Insomnia And Depression: Smartphone App Interventions

During adolescence, the prevalence of prevalent mental disorders, especially depression, increases at a faster rate among adolescents than among adults. Despite the critical need for intervention, many depressed adolescents go untreated due to barriers like stigma. This research explores an innovative approach targeting non-stigmatizing, self-identified issues related to depression. By focusing on prevalent sleep disturbances in adolescents, particularly insomnia, the study assesses the effectiveness of an automated app-based intervention, Sleep Ninja, in alleviating depressive symptoms. This novel approach holds promise for scalable mental health interventions by addressing a significant risk factor for adolescent depression.

 

THE BACKGROUND OF THE STUDY

Adolescence is a crucial developmental period marked by a notable increase in the prevalence of common mental disorders, particularly depression [1]. Recent data indicate that rates of depression are escalating more rapidly in adolescents compared to adults, with approximately 13.2% of individuals aged 12–17 experiencing a major depressive episode in one year [2]. Despite the evident need for timely intervention during this developmental stage, a considerable proportion of depressed adolescents remain untreated, facing barriers such as stigma and a preference for managing their mental health concerns independently [3]. The study explores an alternative avenue for addressing adolescent depression through prevention and early intervention approaches [4].

One potential target for intervention is sleep disturbance, a pervasive issue affecting 25%–66% of adolescents [5]. Insomnia, the most prevalent sleep disorder among adolescents, is acclaimed as a substantial risk factor for the onset of depression [6]. Non-depressed individuals with clinical levels of insomnia are reported to be twice as likely to have depression compared to those without insomnia [6]. The association between sleep and depression is well-documented in adult populations, with insomnia-focused treatments demonstrating improvements in both insomnia and depression [7]. Extending this line of inquiry to adolescents is crucial, given that sleep problems do not carry the same societal stigma as other psychiatric disorders and may, therefore, serve as a more acceptable vector for intervention [8].

The study proposes to evaluate the efficacy of an automated app-based insomnia intervention, Sleep Ninja, in addressing depressive symptoms among adolescents [9]. Previous trials of digital Cognitive Behavioral Therapy for Insomnia (CBT-I) in adolescents have shown promising results, with reductions in insomnia symptoms, depression, and anxiety following intervention [10]. However, these researches have been limited by small sample sizes and a lack of active control groups. In this context, the present research aims to contribute novel insights by assessing the effects of Sleep Ninja on adolescent insomnia and depressive symptoms relative to an active control group. Additionally, the study seeks to investigate potential mediating factors between insomnia and depression in adolescents, thus enhancing theoretical understanding and clinical practice in this domain [11].

In summary, this research addresses a critical gap in the literature by exploring an innovative approach to tackle adolescent depression through a targeted intervention addressing sleep disturbances. The study’s focus on an automated app-based intervention introduces a measurable and accessible method that promises to improve adolescent mental health outcomes.

 

THE STUDY METHOD

This research employed a single-blind Randomized Controlled Trial (RCT) with two parallel arms (intervention and active control) and a 1:1 allocation ratio. The primary endpoints included a six-week post-intervention assessment and a 14-week post-baseline follow-up. Recruitment, screening, consent, checks, allocation, and intervention delivery were conducted using an automated trial management system hosted by the Black Dog Institute. Participants aged 12–16 were recruited through online channels (social media advertisements) and community pathways (schools, psychology clinics, councils). Inclusion criteria involved reporting symptoms of at least subthreshold insomnia, owning a smartphone, residing in Australia, speaking and reading English fluently, and providing parental and personal consent.

Randomization was performed using an automated computer-generated program integrated into the trial management software. Investigators remained blinded to group allocation, and participants independently completed all assessments online without direct contact with the research team.

