Addictive Behaviors, Mental Health, and Quality of Life in Adolescents: Toward Integrative Assessment for Early Identification of Vulnerability

Abstract

Objectives:

Addictive behaviors are frequently associated with anxiety and depressive disorders. Adolescence represents a critical period of heightened vulnerability to both types of disorders. In recent years, a significant global decline in adolescent mental health—especially among girls—has raised concerns about the short-term evolution of addictive behaviors. Despite available data, this relationship remains poorly understood, warranting further research to improve prevention and care strategies.

Methods:

This study analyzed data from a French cross-sectional observational study (DOPADO), which aimed to assess addictive behaviors among adolescents aged 13 to 18 years attending middle and high schools. Data were collected between 2018 and 2020 using electronic surveys, enabling the evaluation of past-12-month psychoactive substance use, associated risky behaviors, sociodemographic characteristics, anxiety and depressive symptoms, psychological distress, and quality of life.

Results:

Among 4844 adolescents, half exhibited anxiety and/or depressive symptoms. These were associated with higher levels of substance use, increased risky behaviors, and impaired quality of life. A Principal Component Analysis revealed 3 distinct profiles: one with preserved well-being, and 2 with significant impairment. These were characterized by higher anxiety-depressive symptoms (especially in the second cluster) and more pronounced addictive behaviors and risky use (notably in the third cluster).

Conclusions:

Our study highlights robust associations between anxiety/depressive symptoms, addictive behaviors, and quality of life. Several distinct adolescent profiles emerged with respect to mental health and addictive behaviors, each characterized by specific patterns of quality of life and functioning, underscoring the importance of integrated assessment, prevention, and treatment strategies in adolescents.

Adolescence is a critical period marked by heightened vulnerability to addictive behaviors and psychiatric disorders due to ongoing brain development, increased risk-taking, and exposure to various social and environmental influences.^1,2 Alcohol consumption is a significant global issue,³ with 26% of adolescents aged 15 to 19 being current drinkers, representing 155 million individuals worldwide.⁴ Alcohol consumption starts mostly before the age of 15, and 13.6% of teenagers, especially boys, engage in binge drinking.⁴ Nearly 31% of 12th graders reported using drugs in the past year. Alcohol, nicotine vaping, and cannabis are the most frequently used substances, though their usage has decreased.⁵ Worldwide,⁴ and in France as well,^6,7 consumption levels among young people remain high. Although the latest data are encouraging and seem to indicate a slight reduction in the experimentation rate, which should be seen in the context of the special period linked to the COVID-19 pandemic (coronavirus disease).^6,7

The co-occurrence of depressive and addictive disorders is prevalent in the general population, with an American study reporting that 30% of individuals with depression also suffer from a substance use disorder.⁸ The coexistence of these 2 clinical conditions can complicate diagnosis, as symptoms may be triggered, worsened, or relieved depending on the substance used. Individuals with anxiety disorders have about a 40% risk of developing a substance use disorder (vs. 32% for alcohol, 15.8% for tobacco, and 3.7% for illicit drugs in the general population—epidemiological data from 2019 to 2021), while roughly 20% of those with substance use disorders have an anxiety disorder (vs. 4.4% in the global population).^9–11 Anxiety symptoms are strong risk factors for addiction, as substances may be used to alleviate negative emotions through sedation, dissociation, or euphoria, with some substances producing multiple effects depending on the dose.¹²

According to the World Health Organization (WHO) estimations, 1 in 7 adolescents (aged 10–19) worldwide suffers from a mental disorder.^13,14 Major depressive disorders and anxiety disorders are the most common diagnostic categories among teenagers, leading to high mortality and disability, with suicide being a major cause of death in this age group (45,800 adolescents die from suicide each year).^14,15 The latest WHO reports show a decline in life satisfaction, self-rated health, and mental well-being among young people between the ages of 11 and 15.^3,16 In Europe, a French national survey shows that the mental health and well-being of adolescents have deteriorated significantly between 2018 and 2022, particularly for girls.¹⁷ The French Health Barometer 2021 shows that the prevalence of depression among 18-24 year-olds nearly doubled from 12% to 21% between 2017 and 2021.¹⁸ Mental health tends to deteriorate during middle school and does not improve during high school.¹⁸ The COVID-19 pandemic appears to have exacerbated this phenomenon, particularly among adolescents.^19,20

