Physiological mechanisms associated with stress

From a physiological standpoint, stress can be understood as an adaptive response to environmental or internal stressors that engages neuroendocrine, immune, and behavioral systems [7]. This response is mainly coordinated through the hypothalamic-pituitary-adrenal (HPA) axis and the autonomic nervous system (ANS) [8,9]. When a stressor is perceived, the HPA axis initiates the release of corticotropin-releasing hormone (CRH) by the hypothalamus, followed by adrenocorticotropic hormone (ACTH) secretion by the anterior pituitary, ultimately stimulating glucocorticoid synthesis, predominantly cortisol, in the adrenal cortex [9,10]. Cortisol contributes to energy mobilization and the regulation of immune and cardiovascular processes, and salivary cortisol is widely used as a noninvasive indicator of HPA-related activity in stress research [11,12]. In parallel, sympathetic activation promotes catecholamine release, leading to acute cardiovascular changes and increases in salivary alpha-amylase (sAA), an enzyme commonly used as a noninvasive indicator of sympathetic arousal in behavioral and psychophysiological studies [13–15].

Although acute stress responses are adaptive, sustained activation can lead to maladaptive changes that compromise well-being [8]. Chronic stress exposure has been associated with dysregulated cortisol output, sleep disturbances, immunological alterations, increased cardiovascular risk, and greater vulnerability to affective disorders, including major depressive disorder [9,11,12]. At the neurobiological level, stress-related alterations have been described in the hippocampus and broader limbic circuits involved in emotional and cognitive regulation [16]. In academic settings, these pathways may translate into concomitant disturbances in eating behavior, sleep, and mood, supporting the rationale for an integrated assessment of psychological, behavioral, and physiological indicators in university students.

This study will use salivary cortisol and sAA as noninvasive indicators of HPA axis and ANS (sympathetic) activity, respectively, which have been widely applied to characterize stress-related activation in nonclinical and student populations [17–19]. For cortisol, we will focus on the cortisol awakening response (CAR), capturing the rapid post-awakening increase, and we will compute the most commonly used derived indices from serial post-awakening measurements, including the area under the curve with respect to increase (AUCi) and with respect to ground (AUCg), which allow estimation of reactivity and overall output within the sampling window [20].

However, the available evidence still shows important limitations, including the predominance of cross-sectional designs, the limited integration of physiological, behavioral, and psychological indicators within a single analytical framework, and the relative scarcity of studies specifically focused on academic stress as an operationalized construct [7,21]. These gaps support the need for longitudinal protocols capable of examining temporal relationships using an integrative approach.

Academic stress

AS can be conceptualized as a transactional phenomenon that emerges when academic demands are appraised as taxing or exceeding a student’s perceived coping resources [21,22]. This construct is common in university settings and has been linked to adverse consequences for health, academic functioning, and overall well-being. However, its boundaries have often been blurred by the coexistence of related terms such as “student stress,” “burnout,” and “academic anxiety,” which complicates conceptual differentiation and contributes to inconsistent operationalization across studies [23]. Consistent with this heterogeneity, the available instruments vary widely and frequently adopt unidimensional or decontextualized approaches that capture diverse sources of stress without embedding them within a systemic, integrative framework [24–27].

To address this conceptual and methodological diversity, this protocol adopts the systemic cognitivist model proposed by Barraza-Macías, which integrates general systems theory with transactional stress theory [23,28]. Within this framework, AS is represented through three interrelated components: academic stressors, indicators of systemic imbalance, and coping strategies. The model proposes that students face specific educational demands (input) that may trigger an imbalance reaction, followed by coping efforts aimed at restoring homeostasis (output). The SISCO Academic Stress Inventory was developed to operationalize these components. In Chile, both the original instrument and its expanded version (SISCO-II-AS) have been validated in university samples, showing robust psychometric properties [29,30]. The SISCO-II-AS retains the original subscales, refines symptom assessment by differentiating physical, psychological, and behavioral reactions, and incorporates reference norms to support standardized interpretation [30].

