How do adolescents experience a newly developed Online Single Session Sleep Intervention? A Think-Aloud Study

Abstract

Background

Sleep problems are common in adolescents and have detrimental impacts on physical and mental health and daily functioning. Evidence-based treatment like cognitive behaviour therapy for insomnia (CBT-I) is often hard to access, and adolescents may not engage in and adhere to longer, clinician-delivered interventions. Brief, self-guided, and accessible sleep interventions are needed.

Objective

To explore the user experience of a prototype online self-help single session sleep intervention developed for adolescents.

Methods

Eleven participants aged 17–19 years (8 females, 3 males) took part in online retrospective think-aloud interviews. Participants first completed the prototype intervention independently and were then shown the intervention page by page and asked to verbalise their thoughts and experiences. Transcripts were analyzed thematically.

Results

Participants found the intervention helpful. Four themes were generated - ‘Educative: Learning, but more fun’, ‘Effortless: Quicker and Easier’, ‘Personalization: Power of Choice’, and ‘Positivity: Just Good Vibes’. The theme ‘Educative: Learning, but more fun’ encompassed two sub-themes ‘Opportunity to Learn’ and ‘Aesthetics and Learning’. These themes reflected participants’ views that the intervention was educative, personalised, solution-oriented and easy to use, but could incorporate more graphics and visuals to aid in learning and could be made more effortless and positive through modifications to its design.

Conclusions

Findings convey the importance of ensuring educative well-designed content, personalization, a positive tone, and ease of use while designing interventions targeting adolescents’s sleep and mental health. They also indicate areas for further developing the intervention.

Keywords

Qualitative think-aloud sleep user experience user experience design single session intervention

Sleep problems are highly prevalent in adolescents, with most adolescents getting an inadequate amount of sleep (Marino et al., 2021). According to Short and colleagues (2013a), 60% of adolescents experience sleep problems, and the prevalence of sleep problems is up to 80% in adolescents with chronic medical, neurodevelopmental, and psychiatric conditions (Owens, 2007). A meta-analysis found that 53% of adolescents sleep for less than 8 hours per night, and 36% report difficulty falling asleep (Gradisar et al., 2011). Social, psychological, and biological factors contribute to these problems in adolescents, such as delayed timing of circadian rhythms (Hagenauer & Lee, 2013), slower build-up of sleep homeostatic pressure (Taylor et al., 2005), social, emotional, and academic stress (Short et al., 2013b), and electronic or social media use (Hale & Guan, 2015; Hamilton et al., 2020).

The consequences of poor sleep in adolescence may be far-reaching. Inadequate sleep has been associated with poor health (Reidy et al., 2016), reduced neurocognitive functioning and learning capacity (Curcio et al., 2006; Lo et al., 2016), increased daytime sleepiness and prevalence of internalizing disorders such as depression (Marino et al., 2021), and suicidal thoughts and behaviours in adolescents (Blake & Allen, 2020; Hamilton et al., 2023). This may be because sleep, arousal, and affect are all products of overlapping regulatory systems with each system impacting the other. Thus, sleep problems during key periods of maturation such as adolescence may prompt affective dysregulation (Dahl, 1996). While there is a bidirectional relationship between sleep and psychopathology, recent research indicates the potential causal role of sleep problems leading to depression (Goldstone et al., 2020; Lovato & Gradisar, 2014; Orchard et al., 2020a) and suicidal thoughts and behaviors (Liu et al., 2020). Poor sleep quality also has been linked to suicidal thoughts and behaviours in adolescents (Kearns et al., 2020), which may be due to its effects on how adolescents regulate affective responses to interpersonal events (Hamilton et al., 2023). As sleep problems are amenable to change (Blake et al., 2016), they may be an important targetable mechanism in the prevention and treatment of adolescent depression (Gee et al., 2019).

Given the importance of sleep for the mental health of adolescents, providing widely available, scalable interventions for sleep disturbances could be of great benefit in alleviating symptoms of anxiety and depression (Luik et al., 2017) and preventing suicide (Blake et al., 2018). This may be particularly important as adolescents transition towards adulthood and gain increasing autonomy and independence, including sleep-related behaviours such as bedtimes and wake-up times (Carskadon, 2011).

Several interventions addressing sleep problems, ranging from pharmacological interventions to mindfulness strategies and school-based sleep education programs, are currently available (Chung et al., 2017; Greeson et al., 2014; Owens et al., 2005). However multiple systematic meta-analytic reviews have identified Cognitive-Behavioural Therapy for Insomnia (CBT-I) as the gold standard for improving sleep (Ballesio et al., 2018; van Straten et al., 2018), and it is often the first-line treatment for adolescent sleep problems (Blake & Allen, 2020). In addition to reducing sleep problems, other meta-analyses have found that CBT-I also led to significant improvements in secondary outcomes such as anxiety and depression in both clinical and non-clinical adolescent populations (Åslund et al., 2020; Blake et al., 2017; Zetterqvist et al., 2021).

However, traditional CBT-I is time-consuming and resource-intensive, and lack of qualified practitioners and high attrition rates may be barriers to its widespread implementation (Ellis et al., 2015). A more cost-effective way of providing treatment at scale is internet-based CBT-I, which is effective and more accessible (McLay et al., 2020). Given a choice, most adolescents chose internet-based CBT-I over face-to-face therapy indicating acceptability (de Bruin et al., 2015). However, engagement and adherence issues remain. For example, in Zetterqvist et al.’s (2021) internet-based CBT-I trial, course completion was low with participants attending an average of just over half the sessions. This is particularly concerning for an internet-based intervention aiming towards greater accessibility and reflects a trend in adolescent treatment in which drop-out and disengagement tend to be high (Cohen & Schleider, 2022). Thus, briefer treatment alternatives that do not necessitate repeat visits in order to benefit are needed (Schleider et al., 2020b).

Brief, single session interventions (SSIs) can be as effective as longer interventions (Schleider & Weisz, 2017). A SSI is intentionally designed as a stand-alone one-off intervention, without the need for a return visit to complete the course. SSIs are guided by a “disruptive innovation” model (Christensen et al., 2006) aiming to provide simpler, cost-effective, scalable interventions delivered through non-traditional means including digital technologies such as computers or smartphones (Schleider et al., 2020b). Results from a meta-analysis of 50 RCTs reported a significant beneficial effect of SSIs for youth mental health reflecting a “small-to-medium” overall effect (g = .32) that is comparable to traditional, multi-session, face-to-face treatment outcomes across disorders such as anxiety, conduct problems, depression, and general distress in multi-problem youths (Schleider & Weisz, 2017). Schleider et al.’s meta-analysis included mostly therapist-administered SSIs, and also 11 included studies tested self-administered SSIs (e.g., attention bias modification; “growth mindset” programs; self-affirmation interventions). Additionally, this meta-analysis included 13 treatment trials and 37 prevention trials. Self-administered digital SSIs are theoretically precise, ranging from 5-to-90-min, and designed to be completed in one sitting (Schleider et al., 2020b). A large recent trial indicated that they can reduce depression symptoms in adolescents at scale and in a pandemic context, including in those from under-served communities who do not tend to access traditional service provisions (Schleider et al., 2022). Thus, such digital SSIs may offer a novel, scalable, cost-effective approach to extending the provision of help for youth mental health, including for sleep problems—although the applicability of an SSI model to addressing sleep problems directly has not been formally assessed.

User experience (UX) plays a key role in ensuring that novel mental health interventions are optimized to support user engagement and completion (Lemon et al., 2020). Incorporating UX testing into the intervention development process can increase user satisfaction, improve treatment adherence, and facilitate greater empowerment of service users (Knowles et al., 2014). However, despite this, UX design has seldom been used in the process of designing new psychosocial interventions. This study thus adopts a more novel bottom-up approach to designing mental health interventions, focusing on UX design in the early stages of development and testing.

