Youth Life After the Pandemic: An Exploratory Study on Mental Health, Online Behaviours, and Daily Functioning of Italian Early Adolescents

Abstract

Early adolescence is a critical developmental period marked by emotional, behavioral, and biological changes. The COVID-19 pandemic disrupted adolescents’ daily routines, potentially producing lasting effects on mental and physical health. This study investigated anxiety and depressive symptoms, risk of extreme social withdrawal, use of technological devices, physical activity, and sleep–wake functioning in 276 early adolescents (54% females; Mage = 12.28, SD = 0.81) living in a forested and geographically isolated area of Emilia-Romagna in Italy. Participants completed validated self-report questionnaires assessing internalizing symptoms, use of technological devices (including problematic online gaming and smartphone use), gaming motivations, physical activity, chronotype, sleep disturbances, and daytime sleepiness. Scores on the anxiety and depressive symptom scales were within the normative range, except for scores on certain scales suggesting a moderate degree of severity that was not clinically significant. Females reported higher levels of anxiety, depressive symptoms, and problematic smartphone use, whereas males showed greater involvement in problematic online gaming and stronger achievement-, social-, and immersion-related motives. A substantial proportion of participants reported excessive daytime sleepiness (42.4% of females; 26.1% of males). Significant patterns of association were found among internalizing symptoms, domains of sleep–wake functioning, use of technological devices, and risk of extreme social withdrawal. Overall, these findings support the relevance of predisposing preventive strategies aimed at improving different domains of physical and mental health among youth in underserved or geographically isolated communities.

1. Introduction

Early adolescence is a critical phase of development, during which individuals begin to redefine their self-concept and interpersonal world. In a context marked by rapid technological, economic, and environmental change, schools serve as key environments for fostering identity development and social inclusion [1]. Attending school supports adolescents’ sense of competence, autonomy, and relatedness [2,3,4], particularly during the transition to middle school (ages 10–14), when puberty brings profound changes in motivation, behavior, and relational needs. This transition introduces new emotional and social experiences, encouraging the expansion of peer relationships [5,6]. Conversely, exclusion from educational settings can hinder the development of identity and social competencies [7].

The Documento per la pianificazione delle attività scolastiche, educative e formative nelle istituzioni del Sistema nazionale di istruzione per l’anno scolastico 2020/2021 [Document for the planning of school, education, and training activities of the Italian National Education System for the 2020–2021 school year] [8] highlights that during the first two years of the COVID-19 pandemic, adolescents were deprived for extended periods of regular in-person schooling, sports activities, and interactions with peers, teachers and extended family. Although children and adolescents were generally less at risk of severe COVID-19 [9], the containment measures significantly affected their daily lives, primarily through social isolation and the disruption of established routines [10].

As a consequence, the pandemic has contributed to a rise in psychopathological symptoms [11], as well as an increased demand for mental health services [12]. Several studies have investigated the psychological effect of the pandemic on youth, identifying depression, anxiety, sleep disturbances, and emotional and behavioral disorders as some of the most frequently reported outcomes [13,14,15]. While the use of Internet-mediated applications, such as online video games or social media, may be adaptive in coping with pandemic-related restrictions, excessive engagement in such applications may have led to impairments in daily functioning [16].

This study was conducted in the post-pandemic period, following a request by local school services in the Emilia-Romagna region (Italy) to assess the psychological distress of early adolescents residing in forested and geographically isolated areas. The primary objective was to identify potential areas of vulnerability and to inform the development of targeted support interventions. The following sections provide an overview of key issues related to the physical and mental health of early adolescents, as outlined by current scientific literature.

1.1. Anxiety, Depression, and Extreme Social Withdrawal

Early adolescents are particularly vulnerable to developing internalizing symptoms such as depression [16] and anxiety [17], even though often clinically undiagnosed. While the estimated prevalence of depressive disorders is relatively low in childhood (i.e., 1–2%), it rises during adolescence, especially after the age of 11 (i.e., 1–7%) [18]. Anxiety symptoms in children and adolescents showed greater variability across studies, with prevalence rates ranging from 2.5 to 30%, reflecting differences in age, assessment, and cultural context [19].

During the COVID-19 pandemic, an increase in both anxious and depressive symptoms among youth was extensively documented at both national and international levels [10,20,21]. In the post-pandemic period, emotional distress has remained high, with a growing number of mood disorder cases among early adolescents [22]. Notably, limited face-to-face interactions during lockdowns may have led some adolescents, particularly those with social anxiety, to feel a sense of relief, as they could restrict their interactions to relationships perceived as safe [23]. However, the reduced exposure to social contexts may have heightened anxiety [10], making the return to in-person schooling more challenging for some students. In this context, a growing body of research has identified an increase in extreme social withdrawal (or “hikikomori”), a clinical condition characterized by home confinement and avoidance of social participation for at least six months, including withdrawal from school [24]. Extreme social withdrawal is associated with introverted and avoidant psychological profiles, disrupted sleep–wake rhythms, and comorbid conditions such as social anxiety, depression, and problematic online gaming among adolescents [25,26,27].

1.2. Sleep Quality Deterioration and Lack of Physical Activity

Adolescence is marked by substantial changes in sleep–wake patterns [28]. Young people tend to go to bed and wake up later, often reducing total sleep duration on weekdays and compensating with longer sleep on weekends. These irregular patterns are influenced by school demands, social pressures, and chronic sleep deprivation, which can lead to increased daytime sleepiness [29]. Furthermore, other physiological and environmental factors may contribute to these changes in sleep habits. Pubertal development [30], reduced parental supervision of bedtime routines [31], and shifts in circadian rhythm regulation [32] have been shown to influence adolescents’ sleep behaviors. Chronotype differences—i.e., individual variations in preferred sleep–wake timing—also play a role [33]. Morning-type individuals tend to fall asleep and wake up earlier and feel more alert in the early part of the day, whereas evening-types prefer later activity schedules and experience peak alertness in the afternoon or evening. Intermediate chronotypes show patterns in between these two extremes [34].

