Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2025 Aug 20.
Published before final editing as: Clin Psychol Sci. 2025 Aug 17:10.1177/21677026251358836. doi: 10.1177/21677026251358836

Longer Single-Session Interventions May Not Be Better: Evidence From Two Randomized Controlled Trials With Online Workers Facing Mental-Health Struggles

Benjamin Kaveladze 1,2, Arka Ghosh 2, Carter J Funkhouser 3, Stephen M Schueller 4, Jessica L Schleider 2
PMCID: PMC12363389  NIHMSID: NIHMS2094479  PMID: 40843242

Abstract

Online self-guided single-session interventions (SSIs), which provide a complete mental health intervention in one brief experience, promise to increase global access to evidence-based support. One way to expand current SSIs’ reach is to shorten them, but doing so could also compromise their effectiveness. We conducted two randomized trials to test if shortening evidence-based SSIs reduces their efficacy among adult online workers facing mental health struggles. In study 1 (n = 262), the 8-minute “Overcoming Loneliness” SSI reduced loneliness over eight weeks more than a 23-minute version of it (b = 2.64; d = 0.22; 95% CI 0.02, 0.41; p = .03). In study 2 (n = 1,145), 15-minute, 9-minute, 5-minute, and 3-minute versions of the “Action Brings Change” SSI did not significantly differ in how much they affected depression eight weeks later (ps > .14). Our results suggest that longer digital SSIs are not necessarily more helpful than shorter ones.

Introduction

Globally, access to effective mental healthcare is low and inequitable (Kazdin & Blase, 2011). Most people who could benefit from professional mental health support cannot or choose not to access it. Online self-guided single-session interventions (SSIs), structured programs that intentionally involve just one encounter (though one might choose to use an SSI multiple times), address a critical gap in existing care options (Schleider et al., 2025). SSIs exist in in-person supporter-guided and self-guided forms, but here we use the term “SSI” only to refer to online self-guided SSIs. SSIs, most of which last under 60 minutes, have a unique capacity for flexible reach, as evidenced by their implementation in diverse healthcare, social media, and education settings (Dobias et al., 2022; Osborn et al., 2020).

Evidence-based SSIs aim to maximize impact within their brief duration by delivering specific intervention components. For example, the Action Brings Change (ABC) Project, a 15-minute SSI based on behavioral activation, includes several active elements drawing from concepts of autonomy, competence, and relatedness from self-determination theory (Ryan & Deci, 2000): “psychoeducation” about mood-boosting activities’ power to reverse negative mood spirals, a “saying is believing” activity in which users offer advice to an imagined peer, and an “action plan” that helps users create a personalized plan to engage in a positive behavior soon (Schleider et al., 2019).

Loneliness and depression are globally prevalent psychological struggles that have been shown to respond well to manualized behavioral treatments, making them great candidates for SSIs (Cuijpers et al., 2023; Hickin et al., 2021). Trials evaluating the efficacy of SSIs for loneliness and depression have had mixed results. The one published trial of an SSI for loneliness in adults did not find it was more efficacious than an active control (about sharing feelings with close others); however, the SSI was also not found to be less efficacious than a three-session version of it delivered over three weeks (Kaveladze et al., 2024). Multiple trials of SSIs for depression in youth have demonstrated efficacy (Schleider, Burnette, et al., 2020; Schleider et al., 2022), but a trial of an SSI for depression in adults failed to show its efficacy (Lorenzo-Luaces & Howard, 2023). These studies’ methods vary considerably, and more research is needed to better understand which SSIs for loneliness and depression are efficacious, and for whom. SSIs’ average effects tend to be small (Kaveladze et al., 2024; Lorenzo-Luaces & Howard, 2023; Schleider et al., 2022, 2025); however, even small effects can exert population-wide mental health impact if they can manage to reach a substantial portion of the population (Funder & Ozer, 2019). Given this possibility, optimizing SSIs’ potential to scale without sacrificing their effectiveness is a valuable objective for the field.

Duration and Effectiveness: How Light a Touch is Too Light?