Research data are limited and primarily based on global estimates. They rarely assess addictive behaviors, mental health, and quality of life collectively. Consequently, there is currently a lack of reliable data on the characteristics of adolescents who use substances in relation to their mental health. Additional research is therefore needed to better understand the association between these dimensions.

The objective of this study was to simultaneously assess addictive behaviors, mental health, and quality of life in adolescents to determine whether these issues are associated. In addition, this study seeks to identify whether specific clinical profiles emerge in adolescents.

METHODS

Study Design

Our research protocol (DOPADOS) studied addictive behaviors among adolescents through epidemiological, psychological, and sociological perspectives. This cross-sectional observational study was conducted in France from 2018 to 2020, with schools selected through randomization and stratified by education sector, type, and geographic setting. Data were collected via an electronic survey using adaptive filtering questions on the REDCap software (Research Electronic Data CAPture), ensuring confidentiality and anonymity. Students completed the questionnaire on tablets under a psychologist’s supervision. A 2-stage stratified cluster sampling method was used, with schools and then classes randomly selected, stratified by public/private status and educational level for representativeness.

Research Instruments

1. The following sociodemographic data were collected: gender, age, place of residence, and education level (middle or high school).

2. In terms of addictive behaviors, a questionnaire was developed to assess the prevalence of licit and illicit substance use over the past 12 months, as well as to evaluate the types and patterns of usage. This information was used to identify profiles of problematic use in our adolescent population. (Appendix 1, Supplemental Digital Content 1, http://links.lww.com/JAM/A750). Psychometric scales were employed to assess the risk of dependence for some substances (Appendix 1, Supplemental Digital Content 1, http://links.lww.com/JAM/A750): Alcohol Use Disorders Identification Test Short (AUDIT-C)²¹ for alcohol and Cannabis Abuse Screening Test (CAST) for cannabis.²² These scales have been validated for use in adolescents.^23,24 Risk behaviors related to psychoactive substances were measured with the Adolescents and Psychoactive Substances scale (ADOSPA),²⁵ and risky internet use was evaluated using the Internet Addiction Scale (IAS).²⁶

3. In terms of psychiatric and psychological assessment (Appendix 1, Supplemental Digital Content 1, http://links.lww.com/JAM/A750), the French version of the Hospital Anxiety and Depression (HAD) scale was used to measure anxiety and depressive symptoms. Considering the young age of our sample, and in order to identify all individuals presenting symptoms (even if they do not meet full criteria for an anxiety or depressive disorder), we selected all participants with a HAD score ≥8. This scale has been validated in adolescents by White et al.^27,28 In our analysis, the participants were then categorized based on the presence of symptoms: anxiety only (group A, anxiety subscale ≥8 and depression subscale <8), depressive only (group D, depression subscale ≥8 and anxiety subscale <8), both anxiety and depressive (group AD, anxiety and depression subscales ≥8), or no symptoms (group NN, anxiety and depression subscales <8). The Kessler Psychological Distress Scale–6 items (K6) was used to assess overall psychological distress. Quality of life was assessed using the VSP-A (Adolescent Perceived Health and Well-being scale) tool.

Data Analysis

Categorical variables were summarized using frequencies and percentages. Continuous variables were described using means and standard deviations or medians and interquartile ranges. Group comparisons relied on ANOVA or Kruskal-Wallis tests, followed, when appropriate, by Tukey-Kramer or Dunn pairwise tests. Associations between categorical variables were assessed using χ² or Fisher exact tests, with post hoc exploration via the Marascuilo procedure.