Evidence from the pandemic has shown that social isolation, home confinement, and remote learning contributed to a deterioration in sleep quality among adolescents [13]. In particular, the absence of Zeitgebers—i.e., external time cues such as sunlight, regular meals, and social interactions—disrupted circadian regulation, especially when paired with prolonged use of electronic devices and reduced daylight exposure [35,36]. These conditions fostered later bedtimes, poorer sleep quality, and greater variability in sleep schedules [37].

Physical inactivity has also played a critical role in compromising sleep quality [38]. According to pre-pandemic research, early adolescents spent 209 min/day (64%) of their school time in sedentary activities, while passing only 16 min/day (5%) in moderate to vigorous physical ones [39]. COVID-19 restrictions further limited opportunities for movement. School closures, the suspension of organized sport activities, and reduced access to recreational spaces led to a sharp decline in physical activity levels [40].

1.3. Problematic Online Gaming and Problematic Smartphone Use

During the COVID-19 emergency, adolescents increasingly relied on digital devices to meet their social and emotional needs, due to social restrictions [41,42]. Within these circumstances, the prevalence of maladaptive engagement in smartphone use and online gaming rose significantly [43,44]. Problematic online gaming refers to a persistent and recurrent involvement in online gaming that leads to psychological distress and functional impairment [45]. Among adolescents, it is frequently associated with reduced physical activity [46], disrupted sleep–wake rhythms [47], and adverse psychosocial outcomes such as anxiety, depression, aggression, and academic difficulties [48].

It is noteworthy that smartphone use can facilitate autonomy, offering young people independent access to entertainment, communication, and social media [49]. However, problematic smartphone use (PSU), i.e., uncontrolled smartphone use that leads to negative consequences in daily life [50], may become pervasive, manifesting in compulsive smartphone-related activities driven by peer engagement and entertainment needs that are not fulfilled in offline environments [51,52]. Problematic online gaming and PSU may represent critical manifestations of distress among adolescents during the COVID-19 pandemic, as these maladaptive behaviors can reflect maladaptive attempts to cope with psychosocial difficulties and unmet needs [53].

2. The Present Study

The present study was conducted in collaboration with school services in a forested area of the Emilia-Romagna region, northeastern Italy. It aimed to examine the physical and mental health of early adolescents in the post-pandemic period, with particular attention to individual vulnerabilities and areas requiring support. The study targeted middle school students living in geographically isolated communities, who may have been especially deprived of typical social interactions during the pandemic years. Various self-report instruments were used to assess anxiety, depression, risk of extreme social withdrawal, motives for gaming, problematic online gaming, PSU, physical activity, sleep quality, chronotype, and daytime sleepiness. The minimal dataset supporting the findings of this study is provided as Supplementary Material.

3. Materials and Methods

3.1. Participants and Procedure

Data were collected between September and December 2023 through an online self-reported survey (Qualtrics, Provo, UT, USA), administered during school hours to middle school students attending the Predappio, Forlì, and Valle del Montone comprehensive schools (Italy). The questionnaire required around 30 min to be completed and was filled out by students voluntarily and anonymously.

The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Prior to participation, written informed consent was obtained from the participants’ parents or legal guardians, who were fully informed about the aims of the research, the voluntary nature of participation, and the procedures involved. Participants were also provided with appropriate age information about the study and were free to withdraw at any time without any consequences. The research was approved by the Research Ethics Board of the University of Parma, protocol no. 0198238.

3.2. Measures

Participants completed an ad hoc form providing demographic information (i.e., age and sex), details about their sleep–wake patterns (i.e., bedtime and wake-up times on weekdays and weekends), and estimates of daily screen time spent on smartphones and online video games (i.e., average hours per day on weekdays and weekends, respectively).

3.2.1. Anxiety and Depressive Symptoms

The Scale Psichiatriche di Autosomministrazione per Fanciulli e Adolescenti (SAFA) (version for 11–13-year-old children) [54], developed and validated in Italy, is widely used in clinical settings to assess psychopathological symptoms in early adolescents. Its item structure and content are closely aligned with the cultural context of the country [55]. The test provides a preliminary yet comprehensive assessment of various psychopathological conditions. It includes scales for anxiety, depression, obsessive-compulsive symptoms, psychogenic eating disorders, somatic symptoms and hypochondria, and phobias, each with age-specific versions. All SAFA items are rated on a 3-point Likert-type scale, ranging from 0 = “never” to 2 = “often”, with higher scores indicating greater symptom severity. Negatively worded items are reverse-scored prior to analyses, and subscale and total scores are computed by summing item responses. For the present study, we administered the Scale Psichiatriche di Autosomministrazione per Fanciulli e Adolescenti—Anxiety scale (SAFA-A) and the Scale Psichiatriche di Autosomministrazione per Fanciulli e Adolescenti—Depression scale (SAFA-D) designed for the 11–13 age group. The SAFA-A consists of 50 items and includes four subscales (Generalized Anxiety, Social Anxiety, Separation Anxiety, and School-related Anxiety) and includes 8 critical items that signal clinically relevant anxiety symptoms but are not included in subscale scoring. In its original validation on non-clinical population, the SAFA-A demonstrated good internal consistency, with Cronbach’s α values ranging from 0.70 to 0.85 across subscales. Also, it demonstrated high convergent validity with the IPAT Anxiety Scale Questionnaire [56], with a correlation coefficient of r = 0.88. In the current study, the SAFA-A scale showed good internal consistency (Cronbach’s α = 0.86). Internal consistency coefficients for the subscales were acceptable to good, with α = 0.75 for Generalized Anxiety, α = 0.82 for Social Anxiety, α = 0.77 for Separation Anxiety, and α = 0.76 for School-Related Anxiety. The SAFA-D includes 56 items divided into seven subscales (Depressed Mood, Anhedonia and Disinterest, Irritable Mood, Feelings of Inadequacy and Low Self-esteem, Insecurity, Guilt, and Hopelessness) along with 10 critical items assessing response accuracy. In its original validation in a non-clinical population, the SAFA-D scale showed good test–retest reliability and high internal consistency (Cronbach’s α = 0.91), a result that was corroborated in the current sample (Cronbach’s α = 0.83). In the present study, internal consistency for the SAFA-D subscales was good to excellent, with Cronbach’s α = 0.89 for Depressed Mood, α = 0.75 for Anhedonia and Disinterest, α = 0.81 for Irritable Mood, α = 0.84 for Feelings of Inadequacy and Low Self-Esteem, α = 0.81 for Insecurity, α = 0.75 for Guilt, and α = 0.77 for Hopelessness. Furthermore, the SAFA-D demonstrated high convergent validity with the Children’s Depression Inventory [57]. In both the SAFA-A and SAFA-D, each item is rated as “true”, “neutral”, or “false”. Raw scores for each scale and subscale are converted into T-scores using normative tables by age and sex in accordance with the SAFA manual [54]; scores < 60 are considered within the normal range, scores 60–69 are classified as borderline, and scores ≥ 70 are deemed clinically significant or pathological. These scores can be used to generate individual profiles across scales and subscales.