SSIs’ light-touch nature enables easier implementation and broader appeal than multi-session interventions (Kaveladze et al., 2024; Odgers et al., 2022). Similarly, brevity is valuable within a single session. In naturalistic contexts (i.e. outside of trials where participants are compensated), people are less likely to drop out of shorter SSIs and might be more willing to begin them (Cohen & Schleider, 2022; Dobias et al., 2022). Briefer SSIs are also easier to implement in real-world settings; for example, a social media platform might prefer to implement a shorter (e.g., 5-minute) SSI to a longer (e.g., 20-minute) SSI because the longer SSI might pull users’ attention away from their platform. In addition, shorter SSIs might enable more diverse approaches to maximizing impact within a single session (beyond the text-based formats of existing SSIs), such as 3-minute TikToks or one-page infographics (McCashin & Murphy, 2023).

While shorter SSIs have demonstrated potential for broader reach, it remains unclear whether reducing SSI duration might decrease effectiveness consequently. As noted, SSIs’ average effects are already small, so compromising on their impact may not be worth the possible gains in reach.

Current Study

We ran two randomized controlled trials to test if reducing SSIs’ duration impacted their efficacy. In study 1, we predicted that a 23-minute cognitive behavioral therapy SSI would be more efficacious than an 8-minute version in reducing loneliness from baseline to 8-week follow-up. In study 2, we predicted that 15-minute, 9-minute, 5-minute, and 3-minute versions of a behavioral activation SSI would differ in how much they affected participants’ depression over 8 weeks.

Transparency and Openness

Preregistration.

We pre-registered both study 1: https://osf.io/8bth2 and study 2: https://osf.io/fu6yc.

Data, materials, code, and online resources.

The study materials, data, and analysis code are available online (study 1: https://osf.io/5ujtc, study 2: https://osf.io/nj498).

Reporting.

We report how we determined our sample size, all data exclusions, all manipulations, and all measures in the study.

Ethical approval.

The procedures were approved by the University of California, Irvine Human Subjects Review Board (Protocol 1253) for study 1, and the Northwestern University Social and Behavioral Sciences Review Board (Protocol STU00220591) for study 2. The studies were carried out in accordance with the provisions of the World Medical Association Declaration of Helsinki.

Deviations from the pre-registration.

We deviated from the pre-registrations in several ways. First, we neglected to pre-register the inclusion criteria of speaking English, being at least 18 years old, and living in the United States, and did not specify that ULS-3 scores needed to be 6 or above for inclusion in Study 1. Second, we accidentally referred to loneliness when we meant depression in one instance in the study 2 pre-registration. Third, we pre-registered paying participants in study 2 $3.00 for competing the baseline session but decided to pay $4.00 before launching the study. Fourth, the SSI durations we pre-registered were rough estimates made prior to having data on median duration; once we collected the study data, we found the median SSI durations in our sample differed slightly from the pre-registered durations, so in the manuscript we reported the durations we observed. Fifth, in Study 1, we decided not to follow one of our pre-registered exclusion criteria: removing responses for responding fraudulently, mischievously, or speeding past all content. We made this modification because we decided the criterion was problematic for our intent-to-treat strategy and it left too much room for researcher discretion.

Methods

Participants

Participants in both trials were recruited through CloudResearch Connect, a platform where online workers complete studies in exchange for payment (Hartman et al., 2023). Participants were eligible for the study if they spoke fluent English, were at least 18 years old, lived in the United States, and met our criteria for struggling with loneliness in study 1 and depression in study 2 (see the Measures section regarding screening criteria).

Procedure

All study procedures took place online, with no synchronous interactions between participants and researchers. Both trials used Qualtrics to collect data, randomize participant assignment, and deliver the SSI content (Qualtrics, 2005). We recruited and compensated participants via CloudResearch Connect (Hartman et al., 2023). Across the screener, intervention, and follow-up, each participant was paid a total of $7.25 in study 1 and $5.25 in study 2.

In study 1, participants were randomized (1:1) to complete either a 23-minute or an 8-minute version of the “Overcoming Loneliness” SSI. The SSI was adapted from a nine-week internet cognitive behavioral therapy intervention targeting negative thinking, social skills, and exposure to social situations (Käll et al., 2020; Kaveladze et al., 2024). In study 2, participants were randomized (1:1:1:1) to the original 15-minute ABC Project behavioral activation SSI (modified slightly so that its examples were more relevant to adults than its original target population, adolescents), or 9-minute, 5-minute, and 3-minute versions of it (Schleider et al., 2019, 2022).