A multiple correspondence analysis (MCA) was then performed, using categorized variables when no established thresholds were available. MCA served to explore relationships among variable modalities and to identify participant groups with similar profiles. Unlike χ²-based tests, which only assess the presence of associations, MCA offers an integrated view of how categorical variables interact and helps reveal underlying patterns. Variables included in the multivariable exploration were selected based on univariate findings and clinical relevance.

Analyses were conducted using Stata 15 and R 3.3.3. A 2-sided significance level of 0.05 was applied. Given the exploratory aim, especially for analyses informing the factor structure, no correction for multiple comparisons was implemented. To complement P values, often significant in large samples, effect sizes were systematically reported: Hedges’ g for quantitative variables and Cramer’s V (with 95% bootstrap confidence intervals) for categorical variables.

Ethical Aspects

This noninterventional study received ethics approval from a French research ethics committee (Comité de Protection des Personnes, Île-de-France 4) on August 28, 2018, under reference ID-RCB: 2018-A00597-48. Appropriate informed consent (personal or parental) was obtained. The French National Agency for the Safety of Medicines and Health Products, as the study sponsor, signed a compliance agreement with this methodology on November 18, 2016.

RESULTS

In this study, a total of 4844 adolescents from 61 educational institutions, including both middle and high schools, were included (Table 1). To study the association between anxiety-depressive disorders and addictive behaviors, we formed 4 groups of participants according to their HAD score: 34.2% had anxiety symptoms only (A), 12.7% had both anxiety and depressive symptoms (AD), 3% had depressive symptoms only (D), and 50.1% had no symptoms (NN).

TABLE 1.

Sociodemographic Characteristics of the 4.844 Adolescents Based on the Presence of Anxiety and/or Depressive Symptoms (Groups A, AD, D, and NN).

	A	AD	D	NN
	N=1656 (34.2%)	N=613 (12.7%)	N=146 (3%)	N=2429 (50.1%)	P
Age (y, mean±SD)	15.5 (1.7)	15.7 (1.9)	15.7 (1.7)	15.4 (1.7)	<0.0001 u,v,x,z
Gender (female, %)	60.0	66.6	30.1	41.1	<0.0001
School level (%)	—	—	—	—	0.002 u,v,x,z
Middle school (%) n=2771	56.0	51.6	54.1	59.6	—
High school (%) n=2073	44.0	48.5	45.9	40.4	—
Living area (%)	—	—	—	—	−0.001 v,x,y,z
Suburbs (%) n=1348	25.1	31.5	26.7	28.8	—
City center (%) n=2108	42.3	44.9	48.0	43.7	—
Isolated (%) n=711	17.5	11.6	11.0	13.8	—
Rural (%) n=677	15.1	12.1	14.4	13.7	—

Each post hoc comparison is labeled by a letter in the table (A vs. AD = u, A vs. D = v, A vs. NN =w, AD vs. A = x, AD vs. NN = y, D vs. NN = z), with the corresponding letter shown in superscript in the P-value column only when the comparison is not significant.

Participants in NN group are significantly younger than A, AD, and D. NN participants are more likely to be in middle school than those presenting with symptoms. In addition, these participants are more often female in the A (60%) and AD (66%) groups. Our participants predominantly come from urban or suburbs environments, over 65% in all groups. However, there is a higher proportion of individuals living in isolated or rural areas in group A, while there is a greater percentage of those living in urban areas in group D, and a higher rate of suburbs residents in the AD group.