A clear and theoretically grounded operational definition of AS is essential to distinguish it from related constructs, reduce interpretive ambiguity, and support transparent sample delimitation [21]. Accordingly, this protocol uses the systemic cognitivist model and the SISCO-II-AS inventory as its conceptual and methodological foundation, given their contextual relevance and empirical support in Chilean university populations [23,29,30]. This approach enables AS to be examined as a multidimensional phenomenon plausibly linked to behavioral and somatic indicators relevant to student health, including eating-related patterns, sleep, cardiovascular risk indicators, and depressive symptoms.

Impact of stress on eating, nutritional, somatic, and psychological variables

Stress is a key determinant of both physical and mental health. Its influence may occur through sustained activation of stress-responsive systems, including the HPA axis and the ANS, and through indirect pathways involving stress-related changes in daily functioning. In student and general populations, these effects have been linked to disruptions in eating-related behaviors, diet quality, sleep patterns, emotional well-being, and broader health outcomes [6,7].

Stress and eating behavior

Chronic stress engages the HPA axis and the sympathetic branch of the ANS and has been linked to changes in appetite-related signaling, including cortisol, ghrelin, and leptin dynamics [31–33]. Sustained cortisol exposure may contribute to a shift toward highly palatable, energy-dense foods, often framed as stress-related food reward, through interactions between metabolic signals and neural reward processes [34–36]. These effects are plausibly supported by reward-circuit mechanisms, including pathways involving the nucleus accumbens, which may heighten motivation for palatable foods under stressful conditions [6,37].

In university settings, stress-related eating changes are particularly relevant because many students begin managing meals more independently, and patterns established during this period may persist into adulthood [38,39]. During high academic demand, increases in fast food, snacks, and high-fat or high-sugar intake have been reported, alongside lower consumption of fruits and vegetables [40–42]. Stress responses in eating can vary, presenting as higher or lower intake depending on individual factors and situational characteristics [40]. A commonly described pattern is repetitive, unplanned intake of small amounts of food, frequently termed grazing, which may function as a short-term coping behavior during exam periods but has been linked to less adaptive eating patterns over time [43,44].

Prolonged stress exposure has also been associated with heightened motivation for ultra-processed foods, and some authors describe an addictive-type pattern of consumption characterized by loss of control, although this does not constitute a clinical diagnosis [45–48]. Despite these associations, most studies remain cross-sectional, seldom focus specifically on academic stress as an operationalized construct, and rarely incorporate physiological indicators within an integrated model, limiting inference about temporal ordering and mechanisms [6,49].

Stress and diet

Diet is another domain that may be shaped by stress-related changes in daily functioning. Population evidence highlights that healthier eating patterns are associated with better mental health and health-related quality of life [50]. In young adults, higher perceived stress has also been linked to poorer diet quality and greater consumption of fast foods and sugar-rich products, often accompanied by higher intake of stimulants such as coffee and energy drinks [40].

A key pathway connecting diet to health is inflammation. Dietary patterns characterized by higher intake of ultra-processed foods and energy-dense products have been associated with a more proinflammatory profile, and proinflammatory dietary patterns have been linked to depressive symptoms and related mental health outcomes in observational studies [51,52]. In university samples, diet-related inflammatory potential has also been examined in relation to stress and anxiety [53,54]. Beyond mental health, higher proinflammatory dietary scores have been associated with less favorable cardiometabolic and endocrine biomarker profiles in large datasets, although much of the evidence remains cross-sectional [55,56].

Stress may also affect nutritional status through sustained glucocorticoid exposure. Higher cortisol output has been associated with adverse body composition features and altered autonomic balance in young adults, and mechanistic evidence supports a role for glucocorticoid signaling in adipose-related pathways [57–59]. Together, these findings support an integrative approach to AS during university life, incorporating dietary patterns and nutritional status alongside physiological and psychological indicators.

