1 Efficacy of a Smartphone Application for Treating Insomnia:
A Randomized Waitlist Controlled Trial L. G. Bogaard
Institution: University of Amsterdam Supervisor: Jaap Lancee
Studentnumber: 10186840 Wordcount: 4069
2 Index Abstract p. 3 Introduction p. 4 Method p. 7 Participants p. 7 Power p. 9 Material p. 9 Intervention p. 10 Procedure p. 11 Analysis p. 11 Results p. 12 Questionnaire results p. 15
Sleep diary results p. 16
Discussion p. 21
Limitations p. 24
Implications p. 25
3
Abstract
Cognitive behavioral therapy is effective in treating insomnia, but the efficacy of administering cognitive behavioral therapy for insomnia (CBT-I) through a smartphone application is unknown. The aim of the current study was to investigate the effect of the Sleepcare smartphone application on decreasing insomnia symptoms. One hundred fifty-one participants were randomized to either the Sleepcare smartphone condition (n = 74) or the waitlist control condition (n = 77). The participants in the smartphone condition received six weeks of adapted CBT-I through the Sleepcare application. Questionnaires were filled in at baseline and after seven weeks. The insomnia symptoms decreased significantly in the smartphone condition and the participants in the smartphone condition improved more than the waitlist control condition (d = 0.7 – 0.91, p < 0.01). These results indicate that providing CBT-I via a Sleepcare smartphone application is effective. However, the effects are not as large as the effects found in internet delivered CBT-I studies. The Sleepcare application needs to be improved further to be able to replace other forms of CBT-I such as face-to-face or internet delivered CBT-I, but could be used in a blended format in combination with face-to-face treatment.
4 About 10% of the adult population suffers from an insomnia disorder (Morin et al., 2011; Morin, LeBlanc, Daley, Gregoire & Mérette, 2006; Ohayon & Sageles, 2010).
According to the DSM-5, insomnia affects people’s sleep quantity or quality negatively. This dissatisfaction is seen in having difficulty initiating sleep, maintaining sleep, and/or early morning awakening. These disturbances occur at least three nights a week and are present for at least three months. The sleep disturbances cause clinically significant distress and/or impairment in occupational, social, academic, or other important areas of functioning (American Psychiatric Association, 2013). Due to a decline in sleep, people experience
fatigue and a decline in cognitive abilities. Thus, insomnia is considered to have a high burden of disease (Fortier-Brochu, Beaulieu-Bonneau, Ivers & Morin, 2010). Impairment in working memory, problem solving, and episodic memory shows that insomnia affects performance on complex tasks (Fortier-Brochu, Beaulieu-Bonneau, Ivers & Morin, 2011). People who suffer from insomnia disorder are also significantly more likely to suffer from depression and anxiety than people without insomnia (Taylor, Lichstein, Durrence, Reidel, & Bush, 2005) and have a twofold risk of developing depression (Baglioni et al., 2011). From a social
perspective, the direct and indirect costs of insomnia disorder are over three times higher than the annual costs of normal sleepers, with an average of around 75% of these costs attributable to reduced productivity and work absences (Daley, Morin, LeBlanc, Grégoire & Savard, 2009).
The high burden of insomnia disorder calls for an effective treatment. In general practice sleep medication is used most often in treating insomnia. However, Cognitive
Behavioral Therapy for insomnia (CBT-I) is the most effective treatment format (Morin et al., 2006). Meta-analyses show that CBT-I and medication are both effective treatments, but CBT-I seems to have more long-term effects (Smith et al., 2002; Mitchell, Gehrman, Perlis &
5 Umscheid, 2012). Therefore, it seems that CBT-I is the preferred treatment format with the largest effects and the least side effects (Mitchell et al., 2012).
A problem that arises with administering CBT-I is that it is more costly in the short term than medication. Also, there is only a small group of therapists trained in CBT-I, and the intensiveness of the treatment for both the patient and the clinician is high (Lamberg, 2008). To decrease the costs and increase the availability of CBT-I, it is proposed to include self-help CBT-I within a stepped care framework (Espie, 2009). Self-help treatment for CBT-I can be administered either through a paper and pencil, or online format (Lancee, van den Bout, van Straten & Spoormaker, 2012). Overall internet-delivered CBT-I appears to be a good
alternative to face-to-face treatment (Zachariae, Lyby, Ritterband & O’Toole, 2015) and is more effective than an active internet-based control group (Kaldo et al., 2015).
The problem with internet-delivered treatment is that it is still bound to specific times. This is because the participants have to wait to receive feedback from their coaches on
assignments, which is typically sent within three days (van Straten, 2013). This internet delivered CBT-I is not as time specific as meeting with a therapist face-to-face, but it still has a strong time specific element to it. For more integrated treatment, it would be preferred to have access to treatment at all times. A smartphone application with an integrated coach that can be accessed at all times would be one possible solution to this time specific problem. If people want access to a treatment or coach, this should be immediately available. Ideally, treatment should no longer be tied to a specific place or time of day as it is currently with face-to-face therapy or online treatment. There is better integration of treatment into a
person’s life than with other treatment forms, which makes it easy and accessible to use. Thus there is great potential use for a CBT-I application specially tailored for smartphones and tablets.