UX testing is based on principles of Design Thinking (DT) such as empathy towards the users’ needs, multidisciplinary ideation, and iterative development (Scholten & Granic, 2019). UX is also closely linked to UX Design theories and principles such as Nielsen’s Usability Heuristics (1994). These principles emphasise using language that is familiar to users, promoting user control and freedom, ensuring consistency and efficiency of use, emphasising recognition over recall, and creating aesthetic designs. UX principles are grounded in theories of cognitive psychology such as colour theory (Kimmons, 2020) and cognitive ergonomics (Turner, 2017). Recognition that UX may be influenced by factors beyond efficiency has also led to research into the emotional usability of interventions and emotional design (Kumar et al., 2019). The emotional design approach holds that designs that evoke positive emotional responses facilitate good user experience, which aligns with the process of user-centred design (Turner, 2017).

To address the need for accessible, scalable interventions for sleep problems in adolescents, a self-help online Sleep SSI has been developed by three paediatric psychology and sleep experts, ML, JH, and FO, drawing on evidence-based principles that have been tested extensively in evidence-based longer-term or briefer in-person interventions, such as Brief-Behavioral Treatment for Insomnia (BBTI; Gunn et al., 2019). The online SSIs developed and tested by Dr Jessica Schleider (e.g. growth mindset, behavioural activation SSIs) were used as a basis for developing the sleep SSI (Schleider et al., 2020a; Schleider et al., 2022; Schleider & Weisz, 2018). The Sleep SSI was developed using a user-centred design approach which involves the active participation of users in the development of the intervention (Zhang & Dong, 2009). Understanding user experiences of this intervention is thus essential to determine whether it meets the needs and preferences of adolescents experiencing sleep problems as a core part of its development. We herewith aimed to explore user experiences of a newly developed online Sleep SSI through retrospective think-aloud interviews to inform further intervention development.

Methods

Study design

We adopted a qualitative approach to gain an in-depth understanding of user experiences of the sleep SSI (Newton et al., 2021; Santesteban-Echarri et al., 2017). We used an exploratory cross-sectional qualitative design, including retrospective think-aloud interviewing techniques. Reflexive Thematic Analysis was used to analyse the data (Clarke & Braun, 2021). Ethical approval was obtained from the University of Bath Psychology Research Ethics Committee (Ref: UGM 22-027).

Participants

Adolescents aged 16–19 years, living in the UK were eligible to participate, and they were recruited using a combination of purposive and convenience sampling methods (Clarke & Braun, 2021). Inclusion criteria included if they felt that they were experiencing sleep problems, could speak English at a conversational level, and confirmed that they were within the specified age range on the consent form (ages 16–19; see Procedure section for details on the recruitment process).

The final sample consisted of 11 participants, mean age 18.18 years (SD = .79, range 17–19). The participants were relatively diverse in identified ethnicity (54.5% Black/Black British, 36.4% Asian/Asian British, 9.1% White/White British) and gender (27.3% Male, 72.7% Female) (see Table 1). Pseudonyms have been used to ensure the confidentiality of the participants.

Table 1.

Demographic characteristics of participants.

Participant	Age	Gender	Ethnic group
Zoe	19	Female	Asian/Asian British
Mel	18	Female	Black/Black British
Sam	18	Male	Black/Black British
Dan	18	Male	Black/Black British
Alina	18	Female	Black/Black British
Maya	17	Female	Asian/Asian British
Sara	17	Female	Asian/Asian British
Amy	19	Female	White/White British
Roy	18	Male	Black/Black British
Erin	19	Female	Black/Black British
Ida	19	Female	Asian/Asian British

Materials

Development of the intervention

To design this intervention, the collaborators drew on prior work by Orchard and colleagues (2020b) who developed a brief intervention for adolescents informed by CBT for Insomnia (e.g., de Bruin et al., 2018) as well as principles of Brief Behavioural Treatment for Insomnia (Kwon et al., 2021) and adolescent-specific interventions of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (Harvey et al., 2021). The treatment draws on components from these cognitive and behavioural interventions for insomnia, including sleep- and psycho-education, sleep hygiene, stimulus control (establishing regular schedules, associating bed with sleep, consolidating sleep to nighttime), and arousal reduction (e.g., wind down routines before bed to calm mind and body). Sleep restriction techniques are not included, due to the absence of direct clinical supervision to determine whether this is appropriate.

To package this into a single session intervention, existing SSIs for youth mental health developed and trialled by Schleider et al. (2020a, 2020b) were used as a template and adolescents were involved in helping to present the intervention as accessible and interesting. This process included sharing preferences and perspectives for phrasing of the intervention, as well as colours and images that they found developmentally appropriate and engaging to be included in the intervention. Schleider et al. (2020a) suggested four primary elements based on qualities common to self-administered SSIs and brief interventions that have reduced psychopathology in youth to guide the development of SSIs for youth mental health known as “B.E.S.T.” elements. They stand for: B: Brain science to normalize concepts in the program; E: Empower youths to a “helper” or “expert” role; S: Saying-is-believing exercises to solidify learning; T: Testimonials and evidence from valued others. The current sleep SSI is based on the “B.E.S.T.” principles. This framework, drawing upon SSI principles and elements of social psychology, provides an example of a helpful guideline. Experts used the “B.E.S.T.” principles to co-create and refine the material, with design input from graduate students. Each component of the SSI has been informed by “B.E.S.T.” principles which are detailed in the SSI roadmap. Additionally, two advisory groups consisting of adolescents between ages 16–19 were recruited from local high schools and universities in the US and UK to give their input and feedback for intervention development purposes, laying the groundwork for a user-centred design. The steps involved in the Sleep SSI have been summarized in Figure 1. The prototype intervention within Qualtrics, which is the intended end user platform for administration, was used for this study.

Figure 1.

SSI roadmap.

Semi-structured questions

Participants were asked some open-ended semi-structured questions on their overall experience of the intervention (see online supplementary materials S1. for topic guide). These questions were developed drawing on other studies that had used similar qualitative protocols to evaluate psychological interventions. We then used think-aloud techniques, which are useful in determining the acceptability and feasibility of online interventions (Davies et al., 2015; Yardley et al., 2015). Such interviews differ from cognitive interviews in that while cognitive interviews use targeted interview questions to evaluate participant comprehension during an activity, in think aloud interviews, participants are asked to freely talk out loud about their thoughts while completing a certain task (Davies, 2018). Certain pre-prepared prompts are also provided asking participants to verbalise their thoughts and feelings about the intervention (Tinner et al., 2020). There are two variations in think-aloud protocols; (1) concurrent approach in which participants are shown the materials for the first time during the interview and asked to think aloud as they look at them, and (2) retrospective approach, in which participants work through the materials independently and are then subsequently asked to recall their thoughts at a later time (Van Den Haak et al., 2003). While the concurrent think-aloud protocol provides insight into the participants’ thought processes in real-time, the process of thinking aloud may cause reactivity resulting in a poorer performance (Van Den Haak et al., 2003). On the other hand, the retrospective think-aloud protocol enables the participants to work through the intervention silently which more closely resembles regular working procedures (Van Den Haak et al., 2003). While such retrospection may lead to incomplete interpretation by the participants, forgetting and fabrication, and less reporting on the affective aspects of participants’ experiences, these shortcomings may be reduced by providing some kind of stimuli to participants to aid them in their retrospective verbalisations (Van Den Haak et al., 2003). Providing screen captures or video recordings to aid retrospection as has been done in this study may thus incorporate the benefits of concurrent methods into retrospective protocols (Van Den Haak et al., 2003). So that our participants experienced the sleep SSI as it is intended to be i.e. as self-help, we opted to use a retrospective think-aloud protocol, and asked the participants to go through the intervention prototype themselves, and then revisited it page by page by screen-sharing during the interviews to facilitate retrospective recall.