3.2.2. Risk of Extreme Social Withdrawal

The Hikikomori Questionnaire-11 (HQ-11) [58] is an 11-item self-report Italian scale including a 5-point Likert response format (0 = “strongly disagree”, 4 = “strongly agree”). It is designed to assess risk of extreme social withdrawal over the past six months. Total scores range from 0 to 44, with higher scores indicating increased risk of extreme social withdrawal. In its original validation study the HQ-11 showed acceptable internal consistency (Cronbach’s α = 0.68), a reliability coefficient that was also observed in the present study (Cronbach’s α = 0.68).

3.2.3. Sleep–Wake Functioning

The Morningness-Eveningness Questionnaire (MEQ) [59] is a 19-item self-report scale used to assess individuals’ chronotype. It includes questions on sleep–wake patterns, preferred times for physical and mental activities, and subjective alertness throughout the day. Unlike traditional Likert scales, each item offers 4 to 5 discrete response options, each associated with a specific numerical value. Based on standard scoring procedures for the MEQ, total scores range from 16 to 86. Participants can be classified into five categories: Definite Evening Type (16–30), Moderate Evening Type (31–41), Intermediate Type (42–58), Moderate Morning Type (59–69), and Definite Morning Type (70–86). Higher scores indicate a greater tendency toward morningness. The full 19-item MEQ demonstrated good internal consistency in its original validation (Cronbach’s α = 0.82), which was also confirmed in our sample (Cronbach’s α = 0.74)

The Level 2—Sleep Disturbance—Child Age 11–17 measure is based on the PROMIS Pediatric Sleep Disturbance Short Form developed in 2018, an 8-item self-report instrument assessing sleep disturbance in children and adolescents [60]. The measure was included among the DSM-5 Level 2 Cross-Cutting Symptom Measures [61]. Each item asks respondents to rate the severity of their sleep difficulties over the past seven days using a 5-point Likert scale (1 = “never”, 5 = “always”). Total raw scores range from 8 to 40, with higher scores indicating greater sleep disturbance. The sum of the 8 item scores yields a total raw score, which can be converted into a T-score using the standardized scoring tables provided in the PROMIS Pediatric Sleep Disturbance Scoring Manual [60]. T-scores are interpreted as follows: Less than 55 = None to Slight Sleep Disturbance; 55.0—59.9 = Mild Sleep Disturbance; 60.0—69.9 = Moderate Sleep Disturbance; 70 and above = Severe Sleep Disturbance. In the present study, the scale showed acceptable internal consistency (Cronbach’s α = 0.64).

The Pediatric Daytime Sleepiness Scale (PDSS) [62] is an 8-item self-report scale assessing daytime sleepiness in school-age children and adolescents (typically ages 11–17). Although the PDSS has been used in previous pediatric samples, no peer-reviewed psychometric validation of an Italian version of the scale is currently available. Each item is rated on a 5-point Likert scale (0 = “never”, 4 = “always”). Total scores range from 0 to 32, with higher scores indicating greater daytime sleepiness. A cutoff score of 15 has been recommended to identify excessive daytime sleepiness in both sexes [63]. The PDSS showed excellent internal consistency in the original validation (Cronbach’s α = 0.89), while in our sample it demonstrated acceptable reliability (Cronbach’s α = 0.72).

3.2.4. Physical Activity

The Physical Activity Questionnaire for Older Children (PAQ-C) [64] is a 7-day self-report questionnaire composed of 10 items assessing general physical activity levels in children aged 8–14 years during the previous week. Items are rated on a 5-point Likert scale (1 = “very low activity”, 5 = “very high activity”). For Item 1 (activity checklist) and Item 9 (weekly frequency checklist), a mean score across all reported activities is first computed. These mean scores are then included as single items in the calculation of the overall PAQ-C score, which is obtained by averaging the nine item scores. Item 10 is not included in the total score calculation, as recommended by the scoring guidelines. Higher scores indicate greater physical activity. In the Italian context, a cutoff score of >2.75 has been suggested to identify physically active children, although it should be interpreted with caution [65]. In its Italian original validation, the PAQ-C demonstrated acceptable internal consistency (Cronbach’s α = 0.74), which was excellent in the present study (Cronbach’s α = 0.91).

3.2.5. Use of Technological Devices

An adapted version of the Online Gaming Motivations Scale (OGMS) [66] was used to assess motivations for online gaming. The original OGMS was developed based on Yee’s [67,68] multidimensional model of online gaming motivations and was later adapted and validated in a 12-item version measuring three motivational domains: Socializing, Immersion, and Achievement [66]. In the present study, we adopted this 12-item version but excluded one item from the Immersion subscale (“Creating a background story and history for your character”), as it was not applicable across all types of video games included in the study. The final instrument therefore consisted of 11 items, which were rated on a 5-point Likert scale (1= “not important at all”, 5 = “extremely important”), that evaluate three motives for online gaming such as Socializing (5 items), Immersion (3 items), and Achievement (3 items). Subscale scores were computed as the mean of the items within each dimension (range: 1–5), with higher scores indicating stronger endorsement of the respective gaming motive. In the original study, the 9-item version of the OGMS demonstrated a three-factor structure, as well as good internal consistency (Cronbach’s α = 0.88 for Socializing, Cronbach’s α = 0.83 for Achievement, and Cronbach’s α = 0.82 for Immersion). Internal consistency in our sample was acceptable: Cronbach’s α = 0.77 for Socializing, Cronbach’s α = 0.74 for Achievement, and Cronbach’s α = 0.75 for Immersion.