In terms of addictive behaviors, usage patterns are associated with the presence or absence of anxious or depressive symptoms (Table 2). For all substances, excessive or problematic use is significantly higher in the A, AD, and D groups compared to the NN group. The largest effect sizes are observed for coffee (0.11), alcohol (0.11), tobacco (0.11), harmful use of cannabis (0.14), and the type of internet use (0.23), which includes no risk (47% of adolescents), at-risk use (42%), and dependency-risk use (11%). NN participants are significantly less affected by harmful use of drugs (0.14). Alcohol binge behaviors are also more frequent among participants with anxious-depressive symptoms (0.11). Among participants with anxiety and/or depressive symptoms, the highest use of coffee, alcohol, tobacco, cannabis, hallucinogens, and amphetamines is found in the AD group. For cannabis, as well as cocaine, heroin, ecstasy, and synthetic drugs, the D group is primarily affected. Overall, the post hoc comparisons indicate significant differences in substance use between the symptomatic groups and the nonsymptomatic group. However, some comparisons are nonsignificant for substances with the lowest prevalence of use in our sample, and the corresponding effect sizes are uniformly small.

TABLE 2.

Characteristics of Addictive Behaviors Over the Past 12 Months in 4.844 Adolescents

	A	AD	D	NN
	N=1656 (34.2%)	N=613 (12.7%)	N=146 (3%)	N=2429 (50.1%)	P	Effect Size
Coffee (%)	9.3	12.1	10.3	4.8	<0.0001u,v,x	0.11
Alcohol (%)	29.8	35.2	30.1	22.4	<0.0001v,x,z	0.11
Alcohol binge (%)	—	—	—	—	<0.0001v,z	0.11
No	40.2	36.1	31.5	35.0	—	—
Yes	18.0	28.9	18.5	13.5	—	—
No lifetime alcohol	41.9	35.1	50.0	51.5	—	—
Tobacco (%)	—	—	—	—	<0.0001v,x	0.11
no	71.6	59.7	66.4	78.5	—	—
occasional	16.9	19.3	16.4	12.9	—	—
regular use	11.6	21.0	17.1	8.7	—	—
Cannabis (%)	—	—	—	—	<0.0001x	0.08
no	86.2	77.2	80.8	88.5	—	—
occasional	10.9	15.3	11.6	7.8	—	—
regular use	3.0	7.5	7.5	3.8	—	—
Risky use of cannabis (%)	32.3	39.3	35.7	22.5	0.003 u,v,x,z	0.14
Cocaine (%)	3.3	4.9	8.1	1.8	<0.0001u,x	0.08
Hallucinogen (%)	3.4	6.9	4.0	2.2	<0.0001v,x,z	0.09
Heroin (%)	1.2	1.6	6.1	0.9	<0.0001u,w,y	0.08
Amphetamine (%)	1.5	4.7	2.0	1.6	<0.0001v,w,x,z	0.08
Ecstasy (%)	1.6	1.6	3.0	2.6	0.2u,v,w,x,y,z	0.03
Synthetic drug (%)	3.0	3.9	7.1	1.6	0.001u,x	0.07
Global psychotimulants (%)	4.2	8.2	8.2	3.7	<0.0001w,x	0.08
Global Illicit substances (%)	6.6	11.1	9.6	4.7	<0.0001v,x	0.09
Harmful use of drugs (%)	25.2	39.6	18.5	16.3	<0.0001v,z	0.14
Type of internet use (%)	—	—	—	—	<0.0001z	0.23
No risk	37.6	28.2	50.7	57.9	—	—
Risky use	48.9	51.2	29.5	35.8	—	—
Risk of dependence	13.5	20.6	19.9	6.3	—	—

Categorized by the presence of anxiety and/or depressive symptoms (groups A, AD, D, and NN). Problematic use was defined as follows: Consumption several times per day for coffee; an AUDIT Score ≥3 for females and >4 for males for alcohol; A Fagerström Score ≥3 for Tobacco; A CAST Score ≥3 for cannabis; and occasional use for cocaine, heroin, hallucinogens, amphetamines, ecstasy, and synthetic drugs.

Each post hoc comparison is labeled by a letter in the table (A vs. AD = u, A vs. D = v, A vs. NN =w, AD vs. A = x, AD vs. NN = y, D vs. NN = z) with the corresponding letter shown in superscript in the P-value column only when the comparison is not significant.

