Engagement Dynamics in Online Mental Health Communities: The Role of Trust and Social Support in Young Adults’ Attitudes and Norms

Abstract

Online Mental Health Communities (OMHCs) are becoming vital spaces for young people to access peer-based mental health support. However, the behavioral factors that influence continued participation in these communities remain underexplored. This study extends the Theory of Planned Behavior (TPB) by including initial trust, OMHC engagement, emotional support, informational support, and perceived anonymity to examine young users’ intentions to sustain their participation in OMHCs. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data from 459 Filipino youth aged 18 to 30 were analyzed to test the model. Results revealed that attitude, subjective norms, and perceived behavioral control significantly predict sustainable use intentions. Emotional and informational support strongly influenced subjective norms, while initial trust and OMHC engagement influenced attitude. Perceived anonymity, however, did not exhibit a significant effect. The findings suggest that trust, peer support, and user engagement play an important role in shaping participation in digital mental health spaces. The article concludes with practical implications for platform developers, mental health professionals, and policymakers who aim to improve access to mental health support online. By addressing the psychosocial dynamics shaping OMHC participation, the research advances the understanding of help-seeking behaviors in low-resource and collectivist settings.

Plain Language Summary

How trust and support affect young people’s involvement in online mental health communities

Access to mental health resources is limited worldwide, especially for young adults in developing countries like the Philippines. This study examines what influences Filipino young adults to join and stay active in online mental health communities (OMHCs). Researchers surveyed 459 participants aged 18-30 and used a statistical model to find patterns in their behavior. We discovered that young people's continued use of OMHCs is influenced by their attitudes, the support they feel from others, and how much control they believe they have over their participation. Trust in the platform and feeling emotionally supported also helped shape positive attitudes. Interestingly, staying anonymous was not as important as expected. The study shows that feeling supported, engaged, and trusting the platform matters more than hiding one’s identity. These insights can help improve the design of mental health platforms and guide professionals and policymakers in making digital support more accessible, especially in places with limited mental health resources like the Philippines.

Keywords

online mental health communities SDG 3: good health and well-being social support theory of planned behavior sustainable use intention

Introduction

Mental health is a global concern and a leading contributor to the disease burden across developed and developing nations (Moitra et al., 2023; Whiteford et al., 2015). Amidst a worldwide mental health crisis, it is now more crucial than ever to prioritize the expansion of mental health resources. The World Health Organization (WHO) emphasizes the inherent connection between individual well-being and broader societal, economic, and environmental health and advocates for a holistic approach to mental health as an essential aspect of public health (WHO, 2022a). The most recent WHO report (WHO, 2022b) reveals that 1 billion individuals worldwide grapple with mental disorders such as depression and anxiety, further intensified by the COVID-19 pandemic’s far-reaching effects (WHO, 2022c). This situation is compounded by a global shortfall in mental health professionals, aggravating disparities in access to essential care (WHO, 2022b). These challenges disproportionately affect young adults and adolescents who are less likely to seek or receive adequate professional assistance (Cuijpers et al., 2023; National Institute of Mental Health, 2023; OECD, 2021; Pedersen et al., 2014).

Online Health Communities (OHCs) have emerged as vital support systems, offering spaces where individuals can share health-related information and get emotional support. These digital communities utilize their members’ combined knowledge and shared experiences that foster a dynamic and collaborative environment for managing health and improving personal well-being (Naslund et al., 2016; Zhou, 2022). OHCs are commonly categorized into patient-to-patient (P2P) and patient-to-doctor (P2D) forums. P2P communities allow users facing similar health challenges to exchange insights and support, promoting empathy and mutual understanding. On the other hand, P2D platforms enable remote consultations with healthcare professionals, significantly expanding access to medical advice (Li et al., 2019).

This study focuses on the use of Online Mental Health Communities (OMHCs) by young adults in the Philippines. It aims to analyze the factors that influence their participation in these platforms. The Theory of Planned Behavior (TPB) serves as the core theoretical framework, incorporating constructs such as initial trust, OMHC engagement, social support, and perceived anonymity to explain the factors driving young adults toward these digital spaces for mental health support.

While TPB offers a robust foundation for understanding behavioral intention through its key constructs, attitude, subjective norms, and perceived behavioral control, it has limitations when applied to digital health settings. TPB does not explicitly account for platform-specific influences such as perceived anonymity or trust in online environments, which can significantly impact user behavior in OMHCs. In contrast, the Health Belief Model (HBM) (Becker, 1974), which emphasizes risk and benefit perceptions, is less suited for analyzing the social and normative dynamics inherent in peer-to-peer digital platforms. Given that OMHC participation involves community interaction and collective validation, TPB remains a more appropriate framework but benefits from contextual enhancement. Accordingly, trust and social support are integrated as exogenous (independent) variables directly influencing TPB constructs, enhancing the framework’s explanatory power in digital mental health contexts.

Drawing on the data from 459 young adult OMHC users and employing Partial Least Squares Structural Equation Modeling (PLS-SEM) for data analysis, this study emphasizes the significant role of informational and emotional social support, initial trust, and community engagement in shaping sustained user participation. The findings offer valuable insights into the potential of OMHCs as supplementary mental health resources and advocate for deeper exploration of the factors influencing young adults’ engagement with these digital platforms.

Literature Review

Young Adults and Their Mental Health

The need to address mental health issues among young adults has become increasingly recognized as critical to individual and societal well-being. Recent studies have highlighted a significant rise in mental health concerns within this demographic, affecting quality of life (GBD 2019 Mental Disorders Collaborators, 2022; Prescott et al., 2020). This life stage, spanning adolescence into early adulthood (ages 11–30), is marked by key transitions and challenges (Moitra et al., 2023; Pedersen et al., 2014). Without early interventions, delays in diagnosing or addressing mental health conditions can have profound, lasting effects (MacDonald et al., 2018; Radez et al., 2021; Westberg et al., 2022).