Stress and somatic variables: Sleep and cardiovascular risk factors

Individual variability in stress response

Stress does not affect individuals uniformly. Interindividual variability in physiological and behavioral stress reactivity reflects differences in both response magnitude and temporal patterning, and individual characteristics such as coping resources, dietary restraint, and biological sex may shape stress-related changes in eating behavior [6,40].

Procedure

Participants: The study will include undergraduate students enrolled in the Faculty of Medicine at the University of Concepción, excluding first-year students and those completing their professional internships. All participants must sign an informed consent form prior to study enrollment.

Timing: Data collection will be organized into three assessment cycles during the academic semester, each lasting three weeks. In each cycle, participants will complete self-administered questionnaires, undergo nutritional interviews, and provide saliva samples according to the protocol schedule. Peripheral blood sampling will be performed at baseline and at the end of the semester to determine biochemical parameters. Somatic indicators will be recorded using wearable devices during the monitoring periods, enabling longitudinal tracking of behavioral, physiological, and psychological variation across the semester. Recruitment is planned to begin in early April 2026, aligned with the start of the academic semester in Chile.

Recruitment: Students will be invited through institutional email distributed by the corresponding academic programs and through official study announcements on social media. Participants who accept the invitation will receive standardized study information and instructions, along with the materials required for saliva collection and a wearable device for monitoring.

Compensation: Participation is voluntary and no monetary incentive will be provided. Wearable devices will be provided new and unused, and participants will keep the device after completing the monitoring procedures.

Inclusion criteria: Eligible participants will be active undergraduate students enrolled in the Faculty of Medicine at the University of Concepción, specifically from the second, third, or fourth year of study, and under 25 years of age at enrollment. This range is intended to maintain comparability in academic structure and developmental stage and to reduce heterogeneity related to markedly different exposure patterns and non-academic responsibilities that may affect sleep, diet, and physiological indicators. All participants must provide written informed consent and complete standardized training for saliva collection procedures and wearable use. Students currently receiving psychological or pharmacological treatment will be eligible to enhance ecological validity and reflect real-world heterogeneity in university populations, where mental health symptoms and service use are common [3,80,81]. Treatment status will be recorded and considered analytically through covariate adjustment and sensitivity analyses.

Exclusion criteria: First-year students and students completing professional internships/residencies will be excluded because their academic structure and exposure patterns differ substantially from standard semester-based coursework, which could compromise comparability across assessment cycles. Individuals under 18 years of age will be excluded because participation as a minor would require additional consent procedures (e.g., parental/guardian authorization) and may introduce heterogeneity related to developmental stage and living arrangements that could affect sleep, diet, and stress-related indicators. Additional exclusion criteria will include pregnancy; a self-reported clinical diagnosis of an eating disorder within the previous six months; age 25 years or older; and voluntary withdrawal at any time during the study.

The instruments used for data collection are summarized in Table 1.

Download:

• PNG
  larger image
• TIFF
  original image

Table 1. Study variables and measurement instruments.

https://doi.org/10.1371/journal.pone.0352779.t001

Instruments for assessing academic stress and perceived stress

SISCO-II academic stress inventory (SISCO-II): The SISCO-II is a 33-item instrument designed to assess academic stress as a multidimensional construct. It comprises three domains: academic stressors (8 items), reactions (17 items), and coping strategies (6 items). The reactions domain is further differentiated into physical and psychological reactions, and social and behavioral reactions. Items are answered on a five-point Likert scale ranging from 1 (never) to 5 (always). For this study, interpretation will be based on normative data established for Chilean university students, allowing contextualized scoring. In the stressors domain, scores below the 40th percentile are classified as low, between the 40th and 60th percentiles as moderate, and above the 60th percentile as high. In the reactions domain, scores below the 35th percentile indicate low reactivity, between the 35th and 65th percentiles indicate moderate reactivity, and above the 65th percentile indicate high reactivity. Robust psychometric properties and internal consistency for the total scale and its domains have been reported, including evidence derived from Chilean validation and scoring studies, supporting its use in this protocol [29,30,82].