In developing the briefer versions of the SSIs in each study, we strove to retain the original SSI’s core elements, while shortening text and cutting time-intensive exercises. In the 8-minute “Overcoming Loneliness” SSI, we cut the interactive writing exercises and focused on the presenting the key messages: set a goal to regularly engage in social behaviors, challenge negative thought patterns, and work on communication skills. In all shortened versions of the “ABC Project” SSI, we aimed to retain the three hypothesized “active elements” of treatment: psychoeducation, testimonials / saying is believing exercises, and action plan (as detailed in the introduction). All versions of “Overcoming Loneliness” (study 1) are available online at https://osf.io/5ujtc and all versions of the “ABC Project” (study 2) are available at https://osf.io/nj498.

Measures

In study 1, we measured loneliness with the reliable and valid 20-item UCLA Loneliness Scale (ULS-20), version 3 (Russell, 1996). The measure asks participants how frequently they experience several feelings, such as “left out” or “close to people” on a scale from 1 (never) to 4 (often). Total scores range from 20 to 80, with a higher total score indicating greater feelings of loneliness. The ULS-20 had a Cronbach’s α of 0.93 at baseline in study 1.

To screen participants for loneliness, we used the valid three-item version of the UCLA Loneliness Scale (ULS-3, Bottaro et al., 2023; Hughes et al., 2004). The measure includes three items from the 20-item version of the scale, rated from 1 (hardly ever) to 3 (often; total score range 1–9). We also asked a single yes/no question of whether one’s loneliness was causing one distress. If one’s score on the ULS-3 was 6 or higher and they reported that their loneliness was causing them distress, they met our criteria for struggling with loneliness and were invited to participate in the study (Hughes et al., 2004; Käll et al., 2020).

In study 2, we measured depression with the valid and reliable Patient Health Questionnaire-8 (PHQ-8, Kroenke et al., 2009). In this scale, participants rate how often they have been bothered by 8 items over the past two weeks (e.g., Poor appetite or overeating) on a scale from 0 (Not at all) to 3 (Nearly every day). The total score ranges from 0 to 24, with higher scores indicating more severe depression. The PHQ-8 had a Cronbach’s α of 0.74 at baseline in study 2. We also used the PHQ-8 as an eligibility requirement for the study; participants were invited to participate in the study if they scored 10 or above.

In addition to the primary outcomes reported in this manuscript, we collected several secondary outcomes in each study. For full lists of these outcomes, see the “Measured variables” section of the study pre-registrations (study 1: https://osf.io/8bth2 and study 2: https://osf.io/fu6yc).

Analysis Plan

The pre-registered main analysis in each study compared change in the primary outcome (loneliness in study 1 and depression in study 2) from baseline to 8-week follow-up across conditions. We used a mixed-effects model with condition, measurement time point, and the 2-way interaction between condition and measurement time point as independent variables and a participant identifier as a random intercept. We did not impute missing outcome data in the primary analyses because doing so does not improve the fit of longitudinal mixed-effects regression models with clinical trial data (Chakraborty & Gu, 2009; Jakobsen et al., 2017). We did not transform the outcome variables because they were normally distributed (see appendix). We also compared secondary outcomes across conditions and re-ran the main analyses adjusting for demographic covariates (see supplemental document, https://osf.io/czujx).

Although we conducted a priori power analyses as described in our pre-registrations, we identified issues with both studies’ power analyses after conducting the studies, such that the analyses had substantially less statistical power than anticipated. In study 1, using the “ANOVA repeated measures between-within” option in G*Power, we calculated the sample size required to power the study to detect differences in change of at least Cohen’s d = 0.20 across conditions with 80% power and alpha = .05 as 139 participants per group; however, after collecting the data, we realized that G*Power’s default setting (as of June 2023) for “within-between interactions” was not appropriate for this kind of analysis (Faul et al., 2007; Thibault et al., 2024). As a result, our sample size was only sufficient to detect an effect of d = 0.30 over time between conditions with 80% power (with the r = 0.74 test-rest correlation in loneliness we observed between baseline and follow-up). In study 2, we simulated data with the simr package and calculated that n = 275 in each condition would be sufficient to detect an effect of d = 0.20 (with 80% power, alpha = .05, and test-retest correlation of 0.74 as found in Study 1) between any two conditions (Green & MacLeod, 2016). However, after collecting data we observed that the main outcome’s test-retest correlation was r = 0.51. As such, n = 275 per condition was only sufficient to detect an effect of d = 0.29 between any two conditions with 80% power.