In terms of mental and psychiatric health, only 50.1% of the students in our sample present no anxiety or depressive symptoms (Table 3). Severe distress affects 65% of group A, 89.9% of group AD, 52.7% of group D, and 16.6% of group NN (Table 3). Perceived quality of life is also significantly impaired, particularly among adolescents presenting psychiatric symptoms (Table 3). A significant difference is observed across all dimensions of the VSP-A between groups. Groups A, AD, and D demonstrate a more deteriorated quality of life compared to the NN group. The strongest associations are observed for physical well-being, mental well-being, and vitality score. Table 3 shows that groups A, AD, and D have significantly fewer individuals with a good quality of life across the various domains (Q4—fourth quartile) and significantly more individuals with poor quality of life (Q1—first quartile).

TABLE 3.

Mental Health Characteristics (K6 Scale) and Quality of Life (VSP-A Scale: Q4 Represents a Very Good Perceived Quality of Life

	A	AD	D	NN
	N=1656 (34.2%)	N=613 (12.7%)	N=146 (3%)	N=2429 (50.1%)	P	Effect Size
Psychological distress (K6)	—	—	—	—	<0.0001	0.42
Mild	2.5	1.3	16.4	26.1	—	—
Moderate	32.5	8.8	30.8	57.3	—	—
Severe	65	89.9	52.7	16.6	—	—
VSP-A (%)	—	—		—	—
Physical well-being score	—	—	—	—	<0.0001v	0.32
Q1	37	62	37	11	—
Q4	18	7	15	47
Mental well-being score	—	—	—	—	<0.0001	0.44
Q1	46	76	25	6	—	—
Q4	8	5	32	46	—	—
Self-image score	—	—	—	—	<0.0001	0.24
Q1	36	50	23	14	—	—
Q4	20	15	32	43	—	—
Friendship score	—	—	—	—	<0.0001x	0.21
Q1	26	54	59	19	—	—
Q4	32	13	12	39	—	—
Relationship with parents score	—	—	—	—	<0.0001	0.23
Q1	30	60	50	18	—	—
Q4	22	8	13	35	—	—
Relationship with teachers' score	—	—		—	<0.0001	0.2
Q1	18	40	32	12	—	—
Q4	31	14	30	43	—	—
School work score	—	—	—	—	<0.0001	0.22
Q1	28	55	43	15	—	—
Q4	36	19	26	51	—	—
Vitality score	—	—	—	—	<0.0001	0.4
Q1	30	80	62	9	—	—
Q4	17	6	9	41	—	—
Leisure score	—	—	—	—	<0.0001x	0.15
Q1	26	46	40	19	—	—
Q4	35	22	31	41	—	—

Q2-Q3 represent a median perceived quality of life, and Q1 represents a poor perceived quality of life of the 4.844 adolescents based on the presence of anxiety and/or depressive symptoms (groups A, AD, D, and NN).

Each post hoc comparison is labeled by a letter in the table (A vs. AD = u, A vs. D = v, A vs. NN =w, AD vs. A = x, AD vs. NN = y, D vs. NN = z), with the corresponding letter shown in superscript in the P-value column only when the comparison is not significant.

Factor analyses revealed the presence of three clusters within our sample (Fig. 1). These clusters show significant differences in terms of mental health, addictive behaviors, and quality of life profiles (Table 4).

FIGURE 1.

Graphical representation of the 3 clusters identified through multiple correspondence analysis, characterized by mental health, addictive and risky behaviors, and associated levels of perceived quality of life (VSP-A scale) (Q4 very good perceived quality of life, Q2-Q3 median perceived quality of life, and Q1 poor perceived quality of life).

TABLE 4.

