The research team surveyed the university community between October 2024 and April 2025, with questionnaire data collected in January 2025. Participants completed three validated instruments in a single standardized questionnaire. Physical activity during the preceding week was assessed with the International Physical Activity Questionnaire Short Form, which captures walking, moderate-intensity activity, vigorous-intensity activity, and sitting time, and converts reported durations into metabolic equivalent of task minutes per week, with walking weighted at 3.3 METs, moderate activity at 4.0, and vigorous activity at 8.0. Quality of life was measured with the EQ-5D-5L, which rates mobility, self-care, usual activities, pain and discomfort, and anxiety and depression across five severity levels, alongside the EuroQol Visual Analog Scale, on which participants score their overall health from 0 to 100. Perceived barriers were assessed with the 21-item Barriers to Being Active Quiz, covering seven domains from lack of time to fear of injury.

The sample skewed young and heavily academic. Of the 353 participants analyzed, 288 were students, 40 were faculty, and 25 were administrative staff. Most were aged 18 to 24 years, and 57.2 percent were female. Pre-existing physical conditions were reported by 15.3 percent and pre-existing mental health conditions by 8.5 percent. Moderate physical activity was the most frequently reported category, at 43.6 percent, followed by high activity at 28.6 percent and low activity at 27.8 percent. The median weekly volume was 1,618.5 MET-minutes, though the range stretched from zero to 15,750, and 47 participants reported no activity at all. Among the 217 participants who provided sitting-time data, the mean was 423.2 minutes per day, roughly seven hours, with a median of 360 minutes and reported values reaching 1,200 minutes per day.

In multivariable regression models adjusted for age, gender, nationality, education level, university role, marital status, monthly income, and pre-existing physical and mental health conditions, physical activity level was not significantly associated with either quality-of-life outcome. Compared with low activity, high physical activity was associated with an EQ-VAS coefficient of 4.055 points, but the bias-corrected and accelerated 95 percent confidence interval spanned from −2.082 to 10.426, with a p-value of 0.197. For the EQ-5D-5L index score, the coefficient for high versus low activity was −0.005, with a confidence interval from −0.035 to 0.028 and a p-value of 0.824. Moderate activity showed no significant association either. Sitting time, analyzed separately, was also not significantly associated with either outcome, and analyses of the five individual EQ-5D-5L dimensions showed no significant links to activity level. A fractional logistic sensitivity analysis, conducted because the index score is bounded at 1.0 and concentrated there, produced the same qualitative conclusion.

What the models did detect was the association of underlying health status with perceived health. A pre-existing mental health condition was associated with an EQ-VAS score lower by 11.891 points, with a confidence interval from −22.157 to −2.653 and a p-value of 0.010, and with an index score lower by 0.088, p = 0.001. Pre-existing physical conditions were associated with lower index scores by 0.042, p = 0.013. In a population where 39.1 percent of participants reported the full-health state and held the maximum index score of 1.000, these conditions stood out as the clearest correlates of how people perceived their own health. The authors note that this pattern aligns with conceptual models in which physical functioning, psychological health, symptoms, and everyday functioning interact to shape an individual’s overall perception of well-being, rather than lifestyle behavior operating in isolation.

The activity data revealed demographic patterns. In the adjusted ordinal logistic regression, female participants had higher odds of being in a lower physical activity category than males, with an adjusted odds ratio of 1.897 and a confidence interval from 1.267 to 2.840, p = 0.002. Saudi participants, by contrast, had lower odds of being in a lower activity category than non-Saudi participants, with an adjusted odds ratio of 0.589, p = 0.009. A supplementary analysis of whether participants achieved at least 150 minutes of weekly activity found that women had lower odds than men of reaching the threshold, with an adjusted odds ratio of 0.452, and participants aged 35 to 44 had lower odds than those aged 18 to 24, with an adjusted odds ratio of 0.050. Age, education, role, income, and health conditions were not significantly associated with activity category after adjustment.

The barriers data may be the study’s most immediately applicable contribution. Lack of willpower was reported by 67 percent of participants, lack of energy by 66 percent, and lack of time by 65 percent, with lack of resources at 43 percent and social influence at 42 percent, while lack of skill at 24 percent and fear of injury at 16 percent trailed behind. Each one-category increase in physical activity level was associated with lower lack-of-willpower scores, with a coefficient of −0.711, and lower lack-of-time scores, with a coefficient of −0.608, both with p-values below 0.001 after adjustment for age, gender, and university role. The authors are careful about direction here: fewer perceived barriers may facilitate greater activity, or regularly active people may simply perceive and manage such barriers differently. Either way, the pattern flags potentially modifiable factors for university wellness programs.

Why did physical activity fail to show the association often reported elsewhere? The authors point to several features of the sample. Participants were largely young, reported generally favorable quality of life, with a mean EQ-VAS of 78.9 and a mean index score of 0.928, and most were moderately or highly active. The substantial ceiling effect, with nearly four in ten participants at the maximum index score, may have limited the instrument’s ability to distinguish differences among healthier participants. The cross-sectional design, self-reported activity measures vulnerable to recall and social desirability bias, convenience sampling that precluded calculating a response rate, and an English-only questionnaire all constrain interpretation. The authors also stress that the absence of a statistically significant association should not be read as evidence that physical activity is unrelated to quality of life; the confidence intervals do not rule out potentially relevant associations, and the established benefits of regular activity in the broader literature remain intact.

The implications reach beyond one campus. The authors suggest that university health promotion strategies may benefit from integrating physical activity initiatives with mental health support and interventions targeting the behavioral barriers of time, energy, and motivation, rather than assuming that activity programs alone will lift perceived well-being. Such an approach aligns with Saudi Arabia’s objectives under Vision 2030, which emphasize active lifestyles, prevention of noncommunicable diseases, and population well-being. The team calls for longitudinal designs to clarify the temporal relationship between activity and quality of life, objective assessment with accelerometers or wearable devices to complement self-reports, and multicenter studies across more diverse university populations. Until such evidence accumulates, the findings from Riyadh suggest that in a young academic community, perceived health may reflect a combination of psychological, physical, and behavioral influences rather than physical activity level alone.

Subject of Research: Physical activity, health-related quality of life, and perceived barriers among a Saudi university community

Article Title: Physical activity, health-related quality of life, and perceived barriers among the Alfaisal University Community in Riyadh, Saudi Arabia: a cross-sectional study

Article References: Alsouss, Y. O., Ameer, O. Z., Alfaraj, G. A., Cahusac, P. M. B., Al Sharif, A. I., & Alhusseini, N. (2026). Physical activity, health-related quality of life, and perceived barriers among the Alfaisal University Community in Riyadh, Saudi Arabia: a cross-sectional study. Frontiers in Public Health, 14, Article 1999364. https://doi.org/10.3389/fpubh.2026.1999364

Image Credits: AI Generated

DOI: 10.3389/fpubh.2026.1999364

Keywords: physical activity, health-related quality of life, EQ-5D-5L, IPAQ-SF, perceived barriers, mental health, university population, Saudi Arabia, cross-sectional study, public health, Physical, activity

The study arrives at a moment when AI adoption in higher education has become one of the most intensively researched topics in educational technology. Bibliometric analyses show an explosion of publications on generative AI in universities, and systematic reviews have catalogued factors influencing student readiness, from technological self-efficacy to prior digital literacy. Yet the authors note a conspicuous gap: most of this evidence comes from urban, well-resourced institutions in North America, Europe and East Asia, or from major Indian metropolitan centers. Rural contexts, where infrastructure is thinner and digital divides run deeper, remain empirically underexplored. India’s own census classifications define rural areas in ways that shape access to electricity, broadband and institutional funding, making the rural-urban distinction far more than a demographic label. It is a structural boundary condition for technology adoption.

To probe this boundary, the team employed a quantitative survey design targeting rural higher education students in Odisha. Data were collected from 111 students using a ten-item instrument built on a Likert scale, with each item probing a different facet of perceived barriers. The researchers organized these facets into five conceptual clusters: access constraints, meaning unreliable connectivity and inadequate devices; knowledge gaps, meaning insufficient training in how AI systems work; attitudinal resistance, meaning skepticism or discomfort toward AI; cognitive concerns, meaning worries about overreliance and the erosion of independent thinking; and ethical apprehensions, meaning anxieties about privacy, bias and academic integrity. This multidimensional structure matters because treating AI barriers as a single blob obscures the different levers policymakers might pull to address them.

The psychometric quality of the instrument was strong. The scale achieved a Cronbach’s alpha of 0.912, a measure of internal consistency that indicates the ten items reliably tapped a coherent underlying construct of perceived barriers. Values above 0.9 are generally considered excellent in psychometric theory, lending credibility to the composite scores the researchers analyzed. Rather than relying solely on classical parametric statistics, which can be fragile with modest sample sizes and ordinal Likert data, the team used non-parametric tests for group comparisons and reported effect sizes alongside p-values. This dual reporting is important because statistical significance in a small sample can be misleading without a sense of how large the differences actually are.

One of the most methodologically notable features of the study is its handling of the multiple comparisons problem. When researchers test many items across many demographic groups simultaneously, the probability of finding at least one spurious statistically significant result inflates rapidly. To guard against this, the authors applied the Benjamini-Hochberg procedure, a widely used technique that controls the false discovery rate, the expected proportion of false positives among all declared significant findings. This correction is often skipped in educational survey research, which means many published group differences may not survive scrutiny. By applying it here, the Odisha team set a higher evidentiary bar, and the results they report are more trustworthy as a consequence.

What did the corrected analyses reveal? In short, demographic segmentation largely dissolved. Gender, age, field of study and family type did not demonstrate statistically robust differences in perceived barriers after the false discovery rate correction, and the effect sizes were generally small, indicating negligible practical differences between groups even where nominal differences appeared. The one demographic variable that showed any differentiation was academic level, and even there the signal was limited and marginal. It surfaced as a single item-level difference and a small but statistically significant difference in the composite summary index, suggesting that students at different stages of their programs may experience slightly different intensities of barrier, perhaps reflecting growing familiarity with academic technology demands as students progress.

The interpretive weight of the study rests on what this null-heavy pattern means. The authors conclude that perceived barriers to AI integration in rural higher education are shaped more by shared contextual conditions than by demographic characteristics. In other words, a female undergraduate in the humanities and a male postgraduate in the sciences at the same rural institution are likely to report similar obstacles: the same patchy internet, the same lack of formal AI training, the same institutional culture of limited exposure. This finding aligns with a broader literature on digital divides, which has increasingly emphasized that structural and situational factors, rather than individual attributes alone, determine how people experience and adopt new technologies. It also echoes work on low socioeconomic digitalization, where citizens’ difficulties with digital services were traced to situated conditions rather than personal deficits.

The theoretical scaffolding of the study draws on the technology acceptance tradition. Fred Davis’s Technology Acceptance Model, introduced in the 1980s, proposed that perceived usefulness and perceived ease of use govern whether people embrace new information systems, and the later Unified Theory of Acceptance and Use of Technology extended this framework with additional moderators. Studies applying these models to AI chatbots and educational AI have repeatedly found that prior AI experience and digital literacy moderate acceptance. The Odisha findings add a rural inflection to this tradition: when the environment supplies little prior exposure and little infrastructure, the usual demographic moderators that animate urban studies may be swamped by the sheer weight of shared disadvantage. The barrier profile becomes a property of the setting, not of the population within it.

For policymakers and university administrators, the practical implication is that institution-wide strategies, not targeted demographic interventions, are the appropriate response in rural settings. If barriers are common across gender, age and discipline, then programs aimed narrowly at, say, women students or science majors would miss the bulk of the problem. Instead, the authors underscore the importance of strategies that operate at the level of the whole institution: improving connectivity and device access, embedding AI literacy across curricula, addressing attitudinal and ethical concerns through transparent institutional policies, and normalizing critical engagement with generative AI in classrooms. Related research on pedagogical approaches to generative AI has argued that fostering critical engagement, rather than mere technical skill, is key to preparing students, and the rural context makes this doubly urgent because students there often lack informal exposure that urban peers acquire organically.

The study’s authors are candid about its preliminary nature. A sample of 111 students from one Indian state cannot settle questions about AI adoption everywhere, and self-reported perceptions are not the same as observed usage behavior. The composite summary index difference by academic level hints at dynamics that larger, longitudinal designs could unpack, tracking whether barriers attenuate as AI tools proliferate or harden into durable inequalities. The dataset and instrument are available from the corresponding author upon reasonable request, enabling replication. Still, the core contribution stands: rare context-specific empirical evidence from rural higher education, gathered with unusually careful statistics, showing that the AI divide in places like Odisha is not a mosaic of demographic micro-divides but a single shared landscape of constraint. Bridging it will require acting on the landscape itself, not on the demographic variations that cross it.

Subject of Research: Perceived barriers to artificial intelligence adoption among rural higher education students in Odisha, India

Article Title: Perceived barriers to adoption of artificial intelligence among rural higher education students

Article References: Satapathy, D., Divya, V. S., Pradhan, D. K., & Nayak, A. K. (2026). Perceived barriers to adoption of artificial intelligence among rural higher education students. SN Social Sciences, 6(10), Article 500. https://doi.org/10.1007/s43545-026-01801-1

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01801-1

Keywords: artificial intelligence, higher education, rural students, digital divide, Odisha, AI adoption, technology acceptance, survey research, perceived barriers, educational technology, India, Benjamini-Hochberg