To clean, analyze, and visualize data, we used R version 4.3.1 (R Core Team, 2015) and the packages lme4 v1.1–35.1 (Bates et al., 2015), lmerTest v3.1–3 (Kuznetsova et al., 2017), and tidyverse v2.0.0 (Wickham et al., 2019). To obtain Cohen’s d estimates from a in a mixed effects model, we divided the predictor’s regression coefficient by its standard deviation at baseline (Feingold, 2009). We used the p < .05 criterion for statistical significance and the Kenward-Rogers method to calculate p-values (Kenward & Roger, 1997). Analyses include all participants who were randomized to a condition.

Results

We collected data from June 26, 2023, to August 22, 2023, in study 1 and from March 18, 2024, to May 22, 2024, in study 2. The trials’ CONSORT diagrams are shown in Figure 1.

Figure 1.

Figure 1.

Open in a new tab

Study 1 and 2 CONSORT Diagrams

The number of participants who participated in each part of each study.

In study 1, The mean participant age was 37.6 (SD = 12.2). 46.6% of the sample identified as a man and 53.4% identified as a woman. 75.6% of the sample identified as White, 9.5% as Black or African American, 6.9% as Asian, and 5.0% as a different race. The loneliness SSIs’ median (IQR) durations were 22.7 (15.3–32.5) minutes and 7.5 (4.6–11.1) minutes. 46.2% had a bachelor’s degree, 18.3% finished some college, 11.1% completed high school, 10.3% had a master’s degree, and 9.9% had an associate’s degree.

In study 2, the mean participant age was 35.9 (SD = 11.2). 45.9% of the sample identified as a man and 54.1% identified as a woman. 73.5% of the sample identified as White, 11.0% as Black or African American, 8.2% as Asian, and 7.3% as a different race. The depression SSIs’ median (IQR) durations were 14.5 (11.2–19.6) minutes, 9.3 (6.9–12.8) minutes, 5.4 (3.6–8.1) minutes, and 2.6 (1.7–3.6) minutes. 40.1% had a bachelor’s degree, 22.5% finished some college, 12.7% completed high school, 10.5% had a master’s degree, and 9.7% had an associate’s degree.

Change in Primary Outcome Across Conditions

In each study, the primary outcome did not significantly differ across conditions at baseline (loneliness in study 1 p = 0.53, depression in study 2 p = 0.24), indicating successful randomization. Moreover, participants who did not complete the follow-up survey were not significantly more or less lonely (study 1 p = 0.18) or depressed (study 2 p = 0.14) at baseline, nor were they significantly more likely to have been randomized to any condition (study 1 p = 0.37, study 2 p = 0.54).

Ignoring experimental condition, on average loneliness decreased from baseline to week 8 in study 1 (b = −5.79; d = −0.47; 95% CI, −0.57, −0.37; p < .001; ICC = 0.71), and depression decreased from baseline to week 8 in study 2 (b = −3.07; d = −0.64; 95% CI, −0.70, −0.57; p < .001; ICC = 0.49). Loneliness at baseline correlated with loneliness at week 8 with Pearson’s r = 0.74, and depression at baseline correlated with depression at week 8 with r = 0.51.

In study 1, contrary to our hypothesis, participants assigned to the 8-minute loneliness SSI showed greater reductions in loneliness from baseline to week 8 than those assigned to the 23-minute loneliness SSI (b = 2.64; d = 0.22; 95% CI, 0.02, 0.41; p = .03; ICC = 0.71).

In study 2, against our hypothesis, neither participants assigned to the 9-minute SSI, 5-minute, or 3-minute SSI significantly differed from participants assigned to the 15-minute SSI in how much their depression changed from baseline to week 8; (9-minute: b = 0.64; d = 0.13; 95% CI, −0.05, 0.32; p = 0.15), (5-minute: b = 0.12; d = 0.02; 95% CI, −0.15, 0.20; p = 0.79), (3-minute: b = 0.30; d = 0.06; 95% CI, −0.12, 0.24; p = 0.49), nor did those conditions significantly differ from one-another (ps > 0.24; ICC = 0.49).

Figure 2 visualizes differences across conditions and Figure 3 shows outcome distributions at baseline and follow-up. See Tables 1 and 2 for the analyses’ full regression output. See the supplemental document for differences in secondary outcomes across conditions, and demographic covariate-adjusted models (https://osf.io/czujx).

Figure 2.

Figure 2.

Open in a new tab

Change Over Time

The points reflect estimates from the study’s primary model and the error bars show 95% confidence intervals.

Table 1.

