and did not change in nonusers pre‐ and poststudy. There were no significant changes in seizure frequency as noted by follow‐up interviews.
Pandey (2020) ²⁸ United States	Prospective cohort study	CE	91/NA	4 and 8	PAUSE to Learn Your Epilepsy	Unspecified	Android mobile phone OR tablet	Patient, caregiver	Personalized self‐management education intervention	NA	Needs internet/cellular network access, allows both patients and caregivers to use simultaneously	App users showed significant improvements in all self‐management behaviors, self‐efficacy, outcome expectancy, quality of life, and personal impact of epilepsy from baseline to 4‐month follow‐up. Improvement was sustained at 8 months follow‐up.
Phakey (2021) ²⁶ India	Prospective cohort study	CE	14/NA	NA	Working Memory Intervention mobile application and activity booklet	Unspecified	Unspecified	Provider, patient	An application designed to improve working memory for children with epilepsy	Wechsler Intelligence Scale‐IV, ADHD/DSM‐IV scales of Conners Rating Scales‐Revised	Allows sharing of results with care team, allows patients and caregivers to use simultaneously, does not need internet/cellular access to function	There was significant improvement in most working memory parameters including digit span, letter‐number sequencing, WMI, and sustained attention.

Open in a new tab

Abbreviations: A, acceptability; ADHD, attention‐deficit/hyperactivity disorder; CBT‐I, cognitive behavioral therapy for insomnia; CE, clinical effectiveness; C‐ESMS, Chinese Epilepsy Self‐Management Scale; DSM‐IV, Diagnostic and Statistical Manual of Mental Disorders, 4th edition; EHR, electronic health record; ESM, epilepsy self‐management; F, feasibility; HCP, health care provider; M, mean; MEEP, Mobile Epilepsy Education Package; MV, measurement validity; NA, not applicable; OS, operating system; PWE, patients with epilepsy; U, usability; WMI, working memory index.

TABLE 4.

Feasibility, acceptability, and usability studies of mobile apps in epilepsy.

First author, year, country	Design	Study type	Study n/control n	Follow‐up time	App name	App development method	App OS and devices	Validation measure if applicable	Target audience	Description of app function	Studied app use	Main study conclusions
Modi (2023) ⁴³ United States	Randomized controlled trial	F A	19/21	3 months	mHealth social norms intervention	Unspecified	Unspecified	NA	Patient, caregiver	Automated digital reminders to take ASM; individualized ASM adherence feedback reports.	Improved adherence to ASM	Feasibility, acceptability, and satisfaction of the mHealth intervention was high in both control and treatment groups.
Biondi (2024) ⁴¹ United Kingdom	Prospective cohort study	F A	12/NA	Monthly follow‐up to month 7 and then at discretion	Seer Medial App	Unspecified	Unspecified	NA	Patient	Seizure diary, seizure questionnaire completion.	Feasibility of app from patient perspective, acceptability of app from patient perspective	EEG@HOME study demonstrated interest and ability of people with epilepsy in active health monitoring using new technologies.
Chinsuwan (2024) ³² Thailand	Randomized controlled trial	CE F	85/73	6 months	Epilepsy Care	Unspecified	Unspecified	Pediatric Epilepsy Medication Self‐Management Questionnaire	Patient	Aimed to be child‐friendly and improve epilepsy self‐management. Its features include epilepsy and ASM information, a seizure diary, medication reminders, and emergency contacts for medical personnel.	Impact of app on self‐ management of epilepsy	Application “Epilepsy Care” may serve as an effective adjunctive therapy to enhance self‐management and seizure control in children and adolescents with epilepsy.
Michaelis (2024) ⁴² Germany	Randomized controlled trial	F A	46/46	NA	Helpilepsy	Commercial	Android mobile phone	NA	Patient	Contains an e‐diary where patients can register seizures, set reminders to take their ASM, and record daily their mood and sleep quality.	Feasibility of added function of self‐management sessions	Larger proportion of participants in the intervention group showed an improvement in quality of life; the difference was not statistically significant.
Glynn (2016) ³⁷ United States	Pilot study	F A	8/NA	1 month (20 min/day)	CT	Commercial	iPad tablet only	NA	Patient, Caregiver	Provide cognitive rehabilitation therapies.	Feasibility of CT in patients with absence epilepsy to address cognitive comorbidities	CT was a feasible (among children and parents) mobile intervention in children with absence epilepsy.
Le Marne (2018) ²⁷ Australia	Prospective cohort study	CE U A	36/NA	2.3 months (mean)	EpApp	Institutional proprietary	iPhone, iPad, and Android phone/tablet (all devices/platforms)	Adolescent Knowledge of Epilepsy questionnaire (general epilepsy); SSES‐C and CATIS for psychosocial outcomes	Patient, caregiver	Provide education and facilitate epilepsy management.	Effect of app use on (1) knowledge of epilepsy; (2) self‐efficacy, epilepsy control	Both self and general epilepsy knowledge significantly improved from baseline to follow‐up; no significant improvement from baseline to follow‐up on measures of attitude toward illness or seizure self‐efficacy. Compared to baseline counts, post hoc comparisons revealed fewer medication reminders were required during intervention and follow‐up; no significant effect on seizure burden. Most patients and parents felt that the app was moderately usable and found the information/design useful and acceptable.
Page (2019) ²⁵ United Kingdom	Pre/post study	CE F A	377/NA	12 months	MyCareCentric Epilepsy Patient App; CareFlow Connect	Commercial	iPhone, iPad, and Android phone/tablet (all devices/platforms)	NA	Provider, patient	Patient portal, integrated EHR, social networking, wearable/sensor integration and transmission, clinical communication to care team.	Real‐time reporting of seizures securely to the relevant epilepsy care team, and reporting of admissions to hospital to the care team	Interval between seizure occurrence and notification of the specialist team reduced by 24–48 h compared to similar notifications arising through advice line. Compared to year prior, there was a 30% reduction in admissions for patients with epilepsy and 10% reduction in length of stay. Patients using the technology reported an increased feeling of empowerment. Rate of seizure reporting increased by at most 50%.
Patterson (2022) ²⁴ Pakistan	Pilot study	CE F MV	59/NA	NA	Epilepsy Management Aid	Unspecified	iPhone, iPad, and Android phone/tablet (all devices/platforms)	NA	Provider	Nonphysician health care worker communication with epilepsy specialist for diagnosis.	Timeliness of response to email referrals via the app, appropriate judgment of epilepsy diagnosis by app versus face‐to‐face assessment, effectiveness of app treatment recommendations	Epilepsy management app improved access to timely and efficient care for people with epilepsy. The app had a sensitivity and specificity of 97% in diagnosing epileptic episodes and misdiagnosed these episodes in 6% of cases that were assessed face‐to‐face. 70% of patients reported feeling better after the treatment recommended by the app.
Choi (2021) ³⁹ South Korea	Prospective cohort study	U MV	51/48	90 days	Brain4U	Unspecified	iPhone, iPad, and Android phone/tablet (all devices/platforms)	NA	Patient, caregiver	Seizure diary, medication reminders with adherence data, comorbidity self‐screening tools, personal dashboard, educational material.	Provision of quality data on an individual's seizures, triggers, medication adherence and adverse events, and comorbidities, qualitative feedback for usability of the app	Providers can obtain more precise information about seizure triggers and events by using data from the Brain4U application versus in clinic visits alone. Medication adherence rates were more precise, but more variable recorded in the app versus in clinic. Overall knowledge of epilepsy and self‐efficacy improved for users of the application. Educational content and medication reminder were the most helpful features for caregivers, and medication reminders were the most useful feature for patients.
Yoo (2020) ³⁸ South Korea	Pilot study	U	8/NA	NA	Brain4U	Unspecified	Unspecified	NA	Provider, patient	Seizure diary, medication reminders with adherence data, comorbidity self‐screening tools, personal dashboard, educational material.	Usability in 10 scenarios (log in, basic survey, main, seizure diary [with and without detail], medication, appointments, outpatient survey, physical development, my epilepsy [trend of disorder])	All participants completed six of 10 tasks, but only 37.5% of participants could record detailed seizures. The overall System Usability Score was 84.5, indicating the app scored satisfactorily in usability.
Costa (2021) ⁴⁰ Italy	Prospective cohort study	F A	22/18	NA	KetApp	Institutional proprietary	Unspecified	NA	Patient, caregiver	Ketogenic diet management app for pediatric patients with refractory epilepsy.	Impact on attitude toward treatment, awareness of dietary management, share treatment options with children and others	Compared to non‐app‐users, app users showed better attitudes toward ketogenic diet treatment, improved awareness of ketogenic diet management, and higher likelihood of caregivers helping families select the ketogenic diet.
Mirpuri (2021) ³¹ India	Randomized controlled trial	CE U A	48/48	NA	Epilepsy medication adherence application	Commercial	Unspecified	MGLS, ESES	Provider, patient, caregiver	Medication reminder system, seizure diary, lifestyle checklist, health education, physician's portal, and real‐time progress tracker.	Medication adherence, self‐efficacy, change in seizure frequency	In the mobile application group, there was a 60% increase in the proportion of medication adherence, compared to no significant change in medication adherence in the control group. Self‐efficacy increased in users of the mobile application and did not change in nonusers pre‐ and poststudy. There were no significant changes in seizure frequency as noted by follow‐up interviews.
Ge (2022) ³⁶ United States	Prospective cohort study	U	999/NA	NA	EpiWatch	Institutional proprietary	iPhone only	NA	Patient, caregiver	A paired mobile iPhone app and Apple Watch system aimed to assess characteristics of seizure triggers.	Usability of the app in reporting seizure types and seizure triggers for each tracked seizure	Of the 999 enrolled people with epilepsy, 23.4% (234 participants) tracked seizures and completed postseizure surveys. 211 completed seizure frequency information, which showed 93.3% reporting seizure frequencies of one or more per month. Of those who did not track seizures during the study, fewer permitted access to their HealthKit profile compared to those tracking seizures (20% compared to 65.4%).