The Internet Gaming Disorder Scale–Short Form (IGDS9-SF) [69] is a 9-item self-report instrument that assesses problematic online gaming in accordance with DSM-5 criteria for Internet Gaming Disorder. Each item is rated on a 5-point Likert scale (1 = “never”, 5 = “very often”). A total score is obtained by summing all items (range: 9–45), with higher values indicating greater risk of problematic online gaming. A cut-off score of 21 has been proposed for the identification of individuals at risk of problematic online gaming. The Italian version of the IGDS9-SF demonstrated excellent internal consistency in its validation study, with Cronbach’s α = 0.96, a finding that was replicated in the present study (Cronbach’s α = 0.87).

The Test of Mobile Phone Dependence—Brief Version (TMD-brief) [70] is a 12-item self-report instrument that assesses four domains of PSU among adolescents, namely: Abstinence, Abuse and Interference with Other Activities, Tolerance, and Lack of Control. Each subscale includes three items, which are rated on a 5-point Likert scale (0 = “never/completely disagree”, 4 = “frequently/completely agree”). Subscale scores were computed as the sum of the three items belonging to each domain (range: 0–12). A total score was obtained by summing all 12 items (range: 0–48), with higher scores reflecting greater levels of PSU. The Italian 12-item TMD-brief demonstrated internal consistency values of Cronbach’s α = 0.81 for Abstinence, Cronbach’s α = 0.62 for Abuse and Interference with Other Activities, Cronbach’s α = 0.59 for Tolerance, and Cronbach’s α = 0.63 for Lack of Control, indicating acceptable reliability only for the abstinence subscale. In the present study, internal consistency coefficients were Cronbach’s α = 0.82 for Abstinence, Cronbach’s α = 0.64 for Abuse and Interference with Other Activities, Cronbach’s α = 0.64 for Tolerance, and Cronbach’s α = 0.71 for Lack of Control, and α = 0.89 for the total score, suggesting adequate reliability for the total scale and two subscales and marginal values for the others.

3.3. Data Analysis

Descriptive statistics (means, standard deviations, and frequencies) were computed for all variables including the subscales considered from the SAFA-A, SAFA-D, HQ-11, OGMS, and TMD-brief instruments. Sex differences in the variables of interest were examined through t-tests. Effect sizes were examined using Cohen’s d. Spearman’s rank-order correlations (ρ) were computed to investigate the associations between the variables of interest such as anxiety and depressive symptoms, risk of extreme social withdrawal, sleep–wake functioning, physical activity, motivations for gaming, problematic online gaming and PSU. This non-parametric method was selected to account for potential deviations from normality in score distributions. Although no global correction for multiple comparisons was applied, Holm’s method was used to adjust p-values for specific sets of theoretically related t-tests, namely the subscales of SAFA-A (n = 5), SAFA-D (n = 8), OGMS (n = 3), and TMD-brief (n = 5), in order to control for Type I error within each construct. All analyses were conducted using IBM SPSS Statistics for Windows, Version 25.0 (IBM Corp., Armonk, NY, USA), and statistical significance was set at p < 0.05 (two-tailed).

4. Results

4.1. Sample

The sample consisted of 276 middle school students (n = 150 females, 54%) recruited from a north-eastern area of Italy, specifically in the Emilia-Romagna region. Participants included 47 early adolescents from Forlì (17%), 132 from Montone (48%), and 97 from Predappio (35%). A binomial test indicated that the proportion of females was not significantly different from that of males (p = 0.17). Participants were aged between 11 and 13 years (M = 12.28, SD = 0.81).

4.2. Descriptive Statistics and Sex Differences

4.2.1. Anxiety, Depression, and Extreme Social Withdrawal Symptoms

Descriptive statistics and sex comparisons based on raw scores of SAFA subscales and HQ-11 are presented in

Table 1. Sex differences in anxiety, depression, and social withdrawal symptoms: descriptive statistics and t-test results based on raw scores among early adolescents (N = 276).

	Total Sample	Female	Male
	M (SD)	M (SD)	M (SD)	t(274)	p	d
SAFA-A Generalized Anxiety	12.00 (5.72)	13.32 (5.79)	10.42 (5.23)	4.33	0.005	−0.52
SAFA-A Social Anxiety	9.63 (5.25)	10.65 (5.31)	8.40 (4.93)	3.62	0.004	−0.44
SAFA-A Separation Anxiety	8.39 (4.64)	8.43 (4.63)	8.33 (4.68)	0.18	0.860	−0.02
SAFA-A School-related Anxiety	10.24 (5.09)	11.08 (5.41)	9.24 (4.52)	3.04	0.060	−0.37
SAFA-A (Anxiety) critical items	9.49 (5.13)	10.54 (5.28)	8.24 (4.67)	3.80	0.003	−0.46
SAFA-A (Anxiety) total scale	40.25 (16.14)	43.49 (16.74)	36.40 (14.56)	3.72	0.006	−0.45
SAFA-D Depressed Mood	5.30 (4.04)	6.25 (4.29)	4.36 (3.45)	3.99	0.010	−0.48
SAFA-D Anhedonia and Disinterest	3.70 (2.94)	3.77 (3.06)	3.62 (2.81)	0.41	1.000	−0.05
SAFA-D Irritable Mood	5.85 (3.39)	6.21 (3.38)	5.42 (3.37)	1.95	0.250	−0.23
SAFA-D Feelings of Inadequacy and Low Self-esteem	5.67 (3.80)	6.37 (4.00)	4.84 (3.38)	3.40	0.009	−0.41
SAFA-D Insecurity	7.95 (3.44)	8.97 (3.35)	6.74 (3.16)	5.65	0.008	0.68
SAFA-D Guilt	4.80 (3.31)	4.67 (3.45)	4.95 (3.13)	−0.71	0.288	−0.09
SAFA-D Hopelessness	4.94 (4.03)	5.50 (4.32)	4.27 (3.54)	2.55	0.330	−0.31
SAFA-D (Depression) critical items	6.07 (4.48)	6.52 (4.65)	5.53 (4.22)	1.83	0.280	−0.22
SAFA-D (Depression) total scale	44.38 (19.38)	47.75 (19.88)	40.38 (18.05)	3.20	0.070	−0.39
HQ-11 (Risk of Extreme Social Withdrawal) total scale	15.25 (8.28)	15.58 (8.84)	14.85 (7.57)	0.73	0.460	0.09

Note. SAFA-A = Scale Psichiatriche di Autosomministrazione per Fanciulli e Adolescenti—Anxiety scale; SAFA-D = Scale Psichiatriche di Autosomministrazione per Fanciulli e Adolescenti—Depression scale; HQ-11= Hikikomori Questionnaire-11. Means (M) and standard deviations (SD) refer to raw scores. p-values for SAFA-A (n = 5), and SAFA-D (n = 8) subscales were adjusted using the Holm—Bonferroni method.