The primary outcomes included insomnia symptoms measured by the Insomnia Severity Index (ISI) and depression symptoms assessed by the Patient Health Questionnaire-Adolescent Version (PHQ-A). Secondary outcomes encompassed anxiety, sleep quality, fatigue, sleepiness, well-being, sleep-related behaviors, beliefs about sleep, and pre-sleep arousal. Intervention adherence was measured by the number of completed lessons (out of six).

A sample size of 308 participants (118 in each arm, accounting for a 30% attrition rate) was targeted to achieve 80% power for detecting an effect size of d = 0.30 in the co-primary outcome, depression.

Both the intervention and control groups were digitally delivered. The intervention group received the Sleep Ninja smartphone app, offering six training sessions covering various aspects of sleep improvement. Gamification principles were incorporated to enhance engagement. The control group received weekly text messages containing sleep tips from the Sleep Ninja app, providing an active educational comparison.

The University of New South Wales Human Research Ethics Committee approved all procedures. 

Participants progressed through consent, screening, baseline assessment, randomization, and intervention. Post-intervention and follow-up assessments were conducted at six and fourteen weeks post-baseline. $10 electronic gift cards were reimbursed for completing assessments at each time point.

 

ANALYSIS

The impact of the intervention on insomnia and depression was assessed using mixed-model repeated measures (MMRM) with time (baseline, post-intervention, follow-up) and condition (intervention vs. control) as factors. Missing data were considered at random. The analysis focused on the interaction effect between time and group, calculating standardized mean differences. The proportion meeting clinical thresholds for insomnia and depression was examined using mixed results logistic regression. Engagement effects were explored by categorizing intervention participants into high and low adherers.

To investigate the modulating role of changes in insomnia on post-treatment depressive symptoms, a model assessed direct and indirect effects. Analyses were adjusted for baseline insomnia severity and depression. Stata 14 was used, with significance set at α = 0.05.

 

RESULTS

Participant Characteristics

   – 264 participants were randomized (131 to Sleep Ninja, 133 to control).

   – Mean age: 14.71 years, predominantly female (71.3%), and mostly born in Australia (94%).

   – Elevated levels of depression (M=13.95; SD=5.83) and anxiety symptoms (M=11; SD=5.06) at baseline.

   – Higher comorbidity levels than the general population.

Primary and Secondary Outcomes

   – The intervention group showed a more substantial reduction in insomnia symptoms post-intervention and at follow-up.

   – Depression symptoms were reduced more in the intervention group at post but not at follow-up.

   – Similar patterns were observed for anxiety symptoms, sleep quality, dysfunctional beliefs about sleep, and pre-sleep arousal.

   – No significant effects for fatigue, daytime sleepiness, well-being, or sleep-related behaviors.

Clinical Status Post-Assessment

   – Significant group differences in depression caseness at post-intervention and follow-up.

   – Sleep outcomes did not show significant between-group changes, but caseness was significantly lower in the Sleep Ninja group. 

Attrition, Adherence, and Intervention Effects

   – Post-intervention, 70% completed primary outcomes (56% intervention, 83% control), decreasing to 56% at follow-up.

   – Older age predicted attrition in the intervention group, with insomnia scores having a negligible effect.

   – Participants in the intervention completed an average of 2.30 of the six modules.

   – Age-predicted lower adherence and high adherence showed a more significant reduction in insomnia and depression symptoms post-intervention and at follow-up.

Mediation Analysis

   – Changes in insomnia partially mediated changes in depression.

   – The intervention’s direct effect on depression was not statistically significant, suggesting mediation through insomnia severity.

   – The direct effect of the intervention on insomnia remained relevant in the presence of the indirect path through depression.

These findings suggest that the Sleep Ninja intervention effectively addressed insomnia and depression symptoms in adolescents, with higher adherence associated with better outcomes. The mediation analysis highlights the role of improved insomnia in the intervention’s impact on depression.

 

DISCUSSION

The study underscores the efficacy of the Sleep Ninja intervention in reducing insomnia symptoms among adolescents compared to the control group (12). Adolescents receiving the intervention also experienced modest reductions in depression symptoms immediately after the intervention. Caseness analysis further supported these outcomes, revealing a significant decrease in participants meeting the criteria for probable depressive disorder in the intervention group compared to the control group (12).