Procedure

The study was advertised on the Department of Psychology Research Participation Scheme at the University of Bath and the MQ Participate website in June–July 2022. We received responses from 17 potential participants to the study advert, of which 11 met the inclusion criteria and attended the online interviews on Microsoft Teams. Informed consent was obtained from all participants. An online information sheet was sent prior to the meeting to provide participants with more details about the study before they consented to participate in the intervention and interviews. Participants were given an opportunity to ask questions at the start of the meeting and were invited to electronically sign a consent form on Qualtrics. Participants were then asked to complete the intervention independently in the absence of the interviewer, which took approximately 25 minutes (M = 23.78; SD = 9.07; Range = 8.03–38.08). Following completion, participants rejoined the Teams Meeting and online think-aloud interviews were conducted and audio-recorded using an external audio recorder. Interviews were approximately 35–50 minutes. Total duration of participation ranged from 60–75 minutes. Participants were awarded 2 credits through the Research Participation Scheme or offered £5 or an equivalent Amazon voucher for their participation.

Data analysis

We undertook our analysis within the Big Q qualitative paradigm which emphasises collecting and analysing data in line with qualitative research values (Clarke & Braun, 2013). We adopted an ontological position of critical realism which postulates the existence of a true reality but holds that experiences of reality are inevitably mediated by language and culture (Maxwell, 2012). Accordingly, an epistemological framework of contextualism was assumed which recognises that human knowledge and experiences are socially and culturally situated (Guba & Lincoln, 1994). These positions enabled the analysis to reflect participants’ views and use them to inform intervention modifications, while also recognising the social situatedness of such views. Following recent criticisms of the data saturation model and its theoretical inconsistency with Reflexive Thematic Analysis (Braun & Clarke, 2021), Malterud et al. (2016) concept of ‘information power’ was used to estimate sample size. The concept of information power is based on the idea that the sample size should be based on the information the sample holds that is relevant to the study, and thus the more relevant information the sample holds, the lower the number of required participants. The information power of the sample and in turn the size of the sample would depend on the aims of the study, the sample specificity, the use of established theory, the quality of interview dialogue, and the data analysis strategy (Malterud et al., 2016). Considering the relatively narrow study aims; novice researcher; exploratory cross-case analysis; and available time and resources (Patton, 2002), a moderate sample size of 10–15 participants was proposed. However, keeping with the iterative nature of qualitative research (Sim et al., 2018), information power was continually assessed during data collection and a final sample of 11 participants (8 females, 3 males) was determined accordingly.

Interviews were audio-recorded and transcribed verbatim. Transcripts were anonymised at the point of transcription. Data was analysed using Reflexive Thematic Analysis (TA) (Braun & Clarke, 2006, 2021). A critical realist approach to TA was adopted taking a broad inductive orientation. However, the theoretical flexibility of TA enabled the analysis to be informed by interpretive accounts of the data and existing research literature (Clarke & Braun, 2021). The first author AM initially read and re-read the transcripts, coded these and developed and refined the themes. They were then shared and discussed with ML and VW, and then with the wider group during meetings, during which time the themes were refined. The analysis process has been summarized in Table 2.

Table 2.

Process of reflexive thematic analysis.

Stage of thematic analysis	Description of stage	Output of stage
Familiarization with the Dataset	- Each transcript was read at least twice	Notes about initial insights and interesting aspects
Coding	- Sections of the transcripts that addressed the research question were assigned codes (primarily inductively) using the comments function on Microsoft Word	31 codes were finally generated from the data
	-Each transcript was coded twice (iterative)
	- Several codes were modified and combined over the course of this stage
Generating initial themes	-Codes were extracted into a table using the DocTools Word extension, and linked interview extracts were arranged according to their codes on Microsoft Excel	9 initial themes were generated from the data
Generating initial themes	- Similar codes were combined to generate initial themes. Using mind-maps	9 initial themes were generated from the data
Developing and Reviewing themes	-Initial themes were merged and refined by reviewing them against the compiled data extracts	4 final themes were generated
	-Themes and subthemes were also reviewed against the entire dataset to ensure that they reflected the meanings conveyed by the participants	Thematic map was created
	-Central organising concept for each theme was identified	Thematic map was created
Refining, Defining, and naming themes	- Themes and subthemes were refined, and the boundaries of the themes were identified
Refining, Defining, and naming themes	-Each theme was named, drawing on words used by the participants to make the theme names informative and memorable
Writing the report	- Data extracts that best conveyed the essence of each theme were selected as illustrative quotes

To ensure credibility and reflexivity, the researcher who led on the analysis (AM) kept a reflective diary, attended regular supervision sessions (with ML & VW), and adhered to the Standards for Reporting Qualitative Research Guidelines (O’Brien et al., 2014).

Results

Participants were initially asked about their overall experience of the intervention using open-ended questions related to accessibility, usefulness, and likability (see online supplementary materials S1. for topic guide). All participants (n = 11) reported finding the Sleep SSI acceptable and likeable. Ten out of the eleven participants stated that they found the Sleep SSI useful. One participant indicated that she did not find it very useful as she was already aware of most of the information presented.

Four themes were generated from the analysis (see Figure 2 for thematic map), namely ‘Educative: Learning, but more fun’, ‘Effortless: Quicker and Easier’, ‘Personalization: Power of Choice’, and ‘Positivity: Just Good Vibes’. Each theme has been described below, with illustrative quotes.

Figure 2.

Final thematic map.

Theme 1 – Educative: Learning, but more fun

This theme encompasses the need to balance providing education and information and the importance of making the intervention appealing and fun. This theme has two subthemes – Opportunity to Learn, and Aesthetics and Learning.

Opportunity to learn

Participants found the intervention interesting and informative. They viewed the intervention as an opportunity to learn more about sleep and its impact on the mind and body, thus increasing their knowledge.

Cause I thought that not sleeping doesn’t affect anything, but now I’ve learned that if you don’t sleep, it affects your brain, how you think, how you do your work, how the body uses its energy (Dan, 18)

Participants also appreciated that the information provided was reliable and some suggested that more references could be added to enable them to learn more.

It’s referenced on the side corner of the page…Honestly, I found this such a nice detail that I would also have appreciated this on like the other slides as well. Just so that I can read up a bit more on all the information (Ida, 19)

Aesthetics and learning

Most participants appreciated the design of the intervention, and suggested that more relevant graphics, videos, and animations be added. Interestingly, these colours and animations not only made the intervention look more appealing, but also facilitated learning and helped make the educational and self-reflective aspects of the intervention more fun. However, participants indicated that it was important that the graphics be relevant and complement the content of the intervention.

I think having a video where you know I could just learn instead of having to read is just easier for me to absorb (Ida, 19)

The graphics here explaining these things. It’s attractive and it just helps explaining it to you. Like you’re learning but it’s more fun (Erin, 19)

Participants also indicated that highlighting important information in different colours was beneficial and suggested that more colours and visuals could be incorporated to help them learn better.

Another interesting thing could have been to have the whole page in a different colour. It would have maybe helped people remember all these points a bit better (Ida, 19)

Theme 2 - Effortlessness: Quicker and easier

Both convenience and ease of use were important to participants undertaking the intervention. In particular, participants appreciated the brevity of content and stated that shorter sentences were easier to read and understand.

It was easy to do. I like the way it was brief and self-explanatory.…. Because we hate reading those long sentences (Mel, 18)

Participants preferred larger fonts, bigger pictures, clear instructions, and brief and concise information. Many participants also suggested that the graphics and font size be made bigger to make the process more effortless.

I think the font was quite small…having it being just more legible and easier to read would have eased the process and made it more mindless to do and just less effortful (Ida, 19)

Most participants also indicated a preference for selecting options rather than having to type into textboxes. Typing answers or even having to scroll down a lot was seen as tedious, and participants indicated that teenagers would prefer quicker and easier alternatives.

Options could have been given here to get people’s opinion. I think, a lot of young people wouldn’t just want to sit down and write things. Like options makes it more quicker and easier (Erin, 19)

With the scrolling, like I probably won’t read them all, having to scroll I would definitely ignore the last few…because I think teenagers are very lazy and we don’t really want to kind of scroll even if it is gonna be beneficial for us (Amy, 19)

This indicates that a more low-effort approach was desirable to adolescents, and many suggested that drop-down menus and options be provided alongside textboxes.

Theme 3 – Personalization: Power of choice

The intervention offered participants information relevant to their own sleep problems based on the choices and options they selected. This ability to personalise the intervention was widely appreciated by participants and made the information more relatable and helpful in dealing with their specific sleep challenges. Furthermore, participants commented on how choices and options made the intervention more interactive and engaging.