The digital age presents both opportunities and challenges, mainly for young adults’ mental health. Increased reliance on digital communications has psychological costs, a reality that became especially apparent during the COVID-19 pandemic (Glowacz & Schmits, 2020; WHO, 2022a). Social media and digital platforms influence mental health in complex ways, linked to both positive outcomes, such as community building, and negative outcomes, such as heightened anxiety, social comparison, and reduced self-esteem (Beyari, 2023; Bonsaksen et al., 2023).

In the Philippines, mental health issues are highly prevalent among the youth. Despite improvement in awareness, persistent challenges remain, particularly regarding access to care, stigma, and service adequacy (Lally et al., 2019; Martinez et al., 2020; University of the Philippines Population Institute, 2022). Cultural attitudes and socioeconomic disparities further shape help-seeking behaviors and mental health service effectiveness (Asiimwe et al., 2023; Gopalkrishnan, 2018; Reiss et al., 2021). The shortage of trained mental health professionals and persistent stigma contribute to unmet needs across the country (Lally et al., 2019; Martinez et al., 2020).

Online Health Communities

The rise of Online Health Communities (OHCs) has transformed how individuals seek health information and support. OHCs play a critical role in democratizing access to health knowledge and fostering peer-to-peer support, especially for those facing barriers to traditional services (Eysenbach et al., 2004; Liu et al., 2020; Zhou, 2022).

Peer-to-peer interactions in digital spaces offer unique benefits for individuals managing mental health challenges, providing shared experiences that strengthen coping strategies (Merchant et al., 2022; Naslund et al., 2016). The broader shift toward online consultations and telemedicine reflects an important evolution in healthcare delivery, expanding access to timely, affordable care, particularly for populations hesitant to seek traditional help due to stigma or logistical barriers (Gong et al., 2021; Pretorius et al., 2019; Zhang et al., 2023). Digital platforms can also enhance the continuity of care and patient engagement (Kalman et al., 2023).

Online Mental Health Communities (OMHCs), specifically, highlight the potential of digital spaces to address digital needs. These platforms allow users to share experiences, seek advice, and receive emotional support, contributions that can significantly affect coping and resilience (Liu & Kong, 2021; Liu & Liu, 2021). However, risks remain, including exposure to misinformation and emotional overload. The presence of unverified information and the potential for emotional distress from engaging with others’ traumatic experiences require careful moderation and support mechanisms (Eysenbach et al., 2004; Naslund et al., 2020; Zhou et al., 2019).

Theoretical Framework and Hypothesis

Theoretical Framework

This study adopts the Theory of Planned Behavior (TPB; Ajzen, 1991) as its core framework and extends it to accommodate the dynamics of digital mental health platforms. Expanding upon the Theory of Reasoned Action (Fishbein & Ajzen, 2011), TPB explains the psychological mechanisms that drive individuals toward specific behavioral outcomes. It is grounded in three core belief-driven constructs: attitude toward behavior, perceived behavioral control, and subjective norms. These constructs collectively formulate a strong theoretical framework for analyzing behavioral intentions by capturing the interaction between individual predispositions, perceived control over actions, and the influence of social norms (Ajzen, 1991; Bosnjak et al., 2020).

In this study, initial trust, informational support, and emotional support are conceptualized as exogenous variables that directly influence the core TPB components, rather than functioning as moderators or mediators. Specifically, initial trust and perceived anonymity are modeled as antecedents of attitude, while informational and emotional support are proposed to shape subjective norms. These constructs are not positioned to mediate or moderate internal TPB relationships; they are included to reflect essential psychosocial dynamics unique to OMHCs. Their inclusion is grounded in prior research highlighting the importance of trust and peer-based support in influencing online health behaviors, particularly in contexts where empathy, community validation, and emotional safety are paramount (Naslund et al., 2016; Zhou, 2022). By integrating these elements, the model enhances the explanatory power of TPB in the context of digital mental health support.

Social support theory explains that social support, both informational and emotional, can significantly influence individual behavior and attitudes. Cohen and Wills (1985) proposed that social support enhances mental health by buffering stress and providing resources to cope with challenges, shaping subjective norms.

This expanded model, TPB, has garnered substantial empirical support across diverse domains, demonstrating its utility in predicting a wide array of human behaviors. For instance, its application ranges from understanding the intention to use technology in healthcare to unraveling the psychological underpinnings of consumer behavior and online social interactions (Arkorful et al., 2022; Banerjee & Ho, 2020; Gallardo et al., 2024; Gu & Wu, 2019; Ifinedo, 2017; Nie et al., 2020; Rozenkowska, 2023; Zhang et al., 2023). Furthermore, the theory’s adaptability is evidenced by its integration with additional psychological constructs such as anticipated regret, self-efficacy, and social loafing. This enhances the theory by providing deeper insights into the complexities of human behavior (Choi & Suh, 2022; Croy et al., 2015; Maddock et al., 2022; Rhodes & Mistry, 2016).

In the context of our study, TPB serves as the fundamental basis for exploring user engagement within OHCs for mental health support. By incorporating constructs such as Perceived Anonymity and Initial Trust, the framework is designed to analyze the multifaceted nature of online participation dynamics, exploring the complex relationship between anonymity, social support mechanisms, and community engagement in shaping user behaviors within OHCs.

Hypothesis

Attitude

The concept of attitude, central to the TPB, relates to an individual’s favorable or unfavorable evaluation of engaging in a specific behavior (Ajzen, 1991). Karahanna et al. (1999) and Bhattacherjee and Premkumar (2004) have empirically established that attitude is a significant predictor of behavioral intention, suggesting that individuals with a positive attitude toward a behavior are more likely to intend to perform it. Cao, Zhang, et al. (2023) illustrate a significant relationship between positive attitudes and their continuous intention to engage with OHCs. This premise suggests that young adults who perceive participation in these communities positively are more inclined to intend to use them. Thus, we state

H1: Positive attitudes toward participating in OMHCs will influence young adults’ sustainable use intentions to use these communities.