, while SAFA T-score means and standard deviations are reported in

Table 2. Mean T-scores and standard deviations of anxiety and depression dimensions among early adolescents (N = 276).

	Total Sample	Female	Male
	M (SD)	M (SD)	M (SD)
SAFA-A Generalized Anxiety	59.22 (11.39)	61.18 (11.73)	56.88 (10.54)
SAFA-A Social Anxiety	60.76 (11.64)	62.07 (10.94)	59.19 (12.28)
SAFA-A Separation Anxiety	51.83 (10.87)	51.18 (11.16)	52.60 (10.50)
SAFA-A School-related Anxiety	56.41 (9.76)	56.63 (10.38)	56.14 (9.01)
SAFA-A (Anxiety) critical items	36.67 (3.68)	36.45 (3.88)	36.93 (3.41)
SAFA-A (Anxiety) total scale	57.71 (9.68)	58.20 (9.13)	57.13 (10.29)
SAFA-D Depressed Mood	58.14 (12.29)	59.67 (12.64)	56.33 (11.64)
SAFA-D Anhedonia and Disinterest	59.40 (12.62)	60.78 (13.07)	57.76 (11.91)
SAFA-D Irritable Mood	53.29 (10.82)	53.85 (11.09)	52.62 (10.51)
SAFA-D Feelings of Inadequacy and Low Self-esteem	59.73 (12.17)	60.13 (12.26)	59.25 (12.09)
SAFA-D Insecurity	56.38 (10.72)	58.49 (10.80)	53.87 (10.10)
SAFA-D Guilt	51.25 (11.37)	51.91 (11.64)	50.47 (11.03)
SAFA-D Hopelessness	59.60 (12.20)	60.67 (12.35)	58.33 (11.93)
SAFA-D (Depression) critical items	53.01 (13.97)	52.97 (14.14)	53.06 (13.82)
SAFA-D (Depression) total scale	60.27 (8.92)	60.60 (7.78)	59.88 (10.12)

Note. SAFA-A = Scale Psichiatriche di Autosomministrazione per Fanciulli e Adolescenti—Anxiety scale; SAFA-D = Scale Psichiatriche di Autosomministrazione per Fanciulli e Adolescenti—Depression scale; T-score classification according to SAFA norms: T < 60 = normal; T = 60–69 = borderline; T ≥ 70 = clinically significant (pathological).

.

As shown in Table 2, the sample reported normal scores on anxiety and depressive symptom scales (T < 60), except for scores on Social Anxiety subscale and Depression total scale, which fell within the borderline range according to normative criteria (T = 60–69). Scores on Social Anxiety (M = 60.76) and Generalized Anxiety (M = 59.22) emerged as the highest among the anxiety scales. Regarding depressive symptoms, the most elevated scores were reported for Anhedonia and Disinterest, Hopelessness, and Feelings of Inadequacy and Low Self-esteem (all > 59).

As reported in Table 1, sex comparisons revealed that females reported significantly higher scores on Generalized Anxiety, Social Anxiety, Anxiety critical items, and total SAFA-A scores than males. Moreover, females exhibited significantly higher levels of depressive symptoms, especially Depressed Mood, Feelings of Inadequacy and Low Self-esteem, and Insecurity. It is noteworthy that, among females, T-scores for Generalized Anxiety, Social Anxiety, Feelings of Inadequacy and Low Self-esteem, and total depressive symptom scales were within the borderline range (see Table 2). Although females also reported T-scores within the borderline range for Anhedonia and Disinterest and Hopelessness, whereas males reported elevated scores that remained within the normal range, raw scores on these scales did not differ significantly between the groups.

Finally, no significant sex difference was found for risk of extreme social withdrawal symptoms, as measured by the HQ-11, indicating comparable risk levels across males and females.

4.2.2. Sleep Quality and Physical Activity

As reported in

Table 3. Descriptive statistics and sex differences in chronotype, sleep, and physical activity among early adolescents (N = 276).

	Total Sample	Female	Male
	M (SD)	M (SD)	M (SD)	t(274)	p	d
MEQ (Morningness–Eveningness) total scale	51.74 (7.28)	50.22 (7.01)	53.54 (7.22)	−3.87	<0.001	−0.47
Level 2 (Sleep Disturbance) total scale	20.98 (3.55)	21.01 (3.65)	20.94 (3.44)	0.16	0.87	0.02
PDSS (Daytime Sleepiness) total scale	14.12 (5.76)	15.62 (5.66)	12.34 (5.37)	4.94	<0.001	0.59
PAQ-C (Physical Activity) total scale	2.47 (0.67)	2.35 (0.66)	2.61 (0.67)	−3.25	0.001	−0.39

Note. MEQ = Morningness- Eveningness Questionnaire; PDSS = Pediatric Daytime Sleepiness Scale; PAQ-C = Physical Activity Questionnaire for Older Children. Level 2 refers to the DSM-5 Level 2—Sleep Disturbance measure for ages 11–17, based on the PROMIS Pediatric Sleep Disturbance Short Form v1.0.

, participants showed an average MEQ score of 51.74 (SD = 7.28), indicating a general tendency toward an intermediate chronotype. A significant sex difference emerged, with males reporting a stronger inclination toward morningness compared to females. Chronotype distribution (

Table 4. Frequency classification of chronotypes among early adolescents (N = 276) based on the Morningness-Eveningness Questionnaire (MEQ) total scale.

