Behavioral and Social Studies in Mental Health

The Role of Artificial Intelligence in Transforming Psychological Help-Seeking Behaviors: A Social–Behavioral Analysis of User Trust, Acceptance, and Resistance

Maryam Ahmadi , Sina Hosseini ^*

This study aimed to explain how artificial intelligence transforms psychological help-seeking behaviors and to analyze the social and behavioral processes underlying users’ trust, acceptance, and resistance. This qualitative study employed a reflexive thematic analysis approach. Participants were 26 residents of Tehran who had used generative artificial intelligence systems or conversational chatbots at least three times during the previous six months to obtain mental health information, seek emotional support, or decide whether to consult a mental health professional. Purposive maximum-variation sampling was used to ensure diversity in gender, age, educational level, occupational status, and experience with artificial intelligence. Data were collected exclusively through individual semistructured interviews. After obtaining informed consent, the interviews were audio-recorded, transcribed verbatim, and organized and analyzed using NVivo 14. Theoretical saturation was reached during the twenty-third interview, followed by three additional interviews to confirm informational adequacy. Four main categories were identified: “low-friction access and a reduced threshold for help-seeking,” “conditional and evaluative trust,” “instrumental acceptance and preference for a human–AI hybrid model,” and “social–emotional resistance and protection of the uniqueness of human relationships.” Participants perceived continuous availability, anonymity, affordability, immediate responses, and the opportunity to disclose embarrassing concerns without fear of judgment as important facilitators of help-seeking. Nevertheless, trust was provisional and was repeatedly reassessed according to response accuracy, transparency, cultural relevance, privacy protection, consistency, and the system’s ability to recognize its limitations. Most participants accepted artificial intelligence as a resource for psychoeducation, emotional clarification, preliminary support, symptom monitoring, and preparation for professional consultation, but rejected its use as a complete substitute for therapists. Artificial intelligence appears to transform psychological help-seeking from a binary decision to seek or avoid professional care into a gradual, experimental, and multi-resource process. User acceptance does not necessarily indicate complete trust; rather, it reflects a continuing negotiation among convenience, perceived usefulness, vulnerability, risk, and the need for human understanding. Responsible integration of artificial intelligence into mental health services therefore requires transparent communication, culturally and linguistically appropriate design, strong data protection, professional oversight, crisis safeguards, and explicit referral pathways to qualified human professionals.

The Impact of Problematic Social Media Use on Young Adults’ Mental Health: The Mediating Role of Social Comparison and Emotion Regulation