Subjective Norms

Subjective norms pertain to the perceived social pressure to engage or not engage in a behavior (Ajzen, 1991). Research by Tomczyk et al. (2020) and Aldalaykeh et al. (2019) has demonstrated a positive relationship between subjective norms and mental health-seeking behavior. Such a relationship underscores the role of social influences in shaping intentions to participate in OMHCs, especially concerning mental health. Based on this, we hypothesize that:

H2: Subjective norms will positively influence young adults’ sustainable use intentions to participate in OMHCs.

Perceived Behavioral Control

Perceived behavioral control refers to an individual’s perceived ease or difficulty in performing a behavior (Ajzen, 1991). Research demonstrates that perceived behavioral control significantly impacts an individual’s intentions to seek mental health services. For instance, Zorrilla et al. (2019) highlight how perceived control over accessing and utilizing mental health resources significantly increases the likelihood of young adults seeking such services. Similarly, Li et al. (2018) confirm this impact among university students, noting that those who perceive greater control and resource availability are more inclined to seek mental health services. Additionally, Tomczyk et al. (2020) demonstrate that perceived behavioral control affects not only intentions but also actual help-seeking behaviors in individuals facing mental health challenges. These findings support the hypothesis that enhancing perceived behavioral control will positively influence young adults’ intentions to participate in OMHCs. Therefore, we hypothesize:

H3: Perceived behavioral control will positively influence young adults’ sustainable use intentions to participate in OMHCs.

Online Social Support

The literature identifies two primary types of online social support—informational and emotional—both recognized for their significant impacts on user behavior (Coulson, 2005; Liu et al., 2022; Wu et al., 2019; Zhou, 2022).

Informational support includes advice and suggestions that aid in problem-solving (Coulson, 2005). It enhances trust and participation intentions by providing valuable knowledge (Wu et al., 2019; Xiang et al., 2023). For instance, users who receive helpful information are likely to develop trust in the community’s expertise, which can increase their intention to participate further. This type of support may also influence subjective norms as users integrate the advice into their belief systems, potentially aligning their views more closely with those of the community (Zhou, 2022). Informational support reinforces the perceived value and credibility of the community, thereby influencing the subjective norms associated with community engagement. Based on this, we propose:

H4: Informational social support from online health communities will positively influence young adults’ subjective norms towards participating in OMHCs.

Emotional support in OHCs involves expressions of empathy, encouragement, and comfort (Wu et al., 2019; Zhou, 2022). This support is crucial as it fosters a sense of community, belonging, and acceptance. Users dealing with negative emotions from health issues, like anxiety or depression, may find that emotional support improves their mood and overall well-being (Liu, Liu, & Guo, 2020; Wu et al., 2019; Zhou, 2022). Additionally, receiving this support builds trust and a sense of goodwill within the community (Wu et al., 2019). It also motivates users to share their experiences and knowledge, which helps them fit in with the community’s norms. Emotional support helps individuals feel understood and valued, promoting alignment with the community’s expectations and behaviors. Therefore, we hypothesize:

H5: Emotional social support from online health communities will positively influence young adults’ subjective norms toward participating in OMHCs.

Perceived Anonymity

Perceived Anonymity may play a crucial role in influencing participation dynamics within OMHCs. Anonymity has been shown to facilitate open discussion and enhance psychological well-being by allowing individuals to express themselves without fear of judgment (Pan et al., 2023). Research by Christie and Dill (2016) and Keipi et al. (2015) underscores the importance of anonymity in shaping online interactions, especially in contexts involving sensitive issues like mental health. The benefits of anonymity are particularly significant in OMHCs, where discussions often concern sensitive health issues. Remaining anonymous can help reduce social stigma and personal vulnerability, encouraging more open and honest communication. This aspect is especially critical for young adults, who may be particularly concerned about judgment and social repercussions related to mental health issues. Based on these insights, we propose the following hypothesis:

H6: Perceived anonymity in online health communities will positively influence young adults’ attitudes toward participating in OMHCs.

OMHC Initial Trust

Trust is critical in adopting and using online platforms, especially those that handle sensitive health data (Xie et al., 2020). In digital platforms, trust in the initial stages of interaction is vital to adoption decisions (Gao & Waechter, 2017; Kaabachi et al., 2019). Initial trust refers to the trustworthiness attributed to a platform or service prior to extensive personal experience with it (Cao, Zheng, & Liu, 2023; Gao & Waechter, 2017). Research into the influence of trust on technology acceptance has consistently found a significant correlation between trust and positive attitudes toward technology (Gallardo et al., 2024; Shrestha et al., 2021). Within OMHCs, initial trust can reduce perceived risks and uncertainties, thus enhancing positive attitudes toward participation. Thus, we propose that:

H7: Initial trust in online health communities will influence young adults’ attitudes toward participating in OMHCs.

OMHC Engagement

Community engagement, involving active participation and commitment, is linked to positive outcomes in online settings. Research by Ray et al. (2014) highlights the critical role of engagement in online communities. They found that more engagement leads to greater contributions from members and increased promotional activities. This strong sense of community and personal connection positively changes members’ attitudes toward their continued participation. Lessard et al. (2023) found that increasing community engagement in health-focused OHCs boosts empowerment and capacity among members. This means that members who feel more empowered and capable view the community more positively and are more willing to participate. Al-Khasawneh et al. (2023) emphasized the importance of how communities engage. They noted that social support, how well members identify with the community, and the community being seen as a cohesive unit enhance engagement. This, in turn, positively affects young adults’ attitudes toward joining and participating in mental health OHCs. The study by Zhou et al. (2022) suggests that more engaged members will likely have more favorable views about participating in these communities. Thus, we suggest:

H8: Greater community engagement in OMHCs will positively influence young adults’ attitudes toward participating in mental health OHCs.