Sociodemographic, Psychopathological, and Addiction Profiles of the 3 Identified Clusters

	Cluster 1	Cluster 2	Cluster 3
	N=2277 (47%)	N=1600 (33%)	N=967 (20%)	P
Gender (female, %)	38.52	70.38	45.50	<0.01
Middle school (%)	70.18	63.19	16.75	<0.01
Private school (%)	31.36	36.50	49.12	<0.01
Age (mean, y)
≤14.5	47.12	41.19	9.41	—
14.6–15.5	26.88	25.31	17.06	—
15.6–16.5	10.72	12.88	22.96	—
>16.5	15.28	20.63	50.57	<0.01
HAD (%)
Group A	18.4	53.4	39.6	<0.01
Group AD	0.8	25.4	19.5	—
Group D	1.5	5.1	3.0	—
Group NN	79.3	16.1	37.9	—
Psychological distress (K6)
Mild	27.8	1.8	4.9	<0.01
Moderate	61.3	20.3	32.1	—
Severe	10.9	78.0	63.1	—
Coffe	3.2	5.6	20.4	<0.01
Global illicit substances	2.6	3.6	19.4	<0.01r
Alcohol	10.7	12.2	88.7	<0.01r
Tobbaco
No	90.8	84.1	14.5	<0.01
Experimentation	7.7	13.1	36.0	—
Regular use	1.5	2.8	49.5	—
Cannabis
No	97.6	98.2	38.7	<0.01r
Experimentation	2.2	1.8	41.5	—
Regular use	0.2	0.1	19.9	—
Alcohol binge (%)	4.0	5.1	67.9	<0.01
Risky internet use (%)	36.2	69.2	65.7	<0.01
Harmful use of drugs (%)	—	—	—	<0.01
0 criterion	67.9	56.3	7.0	<0.01
1 criteria	26.4	30.3	16.3	—
≥2 criteria	5.7	13.4	76.3	—

Each post hoc comparison is labeled by a letter in the table (C1 vs. C2 = r, C1 vs. C3 = s, C2 vs. C3 =t), with the corresponding letter shown in superscript in the P-value column only when the comparison is not significant.

From a psychiatric perspective, C1 is preserved, with 79% showing no symptoms, 18% having anxiety symptoms, 2% showing depressive symptoms, and 1% having both anxiety and depressive symptoms. In C2, mental health is significantly more affected, with 53% experiencing anxiety, 25% showing anxiety-depressive symptoms, 5% presenting depressive symptoms, and only 16% showing no symptoms. In C3, the situation is more mixed but still indicative of impaired mental health, with 40% showing anxiety, 20% anxiety-depressive symptoms, 3% depressive symptoms, and 38% presenting no symptoms. According to the K6 scale, the C1 group is relatively preserved, with only 11% of the participants reporting severe psychological distress. In contrast, the C2 and C3 groups show severe psychological distress in 78% and 63% of the participants, respectively. The quality of life of adolescents in these three clusters shows predominantly impaired profiles in clusters 2 and 3 (Fig. 1). For C1 (blue), most participants are in Q4 and Q2-Q3. In C2 (orange), most participants are found in Q1 across all explored dimensions. In C3 (gray), all dimensions are impaired, except for leisure activities and friendships, which resemble cluster 1, with a perceived quality of life being very satisfactory or satisfactory for leisure and friendships in these clusters.

From a sociodemographic perspective, the data presented in Table 4 detail the profiles of the clusters. The female gender is significantly more prevalent in C2 (70.4%), compared with C1 and C3. Most of the students in C1 (70%) and C2 (63%) are in middle school, compared with C3 (17%), where a larger proportion of students attend private schools rather than public schools. The age of participants tended to be higher in C3, compared with C1 and C2. The data presented in Table 4 outline the substance use profiles across clusters. The highest rates of substance use are found in C3 for all substances. Risky behaviors are also much more prevalent in C3, affecting 76.3% of participants, compared to 13.4% in C2 and only 5.7% in C1. Finally, a significantly higher proportion of at-risk participants is found in C2 and C3 compared to C1 regarding the type of internet use. The post hoc analysis indicated that all between-cluster differences were statistically significant, except for problematic use of alcohol, tobacco, and cannabis between clusters 1 and 2, and school-related impairments between clusters 2 and 3.