Reza Zare , Neda Safari ^*

پژوهش حاضر با هدف واکاوی تجربه زیسته جوانان از تأثیر استفاده مسئله‌زا از شبکه‌های اجتماعی بر سلامت روان و تبیین نقش واسطه‌ای ادراک‌شده مقایسه اجتماعی و تنظیم هیجان انجام شد. این پژوهش با رویکرد کیفی و روش تحلیل محتوای قراردادی انجام شد. مشارکت‌کنندگان شامل 22 جوان 18 تا 30 ساله ساکن تهران بودند که با روش نمونه‌گیری هدفمند و با رعایت اصل بیشینه تنوع انتخاب شدند. معیارهای ورود شامل استفاده روزانه دست‌کم سه ساعت از شبکه‌های اجتماعی طی شش ماه گذشته، تجربه دشواری در کنترل استفاده و ادراک پیامدهای روان‌شناختی یا کارکردی ناشی از آن بود. داده‌ها صرفاً از طریق مصاحبه‌های نیمه‌ساختاریافته فردی گردآوری شدند. مصاحبه‌ها تا دستیابی به اشباع نظری ادامه یافت؛ اشباع اولیه پس از مصاحبه بیستم حاصل شد و دو مصاحبه تکمیلی ثبات مقوله‌ها را تأیید کرد. داده‌ها پس از پیاده‌سازی کلمه‌به‌کلمه، با استفاده از نرم‌افزار NVivo کدگذاری و تحلیل شدند. برای افزایش اعتبار یافته‌ها از بازبینی مشارکت‌کنندگان، مرور همتایان، یادداشت‌نویسی تأملی و ثبت مسیر تحلیل استفاده شد. تحلیل مصاحبه‌ها به استخراج پنج مقوله اصلی منجر شد: «استفاده تکانه‌ای و نشانه‌محور و از دست رفتن کنترل»، «مقایسه صعودی با تصاویر گزینش‌شده و فرسایش خودارزیابی»، «استفاده از شبکه‌های اجتماعی به‌عنوان راهبرد تنظیم هیجان و گریز دیجیتال»، «تجمع پیامدهای روان‌شناختی و اختلال در عملکرد روزمره» و «خودمراقبتی دیجیتال ناپایدار و بازگشت به الگوی مسئله‌زا». تجربه مشارکت‌کنندگان نشان داد که مواجهه مستمر با بازنمایی‌های آرمانی، احساس عقب‌ماندگی، ناکافی‌بودن و ترس از دست دادن را تقویت می‌کند. همچنین استفاده از شبکه‌های اجتماعی برای کاهش موقت اضطراب، تنهایی، ملال و فشار روانی، در بلندمدت با احساس گناه، نشخوار فکری، اختلال خواب و کاهش تمرکز همراه بود. یافته‌ها بیانگر آن است که رابطه استفاده مسئله‌زا با سلامت روان صرفاً حاصل مدت حضور در شبکه‌های اجتماعی نیست، بلکه به انگیزه استفاده، فقدان کنترل، ماهیت منفعلانه مصرف محتوا، مقایسه صعودی و وابستگی به رسانه برای تعدیل هیجان مربوط می‌شود. نتایج از یک الگوی مفهومی حمایت می‌کنند که در آن مقایسه اجتماعی منفی و تنظیم هیجان ناکارآمد، مسیر ارتباط استفاده مسئله‌زا با آشفتگی روان‌شناختی را توضیح می‌دهند؛ بااین‌حال، ماهیت کیفی پژوهش امکان آزمون آماری میانجی‌گری یا استنباط علی را فراهم نمی‌کند. مداخلات پیشگیرانه باید هم‌زمان بر سواد رسانه‌ای، اصلاح مقایسه‌های غیرواقع‌بینانه، مهارت‌های تنظیم هیجان و مدیریت محرک‌های دیجیتال تمرکز کنند.

Mental Health in the Age of Algorithms: Examining the Behavioral Consequences of Human Interaction with Intelligent Digital Platforms

Ali Ahmadi ^*

This study aimed to explain users’ lived experiences of the behavioral and psychological consequences of continuous interaction with intelligent digital platforms and to identify the mechanisms through which algorithmic personalization influences attention, emotion, identity, social relationships, and digital self-regulation. This qualitative study employed an inductive thematic analysis. The participants were 24 adult users residing in Tehran who were selected through purposive maximum-variation sampling and had used platforms incorporating recommendation systems on a daily basis for at least two years. Data were collected exclusively through individual semi-structured interviews. Interviews continued until theoretical saturation was achieved; preliminary saturation occurred during the twenty-first interview, and three additional interviews were conducted to confirm the stability and comprehensiveness of the categories. The interviews were transcribed verbatim and analyzed with the assistance of NVivo software. Analysis involved familiarization with the data, generation of initial codes, development and review of themes, and definition and naming of the final themes. Member checking, peer review, maintenance of an audit trail, maximum-variation sampling, and reflexive documentation were used to enhance credibility and dependability. Five main categories were identified: “algorithmic capture of attention and the development of compulsive use,” “emotional fluctuation, social comparison, and psychological fatigue,” “identity reconstruction in the algorithmic mirror,” “the duality of social connectedness and perceived loneliness,” and “algorithmic awareness, resistance, and restoration of agency.” Participants did not experience themselves as merely passive recipients of technology. Instead, they described a reciprocal feedback cycle in which user behavior trained the algorithm, while algorithmically selected outputs subsequently influenced users’ moods, preferences, decisions, and future behaviors. Intelligent platforms may simultaneously facilitate belonging, learning, identity expression, and access to social or psychological support while threatening mental health through engagement-oriented design, repeated exposure to emotionally arousing content, social comparison, sleep disruption, and reduced perceived control. Therefore, the quality and context of interaction, the nature of recommended content, and users’ degree of transparency and control may be more informative than screen time alone. Algorithmic literacy, well-being-oriented platform design, meaningful user control over recommendations, and digital self-regulation interventions are required to reduce harm while preserving the psychosocial benefits of digital participation.