Chronotype Category	Frequency (%)
Definite Evening Type (16–30)	0 (0%)
Moderate Evening Type (31–41)	23 (8.3%)
Intermediate Type (42–58)	207 (75%)
Moderate Morning Type (59–69)	45 (16.3%)
Definite Morning Type (70–86)	1 (0.4%)

) revealed that the vast majority of participants (75%) were classified as Intermediate Type, while 16.3% fell into the Moderate Morning Type and 8.3% into the Moderate Evening Type. Only one participant showed a Definite Morning Type, and no one fell into the Definite Evening Type category.

Sleep quality, as measured by the raw score of the Level 2—Sleep Disturbance—Child Age 11–17 measure (Table 3), was overall in the normative range (M = 20.98, SD = 3.55) with no significant sex differences. Based on T-score categories (

Table 5. Frequency classification of sleep disturbance among early adolescents (N = 276) based on the Level 2 (Sleep Disturbance) total scale.

T-score Category	Frequency (%)
None to Slight Sleep Disturbance (<55)	237 (59%)
Mild Sleep Disturbance (55–59.9)	35 (8.7%)
Moderate Sleep Disturbance (60–69.9)	4 (1%)
Severe Sleep Disturbance (≥70)	0 (0%)

), 27 participants (9.8%) reported clinically relevant levels of sleep disturbance (55 ≤ T < 70 = Mild/Moderate Sleep Disturbance; T ≥ 70 = severe), whereas the vast majority of participants fell within the None to Slight Sleep Disturbance range (T < 55).

PDSS results showed a mean score of 14.12 (SD = 5.76) (Table 3). Using the recommended cut-off of 15 to identify excessive daytime sleepiness, 42.4% of female participants and 26.1% of males scored above the threshold. The sex difference was statistically significant, indicating higher levels of sleepiness in females.

Table 6. Frequency distribution of early adolescents by bedtime and wake-up time on weekdays and weekends (N = 276).

	Weekdays (%)	Weekends (%)
Bedtime
Between 6:00 and 8:00 p.m.	6 (2.2%)	1 (0.4%)
Between 8:00 and 10:00 p.m.	124 (44.9%)	42 (15.2%)
Between 10:00 p.m. and 12:00 a.m.	124 (44.9%)	137 (49.6%)
After 12:00 a.m.	22 (8%)	96 (34.8%)
Wake-up time
Before 6:00 a.m.	34 (12.3%)	9 (3.3%)
Between 6:00 and 8:00 a.m.	238 (86.2%)	54 (19.6%)
Between 8:00 and 10:00 a.m.	3 (1.1%)	151 (54.7%)
Between 10:00 and 12:00 p.m.	1 (0.4%)	62 (22.5%)

Note. Time categories refer to participants’ self-reported habitual bedtimes and wake-up times. Midnight is indicated as 12 a.m., and noon as 12 p.m. to avoid ambiguity.

shows that weekday bedtimes were mostly between 8:00 p.m. and midnight (89.8%), with the majority waking up between 6:00 and 8:00 a.m. (86.2%). On weekends, bedtime and wake-up times shifted considerably, with over one-third of the sample (34.8%) going to bed after midnight and more than half waking up between 8:00 and 10:00 a.m. (54.7%). This pattern reflects the presence of “social jetlag”, a misalignment between internal circadian rhythms and socially imposed schedules.

In terms of physical activity, males reported significantly higher activity levels on the PAQ-C compared to females (Table 3). However, the overall mean PAQ-C score for both males and females fell below the suggested Italian cut-off of >2.75 [65], indicating generally insufficient physical activity across the sample.

4.2.3. Electronic Devices Use, Problematic Online Gaming and Problematic Smartphone Use

As shown in

Table 7. Descriptive statistics and sex differences in online gaming motivations, problematic online gaming, and problematic smartphone use among early adolescents (N = 276).

	Total Sample	Female	Male
	M (SD)	M (SD)	M (SD)	t(274)	p	d
OGMS Immersion	2.86 (1.00)	2.61 (0.98)	3.16 (0.95)	−4.73	0.002	0.57
OGMS Achievement	2.93 (1.07)	2.67 (1.10)	3.25 (0.95)	−4.69	0.010	0.56
OGMS Socializing	2.75 (1.04)	2.47 (1.05)	3.09 (0.93)	−5.19	0.003	0.63
IGDS9-SF (Problematic Online Gaming) total scale	15.61 (7.08)	13.76 (6.68)	17.81 (6.93)	−4.93	0.001	0.60
TMD Abstinence	5.13 (3.59)	5.76 (3.69)	4.38 (3.32)	3.24	0.001	0.39
TMD Abuse & Interference with other activities	5.19 (3.28)	5.77 (3.28)	4.50 (3.15)	3.27	0.002	0.39
TMD Tolerance	4.71 (3.07)	5.35 (3.18)	3.94 (2.75)	3.90	0.003	0.47
TMD Lack of Control	5.78 (3.48)	6.73 (3.50)	4.65 (3.11)	5.16	0.004	0.62
TMD-brief (Mobile Phone Dependence) total scale	20.81(11.42)	23.61(11.64)	17.48 (10.23)	4.61	0.005	0.56

Note. OGMS = Online Gaming Motivation Scale; IGDS9-SF = Internet Gaming Disorder Scale–Short Form; TMD-brief = Test of Mobile Phone Dependence—Brief Version. A score of ≥21 is considered the cut-off for identifying individuals at risk of problematic online gaming [69] p-values for OGMS subscales were corrected using the Holm—Bonferroni method (n = 3); p-values for TMD subscales and total score were corrected using the Holm—Bonferroni method (n = 5).

, male participants exhibited significantly higher levels of all online gaming motivations. Furthermore, higher levels of problematic online gaming were observed among males, with a moderate effect size. Although the mean score on IGDS9-SF total score in the overall sample was below the cut-off of 21, 66 participants (16.4%) were classified as being at risk for problematic online gaming, of whom 46 were male (69.7%). Significant sex differences were also observed in the domains of PSU, with higher scores on TMD-brief total scale and subscales (Abstinence, Abuse and Interference with Other Activities, Tolerance, and Lack of Control) among females.

