The study, published in Frontiers in Physiology, recruited ninety competitive runners aged fourteen to eighteen from regional athletics clubs and schools in Tamil Nadu, India. All were experienced middle-distance athletes competing in events from 800 meters to 3000 meters, with at least two years of structured training and a current volume of four or more sessions per week. The researchers randomly assigned thirty participants each to an inspiratory muscle training group, a plyometric training group, or a control group that continued normal running routines. Eighty-six participants, or 95.6 percent, completed the full twelve-week protocol and were included in the primary analysis.

The inspiratory muscle training protocol was demanding and precisely dosed. Using threshold devices that provide consistent resistance throughout the inspiratory range of motion, athletes performed thirty inspiratory efforts twice daily, five days per week, amounting to three hundred efforts weekly. Training intensity began at 30 percent of each athlete’s maximal inspiratory pressure in the first week and progressed by five percentage points weekly, reaching 75 percent by week ten and holding at 80 percent through week twelve. Weekly assessments of maximal inspiratory pressure allowed the load to be adjusted as the respiratory muscles strengthened, applying the same progressive-overload principle that weightlifters use for skeletal muscle.

The plyometric group, by contrast, trained three times per week in supervised sessions lasting forty-five to sixty minutes. Their program included depth jumps, box jumps, horizontal bounds, reactive jumps, and medicine ball throws, with training volume ranging from eighty to one hundred twenty foot contacts per session. Intensity progressed from bilateral movements to unilateral exercises while drop heights and reactive components increased, all designed to enhance the stretch-shortening cycle that underpins explosive athletic movement. Certified strength and conditioning specialists supervised every session, while the inspiratory training group’s home sessions from week three onward were monitored through adherence logs and weekly check-ins.

When the researchers measured lung function at baseline and again after twelve weeks, the differences between the groups were striking. The inspiratory training group improved forced expiratory volume in one second from 4.12 to 4.45 liters, an 8 percent gain, and forced vital capacity from 4.89 to 5.28 liters, also an 8 percent increase. Peak expiratory flow rose from 628 to 672 liters per minute. The plyometric group showed more modest but still significant improvements, with forced expiratory volume rising about 4.2 percent. The control group remained essentially unchanged across all measures. The most dramatic effect appeared in maximal inspiratory pressure, the direct measure of inspiratory muscle strength, which surged by 38 centimeters of water in the inspiratory training group, an effect size of 2.25 that the authors classified as very large, compared with only 12 centimeters of water in the plyometric group.

Importantly, the ratio of forced expiratory volume to forced vital capacity remained stable in all three groups. This preservation indicates that the improvements came from enhanced respiratory muscle strength rather than any change in airway caliber, a reassuring safety signal given concerns about exercise-induced bronchoconstriction in young athletes. Neither training modality induced pathological airway changes, and no serious adverse events were reported throughout the trial.

The most novel dimension of the study lay in its use of pupillometry. In a darkened room after five minutes of dark adaptation, each athlete received a standardized light stimulus to the right eye while a portable dynamic pupillometer recorded pupil diameter from both eyes at thirty hertz. The pupillary light reflex is governed by the autonomic nervous system: constriction velocity and maximum constriction percentage are associated with parasympathetic responsiveness, while re-dilation velocity has been linked to sympathetic function, though it is also influenced by parasympathetic withdrawal and measurement conditions. No previous study had examined how these reflex dynamics respond to inspiratory muscle or plyometric training in youth athletes.

The results revealed what the authors describe as distinct autonomic signatures. The inspiratory training group showed a 17.3 percent increase in constriction velocity, rising from 5.2 to 6.1 millimeters per second, along with a 10.5 percent increase in maximal constriction percentage. The plyometric group, meanwhile, displayed the largest improvement in re-dilation velocity, a 22.6 percent increase from 3.1 to 3.8 millimeters per second. The researchers propose that the inspiratory group’s faster constriction may reflect enhanced parasympathetic responsiveness, potentially arising from strengthened respiratory muscles reducing the work of breathing and its associated sympathetic activation, or from repetitive inspiratory resistance loading activating mechanoreceptors in the chest wall. The plyometric group’s faster re-dilation, conversely, may represent adaptive sympathetic responsiveness to the repeated high-intensity stretch-shortening cycles that demand rapid neural activation for force production.

Exploratory correlation analyses added another layer of intrigue. Changes in maximal inspiratory pressure were moderately associated with changes in pupillary constriction velocity across the pooled sample, and changes in forced expiratory volume correlated with changes in maximal constriction percentage. The authors caution that these correlations, computed on change scores across all three groups, may partly reflect between-group separation rather than pure within-participant physiological coupling, and they remain hypothesis-generating rather than proof of a mechanistic link between respiratory strength and autonomic regulation.