Perceived stress scale (PSS14): The PSS-14 is a widely used measure of perceived stress that assesses the extent to which individuals appraise their life situations as stressful [83]. Psychometric evidence supports its use across diverse populations, including European Spanish validation studies and broader reviews of the instrument’s measurement performance [84,85]. In Chilean samples, the PSS has also been applied in research settings, supporting its feasibility of use in the local context [86]. In the present study, internal consistency will be reported for the total score and relevant subscales in the study sample.

Instruments for assessing nutritional variables (diet and nutritional status)

The instruments used to evaluate dietary intake and nutritional status are described below.

Diet assessment

24-hour dietary recall: Dietary intake will be assessed using a structured, interviewer-administered 24-hour dietary recall. Individual interviews will be conducted by a trained nutritionist on three non-consecutive days during each assessment cycle to estimate total food and beverage intake over the preceding 24 hours. To ensure methodological consistency across cycles, dietary recalls will be standardized through uniform collection, evaluation, and coding procedures applied throughout the study. Each recall will be treated as a time-specific observation and modeled longitudinally as a time-varying measure, without averaging across cycles. Comparability across measurement occasions will be supported by identical assessment procedures, standardized coding criteria, and a single food composition database to estimate total energy intake and macro- and micronutrient composition [87]. A standardized multi-pass approach will be used to reduce recall bias and improve completeness [88].

Dietary Inflammatory Index (DII): The DII is a literature-derived index designed to estimate the overall inflammatory potential of the diet along a continuum from more anti-inflammatory to more pro-inflammatory [89]. In this protocol, the DII will be computed using dietary intake data derived from the 24HR assessments, following the standard method described by the developers [89]. The index is supported by evidence linking specific dietary components to systemic inflammatory markers and is applicable across populations where dietary intake data are available [90].

Chilean Self-administered Mediterranean Diet Index (IDM-Chile): The IDM-Chile is a self-administered instrument developed to assess adherence to the Mediterranean dietary pattern in the Chilean population as an indicator of diet quality [91]. It includes 14 items assessing portion size and frequency of consumption across key food groups. Items are scored from 0 to 2 according to predefined criteria, yielding a total score that is classified as high adherence (9–14 points), moderate adherence (5–8.5 points), or low adherence (<5 points) [91]. High reliability has been reported in a large Chilean adult sample (Spearman’s ρ = 0.94) [91].

Assessment of nutritional status

Anthropometric measurements: Weight, height, and waist circumference will be assessed as indicators of nutritional status and body composition. Weight and height will be used to calculate body mass index (BMI), expressed as kg/m², and categorized according to World Health Organization criteria [92]. Waist circumference will be used as an indicator of central adiposity, applying cut-off points of ≥80 cm for women and ≥90 cm for men [93]. Anthropometric measurements will be obtained using standardized procedures following established guidelines for nutritional assessment [94].

Biochemical parameters associated with nutritional status: Metabolic and protein-related parameters will be assessed using peripheral blood samples, including fasting blood glucose, lipid profile, albumin, globulin, and total protein concentrations. Analyses will be performed using standardized automated spectrophotometric procedures routinely used in clinical chemistry laboratories, following laboratory quality procedures and manufacturer instructions [95].

Instruments for assessing eating behavior

Yale Food Addiction Scale 2.0 (YFAS-2.0): The YFAS 2.0 is used as a screening instrument to identify addictive-type eating patterns based on the 11 substance use disorder criteria described in the DSM-5 framework. It includes 35 items rated on a Likert-type frequency scale ranging from 0 (never) to 7 (every day), allowing estimation of symptom presence and related scoring outputs according to the instrument guidelines [46]. The YFAS 2.0 has been validated in Spanish and has shown good psychometric performance [96,97]. In Chile, validation evidence in nonclinical samples, including university students, has reported high reliability (McDonald’s ω = 0.88), supporting its use in this protocol [98].