The mediation analysis suggests a relationship between sleep and depression symptoms, indicating that improvements in sleep played a role in reducing depressive symptoms (12). This trial contributes to the growing body of evidence supporting the positive impact of digital sleep interventions and hints at a potential causal connection between insomnia symptoms and depression, particularly in the adolescent population (12).

Effect sizes, falling within the small to medium range, align with expectations in non-clinical settings and the specific nature of the sample (12). Despite the brevity and simplicity of the Sleep Ninja intervention, the study highlights its significant impact, indicating the potential for widespread effects when implemented on a larger scale (12).

The core components of Sleep Ninja, encompassing psychoeducation, stimulus control, sleep hygiene, and sleep-focused cognitive therapy, were delivered in concise sessions (12). Intriguingly, even without including sleep restriction, a component commonly found in similar programs, Sleep Ninja demonstrated significant benefits, emphasizing its adaptability and effectiveness for adolescents (12).

Adherence rates, while modest, did not overshadow the positive outcomes observed (12). Younger participants displayed higher completion rates, suggesting the importance of tailoring interventions to specific age groups (12). The study encourages further exploration of strategies to enhance engagement, possibly through human-supported components (12).

Future research should delve into refining engagement strategies and confirming the program’s applicability across diverse adolescent populations (12). The study’s limitations, including its community sample design and potential influences of time and attention in the control group, call for cautious interpretation (12). Moreover, the trial’s preventive or treatment effects warrant further investigation in subsequent studies (12).

This study underscores the significance of sleep-focused interventions, such as Sleep Ninja, in ameliorating insomnia and mental health symptoms in adolescents (12). The findings provide a fresh perspective on addressing depression and pave the way for future research aimed at optimizing engagement and ensuring the intervention’s effectiveness across a broad spectrum of adolescent experiences (12).

 

LIMITATIONS OF THE STUDY

1. Sample Composition: The study’s reliance on a community sample with insomnia symptoms introduces a blend of prevention, early intervention, and treatment effects, challenging the conclusive determination of specific preventive or therapy impacts.
2. Active Control Group: While the active control group strengthens comparisons, the lack of complete matching for time and attention raises potential confounding factors that could influence outcomes.
3. Attrition Bias: Notably, older participant age correlates with higher attrition rates, suggesting a potential bias. It prompts questions about the intervention’s appeal to older adolescents, emphasizing the need for age-specific tailoring.
4. Adherence Challenges: Modest adherence rates, with over a quarter of intervention participants not initiating the program, and caution in interpreting outcomes for those with lower engagement underscore the necessity of exploring strategies to enhance adherence.

In summary, the study faces limitations related to the mixed nature of the community sample, potential confounding in the active control group, age-related attrition bias, and modest adherence rates. Acknowledging these constraints is crucial for nuanced result interpretation and underscores the need for refined research approaches in future investigations.

 

CONCLUSION

In conclusion, this groundbreaking study illuminates the transformative potential of Sleep Ninja, a concise and self-guided intervention targeting adolescent insomnia. The findings reveal significant reductions in insomnia symptoms and depressive tendencies, marking a paradigm shift in adolescent mental health interventions. The study’s brevity and simplicity challenge traditional expectations, showcasing the potency of a streamlined approach. While acknowledging limitations, such as attrition concerns and modest adherence rates, the promising outcomes open avenues for reimagining how we address adolescent mental health. Sleep Ninja not only introduces a novel chapter in digital interventions but also underscores the profound impact of sleep-focused treatments on the intricate tapestry of adolescent well-being. As we navigate the evolving landscape of mental health interventions, this study beckons us to embrace innovative, youth-centric approaches that resonate with the unique needs of this population.