Modeling Psychological and Social Factors Affecting the Acceptance of Digital Mental Health Interventions: A Multilevel Structural Modeling Approach

Yaser Sadeghi , Mahsa Salehi , Farhad Taheri ^*

This study aimed to identify and conceptually model the relationships among psychological, social, technological, and structural factors influencing the acceptance and sustained use of digital mental health interventions among adults residing in Tehran. An exploratory qualitative design using inductive thematic analysis was employed. The participants were 24 adults residing in Tehran who were purposively recruited with maximum variation according to their status as active users, discontinued users, or nonadopters of digital mental health interventions. Data were collected exclusively through individual semistructured interviews lasting between 45 and 75 minutes. Interviews continued until theoretical saturation was reached. Initial saturation was identified after the twenty-first interview, followed by three additional interviews to confirm the stability and comprehensiveness of the emerging categories. The verbatim transcripts were organized and analyzed using NVivo 14 through initial coding, constant comparison, category development, theme refinement, and construction of a multilevel conceptual network. Five major categories were identified: cognitive appraisal and psychological readiness; digital trust, safety, and clinical credibility; social legitimacy and human connectedness; user-experience fit and intervention personalization; and structural conditions, accessibility, and digital equity. Acceptance emerged as a dynamic process involving initial awareness, appraisal of benefits and risks, limited experimentation, formation or erosion of trust, and eventual sustained use or disengagement. Perceived usefulness, digital self-efficacy, autonomy, and emotional readiness operated at the individual level. Professional endorsement, family and peer attitudes, stigma, and human support were influential at the interpersonal level. Privacy protection, evidence-based content, usability, cost, technical infrastructure, regulation, and integration with formal health services shaped acceptance at the intervention, organizational, and policy levels. Acceptance of digital mental health interventions cannot be adequately explained solely by ease of use or general attitudes toward technology. It results from interactions among individual, relational, technological, organizational, and societal conditions. Perceived trust functioned as a cross-level mechanism connecting these domains. Human-centered and culturally responsive design, transparent privacy practices, evidence disclosure, optional professional support, digital mental health literacy programs, equitable access, and regulatory oversight are required to improve adoption and sustained engagement.

Predicting the Risk of Psychological Disorders Using Digital Behavioral Markers: An Artificial Intelligence and Behavioral Science Framework

