Beyond accessibility: co-designing mHealth to bridge the physical activity gap for people with disabilities

Introduction

Physical activity (PA) participation is essential for improving physical fitness and cardiovascular health while reducing the risk of non-communicable diseases (e.g., diabetes, cancer) and mental health conditions such as anxiety and depression in people with disabilities (1-4). However, PA participation levels remain low for people with disabilities (5). Both children and adults with disabilities engage in PA at significantly lower rates than their non-disabled peers (6-8). A recent U.S. study using accelerometer data from 330 adolescents and adults with intellectual and developmental disabilities (IDD) found that these individuals engaged in an average of only 14 minutes of moderate-to-vigorous PA per day (9). Similarly, in the United Kingdom, only 17% of adults with a disability undertook at least one session of exercise per week compared to 40% of adults without a disability (10). This disparity not only affects health outcomes but also reinforces broader inequalities in wellbeing and quality of life among individuals with disabilities who face diverse and intersecting barriers to PA participation.

Mobile health (mHealth) technologies offer a promising strategy to reduce this gap (11). Smartphone applications (or apps) are relatively low-cost, widely accessible, and adaptable, offering opportunities for personalised tracking, feedback, and can help overcome transportation and environmental barriers to PA (12-14). Among emerging mHealth strategies, tele-exercise, defined as interventions delivered via videoconferencing, mobile apps, or web-based platforms that offer physical training such as weightlifting and stretching (15), is deemed a promising area of technology that may encourage sustainable PA participation in people with SCI (16,17). Tele-exercise allows individuals to engage in PA remotely without needing to be physically at a fitness facility (18). This approach offers flexible, accessible, and personalised exercise options that can accommodate diverse populations.

Despite this potential, people with disabilities have not typically been a primary target for mHealth interventions with very few mHealth apps being designed specifically with (i.e., co-produced) and for people with disabilities (19). In this commentary, we explore how inclusively designed mHealth apps can help people with disabilities overcome PA barriers. Using Accessercise and PuzzleWalk as case studies, we highlight how co-production, accessibility, and innovative behavior change strategies can advance PA participation and equity in these populations.

Barriers to PA for people with disabilities

Reduced levels of PA among people with disabilities can likely be explained by the many barriers they experience (20). These barriers are commonly grouped into four categories: (I) physical and environmental, (II) social and attitudinal, (III) informational and technological, and (IV) economic.

Physical and environmental barriers include inaccessible facilities, lack of adaptive equipment, weather conditions (e.g., wind, rain, snow) and transportation difficulties (21-24). They may also involve hill terrain, and rocky paths that for example, making it challenging to manoeuvre a wheelchair over (25-27). Social and attitudinal barriers involve negative attitude/beliefs and discriminatory behavior that contribute to exclusion and discourage participation in PA (22). Information and technological barriers restrict people with disabilities from communicating and obtaining information (28). Information barriers refer to difficulties in accessing information when it is unavailable or irrelevant. Such barriers can be especially consequential to people with disabilities because accessing and utilising information is a basic human right that they are often denied (29). Communication barriers commonly occur due to limited knowledge regarding different styles and alternative modes of communication (e.g., verbal, written, picture, or sign) (30). Economic barriers also play a major role, as the cost of participation, limited government funding, and the inability to afford gym memberships can restrict access to facilities and programs (22,23,31). In addition, the considerable expense of high-end smartphones and the data plans required for the mHealth apps present a barrier, particularly because individuals with disabilities tend to have lower income levels than those without disabilities (32). Despite this barrier, 72% of people with disabilities own a smartphone which can be harnessed for an mHealth app (33). Although activities such as walking in nearby parks or moving around one’s neighbourhood can meaningfully contribute to overall PA levels, individuals with disabilities often demonstrate lower intrinsic motivation to participate in such activities (34,35). They may therefore benefit from tailored and accessible behavior change programs that address their unique needs and ensure safety.

On this basis, these barriers highlight the need for inclusive, tailored, and supportive interventions allowing people with disabilities to participate in regular PA safely and effectively. One promising approach to addressing these barriers is the use of health-based interventions delivered through applications (or apps) on mobile devices, such as smartphones, tables or computers, which is collectively referred to as mHealth.