As shown in

Table 8. Frequency distribution of smartphone and video game use on weekdays and weekends among early adolescents (N = 276).

	Weekdays (%)		Weekends (%)
	Smartphones	Videogames	Smartphones	Videogames
Less than 1 h	47 (17%)	155 (56.2%)	47 (17%)	158 (57.2%)
1–2 h daily	93 (33.7%)	71 (25.7%)	106 (38.4%)	68 (24.6%)
3–4 h daily	80 (29%)	32 (11.6%)	57 (20.7%)	29 (10.5%)
More than 4 h daily	56 (20.3%)	18 (6.5%)	66 (23.9%)	21 (7.6%)

, most participants reported using smartphones for at least 1–2 h per day on weekdays (33.7%) or longer (49.3%), with a further increase during weekends (38.4% reported 1–2 h, and 23.9% reported over 4 h). In contrast, time spent gaming was lower: on weekdays, 56.2% of participants reported playing less than one hour daily, and this trend remained stable at weekends (57.2%). Only a small proportion of participants reported playing online video games for more than 4 h per day (6.5% on weekdays; 7.6% on weekends).

4.3. Correlation Analysis

Table 9. Spearman’s rank correlation coefficient (Spearman’s rho) for the research variables.

	1.	2.	3.	4.	5.	6.	7.	8.	9.	10.	11.	12.
1. SAFA-A (Anxiety) total scale	-
2. SAFA-D (Depression) total scale	0.63 **	-
3. HQ-11 (Risk of Extreme Social Withdrawal) total scale	0.48 **	0.47 **	-
4. MEQ (Morningness—Eveningness) total scale	−0.24 **	−0.22 **	−0.13 *	-
5. Level 2 (Sleep Disturbance) total scale	0.01	0.06	0.09	0.00	-
6. PDSS (Daytime Sleepiness) total scale	0.41 **	0.29 **	0.27 **	−0.53 **	−0.003	-
7. PAQ-C (Physical Activity) total scale	−0.09	−0.01	−0.15 *	0.09	0.09	−0.12 *	-
8. OGMS Immersion	0.16 **	0.26 **	0.18 **	−0.11	0.08	−0.01	0.06	-
9. OGMS Achievement	0.17 **	0.19 **	0.23 **	−0.06	0.10	−0.04	−0.08	0.61 **	-
10. OGMS Socializing	0.15 *	0.27 **	0.15 *	−0.16 **	0.05	0.09	0.09	0.66 **	0.58 **	-
11. IGDS9-SF (Problematic Online Gaming) total scale	0.28 **	0.45 **	0.35 **	−0.18 **	0.13 *	0.16 **	−0.01	0.45 **	0.52 **	0.56 **	-
12. TMD-brief (Mobile Phone Dependence) total scale	0.32 **	0.28 **	0.17 **	−0.42 **	0.09	0.35 **	0.02	0.18 **	0.03	0.24 **	0.20 **	-

Note. SAFA-A= Scale Psichiatriche di Autosomministrazione per Fanciulli e Adolescenti—Anxiety scale; SAFA-D= Scale Psichiatriche di Autosomministrazione per Fanciulli e Adolescenti—Depression scale; HQ-11= Hikikomori Questionnaire-11; MEQ = Morningness—Eveningness Questionnaire; Level 2 = DSM-5 Level 2—Sleep Disturbance—Child Age 11–17 measure (PROMIS Pediatric Sleep Disturbance Short Form v1.0); PDSS = Pediatric Daytime Sleepiness Scale; PAQ-C = Physical Activity Questionnaire for Children; OGMS = Online Gaming Motivation Scale; IGDS9-SF = Internet Gaming Disorder Scale—Short Form; TMD-brief = Test of Mobile Phone Dependence—Brief Version. * p < 0.05; ** p < 0.01.

displays Spearman’s rank-order correlation matrix for all study variables. Anxiety and depression symptoms were strongly correlated with each other. Furthermore, both anxious and depressive symptoms showed moderate associations with an increased risk of extreme social withdrawal; small-to-moderate positive correlations with daytime sleepiness, PSU, problematic online gaming and online gaming motives; and small negative associations with morningness chronotype.

Risk of extreme social withdrawal was moderately associated with increased problematic online gaming and was weakly correlated with increased motivations for online gaming, PSU, and daytime sleepiness, as well as decreased levels of morningness and physical activity.

Morningness was moderately and negatively associated with daytime sleepiness and showed small-to-moderate negative correlations with PSU, problematic online gaming, and socializing motivation for online gaming.

Sleep disturbance showed a weak positive correlation with problematic online gaming. In contrast, daytime sleepiness was positively moderately associated with PSU, positively weakly associated with problematic online gaming, and negatively weakly correlated with physical activity levels.

Also, problematic online gaming and PSU were weakly positively correlated with each other. Finally, problematic online gaming was moderately and positively associated with all three online gaming motivation dimensions, whereas PSU showed weak positive associations with Immersion and Socializing motives.

5. Discussion

The present study aimed to investigate the physical and mental health of early adolescents living in a forested area of the Emilia-Romagna region (Italy), in the aftermath of the COVID-19 pandemic. As this area comprises geographically isolated communities, early adolescents residing in these communities may have experienced greater deprivation of opportunities for social interaction during the pandemic, potentially contributing to increased vulnerability to pandemic–related limitations in physical and mental health. The sample included 276 middle school students regularly attending school in Forlì, Predappio, and Valle del Montone. We assessed a range of indicators spanning emotional symptoms, risk of extreme social withdrawal, sleep–wake functioning, physical activity, and problematic use of technological devices.

5.1. Internalizing Symptoms and Digital Behaviors

Although participants did not report clinically significant levels of anxiety or depression, their scores on Social Anxiety and overall depressive symptoms fell within the borderline range when compared with normative data. Accordingly, early adolescents in our sample may experience significant levels of distress that could heighten vulnerability to negative mental health outcomes. Previous research showed increased rates of anxiety and depressive disorders among adolescents during the COVID-19 pandemic [71]. The mild vulnerability observed in our sample may partly reflect the enduring effects of the pandemic on adolescent mental health, underscoring the need for ongoing monitoring and targeted prevention programs.