The grazing questionnaire: The Grazing Questionnaire assesses repetitive, often unplanned consumption of small amounts of food over extended periods. It comprises seven items grouped into two factors. The instrument has shown good internal consistency (Cronbach’s α = 0.82) in university samples without a clinical history [43]. In this protocol, the questionnaire will be applied to characterize grazing-related patterns in the study population.

Instruments for assessing somatic variables (sleep and cardiovascular risk factors)

Sleep patterns: Sleep-related patterns will be assessed using a wearable device (Redmi Watch 5 Active), which provides device-derived sleep metrics during the monitoring periods. Outputs will include sleep duration, sleep efficiency, and device-estimated sleep stages (e.g., REM and non-REM stage estimates, as defined by the device). These outputs will be treated as nonclinical somatic indicators intended to capture within-person patterns over time rather than clinical-grade measurements [78,79].

Cardiovascular risk factors in young adults: Cardiometabolic risk will be examined across three complementary domains: (i) behavioral risk factors (e.g., smoking, alcohol use, physical inactivity, and sleep-related problems) [71]; (ii) biochemical and metabolic indicators derived from blood sampling (e.g., lipid profile and fasting glucose) [72]; and (iii) metabolic syndrome, operationalized using standard criteria defined a priori in the protocol.

Metabolic syndrome (operational definition): Metabolic syndrome will be defined using the harmonized criteria (Joint Interim Statement), which considers the presence of any three of five components: elevated waist circumference (using country- or ethnicity-specific cutoffs), elevated triglycerides, reduced HDL-cholesterol, elevated blood pressure, and elevated fasting glucose [99]. In this protocol, waist circumference cutoffs will follow the thresholds already specified for central adiposity (≥80 cm for women and ≥90 cm for men), and medication for any component will be treated as meeting the corresponding criterion when applicable.

Instrument for assessing depressive symptoms

Patient Health Questionnaire-9 (PHQ-9, Spanish version): Depressive symptoms will be assessed using the PHQ-9, a self-administered measure based on DSM symptom criteria for major depressive disorder [100]. The instrument consists of nine items with Likert-type response options and has shown good reliability in Chilean samples [101].

Data collection mechanism: Data collection and management will be conducted using REDCap (Research Electronic Data Capture), a secure, web-based platform designed for biomedical and academic research [102]. The platform will be used to administer online surveys, store data centrally, and support confidentiality and access control. Participants will receive survey links via text message, allowing completion from mobile phones or computers without requiring additional applications. REDCap will also support automated reminders and real-time monitoring of data collection progress.

Use of smart devices: The study will incorporate smart wristband-type devices selected for affordability, broad availability, and feasibility for implementation in university populations. These devices enable non-invasive recording of somatic indicators such as heart rate, oxygen saturation, and sleep-related metrics during monitoring periods [78,79]. Because the device operates as a closed system and does not allow clinical calibration, wearable outputs will be treated as nonclinical indicators and used to identify longitudinal patterns rather than clinical-grade absolute values. Standardized procedures will be applied to support data quality and device functioning, including device delivery checks, monitoring of completeness, and artifact-oriented plausibility rules (e.g., extended missingness or prolonged flatline segments). No rigid assumptions about minimum sleep duration will be used, because extremely short or even zero recorded sleep may reflect true behavior in some instances. Wearable data completeness will be monitored throughout the monitoring periods. Missingness will be handled at the analysis stage using the prespecified statistical strategy, based on derived wearable metrics rather than imputing raw time-series signals.

Collection of saliva samples

Saliva samples will be collected twice per week during each assessment cycle to quantify salivary cortisol and sAA as noninvasive physiological indicators of stress-related activation [11,12,15]. Before starting, participants will receive practical training and written instructions to support standardized home collection using Salivette® devices.