Methods and Data

Sample and Data Collection

We invited young adults aged 18 to 30 who are users of OMHCs due to their recognized vulnerability to mental health issues, a pattern well-documented in prior research (Pedersen et al., 2014; Takino et al., 2021). Among Filipino youth, mental health concerns such as suicide ideation and attempts have risen significantly in recent years, particularly among females (University of the Philippines Population Institute, 2022). This reinforces the relevance of focusing on this demographic when exploring patterns of mental health engagement and online support-seeking. We adopted a non-random convenience sampling strategy to maximize respondent participation efficiently and cost-effectively (Easterby-Smith et al., 2012).

Our theoretical framework, illustrated in Figure 1, extends the TPB by incorporating constructs relevant to OMHC participation. Before full deployment, we conducted a preliminary pilot test with 30 OMHC users, who provided feedback on item clarity, wording, and ease of understanding. Data was collected using Google Forms between February 6 and March 16, 2024. To maximize reach and diversity across the Philippines, the survey link was distributed via multiple online platforms, including mental health-related Facebook groups and messenger chat groups. A screening question was used to ensure that participants were actively engaging with mental health discussions in OMHCs. Only fully completed responses were accepted, resulting in 459 valid responses.

Figure 1.

Extended TPB framework for understanding OMHC participation.

While this approach facilitated broad online participation, convenience sampling introduces the risk of selection bias. This method, though commonly used in online mental health research, may over-represent individuals who are more digitally literate, socially engaged, or personally invested in mental health issues (Andrade, 2021; Etikan et al., 2016). As such, the findings represent young digitally connected Filipino adults who are already active in online communities. Future research could improve generalizability by employing probability-based or stratified sampling techniques to reach more demographically diverse populations.

Additionally, using Google Forms disseminated through social media carries the potential for self-selection bias, where participants who are already interested or involved in mental health topics are more likely to respond (Bethlehem, 2010). Although formal stratified sampling was not conducted, we monitored respondent demographics throughout the data collection period to ensure balance across gender, educational background, and geographic distribution. We also disseminated the survey in mental health-focused and general-interest Facebook groups to reach participants with varying levels of engagement in mental wellness discourse. Nonetheless, this potential bias is acknowledged as a limitation in interpreting the results.

We employed procedural and statistical strategies to address common method bias (CMB). Procedurally, anonymity was emphasized to reduce socially desirable responding, an established method for mitigating CMB (Podsakoff et al., 2003). Statistically, we conducted Harman’s single-factor test by loading all items into an unrotated exploratory factor analysis (EFA). The results showed that the first factor accounted for approximately 50% of the total variance, approaching the threshold commonly used to indicate potential CMB concerns (Podsakoff et al., 2003). Although this suggests that CMB may be present to a limited extent, several procedural remedies such as separating predictor and outcome items, using non-redundant language, and reinforcing confidentiality were implemented to reduce its influence.

Lastly, we examined the Variance Inflation Factor (VIF) scores to assess potential multicollinearity, which can also signal CMB. All VIF scores remained below the accepted threshold of 5, indicating minimal multicollinearity and further reducing concern regarding CMB in our dataset.

Measures

The survey instrument was structured into three sections. The first section introduced the study, explained its objectives, presented a data privacy statement, and was followed by a request for informed consent. Participants were informed that their participation was voluntary and could be withdrawn at any time.

The second section collected demographic information, including age, gender, educational background, and online engagement frequency. A control question was included to validate response consistency and filter inattentive respondents (Ramírez-Correa et al., 2020).

The third section consisted of 29 items designed to measure the key constructs of the extended TPB model: attitude, subjective norms, perceived behavioral control, initial trust, perceived anonymity, informational and emotional support, OMHC engagement, and sustainable use intention. All items were measured using a five-point Likert scale ranging from 1 (“Strongly Disagree”) to 5 (“Strongly Agree”). This format is widely used for measuring attitudes and perceptions due to its psychometric stability and ease of interpretation (Müller et al., 2021; Sousa et al., 2022). The constructs were adapted from previously validated instruments. Items related to attitude and perceived behavioral control were drawn from Ramírez-Correa et al. (2020), while subjective norms were adapted from Gong et al. (2019) and Cao, Zheng, and Liu (2023). Measures of informational and emotional support were based on the framework by Liang et al. (2011), and perceived anonymity was adapted from Jung et al. (2012). The OHC engagement was informed by prior work by Tseng et al. (2022), Baldus et al. (2015), and Hajli and Lin (2016). The construct OHC initial trust was adapted from Zhou (2012) and Cao et al. (2020), while sustainable use intention was assessed using items based on Davis (1989).

A summary of constructs, sources, and corresponding measurement items is presented in Table 1.

Table 1.

Survey Instrument.

Construct	Item codes	Example items	Sources
Attitude	A1–A3	Using OHCs for mental health gives me a pleasant experience	Ramírez-Correa et al. (2020)
Subjective norms	SN1–SN3	People important to me think I should use OMHCs	Cao, Zheng, and Liu (2023), Gong et al. (2019)
Perceived behavioral control	PBC1–PBC3	I have the knowledge and the ability to use the OMHC platform	Ramírez-Correa et al. (2020)
Informational support (IS)	IS1–IS3	People in the OMHC provide helpful suggestions and information	Liang et al. (2011)
Emotional support (ES)	ES1–ES4	People on the OMHC express interest and concern for my well-being	Liang et al. (2011)
OMHC engagement	OE1–OE3	I am willing to share my own experiences with OMHCs	Algesheimer et al. (2005), Baldus et al. (2015), Hajli and Lin (2016), Tseng et al. (2022)
Perceived anonymity	PA1–PA4	People cannot identify me based on my messages in the OMHC	Jung et al. (2012)
OHC initial trust	IT1–IT3	I believe this OMHC can effectively provide mental health support	Cao et al. (2020), Zhou (2012)
Sustainable use intention	SU1–SU3	I intend to use OMHC in the future	Davis (1989)

To ensure instrument validity, we followed a two-step validation process. First, the survey was reviewed by four digital health experts to establish content and face validity. Their feedback led to minor refinements in item phrasing and relevance. Second, a pilot test with 30 OMHC users was conducted to assess clarity and contextual appropriateness. Based on pilot feedback, adjustments were made to improve comprehension and flow.

