Background: Digital mental health interventions (DMHIs) are a promising approach to reducing the public health burden of mental illness. DMHIs are efficacious, can provide evidence-based treatment with few resources, and are highly scalable relative to one-on-one face-to-face psychotherapy. There is potential for DMHIs to substantially reduce unmet treatment needs by circumventing structural barriers to treatment access (eg, cost, geography, and time). However, epidemiological research on perceived barriers to mental health care use demonstrates that attitudinal barriers, such as the lack of perceived need for treatment, are the most common self-reported reasons for not accessing care. Thus, the most important barriers to accessing traditional psychotherapy may also be barriers to accessing DMHIs. Objective: This study aimed to explore whether attitudinal barriers to traditional psychotherapy access might also serve as barriers to DMHI uptake. We explored the relationships between individuals’ structural versus attitudinal barriers to accessing psychotherapy and their indicators of potential use of internet-delivered guided self-help (GSH). Methods: We collected survey data from 971 US adults who were recruited online via Prolific and screened for the presence of psychological distress. Participants provided information about demographic characteristics, current symptoms, and the use of psychotherapy in the past year. Those without past-year psychotherapy use (640/971, 65.9%) answered questions about perceived barriers to psychotherapy access, selecting all contributing barriers to not using psychotherapy and a primary barrier. Participants also read detailed information about a GSH intervention. Primary outcomes were participants’ self-reported interest in the GSH intervention and self-reported likelihood of using the intervention if offered to them. Results: Individuals who had used psychotherapy in the past year reported greater interest in GSH than those who had not (odds ratio [OR] 2.38, 95% CI 1.86-3.06; P<.001) and greater self-reported likelihood of using GSH (OR 2.25, 95% CI 1.71-2.96; P<.001). Attitudinal primary barriers (eg, lack of perceived need; 336/640, 52.5%) were more common than structural primary barriers (eg, money or insurance; 244/640, 38.1%). Relative to endorsing a structural primary barrier, endorsing an attitudinal primary barrier was associated with lower interest in GSH (OR 0.44, 95% CI 0.32-0.6; across all 3 barrier types, P<.001) and lower self-reported likelihood of using GSH (OR 0.61, 95% CI 0.43-0.87; P=.045). We found no statistically significant differences in primary study outcomes by race or ethnicity or by income, but income had a statistically significant relationship with primary barrier type (ORs 0.27-3.71; P=.045). Conclusions: Our findings suggest that attitudinal barriers to traditional psychotherapy use may also serve as barriers to DMHI use, suggesting that disregarding the role of attitudinal barriers may limit the reach of DMHIs. Future research should seek to further understand the relationship between general treatment-seeking attitudes and attitudes about DMHIs to inform the design and marketing of DMHIs.

Background: Digital health technologies (DHTs) for psychosis have been developed and tested rapidly in recent years, and the COVID-19 pandemic accelerated the transition to telehealth and digital health. However, research examining mental health professionals’ views on the use of DHTs for people with psychosis is limited. Given the increased accessibility and availability of DHTs for psychosis, an up-to-date understanding of staff perception of DHTs for psychosis is warranted. Objective: In this study, we investigated: i) staff technology usage and their perception of service users’ technology usage; ii) staff views towards the use of DHTs for psychosis; and iii) the differences in staff technology usage and views towards the use of DHTs in clinical practice before and after the COVID-19 pandemic. Methods: Two cross-sectional surveys were deployed before and after the pandemic. Both surveys were distributed to mental healthcare staff who had experience of working with individuals experiencing psychosis in the UK. Results: A total of 155 and 352 participants completed the Phase 1 and Phase 2 surveys respectively. Staff reported high levels of technology ownership and usage. In general, staff expressed positive views regarding the use of DHTs for psychosis; however, barriers and concerns, including affordability, digital literacy, and potential negative effects on service users’ mental health, were also reported. There was no change in staff use of digital technology in clinical practice pre-post pandemic. Conclusions: Staff expressed optimism about the potential implementation of DHTs in practice, though they also noted some concerns regarding safety and access. Although the COVID-19 pandemic accelerated the adoption of digital tools for healthcare, the sustainability of this shift from traditional to digital healthcare has been less than optimal. To promote the implementation of DHTs, systematic evaluation of adverse effects of using DHTs and dissemination of evidence are needed to address concerns staff expressed regarding safety. Organisational support and training should be offered to staff to help address barriers and increase confidence in recommending and utilising DHTs with service users.