Why mHealth holds unique promise

With over 3.3 billion users worldwide (36), mHealth technology solutions hold significant potential to support people with disabilities (37). Research highlights that mHealth apps are a promising tool supporting healthy changes in PA (38,39). For individuals with restricted mobility, such as wheelchair users, mHealth apps can help overcome many common barriers to participation in PA (39). mHealth apps can support individuals to perform physical activities remotely in their own homes (14) and help relieve transportation and built environmental barriers to access (22). For individuals with IDD, including autism spectrum disorder and Down syndrome, mHealth apps can help encourage PA by providing structure and engaging users through visual cues and gamified elements (e.g., point systems, leaderboards) (40,41). By leveraging customizable features that support behavior change such as feedback on performance, reminders, and social support, mHealth platforms have potential to deliver tailored experiences for individuals with limited motivation and self-regulation and higher support needs.

There is growing evidence supporting the use of tele-exercise interventions for individuals with disabilities. Longitudinal findings indicate that tele-exercise programs can achieve training loads consistent with moderate-intensity spinal cord injury (SCI)-specific PA guidelines, although vigorous-intensity thresholds may not always be achieved (42). Both synchronous and asynchronous delivery models have demonstrated feasible implementation and comparable training loads (17). In addition, studies involving wheelchair users report similar acute performance responses across remotely delivered training protocols, despite differences in exercise intensity (43). Overall, these findings suggest that tele-exercise represents a viable approach for delivering structured yet autonomous PA opportunities for disability populations.

However, people with disabilities remain underrepresented in mHealth development, with few available apps co-designed with them as end users using community-based participatory research principles (19). This may explain, in part, why there are increasing health care service disparities between people with disabilities and the general population (44). To highlight what inclusive mHealth apps can look like, the next section explores Accessercise and PuzzleWalk, mHealth apps designed specifically to enable individuals with disabilities to overcome barriers and participate meaningfully in PA.

Case study 1: Accessercise Ltd.

Accessercise, founded in 2021, is a novel fitness and healthy living smartphone app created for people with disabilities. Accessercise, co-founded by a paralympic champion and developed together with people who have disabilities, fills a gap in the market and makes PA more accessible and inclusive. The app was developed using an iterative, user-centred development process informed by research, testing and user input to ensure it reflected the needs and experiences of people with disabilities. Accessercise aims to get people with disabilities fit, strong and healthy, offering an asynchronous experience that guides users through performing PA in numerous settings (e.g., gym, home, park) at their own pace and at a convenient time. Inclusive features from the Accessercise application include:

• A video library tailored to participants’ needs and impairments to help illustrate suitable exercises that are demonstrated by a role model with the same impairment.
• A track progress function where users can log workouts, track progress, and meet or exceed their goals.
• An explore section to search a directory of fitness facilities ranked for accessibility.
• A social hub where users can connect with others, share their progress with followers and groups and be part of a diverse, supportive, and passionate community.

The overarching focus of this app is to offer educational knowledge, tracking abilities and behavioral prompts to increase PA among its users (namely, people with physical disabilities). Early evaluations (45,46) show that the app’s customisable, easy-to-navigate design and adapted exercise content make it useable for diverse needs, while features like progress tracking, social support, and goal setting help overcome motivational and environmental barriers.

Case study 2: PuzzleWalk

PuzzleWalk is a theory-driven, gamified mHealth app designed to promote free-living PA among autistic adults (see Figure 1) (41). Grounded in Self-Determination Theory (47), the app integrates key psychological determinants, including autonomy, competence, and relatedness, to enhance motivation and initiate PA behavior change. Its design uniquely leverages visuospatial interaction, a common strength among autistic individuals, through gamified puzzle games. The app takes users to major cities across the world, tracking and converting step counts into gameplay time to incentivize walking behavior, a key gamification element. PuzzleWalk incorporates behavior change features such as self-monitoring of step counts, feedback on performance, goal setting, and contingent rewards to strengthen both intrinsic and extrinsic motivation for PA engagement (48,49). Accessibility and usability are further supported through step-by-step visual guides, simplified interface navigation, and clear visual feedback to accommodate diverse cognitive and sensory needs among autistic adults. PuzzleWalk was developed through an iterative, participatory co-design process with autistic adults, caregivers, and domain experts (50,51) and has demonstrated potential to increase free-living PA and reduce sedentary time in a feasibility trial (49). This theory-driven, user-centered approach highlights PuzzleWalk’s potential as a scalable and sustainable digital tool for promoting PA among broader populations of individuals with IDD.

Figure 1 Functional interfaces of PuzzleWalk app.

Autism is commonly described within the neurodiversity framework as a form of neurodivergence reflecting varying profiles of neurological and behavioral functioning among individuals with autism spectrum disorder (52). Within health research and policy contexts, however, autism is also recognized as a neurodevelopmental disorder that may require varying levels of support needs due to differences in cognitive, social, and behavioral functioning (53). While some autistic individuals may require substantial support associated with co-occurring intellectual disability, deficits in social and spoken communication, or behavioral challenges, others may require little to no support across most domains of daily living.