It just gives you the ability to choose…The power of choice really comes into play here so it’s really perfect (Roy, 18)

I really like that it like allowed you to personalize what you wanted to learn about…like this was really, really helpful because it’s very relatable. (Zoe, 19)

This also helped the participants reflect on their own sleep and gave them more confidence in their ability to cope with sleep problems.

This kind of pushed me towards thinking of like small changes myself because it says that Riley felt overwhelmed…but then started with something very small…That gave me more hope about my sleep (Ida, 19)

Participants also suggested ways to make the intervention more personalized by providing greater variation in choices of sleep challenges, allowing them to pick multiple sleep problems, and providing options to learn more about the impact of poor sleep in chosen areas.

Maybe adding more problems that could be tackled to do with sleep. Because I feel like there are other problems people have….I think like personally, I struggle with getting consistent sleep like I wake up randomly, so maybe that (Sara, 17)

Theme 4 – Positivity: Just good vibes

Positivity and a light-hearted optimistic tone were essential to making this intervention acceptable to adolescents. Participants indicated strong preferences for messages that were reassuring and less stressful.

I feel like a lot of people, especially teenagers, we don’t like things that stresses us out (Mel, 18)

In particular, participants appreciated that the content was encouraging and solution-oriented, as opposed to focusing on the negative consequences of poor sleep.

I like how it’s really positive in terms of actually convincing you like you can take control of your sleep…it’s not all-consumingly negative despite the negative impacts that sleep problems have (Zoe, 19)

This positive tone encouraged participants to re-evaluate their beliefs and perceptions of control regarding their own sleep.

Participants also commented favourably on the light-hearted humorous aspects of the intervention and found the pictures and animations uplifting and comforting. The theme of positivity thus also extended to the graphics and design of the intervention.

The graphic makes me like it more…I think the slide is just good vibes in general…and maybe use more positive colours….Like maybe green (Sara, 17)

I particularly liked the GIFs….a GIF came up on the next page correcting me and I thought that was quite funny because it softened the blow for getting something not perfectly correct (Ida, 19)

Both Sara and Ida highlight how graphics and colours play an integral role in conveying a positive optimistic tone and provide suggestions on how to improve this aspect of the intervention. Participants also found colours such as red alarming and preferred colours like green and blue which they found more positive and relaxing.

Discussion

This is a qualitative exploration of the user experience of a newly developed self-help digital single session intervention (SSI) for adolescents who self-identify as struggling with sleep. Overall, the adolescents who took part in our think aloud interviews said they found the intervention helpful and useful. They particularly liked that it was educational but also fun, relatively low effort, and that there was the ability to choose what aspects of sleep to focus on. They also liked the positive focus of the intervention.

It is important to provide credible, reliable information in evidence-informed interventions such as the current SSI for sleep problems. Our participants appreciated the educational aspects of the intervention and perceived the content to be informative and reliable. Providing references to the source material aided their perception of credibility, and participants suggested that more references could be added so that they could learn more about the information provided on their own. This is supported by research suggesting that shaping knowledge around sleep by providing credible information may be an effective behaviour change technique (Arroyo & Zawadzki, 2022; Michie et al., 2015).

General principles about user design, and particularly the Aesthetic Usability principle, hold that visually appealing designs may boost usability and user engagement (Nielsen, 1994), i.e., “attractive things work better” (Norman, 2002 p. 36). Consistent with this, our participants preferred visual modes of learning and suggested that more graphics, animations, and colours be added to make the design more appealing. Research on other digital health interventions has also found that presenting interventions in an attractive and interactive way can improve their efficacy in adolescents (Hollis et al., 2017). The notion that graphics may improve learning aligns with the dual-coding theory which holds that pairing text with visual representations makes information easier to recall (Reed, 2012). Therefore, incorporating more references and visual elements may be avenues for further developing this intervention and other similar interventions.

Our participants favoured features that meant minimal effort was needed on their part, which highlights the important links between UX and cognitive ergonomics (i.e. the study of the cognitive processes involved in interactions between individuals and other systems such as technology, Zolotova & Giambattista, 2019). The capacity of the human working memory system is limited, and behaviour is often motivated by the urge to minimise effort (Zipf, 2016). These principles may underlie our participants’ preference for brief content, larger fonts, and audio-visual formats. As mobile devices have become more commonplace, it may be that small screens used to view online information also explain this preference. Our participants also preferred a list of pre-specified options to choose from rather than free textboxes, saying that they found these easier to use. Research on SSIs has reported high drop-out rates on pages where participants were asked to type out answers (Dobias et al., 2022). This mirrors another usability heuristic that emphasizes on recognition over recall (Nielsen, 1994). As giving options enabled participants to recognise rather than recall answers, they required less cognitive effort and may have thus facilitated better UX (Pereverseff & Bodner, 2020). Other research on digital mental health interventions has suggested that the high cognitive load demanded by them may be a barrier to their adoption by adolescents (Stiles-Shields et al., 2016). Thus, minimising the cognitive effort by, for example, providing pre-specified options as well as or instead of free textboxes may make this intervention more acceptable and engaging for adolescents. Part of the appeal of a single session intervention may be that it is briefer and easier to access compared to traditional, longer term interventions (Schleider et al., 2020b). Whilst completion rates for unguided digital mental health interventions are generally low as they require substantial mental and emotional effort during times of distress (Fleming et al., 2018), SSIs could be a potentially promising solution as they have been intentionally designed to lower time and effort demands, which may improve user engagement with interventions (Dobias et al., 2022).

Consistent with findings from several systematic reviews of digital mental health interventions (e.g. Borghouts et al., 2021), including in adolescents (e.g. Rasing et al., 2020), personalization features were liked by our participants. Even though the sleep SSI is self-directed, our participants appreciated being able to choose what they would like to learn more about and to have the subsequent intervention tailored accordingly. This aligns with research suggesting that adolescents value personal control when accessing mental health services (Plaistow et al., 2014). Personalization can also satisfy psychological needs such as autonomy in adolescents which may ensure future use of services (van der Velden et al., 2015). This also demonstrates a behavioural economics concept, the ‘IKEA effect’, which holds that offering users more choices for customisation provides greater opportunity for self-expression which can increase users’ retention of services (Ling et al., 2020). For adolescents who are essentially “digital natives”, ensuring that digital mental health interventions reflect the level of personalization and interactivity that they are accustomed to may thus be essential to making them more engaging and appealing (Knowles et al., 2014; Scholten & Granic, 2019).

It is also interesting that the positive tone, both in terms of the intervention content and its presentation visually, was important to participants. Adolescents appreciated the positive solution-oriented focus of the intervention and expressed dislike for content that was negative and stressful. As anxiety and stress may interfere with sleep (Alvaro et al., 2013), information focusing on the negative consequences of poor sleep can increase anxiety and worry about the consequences of not sleeping (Hiller et al., 2014). Thus, more encouraging content focusing on ways to improve sleep may be more helpful (Arroyo & Zawadzki, 2022). Participants also noted that this positivity helped them re-evaluate their perceived control over sleep, and this could be a candidate mechanism to explore in work in the future. Positivity and ‘good vibes’ were also expressed through the colours and graphics of the intervention, finding the graphics comforting and preferring positive colours such as green over colours like red that they perceived as alarming. Research on colour theory and positive aesthetics suggests that colours may impact attitudes and emotions (Kimmons, 2020). Cool colours such as blue or green typically evoke feelings of comfort (Kimmons, 2020), while colours like red are more arousing and often associated with danger (Elliot & Maier, 2007). There is a need for more in-depth exploration into the emotional usability of the sleep SSI and other similar interventions, focusing on participants’ feelings and emotions, to create a more positive, satisfying user experience (Marchitto & Canas, 2011).