Reliability analysis using Cronbach’s alpha showed that all constructs demonstrated acceptable internal consistency, with alpha values exceeding the recommended threshold of .70.

Ethical Considerations

Ethical oversight was embedded in the course framework under which this study was conducted. All data collection adhered to institutional academic guidelines for minimal-risk research, as well as the ethical standards outlined in the Declaration of Helsinki and the Philippine Data Privacy Act of 2012. Participation was voluntary, with informed consent obtained, and measures were taken to ensure anonymity and data confidentiality. Participants were clearly informed about the study’s purpose, their right to withdraw at any time, and how their information would be protected.

The survey focused exclusively on participants’ perceptions and usage patterns of Online Mental Health Communities (OMHCs), without soliciting clinical, diagnostic, or sensitive mental health disclosures. To further safeguard participant well-being, a list of accessible mental health resources—including support hotlines and local psychological services—was provided at the conclusion of the survey.

Data Analysis

We employed Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS version 4, chosen for its suitability in exploratory research, flexibility in model complexity, and efficiency in handling small sample sizes without strict distributional assumptions (Hair et al., 2019; Henseler et al., 2016). PLS-SEM facilitated a comprehensive assessment of the measurement model’s reliability and validity and the structural model’s predictive accuracy. Applying this analytical approach was pivotal in examining the relationships between constructs and understanding the determinants influencing young adults’ intentions to engage with OMHCs.

Results

This section presents the findings from our empirical analysis using PLS-SEM. We begin by assessing the measurement model’s reliability and validity, followed by the structural model’s hypothesis testing outcomes.

Demographic Profile of the Study Participants

Table 2 presents the demographic makeup and mental health interests of participants engaged in OMHCs. The sample exhibited a male majority of 66.49% (n = 305), with females comprising 33.55% (n = 154). Age distribution primarily spanned the younger cohorts, with 35.08% (n = 161) aged 18 to 19 and 37.91% (n = 174) within the 20 to 21 bracket. Participation in OMHCs is mainly singular, with 86.49% (n = 397) belonging to one community. Results in the mental health topics of interest reveal a significant focus on anxiety disorders and depression, engaging 80.61% (n = 370) and 77.34% (n = 355) of participants, respectively. The strong interest in anxiety and depression-related discussions mirrors global and regional trends where young people increasingly seek peer support for emotional distress through online channels (Naslund et al., 2020). Other mental health topics of interest include eating disorders (34.64%, n = 159), panic disorder (33.77%, n = 155), and bipolar disorder (27.89%, n = 128). The diversity in mental health topics suggests that OMHCs are versatile platforms catering to a wide array of mental health concerns among young adults.

Table 2.

Demographic Profile of the Participant.

Demographics	Item	Subjects (N = 459)
Demographics	Item	Frequencies	Percentage (%)
Gender	Male	305	66.49
Gender	Female	154	33.55
Age	18–19	161	35.08
	20–21	174	37.91
	22–23	66	14.38
	24–25	30	6.54
	26–30	28	6.10
Hours/week spent participating in OMHCs	<1 hr	359	78.29
	1–2 hrs	75	16.34
	3–5 hrs	17	3.70
	6–9 hrs	3	0.65
	>10 hrs	5	1.09
No. OMHC membership	1 OMHC	397	86.49
	2 OMHCs	35	7.65
	3–4 OMHCs	21	4.58
	5–6 OMHCs	3	0.65
	7 or more	3	0.65
Mental Health topics interested in learning about	Anxiety disorders	370	80.61
	Depression	355	77.34
	Eating disorders	159	34.64
	Panic disorder	155	33.77
	Bipolar disorder	128	27.89
	Paranoia	92	20.04
	Post-traumatic stress disorder (PTSD)	157	34.20
	Schizophrenia	94	20.48
	Disruptive behavior and dissocial disorders	93	20.26
	Obsessive-compulsive disorder (OCD)	123	26.80
	Specific phobia	77	16.78
	Other	1	0.22

Evaluation of the Measurement Model

The measurement model of this study was analyzed using PLS-SEM to ensure accurate and reliable construct measurements. This step was crucial for validating the construct measurements by examining Cronbach’s alpha, Composite Reliability (CR), and the Average Variance Extracted (AVE). This analysis provided a necessary foundation for evaluating the structural model (Fornell & Larcker, 1981; Hair et al., 2014, 2019).

In assessing the structural model, the Standardized Root Mean Square Residual (SRMR) fit index was used to evaluate model fit, with values below .08 (SRMR < .08 or at most <.10) indicating an acceptable fit (Henseler et al., 2016). The study’s model achieved an SRMR of .06. CR values above the .7 threshold indicated good internal consistency, while AVE values greater than .5 confirmed convergent validity, showing that the indicators appropriately represented their constructs (Pahlevan Sharif & Sharif-Nia, 2018). The study also employed a bias-corrected bootstrapping method with 5,000 iterations to assess the structural model, considering p-values below .05 indicative of statistical significance. This approach helped confirm the variables’ outer loadings, ensuring that they effectively represented their respective constructs (Hair et al., 2019). The constructs’ internal consistency assessment was primarily conducted using CR, which is preferred over Cronbach’s alpha as it more accurately reflects how construct components represent the underlying concept within the PLS path model (Hair et al., 2014). The analysis then moved to evaluate convergent and discriminant validity. Convergent validity was verified through factor loadings and AVE values that met or exceeded minimum requirements, indicating that a set of indicators reliably represents a construct (Henseler et al., 2016).

This evaluation confirmed that the measurement and structural models were adequately constructed, demonstrating reliable construct measurement. Based on these findings, the questionnaire was validated for further data collection.