Furthermore, females reported higher levels of anxious symptoms—including Generalized and Social Anxiety—as well as higher scores on certain depressive dimensions, namely Depressed Mood, Feelings of Inadequacy and Low Self-esteem, and Insecurity, than males. These findings are consistent with previous research showing that females are more prone to exhibiting internalizing symptoms [72,73].

Correlation analyses showed that both anxiety and depressive symptoms were associated with increased problematic online gaming and PSU. The maladaptive use of technological devices may function as a temporary coping strategy for managing psychosocial difficulties and addressing unmet needs [53,74,75,76]. Accordingly, early adolescents experiencing distress may be at higher risk of using technological devices as a coping strategy [76]. Furthermore, significant sex differences emerged in problematic online gaming and PSU, potentially reflecting different compensatory strategies in response to distressing circumstances. Specifically, females reported greater severity across PSU dimensions, including Abstinence, Abuse and Interference with Other Activities, Tolerance, and Lack of Control; in contrast, males reported higher levels of problematic online gaming. Additionally, males exhibited stronger motivations to engage in online gaming for Achievement, Immersion, and Socializing motives. Importantly, 16.4% of the sample scored above the cut-off for problematic online gaming, with a clear overrepresentation of males (69.7%). These individuals may be particularly vulnerable to certain negative psychosocial outcomes, including school disengagement, sleep disruption, and impaired mood regulation [48]. Our findings are consistent with previous research suggesting that girls are more inclined to seek social reassurance or escape from negative emotions through smartphone engagement [77], whereas boys show a stronger preference for gaming as both a leisure activity and a means of social connection [78].

5.2. Sleep–Wake Functioning

Circadian functioning in our sample was predominantly characterized by the Intermediate Type chronotype, with males reporting a higher morning orientation than females. Notably, average scores on the scales assessing sleep disturbance and daytime sleepiness were within the normative range. However, approximately half of females (42.4%) and one quarter of males (26.1%) reported clinically significant levels of daytime sleepiness. The high proportion of females reporting clinically relevant daytime sleepiness may be closely related to a higher evening orientation and greater severity of certain internalizing symptoms. Notably, we found significant correlations between internalizing symptoms, evening orientation, and daytime sleepiness. These findings are consistent with previous research suggesting that adolescents with a Definite Evening Type chronotype are more likely to exhibit elevated internalizing symptoms [79], as well as heightened daytime sleepiness [80].

5.3. Risk of Extreme Social Withdrawal

In the current study, the mean score on the risk of extreme social withdrawal scale (M = 15.25, SD = 8.28) was higher than the mean score reported in a sample of 1814 Italian adolescents at the baseline of a longitudinal study (M = 12.86, SD = 10.59) [81]. Furthermore, the risk of extreme social withdrawal was associated with enhanced internalizing symptoms, problematic online gaming, and daytime sleepiness. These findings indicate that early signs of extreme social withdrawal may manifest even in school-attending youth, particularly in those exhibiting distress indicators.

5.4. Physical Activity

Finally, physical activity was generally low across the sample and significantly lower among females. The sex differences observed in the current study are in line with the evidence already summarized in a previous umbrella review [82]. However, higher activity levels were negatively associated with risk of extreme social withdrawal and daytime sleepiness. It could be speculated a potential protective role of physical activity for psychological and mental traits, confirming previous evidence regarding the associations of physical activity with decreased anxiety and heightened quality of life [83]. Therefore, engagement in physical activity remains a crucial strategy to promote a healthy lifestyle, including the encouragement of sports participation, that may support early adolescents in maintaining healthier daily routines and social ties, thus acting as protective buffers against emotional and behavioral difficulties [84].

5.5. Limitations

Our results should be interpreted with caution in light of certain limitations. The variables of interest were assessed through self-report instruments, which may increase the risk of bias. Although the cross-sectional design of the current study is consistent with its primary aim—namely, to evaluate indicators of mental and physical health among early adolescents living in forested and geographically isolated areas in the post-pandemic period—it does not allow for the establishment of causal relationships among the variables examined. Furthermore, this study design does not enable the ascertainment of the effects of the COVID-19 pandemic on physical and mental health in our sample, as no pre- and post-pandemic comparisons were conducted for the variables of interest. Future studies might adopt multi-method approaches, including the assessment of variables of interest through structured interviews, informant reports, or experimental procedures. Additionally, longitudinal research might allow for the detection of the directionality of the associations between the variables of interest, and follow-up assessments could be highly relevant for monitoring potential changes in physical and mental health. Finally, future studies might evaluate potential factors that could explain such changes (e.g., social skills, social support).

Despite these limitations, our findings emphasize the need for integrated, school-based strategies to support emotional well-being, promote healthy online habits, and regulate circadian rhythms in early adolescence. In geographically isolated contexts, such efforts may be especially crucial to offset the lingering psychosocial effects of the pandemic.

6. Conclusions

This study provides an overview of the mental and physical health of early adolescents in a post-pandemic context, focusing on a geographically isolated sample from the Emilia-Romagna region in Italy. Overall, the findings suggest that early adolescents may exhibit certain vulnerabilities—although not overtly clinically significant—in emotional functioning, sleep–wake regulation, and the use of technological devices after the pandemic, with clear sex-specific patterns. In particular, internalizing symptoms, PSU, problematic online gaming, and daytime sleepiness emerged as interrelated indicators of psychosocial vulnerability, also associated with an increased risk of extreme social withdrawal. Conversely, physical activity appeared to play a protective role—especially among males—highlighting its potential relevance for promoting healthier routines and social engagement in early adolescence. These results underscore the importance of integrated preventive strategies that combine emotional support, sleep hygiene education, and the promotion of more adaptive engagement with technological devices, which may address not only problematic use of such devices but also other relevant phenomena, such as difficulties in managing misinformation and cyberbullying victimizations [85]. School settings and structured physical activities represent key contexts for fostering emotional regulation, social connectedness, and circadian stability, particularly for adolescents living in rural or geographically isolated areas where access to resources may be limited.