Open in a new tab

Abbreviations: A, acceptability; ASM, antiseizure medication; CATIS, Child Attitude Towards Illness Scale; CE, clinical effectiveness; CT, Constant Therapy; EHR, electronic health record; ESES, Epilepsy Self Efficacy Scale; F, feasibility; MGLS, Morisky Green Levine Scale; MV, measurement validity; NA, not applicable; OS, operating system; SSES‐C, Seizure Self‐Efficacy Scale for Children and Adolescents with Epilepsy; U, usability.

3.1.2. Study quality

The median NOQAS score among cohort studies was 5.0 (IQR = 5.0–5.7), and all three studies that utilized the JBI Critical Appraisal Checklist were deemed eligible for inclusion based on consensus agreement. Of the seven randomized studies, four (57.1%) were rated as having some concern for bias utilizing the RoB2 tool. Particularly, three (42.9%) studies had concerns regarding the randomization process, one (14.3%) study had concerns regarding deviations from intended interventions, and one (14.3%) study had concerns regarding missing outcome data.

3.2. Studies of clinical effectiveness

3.2.1. Study charaacteristics

The 12 CE studies ²⁴ , ²⁵ , ²⁶ , ²⁷ , ²⁸ , ²⁹ , ³⁰ , ³¹ , ³² , ³³ , ³⁴ , ³⁵ we identified were published between 2018 and 2024 (shown in Table 3). Of the five (41.7%) CE studies that also investigated FAU, ²⁴ , ²⁵ , ²⁷ , ³¹ , ³² acceptability was the studied metric in four (66.7%). The median study period length and population size were 10.0 months (IQR = 2.3–12.0) and 82 individuals (IQR = 52.5–239), respectively. Development methods were mostly not specified within the studies (n = 7, 58.3%), but when specified, were mostly through institutional proprietary mechanisms (n = 3, 25.0%), whereas joint institutional–commercial ventures (n = 1, 8.3%) were the least frequent (Table 2). Eight (66.7%) studies utilized validated scales to assess CE.

3.2.2. Study quality

Among the three cohort studies, the median NOQAS score was 5.0 (IQR = 5.0–5.5). In two cohort studies, the JBI Critical Appraisal Checklist was used, and the studies were determined to be eligible for inclusion based on consensus. Among five randomized studies, three (60.0%) studies had some concerns for bias and two (40.0%) had low concerns for bias utilizing the RoB2 tool.

3.2.3. CE metrics

We found variability in specific CE metrics, which included medication adherence, self‐efficacy, seizure frequency, postseizure specialist notification time, number of hospital admissions, length of stay, feelings after treatment, sleep impact, and working memory parameters. Main study conclusions with respect to these outcomes are detailed in Table 3. Seizure frequency and medication management were a common focus, as stratified by MARS app targets: symptom reporting (n = 8, 66.7%), medication management (n = 7, 58.3%), physical health (n = 6, 50.0%), and symptom management (n = 6, 50.0%). MARS theoretical backgrounds were most commonly monitoring/tracking (n = 7, 58.3%) and information/education (n = 7, 58.3%).

3.2.4. Geographic location

Originating study countries varied, with two (16.7%) studies being based in each of India, China, and the United States and the remaining studies originating from Pakistan (8.33%), the United Kingdom, Australia, Turkey, Iran, and Thailand (Table 3).

3.3. Studies of FAU

3.3.1. Study characteristics

The 13 FAU ²⁴ , ²⁵ , ²⁷ , ³¹ , ³² , ³⁶ , ³⁷ , ³⁸ , ³⁹ , ⁴⁰ , ⁴¹ , ⁴² , ⁴³ studies were published between 2016 and 2024, of which nine (69.2%) addressed acceptability, five (38.4%) usability, and eight (61.5%) feasibility, although not exclusively of one another (Table 4). The median study duration and population size were 5 months (IQR = 2.65–12.0) and 59 individuals (IQR = 24.0–128.5), respectively. Six (46.2%) apps had unspecified app development, whereas three (23.1%) were developed internally by institutions and commercially, and one (7.7%) was developed via joint institutional–commercial venture.

3.3.2. Study quality

Of the four applicable studies, the median NOQAS score was 5.0 (IQR = 4.2–5.0). The one study using the JBI Critical Appraisal Checklist was deemed eligible for inclusion. The median Silva score among five usability studies was 10.0 (IQR = 9.5–11.0; Table 2). Of the two randomized controlled trials that studied FAU, one demonstrated some concerns for bias utilizing the RoB2 tool.