Mixed Effects Model Predicting Change in Loneliness

Predictors Estimates CI p df
(Intercept) 57.46 55.35 – 59.56 < .001 338.38
condition [23min] 0.95 −2.02 – 3.93 .529 338.37
time [Week 8] −7.14 −8.87 – −5.42 < .001 238.21
condition [23min] × time [Week 8] 2.64 0.23 – 5.05 .032 236.82
Random Effects
σ2 42.74
τ00 pid 106.00
ICC 0.71
N pid 262
Observations 488
Marginal R2 / Conditional R2 0.063 / 0.731
Open in a new tab

Table 2.

Mixed Effects Model Predicting Change in Depression

Predictors Estimates CI p df
(Intercept) 14.03 13.48 – 14.58 < .001 1725.59
condition [9min] −0.60 −1.39 – 0.20 .140 1725.58
condition [5min] −0.30 −1.08 – 0.49 .460 1725.57
condition [3 min] 0.05 −0.73 – 0.82 .906 1725.58
time [Week 8] −3.33 −3.94 – −2.72 <.001 1035.84
condition [9min] × time [Week 8] 0.64 −0.23 – 1.52 .148 1029.04
condition [5min] × time [Week 8] 0.12 −0.75 – 0.98 .786 1025.02
condition [3min] × time [Week 8] 0.30 −0.55 – 1.15 .492 1029.38
Random Effects
σ2 11.82
τ00 pid 11.21
ICC 0.49
N pid 1145
Observations 2076
Marginal R2 / Conditional R2 0.094 / 0.535
Open in a new tab

Discussion

SSIs hold promise to expand access to evidence-based support radically. To make SSIs more helpful for more people, we should aim to optimize their reach without sacrificing their effectiveness. In study 1, participants assigned to an 8-minute cognitive-behavioral therapy SSI reported greater reductions in loneliness over 8 weeks than participants assigned to a 23-minute SSI. In study 2, participants assigned to 15-minute, 9-minute, 5-minute, and 3-minute behavioral activation SSIs did not significantly differ in how much their self-rated depression changed over eight weeks. These studies suggest that reducing the amount of content in SSIs (while retaining their core elements) does not necessarily decrease their efficacy. This finding might inform implementations aiming to optimize SSIs’ impact at scale.

Interpreting the Findings

Our finding that an 8-minute loneliness SSI outperformed the 23-minute SSI on which it was based runs counter to common notions of how self-guided psychological interventions work. Participants may have found the 8-minute SSI more memorable and less cognitively demanding than the 23-minute version. Regardless, a result as surprising as this one warrants both further exploration and replication (Ioannidis, 2008).

The finding in study 2 that an SSI that took under three minutes, on average, to complete was not significantly less efficacious than the 15-minute SSI on which it was based (or its 5- or 9-minute versions) was also unexpected. It may be that there are meaningful differences in the SSIs’ efficacy but our study lacked sufficient statistical power to detect them. While the study had sufficient power to detect a difference between two groups of at least d = 0.29 80% of the time, the true difference between groups may be smaller but meaningful. Overall, we interpret the findings from these studies as providing some, but not conclusive, evidence that shorter SSIs are not less efficacious than longer ones.

Strengths and Limitations

These studies had several strengths, including being randomized controlled trials, being pre-registered, and having low attrition. As such, we were able to test our hypotheses fairly cleanly. The studies also had similar intervention formats, samples, and experimental designs, allowing for easy comparison.

The studies also had several limitations. First, they differed in intervention targets and primary outcomes, with one study targeting loneliness within an unspecified time frame and the other targeting depression over the past two weeks. In addition, findings from these studies may not generalize to other SSIs, especially human-guided SSIs, those delivered in-person, and those well beyond the 18-minute duration of the longest SSIs we examined (e.g., school interventions that last 6 or 7 hours). Future studies can examine if longer SSIs might be more helpful in other contexts.

An important limitation of these studies is that they lacked a no-treatment control condition. Thus, we cannot conclude from these studies alone that any of the SSIs we evaluated were more efficacious than receiving no intervention in our samples. We chose to omit a no-treatment control condition because of evidence from well-powered studies supporting the efficacy of digital SSIs for depression against passive and active controls (Schleider et al., 2020, 2022, 2025). However, recent studies found that SSIs for depression and anxiety did not improve outcomes at multi-week follow-ups in adult samples (Lorenzo-Luaces et al., 2024; Lorenzo-Luaces & Howard, 2023). Future controlled trials can address this limitation, providing better estimates of the effects of SSIs by comparing them to a no-treatment control condition.