DISCUSSION

To our knowledge, our study is the first to concurrently assess addictive behaviors, mental health, quality of life, and to investigate vulnerability profiles in a large sample of 4844 adolescents. Our data indicate that mental health is often impaired, with a significant level of anxiety and depressive symptoms in our sample.^3,14 They also show that, despite the young age of our participants, impaired mental health (anxiety and/or depressive symptoms) is strongly associated with a high prevalence of excessive or problematic use of psychoactive substances, particularly alcohol, and with a marked deterioration in quality of life.^3,5 Specific clinical profiles have emerged, confirming a reinforced vulnerability between addictive behaviors and mental health among certain individuals, with patterns of substance use that may reflect self-medication or sensation-seeking mechanisms.

Our findings are consistent with recent studies conducted among 17-year-old adolescents, which report that alcohol (particularly binge drinking), tobacco, and cannabis are the most prevalent substances in this age group. Other illicit substances are used only occasionally and mostly by older adolescents, likely due to greater autonomy and financial resources.^6,7,29,30

Our mental health data mirror national and international findings: a substantial proportion of adolescents report psychiatric symptoms, especially anxiety and depression.¹⁴ In our analyses, half of the participants exhibited anxious and/or depressive symptoms, and even among those without HAD-defined symptoms, nearly 75% reported moderate or high distress—suggesting heightened awareness of these issues among younger generations, even at low symptom levels. Consistent with prior studies, young girls show the highest rates of these disorders in our sample, with marked levels of anxiety and anxious-depressive symptoms.¹⁷ Participants with anxious-depressive symptoms were also significantly older, consistent with evidence that adolescent mental health declines from early middle school to the end of high school—marked by decreasing life satisfaction and worsening mental health scores during this period.¹⁷ Because data were collected pre-COVID, and adolescent mental health has since worsened, our results likely underestimate current symptom levels and highlight the need for stronger public health action.

In our sample, addictive behaviors were more frequent among adolescents with anxious or depressive symptoms, echoing global findings on dual diagnosis.^31,32 This supports a higher co-occurrence of mental health problems and substance use, likely driven by shared multifactorial vulnerabilities, including genetic and neurobiological factors, as supported by accumulating evidence in the scientific literature.^33,34 Our findings suggest that adolescents with greater psychiatric symptom burdens may use addictive behaviors for short-term relief, challenging the straightforward applicability of Incentive Sensitization Theory in this group.³⁵ Conversely, such behaviors may worsen their symptoms, indicating a bidirectional relationship that reinforces the need for early, integrated interventions.³⁶ These adolescents also show higher levels of risk-taking, binge drinking, and problematic internet use, reflecting a particularly vulnerable profile with co-occurring conditions linked to increased morbidity and mortality. Given the global decline in youth mental health, a parallel worsening of addiction indicators is a concern.^3,14,18 A substantial increase in political and individual awareness is needed to improve detection and care, especially given the post-COVID deterioration in youth mental health.³⁷ International guidelines, including the WHO Mental Health Action Plan,³⁸ call for integrating substance use into school-based interventions, and the French 2025–2030 plan makes progress but insufficiently addresses adolescents.³⁹ Our findings underscore the need to prioritize this association, strengthen early detection, and promote integrated, psychosocial skills-based programs such as Unplugged.⁴⁰ Enhancing screening would require broader detection points, better-resourced youth services, and digital self-assessment tools.

As expected, quality of life was closely associated with mental health: adolescents without anxious-depressive symptoms reported the highest levels, whereas those with symptoms showed the lowest scores across all dimensions. This is concerning given that many adolescents are less aware of behaviors that could enhance well-being and face multiple barriers to seeking care.¹⁴ Identifying the most vulnerable youths is therefore essential, and perceived quality of life—simple to assess—could serve as a useful self-screening indicator.