Background: Therapist guided internet-delivered cognitive behavioral therapy (guided ICBT) appears to be efficacious for depression, panic disorder (PD) and social anxiety disorder (SAD) in routine care clinical settings. However, implementation of guided ICBT in specialist mental health services is limited partly due to low referral rates from general practitioners (GP), which may stem from lack of awareness, limited knowledge of its effectiveness, or negative attitudes toward the treatment format. In response, self-referral systems were introduced in mental health care about a decade ago to improve access to care. Yet, little is known about how referral pathways may affect treatment outcomes in guided ICBT. Objective: This study aims to compare the overall treatment effectiveness of GP-referral and self-referral to guided ICBT for patients with depression, PD or SAD in a specialized routine care clinic. This study also explores if the treatment effectiveness varies between referral pathways and the respective diagnoses. Methods: This naturalistic open effectiveness study compares treatment outcomes from pre-treatment to post-treatment and from pre- to 6-month follow-up across two referral pathways. All patients underwent module based guided ICBT lasting up to 14 weeks. The modules covered psychoeducation, working with negative or automatic thoughts, exposure training, and relapse prevention. Patients received weekly therapist guidance through asynchronous messaging, with therapists spending an average of 10–30 minutes per patient per week. Patients self-reported symptoms before, during, immediately after, and 6-months post-treatment. Level and change in symptom severity were measured across all diagnoses. Results: In total 460 patients met inclusion criteria, 305 GP-referred (GP), and 155 self-referred (Self). Across the total sample about 60% were female, mean age 32 years, average duration of disorder 10 years. We found no significant differences in pre-treatment symptom levels between referral pathways, across the diagnoses. Estimated effect sizes based on Linear Mixed Modelling showed large improvements from pre- to post-treatment and from pre- to follow-up across all diagnoses, with statistically significant differences between referral pathways (GP: 0.97 - 1.22, Self: 1.34 - 1.58, P: <.001 - .002) and for the diagnoses separately: depression (GP: 0.86 – 1.26, Self: 1.97 -2.07, P: <.001 - .018), PD (GP: 1.32 – 1.60, Self: 1.64 – 2.08, P: .065 - .016 ) and SAD (GP: 0.80 - 0.99, Self: 0.99 – 1.19, P: .178 - .222 ). Conclusions: Self-referral to guided ICBT for depression and panic disorder appears to yield greater treatment outcomes compared to GP-referrals. We found no difference in outcome between referral pathway for SAD. This study underscores the potential of self-referral pathways to enhance access to evidence-based psychological treatment, improve treatment outcomes, and promote sustained engagement in specialist mental health services. Future studies should examine the effect of the self-referral pathway when it is implemented on a larger scale.

Background: This scoping review examines the effects of various sound interventions, including music, natural sounds, and speech, on the stress response in adults. Objective: The review aims to identify key therapeutic factors, including sound type, individual listener characteristics, and environmental influences. It also synthesizes evidence on physiological responses to sound interventions and highlights current research gaps. Methods: We conducted a comprehensive search using databases such as PubMed, Web of Science, Scopus, and PsycINFO, focusing on studies from 1990 to 2024. Eligible studies included randomized controlled trials, clinical trials, and laboratory experiments that measured stress through physiological markers (eg, heart rate variability and cortisol) and self-reports. A total of 34 studies were included, and thematic analysis was conducted to identify recurring themes in the findings. Results: The findings suggest that music, especially classical and self-selected pieces, effectively reduces physiological stress markers, including cortisol levels, heart rate variability, and blood pressure. Nonmusical sounds, such as nature sounds and calming voices, also demonstrate potential for stress relief, although research in this area remains limited. While most sound interventions showed positive effects, some studies reported adverse effects, indicating that sound can both alleviate and induce stress. The outcomes were substantially affected by contextual factors such as personal preferences, delivery methods, cultural context, and emphasizing the importance of personalized interventions. Conclusions: Sound interventions offer promising, noninvasive methods for stress reduction. This review suggests that future research should address gaps in the study of nonmusical sound interventions and further investigate the neural mechanisms underlying stress responses to sound.