Finally, our decision to recruit participants from CloudResearch Connect had advantages and disadvantages. We chose this sample because it made recruiting many legitimate participants relatively easy and ensured that most participants would complete their assigned SSI and return for follow-up measurement (Chapkovski et al., 2024). Additionally, online workers are an underserved population with notably high rates of depression (Ophir et al., 2020). Yet, including only online workers limits the results’ generalizability, as the online workers in our sample may differ in important ways from other help-seekers who might be expected to use these SSIs once disseminated. For one, they were paid to participate, so they may have been more motivated to complete the intervention and, perhaps, less inclined to implement lifestyle changes after an intervention, compared to individuals who encounter an intervention while actively seeking mental health support. Relatedly, many participants in these trials had moderate levels of loneliness and depression at baseline (see Appendix), limiting generalizability to populations with greater clinical severity, as well as those with subclinical symptoms.

Conclusions

Our findings challenge the assumption that more content necessarily yields more benefit–even within already-brief interventions. Instead, they suggest that, in some implementations, shorter SSIs may be a better choice for broad impact. Future research should investigate the relationship between SSI length and effectiveness in larger, real-world samples, ideally including no-treatment control conditions.

Funding

This work was supported by a grant from the Jacobs CERES (Connecting the EdTech Research Ecosystem) Center. BTK used funding from the National Institute of Mental Health (T32 MH115882) to fund this work.

Outside of the present project, J.L.S. has received funding from the National Institutes of Health Office of the Director (DP5OD028123), the National Institute of Mental Health (R43MH128075), the Upswing Fund for Adolescent Mental Health, the National Science Foundation (2141710), the Health Research and Services Administration (U3NHP45406-01-00), the Society of Clinical Child and Adolescent Psychology, Hopelab, the Child Mind Institute, Alongside, Kooth, and the Klingenstein Third Generation Foundation.

SMS serves on the Scientific Advisory Board for Headspace for which he receives compensation and has received consulting payments from Boehringer Ingelheim and Otsuka Pharmaceuticals for unrelated work. J.L.S. serves on the Scientific Advisory Board for Walden Wise and the Clinical Advisory Board for Koko, has received consulting fees from United Health and Woebot, and receives book royalties from New Harbinger; Oxford University Press; and Little, Brown Book Group. She is co-founder and chief scientific advisor for Mindly. No Mindly products were used or are referenced in the present manuscript.

Appendix

Figure 3.

Figure 3.

Open in a new tab

Outcome Distributions Across Timepoints for Each Study

Footnotes

We pre-registered both study 1: https://osf.io/8bth2 and study 2: https://osf.io/fu6yc. The study materials, data, and analysis code are available online (study 1: https://osf.io/5ujtc, study 2: https://osf.io/nj498). The data in this paper have not been published and are not currently under review for publication elsewhere.