Table 3 presents the results of the reliability and validity assessment of the measurement model. All constructs exhibited strong internal consistency, as indicated by Cronbach’s alpha and composite reliability (CR) values exceeding the .70 threshold. Convergent validity was confirmed through Average Variance Extracted (AVE) scores above .50 for all constructs. The factor loadings of individual items also surpassed the recommended minimum of .70, demonstrating strong item reliability. These results support the internal consistency of both the core TPB constructs and the extended variables: initial trust, informational support, emotional support, perceived anonymity, and OHC engagement.

Table 3.

Items, Loadings, CR, and AVE Values.

Construct	Items	Loadings	Cronbach’s alpha	Composite reliability (rho_a)	Composite reliability (rho_c)	Average variance extracted (AVE)
Attitude	A1	.921	.903	.905	.939	.838
	A2	.907
	A3	.918
Sustainable use intention	SUI1	.968	.967	.967	.978	.937
	SUI2	.964
	SUI3	.973
Emotional support	ES1	.846	.921	.924	.944	.809
	ES2	.93
	ES3	.88
	ES4	.937
Information support	IS1	.854	.856	.862	.912	.776
	IS2	.872
	IS3	.916
OHC initial trust	IT1	.886	.889	.903	.931	.818
	IT2	.894
	IT3	.933
OHC engagement	OE1	.822	.861	.88	.915	.783
	OE2	.930
	OE3	.899
Perceived anonymity	PA1	.763	.841	.973	.898	.747
	PA2	.932
	PA3	.890
Perceived behavioral control	PBC1	.887	.813	.857	.889	.730
	PBC2	.924
	PBC3	.742
Subjective norms	SN1	.881	.856	.865	.912	.775
	SN2	.906
	SN3	.853

Discriminant Validity (DV) is established when the square roots of the Average Variance Extracted (AVE) are greater than the correlations among constructs, which are placed in the diagonal cells (Hair et al., 2019). To evaluate discriminant validity, we applied the Fornell–Larcker criterion (Fornell & Larcker, 1981). Table 4 presents the outcomes from the Fornell–Larcker criterion test for discriminant validity. The diagonal values in bold signify the square roots of the AVEs, whereas the off-diagonal figures represent the correlations between the constructs. Based on Table 4, the constructs exhibit satisfactory discriminant validity as the diagonal values surpass the off-diagonal values in their respective columns and rows (Hair et al., 2019).

Table 4.

Fornell–Larcker Discriminant Validity Test.,

Constructs	Attitude	Sustainable use intention	Emotional support	Information support	OHC initial trust	OHC engagement	Perceived anonymity	Perceived behavioral control	Subjective norms
Attitude	.915
Sustainable use intention	.686	.968
Emotional support	.726	.739	.899
Information support	.686	.816	.856	.881
OHC initial trust	.823	.88	.745	.744	.904
OHC engagement	.67	.777	.913	.807	.721	.885
Perceived anonymity	.481	.633	.626	.692	.646	.565	.865
Perceived behavioral control	.696	.809	.642	.725	.729	.692	.537	.854
Subjective norms	.821	.756	.755	.741	.858	.683	.563	.609	.88

Note. The diagonal values in bold represent the square roots of the AVE for each construct, while the off-diagonal values indicate the correlations among constructs. Discriminant validity is supported when the square root of each construct's AVE (diagonal) is greater than its corresponding inter-construct correlations.

Structural Model Analysis

The structural model (Figure 2) was evaluated using a bootstrapping method through PLS-SEM, with findings detailed in Table 5. Hypotheses H1 (Attitude → Sustainable Use Intention), H2 (Subjective Norms → Sustainable Use Intention), and H3 (Perceived Behavioral Control → Sustainable Use Intention) displayed positive path coefficients of .404, .364, and .400, respectively. This reveals the positive effect of these constructs on the intention to use OMHCs, validated by significant t-statistics (5.523 for H1, 4.693 for H2, and 5.391 for H3) and p-values of .000.

Figure 2.

Results of the structural model.

Table 5.

Hypothesis Testing Results.

Hypothesis	Beta (β)	R ²	f ²	T-test	p-Values	Decision
H1: Att → SUI	.404	.616	.050	5.523	.000	Supported
H2: SN → SUI	.364	.616	.061	4.693	.000	Supported
H3: PBC → SUI	.400	.616	.032	5.391	.000	Supported
H4: IS → SN	.330	.458	.117	5.707	.000	Supported
H5: ES → SN	.465	.458	.255	8.499	.000	Supported
H6: PA → Att	.051	.593	.001	1.277	.202	Not supported
H7: IT → Att	.177	.593	.114	2.809	.005	Supported
H8: OE→ Att	.256	.593	.085	3.531	.000	Supported

Hypotheses H4 (Informational Support → Subjective Norms) and H5 (Emotional Support → Subjective Norms) showed positive path coefficients of .330 and .465, respectively, with significant t-statistics (5.707 for H4 and 8.499 for H5) and p-values of .000. This demonstrates a strong positive link between support types and subjective norms around OMHC use, indicating that informational and emotional support significantly affect social perceptions of OMHC engagement.

However, the influence of perceived anonymity on attitudes towards OMHCs (H6) was minor, with a path coefficient of .051, t-statistic of 1.277, and p-value of .202, suggesting insignificance. In contrast, initial trust (H7) and OMHC engagement (H8) significantly influenced attitudes towards OMHCs, with path coefficients of .177 and .256, t-statistics of 2.809 and 3.531, and p-values of .005 and .000, respectively. These results underline the positive impact of initial trust and engagement on attitudes toward OMHCs.

Table 5 displays the structural model results, including path coefficients, R-squared values, and hypothesis testing outcomes. We evaluated the R-squared (R²) values to assess the model’s in-sample predictive power (Hair et al., 2022; Henseler et al., 2016). For the Attitude (Att) construct, the R²value of .593 (adjusted R² = .580) indicates that the model accounts for 59.3% of the variance in users’ attitudes toward OMHC participation, a substantive level of explanatory strength. Similarly, for Sustainable Use Intention (SUI), the R² value of .616 (adjusted R² = .604) shows that 61.6% of the variance, suggesting a strong predictive capacity. Meanwhile, Subjective Norms (SN) presents a moderate R² value of .458 (adjusted R² = .447), indicating that the model has a moderate explanatory power regarding the influence of emotional and informational support on perceived social pressure to use OMHCs.