3.3.3. FAU metrics

MARS app targets were most commonly symptom reporting (n = 9, 69.2%), symptom management (n = 8, 61.5%), and medication management (n = 7, 53.8%). MARS theoretical backgrounds were most commonly monitoring or tracking (n = 10, 76.9%) and information or education (n = 7, 53.8%). Three (23.1%) studied apps were implemented with additional wearable devices, which included the AppleWatch (Apple Corporation) and Band/Band 2 (Microsoft Corporation).

3.3.4. Geographic location

Originating study countries varied, with three (23.1%) studies based in the United States, two in South Korea (15.4%), two in the United Kingdom, and the remainder originating in Pakistan, Australia, Italy, Germany, Thailand, and India (Table 4).

3.4. Comparing studies of CE and FAU

There were several distinctions and similarities in features between the types of apps evaluated by CE and FAU studies, as shown in Table 2. Patients were the most common target user audience in both CE and FAU studies, although a higher proportion of FAU studies targeted patients (n = 12, 92.3%) than CE studies (n = 9, 75%).

3.4.1. MARS App target

Apps that were evaluated in FAU studies more frequently focused on symptom reporting (n = 9, 69.2% vs. n = 8, 66.7% in CE), symptom management (n = 8, 61.5% vs. n = 6, 50% in CE), quality improvement (n = 4, 30.8% vs. n = 2, 16.7% in CE), and enhancement of provider assessments (n = 5, 38.5% vs. n = 2, 16.7% in CE). CE studies more frequently focused on medication management (n = 7, 58.3% vs. n = 7, 53.8% in FAU), physical health (n = 6, 50% vs. n = 6, 46.2% in FAU), care coordination (n = 5, 41.7% vs. n = 3, 23.1% in FAU), and behavior change (n = 4, 33.3% vs. n = 1, 7.7% in FAU).

3.4.2. MARS theoretical background

Apps that were evaluated in FAU studies more frequently used monitoring or tracking (n = 10, 76.9% vs. n = 7, 58.3% in CE) and feedback (n = 2, 15.4% vs. n = 0, 0% in CE). Neither CE nor FAU apps incorporated acceptance commitment therapy, measured patient/caregiver gratitude, or included theoretical background or strategies.

3.4.3. App technical features

Apps in FAU studies more frequently were used to send reminders (n = 10, 76.9% vs. n = 7, 58.3% in CE) and had associated wearable devices (n = 3, 23.1% vs. n = 1, 8.3%). Apps in CE studies more frequently required an internet connection (n = 6, 50% vs. n = 5, 30.8% in FAU).

4. DISCUSSION

In this systematic review, we found 20 studies ²⁴ , ²⁵ , ²⁶ , ²⁷ , ²⁸ , ²⁹ , ³⁰ , ³¹ , ³² , ³³ , ³⁴ , ³⁵ , ³⁶ , ³⁷ , ³⁸ , ³⁹ , ⁴⁰ , ⁴¹ , ⁴² , ⁴³ published between 2016 and 2024 that assessed either the CE or FAU of mobile apps in epilepsy. Although FAU studies were marginally more frequent than CE studies, approximately half of the included FAU studies also evaluated CE as an outcome. Overall, apps evaluated in this systematic review were more often intended for patient use than provider or caregiver use, mostly focused on symptom reporting or medication management, made frequent use of reminder functionality, and were implemented in all mobile operating systems and devices. However, we did find that the included studies demonstrated variability in study CE metrics, originating study countries, and MARS backgrounds and targets for studied apps.

We observed several trends with respect to study designs, settings, and intended user populations. Despite similar study measures of bias and methodological quality across both groups, we found that CE studies more often used randomized controlled trial methodology. As such, future FAU studies of mobile apps in epilepsy may benefit from a randomized controlled trial design, as this approach has been utilized in FAU studies of apps for other conditions such as cancer and Parkinson disease. ⁴⁴ , ⁴⁵

We found that many studies commonly developed or evaluated apps in academic medical center outpatient settings, highlighting the effectiveness of this setting for conducting research and informing future investigative efforts. It is important to note that worldwide, most patients with epilepsy are treated in nonacademic settings, and evidence from resource‐rich countries such as the United States continues to suggest that access to epilepsy specialists demonstrates significant geographic variability. ¹² , ⁴⁶ Our findings therefore emphasize important differences between locations of app evaluation and implementation, ⁴⁷ raising the question of whether the results of such app studies are truly generalizable to real‐world practice settings.

Third, although we found that most studied apps were overall directed toward patient users, apps in FAU studies mostly investigated apps targeting use by both patients and caregivers, perhaps underlining the importance of caregivers in epilepsy care. Although many studies examine perspectives and quality of life in individuals caring for people with epilepsy, ⁴⁸ , ⁴⁹ , ⁵⁰ , ⁵¹ few studies concretely characterize the role that app‐using caregivers can play in the management of epilepsy. Similar to how other studies have explored the potential for mobile apps to reduce caregiver burden in stroke, cancer, or geriatric specialties, ⁵² , ⁵³ , ⁵⁴ the apps identified in this review highlight the benefits of caregiver involvement in the self‐management of epilepsy. This emphasizes a need for studies that more specifically evaluate how caregivers can best utilize mobile apps to assist patients with epilepsy in optimally managing their condition.

Despite fragmented outcomes, the studies of the two most frequently reported CE measures (medication adherence and seizure frequency) generally drew opposing conclusions. Both studies of medication adherence found a positive effect of app usage, ²⁷ , ³¹ whereas only one in three studies concluded that app use reduced seizure frequency. ²⁷ , ³⁰ , ³¹ Coupled with the fragmentation we found in CE outcome measures and the relatively infrequent use of validated clinical assessment scales, these findings suggest that CE appraisals of apps in epilepsy are most likely based on few studies. Additional randomized analyses are necessary to better establish the favorable clinical effectiveness of apps for epilepsy care. Finally, we also found that FAU studies often addressed variable combinations of implementation metrics. ²⁴ , ²⁵ , ²⁷ , ³¹ , ³² , ³⁶ , ³⁷ , ³⁸ , ³⁹ , ⁴⁰ The development and use of standardized scales to measure FAU would allow more consistent reporting of these metrics, which will provide a clearer picture of the capabilities of these apps.