The authors are careful about the limits of their findings. Running performance was not assessed, so the results must be interpreted as physiological rather than performance evidence. The trial was not prospectively registered, participants and trainers could not be blinded, and the complete-case analysis excluded four withdrawn participants rather than imputing their data. Only forced expiratory volume was used in the sample-size calculation, making the remaining outcomes exploratory despite Bonferroni-corrected testing. Factors known to influence pupillary responses, such as sleep, screen exposure, and stress, were not systematically documented, and no formal pubertal maturation staging was performed across the fourteen-to-eighteen age range.

Nevertheless, the study represents the first randomized controlled trial to directly compare these two conditioning modalities on both pulmonary function and autonomic markers in youth middle-distance runners. The 8 percent gains in forced expiratory volume and vital capacity exceed the typical 2 to 3 percent year-over-year improvements attributable to natural adolescent growth, consistent with a genuine training effect. The authors suggest that a periodized approach combining both modalities might prove optimal: foundational inspiratory training during general preparation phases to build respiratory muscle strength, concurrent training during specific preparation, and maintenance-level inspiratory work during competition while emphasizing plyometric and speed work. Whether such combined programs deliver synergistic benefits, and whether the pupillary signatures observed here predict competitive performance or overtraining risk, remain questions for future trials. For now, the message for young runners and their coaches is clear: the muscles that power every breath respond to targeted training with remarkable specificity, and the eyes may reveal how the nervous system adapts alongside them.

Subject of Research: Comparative effects of inspiratory muscle training and plyometric training on pulmonary function and pupillary light reflex in youth middle-distance runners

Article Title: Effects of twelve-week inspiratory muscle vs. plyometric training on pulmonary function and pupillary light reflex in youth middle-distance runners—a three-arm randomized controlled trial

Article References: W., V., Tejaswi, J., Azeezul Kabeer, D., Karmakar, D., Kambitta Valappil, I. N., Parpa, K., Katanic, B., Geantă, V. A., Vveinhardt, J., & Govindasamy, K. (2026). Effects of twelve-week inspiratory muscle vs. plyometric training on pulmonary function and pupillary light reflex in youth middle-distance runners—a three-arm randomized controlled trial. Frontiers in Physiology, 17, Article 1781766. https://doi.org/10.3389/fphys.2026.1781766

Image Credits: AI Generated

DOI: 10.3389/fphys.2026.1781766

Keywords: inspiratory muscle training, plyometric training, pulmonary function, pupillary light reflex, autonomic nervous system, youth athletes, middle-distance running, spirometry, maximal inspiratory pressure, randomized controlled trial, exercise physiology, adolescent athletes

The clinical backdrop explains why the work matters. Autism spectrum disorder affects a substantial and growing share of children worldwide, yet the gold-standard diagnostic pathway still depends on behavioral observation and caregiver interviews, most notably instruments such as the Modified Checklist for Autism in Toddlers, or M-CHAT, and the Autism Diagnostic Observation Schedule-2, known as ADOS-2. These tools are effective in trained hands, but they are slow, expensive, and heavily dependent on the child’s willingness and ability to cooperate, something that is notoriously difficult in toddlers, the very age group in which early intervention delivers its greatest benefit. In resource-limited settings, where specialist clinicians are scarce and waiting lists stretch for years, many children reach school age without ever receiving a formal diagnosis. The research team, led by Juan Flores and Andrea E. Montero of Universidad Peruana Cayetano Heredia in Lima, together with collaborators at Johns Hopkins Bloomberg School of Public Health, Yale Child Study Center, and other institutions, set out to test whether a biological measurement that requires almost nothing from the child could help close that gap.

Their approach rests on a well-established body of prior evidence. Earlier studies had already reported atypical PLR characteristics in individuals with autism, including abnormal transient pupillary responses documented as far back as 2009, and enhanced pupillary light reflex in infants who were later diagnosed with autism in toddlerhood, a finding reported in Nature Communications in 2018. A systematic review and meta-analysis published in 2021 concluded that pupillometry shows consistent group differences between autistic and non-autistic individuals, though with heterogeneous methods across studies. What remained missing was a practical, low-cost pipeline that could capture and quantify the reflex in very young children using nothing more exotic than a video camera and careful image processing, and then test whether the resulting numbers could discriminate diagnosed children from typically developing ones in a real clinical screening context.

The study enrolled 30 children between the ages of two and five: 14 with a confirmed diagnosis of autism spectrum disorder, three of them girls, and 16 typically developing peers, ten of them girls. The diagnoses were not taken at face value. Each child in the autism group was evaluated with the M-CHAT screening questionnaire and the ADOS-2, the observational assessment widely regarded as a clinical reference standard, to confirm that the classification was sound. The pupillary testing itself was deliberately minimal. Researchers recorded five-second videos of each child’s eyes while delivering a controlled light stimulus, a protocol short enough that even a restless toddler could tolerate it. From those recordings, the team extracted three quantitative features of the reflex: the degree of pupillary constriction, meaning how much the pupil shrank in response to the light; the latency, meaning how long it took for constriction to begin; and the time to maximum contraction, meaning how quickly the pupil reached its tightest point.

The image-processing pipeline behind those measurements is where the engineering becomes interesting. Extracting a reliable pupil diameter from video frames of a moving child’s face is a nontrivial computer vision problem. The researchers drew on classical techniques that are foundational to the field, including the Canny edge detection algorithm for locating boundaries in each frame, direct least-squares ellipse fitting for modeling the pupil’s shape, the Viola-Jones cascade framework for detecting facial regions, and RANSAC-based robust model fitting to reject outlier measurements caused by blinks, head movements, or partial occlusion. Signal-processing steps, including Savitzky-Golay smoothing of the diameter time series, cleaned up the raw traces before the characteristic parameters of the reflex were computed. The result is a system that converts a short video clip into a compact set of numbers describing the dynamics of an involuntary neural reflex, without requiring any specialized pupillometer hardware beyond a camera and a light source.

The headline result concerns the degree of constriction. Children with autism showed a significantly greater degree of pupillary constriction in response to the light stimulus than their typically developing peers, a difference that reached statistical significance in the quantitative analysis. The other two features, latency and time to maximum contraction, were shorter in the autism group on average, but these differences rose only to the level of statistical trends rather than definitive effects. The direction of the finding is consistent with prior work suggesting enhanced or exaggerated pupillary light responses in autistic children, which some investigators have linked to atypical functioning of the locus coeruleus-norepinephrine system, the brain’s principal arousal circuitry. If the pupil of a child with autism clamps down harder and faster under the same light, that overshoot may reflect a nervous system tuned differently, and potentially measurable, long before behavioral symptoms can be reliably scored by a clinician.

To test whether those differences could translate into an actual screening decision, the team built an exploratory logistic regression model using the pupillary features as predictors of diagnostic status. The in-sample performance was remarkable: the model achieved a sensitivity of 93 percent, meaning it correctly identified nearly all of the children with autism, and a specificity of 86 percent, meaning it correctly cleared most of the typically developing children. The area under the receiver operating characteristic curve, a standard summary of discriminative power that ranges from 0.5, equivalent to a coin flip, to 1.0, perfect classification, came in at 0.93. Those are numbers that, if they held up in the real world, would place pupillometry among the more accurate screening tools available for early autism detection.

But the authors are careful, and rightly so, about what those numbers do and do not mean. The performance estimates were obtained without external validation or cross-validation, meaning the model was evaluated on the same 30 children from which it was built. In machine learning terms, the model may be overfitted to this particular sample, and its true performance on new, unseen children from different populations could be considerably lower. The sample was also small, with only 14 autistic children, and skewed toward girls in the typically developing group, which limits how broadly the findings can be generalized. These caveats do not undermine the proof of concept, but they mark the distance between a promising pilot and a deployable clinical tool. Larger, preregistered studies with independent validation cohorts, ideally across multiple sites and demographic groups, are the necessary next step before any claim of diagnostic utility can be made responsibly.

Even with those limitations, the practical appeal of the approach is hard to overstate. Pupillometry demands almost no cooperation from the child, unlike behavioral assessments that require sustained social engagement, which is precisely the capacity that autism affects. The equipment is inexpensive, the capture takes seconds, and the analysis can be automated, making the technique attractive for primary care clinics, community health posts, and low-resource settings where traditional diagnostic facilities are scarce. The researchers frame it as a complement to, not a replacement for, comprehensive clinical evaluation: a fast, objective first filter that could flag children who need full diagnostic workup, shortening the path from first concern to intervention during the developmental window when therapy has the greatest impact. For a condition in which every month of delayed diagnosis matters, a five-second video of a toddler’s eye responding to a flash of light is an unusually elegant place to start.

Subject of Research: Pupillary light reflex measurement as a potential biological screening tool for autism spectrum disorder in young children

Article Title: Analysis of the Pupillary Light Reflex Response Against Light Stimuli for Autism Screening in Children

Article References: Flores, J., Montero, A. E., Barrientos, F., Zavaleta, R., Dioses, A., Noazin, S., Powers, M., Gilman, R. H., Roman-Gonzalez, A., & Zimic, M. (2026). Analysis of the Pupillary Light Reflex Response Against Light Stimuli for Autism Screening in Children. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-026-07501-2

Image Credits: AI Generated

DOI: 10.1007/s10803-026-07501-2

Keywords: autism spectrum disorder, pupillometry, pupillary light reflex, early screening, M-CHAT, ADOS-2, toddlers, logistic regression, biomarkers, computer vision, locus coeruleus, resource-limited settings