1) How do rehabilitation professionals in Nigeria perceive the use of telerehabilitation in their clinical practice?

2) What factors influence the willingness of rehabilitation professionals in Nigeria to adopt telerehabilitation services?

This study employs a quantitative cross-sectional survey design to assess the acceptance and use of telerehabilitation among rehabilitation professionals.

The target population for this study comprised rehabilitation professionals registered to practice in Nigeria, including physiotherapists, occupational therapists, speech-language therapists, and audiologists. Participants were recruited using a non-probability convenience sampling approach through an official WhatsApp group managed by the professional regulatory body, which includes registered rehabilitation professionals nationwide. This platform enabled broad dissemination of the survey and facilitated participation from professionals practicing in both urban and rural settings. A total of 100 respondents completed the survey. While the recruitment platform included multiple rehabilitation disciplines, the final sample consisted predominantly of physiotherapists and Speech Language Therapists, with fewer responses from other professional groups.

Data was collected using a structured online questionnaire designed to assess key variables relevant to telerehabilitation adoption. The questionnaire consisted of several sections. The first section gathered demographic information, including respondents’ age, gender, professional background, and location of practice. Subsequent sections focused on constructs from the Unified Theory of Acceptance and Use of Technology (UTAUT).

Perceived Usefulness (PU) items assessed whether respondents believed that telerehabilitation improves the accessibility, efficiency, and overall quality of rehabilitation services. Perceived Ease of Use (PEOU) items evaluated how user-friendly telerehabilitation technologies were perceived to be, including the ease of integration into existing clinical routines.

Social Influence (SI) was measured by exploring the extent to which colleagues, peers, and professional bodies influenced or encouraged the adoption of telerehabilitation. Facilitating Conditions (FC) assessed the availability of resources, infrastructure, and institutional support necessary for effective implementation.

Lastly, Behavioral Intention (BI) items measured the likelihood that respondents would adopt and integrate telerehabilitation into their clinical practice.

Data for the study was collected using a structured online questionnaire developed based on the Unified Theory of Acceptance and Use of Technology (UTAUT). The questionnaire was created using Google Forms and designed to capture responses on demographic variables as well as the key UTAUT constructs: Perceived Usefulness, Perceived Ease of Use, Social Influence, Facilitating Conditions, and Behavioral Intention to use telerehabilitation.

To reach the target population of rehabilitation professionals across Nigeria, the survey link was distributed through a professional WhatsApp group managed by the Medical Rehabilitation Therapists Board of Nigeria (MRTB), the official regulatory body responsible for licensing and maintaining the register of physiotherapists, occupational therapists, speech-language therapists, and other allied professionals in the country. This platform was used due to its wide reach and direct engagement with practicing professionals.

An introductory message accompanied the survey link, clearly outlining the purpose of the study, ensuring participants of confidentiality, and requesting informed consent. Participation was voluntary, and respondents indicated their consent before proceeding with the questionnaire. The use of WhatsApp as a dissemination channel ensured easy access and convenience, facilitating participation across various regions, including both urban and rural locations.

Data analysis was conducted using the Statistical Package for the Social Sciences (SPSS). The analysis included descriptive statistics such as means, frequencies, and standard deviations to summarize the demographic characteristics and responses to survey items. To assess the internal consistency of the measurement instruments, reliability testing was carried out using Cronbach’s Alpha. Furthermore, regression analysis was employed to identify the key factors that predict the adoption of telerehabilitation and other interacting variables.

This study was considered minimal risk and therefore did not require formal ethical approval from the Rehabilitation Therapists Board. However, it adhered strictly to recognized ethical standards. Participation was entirely voluntary, and respondents were assured of confidentiality and anonymity. Prior to accessing the questionnaire, participants were presented with an electronic information sheet outlining the study’s purpose, procedures, and their rights. Informed consent was explicitly obtained electronically, with participants required to indicate agreement before proceeding to complete the survey.

A total of 100 rehabilitation professionals participated in the study. The gender distribution was nearly equal, with 52% male and 48% female respondents. The age distribution showed the largest proportion (40%) in the 30 - 39 years group, followed by 30% aged 40 - 49 years, 20% aged 20 - 29 years, and 10% aged 50 years and above.

Professionally, the sample comprised 60% physiotherapists, 25% occupational therapists, and 15% speech-language therapists. Regarding geographic distribution, 68% of participants worked in urban areas, while 32% were based in rural settings.

The reliability of the UTAUT constructs was assessed using Cronbach’s Alpha. All constructs demonstrated good internal consistency, with values above the acceptable threshold of 0.70. This indicates that the survey items were reliable measures of their respective constructs.

The Cronbach’s Alpha Values for UTAUT Constructs are presented in Table 1.