Optimal implementation and usage of epilepsy apps for the benefit of patient care requires a firm understanding of app characteristics. A thorough grasp of mHealth apps and their features can empower clinicians to take an informed approach in tailoring mobile app adoption to individual patients, encourage investigators to pursue formal scientific evaluations of apps, develop optimal approaches to app implementation, and better understand gaps in evidence supporting epilepsy app adoption. For example, a review of MARS characteristics for apps targeting epilepsy self‐management and seizure detection identified medication management and seizure logs as particularly useful functionalities, although patient engagement quality metrics were low overall, thereby raising concerns regarding acceptability and long‐term app usability. ²⁹ Additionally, in response to variable disease self‐management features across multiple epilepsy apps, ¹⁸ some experts have suggested that comprehensive educational and self‐management tools be required in developing such apps. ¹³ , ⁵⁵

Our findings build on prior app feature characterization endeavors by describing apps that have been evaluated for CE and implementation measures in epilepsy. Our results may further support the need for epilepsy app development guidelines ⁵⁶ and frameworks for categorizing apps in the available epilepsy app ecosystem. ⁵⁷ Eventually, these additional outcomes can promote an informed approach to prescribing epilepsy apps by matching individual patient needs and app functionality. As such, physicians and app developers can work to optimize additional dimensions of app utilization that were not evaluated in this study but are crucial to successful use, such as persistence of use (i.e., the likelihood that an app user will continue to use an app after initiation). Similar approaches to app categorization have been taken in psychiatry and mental health. ⁵⁸ Improved classification of epilepsy mobile apps may also improve the way patients, providers, and caregivers parse through available resources for management of epileptic disorders.

Another important finding of our review is the gap between commercial app availability and published studies investigating app use in epilepsy. Whereas a recent review identified 55 commercially available epilepsy apps, our review identified only 20 studies of FAU or CE related to mobile app usage in epilepsy. ¹⁴ Although not all of the apps studied in the current systematic review were commercially available and our inclusion criteria did exclude a number of studies based on outcomes and design, our results suggest that despite wide commercialization of mobile apps in epilepsy, relatively few studies have formally investigated the impact of commercialized app usage on CE or implementation measures. Although not necessarily specific to epileptic disorders, these findings raise the possibility that epilepsy apps may be developed and released without adequate evaluation of their impact on clinical outcomes or their ease of implementation.

Some of our results may be due to the structure of app regulation before commercial release to consumers. In the United States, the US Food and Drug Administration (FDA) regulates apps that function as medical devices but enforces regulatory discretion over others that the agency may consider in a gray area, such as those that supplement clinical care, facilitate self‐management, and improve patient–provider communication. ⁵⁹ Although the presence and type of FDA regulation for epilepsy apps has not been reliably documented, ¹⁴ our findings suggest additional opportunities for investigating the pathway taken by epilepsy apps to market, and whether this oversight should be expanded to better ensure quality interventions and tools for patients.

This study was not without limitations. Our outcomes of interest were broad and sometimes not mutually exclusive, limiting our ability to statistically compare both study groups and draw definitive conclusions, or generalize our results. Because we adapted aspects of the MARS app quality framework to characterize app targets, technical features, and theoretical backgrounds, we may have introduced qualitative judgments that may have also limited generalizability. Another limitation is that we focused on peer reviewed publications, which limited our inclusion of many mobile apps for epilepsy that exist but that were not formally evaluated or may have been tested but never published. Additionally, we found that studies largely did not specify which app features were specifically associated with our outcomes (e.g., high usability, clinical effectiveness). More data are needed to determine which specific components of these apps are associated with high CE and FAU.

5. CONCLUSIONS

In this systematic review of the CE and FAU of mobile apps in epilepsy, we found a range of articles, generally comprising prospective cohort designs derived from academic center clinic populations and presenting moderate bias in randomized studies and fair methodological quality. Among the apps studied, most were intended for patient use, focused on symptom reporting, medication/symptom management, and physical health, and were developed for both major mobile operating system and device implementations. Although most CE studies offered limited generalizability, they identified a positive effect of app usage on medication adherence in epilepsy, as well as an opportunity for additional prospective randomized studies investigating FAU. mHealth has shown promising positive outcomes that could have a marked impact on access to care for people in geographically diverse areas with internet access. Future work toward higher quality studies of app outcomes and improved app categorization will likely enable clinicians and patients to optimally utilize mobile apps in epilepsy care.

AUTHOR CONTRIBUTIONS

Evelyn Gotlieb generated study data, analyzed study data, and drafted and revised the manuscript for key intellectual content. Shahab Marzoughi generated study data, analyzed study data, and revised the manuscript for key intellectual content. Churl‐Su Kwon generated study data and revised the manuscript for key intellectual content. Michael Harmon generated study data and revised the manuscript for key intellectual content. Maren Kimura generated study data, analyzed study data, and revised the manuscript for key intellectual content. Ashley Truesdale analyzed study data and revised the manuscript for key intellectual content. Chloe Sweetnam generated study data and revised the manuscript for key intellectual content. Céline Soudant generated study data, analyzed study data, and revised the manuscript for key intellectual content. Margaret H. Downes generated study data, analyzed study data, and revised the manuscript for key intellectual content. Neil A. Busis revised the manuscript for key intellectual content. Benjamin R. Kummer conceived the study, generated study data, interpreted study data, drafted the manuscript, and revised the manuscript for key intellectual content. Nathalie Jetté conceived the study, generated study data, interpreted study data, and revised the manuscript for key intellectual content.

CONFLICT OF INTEREST STATEMENT

B.R.K. holds equity in and serves as scientific advisor to Syntrillo and has served as a consultant to NeuraHealth, MD Aware, Syapse, AlphaSights, Atheneum, Guidepoint Global, and Gehrson Lehrman Group. NJ receives funding for grants unrelated to this work from NINDS (R01NS130119; 4R00HD106060‐03). She receives an honorarium for her work as an Associate Editor of Epilepsia. The remaining authors have no conflicts of interest. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

Supporting information

File S1.

EPI-66-1349-s002.docx^{(16.5KB, docx)}

File S2.

EPI-66-1349-s001.docx^{(16.9KB, docx)}

ACKNOWLEDGMENTS

None.

Gotlieb E, Marzoughi S, Kwon C‐S, 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;66:1349–1373. 10.1111/epi.18288

Evelyn Gotlieb and Shahab Marzoughi are co‐first authors.

Benjamin R. Kummer and Nathalie Jetté are co‐senior authors.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available in MEDLINE and Embase at https://www.medline.com/ and https://www.embase.com/landing?status=grey, respectively.