Strengths and limitations

Whereas digital mental health interventions are commonly tested on majority white populations (Valdez et al., 2012), this study included participants from diverse ethnic backgrounds, although the sample was predominantly female. This is important as ethnic minorities may be disproportionately affected by and less likely to seek help for sleep problems (Jackson et al., 2020), and SSIs have documented potential to reach these underserved populations (Schleider et al., 2020a). Another strength of this study lies in its user-centred design approach.

However, the sample size was small and recruited solely from the UK, and was not intended to be a representative sample. Participants were self-selecting and were also provided an information sheet about the study and then asked certain semi-structured questions prior to the think-aloud portion of the interviews, which could have influenced their experience of the intervention and their reporting of it. The use of retrospective think-aloud interviews may also have led to participants forgetting some of their initial thoughts about the intervention. Lastly, participants self-identified as experiencing sleep problems and no data was collected on the severity of such problems. Thus, it is not possible to determine how these factors may have shaped user experiences, and this remains an important area of future research. Furthermore, while this study found this sleep SSI to be acceptable to adolescents, further research is required to determine its effectiveness in addressing sleep problems in adolescents.

Clinical implications

Promisingly, adolescents in our study found the newly developed sleep SSI acceptable, and it may hold promise as a means of providing a brief, scalable, accessible addition to traditional interventions. Building on this study, feedback will be incorporated and future work will aim to further develop, refine, and evaluate the effectiveness of the sleep SSI using rigorous research designs. There may also be the potential to use the sleep SSI as an adjunct to other clinical services addressing sleep problems or as a preventive measure for sleep or other mental health problems.

Conclusion

Overall, our findings will inform our further developments of this Sleep SSI but are also applicable to other similar interventions for adolescents. This study also highlights the value of involving adolescents in the development of digital mental health interventions aimed at their cohort, and of using think-aloud interviews to explore their experience of user design. It is apparent that educational aspects, positivity, ease of use, and scope for personalization impacted user experience, and participants provided suggestions on how to improve these aspects. Next steps involve incorporating these suggestions into the intervention. Future research is also needed to determine how best to trial this intervention to determine feasibility and effectiveness among adolescents.

Supplemental Material

Supplemental Material - How do adolescents experience a newly developed Online Single Session Sleep Intervention? A Think-Aloud Study

Supplemental Material for How do adolescents experience a newly developed Online Single Session Sleep Intervention? A Think-Aloud Study by Ananya Maity, Angela W. Wang, Melissa J. Dreier, Vuokko Wallace, Faith Orchard, Jessica L. Schleider, Maria E. Loades, and Jessica L. Hamilton in Clinical Child Psychology and Psychiatry.

Footnotes

Acknowledgments

Dr Jessica L. Hamilton is funded by National Institute for Mental Health (K01MH121584). Dr. Maria Loades (Development and Skills Enhancement Award, 302367; Advanced Fellowship, 302929) is funded by the National Institute for Health Research (NIHR) for this research project. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR, NHS or the UK Department of Health and Social Care. Thanks to all the young people who took the time out to participate in this study and provide their valuable input and suggestions. We also appreciate the support and advice we received from the Lab for Scalable Mental Health at Stony Brook University, and the assistance from Amelia Peck at the University of Bath in the early stages of developing the intervention.

Declaration of conflicting interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute for Mental Health (K01MH121584) and National Institute for Health Research (Development and Skills Enhancement Award, 302367; Advanced Fellowship, 302929).

ORCID iDs

Ananya Maity

Melissa Dreier

Faith Orchard

Maria Loades

Jessica Hamilton

Supplemental Material

Supplemental material for this article is available online.

Author biographies

Ananya Maity recently received her MSc in Applied Clinical Psychology from the University of Bath. She is currently working as an Assistant Psychologist at the Avon and Wiltshire Partnership NHS Trust, and is working towards getting her doctorate in clinical psychology.

Angela W Wang is a Ph.D. candidate in Clinical Psychology at Rutgers University and received her Masters of Education from Harvard University in the USA. Angela is broadly interested in developing, evaluating, and disseminating innovative and scalable transdiagnostic interventions for low resource youth.

Melissa J Dreier is a PhD student in clinical psychology at Rutgers University. Her research broadly focuses on the intersection of adolescent development and mental health problems, with an emphasis on risk and protective factors.

Dr. Vuokko Wallace is a Clinical Psychologist and a Lecturer on the Doctorate in Clinical Psychology programme at the University of Bath.

Dr Faith Orchard is a Research Psychologist and Lecturer on the Undergraduate Psychology programme at the University of Sussex. She holds an ESRC New Investigator grant to investigate the relationship between sleep and depression in adolescents.

Dr. Jessica L Schleider is an Associate Professor of Medical Social Sciences and Psychology at Northwestern University, where she directs the Lab for Scalable Mental Health. Her professional mission is to build, test, and disseminate scalable, evidence-based mental health solutions that bridge gaps in mental health ecosystems, with a focus on single-session interventions for underserved youth.

Dr Maria E Loades is a Clinical Psychologist and Reader on the Doctorate in Clinical Psychology programme at the University of Bath. She holds an NIHR Advanced Fellowship to investigate improving access to timely help for adolescents with depression symptoms.

Dr. Jessica L Hamilton is an Assistant Professor in the Department of Psychology at Rutgers University in the USA. Her research focuses on identifying modifiable risk and protective factors for suicidal thoughts and behaviors, with a focus on sleep and social media as opportunities for prevention and intervention.

References

Alvaro

P. K.

Roberts

R. M.

Harris

J. K.

(2013). A systematic review assessing bidirectionality between sleep disturbances, anxiety, and depression. Sleep, 36(7), 1059–1068. https://doi.org/10.5665/sleep.2810

Arroyo

A. C.

Zawadzki

M. J.

(2022). The implementation of behavior change techniques in mHealth apps for sleep: Systematic review. Journal of Medical Internet Research, MHealth and UHealth, 10(4), Article e33527. https://doi.org/10.2196/33527

Åslund

Lekander

Wicksell

R. K.

Henje

Jernelöv

(2020). Cognitive-behavioral therapy for insomnia in adolescents with comorbid psychiatric disorders: A clinical pilot study. Clinical Child Psychology and Psychiatry, 25(4), 958–971. https://doi.org/10.1177/1359104520929376

Ballesio

Aquino

M. R. J. V.

Feige

Johann

A. F.

Kyle

S. D.

Spiegelhalder

Lombardo

Rücker

Riemann

Baglioni

(2018). The effectiveness of behavioural and cognitive behavioural therapies for insomnia on depressive and fatigue symptoms: A systematic review and network meta-analysis. Sleep Medicine Reviews, 37, 114–129. https://doi.org/10.1016/j.smrv.2017.01.006.

Blake

Waloszek

J. M.

Schwartz

Raniti

Simmons

J. G.

Blake

Allen

N. B.

(2016). The SENSE study: Post intervention effects of a randomized controlled trial of a cognitive–behavioral and mindfulness-based group sleep improvement intervention among at-risk adolescents. Journal of Consulting and Clinical Psychology, 84(12), 1039–1051. https://doi.org/10.1037/ccp0000142

Blake

M. J.

Allen

N. B.

(2020). Prevention of internalizing disorders and suicide via adolescent sleep interventions. Current Opinion in Psychology, 34, 37–42. https://doi.org/10.1016/j.copsyc.2019.08.027.

Blake

M. J.

Sheeber

L. B.

Youssef

G. J.

Raniti

M. B.

Allen

N. B.

(2017). Systematic review and meta-analysis of adolescent cognitive–behavioral sleep interventions. Clinical Child and Family Psychology Review, 20(3), 227–249. https://doi.org/10.1007/s10567-017-0234-5

Blake

M. J.

Trinder

J. A.

Allen

N. B.

(2018). Mechanisms underlying the association between insomnia, anxiety, and depression in adolescence: Implications for behavioral sleep interventions. Clinical Psychology Review, 63, 25–40. https://doi.org/10.1016/j.cpr.2018.05.006.

Borghouts

Eikey

Mark

Leon

C. D.

Schueller

S. M.

Schneider

Sorkin

D. H.

(2021). Barriers to and facilitators of user engagement with digital mental Health Interventions: Systematic review. Journal of Medical Internet Research, 23(3), Article e24387. https://doi.org/10.2196/24387

10.