In the present case study, PuzzleWalk focused on promoting PA behaviors among cognitively able autistic adults, including those with and without mild intellectual disabilities. Nevertheless, the underlying conceptual framework (i.e., gamified behavior change strategies) may also be applicable to broader IDD populations (e.g., Down syndrome) as shown in prior research (41,54).

Principles for inclusive mHealth design

We found several guiding principles to be helpful when co-developing Accessercise and PuzzleWalk alongside people with disabilities. These include the following.

Co-production and participatory design are essential for developing effective interventions. Accessercise and PuzzleWalk were co-produced with individuals with physical and developmental disabilities. Involving users in this way ensures that apps focused on changing behavior are engaging, satisfactory, and useful, aligning with the desires of potential end-users who can express what they want from the early stages of development (55,56).

Accessibility and universal design principles were embedded throughout the development of Accessercise and PuzzleWalk including clear navigation, accessible visuals, and plain language to ensure people with disabilities can use the app confidently. Poor design features, including complex navigation, are poorly accepted by users in real-world settings (57). Additionally, good visuals are crucial as the design (visual display) can significantly impact a user’s experience and their willingness to engage with the app (58). Similarly, plain language improves user experiences, making apps more functional by providing action-oriented information (59).

While co-production is central to developing inclusive mHealth apps, it can present challenges when communication barriers arise or user preferences conflict. For example, visual elements that enhance engagement for deaf users may inadvertently create sensory overload for autistic users. To navigate these challenges, both Accessercise and PuzzleWalk integrated the principles of Universal Design as a framework for creating designs that are inherently more inclusive, flexible, and accessible for all users, reducing the risk of unintended negative impacts while maintaining the benefits of co-production. Universal Design is a design approach in which products and environments are designed to be used by the widest range of individuals possible, regardless of disability, impairment, age, race or gender (60).

To ensure mHealth solutions are inclusive and accessible, the seven principles of Universal Design should be followed (61).

• Equitable use—the design is useful and accessible to people with diverse abilities, ensuring that all users can engage with core features without discrimination or exclusion.
• Flexibility in use—the design accommodates a wide range of individual preferences and abilities, including options for personalisation (e.g., language, notifications, display settings).
• Simple and intuitive use—the interface is easy to understand and navigate, regardless of the user's experience, knowledge, language proficiency or digital literacy.
• Perceptible information—essential information is communicated effectively using multiple formats (e.g., visual, auditory), ensuring accessibility for users with varying sensory abilities.
• Tolerance for error—the design minimizes risks and unintended actions through features including input validation, confirmations, and easy recovery from mistakes (e.g., undo or edit functions).
• Low physical effort—the application can be used comfortably with minimal physical strain, supporting efficient interaction through streamlined workflows and reduced repetitive actions.
• Size and space for approach and use—texts, icons and interactive features are appropriately sizes, well-spaced and scalable to ensure readability, touch accuracy, and easy interaction across devices and for users with visual, motor, or cognitive impairments.

By combining co-production with Universal Design principles, the research team and app developers can better manage communication challenges, reconcile conflicting user needs, and create apps that are genuinely inclusive and accessible.

Personalisation and adaptability were key features of Accessercise and PuzzleWalk allowing users to tailor exercises by impairment type, environment, or available equipment. As personalisation is often lacking in mHealth apps (62), offering such flexibility can enhance engagement and long-term adherence (63).

Social and community features, including Accessercise’s social hub, help facilitate peer interaction, shared experiences, and social accountability. These elements are essential to create social connections in mHealth apps, as they are likely to encourage participation in an activity, and help people with physical and developmental disabilities overcome common challenges such as loneliness, low perceived social support and social isolation (64-66).

Finally, Accessercise encourages users to participate in PA by providing regular feedback (67). Participant feedback and self-monitoring are crucial when attempting to increase PA, allowing users to stay aligned with their goals (69,70). Therefore, implementing feedback on mHealth apps can foster progress towards goals, enhancing self-efficacy (62).

Together, these principles were used to develop Accessercise and PuzzleWalk, making these mHealth apps inclusive, accessible and evidence informed. Through this co-development process, Accessercise and PuzzleWalk are uniquely positioned to address barriers and increase PA among people with physical and developmental disabilities.