Beyond the overall association, 3 clusters emerged, 2 combining addictive behaviors with poorer mental health and QoL. These patterns suggest distinct pathways to substance use—some reflecting self-medication, others sensation-seeking—each linked to specific quality-of-life impairments. A need for social belonging may also drive use and could explain the relatively higher friendship and leisure scores observed in cluster 3.

The first cluster (C1) consists of adolescents in good mental health, with a satisfactory life and minimal use of psychoactive substances. They tend to be younger and predominantly male, which aligns with the literature.¹⁷

Cluster 2 appears to reflect a self-soothing profile, combining marked psychiatric impairment, reduced quality of life, and moderate but higher substance use than that observed in cluster 1, despite the participants’ young age. This pattern is unlikely to stem from chronic use and may indicate a subgroup at risk for worsening trajectories and more severe addictions if not identified and supported early.

Cluster 3 seems to reflect a sensation-seeking profile, marked by the highest levels of substance use, binge drinking, and risky behaviors, alongside clear mental health deterioration and reduced quality of life. Despite this, these adolescents report satisfying friendships and active leisure lives, illustrating that rich social engagement does not preclude significant distress or addiction. Their patterns likely reflect substance use embedded in peer contexts. They are also older and more often in high school, which may ease access to substances and could represent, for some, a later-stage progression from the C2 profile—an idea that warrants longitudinal confirmation.

Recognizing these profiles could help tailor prevention and intervention strategies that jointly address mental health, quality of life, and addictive behaviors, with interventions adapted to the specific vulnerabilities of each profile and incorporating approaches that address social norm dynamics.

This study has several limitations. Its cross-sectional design prevents any causal inference, and longitudinal data would be needed to clarify directionality. A selection bias is also possible: although participants were diverse, all came from the Auvergne-Rhône-Alpes region. This large, socially heterogeneous region is broadly comparable to France as a whole, but slightly more advantaged, and its adolescents show somewhat better perceived health and similar substance use patterns—except for higher alcohol use—so generalizability should be interpreted with caution. Data were collected through self-administered questionnaires in schools, supervised by a psychologist, which may have reduced nonresponse bias, but social desirability and underreporting remain likely. Confounding bias is another inherent limitation of observational studies, and unmeasured factors may have influenced the results. Future work should integrate complementary measures, such as biological markers or peer network data, to strengthen validity.

CONCLUSIONS

It is particularly concerning that, despite the young age of participants, we identified profiles combining high anxiety-depressive symptoms, significant substance use, and reduced quality of life. These 3 dimensions were strongly associated, yielding 3 distinct adolescent profiles: 2 subgroups with marked mental health deterioration and elevated substance use, and a third, comparatively more preserved group. Patterns of quality-of-life impairment also differed clearly across profiles, both in overall severity and in the specific domains affected.

These findings offer new insight into how psychiatric symptoms, addictive behaviors, and quality of life are associated during adolescence, a period of heightened vulnerability. They suggest distinct pathways toward substance use—such as self-medication or sensation seeking—each linked to specific patterns of quality-of-life decline. Quality-of-life profiles may therefore serve as valuable markers for the early detection of problematic addictive behaviors, with or without co-occurring psychiatric symptoms. To our knowledge, this is among the first studies to jointly examine addictive behaviors, psychiatric symptoms, and multidimensional quality of life in a large adolescent sample.

Taken together, these results refine the current understanding of adolescent vulnerability profiles in terms of addictive behaviors, psychiatric symptoms, and quality of life. They advocate for integrated early screening strategies that incorporate these 3 dimensions and highlight the urgent need for comprehensive detection, prevention, and intervention approaches that simultaneously address mental health, substance use, and quality-of-life improvement in adolescents.

Supplementary Material

adm-20-575-s001.docx ^{(51.5KB, docx)}