Braun

Clarke

(2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

11.

Braun

Clarke

(2021). To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales. Qualitative Research in Sport, Exercise and Health, 13(2), 201–216. https://doi.org/10.1080/2159676X.2019.1704846

12.

Carskadon

M. A.

(2011). Sleep in adolescents: The perfect storm. Pediatric Clinics of North America, 58(3), 637–647. https://doi.org/10.1016/j.pcl.2011.03.003

13.

Christensen

C. M.

Baumann

Ruggles

Sadtler

T. M.

(2006). Disruptive innovation for social change. Harvard Business Review, 84(12), 94–101. PMID: 17183796.

14.

Chung

K.-F.

Chan

M.-S.

Lam

Y.-Y.

Lai

C. S.-Y.

Yeung

W.-F.

(2017). School-based sleep education programs for short sleep duration in adolescents: A systematic review and meta-analysis. Journal of School Health, 87(6), 401–408. https://doi.org/10.1111/josh.12509

15.

Clarke

Braun

(2013). Successful qualitative research: A practical guide for beginners. Successful Qualitative Research, (1), 1–400.

16.

Clarke

Braun

(2021). Thematic analysis: A practical guide. Thematic Analysis, (1), 1–100.

17.

Cohen

K. A.

Schleider

J. L.

(2022). Adolescent dropout from brief digital mental health interventions within and beyond randomized trials. Internet Interventions, 27, 100496. https://doi.org/10.1016/j.invent.2022.100496

18.

Curcio

Ferrara

De Gennaro

(2006). Sleep loss, learning capacity and academic performance. Sleep Medicine Reviews, 10(5), 323–337. https://doi.org/10.1016/j.smrv.2005.11.001

19.

Dahl

R. E.

(1996). The regulation of sleep and arousal. Development and Psychopathology, 8(1), 3–27. https://doi.org/10.1017/S0954579400006945

20.

Davies

E. L.

(2018). Feasibility of the prototype willingness model as the basis for school-delivered alcohol misuse prevention: A qualitative think-aloud study to explore acceptability of ‘the alcohol smart quiz’ with adolescents and teachers. Journal of Health Psychology, 23(9), 1196–1210. https://doi.org/10.1177/1359105316648481

21.

Davies

E. L.

Martin

Foxcroft

D. R.

(2015). Development and acceptability of a co-produced online intervention to prevent alcohol misuse in adolescents: A think aloud study. JMIR Human Factors, 2(2), Article e4452. https://doi.org/10.2196/humanfactors.4452

22.

de Bruin

E. J.

Bögels

S. M.

Oort

F. J.

Meijer

A. M.

(2015). Efficacy of cognitive behavioral therapy for insomnia in adolescents: A randomized controlled trial with internet therapy, group therapy and a waiting list condition. Sleep, 38(12), 1913–1926. https://doi.org/10.5665/sleep.5240

23.

de Bruin

E. J.

Bögels

S. M.

Oort

F. J.

Meijer

A. M.

(2018). Improvements of adolescent psychopathology after insomnia treatment: Results from a randomized controlled trial over 1 year. Journal of Child Psychology and Psychiatry, 59(5), 509–522. https://doi.org/10.1111/jcpp.12834

24.

Dobias

M. L.

Morris

R. R.

Schleider

J. L.

(2022). Single-session interventions embedded within tumblr: Acceptability, feasibility, and utility study. Journal of Medical Internet Research Formative Research, 6(7), Article e39004. https://doi.org/10.2196/39004

25.

Elliot

A. J.

Maier

M. A.

(2007). Color and psychological functioning. Current Directions in Psychological Science, 16(5), 250–254. https://doi.org/10.1111/j.1467-8721.2007.00514.x

26.

Ellis

J. G.

Cushing

Germain

(2015). Treating acute insomnia: A randomized controlled trial of a “single-shot” of cognitive behavioral therapy for insomnia. Sleep, 38(6), 971–978. https://doi.org/10.5665/sleep.4752

27.

Fleming

Bavin

Lucassen

Stasiak

Hopkins

Merry

(2018). Beyond the Trial: Systematic review of real-world uptake and engagement with digital self-help interventions for depression, low mood, or anxiety. Journal of Medical Internet Research, 20(6), Article e9275. https://doi.org/10.2196/jmir.9275

28.

Gee

Orchard

Clarke

Joy

Clarke

Reynolds

(2019). The effect of non-pharmacological sleep interventions on depression symptoms: A meta-analysis of randomised controlled trials. Sleep Medicine Reviews, 43, 118–128. https://doi.org/10.1016/j.smrv.2018.09.004

29.

Goldstone

Javitz

H. S.

Claudatos

S. A.

Buysse

D. J.

Hasler

B. P.

Zambotti

M. de, . .

Baker

F. C.

(2020). Sleep disturbance predicts depression symptoms in early adolescence: Initial findings from the adolescent brain cognitive development study. Journal of Adolescent Health, 66(5), 567–574. https://doi.org/10.1016/j.jadohealth.2019.12.005

30.

Gradisar

Gardner

Dohnt

(2011). Recent worldwide sleep patterns and problems during adolescence: A review and meta-analysis of age, region, and sleep. Sleep Medicine, 12(2), 110–118. https://doi.org/10.1016/j.sleep.2010.11.008

31.

Greeson

J. M.

Juberg

M. K.

Maytan

James

Rogers

(2014). A randomized controlled trial of koru: A mindfulness program for college students and other emerging adults. Journal of American College Health, 62(4), 222–233. https://doi.org/10.1080/07448481.2014.887571

32.

Guba

E. G.

Lincoln

Y. S.

(1994). Competing paradigms in qualitative research. Handbook of qualitative research, 2(163–194), 105.

33.

Gunn

H. E.

Tutek

Buysse

D. J.

(2019). Brief behavioral treatment of insomnia. Sleep Medicine Clinics, 14(2), 235–243. https://doi.org/10.1016/j.jsmc.2019.02.003

34.

Hagenauer

M. H.

Lee

T. M.

(2013). Adolescent sleep patterns in humans and laboratory animals. Hormones and Behavior, 64(2), 270–279. https://doi.org/10.1016/j.yhbeh.2013.01.013

35.

Hale

Guan

(2015). Screen time and sleep among school-aged children and adolescents: A systematic literature review. Sleep Medicine Reviews, 21, 50–58. https://doi.org/10.1016/j.smrv.2014.07.007

36.

Hamilton

J. L.

Chand

Reinhardt

Ladouceur

C. D.

Silk

J. S.

Moreno

Bylsma

L. M.

(2020). Social media use predicts later sleep timing and greater sleep variability: An ecological momentary assessment study of youth at high and low familial risk for depression. Journal of Adolescence, 83(2), 122–130. https://doi.org/10.1016/j.adolescence.2020.07.009

37.

Hamilton

J. L.

Tsypes

Zelazny

Sewall

C. J. R.

Rode

Merranko

Franzen

P. L.

(2023). Sleep influences daily suicidal ideation through affective reactivity to interpersonal events among high-risk adolescents and young adults. Journal of Child Psychology and Psychiatry, 64(1), 27–38. https://doi.org/10.1111/jcpp.13651.

38.

Harvey

A. G.

Dong

Hein

S. H.

Martinez

A. J.

Gumport

N. B.

Smith

F. L.

Chapman

Lisman

Mirzadegan

I. A.

Mullin

A. C.

Fine

Dolsen

E. A.

Gasperetti

C. E.

Bukosky

Alvarado-Martinez

C. G.

Kilbourne

A. M.

Rabe-Hesketh

Buysse

D. J.

(2021). A randomized controlled trial of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) to improve serious mental illness outcomes in a community setting. Journal of Consulting and Clinical Psychology, 89(6), 537–550. https://doi.org/10.1037/ccp0000650

39.

Hiller

R. M.

Lovato

Gradisar

Oliver

Slater

(2014). Trying to fall asleep while catastrophising: What sleep-disordered adolescents think and feel. Sleep Medicine, 15(1), 96–103. https://doi.org/10.1016/j.sleep.2013.09.014

40.