References

  1. Bates D, Mächler M, Bolker B, & Walker S (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67, 1–48. 10.18637/jss.v067.i01 [DOI] [Google Scholar]
  2. Bottaro R, Valenti GD, & Faraci P (2023). Assessment of an Epidemic Urgency: Psychometric Evidence for the UCLA Loneliness Scale. Psychology research and behavior management, 16, 2843–2855. 10.2147/PRBM.S406523 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Chakraborty H, & Gu H (2009). A Mixed Model Approach for Intent-to-Treat Analysis in Longitudinal Clinical Trials with Missing Values. RTI Press. 10.3768/rtipress.2009.mr.0009.0903 [DOI] [PubMed] [Google Scholar]
  4. Chapkovski P, Peer E, & Brandt E (2024). From WEIRD to GREAT? Exploring data quality of global platforms for online research. OSF. 10.31234/osf.io/xvfqn [DOI] [Google Scholar]
  5. Cohen K, & Schleider J (2022). Adolescent dropout from brief digital mental health interventions within and beyond randomized trials. Internet Interventions, 27, 100496. 10.1016/j.invent.2022.100496 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cuijpers P, Miguel C, Harrer M, Plessen CY, Ciharova M, Papola D, Ebert D, & Karyotaki E (2023). Psychological treatment of depression: A systematic overview of a ‘Meta-Analytic Research Domain.’ Journal of Affective Disorders, 335, 141–151. 10.1016/j.jad.2023.05.011 [DOI] [PubMed] [Google Scholar]
  7. Dobias ML, Morris RR, & Schleider J (2022). Single-session interventions embedded within Tumblr: Acceptability, feasibility, and utility study. JMIR Formative Research, 6(7), e39004. 10.2196/39004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Faul F, Erdfelder E, Lang A-G, & Buchner A (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. 10.3758/BF03193146 [DOI] [PubMed] [Google Scholar]
  9. Feingold A (2009). Effect sizes for growth-modeling analysis for controlled clinical trials in the same metric as for classical analysis. Psychological Methods, 14(1), 43–53. 10.1037/a0014699 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Funder DC, & Ozer DJ (2019). Evaluating effect size in psychological research: Sense and nonsense. Advances in Methods and Practices in Psychological Science, 2(2), 156–168. 10.1177/2515245919847202 [DOI] [Google Scholar]
  11. Green P, & MacLeod CJ (2016). SIMR: An R package for power analysis of generalized linear mixed models by simulation. Methods in Ecology and Evolution, 7(4), 493–498. 10.1111/2041-210X.12504 [DOI] [Google Scholar]
  12. Hartman R, Moss AJ, Jaffe SN, Rosenzweig C, LItman L, & Robinson J (2023). Introducing Connect by CloudResearch: Advancing Online Participant Recruitment in the Digital Age. OSF. 10.31234/osf.io/ksgyr [DOI] [Google Scholar]
  13. Hickin N, Käll A, Shafran R, Sutcliffe S, Manzotti G, & Langan D (2021). The effectiveness of psychological interventions for loneliness: A systematic review and meta-analysis. Clinical Psychology Review, 88, 102066. 10.1016/j.cpr.2021.102066 [DOI] [PubMed] [Google Scholar]
  14. Hughes ME, Waite LJ, Hawkley LC, & Cacioppo JT (2004). A short scale for measuring loneliness in large surveys: Results From two population-based studies. Research on Aging, 26(6), 655–672. 10.1177/0164027504268574 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Ioannidis JPA (2008). Why Most Discovered True Associations Are Inflated. Epidemiology, 19(5), 640. 10.1097/EDE.0b013e31818131e7 [DOI] [PubMed] [Google Scholar]
  16. Jakobsen JC, Gluud C, Wetterslev J, & Winkel P (2017). When and how should multiple imputation be used for handling missing data in randomised clinical trials – a practical guide with flowcharts. BMC Medical Research Methodology, 17(1), 162. 10.1186/s12874-017-0442-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Käll A, Jägholm S, Hesser H, Andersson F, Mathaldi A, Norkvist BT, Shafran R, & Andersson G (2020). Internet-based cognitive behavior therapy for loneliness: A pilot randomized controlled trial. Behavior Therapy, 51(1), 54–68. 10.1016/j.beth.2019.05.001 [DOI] [PubMed] [Google Scholar]
  18. Kaveladze BT, Gastelum SF, Ngo D-AC, Delacruz P, Cohen KA, Käll A, Andersson G, Schleider J, & Schueller SM (2024). A randomized controlled trial comparing brief online self-guided interventions for loneliness. Journal of Consulting and Clinical Psychology, No Pagination Specified-No Pagination Specified. 10.1037/ccp0000908 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Kazdin AE, & Blase SL (2011). Rebooting psychotherapy research and practice to reduce the burden of mental illness. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 6(1), 21–37. 10.1177/1745691610393527 [DOI] [PubMed] [Google Scholar]
  20. Kenward MG, & Roger JH (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics, 53(3), 983–997. [PubMed] [Google Scholar]