Sampling schedule and compliance procedures: Each sampling day will include three consecutive samples: immediately upon awakening (0 min), and 30 and 60 minutes thereafter. Sampling compliance criteria: To compute CAR and AUC-derived indices, the awakening sample will be considered compliant when collected within 5 minutes of self-reported wake time. The + 30 and +60 minute samples will be considered compliant when collected within ±10 minutes of their target times. Sampling days outside these windows will be flagged and excluded from CAR/AUC computation and considered in sensitivity analyses [20]. Participants will record wake time and sampling times using time-stamped entries in REDCap and will be instructed to follow standardized pre-collection conditions (e.g., no food or beverages except water, no smoking, and no tooth brushing immediately before collection). Because CAR estimates are sensitive to deviations from the awakening schedule, time compliance will be monitored, and noncompliant sampling days will be flagged for sensitivity analyses [20]. Wearable-derived sleep timing will be used as an auxiliary reference to support consistency checks of reported wake time when available.

Handling, labeling, and storage: Samples will be labeled with unique participant codes to ensure traceability while preserving confidentiality. Participants will store samples under refrigeration until delivery to the research team according to study instructions. In the laboratory, samples will be processed following standardized procedures, including centrifugation, and stored at −20 °C until analysis to preserve sample integrity.

Determination of salivary cortisol

Frozen samples will be thawed under controlled conditions prior to analysis. Cortisol concentration will be determined using an ELISA-based immunoassay with the commercial Salivary Cortisol ELISA Kit (Eagle Biosciences, CRT32-K01), following the manufacturer’s instructions [103]. Absorbance will be read at a primary wavelength of 405 nm with spectral correction between 570 and 590 nm, as specified by the kit insert. Cortisol awakening response (CAR) metrics will be derived from the serial post-awakening measurements, and the AUCi and AUCg indices will be computed to characterize cortisol dynamics within the post-awakening sampling window [20].

Determination of salivary alpha-amylase: Salivary alpha-amylase activity will be assessed using a standardized spectrophotometric method based on 2-chloro-p-nitrophenyl-maltotriose (CNPG₃) as substrate. Hydrolysis releases 2-chloro-4-nitrophenol, which will be quantified at 405 nm, with the rate of increase in absorbance proportional to enzymatic activity. The procedure will follow the laboratory protocol and reagent specifications for clinical chemistry photometric methods [95,104], and sAA will be interpreted as a noninvasive indicator of sympathetic arousal in psychophysiological research [13–15].

Blood sample collection: Peripheral venous blood samples will be obtained at the Neuroscience, Psychiatry, and Mental Health Laboratory of the University of Concepción (NEPSAM-UdeC). Blood draws will be performed by a Medical Technologist trained in phlebotomy and biospecimen handling procedures. Sample collection will be scheduled to coincide with anthropometric assessment, blood pressure measurement, and the dietary interview to optimize integration of physiological and behavioral data. Participants will be instructed to arrive after at least 12 hours of fasting.

Determination of biochemical parameters

Biochemical determinations will include lipid profile (total cholesterol, LDL-cholesterol, HDL-cholesterol, and triglycerides), fasting blood glucose, albumin, globulin, and total proteins as metabolic and protein-related indicators of nutritional status. Analyses will be conducted using standardized automated spectrophotometric procedures with commercial reagents from Human Diagnostics Worldwide [104]. After collection, tubes will be centrifuged at 2500 × g for 10 minutes. Serum or plasma will be separated according to the analytical requirements of each parameter and stored in labeled microtubes at −80 °C until analysis.