 

References

1. World Health Organization. (2014). Adolescent mental health. Geneva: World Health Organization. https://www.who.int/news-room/fact-sheets/detail/adolescent-mental-health
2. Twenge, J. M., Cooper, A. B., Joiner, T. E., Duffy, M. E., & Binau, S. G. (2019). Age, period, and cohort trends in mood disorder indicators and suicide-related outcomes in a nationally representative dataset, 2005–2017. Journal of Abnormal Psychology, 128(3), 185–199. https://pubmed.ncbi.nlm.nih.gov/30869927/
3. Islam, M. A., Khanam, F., & Kabir, M. A. (2020). Depression and anxiety among university students in Bangladesh during the COVID-19 pandemic: A web-based cross-sectional survey. PLOS ONE, 15(8), e0238162. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7449469/
4. Lillevoll, K. R., Vangberg, H. C., Griffiths, K. M., Waterloo, K., & Eisemann, M. R. (2014). Uptake and adherence of a self-directed internet-based mental health intervention with tailored e-mail reminders in senior high schools in Norway. BMC Psychiatry, 14, 14. https://pubmed.ncbi.nlm.nih.gov/24443820/
5. Ohayon, M. M., Roberts, R. E., Zulley, J., Smirne, S., & Priest, R. G. (2000). Prevalence and patterns of problematic sleep among older adolescents. Journal of the American Academy of Child & Adolescent Psychiatry, 39(12), 1549–1556. https://pubmed.ncbi.nlm.nih.gov/11128333/
6. Baglioni, C., Battagliese, G., Feige, B., Spiegelhalder, K., Nissen, C., Voderholzer, U., … & Riemann, D. (2011). Insomnia as a predictor of depression: A meta-analytic evaluation of longitudinal epidemiological studies. Journal of Affective Disorders, 135(1–3), 10–19. https://pubmed.ncbi.nlm.nih.gov/21300408/
7. Ritterband, L. M., Thorndike, F. P., Ingersoll, K. S., Lord, H. R., Gonder-Frederick, L., Frederick, C., … & Morin, C. M. (2009). Effect of a web-based cognitive-behavioral intervention for insomnia on comorbid depression and anxiety in adults. Journal of Consulting and Clinical Psychology, 77(4), 607. https://pubmed.ncbi.nlm.nih.gov/27902836/
8. Ribeiro, J. D., Pease, J. L., Gutierrez, P. M., Silva, C., Bernert, R. A., Rudd, M. D., … & Joiner, T. E. (2011). Sleep problems outperform depression and hopelessness as cross-sectional and longitudinal predictors of suicidal ideation and behavior in young adults in the military. Journal of Affective Disorders, 136(3), 743–750. https://pubmed.ncbi.nlm.nih.gov/22032872/
9. Luik, A. I., Kyle, S. D., & Espie, C. A. (2017). Digital cognitive behavioral therapy (dCBT) for insomnia: A state-of-the-science review. Current Sleep Medicine Reports, 3(2), 48–56. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5427093/
10. Cliffe, B., Croker, A., Denne, L. D., Smith, K., & Stallard, P. (2020). Digital cognitive behavioral therapy for insomnia for young people: A systematic review. Sleep Medicine Reviews, p. 49, 101222. https://pubmed.ncbi.nlm.nih.gov/32134720/
11. de Bruin, E. J., Oort, F. J., Bögels, S. M., & Meijer, A. M. (2014). Efficacy of Internet and group-administered cognitive behavioral therapy for insomnia in adolescents: A pilot study. Behavioral Sleep Medicine, 12(3), 235–254. https://pubmed.ncbi.nlm.nih.gov/23767888/
12. Werner-Seidler, A., et al. (2023). Effects of insomnia intervention on sleep and depression. Journal of Child Psychology and Psychiatry, 64(9), 1324–1335. doi:10.1111/jcpp.13795. https://acamh.onlinelibrary.wiley.com/doi/full/10.1111/jcpp.13795

 

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