Challenges and caveats

Our experience and limited evidence suggest that there are still significant challenges to overcome for ensuring that mHealth apps truly deliver on their inclusive potential. Firstly, only a limited number of mHealth apps have been specifically developed for people with physical and developmental disabilities (19), with this limited availability increasing healthcare service disparities between people with disabilities and the general population (45). The apps that do exist are still in its infancy, with most apps not being fully developed programs (70), are not thorough enough, are not evidence-based, and are of low quality (71,72). Empirical assessments indicate that many of the existing mHealth apps demonstrate poor usability and design quality, often due to limited software development resources or lack of adherence to established design standards (73). For this reason, mHealth apps can experience technical ‘bugs’, be underdeveloped and improperly designed (74,75), with these technical challenges negatively impacting adherence to app-based interventions (76). Most importantly, end-users (e.g., people with disabilities, family members, caregivers, etc.) rarely engage in the development of interventions, which impacts the effectiveness of interventions as people with physical and developmental disabilities are heterogenous, and the needs and preferences of one group may not necessarily be the same for all (77).

The co-design process with individuals with IDD must also take into account their unique and varied communication abilities including the possibility of limited literacy or expressive language (78). Whereas many co-designers with IDD will be able to easily express preferences and opinions for app design, others many acquiesce to what they perceive the research team wants (79), some may have limited language expression (80) which requires accommodation by the research, and some may prefer discrete options rather than abstract concepts when choosing design features (81). Visual displays of app features may be needed to garner feedback, and a skilled facilitator with experience working with the population will ensure that the best input is facilitated. Further, hands-on workshops where app usability can be observed and assessed in real-time will help if expressive communication is limited. On that basis, these challenges with PA mHealth apps for people with physical and developmental disabilities may contribute to lower engagement and increased risk of physical inactivity in this population.

Artificial intelligence (AI), when integrated into mHealth apps, has the potential to enhance accessibility, offers personalised exercise programmes tailored to individual needs and adapts interfaces in real time, promoting active participation and progress tracking (82). AI denotes a computer system possessing intelligence comparable to that of humans (83). Building on these AI capabilities, generative AI can further enable hyper-personalisation by dynamically creating routines and content tailored to individual needs based on user information and behavioral data. However, the integration of AI into mHealth apps raises important questions about data privacy, algorithmic bias, regulatory compliance, and the changing nature of the user-provider relationship (84). mHealth apps collect sensitive information (e.g., name, age, weight, fitness goals) requiring robust data protection measures and complications with regulations like General Data Protection Regulation (GDPR) to maintain trust and prevent data breaches (84). In addition, there is growing concern about the potential for algorithmic bias in AI systems, which could exacerbate existing health disparities. If AI models are not trained on non-representative data sets, they may perform poorly for certain demographic groups, potentially leading to inappropriate health recommendations (84).

The use of AI for health purposes raises important ethical considerations. Informed consent and data ownership are critical issues, as users must understand how their data will be used and retain control over their personal health information (84). Clear policies on data usage, sharing, and ownership are essential to protect users’ rights and maintain trust in AI-powered solutions. On that basis, ongoing collaboration between app developers, healthcare providers, policymakers and end-users to address AI challenges such as data standardisation and ethical AI development is needed.

Future directions and call to action

The way forward requires a collaborative, interdisciplinary, and user-centred approach. Key recommendations include:

• Co-production and personalisation: the development of future mHealth apps should pursue a co-design process, actively engaging end-users from the initial idea through the evaluation, to ensure the mHealth tools are engaging and personalised to the population’s needs and preferences. Apps should also consider personalised features, such as tailoring content to different disability types, environments (e.g. home, gym, outside), or available equipment, as this can increase engagement and long-term adherence.
• Investment and policy support: with smartphones now widely used across the world, funding agencies should prioritise and invest in inclusive digital health solutions, particularly mHealth apps. Integrating these apps into broader public health and rehabilitation strategies can maximise accessibility and impact, which ensures that mHealth apps can benefit a range of populations with diverse health needs.
• Awareness and dissemination: systematic promotion of inclusive mHealth apps can help challenge stereotypes and negative perceptions about disability, while increasing public awareness of accessible PA opportunities. Such efforts may address persistent barriers related to limited visibility, marketing, and dissemination of inclusive PA opportunities.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was a standard submission to the journal. The article has undergone external peer review.