Statistical analysis

Statistical analyses will be conducted in R [105]. Analyses will be performed in three main stages:

1. (i). Descriptive analysis: Sample characteristics will be summarized using measures of central tendency and dispersion (mean and standard deviation for approximately normally distributed variables; median and interquartile range for skewed variables). Categorical variables will be described using absolute frequencies and percentages. Distributions will be inspected graphically and, when appropriate, transformations will be considered for markedly skewed biomarkers.
2. (ii). Bivariate analysis: Associations between variables will be explored using graphical inspection and correlation analyses. Correlations will be computed using Pearson or Spearman coefficients depending on distributional assumptions and will be examined at each assessment cycle to describe contemporaneous relationships. Correlation matrices will be used to identify patterns among key variables, including AS, eating-related patterns, diet indices, nutritional status indicators, sleep-related metrics, cardiometabolic indicators, and depressive symptoms.
3. (iii). Multivariate and longitudinal analysis: To examine adjusted associations, regression models will be specified according to outcome type (linear models for continuous outcomes; generalized models, including logistic models, for categorical outcomes). To evaluate within-participant changes over time, mixed-effects longitudinal models will be used, including participant-level random intercepts to account for within-person correlation across repeated measurements. Time will be represented by assessment cycle, and key predictors (including AS and relevant covariates) will be treated as time-varying when applicable. Dietary variables derived from the 24-hour dietary recalls (including DII-derived measures) will be analyzed at the recall level as time-specific observations, nested within assessment cycles, and modeled longitudinally as time-varying outcomes within mixed-effects models, without averaging across cycles. Wearable data will be analyzed using derived metrics (e.g., daily or cycle-level summaries) rather than raw time-series signals. Illicit drug use was not assessed in this protocol and will therefore be considered as a potential source of residual confounding, particularly for outcomes related to sleep and mood.
4. (iv). RI-CLPM analyses will be restricted to constructs assessed once per assessment cycle; therefore, diet in the cross-lagged framework will be represented using the cycle-level IDM-Chile. To examine temporal cross-lagged associations while separating stable between-person differences from within-person fluctuations, the random-intercept cross-lagged panel model (RI-CLPM) will be applied across the three assessment cycles. This approach will be used to evaluate cross-lagged associations between AS and selected outcomes (e.g., depressive symptoms, eating-related patterns, diet indices, sleep-related metrics, and cardiometabolic indicators) and to explore reciprocal patterns where theoretically justified. All interpretations will be framed as associational given the observational design.

Missing data: Because incomplete follow-up and intermittent missingness are expected in repeated-measures designs, missing data will be addressed using the prespecified longitudinal strategy. Mixed-effects models will be estimated using likelihood-based approaches that accommodate unbalanced repeated measures under Missing At Random assumptions. For RI-CLPM and related longitudinal models, full-information estimation will be used when applicable. If required (e.g., missing covariates), multiple imputation procedures will be implemented at the level of derived variables, and sensitivity analyses will be conducted to assess robustness to missingness patterns.

General data plan: At the end of the project, all de-identified data generated will be shared with the scientific community through deposition in an open-access repository. The dataset will be deposited in the University of Concepción institutional Dataverse repository (Dataverse UdeC), together with a data dictionary/codebook and sufficient metadata to support transparency, independent verification, and reproducibility. Procedures for data organization, secure storage, access control, and confidentiality will be implemented throughout the study.

Publication and dissemination of protocol results: Before any public dissemination, all study data will undergo a structured de-identification process to remove direct identifiers and reduce re-identification risk. Once de-identified, the dataset, together with a codebook and relevant metadata, will be deposited in the University of Concepción institutional repository (Dataverse UdeC) to support transparency, independent verification, and reproducibility. If any subset of variables is considered potentially identifying, access restrictions will be applied according to repository policies, while maintaining public metadata describing the dataset.

Strengths

A main strength of this protocol is the repeated-measures, longitudinal design across an academic semester. This structure supports the examination of within-person variability over time and reduces reliance on cross-sectional snapshots, allowing a more precise characterization of how AS aligns with concurrent changes across physiological, behavioral, somatic, and psychological domains.

A second strength is the feasibility-oriented integration of multiple measurement modalities using scalable procedures. The protocol combines noninvasive salivary indicators with wearable-derived nonclinical somatic metrics and validated self-report instruments, creating a replicable framework that can be implemented in university settings. This integrative approach provides a practical methodological template for future longitudinal and comparative research on AS.