REFERENCES

1. Kay M, Santos J, Takane M. mHealth: new horizons for health through mobile technologies. Vol 64. Geneva: World Health Organization; 2011. p. 66–71. [Google Scholar]
2. Rowland SP, Fitzgerald JE, Holme T, Powell J, Mcgregor A. What is the clinical value of mHealth for patients? NPJ Digit Med. 2020;3(1):1–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
3. Pew Research Center . Mobile fact sheet. 2021.
4. Statista . Number of smartphone subscriptions worldwide from 2016 to 2021, with forecasts from 2022 to 2027. 2022.
5. Årsand E, Frøisland DH, Skrøvseth SO, Chomutare T, Tatara N, Hartvigsen G, et al. Mobile health applications to assist patients with diabetes: lessons learned and design implications. J Diabetes Sci Technol. 2012;6(5):1197–1206. [DOI] [PMC free article] [PubMed] [Google Scholar]
6. Lewis J, Ray P, Liaw S‐T. Recent worldwide developments in eHealth and mHealth to more effectively manage cancer and other chronic diseases–a systematic review. Yearb Med Inform. 2016;25(1):93–108. [DOI] [PMC free article] [PubMed] [Google Scholar]
7. Belisario JSM, Huckvale K, Greenfield G, Car J, Gunn LH. Smartphone and tablet self management apps for asthma. Cochrane Database Syst Rev. 2013;11:CD010013. [DOI] [PMC free article] [PubMed] [Google Scholar]
8. Lee J‐A, Choi M, Lee SA, Jiang N. Effective behavioral intervention strategies using mobile health applications for chronic disease management: a systematic review. BMC Med Inform Decis Mak. 2018;18(1):1–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
9. Anderson LM, Leonard S, Jonassaint J, Lunyera J, Bonner M, Shah N. Mobile health intervention for youth with sickle cell disease: impact on adherence, disease knowledge, and quality of life. Pediatr Blood Cancer. 2018;65(8):e27081. [DOI] [PubMed] [Google Scholar]
10. Marrie RA, Leung S, Tyry T, Cutter GR, Fox R, Salter A. Use of eHealth and mHealth technology by persons with multiple sclerosis. Mult Scler Relat Disord. 2019;27:13–19. [DOI] [PubMed] [Google Scholar]
11. World Health Organization . Epilepsy: a public health imperative. Geneva: World Health Organization; 2019. [Google Scholar]
12. Szaflarski M, Wolfe JD, Tobias JGS, Mohamed I, Szaflarski JP. Poverty, insurance, and region as predictors of epilepsy treatment among US adults. Epilepsy Behav. 2020;107:107050. [DOI] [PMC free article] [PubMed] [Google Scholar]
13. Escoffery C, McGee R, Bidwell J, Sims C, Thropp EK, Frazier C, et al. A review of mobile apps for epilepsy self‐management. Epilepsy Behav. 2018;81:62–69. [DOI] [PubMed] [Google Scholar]
14. Abreu M, Carmo AS, Franco A, Parreira S, Vidal B, Costa M, et al. Mobile applications for epilepsy: where are we? Where should we go? A systematic review. Signals. 2022;3(1):40–65. [Google Scholar]
15. Newzoo . Countries/Markets by Smartphone Users and Penetration . 2022.
16. Esmaeili B, Vieluf S, Dworetzky BA, Reinsberger C. The potential of wearable devices and Mobile health applications in the evaluation and treatment of epilepsy. Neurol Clin. 2022;40:729–739. [DOI] [PubMed] [Google Scholar]
17. Ranganathan LN, Chinnadurai SA, Samivel B, Kesavamurthy B, Mehndiratta MM. Application of mobile phones in epilepsy care. Int J Epilepsy. 2015;2(1):28–37. [Google Scholar]
18. Alzamanan MZ, Lim KS, Akmar Ismail M, Abdul Ghani N. Self‐management apps for people with epilepsy: systematic analysis. JMIR Mhealth Uhealth. 2021;9(5):e22489. [DOI] [PMC free article] [PubMed] [Google Scholar]
19. GBD 2015 Neurological Disorders Collaborator Group . Global, regional, and national burden of neurological disorders during 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Neurol. 2017;16(11):877–897. [DOI] [PMC free article] [PubMed] [Google Scholar]
20. Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth. 2015;3(1):e27. [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Penson D, Krishnaswami S, Jules A. Newcastle‐Ottawa quality assessment form for cohort studies. Ottawa: Ottawa Hospital Research Institute; 2012. [Google Scholar]
22. Higgins J. Assessing risk of bias in included studies. Cochrane Handbook for Systematic Reviews of Interventions, version 5.1.0. 2011.
23. Silva AG, Simões P, Santos R, Queirós A, Rocha NP, Rodrigues M. A scale to assess the methodological quality of studies assessing usability of electronic health products and services: Delphi study followed by validity and reliability testing. J Med Internet Res. 2019;21(11):e14829. [DOI] [PMC free article] [PubMed] [Google Scholar]
24. Patterson V, Larik AB, Soomro MZ, Coates S. Validation of an epilepsy management smartphone application in Pakistan. Acta Epileptol. 2022;4(1):1–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
25. Page RA. Technology‐enabled seizure detection and reporting: the epilepsy network project. Epilepsy Res. 2019;153:85–87. [DOI] [PubMed] [Google Scholar]
26. Phakey N, Sharma S, Garg D, Mukherjee SB, Sapra S, Wadhawan AN, et al. Development and evaluation of a working memory intervention kit in children with epilepsy in low‐resource settings. Indian J Pediatr. 2021;88(6):589–592. [DOI] [PubMed] [Google Scholar]
27. Le Marne FA, Butler S, Beavis E, Gill D, Bye AME. EpApp: development and evaluation of a smartphone/tablet app for adolescents with epilepsy. J Clin Neurosci. 2018;50:214–220. [DOI] [PubMed] [Google Scholar]
28. Pandey DK, Dasgupta R, Levy J, Wang H, Serafini A, Habibi M, et al. Enhancing epilepsy self‐management and quality of life for adults with epilepsy with varying social and educational backgrounds using PAUSE to learn your epilepsy. Epilepsy Behav. 2020;111:107228. [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Ahorsu DK, Lin CY, Imani V, Carlbring P, Nygårdh A, Broström A, et al. Testing an app‐based intervention to improve insomnia in patients with epilepsy: a randomized controlled trial. Epilepsy Behav. 2020;112:107371. [DOI] [PubMed] [Google Scholar]
30. Si Y, Xiao X, Xia C, Guo J, Hao Q, Mo Q, et al. Optimising epilepsy management with a smartphone application: a randomised controlled trial. Med J Aust. 2020;212(6):258–262. [DOI] [PubMed] [Google Scholar]
31. Mirpuri P, Chandra PP, Samala R, Agarwal M, Doddamani R, Kaur K, et al. The development and efficacy of a mobile phone application to improve medication adherence for persons with epilepsy in limited resource settings: a preliminary study. Epilepsy Behav. 2021;116:107794. [DOI] [PubMed] [Google Scholar]