Hollis

Falconer

C. J.

Martin

J. L.

Whittington

Stockton

Glazebrook

Davies

E. B.

(2017). Annual Research Review: Digital health interventions for children and young people with mental health problems – a systematic and meta-review. Journal of Child Psychology and Psychiatry, 58(4), 474–503. https://doi.org/10.1111/jcpp.12663

41.

Jackson

C. L.

Powell-Wiley

T. M.

Gaston

S. A.

Andrews

M. R.

Tamura

Ramos

(2020). Racial/ethnic disparities in sleep health and potential interventions among women in the United States. Journal of Women’s Health, 29(3), 435–442. https://doi.org/10.1089/jwh.2020.8329

42.

Kearns

J. C.

Coppersmith

D. D. L.

Santee

A. C.

Insel

Pigeon

W. R.

Glenn

C. R.

(2020). Sleep problems and suicide risk in youth: A systematic review, developmental framework, and implications for hospital treatment. General Hospital Psychiatry, 63, 141–151. https://doi.org/10.1016/j.genhosppsych.2018.09.011

43.

Kimmons

(2020). Color theory in experience design. Learner and user experience research: An introduction for the field of learning design & technology. EdTech Books. https://edtechbooks.org/-LIV.

44.

Knowles

S. E.

Toms

Sanders

Bee

Lovell

Rennick-Egglestone

Coyle

Kennedy

C. M.

Littlewood

Kessler

Gilbody

Bower

(2014). Qualitative meta-synthesis of user experience of computerised therapy for depression and anxiety. Public Library of Science One, 9(1), Article e84323. https://doi.org/10.1371/journal.pone.0084323

45.

Kumar

J. A.

Muniandy

Wan Yahaya

W. A. J.

(2019). Exploring the effects of emotional design and emotional intelligence in multimedia-based learning: An engineering educational perspective. New Review in Hypermedia and Multimedia, 25(1–2), 57–86. https://doi.org/10.1080/13614568.2019.1596169

46.

Kwon

Wang

Wilding

Dickerson

S. S.

Dean

G. E.

(2022). Brief behavioral treatment for insomnia: A meta-analysis. Behavioral Sleep Medicine, 20(6), 674–694. https://doi.org/10.1080/15402002.2021.1982715

47.

Lemon

Huckvale

Carswell

Torous

(2020). A narrative review of methods for applying user experience in the design and assessment of mental health smartphone interventions. International Journal of Technology Assessment in Health Care, 36(1), 64–70. https://doi.org/10.1017/S0266462319003507

48.

Ling

I.-L.

Liu

Y.-F.

Lin

C.-W.

Shieh

C.-H.

(2020). Exploring IKEA effect in self-expressive mass customization: Underlying mechanism and boundary conditions. Journal of Consumer Marketing, 37(4), 365–374. https://doi.org/10.1108/JCM-09-2017-2373

49.

Liu

R. T.

Steele

S. J.

Hamilton

J. L.

Q. B. P.

Furbish

Burke

T. A.

Gerlus

(2020). Sleep and suicide: A systematic review and meta-analysis of longitudinal studies. Clinical Psychology Review, 81, 101895. https://doi.org/10.1016/j.cpr.2020.101895

50.

J. C.

Ong

J. L.

Leong

R. L. F.

Gooley

J. J.

Chee

M. W. L.

(2016). Cognitive performance, sleepiness, and mood in partially sleep deprived adolescents: The need for sleep study. Sleep, 39(3), 687–698. https://doi.org/10.5665/sleep.5552

51.

Lovato

Gradisar

(2014). A meta-analysis and model of the relationship between sleep and depression in adolescents: Recommendations for future research and clinical practice. Sleep Medicine Reviews, 18(6), 521–529. https://doi.org/10.1016/j.smrv.2014.03.006

52.

Luik

A. I.

Bostock

Chisnall

Kyle

S. D.

Lidbetter

Baldwin

Espie

C. A.

(2017). Treating depression and anxiety with digital cognitive behavioural therapy for insomnia: A real world nhs evaluation using standardized outcome measures. Behavioural and Cognitive Psychotherapy, 45(1), 91–96. https://doi.org/10.1017/S1352465816000369

53.

Malterud

Siersma

V. D.

Guassora

A. D.

(2016). Sample Size in qualitative interview studies: Guided by information power. Qualitative Health Research, 26(13), 1753–1760. https://doi.org/10.1177/1049732315617444

54.

Marchitto

Canas

J. J.

(2011). User experience as a challenge for cognitive psychology and ergonomics. Human Technology: An Interdisciplinary Journal on Humans in ICT Environments, 7(3), 268–280. https://doi.org/10.17011/ht/urn.2011112211715

55.

Marino

Andrade

Campisi

S. C.

Wong

Zhao

Jing

Aitken

Bonato

Haltigan

Wang

Szatmari

(2021). Association between disturbed sleep and depression in children and youths: A systematic review and meta-analysis of cohort studies. Journal of the American Medical Association Network Open, 4(3), Article e212373. https://doi.org/10.1001/jamanetworkopen.2021.2373

56.

Maxwell

J. A.

(2012). Qualitative research design: An interactive approach (3rd ed.). Sage Publications, Inc.

57.

McLay

Sutherland

Machalicek

Sigafoos

(2020). Systematic review of telehealth interventions for the treatment of sleep problems in children and adolescents. Journal of Behavioral Education, 29(2), 222–245. https://doi.org/10.1007/s10864-020-09364-8

58.

Michie

Wood

C. E.

Johnston

Abraham

Francis

J. J.

Hardeman

(2015). Behaviour change techniques: The development and evaluation of a taxonomic method for reporting and describing behaviour change interventions (a suite of five studies involving consensus methods, randomised controlled trials and analysis of qualitative data). Health Technology Assessment, 19(99), 1–188. https://doi.org/10.3310/hta19990

59.

Newton

A. S.

March

Gehring

N. D.

Rowe

A. K.

Radomski

A. D.

(2021). Establishing a working definition of user experience for ehealth interventions of self-reported user experience measures with ehealth researchers and adolescents: Scoping review. Journal of Medical Internet Research, 23(12), Article e25012. https://doi.org/10.2196/25012

60.

Nielsen

(1994). Usability Engineering. Morgan Kaufmann.

61.

Norman

(2002). Emotion & Design: attractive things work better. Interactions, 9(4), 36–42. DOI:https://doi.org/10.1145/543434.543435

62.

O’Brien

B. C.

Harris

I. B.

Beckman

T. J.

Reed

D. A.

Cook

D. A.

(2014). Standards for reporting qualitative research: A synthesis of recommendations. Academic Medicine, 89(9), 1245–1251. https://doi.org/10.1097/ACM.0000000000000388

63.

Orchard

Gregory

A. M.

Gradisar

Reynolds

(2020a). Self-reported sleep patterns and quality amongst adolescents: Cross-sectional and prospective associations with anxiety and depression. Journal of Child Psychology and Psychiatry, 61(10), 1126–1137. https://doi.org/10.1111/jcpp.13288

64.

Orchard

Pass

Chessell

Moody

Ellis

Reynolds

(2020b). Adapting brief CBT-I for depressed adolescents: A case illustration of the sleeping better program. Cognitive and Behavioral Practice, 27(3), 336–346. https://doi.org/10.1016/j.cbpra.2019.07.010

65.

Owens

(2007). Classification and epidemiology of childhood sleep disorders. Sleep Medicine Clinics, 2(3), 353–361. https://doi.org/10.1016/j.jsmc.2007.05.009

66.

Owens

J. A.

Babcock

Blumer

Chervin

Ferber

Goetting

Glaze

Ivanenko

Mindell

Rappley

Rosen

Sheldon

(2005). The use of pharmacotherapy in the treatment of pediatric insomnia in primary care: Rational Approaches. A consensus meeting summary. Journal of Clinical Sleep Medicine, 01(01), 49–59. https://doi.org/10.5664/jcsm.26297

67.