Effect sizes (f²) gauge the individual predictor variables’ effect sizes on the dependent constructs within the model (Hair et al., 2022; Henseler et al., 2015). Attitude suggests a modest effect factor on Sustainable Use Intention (f² = .050). This aligns with the findings in prior research that point to more complex interplays between attitude and behavioral intentions (Ajzen, 1991). Emotional Support had a moderate effect on Subjective Norms (f² = .061), whereas Informational Support had a smaller influence (f² = .032), underscoring the varied weight of emotional versus informational cues in shaping social perceptions. Initial Trust had a medium effect on Attitude (f² = .117) while OMHC Engagement exerted a large effect (f² = .255) on Attitude, emphasizing the impact of active participation on attitude formation (Al-Khasawneh et al., 2023).

Conversely, perceived anonymity had a negligible effect on Attitude (f² = .001), reinforcing its limited relevance in this context. Perceived Behavioral Control shows a substantial medium effect (f² = .114), reflecting the importance of self-efficacy in predicting sustained use. Subjective Norms contributed a small to medium effect (f² = .085), further emphasizing the role of social influences in shaping behavioral intent.

Of the eight hypotheses, seven were supported (H1 through H5, H7, and H8), demonstrating significant path coefficients and t-values consistent with established benchmarks. These results affirm the predictive relevance of key TPB constructs and extended variables such as Initial Trust, Emotional and Informational Support, and OMHC Engagement. Conversely, Hypothesis 6 posited a positive relationship between Perceived Anonymity and Attitude toward OMHCs, but this was not supported. The path coefficient for H6 was minimal (β = .051), with a t-statistic of 1.277 and a non-significant p-value of .202. This finding indicates that, within this study’s sample, perceived anonymity did not significantly influence young adults’ attitudes toward participation in OMHCs, suggesting a diminished role of anonymity in the attitudinal formation process compared to other psychosocial and community-related factors.

The decision to accept or reject each hypothesis was based on established statistical thresholds from PLS-SEM, including t-values greater than 1.96 and p-values below .05. Effect sizes were also considered to gauge the practical significance of each relationship. Taken together, these results validate the explanatory power of the extended TPB model and offer culturally contextualized insights into the psychosocial mechanisms driving OMHC engagement among Filipino youth.

Discussion

This research applies the TPB to explore how young adults in the Philippines engage with OMHCs. By extending the TPB to include constructs such as Attitude, Subjective Norms, Perceived Behavioral Control, along with Initial Trust, OHC Engagement, Social Support, and Perceived Anonymity, we provide a comprehensive mapping of the factors that draw young adults to these digital platforms, thereby enriching both theoretical and practical understanding in the field of mental health support.

The affirmation of hypotheses H1 through H3 confirms the TPB’s core constructs—Attitude, Subjective Norms, and Perceived Behavioral Control—as reliable predictors of sustainable use intentions within OMHCs. This aligns with prior studies, accentuating the critical role of positive attitudes and social endorsement in digital settings (Cao, Zhang, et al., 2023; Xiang et al., 2023). Furthermore, by breaking down Social Support into informational and emotional components reveal their influence on subjective norms, we underscore the critical role of community dynamics in creating supportive environments for mental health discourse (Hu et al., 2022; Kim et al., 2023; Longest & Kang, 2022; Naslund et al., 2016).

Our findings reveal that Perceived Anonymity (H6) does not significantly influence attitudes toward OMHC participation, contrasting with earlier studies where anonymity was central in encouraging online self-disclosure (Jung et al., 2012). This result may reflect the cultural and contextual dynamics unique to the Philippines, where collectivist values, strong interpersonal ties, and shared emotional expression may reduce the perceived need for anonymity (Jocano, 1999; Reyes, 2015). In such settings, relational trust and community validation may outweigh the desire to remain anonymous, especially when emotional connection and support are foundational elements of the platform (Martinez et al., 2020; Naslund et al., 2016).

Additionally, users may already assume a baseline level of anonymity when engaging in digital spaces, making perceived anonymity a less salient or variable construct in influencing attitudes. The type of online health interaction may also shape perception; users may be less concerned about anonymity in patient-to-doctor exchanges where privacy supports quality care, but more cautious in peer-to-peer forums where recognition or social judgment could arise.

Theoretically, anonymity is more influential in contexts involving taboo, stigma, or high disclosure risk (Christie & Dill, 2016; Keipi et al., 2015). However, our findings suggest that in trust-oriented OMHCs, especially within culturally empathetic environments like the Philippines, users may prioritize authenticity, emotional resonance, and community credibility over anonymity. Practically, this underscores a potential shift in user values, where trust, emotional safety, and engagement play more prominent roles in shaping attitudes than identity concealment. Previous studies have extensively explored the intentions of TPB rather than the sustained use of platforms, particularly in OMHCs. For instance, research by Cao, Zhang, et al. (2023) and Arkorful et al. (2022) primarily examines initial adoption rather than continuous engagement. Our study addresses this gap by focusing on sustainable use intention, providing a deeper understanding of long-term engagement dynamics within OMHCs. Additionally, our study incorporates constructs such as initial trust, OMHC engagement, and perceived anonymity, which are less frequently integrated into TPB models in this context. By doing so, we overcome the limitations of previous work that often overlook the multifaceted nature of online community participation.