32. Chinsuwan P, Wilodjananunt W, Wanvarie D, Suksumek N, Sakpichaisakul K, Simasathien T, et al. Feasibility of mobile phone application “epilepsy care” for self‐management of children and adolescents with epilepsy in Phramongkutklao hospital: a randomized controlled trial. Epilepsy Behav. 2024;151:109598. 10.1016/j.yebeh.2023.109598 [DOI] [PubMed] [Google Scholar]
33. Li W, Gu W, Liang C, Tu F, Ding L, Lu X, et al. Efficacy and safety of a smartphone application‐based treatment of ketogenic diet in pediatric refractory epilepsy. Epilepsia Open. 2024;9(1):278–286. 10.1002/epi4.12867 [DOI] [PMC free article] [PubMed] [Google Scholar]
34. Sayik D, Acikgoz A, Yimenicioglu S. A randomized controlled study: evaluating the efficacy of a mobile application developed for mothers who have children with epilepsy in Turkiye. J Pediatr Nurs. 2023;70:103–110. 10.1016/j.pedn.2023.02.010 [DOI] [PubMed] [Google Scholar]
35. Sepulveda R, Chong J, Shegog R, Martin K, Begley C, Addy R, et al. Experiences of using the MINDSET self‐management mobile health app among Hispanic patients: results of a qualitative study. Epilepsy Behav. 2024;153:109702. 10.1016/j.yebeh.2024.109702 [DOI] [PubMed] [Google Scholar]
36. Ge A, Gutierrez EG, Wook Lee S, Shah S, Carmenate Y, Collard M, et al. Seizure triggers identified postictally using a smart watch reporting system. Epilepsy Behav. 2022;126:108472. [DOI] [PubMed] [Google Scholar]
37. Glynn P, Eom S, Zelko F, Koh S. Feasibility of a mobile cognitive intervention in childhood absence epilepsy. Front Hum Neurosci. 2016;10:575. [DOI] [PMC free article] [PubMed] [Google Scholar]
38. Yoo S, Lim K, Baek H, Jang SK, Hwang GY, Kim H, et al. Developing a mobile epilepsy management application integrated with an electronic health record for effective seizure management. Int J Med Inform. 2020;134:104051. [DOI] [PubMed] [Google Scholar]
39. Choi SA, 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 Behav. 2021;119:107982. [DOI] [PubMed] [Google Scholar]
40. Costa A‐M, Marchiò M, Bruni G, Bernabei SM, Cavalieri S, Bondi M, et al. Evaluation of e‐health applications for paediatric patients with refractory epilepsy and maintained on ketogenic diet. Nutrients. 2021;13(4):1240. [DOI] [PMC free article] [PubMed] [Google Scholar]
41. Biondi A, Simblett SK, Viana PF, Laiou P, Fiori AMG, Nurse E, et al. Feasibility and acceptability of an ultra‐long‐term at‐home EEG monitoring system (EEG@HOME) for people with epilepsy. Epilepsy Behav. 2024;151:109609. 10.1016/j.yebeh.2023.109609 [DOI] [PubMed] [Google Scholar]
42. Michaelis R, Knake S, Rosenow F, Grönheit W, Hamer H, Schmitz B, et al. A multicenter randomized controlled feasibility trial of a digital self‐management intervention for adults with epilepsy. Epilepsia Open. 2024;9(3):1021–1033. 10.1002/epi4.12933 [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Modi AC, Patel AD, Mara CA, Schmidt M, Tenney JR, Stevens J. Pilot randomized controlled clinical trial of an adherence social norms intervention for adolescents with epilepsy. Epilepsy Behav. 2023;140:109082. 10.1016/j.yebeh.2022.109082 [DOI] [PMC free article] [PubMed] [Google Scholar]
44. Kubo A, Kurtovich E, McGinnis MA, Aghaee S, Altschuler A, Quesenberry C Jr, et al. A randomized controlled trial of mHealth mindfulness intervention for cancer patients and informal cancer caregivers: a feasibility study within an integrated health care delivery system. Integr Cancer Ther. 2019;18:1534735419850634. [DOI] [PMC free article] [PubMed] [Google Scholar]
45. Gatsios D, Antonini A, Gentile G, Marcante A, Pellicano C, Macchiusi L, et al. Feasibility and utility of mhealth for the remote monitoring of parkinson disease: ancillary study of the PD_manager randomized controlled trial. JMIR Mhealth Uhealth. 2020;8(6):e16414. [DOI] [PMC free article] [PubMed] [Google Scholar]
46. Schiltz NK, Koroukian SM, Singer ME, Love TE, Kaiboriboon K. Disparities in access to specialized epilepsy care. Epilepsy Res. 2013;107(1–2):172–180. [DOI] [PMC free article] [PubMed] [Google Scholar]
47. Caraballo R, Fejerman N. Management of epilepsy in resource‐limited settings. Epileptic Disord. 2015;17(1):13–18. [DOI] [PubMed] [Google Scholar]
48. Lai S‐T, Tan WY, Wo MCM, Lim KS, Ahmad SB, Tan CT. Burden in caregivers of adults with epilepsy in Asian families. Seizure. 2019;71:132–139. [DOI] [PubMed] [Google Scholar]
49. Saada F, Wang ZS, Bautista RED. In focus: the everyday lives of families of adult individuals with epilepsy. Epilepsy Behav. 2015;50:10–13. [DOI] [PubMed] [Google Scholar]
50. Smith G, Wagner J, Andrews J, Austin J, Mueller M, Carter E, et al. Caregiving in pediatric epilepsy: results of focus groups and implications for research and practice. Epilepsy Behav. 2014;34:34–41. [DOI] [PubMed] [Google Scholar]
51. Asato MR, Manjunath R, Sheth RD, Phelps SJ, Wheless JW, Hovinga CA, et al. Adolescent and caregiver experiences with epilepsy. J Child Neurol. 2009;24(5):562–571. [DOI] [PubMed] [Google Scholar]
52. Garnett A, Northwood M, Ting J, Sangrar R. mHealth interventions to support caregivers of older adults: equity‐focused systematic review. JMIR Aging. 2022;5(3):e33085. [DOI] [PMC free article] [PubMed] [Google Scholar]
53. Lobo EH, Frølich A, Kensing F, Rasmussen LJ, Livingston PM, Grundy J, et al. mHealth applications to support caregiver needs and engagement during stroke recovery: a content review. Res Nurs Health. 2021;44(1):213–225. [DOI] [PubMed] [Google Scholar]
54. Faieta J, Sheehan J, DiGiovine C. Mhealth interventions to improve health and quality of life related outcomes for informal dementia caregivers: a scoping review. Assist Technol. 2022;34(3):362–374. [DOI] [PubMed] [Google Scholar]
55. Pandher PS, Bhullar KK. Smartphone applications for seizure management. Health Informatics J. 2016;22(2):209–220. [DOI] [PubMed] [Google Scholar]
56. 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
57. Paglialonga A, Lugo A, Santoro E. An overview on the emerging area of identification, characterization, and assessment of health apps. J Biomed Inform. 2018;83:97–102. [DOI] [PubMed] [Google Scholar]
58. Huckvale K, Nicholas J, Torous J, Larsen ME. Smartphone apps for the treatment of mental health conditions: status and considerations. Curr Opin Psychol. 2020;36:65–70. [DOI] [PubMed] [Google Scholar]
59. U.S. Food and Drug Administration . Examples of software functions that are not medical devices [Internet]. FDA; 2022. https://www.fda.gov/medical‐devices/device‐software‐functions‐including‐mobile‐medical‐applications/examples‐software‐functions‐are‐not‐medical‐devices [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