Background: Large language models (LLMs) are increasingly used in the mental health field, with promising results in assessing mental disorders. However, correctness, dependability, and equity of LLM-generated information have been questioned. Amongst other, societal biases and research underrepresentation of certain population strata may affect LLMs. Because LLMs are already used for clinical practice, including decision support, it is important to investigate potential biases to ensure a responsible use of LLMs. Anorexia nervosa (AN) and bulimia nervosa (BN) show a lifetime prevalence of 1–2%, affecting more women than men. For men, sexual orientation was identified as a risk factor, with homosexual men having a higher risk of developing an eating disorder (ED) than heterosexual men. However, men are underrepresented in ED research and research on the association between gender and sexual orientation with prevalence, symptoms, and treatment outcomes of AN and BN is scarce. Objective: We aimed to estimate the presence and size of bias related to gender and sexual orientation produced by a common LLM as well as a smaller LLM specifically trained for mental health analyses, exemplified in the context of ED symptomatology and health-related quality of life (HRQoL) of patients with AN or BN. Methods: We extracted 30 case vignettes (22 AN, 8 BN) from scientific articles. We adapted each vignette to create 4 versions, describing a female vs. male patient living with their female vs. male partner (2x2 design), yielding n=120 vignettes. We then fed each vignette into Chat Generative Pre-trained Transformer-4 (ChatGPT-4) and to “MentaLLaMA” based on the Large Language Model Meta AI (LLaMA) architecture thrice with the instruction to evaluate them by providing responses to two psychometric instruments, the RAND-36 questionnaire assessing HRQoL and the eating disorder examination questionnaire (EDE-Q). With the resulting LLM-generated scores, we calculated multilevel models (MLMs) with a random intercept for gender and sexual orientation (accounting for within-vignette variance), nested in vignettes (accounting for between-vignette variance). Results: In ChatGPT-4, the MLM with N=360 observations indicated for the RAND-36 mental composite summary, a significant association with gender (conditional means: 12.8 for male and 15.1 for female cases; 95% CI of the effect=[-6.15, -0.35]; p=.037) but neither with sexual orientation nor an interaction effect (ps>.370). We found no indications for main or interaction effects of gender or sexual orientation for the EDE-Q overall score (conditional means: 5.59-5.65; ps>.611). MentaLLaMA did not yield reliable results. Conclusions: LLM-generated estimates of mental HRQoL in AN or BN case vignettes are at risk of being affected by cases’ gender, with male cases scoring lower. Given the lack of real-world epidemiological evidence for such a pattern, our study highlights relevant risk of bias when applying generative AI in the context of mental health. Better understanding and mitigation of risk of bias related to gender and other factors, such as ethnicity or socioeconomic status, are highly warranted to ensure responsible use of LLMs when conducting diagnostic assessments or providing treatment recommendations.

Background: In the interest of accurately assessing the effects of digital technology use in early childhood, researchers and experts have emphasized the need to conceptualize and measure children’s digital technology use beyond screen time. Researchers have argued that many patterns of early digital technology use could be problematic, resulting in the emerging need to list and examine their measures. Objective: We aimed to review existing empirical literature that is using measures for problematic digital technology use in preschool children with the end goal of identifying a set of reliable and valid measures, predicting negative outcomes for children’s health, development, or well-being. Methods: We conducted a scoping review across the Web of Science, PubMed, and Google Scholar databases to identify peer-reviewed publications that were published from January 2012 to December 2023, were written in the English language, described an empirical study, and included a measure of problematic digital technology use beyond exposure (ie, screen time) in children aged 0 to 6 years. Results: The search yielded 95 empirical studies, in which 18 composite measures of problematic use and 23 measures of specific problematic use aspects were found. Existing composite measures conceptualize problematic use as either a group of risky behaviors or as a group of symptoms of a presumed underlying disorder, with the latter being more common. Looking at their conceptual background and psychometric properties, existing composite measures fall short of reliably assessing all the crucial aspects of problematic digital technology use in early childhood. Therefore, the benefits and shortcomings of single-aspect problematic digital technology use measures are evaluated and discussed. Conclusions: On the basis of current research, early exposure to digital technologies, device use before sleep, and solitary device use represent measures that have been consistently associated with negative outcomes for children. In addition, potential measures of problematic use include device use during meals, device use for emotional regulation, device multitasking, and technoference, warranting further research. Public health benefits of defining problematic digital technology use as a group of risky behaviors rather than a group of addiction symptoms are discussed.