Mohammad Moradi , Mina Ebrahimi , Hassan Rostami

This study aimed to identify the components, requirements, and mechanisms of a culturally responsive, ethically governed, and person-centered framework for predicting the risk of psychological disorders using digital behavioral markers, artificial intelligence, and behavioral science principles. This qualitative study was conducted using conventional content analysis. The participants were 22 clinical psychologists, psychiatrists, artificial intelligence and data science specialists, and digital health professionals working in Tehran, Iran. They were selected through purposive maximum-variation sampling. Data were collected exclusively through in-depth semi-structured interviews. Theoretical saturation was reached after the nineteenth interview, followed by three additional interviews to confirm the stability and completeness of the categories. The interviews were transcribed verbatim and analyzed through initial coding, constant comparison, aggregation of conceptually similar codes, and development of subcategories and main categories. NVivo software was used to organize, manage, retrieve, and compare the qualitative data. Credibility and dependability were enhanced through member checking, peer debriefing, maximum-variation sampling, documentation of the analytical process, and examination of alternative interpretations. The analysis generated five main categories: “multimodal architecture of digital behavioral markers,” “dynamic and person-specific prediction,” “understandable artificial intelligence with human oversight,” “ethical governance, data security, and algorithmic justice,” and “clinical translation and cultural localization.” Participants emphasized that no single digital marker could reliably indicate psychological risk. Changes in mobility, sleep, circadian regularity, communication, smartphone interaction, language, voice, and physiological activity needed to be interpreted relative to the individual’s baseline and social context. The proposed system was therefore conceptualized as a probabilistic early-warning mechanism rather than an autonomous diagnostic instrument. Reliable psychological risk prediction requires multimodal longitudinal data, individualized temporal models, explicit uncertainty estimation, explainability, fairness auditing, dynamic consent, data minimization, and continuous professional oversight. The proposed framework may support early screening, prioritization of referrals, monitoring of symptom trajectories, and timely preventive interventions. Nevertheless, algorithmic outputs should be treated as decision-support information and should not replace clinical interviews, professional judgment, contextual assessment, or the therapeutic relationship.

The Relationship Between Digital Mental Health Literacy and Psychological Help-Seeking Behaviors Among Young People: The Roles of Trust and Social Stigma

Sara Hosseini

This study aimed to explain how digital mental health literacy shapes young people’s recognition of psychological difficulties and their decisions to seek help, with particular attention to the roles of trust and social stigma. A qualitative design using thematic analysis was employed. Participants were 24 young people aged 18–29 years residing in Tehran, Iran, who were recruited through purposive maximum-variation sampling. Eligibility required having used the internet, social media, websites, or digital applications to obtain mental health information during the preceding year. Data were collected exclusively through individual semi-structured interviews. Interviews continued until theoretical saturation was reached; preliminary saturation occurred after the twenty-first interview, and three additional interviews were conducted to confirm it. The data were analyzed through the six-phase thematic analysis process, supported by NVivo 14 software. Five main categories were identified: “digital literacy as a means of naming and initially recognizing psychological experiences,” “critical appraisal versus confusion and self-diagnosis,” “conditional trust based on source credibility and privacy protection,” “social stigma and reliance on concealed help-seeking,” and “transition from digital searching to professional support.” Digital mental health literacy facilitated symptom recognition, communication of distress, and identification of potential sources of support when it included critical evaluation skills. However, conflicting information, algorithmically amplified content, privacy concerns, low institutional trust, and fear of social judgment delayed professional help-seeking or confined help-seeking to anonymous digital environments. The relationship between digital mental health literacy and psychological help-seeking is neither direct nor linear. Trust and social stigma operate as contextual and intermediary mechanisms that determine whether digital knowledge is translated into professional action. Merely increasing access to mental health information is therefore insufficient. Digital mental health literacy initiatives should incorporate source verification, awareness of the limits of digital self-help, privacy and data-security education, clear referral pathways, and culturally appropriate stigma-reduction strategies.

Analysis of Online Behaviors and Their Relationship with Psychological Vulnerability: A Data-Driven Approach to Social Mental Health