File S1.

EPI-66-1349-s002.docx^{(16.5KB, docx)}

File S2.

EPI-66-1349-s001.docx^{(16.9KB, docx)}

Data Availability Statement

The data that support the findings of this study are available in MEDLINE and Embase at https://www.medline.com/ and https://www.embase.com/landing?status=grey, respectively.

[epi18288-bib-0001] 1. Kay M, Santos J, Takane M. mHealth: new horizons for health through mobile technologies. Vol 64. Geneva: World Health Organization; 2011. p. 66–71. [Google Scholar]

[epi18288-bib-0002] 2. Rowland SP, Fitzgerald JE, Holme T, Powell J, Mcgregor A. What is the clinical value of mHealth for patients? NPJ Digit Med. 2020;3(1):1–6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0003] 3. Pew Research Center . Mobile fact sheet. 2021.

[epi18288-bib-0004] 4. Statista . Number of smartphone subscriptions worldwide from 2016 to 2021, with forecasts from 2022 to 2027. 2022.

[epi18288-bib-0005] 5. Årsand E, Frøisland DH, Skrøvseth SO, Chomutare T, Tatara N, Hartvigsen G, et al. Mobile health applications to assist patients with diabetes: lessons learned and design implications. J Diabetes Sci Technol. 2012;6(5):1197–1206. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0006] 6. Lewis J, Ray P, Liaw S‐T. Recent worldwide developments in eHealth and mHealth to more effectively manage cancer and other chronic diseases–a systematic review. Yearb Med Inform. 2016;25(1):93–108. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0007] 7. Belisario JSM, Huckvale K, Greenfield G, Car J, Gunn LH. Smartphone and tablet self management apps for asthma. Cochrane Database Syst Rev. 2013;11:CD010013. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0008] 8. Lee J‐A, Choi M, Lee SA, Jiang N. Effective behavioral intervention strategies using mobile health applications for chronic disease management: a systematic review. BMC Med Inform Decis Mak. 2018;18(1):1–18. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0009] 9. Anderson LM, Leonard S, Jonassaint J, Lunyera J, Bonner M, Shah N. Mobile health intervention for youth with sickle cell disease: impact on adherence, disease knowledge, and quality of life. Pediatr Blood Cancer. 2018;65(8):e27081. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0010] 10. Marrie RA, Leung S, Tyry T, Cutter GR, Fox R, Salter A. Use of eHealth and mHealth technology by persons with multiple sclerosis. Mult Scler Relat Disord. 2019;27:13–19. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0011] 11. World Health Organization . Epilepsy: a public health imperative. Geneva: World Health Organization; 2019. [Google Scholar]

[epi18288-bib-0012] 12. Szaflarski M, Wolfe JD, Tobias JGS, Mohamed I, Szaflarski JP. Poverty, insurance, and region as predictors of epilepsy treatment among US adults. Epilepsy Behav. 2020;107:107050. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0013] 13. Escoffery C, McGee R, Bidwell J, Sims C, Thropp EK, Frazier C, et al. A review of mobile apps for epilepsy self‐management. Epilepsy Behav. 2018;81:62–69. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0014] 14. Abreu M, Carmo AS, Franco A, Parreira S, Vidal B, Costa M, et al. Mobile applications for epilepsy: where are we? Where should we go? A systematic review. Signals. 2022;3(1):40–65. [Google Scholar]

[epi18288-bib-0015] 15. Newzoo . Countries/Markets by Smartphone Users and Penetration . 2022.

[epi18288-bib-0016] 16. Esmaeili B, Vieluf S, Dworetzky BA, Reinsberger C. The potential of wearable devices and Mobile health applications in the evaluation and treatment of epilepsy. Neurol Clin. 2022;40:729–739. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0017] 17. Ranganathan LN, Chinnadurai SA, Samivel B, Kesavamurthy B, Mehndiratta MM. Application of mobile phones in epilepsy care. Int J Epilepsy. 2015;2(1):28–37. [Google Scholar]

[epi18288-bib-0018] 18. Alzamanan MZ, Lim KS, Akmar Ismail M, Abdul Ghani N. Self‐management apps for people with epilepsy: systematic analysis. JMIR Mhealth Uhealth. 2021;9(5):e22489. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0019] 19. GBD 2015 Neurological Disorders Collaborator Group . Global, regional, and national burden of neurological disorders during 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Neurol. 2017;16(11):877–897. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0020] 20. Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth. 2015;3(1):e27. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0021] 21. Penson D, Krishnaswami S, Jules A. Newcastle‐Ottawa quality assessment form for cohort studies. Ottawa: Ottawa Hospital Research Institute; 2012. [Google Scholar]

[epi18288-bib-0022] 22. Higgins J. Assessing risk of bias in included studies. Cochrane Handbook for Systematic Reviews of Interventions, version 5.1.0. 2011.