Theoretical and Practical Implications

Theoretical Implications

This study provides empirical support for the TPB in the context of sustainable use of OMHCs. Beyond reaffirming the TPB’s foundational constructs—attitude, subjective norms, and perceived behavioral control—this research extends the model by incorporating initial trust, OHC engagement, and social support as additional predictors. These additions deepen our understanding of individuals’ intentions to engage with OMHCs. By investigating these extensions, the study contributes to the growing body of research that situates TPB within digital contexts, where relational dynamics, emotional safety, and trust in technology play a critical role. The findings suggest that TPB remains a valuable theoretical lens for understanding behavior in technology-mediated mental health environments when contextually expanded.

Practical Implications

The strong influence of initial trust on attitude and behavioral intention underscores the need for intentional trust-building mechanisms in OMHC design. Platform designers can operationalize this finding by embedding visible and verifiable trust signals throughout the user experience, including:

Transparent privacy policies and visible data encryption symbols to reassure users about confidentiality;

Badges or certifications for licensed mental health professionals to distinguish verified experts;

User testimonials and ratings from community members to reflect authenticity and social proof;

Moderation protocols that prevent harmful or stigmatizing content while promoting respectful dialogue;

Onboarding walkthroughs that explain how user data is handled and how interactions are kept safe;

Emotionally sensitive design elements include empathetic tone, inclusive language, and customizable sharing controls.

Trust can also be reinforced through consistent engagement patterns, such as predictable response times from moderators or professionals and AI-powered suggestions for credible mental health resources. These features help reduce uncertainty and increase the user’s perception of platform reliability, particularly during initial encounters when trust and attitudes are still forming.

Mental health professionals also play a vital role by providing clinical support and enhancing platform credibility. Their presence can encourage user engagement and trust, especially in emotionally vulnerable settings (Pretorius et al., 2019). Furthermore, our findings support the integration of OMHC features into popular social media platforms, which are already widely used by young adults to explore health topics (Chen & Wang, 2021; Pretorius et al., 2019). Embedding wellness tools in familiar platforms could help normalize mental health discourse and expand the reach of digital support systems.

The potential of AI-driven analytics is also promising. Tools that tailor interactions based on individual users may improve engagement and relevance (Thakkar et al., 2024). However, the use of AI must be matched by strong data privacy policies, ethical standards, and user-informed design to ensure digital equity and trust.

From a policy perspective, robust frameworks that support privacy, interoperability, quality assurance, and research-driven innovation are essential for maintaining OMHCs as viable and trustworthy alternatives to traditional care. Our findings align with the World Health Organization’s (2021) digital mental health guidelines, emphasizing trust, user safety, inclusivity, and context-specific adaptation.

In the Philippine context, these insights may inform existing national initiatives such as the Philippine Council for Mental Health Strategic Framework 2024 to 2028 by integrating community-based trust features into telemental health platforms and digital referral systems. International models such as Australia’s headspace (https://headspace.org.au/) and the UK’s Every Mind Matters (https://www.nhs.uk/every-mind-matters/) offer further guidance, highlighting the importance of verified professionals, transparent data policies, and structured peer support in building trust and driving sustained engagement.

Conclusions, Limitations, and Future Research

This study contributes to the digital mental health support dialogue by examining the complex factors driving young adults’ interactions with OMHCs in the Philippines. Utilizing the TPB and incorporating additional constructs such as initial trust, community engagement, and informational and emotional support, the study offers a deeper understanding of the motivations that shape online help-seeking behaviors among young adults. Our findings highlight the critical role of these constructs in fostering positive attitudes toward participation in OMHCs.

Despite its contributions, this study has several limitations. Its focus on a specific demographic within the Philippines may limit the generalizability to other populations or cultural settings. Nonetheless, it provides essential insights into the evolving digital mental health landscape. Additionally, the reliance on self-reported data may introduce bias, and the cross-sectional study design restricts our ability to infer causal relationships among variables.

To address these limitations, future research could benefit from expanding the demographic and cultural scope of the sample. Including participants from diverse geographic, socioeconomic, and cultural contexts would enhance the external validity of the findings. Longitudinal designs are also recommended for behavioral changes over time and to better assess the stability of user engagement with OMHCs. Moreover, exploring the roles of anonymity and privacy in digital health platforms remains a promising direction, particularly in understanding how these factors influence engagement, trust, and disclosure.

Future studies are encouraged to adopt a mixed-methods approach to uncover deeper insights that are not captured through quantitative methods alone. While this study provides valuable statistical evidence, qualitative components such as in-depth interviews or open-ended survey responses could reveal context-specific factors and user narratives that influence attitudes and behaviors. Employing mixed methods would also facilitate the triangulation of findings, enrich theory development, and provide a more holistic view of user motivations, especially within culturally sensitive or emotionally charged domains like mental health. The findings reinforce the theoretical underpinnings of user engagement with OMHCs and offer practical strategies for enhancing digital mental health support systems. By dissecting the various factors influencing young adults’ participation in OMHCs, this research provides a foundational framework for future studies to optimize digital platforms as supplementary mental health resources. As these platforms continue to gain traction, especially among younger populations, further research into the factors that sustain meaningful engagement is essential. Advancing this knowledge is critical to improving the effectiveness, reach, and impact of digital interventions in addressing the broader mental health challenges faced by today’s youth.

Footnotes

Acknowledgements

The authors thank the respondents, the editor, and the reviewers for their constructive comments.

ORCID iDs

January Naga

Mia Quidato

Ma. Celsa Gregoria Paula Pascual

Ryan Ebardo

Ethical Considerations

The ethical standards and guidelines governing this study were embedded within the course framework under which the research was conducted. All data collection adhered to institutional academic protocols for minimal-risk research, as well as to the ethical principles outlined in the Declaration of Helsinki and the Philippine Data Privacy Act of 2012. Participation was entirely voluntary, and informed consent was obtained from all respondents. Clear information was provided regarding the study’s purpose, the right to withdraw at any time, and the measures taken to protect their anonymity and data confidentiality. All participants consented willingly, and their privacy rights were fully respected throughout the research process.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets generated and/or analyzed during the current study are not publicly available and are not available from the corresponding author due to ethical and privacy considerations.

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