Reza Karimi

This study aimed to identify online behavioral patterns associated with psychological vulnerability and explain the individual and social mechanisms underlying this relationship among adult users living in Tehran. This qualitative study employed an inductive thematic analysis approach. The participants were 24 adult internet and social media users living in Tehran, including 14 women and 10 men aged 19–42 years, who were selected through purposive maximum-variation sampling. Data were collected exclusively through individual semi-structured interviews. Each interview lasted approximately 45–75 minutes, was audio-recorded with informed consent, and was transcribed verbatim. Theoretical saturation was reached after the twenty-first interview, followed by three confirmatory interviews to ensure data adequacy. The data were managed and analyzed using NVivo software through familiarization, initial coding, category development, theme review, theme definition, and interpretive synthesis. Credibility and trustworthiness were enhanced through participant checking, peer review, an audit trail, reflexive note-taking, and maximum-variation sampling. Five main categories emerged from the analysis: compensatory online use for emotion regulation; social comparison and vulnerability in self-evaluation; information overload, cognitive fatigue, and sleep disruption; the dual experience of online social support and interpersonal threats; and observable behavioral indicators for the early identification of psychological vulnerability. Recurrent checking of notifications, aimless scrolling, nighttime use, mood dependence on online feedback, repeated deletion and reinstallation of applications, withdrawal from face-to-face relationships, and heightened sensitivity to online visibility were described as salient indicators of increasing distress. The findings also showed that supportive online interactions could reduce loneliness, whereas rejection, cyberaggression, privacy concerns, and exposure to idealized content intensified psychological vulnerability. Psychological vulnerability appeared to be associated not merely with the amount of time spent online but with motives for use, interaction quality, exposure context, perceived control, and the displacement of protective offline activities. Data-driven mental health approaches should therefore move beyond screen-time indicators and integrate behavioral, emotional, interpersonal, and contextual markers. Digital mental health literacy, emotion-regulation training, notification management, sleep-related interventions, and ethically designed early-warning systems may contribute to the promotion of social mental health.

Social Inequality and Mental Health: Exploring the Roles of Social Capital, Economic Deprivation, and Access to Psychological Services

Elham Mousavi , Hamid Yousefi

This study aimed to explain Tehran residents’ lived experiences of the effects of social inequality on mental health, focusing on social capital, economic deprivation, and access to psychological services. This qualitative study was conducted using conventional qualitative content analysis. The proposed sample consisted of 24 Tehran residents aged 23–58 years who were recruited through purposive maximum-variation sampling based on gender, age, educational attainment, employment status, residential area, socioeconomic circumstances, and experience of using psychological services. Data were collected exclusively through individual semi-structured interviews. Interviews lasted approximately 45–75 minutes and were audio-recorded and transcribed verbatim after obtaining informed consent. Theoretical saturation was reached after the twenty-first interview, followed by three additional interviews to confirm data adequacy. Data collection and analysis were conducted concurrently. An inductive coding process was managed using NVivo 14. Credibility and trustworthiness were enhanced through participant checking, peer review of codes and categories, maintenance of an audit trail, maximum-variation sampling, and reflexive documentation. Four main categories were identified: “chronic economic pressure and erosion of psychological security,” “the dual nature of social capital: protective resource and source of pressure,” “class-based and discontinuous access to psychological services,” and “the reproduction cycle of inequality and survival strategies.” Participants associated job insecurity, rising housing expenses, indebtedness, uncertainty about the future, and difficulty meeting basic needs with anxiety, sleep disturbance, helplessness, irritability, and family conflict. Family, friendship, and neighborhood support sometimes buffered the effects of economic hardship. Nevertheless, exhausted support networks, social comparison, shame, and fear of judgment weakened this protective function. The cost of treatment, inadequate insurance coverage, geographical concentration of services, inconvenient appointment times, stigma, and distrust of service quality were described as major barriers to professional help-seeking. Mental-health inequalities were experienced as outcomes of interacting economic, relational, cultural, and service-system conditions rather than isolated individual vulnerabilities. Economic deprivation simultaneously increased exposure to psychological distress and reduced the ability to obtain timely and continuous care. Social capital was protective when it provided dependable emotional, informational, and material resources, but could become ineffective or stressful in unequal and depleted communities. Reducing mental-health inequalities therefore requires intersectoral policies that combine socioeconomic protection, affordable insurance coverage, community-based psychological services, culturally responsive care, and systematic stigma reduction

Behavioral and Social Studies in Mental Health

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