Background: Secondary use of routinely collected health care data has great potential benefits in epidemiological studies primarily due to the large scale of preexisting data. Objective: This study aimed to engage respondents with and without a history of self-harm, gain insight into their views on the use of their data for research, and determine whether there were any differences in opinions between the 2 groups. Methods: We examined young people’s views on the use of their routinely collected data for mental health research through a web-based survey, evaluating any differences between those with and without a history of self-harm. Results: A total of 1765 respondents aged 16 to 24 years were included. Respondents’ views were mostly positive toward the use and linkage of their data for research purposes for public benefit, particularly with regard to the use of health care data (mental health or otherwise), and generally echoed existing evidence on the opinions of older age groups. Individuals who reported a history of self-harm and subsequently contacted health services more often reported being “extremely likely” or “likely” to share mental health data (contacted: 209/609, 34.3%; 95% CI 28.0-41.2; not contacted: 169/782, 21.6%; 95% CI 15.8-28.7) and physical health data (contacted: 117/609, 19.2%; 95% CI 12.7-27.8; not contacted: 96/782, 12.3%; 95% CI 6.7-20.9) compared with those who had not contacted services. Respondents were overall less likely to want to share their social media data, which they considered to be more personal compared to their health care data. Respondents stressed the importance of anonymity and the need for an appropriate ethical framework. Conclusions: Young people are aware, and they care about how their data are being used and for what purposes, irrespective of having a history of self-harm. They are largely positive about the use of health care data (mental or physical) for research and generally echo the opinions of older age groups raising issues around data security and the use of data for the public interest.

Background: Longitudinal investigation of the association between mobile health (mHealth) app use and attitudes toward one’s body during adolescence is scarce. mHealth apps might shape adolescents’ body image perceptions by influencing their attitudes toward their bodies. Adolescents might also use mHealth apps based on how they feel and think about their bodies. Objective: This prospective study examined the longitudinal within-person associations between mHealth app use, body dissatisfaction, and physical self-worth during adolescence. Methods: The data were gathered from a nationally representative sample of Czech adolescents aged between 11 and 16 years (N=2500; n=1250, 50% girls; mean age 13.43, SD 1.69 years) in 3 waves with 6-month intervals. Participants completed online questionnaires assessing their mHealth app use, physical self-worth, and body dissatisfaction at each wave. The mHealth app use was determined by the frequency of using sports, weight management, and nutritional intake apps. Physical self-worth was assessed using the physical self-worth subscale of the Physical Self Inventory-Short Form. Body dissatisfaction was measured with the items from the body dissatisfaction subscale of the Eating Disorder Inventory-3. The random intercept cross-lagged panel model examined longitudinal within-person associations between the variables. A multigroup design was used to compare genders. Due to the missing values, the final analyses used data from 2232 adolescents (n=1089, 48.8% girls; mean age 13.43, SD 1.69 years). Results: The results revealed a positive within-person effect of mHealth app use on the physical self-worth of girls: increased mHealth app use predicted higher physical self-worth 6 months later (β=.199, P=.04). However, this effect was not consistent from the 6th to the 12th month: a within-person increase in using apps in the 6th month did not predict changes in girls’ physical self-worth in the 12th month (β=.161, P=.07). Regardless of gender, the within-person changes in the frequency of using apps did not influence adolescents’ body dissatisfaction. In addition, neither body dissatisfaction nor physical self-worth predicted app use frequency at the within-person level. Conclusions: This study highlighted that within-person changes in using mHealth apps were differentially associated with adolescents’ body-related attitudes. While increased use of mHealth apps did not influence body dissatisfaction across genders, it significantly predicted higher physical self-worth in adolescent girls 6 months later. A similar association was not observed among boys after 6 months. These findings indicate that using mHealth apps is unlikely to have a detrimental impact on adolescents’ body dissatisfaction and physical self-worth; instead, they may have a positive influence, particularly in boosting the physical self-worth of adolescent girls.