Patton

M. Q.

(2002). Qualitative research and evaluation methods (3 ed). Sage Publications. https://catdir.loc.gov/catdir/enhancements/fy0658/2001005181-t.html

68.

Pereverseff

R. S.

Bodner

G. E.

(2020). Comparing recollection and nonrecollection memory states for recall of general knowledge: A nontrivial pursuit. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46(111), 2207–2225. https://doi.org/10.1037/xlm0000941

69.

Plaistow

Masson

Koch

Wilson

Stark

R. M.

Jones

P. B.

Lennox

B. R.

(2014). Young people’s views of UK mental health services. Early Intervention in Psychiatry, 8(1), 12–23. https://doi.org/10.1111/eip.12060

70.

Rasing

S. P. A.

Stikkelbroek

Y. A. J.

Bodden

D. H. M.

(2020). Is digital treatment the holy grail? Literature review on computerized and blended treatment for depressive disorders in youth. International Journal of Environmental Research and Public Health, 17(1), 153. https://doi.org/10.3390/ijerph17010153

71.

Reed

S. K.

(2012). Cognition: Theories and applications. Cengage Learning.

72.

Reidy

B. L.

Raposa

E. B.

Brennan

P. A.

Hammen

C. L.

Najman

J. M.

Johnson

K. C.

(2016). Prospective associations between chronic youth sleep problems and young adult health. Sleep Health, 2(1), 69–74. https://doi.org/10.1016/j.sleh.2015.11.005

73.

Santesteban-Echarri

Rice

Wadley

Lederman

D’Alfonso

Russon

Chambers

Miles

C. J.

Gilbertson

Gleeson

J. F.

McGorry

P. D.

Álvarez-Jiménez

(2017). A next-generation social media-based relapse prevention intervention for youth depression: Qualitative data on user experience outcomes for social networking, safety, and clinical benefit. Internet Interventions, 9, 65–73. https://doi.org/10.1016/j.invent.2017.06.002

74.

Schleider

Weisz

(2018). A single-session growth mindset intervention for adolescent anxiety and depression: 9-month outcomes of a randomized trial. Journal of Child Psychology and Psychiatry, 59(2), 160–170. https://doi.org/10.1111/jcpp.12811

75.

Schleider

J. L.

Dobias

M. L.

Sung

J. Y.

Mullarkey

M. C.

(2020a). Future directions in single-session youth mental health interventions. Journal of Clinical Child and Adolescent Psychology, 49(2), 264–278. https://doi.org/10.1080/15374416.2019.1683852

76.

Schleider

J. L.

Dobias

Sung

Mumper

Mullarkey

M. C.

(2020b). Acceptability and utility of an open-access, online single-session intervention platform for adolescent mental health. Journal of Medical Internet Research Mental Health, 7(6), Article e20513. https://doi.org/10.2196/20513

77.

Schleider

J. L.

Mullarkey

M. C.

Fox

K. R.

Dobias

M. L.

Shroff

Hart

E. A.

Roulston

C. A.

(2022). A randomized trial of online single-session interventions for adolescent depression during COVID-19. Nature Human Behaviour, 6(2), 258–268. https://doi.org/10.1038/s41562-021-01235-0

78.

Schleider

J. L.

Weisz

J. R.

(2017). Little treatments, promising effects? Meta-Analysis of single-session interventions for youth psychiatric problems. Journal of the American Academy of Child & Adolescent Psychiatry, 56(2), 107–115. https://doi.org/10.1016/j.jaac.2016.11.007

79.

Scholten

Granic

(2019). Use of the principles of design thinking to address limitations of digital mental health interventions for youth: Viewpoint. Journal of Medical Internet Research, 21(1), Article e11528. https://doi.org/10.2196/11528

80.

Short

M. A.

Gradisar

Lack

L. C.

Wright

H. R.

(2013b). The impact of sleep on adolescent depressed mood, alertness and academic performance. Journal of Adolescence, 36(6), 1025–1033. https://doi.org/10.1016/j.adolescence.2013.08.007

81.

Short

M. A.

Gradisar

Lack

L. C.

Wright

H. R.

Dohnt

(2013a). The sleep patterns and well-being of Australian adolescents. Journal of Adolescence, 36(1), 103–110. https://doi.org/10.1016/j.adolescence.2012.09.008

82.

Sim

Saunders

Waterfield

Kingstone

(2018). Can sample size in qualitative research be determined a priori? International Journal of Social Research Methodology, 21(5), 619–634. https://doi.org/10.1080/13645579.2018.1454643

83.

Stiles-Shields

Mohr

D. C.

(2016). A review of design characteristics of cognitive behavioral therapy-informed behavioral intervention technologies for youth with depression and anxiety. Digital Health, 2, 2055207616675706. https://doi.org/10.1177/2055207616675706

84.

Taylor

D. J.

Jenni

O. G.

Acebo

Carskadon

M. A.

(2005). Sleep tendency during extended wakefulness: Insights into adolescent sleep regulation and behavior. Journal of Sleep Research, 14(3), 239–244. https://doi.org/10.1111/j.1365-2869.2005.00467.x

85.

Tinner

L. E.

Kaner

Garnett

Mitchell

Hickman

Campbell

MacArthur

(2020). Qualitative evaluation of web-based digital intervention to prevent and reduce excessive alcohol use and harm among young people aged 14–15 years: A “think-aloud” study. Journal of Medical Internet Research Pediatrics and Parenting, 3(2), Article e19749. https://doi.org/10.2196/19749

86.

Turner

(2017). A psychology of user experience. Springer International Publishing. https://doi.org/10.1007/978-3-319-70653-5

87.

Valdez

R. S.

Gibbons

M. C.

Siegel

E. R.

Kukafka

Brennan

P. F.

(2012). Designing consumer health IT to enhance usability among different racial and ethnic groups within the United States. Health Technology, 2(4), 225–233. https://doi.org/10.1007/s12553-012-0031-6

88.

Van Den Haak

De Jong

Jan Schellens

(2003). Retrospective vs. concurrent think-aloud protocols: testing the usability of an online library catalogue. Behaviour & Information Technology, 22(5), 339–351. https://doi.org/10.1080/0044929031000

89.

van der Velden

Sommervold

M. M.

Culén

A. L.

(2015). Patient-initiated personalisation: Privacy, moods and colours. In: Proceedings of the international conference on e-health 2015 (pp. 95–103). IADIS Press. https://d1wqtxts1xzle7.cloudfront.net/38819719/personalisation_colours_submitted-libre.pdf?1442656282=&response-content-disposition=inline%3B+filename%3DPatient_Intitiated_Personalisation_Priva.pdf&Expires=1680300066

90.

van Straten

van der Zweerde

Kleiboer

Cuijpers

Morin

C. M.

Lancee

(2018). Cognitive and behavioral therapies in the treatment of insomnia: A meta-analysis. Sleep Medicine Reviews, 38, 3–16. https://doi.org/10.1016/j.smrv.2017.02.001

91.

Yardley

Ainsworth

Arden-Close

Muller

(2015). The person-based approach to enhancing the acceptability and feasibility of interventions. Pilot and Feasibility Studies, 1(1), 37. https://doi.org/10.1186/s40814-015-0033-z

92.

Zetterqvist

Lundén

Herrmann

Hasbar

Khalifa

Lekander

Åslund

Jernelöv

(2021). Internet-delivered cognitive behaviour therapy for adolescents with insomnia comorbid to psychiatric conditions: A non-randomised trial. Clinical Child Psychology and Psychiatry, 26(2), 475–489. https://doi.org/10.1177/1359104520978464

93.

Zhang

Dong

(2009). Human-centred design: An emergent conceptual model. Royal College of Art. https://bura.brunel.ac.uk/handle/2438/3472

94.

Zipf

G. K.

(2016). Human behavior and the principle of least effort: An introduction to human ecology. Ravenio Books.

95.

Zolotova

Giambattista

(2019). Designing cognitive ergonomics features of medical devices. Aspects of cognitive interaction. The Design Journal, 22(sup1), 463–474. https://doi.org/10.1080/14606925.2019.1595432

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