Peer Review File: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-2026-0003/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-2026-0003/coif). A.B. reports serving on the NIH Data Safety Monitoring Board at the University of North Carolina-Chapel Hill as well as receiving the University of Kansas Medical Center Implementation Science for Equity Center Pilot Grant (NIH: U01HD116477, R01 HD116832, KL2TR002367, and R01 AG063909). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Martin Ginis KA, van der Ploeg HP, Foster C, et al. Participation of people living with disabilities in physical activity: a global perspective. Lancet 2021;398:443-55. [Crossref] [PubMed]
2. Bouchard CE, Shephard RJ, Stephens TE. Physical activity, fitness, and health: international proceedings and consensus statement. In: International Consensus Symposium on Physical Activity, Fitness, and Health, 2nd. Toronto: Human Kinetics Publishers; 1994.
3. Heath GW, Fentem PH. Physical activity among persons with disabilities--a public health perspective. Exerc Sport Sci Rev 1997;25:195-234.
4. Battalio SL, Huffman SE, Jensen MP. Longitudinal associations between physical activity, anxiety, and depression in adults with long-term physical disabilities. Health Psychol 2020;39:529-38. [Crossref] [PubMed]
5. Ives B, Clayton B, Brittain I, et al. ‘I’ll always find a perfectly justified reason for not doing it’: challenges for disability sport and physical activity in the United Kingdom. Sport in Society 2021;24:588-606.
6. Sport England. Active Lives Adult Survey November 2022-23 Report. 2024. Available online: https://www.sportengland.org/know-your-audience/data/active-lives
7. Stanish HI, Curtin C, Must A, et al. Does physical activity differ between youth with and without intellectual disabilities? Disabil Health J 2019;12:503-8. [Crossref] [PubMed]
8. Johnson M, Nocera V, Kaushal N, et al. Why we must prioritize equitable access to physical activity for children with disabilities. American College of Sports Medicine. 2021. Available online: https://acsm.org/equitable-access-physical-activity-children-with-disabilities/
9. Helsel BC, Bodde AE, Ptomey LT, et al. Physical Activity Patterns in Adolescents and Adults with Intellectual and Developmental Disabilities. Exerc Sport Mov 2024;2:e00024. [Crossref] [PubMed]
10. Sport England. Active lives survey. 2015-16. Available online: https://www.sportsthinktank.com/research/121363
11. Neuhauser L, Kreps GL. Rethinking Communication in the E-health Era. J Health Psychol 2003;8:7-23. [Crossref] [PubMed]
12. LeBeau K, Huey LG, Hart M. Assessing the Quality of Mobile Apps Used by Occupational Therapists: Evaluation Using the User Version of the Mobile Application Rating Scale. JMIR Mhealth Uhealth 2019;7:e13019. [Crossref] [PubMed]
13. Marco-Ahulló A, Montesinos-Magraner L, González LM, et al. Impact of COVID-19 on the self-reported physical activity of people with complete thoracic spinal cord injury full-time manual wheelchair users. J Spinal Cord Med 2022;45:755-9. [Crossref] [PubMed]
14. Toquero CM. Mobile Healthcare Technology for People with Disabilities amid the COVID-19 pandemic. European Journal of Environment and Public Health 2021;5:em0060.
15. Lai B, Rimmer J, Barstow B, et al. Teleexercise for Persons With Spinal Cord Injury: A Mixed-Methods Feasibility Case Series. JMIR Rehabil Assist Technol 2016;3:e8. [Crossref] [PubMed]
16. Chemtob K, Rocchi M, Arbour-Nicitopoulos K, et al. Using tele-health to enhance motivation, leisure time physical activity, and quality of life in adults with spinal cord injury: A self-determination theory-based pilot randomized control trial. Psychology of Sport and Exercise 2019;43:243-52.
17. Costa RRG, Dorneles JR, Veloso JH, et al. Synchronous and asynchronous tele-exercise during the coronavirus disease 2019 pandemic: Comparisons of implementation and training load in individuals with spinal cord injury. J Telemed Telecare 2023;29:308-17. [Crossref] [PubMed]
18. Garrido ND, Reis VM, Vilaça-Alves JM, et al. Impact of tele-exercise on quality of life, physical fitness, functional capacity and strength in different adult populations: a systematic review of clinical trials. Front Sports Act Living 2025;7:1505826. [Crossref] [PubMed]
19. Jones M, Morris J, Deruyter F. Mobile Healthcare and People with Disabilities: Current State and Future Needs. Int J Environ Res Public Health 2018;15:515. [Crossref] [PubMed]