  21. Kroenke K, Strine TW, Spitzer RL, Williams JBW, Berry JT, & Mokdad AH (2009). The PHQ-8 as a measure of current depression in the general population. Journal of Affective Disorders, 114(1), 163–173. 10.1016/j.jad.2008.06.026 [DOI] [PubMed] [Google Scholar]
  22. Kuznetsova A, Brockhoff PB, & Christensen RHB (2017). lmerTest Package: Tests in Linear Mixed Effects Models. Journal of Statistical Software, 82, 1–26. 10.18637/jss.v082.i13 [DOI] [Google Scholar]
  23. Lorenzo-Luaces L, Dierckman C, Lind C, Peipert A, de Jesús-Romero R, Buss JF, Ramirez I, Starvaggi I, Adams S, Howard J, & Fite RE (2024). A Pragmatic Randomized Controlled Trial of Stepped Care Cognitive-behavioral Therapy for Internalizing Distress in Adults. Cognitive Therapy and Research, 48(5), 998–1013. 10.1007/s10608-024-10489-7 [DOI] [Google Scholar]
  24. Lorenzo-Luaces L, & Howard J (2023). Efficacy of an Unguided, Digital Single-Session Intervention for Internalizing Symptoms in Web-Based Workers: Randomized Controlled Trial. Journal of Medical Internet Research, 25, e45411. 10.2196/45411 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. McCashin D, & Murphy CM (2023). Using TikTok for public and youth mental health–A systematic review and content analysis. Clinical child psychology and psychiatry, 28(1), 279–306. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Odgers K, Kershaw KA, Li SH, & Graham BM (2022). The relative efficacy and efficiency of single- and multi-session exposure therapies for specific phobia: A meta-analysis. Behaviour Research and Therapy, 159, 104203. 10.1016/j.brat.2022.104203 [DOI] [PubMed] [Google Scholar]
  27. Ophir Y, Sisso I, Asterhan CSC, Tikochinski R, & Reichart R (2020). The Turker Blues: Hidden Factors Behind Increased Depression Rates Among Amazon’s Mechanical Turkers. Clinical Psychological Science, 8(1), 65–83. 10.1177/2167702619865973 [DOI] [Google Scholar]
  28. Osborn TL, Rodriguez M, Wasil AR, Venturo-Conerly KE, Gan J, Alemu RG, Roe E, Arango G S, Otieno BH, Wasanga CM, Shingleton R, & Weisz JR (2020). Single-session digital intervention for adolescent depression, anxiety, and well-being: Outcomes of a randomized controlled trial with Kenyan adolescents. Journal of Consulting and Clinical Psychology, 88(7), 657–668. 10.1037/ccp0000505 [DOI] [PubMed] [Google Scholar]
  29. Qualtrics (Version November, 2022). (2005). [Computer software]. Qualtrics. https://www.qualtrics.com/ [Google Scholar]
  30. R Core Team. (2015). R: A Language and Environment for Statistical Computing [Computer software]. R Foundation for Statistical Computing. [Google Scholar]
  31. Russell DW (1996). UCLA Loneliness Scale (Version 3): Reliability, validity, and factor structure. Journal of Personality Assessment, 66(1), 20–40. 10.1207/s15327752jpa6601_2 [DOI] [PubMed] [Google Scholar]
  32. Ryan RM, & Deci EL (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. The American Psychologist, 55(1), 68–78. 10.1037//0003-066x.55.1.68 [DOI] [PubMed] [Google Scholar]
  33. Schleider JL, Burnette JL, Widman L, Hoyt C, & Prinstein MJ (2020). Randomized trial of a single-session growth mind-set intervention for rural adolescents’ internalizing and externalizing problems. Journal of Clinical Child & Adolescent Psychology, 49(5), 660–672. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Schleider J, Dobias ML, Sung JY, & Mullarkey MC (2020). Future directions in single-session youth mental health interventions. Journal of Clinical Child & Adolescent Psychology, 49(2), 264–278. 10.1080/15374416.2019.1683852 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Schleider J, Mullarkey MC, Fox KR, Dobias ML, Shroff A, Hart EA, & Roulston CA (2022). A randomized trial of online single-session interventions for adolescent depression during COVID-19. Nature Human Behaviour, 6(2), Article 2. 10.1038/s41562-021-01235-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Schleider J, Mullarkey MC, Mumper E, & Sung J (2019). The ABC Project: Action brings change. 10.17605/OSF.IO/QJ94C [DOI] [Google Scholar]
  37. Schleider J, Zapata JP, Rapoport A, Wescott A, Ghosh A, Kaveladze B, Szkody E, & Ahuvia IL (2025). Single-Session Interventions for Mental Health Problems and Service Engagement: Umbrella Review of Systematic Reviews and Meta-Analyses. Annual Review of Clinical Psychology. 10.1146/annurev-clinpsy-081423-025033 [DOI] [PubMed] [Google Scholar]
  38. Thibault RT, Zavalis EA, Malički M, & Pedder H (2024). An evaluation of reproducibility and errors in published sample size calculations performed using G*Power (p. 2024.07.15.24310458). medRxiv. 10.1101/2024.07.15.24310458 [DOI] [Google Scholar]
  39. Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, … Yutani H (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686. 10.21105/joss.01686 [DOI] [Google Scholar]

RESOURCES