Table 1. Cronbach’s alpha values for UTAUT constructs.

As shown inTable 2 below, participants reported a generally positive perception across all UTAUT constructs. The highest mean score was recorded for Behavioral Intention (BI) to use telerehabilitation (M = 4.28, SD = 0.87), indicating strong willingness among rehabilitation professionals to adopt the technology. This was followed by Perceived Usefulness (PU) (M = 4.04, SD = 0.99) and Perceived Ease of Use (PEOU) (M = 3.97, SD = 0.81), suggesting favorable attitudes toward both the utility and usability of telerehabilitation. Social Influence (SI) showed a moderate mean score (M = 3.82, SD = 0.91), reflecting a fair level of peer or societal support. In contrast, Facilitating Conditions (FC) had the lowest mean score (M = 3.29, SD = 0.86), suggesting that participants perceived relatively lower availability of resources and institutional support for effective implementation.

The Descriptive Statistics for UTAUT Constructs are presented in Table 2.

Table 2. Descriptive statistics for UTAUT constructs.

Pearson’s correlation analysis showed that all UTAUT constructs were positively associated with Behavioral Intention (BI). The strongest relationships were with Perceived Usefulness (PU) and Perceived Ease of Use (PEOU), supporting their relevance in predicting intention to adopt telerehabilitation.

The Correlation Matrix of UTAUT Constructs are presented in Table 3.

Table 3. Correlation matrix of UTAUT constructs.

A multiple regression model was conducted to identify which UTAUT constructs significantly predicted Behavioral Intention (BI). The overall model was statistically significant:

F (4, 95) = 5.41, p = .002, R² = 0.389

Among the constructs, only Perceived Usefulness (PU) was a significant predictor of Behavioral Intention.

The Regression Analysis Predicting Behavioral Intention are presented in Table 4.

Table 4. Regression analysis predicting behavioral intention.

The Gender Differences in Behavioral Intention and UTAUT Constructs are presented in Table 5.

Table 5. Gender differences in behavioral intention and UTAUT constructs.

There are no statistically significant gender differences were found in any of the UTAUT constructs or in behavioral intention to use telerehabilitation. Males and females perceive telerehabilitation similarly

The Differences in UTAUT Constructs and Behavioral Intention by Location are presented in Table 6.

Table 6. Differences in UTAUT constructs and behavioral intention by location.

There were no statistically significant differences between urban and rural respondents in behavioral intention to use telerehabilitation, or in most UTAUT constructs. However, urban participants rated Perceived Usefulness significantly higher than their rural counterparts. Furthermore, location moderated the relationship between Perceived Usefulness and Behavioral Intention, suggesting that perceptions of usefulness had a stronger influence on intention among urban professionals.

The Differences in UTAUT Constructs and Behavioral Intention by Age Group are presented in Table 7.

Table 7. Differences in UTAUT constructs and behavioral intention by age group.

There were no statistically significant differences across age groups in any of the UTAUT constructs or in behavioral intention to use telerehabilitation. Respondents aged 20 - 29, 30 - 39, 40 - 49, and 50+ all reported similar perceptions of usefulness, ease of use, social influence, facilitating conditions, and intention to adopt telerehabilitation.

The Differences in UTAUT Constructs and Behavioral Intention by Profession are presented in Table 8.

Table 8. Differences in UTAUT constructs and behavioral intention by profession.

There were no statistically significant differences across professional groups in any of the UTAUT constructs or in behavioral intention to use telerehabilitation. Physiotherapists, occupational therapists, and speech-language therapists all reported similar perceptions of usefulness, ease of use, social influence, facilitating conditions, and intention to adopt telerehabilitation.

This study examined the acceptance of telerehabilitation among rehabilitation professionals in Nigeria using the Unified Theory of Acceptance and Use of Technology (UTAUT) as the guiding framework. The results reveal that perceived usefulness (PU) emerged as the strongest and only statistically significant predictor of behavioral intention (BI) to use telerehabilitation. This aligns with findings from prior studies in both high- and low-resource settings, where PU has consistently been identified as a central determinant of technology adoption in healthcare contexts [13][18]. Specifically, in resource-constrained environments such as Nigeria, the potential for telerehabilitation to mitigate barriers related to geographical access, time, and workforce shortages may enhance its perceived value among practitioners, echoing similar conclusions from studies conducted in comparable low- and middle-income countries (LMICs) [19].