Background: The EVADD study (EVAluation of Digital aDdiction) investigates problematic smartphone use in the digital age, as global smartphone users reached 55.88 million in France in 2023. With increased screen time from digital devices, especially smartphones, the study highlights adult usage issues and associated risks. Objective: To assess the prevalence of compulsive smartphone use among French adults and identify patterns of problematic behaviors, including their impact on daily activities, sleep, and safety. Methods: The EVADD study employed a cross-sectional, non-probability sample design, conducted from May 3rd to June 5th, 2023. Participants were recruited through the French mutual insurance company PRO-BTP. Data were collected anonymously via an online questionnaire, including the Smartphone Compulsive Use Test, capturing information on social network engagement, device ownership, and daily screen use. A total of 21,244 adults (average age 53 years; 48% women) participated. Results: Among 21,244 participants (mean age: 53 ± 15 years; 48% women), 8,025 out of 12,034 respondents (66.7%) exhibited compulsive smartphone use (P < 0.001). Additionally, 4,569 participants (38%) scored ≥ 8 on the Smartphone Compulsion Test, indicating addiction. Younger participants (18-39 years) were significantly more likely to show signs of addiction (57% or 2,504 out of 4,394 participants; Odds Ratio: 2.5, 95% CI: 1.9-3.2) compared to participants aged ≥ 60. Problematic behaviors included unsafe smartphone use while driving (5,736 out of 12,953 participants; 44.3%), frequent smartphone use before bedtime (9,136 out of 21,244 participants; 43.0%), and using smartphones in the bathroom (7,659 out of 21,244 participants; 36.1%). Sleep disturbances and risky behaviors correlated strongly with higher compulsion scores (P < 0.01). Conclusions: The EVADD study highlights the complex relationship between adults and smartphones, revealing the prevalence of compulsive behaviors and their impact on daily life, sleep, and safety. These findings emphasize the need for public awareness campaigns, preventive strategies, and therapeutic interventions to mitigate health risks associated with excessive smartphone use. Clinical Trial: https://clinicaltrials.gov/ct2/show/NCT05860660

Background: Web-based cognitive behavioral therapy (wb-CBT) is a scalable way to reach distressed university students. Guided wb-CBT is typically superior to self-guided wb-CBT over short follow-up periods, but evidence is less clear over longer periods. Objective: This study aimed to compare short-term (3 months) and longer-term (12 months) aggregate effects of guided and self-guided wb-CBT versus treatment as usual (TAU) in a randomized controlled trial of Colombian and Mexican university students and carry out an initially unplanned secondary analysis of the role of differential predicted compliance in explaining these differences. Methods: The 1319 participants, recruited either through email and social media outreach invitations or from waiting lists of campus mental health clinics, were undergraduates (1038/1319, 78.7% female) with clinically significant baseline anxiety (Generalized Anxiety Disorder–7 score≥10) or depression (Patient Health Questionnaire–9 score≥10). The intervention arms comprised guided wb-CBT with weekly asynchronous written human feedback, self-guided wb-CBT with the same content as the guided modality, and TAU as provided at each university. The prespecified primary outcome was joint remission (Generalized Anxiety Disorder–7 score=0-4 and Patient Health Questionnaire–9 score=0-4). The secondary outcome was joint symptom reduction (mean scores on the Patient Health Questionnaire Anxiety and Depression Scale) at 3 and 12 months after randomization. Results: As reported previously, 3-month outcomes were significantly better with guided wb-CBT than self-guided wb-CBT (P=.02) or TAU (P=.02). However, subsequent follow-up showed that 12-month joint remission (adjusted risk differences=6.0-6.5, SE 0.4-0.5, and P<.001 to P=.007; adjusted mean differences=2.70-2.69, SE 0.7-0.8, and P<.001 to P=.001) was significantly better with self-guided wb-CBT than with the other interventions. Participants randomly assigned to the guided wb-CBT arm spent twice as many minutes logged on as those in the self-guided wb-CBT arm in the first 12 weeks (mean 12.5, SD 36.9 vs 5.9, SD 27.7; χ²₁=107.1, P<.001), whereas participants in the self-guided wb-CBT arm spent twice as many minutes logged on as those in the guided wb-CBT arm in weeks 13 to 52 (mean 0.4, SD 7.5 vs 0.2, SD 4.4; χ²₁=10.5, P=.001). Subgroup analysis showed that this longer-term superiority of self-guided wb-CBT was confined to the 40% (528/1319) of participants with high predicted self-guided wb-CBT compliance beyond 3 months based on a counterfactual nested cross-validated machine learning model. The 12-month outcome differences were nonsignificant across arms among other participants (all P>.05). Conclusions: The results have important practical implications for precision intervention targeting to maximize longer-term wb-CBT benefits. Future research needs to investigate strategies to increase sustained guided wb-CBT use once guidance ends. Trial Registration: ClinicalTrials.gov NCT04780542; https://www.clinicaltrials.gov/study/NCT04780542