20. Wilroy J, Lai B, Currie M, et al. Teleassessments for Enrollment of Adults With Physical or Mobility Disability in a Home-Based Exercise Trial in Response to COVID-19: Usability Study. JMIR Form Res 2021;5:e29799. [Crossref] [PubMed]
21. Derakhshan P, Miller WC, Bundon A, et al. Adaptive outdoor physical activities for adults with mobility disability: a scoping review. Front Rehabil Sci 2023;4:1331971. [Crossref] [PubMed]
22. Martin Ginis KA, Ma JK, Latimer-Cheung AE, et al. A systematic review of review articles addressing factors related to physical activity participation among children and adults with physical disabilities. Health Psychol Rev 2016;10:478-94. [Crossref] [PubMed]
23. Rimmer JH, Riley B, Wang E, et al. Physical activity participation among persons with disabilities: barriers and facilitators. American Journal of Preventive Medicine 2004;26:419-25.
24. Williams TL, Smith B, Papathomas A. The barriers, benefits and facilitators of leisure time physical activity among people with spinal cord injury: a meta-synthesis of qualitative findings. Health Psychology Review 2014;8:404-25.
25. Seligman, M, Darling R. Ordinary families, special children: A systems approach to childhood disability. Guilford Press; 2009.
26. Cruz-Cunha MM, Miranda IM, Goncalves P. Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care. Medical Information Science Reference. 2013.
27. Michopoulou E, Hilton SJ. Accessibility to Spa Experiences. In: Eusébio C, Teixeira L, Carneiro MJ. editors. ICT Tools and Applications for Accessible Tourism. IGI Global; 2021:146-68.
28. Randle M, Dolnicar S. Enabling people with impairments to use Airbnb. Annals of Tourism Research 2019;76:278-89.
29. Omekwu CO, Nwafor OO. Availability and use of information and communication technology resources for people with disabilities in Nnamdi Azikiwe Library, University of Nigeria, Nsukka. Journal of Applied Information Science and Technology 2014;7:26-37.
30. Hashemi G, Wickenden M, Bright T, et al. Barriers to accessing primary healthcare services for people with disabilities in low and middle-income countries, a Meta-synthesis of qualitative studies. Disabil Rehabil 2022;44:1207-20. [Crossref] [PubMed]
31. Mulligan HF, Hale LA, Whitehead L, et al. Barriers to physical activity for people with long-term neurological conditions: a review study. Adapt Phys Activ Q 2012;29:243-65. [Crossref] [PubMed]
32. Walls RT, Dowler DL. Disability and income. Rehabilitation Counseling Bulletin 2015;58:146-53.
33. Perrin A. Americans with disabilities less likely than those without to own some digital devices. Pew Research Center. 2021. Available online: https://www.pewresearch.org/short-reads/2021/09/10/americans-with-disabilities-less-likely-than-those-without-to-own-some-digital-devices/
34. Bossink LWM, van der Putten AA, Vlaskamp C. Understanding low levels of physical activity in people with intellectual disabilities: A systematic review to identify barriers and facilitators. Res Dev Disabil 2017;68:95-110. [Crossref] [PubMed]
35. Watson PK, Stendell L, Quel de Oliveira C, et al. Leisure-time physical activity motives and perceived gains for individuals with spinal cord injury. Spinal Cord 2024;62:546-52. [Crossref] [PubMed]
36. Montag C, Wegmann E, Sariyska R, et al. How to overcome taxonomical problems in the study of Internet use disorders and what to do with "smartphone addiction"? J Behav Addict 2021;9:908-14. [Crossref] [PubMed]
37. DeRuyter F, Jones M, Morris J. Mobile health apps and needs of people with disabilities: a national survey. Journal on Technology and Persons with Disabilities 2018;2018:149-61.
38. Vandelanotte C, Müller AM, Short CE, et al. Past, Present, and Future of eHealth and mHealth Research to Improve Physical Activity and Dietary Behaviors. J Nutr Educ Behav 2016;48:219-228.e1. [Crossref] [PubMed]
39. Hoevenaars D, Holla JFM, Te Loo L, et al. Mobile App (WHEELS) to Promote a Healthy Lifestyle in Wheelchair Users With Spinal Cord Injury or Lower Limb Amputation: Usability and Feasibility Study. JMIR Form Res 2021;5:e24909. [Crossref] [PubMed]
40. Lee D, Frey GC, Shih PC. Gamified Mobile Health Strategies for Promoting Physical Activity in Autistic Adults. Exerc Sport Sci Rev 2025;53:68-76. [Crossref] [PubMed]
41. Mahmoudi E, Yejong Yoo P, Chandra A, et al. Gamification in Mobile Apps for Children With Disabilities: Scoping Review. JMIR Serious Games 2024;12:e49029. [Crossref] [PubMed]
42. Gomes Costa RR, Dorneles JR, Veloso JHCL, et al. Does tele-exercise training for tetraplegia meet the spinal cord injury-specific physical activity guidelines? A 7-month longitudinal study. J Telemed Telecare 2025;31:338-46.
43. Ribeiro Neto F, Costa Bezerra GH, Xavier Couto de Oliveira R, et al. Effects of different training methods in tele-exercise on acute performance decrement in adults with disability and wheelchair users. Disabil Rehabil Assist Technol 2024;19:2491-7. [Crossref] [PubMed]
44. Singh K, Drouin K, Newmark LP, et al. Many Mobile Health Apps Target High-Need, High-Cost Populations, But Gaps Remain. Health Aff (Millwood) 2016;35:2310-8. [Crossref] [PubMed]
45. Haley JA, Rhind DJA, Maidment DW. Applying the behaviour change wheel to assess the theoretical underpinning of a novel smartphone application to increase physical activity in adults with spinal cord injuries. Mhealth 2023;9:10. [Crossref] [PubMed]
46. Haley JA, Rhind DJA, Maidment DW. Assessing the usability of Accessercise to increase physical activity in adults with physical disabilities: A qualitative think-aloud study. PLoS One 2025;20:e0321109. [Crossref] [PubMed]
47. Deci EL, Ryan RM. Self-determination theory: A macrotheory of human motivation, development, and health. Canadian psychology/Psychologie canadienne 2008;49:182-5.
48. Lee D, Frey G, Cheng A, et al. Puzzle walk: A gamified mobile app to increase physical activity in adults with autism spectrum disorder. In: 2018 10th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games). IEEE: 2018:1-4.
49. Lee D, Frey GC, Cothran DJ, et al. Effects of a Gamified, Behavior Change Technique-Based Mobile App on Increasing Physical Activity and Reducing Anxiety in Adults With Autism Spectrum Disorder: Feasibility Randomized Controlled Trial. JMIR Form Res 2022;6:e35701. [Crossref] [PubMed]
50. Lee D, McNulty L, Kathiravan S, et al. A Gamified mHealth App to Promote Physical Activity and Reduce Sedentary Behavior in Autistic Adults: Protocol for a Remotely Delivered Pilot Intervention Study. JMIR Res Protoc 2025;14:e71631. [Crossref] [PubMed]
51. Kim B, Lee D, Min A, et al. PuzzleWalk: A theory-driven iterative design inquiry of a mobile game for promoting physical activity in adults with autism spectrum disorder. PLoS One 2020;15:e0237966. [Crossref] [PubMed]
52. Dwyer P. The Neurodiversity Approach(es): What Are They and What Do They Mean for Researchers? Hum Dev 2022;66:73-92. [Crossref] [PubMed]
53. Hodges H, Fealko C, Soares N. Autism spectrum disorder: definition, epidemiology, causes, and clinical evaluation. Transl Pediatr 2020;9:S55-65. [Crossref] [PubMed]
54. Johnson RW, White BK, Gucciardi DF, et al. Intervention Mapping of a Gamified Therapy Prescription App for Children With Disabilities: User-Centered Design Approach. JMIR Pediatr Parent 2022;5:e34588. [Crossref] [PubMed]
55. Martin A, Caon M, Adorni F, et al. A mobile phone intervention to improve obesity-related health behaviors of adolescents across Europe: iterative co-design and feasibility study. JMIR Mhealth Uhealth 2020;8:e14118. [Crossref] [PubMed]
56. Bolster EAM, van Gessel C, Welten M, et al. Using a Co-design Approach to Create Tools to Facilitate Physical Activity in Children With Physical Disabilities. Front Rehabil Sci 2021;2:707612. [Crossref] [PubMed]
57. Ting YL. The pitfalls of mobile devices in learning: A different view and implications for pedagogical design. Journal of Educational Computing Research 2012;46:119-34.
58. Lazard AJ, Brennen JS, Belina SP. App designs and interactive features to increase mHealth adoption: user expectation survey and experiment. JMIR Mhealth Uhealth 2021;9:e29815. [Crossref] [PubMed]
59. Broderick J, Devine T, Langhans E, et al. Designing health literate mobile apps. NAM Perspectives 2014. Available online: https://nam.edu/wp-content/uploads/2015/06/HealthLiterateApps.pdf
60. Sanford JA. Universal design as a rehabilitation strategy: Design for the ages. Springer Publishing Company; 2012.
61. Mace R. What is universal design. The Center for Universal Design at North Carolina State University 1997;19:2004. Available online: https://design.ncsu.edu/research/center-for-universal-design/
62. Cavallo FR, Toumazou C. Personalised lifestyle recommendations for type 2 diabetes: Design and simulation of a recommender system on UK Biobank Data. PLOS Digit Health 2023;2:e0000333. [Crossref] [PubMed]
63. Jakob R, Harperink S, Rudolf AM, et al. Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review. J Med Internet Res 2022;24:e35371. [Crossref] [PubMed]
64. Steinhubl SR, Muse ED, Topol EJ. The emerging field of mobile health. Sci Transl Med 2015;7:283rv3. [Crossref] [PubMed]
65. Peever N, Vella K, Johnson D, et al. Understanding initial experiences with Mindmax, an mHealth app that draws on shared interests in sports and video games. In: Proceedings of the 29th Australian Conference on Computer-Human Interaction. Paper presented at OzCHI '17: 29th Australian Conference on Human-Computer Interaction. 2017:438-42.
66. Emerson E, Fortune N, Llewellyn G, et al. Loneliness, social support, social isolation and wellbeing among working age adults with and without disability: Cross-sectional study. Disabil Health J 2021;14:100965. [Crossref] [PubMed]
67. Tong HL, Coiera E, Laranjo L. Using a Mobile Social Networking App to Promote Physical Activity: A Qualitative Study of Users' Perspectives. J Med Internet Res 2018;20:e11439. [Crossref] [PubMed]
68. Fjeldsoe B, Neuhaus M, Winkler E, et al. Systematic review of maintenance of behavior change following physical activity and dietary interventions. Health Psychol 2011;30:99-109. [Crossref] [PubMed]
69. Voth EC, Oelke ND, Jung ME. A Theory-Based Exercise App to Enhance Exercise Adherence: A Pilot Study. JMIR Mhealth Uhealth 2016;4:e62. [Crossref] [PubMed]
70. Seçkin G. Digitized emotions in the pocket: social computing and mobile mental health (mMH) applications. In: Emotions, technology, and health. Academic Press; 2016:41-61.
71. Oh H, Rizo C, Enkin M, et al. What is eHealth (3): a systematic review of published definitions. J Med Internet Res 2005;7:e1. [Crossref] [PubMed]
72. Domnich A, Arata L, Amicizia D, et al. Development and validation of the Italian version of the Mobile Application Rating Scale and its generalisability to apps targeting primary prevention. BMC Med Inform Decis Mak 2016;16:83. [Crossref] [PubMed]
73. Martínez-Pérez B, de la Torre-Díez I, López-Coronado M. Experiences and Results of Applying Tools for Assessing the Quality of a mHealth App Named Heartkeeper. J Med Syst 2015;39:142. [Crossref] [PubMed]
74. Paschou M, Sakkopoulos E, Tsakalidis A. easyHealthApps: e-Health Apps dynamic generation for smartphones & tablets. J Med Syst 2013;37:9951. [Crossref] [PubMed]
75. de la Torre-Díez I, Martínez-Pérez B, López-Coronado M, et al. Decision support systems and applications in ophthalmology: literature and commercial review focused on mobile apps. J Med Syst 2015;39:174. [Crossref] [PubMed]
76. Bentley CL, Powell L, Potter S, et al. The Use of a Smartphone App and an Activity Tracker to Promote Physical Activity in the Management of Chronic Obstructive Pulmonary Disease: Randomized Controlled Feasibility Study. JMIR Mhealth Uhealth 2020;8:e16203. [Crossref] [PubMed]
77. Ma JK, Ginis KAM. A meta-analysis of physical activity interventions in people with physical disabilities: content, characteristics, and effects on behaviour. Psychology of Sport and Exercise 2018;37:262-73.
78. van Calis JFE, Naaldenberg J, van der Cruijsen AWC, et al. Inclusive digital platforms: Designing for and with users with mild intellectual disabilities or low literacy skills. Computers in Human Behavior Reports 2025;17:100617.
79. Matikka LM, Vesala HT. Acquiescence in quality-of-life interviews with adults who have mental retardation. Ment Retard 1997;35:75-82. [Crossref] [PubMed]
80. Friedman C, Spassiani NA. Making survey research accessible for people with intellectual and developmental disabilities. In: The Palgrave Encyclopedia of Disability. Cham: Springer Nature Switzerland; 2024:1-10.
81. Schalock RL, Luckasson R, Tassé MJ. An Overview of Intellectual Disability: Definition, Diagnosis, Classification, and Systems of Supports (12th ed.). Am J Intellect Dev Disabil 2021;126:439-42.
82. Rosa JPP. The potential role of artificial intelligence to promote the participation and inclusion in physical exercise and sports for people with disabilities: A narrative review. J Bodyw Mov Ther 2025;42:127-31. [Crossref] [PubMed]
83. Russell S, Norvig P, Intelligence A. A modern approach. Artificial Intelligence. Englewood Cliffs, NJ, USA: Prentice-Hall; 1995. Available online: https://people.engr.tamu.edu/guni/csce625/slides/AI.pdf
84. Muthineni SR. AI in mobile health apps: transforming chronic disease management. International Journal of Scientific Research in Computer Science Engineering and Information Technology 2025;11:108-16.