Although, perceived ease of use (PEOU) showed moderate correlation with behavioral intention, it did not reach statistical significance in the regression model. This is consistent with some prior research indicating that while ease of use may influence user satisfaction and initial impressions, it becomes less critical when users are convinced of the tool’s utility [20]. This trend suggests that in professional environments, particularly within clinical settings, the emphasis is often placed on the functional benefits of technology rather than its usability alone. Similarly, social influence (SI) and facilitating conditions (FC) did not significantly predict behavioral intention, a finding that diverges from several studies conducted in more structured or technologically advanced health systems [21][22], where institutional endorsement and peer influence are often strong enablers of digital health adoption. One plausible explanation for the limited influence of SI and FC in this study may be the relative infancy of telerehabilitation integration within Nigerian health institutions. In contexts where policy support, training programs, and organizational infrastructure are limited or inconsistent, professionals may not perceive these external factors as relevant to their decision-making. FC recorded the lowest mean among UTAUT constructs suggesting that participants were aware of infrastructural inadequacies, it however did not significantly predict intention. This paradox has been observed in prior studies [23] and may reflect a pragmatic willingness among professionals to adopt telerehabilitation even in suboptimal conditions, provided they believe in its efficacy.

Importantly, this finding carries critical implications for health system managers and policymakers. While professionals may demonstrate high intention to adopt telerehabilitation, sustained implementation is unlikely to succeed if institutional support does not follow. Inadequate facilitating conditions may lead to professional frustration, increased workload burden, reduced quality of care, and eventual abandonment of telerehabilitation initiatives despite initial enthusiasm. Health system managers should therefore interpret high behavioral intention as a window of opportunity that must be matched with investments in infrastructure, training, technical support, and clear organizational policies. Without such alignment, the mismatch between professional willingness and system readiness may result in implementation failure and diminished trust in future digital health interventions.

Beyond system-level considerations, it is also important to explore whether individual characteristics influence telerehabilitation acceptance. Contrary to some previous literature which suggests that demographic factors such as age, gender, and profession may moderate technology acceptance [24][25], this study found no statistically significant differences across these variables. This homogeneity could suggest a shared professional disposition toward innovation, possibly due to common training backgrounds or exposure levels among rehabilitation professionals in Nigeria. Practice location however, did play a moderating role as urban-based professionals reported significantly higher levels of perceived usefulness than their rural counterparts. This is consistent with studies indicating that urban health workers often have better exposure to digital infrastructure, peer networks, and training opportunities, which shape more favorable attitudes toward digital tools [25].

There were no significant differences across professional groups including physiotherapists, occupational therapists, and speech-language therapists. This could be partly attributed to a skewed sample composition as the majority of respondents were physiotherapists, with very limited responses from other rehabilitation professionals despite their inclusion in the study population. This presents a limitation in assessing the true generalizability of the results across all rehabilitation professions. It also raises concerns about professional engagement with digital health discussions.

Overall, this study offers important contributions to the discourse on digital health adoption in LMICs, especially in under-researched fields such as rehabilitation. The UTAUT model proved valuable in highlighting the core motivators and inhibitors of telerehabilitation acceptance, although its applicability may be influenced by contextual factors such as infrastructural readiness, policy direction, and professional exposure. As the health system in Nigeria continues to explore digital health integration, targeted strategies that focus on demonstrating the clinical value of telerehabilitation, improving infrastructural support, and encouraging interprofessional collaboration may be most effective in promoting its uptake.

This study offers valuable insights into the acceptance of telerehabilitation among rehabilitation professionals in Nigeria; however, it is not without limitations. First, the sample size of 100 participants, while informative, is relatively modest and may not fully represent the broader population of rehabilitation professionals in the country. Although efforts were made to ensure geographic and professional diversity, the voluntary nature of participation and the use of WhatsApp groups for recruitment may have introduced self-selection bias, potentially attracting individuals who are more familiar or receptive to digital health technologies. Furthermore, despite the questionnaire being shared in a professional group comprising various rehabilitation disciplines, responses were predominantly from physiotherapists. This limited representation restricts the generalizability of the findings across other rehabilitation professions such as occupational therapists, speech-language pathologists, and orthotists/prosthetists who were underrepresented or did not respond.

Second, the cross-sectional design limits the ability to infer causality between the UTAUT constructs and behavioral intention. Longitudinal studies would be necessary to explore how perceptions and intentions evolve over time with increased exposure to and institutional support for telerehabilitation. Additionally, data collection relied solely on self-reported measures, which are susceptible to social desirability bias and recall inaccuracies. Participants may have overestimated their willingness to adopt telerehabilitation or their perceptions of available infrastructure. Additionally, while the UTAUT model offers a robust theoretical framework, it may not fully capture other relevant contextual factors such as organizational culture, regulatory support, and prior exposure to telehealth platforms, all of which could influence actual adoption behavior. Additionally, the study focused on behavioral intention without assessing actual usage. Future research should incorporate objective measures of adoption and implementation outcomes to better understand the transition from intention to practice