[epi18288-bib-0023] 23. Silva AG, Simões P, Santos R, Queirós A, Rocha NP, Rodrigues M. A scale to assess the methodological quality of studies assessing usability of electronic health products and services: Delphi study followed by validity and reliability testing. J Med Internet Res. 2019;21(11):e14829. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0024] 24. Patterson V, Larik AB, Soomro MZ, Coates S. Validation of an epilepsy management smartphone application in Pakistan. Acta Epileptol. 2022;4(1):1–6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0025] 25. Page RA. Technology‐enabled seizure detection and reporting: the epilepsy network project. Epilepsy Res. 2019;153:85–87. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0026] 26. Phakey N, Sharma S, Garg D, Mukherjee SB, Sapra S, Wadhawan AN, et al. Development and evaluation of a working memory intervention kit in children with epilepsy in low‐resource settings. Indian J Pediatr. 2021;88(6):589–592. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0027] 27. Le Marne FA, Butler S, Beavis E, Gill D, Bye AME. EpApp: development and evaluation of a smartphone/tablet app for adolescents with epilepsy. J Clin Neurosci. 2018;50:214–220. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0028] 28. Pandey DK, Dasgupta R, Levy J, Wang H, Serafini A, Habibi M, et al. Enhancing epilepsy self‐management and quality of life for adults with epilepsy with varying social and educational backgrounds using PAUSE to learn your epilepsy. Epilepsy Behav. 2020;111:107228. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0029] 29. Ahorsu DK, Lin CY, Imani V, Carlbring P, Nygårdh A, Broström A, et al. Testing an app‐based intervention to improve insomnia in patients with epilepsy: a randomized controlled trial. Epilepsy Behav. 2020;112:107371. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0030] 30. Si Y, Xiao X, Xia C, Guo J, Hao Q, Mo Q, et al. Optimising epilepsy management with a smartphone application: a randomised controlled trial. Med J Aust. 2020;212(6):258–262. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0031] 31. Mirpuri P, Chandra PP, Samala R, Agarwal M, Doddamani R, Kaur K, et al. The development and efficacy of a mobile phone application to improve medication adherence for persons with epilepsy in limited resource settings: a preliminary study. Epilepsy Behav. 2021;116:107794. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0032] 32. Chinsuwan P, Wilodjananunt W, Wanvarie D, Suksumek N, Sakpichaisakul K, Simasathien T, et al. Feasibility of mobile phone application “epilepsy care” for self‐management of children and adolescents with epilepsy in Phramongkutklao hospital: a randomized controlled trial. Epilepsy Behav. 2024;151:109598. 10.1016/j.yebeh.2023.109598 [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0033] 33. Li W, Gu W, Liang C, Tu F, Ding L, Lu X, et al. Efficacy and safety of a smartphone application‐based treatment of ketogenic diet in pediatric refractory epilepsy. Epilepsia Open. 2024;9(1):278–286. 10.1002/epi4.12867 [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0034] 34. Sayik D, Acikgoz A, Yimenicioglu S. A randomized controlled study: evaluating the efficacy of a mobile application developed for mothers who have children with epilepsy in Turkiye. J Pediatr Nurs. 2023;70:103–110. 10.1016/j.pedn.2023.02.010 [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0035] 35. Sepulveda R, Chong J, Shegog R, Martin K, Begley C, Addy R, et al. Experiences of using the MINDSET self‐management mobile health app among Hispanic patients: results of a qualitative study. Epilepsy Behav. 2024;153:109702. 10.1016/j.yebeh.2024.109702 [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0036] 36. Ge A, Gutierrez EG, Wook Lee S, Shah S, Carmenate Y, Collard M, et al. Seizure triggers identified postictally using a smart watch reporting system. Epilepsy Behav. 2022;126:108472. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0037] 37. Glynn P, Eom S, Zelko F, Koh S. Feasibility of a mobile cognitive intervention in childhood absence epilepsy. Front Hum Neurosci. 2016;10:575. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0038] 38. Yoo S, Lim K, Baek H, Jang SK, Hwang GY, Kim H, et al. Developing a mobile epilepsy management application integrated with an electronic health record for effective seizure management. Int J Med Inform. 2020;134:104051. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0039] 39. Choi SA, 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 Behav. 2021;119:107982. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0040] 40. Costa A‐M, Marchiò M, Bruni G, Bernabei SM, Cavalieri S, Bondi M, et al. Evaluation of e‐health applications for paediatric patients with refractory epilepsy and maintained on ketogenic diet. Nutrients. 2021;13(4):1240. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0041] 41. Biondi A, Simblett SK, Viana PF, Laiou P, Fiori AMG, Nurse E, et al. Feasibility and acceptability of an ultra‐long‐term at‐home EEG monitoring system (EEG@HOME) for people with epilepsy. Epilepsy Behav. 2024;151:109609. 10.1016/j.yebeh.2023.109609 [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0042] 42. Michaelis R, Knake S, Rosenow F, Grönheit W, Hamer H, Schmitz B, et al. A multicenter randomized controlled feasibility trial of a digital self‐management intervention for adults with epilepsy. Epilepsia Open. 2024;9(3):1021–1033. 10.1002/epi4.12933 [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0043] 43. Modi AC, Patel AD, Mara CA, Schmidt M, Tenney JR, Stevens J. Pilot randomized controlled clinical trial of an adherence social norms intervention for adolescents with epilepsy. Epilepsy Behav. 2023;140:109082. 10.1016/j.yebeh.2022.109082 [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0044] 44. Kubo A, Kurtovich E, McGinnis MA, Aghaee S, Altschuler A, Quesenberry C Jr, et al. A randomized controlled trial of mHealth mindfulness intervention for cancer patients and informal cancer caregivers: a feasibility study within an integrated health care delivery system. Integr Cancer Ther. 2019;18:1534735419850634. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0045] 45. Gatsios D, Antonini A, Gentile G, Marcante A, Pellicano C, Macchiusi L, et al. Feasibility and utility of mhealth for the remote monitoring of parkinson disease: ancillary study of the PD_manager randomized controlled trial. JMIR Mhealth Uhealth. 2020;8(6):e16414. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0046] 46. Schiltz NK, Koroukian SM, Singer ME, Love TE, Kaiboriboon K. Disparities in access to specialized epilepsy care. Epilepsy Res. 2013;107(1–2):172–180. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0047] 47. Caraballo R, Fejerman N. Management of epilepsy in resource‐limited settings. Epileptic Disord. 2015;17(1):13–18. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0048] 48. Lai S‐T, Tan WY, Wo MCM, Lim KS, Ahmad SB, Tan CT. Burden in caregivers of adults with epilepsy in Asian families. Seizure. 2019;71:132–139. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0049] 49. Saada F, Wang ZS, Bautista RED. In focus: the everyday lives of families of adult individuals with epilepsy. Epilepsy Behav. 2015;50:10–13. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0050] 50. Smith G, Wagner J, Andrews J, Austin J, Mueller M, Carter E, et al. Caregiving in pediatric epilepsy: results of focus groups and implications for research and practice. Epilepsy Behav. 2014;34:34–41. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0051] 51. Asato MR, Manjunath R, Sheth RD, Phelps SJ, Wheless JW, Hovinga CA, et al. Adolescent and caregiver experiences with epilepsy. J Child Neurol. 2009;24(5):562–571. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0052] 52. Garnett A, Northwood M, Ting J, Sangrar R. mHealth interventions to support caregivers of older adults: equity‐focused systematic review. JMIR Aging. 2022;5(3):e33085. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0053] 53. Lobo EH, Frølich A, Kensing F, Rasmussen LJ, Livingston PM, Grundy J, et al. mHealth applications to support caregiver needs and engagement during stroke recovery: a content review. Res Nurs Health. 2021;44(1):213–225. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0054] 54. Faieta J, Sheehan J, DiGiovine C. Mhealth interventions to improve health and quality of life related outcomes for informal dementia caregivers: a scoping review. Assist Technol. 2022;34(3):362–374. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0055] 55. Pandher PS, Bhullar KK. Smartphone applications for seizure management. Health Informatics J. 2016;22(2):209–220. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0056] 56. 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. [DOI] [PMC free article] [PubMed] [Google Scholar]

[epi18288-bib-0057] 57. Paglialonga A, Lugo A, Santoro E. An overview on the emerging area of identification, characterization, and assessment of health apps. J Biomed Inform. 2018;83:97–102. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0058] 58. Huckvale K, Nicholas J, Torous J, Larsen ME. Smartphone apps for the treatment of mental health conditions: status and considerations. Curr Opin Psychol. 2020;36:65–70. [DOI] [PubMed] [Google Scholar]

[epi18288-bib-0059] 59. U.S. Food and Drug Administration . Examples of software functions that are not medical devices [Internet]. FDA; 2022. https://www.fda.gov/medical‐devices/device‐software‐functions‐including‐mobile‐medical‐applications/examples‐software‐functions‐are‐not‐medical‐devices [Google Scholar]

PERMALINK

Clinical effectiveness, feasibility, acceptability, and usability of mobile health applications for epilepsy: A systematic review

Evelyn Gotlieb

Shahab Marzoughi

Churl‐Su Kwon

Michael Harmon

Maren Kimura

Ashley Truesdale

Chloe Sweetnam

Céline Soudant

Margaret H Downes

Neil A Busis

Benjamin R Kummer

Nathalie Jetté

Abstract

Key points.

1. INTRODUCTION

2. MATERIALS AND METHODS

2.1. Literature search strategy and inclusion criteria

2.2. Search strategy and data extraction

2.3. Study quality and assessment of bias

2.4. Statistical analyses

3. RESULTS

3.1. Included studies

FIGURE 1.

3.1.1. Study characteristics

TABLE 1.

TABLE 2.

TABLE 3.

TABLE 4.

3.1.2. Study quality

3.2. Studies of clinical effectiveness

3.2.1. Study charaacteristics

3.2.2. Study quality

3.2.3. CE metrics

3.2.4. Geographic location

3.3. Studies of FAU

3.3.1. Study characteristics

3.3.2. Study quality

3.3.3. FAU metrics

3.3.4. Geographic location

3.4. Comparing studies of CE and FAU

3.4.1. MARS App target

3.4.2. MARS theoretical background

3.4.3. App technical features

4. DISCUSSION

5. CONCLUSIONS

AUTHOR CONTRIBUTIONS

CONFLICT OF INTEREST STATEMENT

Supporting information

ACKNOWLEDGMENTS

DATA AVAILABILITY STATEMENT

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases