Nutritional Screening in Children with Neurodevelopmental and Neuromotor Disabilities: Lessons from a Cross-Sectional Clinical Assessment

1. Introduction

Childhood obesity is a chronic, multifactorial disease shaped by biological susceptibility, medication exposure, family and social environments, access to health-promoting resources, sleep, dietary patterns, and opportunities for physical activity. Contemporary pediatric guidance recommends assessment within a family-centered, non-stigmatizing framework rather than attributing excess weight to individual choice alone [1]. In children and adolescents, BMI is not interpreted using fixed adult cut-offs; instead, it must be evaluated relative to age and sex using a defined pediatric growth reference because expected BMI changes substantially across growth and puberty [2,3].

Children with disabilities warrant particular attention because they face interacting risks for both overnutrition and undernutrition. Mobility limitations may reduce energy expenditure, while feeding difficulties, dysphagia, sensory selectivity, gastrointestinal symptoms, sleep disruption, caregiver burden, socioeconomic constraints, and weight-altering medicines can modify intake and nutritional risk [4,5,6,7]. Recent work in cerebral palsy continues to emphasize that nutritional screening should integrate functional severity, swallowing and feeding difficulties, energy intake, and more than one anthropometric indicator rather than rely on body weight alone [8,9,10].

The clinical interpretation of body size is especially complex in cerebral palsy. Contractures, scoliosis, inability to stand, altered body composition, and reduced muscle mass can compromise standing-height measurement and make BMI an imperfect proxy for adiposity [4,8,9,10,11,12,13,14]. Segmental measures, recumbent length, knee height, or other validated alternatives may be required when standing height cannot be measured reliably [11,14]. Condition-specific growth charts may describe how children with cerebral palsy grow, but they are not standards of optimal growth or direct measures of adiposity [12,13]. Contemporary evidence also supports integrating functional level, dietary intake, and body-composition information where feasible [9,10]. Consequently, a high or low BMI should prompt broader nutritional assessment rather than function as a stand-alone diagnosis.

Neurodevelopmental conditions also have distinct nutritional risk pathways. Children with autism spectrum disorder (ASD) may have restricted food repertoires, sensory-related eating patterns, food refusal, gastrointestinal symptoms, sleep problems, and exposure to medicines associated with weight change [15,16,17,18,19]. Recent systematic reviews confirm that food selectivity is common and can coexist with overweight, obesity, or micronutrient inadequacy, meaning that apparently normal or elevated BMI does not exclude poor diet quality [18,19]. In attention-deficit/hyperactivity disorder (ADHD), impulsivity, executive-function difficulties, sleep, dietary organization, and pharmacotherapy may influence weight trajectories in different directions [20,21]. Stimulant therapy commonly suppresses appetite during treatment, whereas several antipsychotic and selected antiepileptic or antidepressant agents may promote weight gain; class-level labels cannot substitute for agent-, dose-, and duration-specific analysis [21,22,23].

Family context is relevant because children and caregivers share food availability, routines, health literacy, stressors, and opportunities for activity. Associations between parental and child BMI are well described, but a cross-sectional correlation cannot distinguish genetic, prenatal, environmental, socio-economic, or behavioral pathways [24,25]. In disability care, family-centered nutritional assessment should therefore identify modifiable barriers without blaming caregivers.

Against this background, our work reports a single exploratory cross-sectional clinical assessment performed directly by the authors between February and April 2023. The primary objective was to describe child weight-status categories overall and by broad diagnostic subgroup. A secondary objective was to describe maternal BMI categories among children with obesity. In addition, we used the experience of this assessment to identify practical limitations of an anthropometry-centered approach and to formulate a more complete, disability-sensitive nutritional screening framework for future clinical use. The study was not designed as a comprehensive nutritional-status evaluation, nor as a secondary analysis of pre-existing medical records.

2. Materials and Methods

2.1. Study Design and Setting

This was a single-center, exploratory cross-sectional descriptive study conducted between February and April 2023 at the Mihai Neșu Foundation for children with disabilities in Oradea, Romania. Members of the author team participated directly in the assessment and data collection during the study period. Each participant underwent one clinical assessment; no later re-analysis of an external or previously assembled clinical database constituted the study design. Reporting was revised with reference to the STROBE checklist for observational studies [26]. The study did not evaluate the effectiveness of a behavioral, nutritional, rehabilitation, or pharmacologic intervention.

2.2. Participants and Study Assessment

The convenience sample comprised 20 children recruited from multiple cities across Romania who were receiving rehabilitation services at the Foundation: 10 with spastic quadriplegia, five with ASD, and five with ADHD. Ages ranged from 9 to 15 years; eight participants were girls and 12 were boys. The variables reported in this manuscript are those collected during the 2023 study assessment.

2.3. Anthropometric Assessment

During the 2023 study assessment, body weight was obtained in the morning using an electronic scale available at the study site, with children in light clothing or underwear and without shoes. Standing height was obtained with a stadiometer; when independent standing was not possible, assisted positioning was used. BMI was calculated as weight in kilograms divided by height in meters squared. Because assisted standing, contractures, scoliosis, and motor impairment can affect stature measurement, BMI in the spastic-quadriplegia subgroup was interpreted cautiously as a screening indicator rather than a definitive measure of adiposity.

Pediatric BMI was interpreted using age- and sex-specific categories rather than fixed adult thresholds. Healthy weight was defined as the 5th to <85th percentile, overweight as the 85th to <95th percentile, and obesity as ≥95th percentile using the pediatric reference [1,2,3].

Maternal BMI was calculated from maternal weight and height and categorized using adult thresholds: healthy weight, 18.5-24.9 kg/m², overweight, 25.0-29.9 kg/m² and obesity, ≥ 30.0 kg/m² [27].

2.4. Clinical Variables

Diagnosis, ongoing rehabilitation modalities, and medication classes were recorded during the same assessment. Rehabilitation included physiotherapy, TheraSuit therapy, speech and language therapy, psychotherapy, behavioral therapy, and occupational therapy; sessions were approximately 50 minutes. Medication information was communicated by mothers and grouped as antispastic, antidepressant, antiepileptic, psychostimulant, anxiolytic, antipsychotic, and vitamin preparations. Exact agents, doses, indications, treatment duration, adherence, and temporal relationships with weight change were not systematically collected. The protocol did not include a structured 24-hour dietary recall, food record, food-frequency questionnaire, or comparable dietary instrument. Food selectivity or aversion, sensory-related eating behavior, chewing or swallowing difficulty, texture modification, feeding assistance, mealtime duration, physical activity, pubertal stage, and body composition were also not systematically assessed. These variables are therefore reported as unmeasured domains of the original study, not as absent clinical problems.

2.5. Outcomes and Statistical Analysis

The primary descriptive outcome was the distribution of child weight-status categories. Secondary descriptive outcomes were distributions by broad diagnostic subgroup and maternal BMI category among children with obesity. Counts, percentages, ranges, means, and standard deviations were reported where available and internally coherent. No hypothesis test, p value, correlation coefficient, regression model, or claim of statistical significance was used because n = 20 and subgroup cell counts were very small. As part of the interpretation of this clinical assessment, we also identified important nutritional domains that had not been incorporated systematically into the 2023 protocol. These were treated as limitations of the assessment and as targets for a future standardized screening framework, not as retrospectively reconstructed study variables.

2.6. Data-Quality and Bias Safeguards

The data-quality review was limited to information actually collected during the 2023 assessment. No missing value was imputed and no unmeasured clinical feature was inferred. Particular attention was paid to whether the assessment protocol provided sufficient context for interpretation of BMI in children with neuromotor or neurodevelopmental disability. Variables not incorporated systematically into the 2023 protocol were explicitly classified as unmeasured rather than interpreted as absent clinical findings.

2.7. Ethics

The study was conducted at the Mihai Neșu Foundation with the institution’s prior written authorization. Verbal informed consent was obtained from parents for the children’s participation in the 2023 assessment and for use of the collected study data for scientific purposes. The Foundation did not operate a formal institutional review board/ethics committee for this non-interventional project and therefore did not issue a separate ethics approval number. No additional participant contact, intervention, or later secondary analysis of unrelated clinical records was performed for the present manuscript.

3. Results

3.1. Participant Characteristics

The study data included 20 children: 10 with spastic quadriplegia, five with ASD, and five with ADHD. Eight were girls and 12 were boys; girls were 11–15 years old and boys 9–14 years old. In the spastic-quadriplegia subgroup, the recorded mean age was 13.1 ± 1.37 years, mean weight 49.3 ± 19.27 kg, and mean height 142 ± 16.67 cm. The 2023 assessment did not provide sufficiently complete, standardized subgroup data for formal between-diagnosis comparisons.

3.2. Child Weight Status

Overall, 10 children (50%) were classified as having obesity, three (15%) as overweight, and seven (35%) as healthy weight. The internally coherent diagnostic distribution was four children with obesity, one with overweight, and five with healthy weight among those with spastic quadriplegia; and six with obesity, two with overweight, and two with healthy weight among those with ASD or ADHD. These counts are descriptive characteristics of the study sample and should not be interpreted as prevalence estimates. The distribution is summarized in Table 1.

3.3. Maternal BMI Categories

Among the ten children classified as having obesity, two mothers had obesity, four had overweight, and four had healthy-weight BMI. Thus, six of ten children with obesity had a mother with overweight or obesity. This is a descriptive co-occurrence only; the available records do not establish an association, direction of effect, or causal pathway between maternal and child BMI. Table 2 summarizes the recorded maternal BMI categories among children with obesity.

3.4. Rehabilitation and Medication Context

All ten children with spastic quadriplegia reportedly received physiotherapy and TheraSuit therapy and used antispastic medication; three also received antidepressants and three antiepileptic medicines. Children with ADHD received behavioral therapy and psychostimulants. Children with ASD received combinations of speech and language therapy, occupational therapy, psychotherapy, behavioral therapy, and psychotropic medication. These data did not systematically capture agent, dose, duration, pretreatment weight trajectory, functional severity, dietary intake, feeding assistance, swallowing status, physical activity, or energy expenditure, so treatment-related effects on weight cannot be inferred.

3.5. Nutritional Domains Not Systematically Included in the study

The 2023 assessment did not systematically include several domains that would now be considered important for a more comprehensive nutritional evaluation in this population. These included standardized functional classification; feeding route; chewing and swallowing difficulties; food texture modification and feeding assistance; food selectivity or aversion and sensory-related eating behaviors; structured dietary assessment such as a 24-hour dietary recall, food record, or appropriate food-frequency questionnaire; physical activity; body-composition assessment; pubertal stage; and agent-, dose-, and duration-specific medication exposure. These omissions do not indicate that such problems were present or absent in individual participants. Rather, they define the limits of the information that could be inferred from this one-time clinical assessment and informed the practical screening framework proposed below.

4. Discussion

4.1. Principal Findings

This study provides a snapshot of weight status within a small, heterogeneous rehabilitation cohort and, equally importantly, illustrates the practical limitations of interpreting anthropometric findings when key nutritional and functional domains are not assessed systematically. Half of the children were classified as having obesity and a further 15% as overweight; however, these figures should not be interpreted as prevalence estimates for Romanian children with disabilities. The most useful contribution of the study is therefore not a claim of comprehensive nutritional characterization, but the clinical lessons arising from a real-world cross-sectional assessment and the identification of elements that should be incorporated into future disability-sensitive nutritional screening.

The limitations of BMI are particularly relevant in cerebral palsy, where reduced lean mass, contractures, scoliosis, and altered fat distribution can produce misleading weight-for-height relationships. Severe motor impairment, feeding difficulty, and dysphagia may increase undernutrition risk, whereas reduced mobility and low energy expenditure may favor fat accumulation [4,9,10,11,12,13]. Recent reviews therefore support combining anthropometry with functional information, feeding and swallowing assessment, dietary intake, and body-composition measures when feasible [9,10]. In this context, the absence of these complementary data in the reviewed records is itself clinically meaningful.

4.2. Interpretation in the Context of Existing Evidence

The broader literature reinforces the need to move beyond anthropometry alone. In ASD, recent systematic reviews describe frequent food selectivity and sensory-related restriction and show that nutritional inadequacy can coexist with normal or elevated BMI [18,19]. In cerebral palsy, contemporary reviews emphasize swallowing dysfunction, feeding difficulty, functional severity, energy intake, and the limitations of conventional anthropometry as core elements of nutritional interpretation [8,9,10]. These approaches reflect a wider good-practice principle in disability nutrition: measurement must be adapted to functional ability, and screening should link anthropometry to feeding safety, diet quality, medication exposure, and longitudinal growth rather than treat a single BMI value as a complete nutritional diagnosis. The gaps identified in our study therefore have direct implications for routine healthcare documentation, not only for research quality.

Medication-related interpretation also requires precision. Second-generation antipsychotics, particularly agents such as olanzapine and risperidone, can produce clinically important weight and metabolic effects in youth, while stimulant treatment more commonly reduces appetite and may slow weight gain during active treatment [21,22,23]. Antiepileptic and antidepressant medicines differ markedly within their classes. Because only broad medication categories were collected during the study assessment, no statement attributing weight status to medication is justified.

The observed maternal-child BMI co-occurrence is compatible with shared familial risk but is not mechanistically specific. Parent-child resemblance may reflect inherited susceptibility, prenatal influences, food environments, sleep schedules, stress, socioeconomic circumstances, and reciprocal behavior [24,25]. For clinical practice, caregiver anthropometry is best interpreted alongside household food access, dietary routines, activity opportunities, sleep, caregiver burden, and readiness for feasible change. This approach avoids using maternal BMI as a proxy for lifestyle and is consistent with family-centered, non-stigmatizing pediatric obesity care [1].

4.3. Implications for Practice and Research

The immediate implication is practical: nutritional screening in rehabilitation services should combine feasible anthropometry with the clinical context required to interpret it. At each assessment, staff should document the measurement method and positioning; repeat weight and an appropriate length or height measure; record exact age, sex, pubertal stage when relevant, and functional classification; assess feeding route, chewing/swallowing safety, food texture, feeding assistance, food selectivity and sensory-related eating behavior; obtain a structured dietary assessment such as a 24-hour recall, food record, or appropriate food-frequency questionnaire; record physical activity, sleep, gastrointestinal symptoms, and exact medication exposure; and consider waist or validated body-composition measures where feasible. These additions would improve immediate clinical decision-making and, secondarily, the quality and comparability of data available for future research.

Table 3 translates these lessons into a practical minimum dataset for disability-sensitive nutritional screening. Table 4 provides a concise sample form that could be used prospectively in rehabilitation settings. These tools are proposed as practice-oriented outputs derived from the limitations encountered during the 2023 assessment;

4.4. Strengths and Limitations

The principal strength of this work is that it derives practical recommendations from a real-world clinical assessment performed directly in an understudied population. Rather than presenting BMI as a complete nutritional diagnosis, the study shows how interpretation can become uncertain when functional severity, feeding safety, diet, body composition, activity, and detailed medication exposure are not assessed alongside anthropometry. The resulting practice framework offers a concrete route to improve future screening in rehabilitation settings.

Limitations remain substantial. The study was small and single-center; no nondisabled comparison group was available, and the diagnostic groups differed in mobility and clinical context. The 2023 assessment did not systematically include standardized diagnostic confirmation, pubertal stage, functional severity, structured dietary intake, feeding and swallowing measures, food selectivity, physical activity, sleep, socioeconomic context, body composition, or detailed medication exposure.

5. Conclusions

This exploratory cross-sectional study should be read as a set of clinical lessons from a single 2023 assessment rather than as a comprehensive nutritional-status study. Excess weight was frequently identified in the small cohort, but BMI alone could not capture the nutritional complexity of children with neurodevelopmental and neuromotor disabilities. The assessment highlighted the need to pair disability-sensitive anthropometry with structured information on feeding and swallowing, dietary intake, functional status, medication exposure, activity, body composition where feasible, and family context. The practical framework proposed here may support more consistent clinical screening and generate better-quality data for future research, but it requires prospective validation in larger and more diverse populations.