6 The Sleepcare project answers the needs of this modern CBT-I application. In this project an application is developed in which CBT-I is administered through a Smartphone. After beta tests, a pilot study1 was conducted on 15 participants. Overall the participants in the pilot study showed a significant improvement in their insomnia complaints after using the smartphone application. However, this was only a pilot study with a small number of
participants and without a control group. In the current study we try to answer the question of whether CBT-I administered through a smartphone application is effective. To this aim we set up a randomized controlled trial comparing the Sleepcare application to a waitlist control condition.
The overall question if CBT-I is effective in reducing insomnia through a smartphone application can be operationalized by splitting it into the following three different hypotheses. The first hypothesis suggests that CBT-I administered through the Sleepcare smartphone application will reduce insomnia symptoms over time on the Insomnia Severity Index (ISI) scale, when compared to those in the waitlist control condition. The second hypothesis suggests that CBT-I administered through the Sleepcare smartphone application will improve sleep diary indices over time, when compared to those without treatment. These indices are the Sleep Efficiency (SE), Sleep Quality (SQ), Total Sleep Time (TST), Wake time After Sleep Onset (WASO) and Sleep Onset Latency (SOL). The third hypothesis suggests that CBT-I administered through the Sleepcare smartphone application will reduce dysfunctional beliefs about sleep over time on the Dysfunctional Beliefs and Attitudes about Sleep scale (DBAS), when compared to those without treatment.
1 In this pilot several problems in the application were discovered. These problems included bugs in the application code that stopped the participants from continuing their therapy conversations and bugs in selecting sleeping time in the diary. These bugs have all been fixed before starting the Randomized Controlled Trial.
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Method
Participants
Participants were obtained through websites, social media, online advertisements, flyers, and a press release. Prior to participating in the study, a selection process took place through an online questionnaire. This questionnaire was answered after filling in a digital informed consent form. The questionnaire contained items about demographic and general information, chronic insomnia, and dysfunctional beliefs (ISI, DBAS). Because this master thesis is part of a larger project, additional questionnaires were also administered. Inclusion criteria were being 18 years of age or older, suffer from insomnia according to the DMS-5 criteria, an ISI score of seven or higher (Morin, 1993), stable medication use, a valid email address, a connection to the internet, and an Android (version 4.1 or higher) smartphone. People were excluded if they reported an average of less than 5 hours of sleep a night in the sleep diary (Kyle et al., 2015), had an ISI-score lower than seven, had received previous CBT-I for chronic insomnia, had started psychotherapy in the last six months, showed signs of psychosis/schizophrenia, had more than two glasses of alcohol a day for at least 21 days a month, used marijuana more than once a week, had symptoms that point to sleep apnea, were pregnant or breastfeeding, were working in unstable shifts, or showed symptoms of a current depressive episode.
If the participants fulfilled the inclusion criteria, they filled in a daily online sleep diary for seven days before starting the training and being included in the study. After successfully filling in at least six days of the sleep diary and not sleeping less than an average of five hours a night, the participants were randomly assigned to the treatment condition or the waitlist control condition by an independent investigator. Both conditions took seven weeks to complete, after which the waitlist control condition received the same treatment with the smartphone application as the treatment condition after filling in the post-test questionnaire.
8 The therapy for insomnia was administered through the Sleepcare smartphone application. Figure 1 shows the amount of participants included and randomized into the conditions.
Figure 1. Flowchart of amount of participants during the pre and post-tests that were excluded and the amount of
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Power
The effect of online administered CBT-I in treating chronic insomnia seems to be large in previous research with a Hedges’ g round about 1.03 on the ISI scale (Zachariae et al., 2015). Since this is the first study to use a Smartphone application to administer CBT-I, there is no certainty of obtaining similar effect size magnitudes. Therefore at least a medium effect was expected. To be able to detect a medium effect (f2 = .15) with a power of 0.8 using a repeated measures mixed ANOVA (within-between interaction), a sample of 90 participants is required. Taking into account a 50% dropout rate, 180 participants needed to be included in this study.
Material
The questionnaire that was used to measure the severity of insomnia was the Insomnia Severity Index Scale (ISI). The ISI has a high internal consistency with a Cronbach α = 0.9 and a high validity (Morin, Belleville, Bélanger & Ivers, 2011). The scale consists of seven items, with a total score ranging from 0 to 28. The sub-threshold for insomnia is eight, with eight or higher meaning that the participant suffers from insomnia. An example question from the ISI scale is : “How worried/distressed are you about your current sleep problem?”. The answers range from not at all worried (0) to very much worried (4).
Dysfunctional beliefs and attitudes about sleep were measured through the
Dysfunctional Beliefs and Attitudes about Sleep (DBAS) scale. The DBAS-16 has a high internal consistency with a Cronbach α = 0.77 (Morin, Vallières & Ivers, 2007) and also has a high reliability. This scale consists of 16 items, selected from the original DBAS with 30 items. An example of a statement of the DBAS scale would be “I need 8 hours of sleep to feel refreshed and function well during the day”, with the answers ranging from strongly disagree (0) to strongly agree (10). The questionnaires used are all translated into Dutch for this study.
10 The sleep diary consists of fifteen questions regarding the participants’ sleep. After waking up, the participant was asked to remember the previous night and fill in the diary regarding when they went to bed, when they fell asleep, what time they woke up, and what time they got out of bed (Carney, 2012). The participants were also asked to fill in how many awakenings occurred during the night, and how they would rate their sleep quality on a scale of one to ten. This diary was filled in over six to seven consecutive nights. The questions regarding sleep quality, total sleeping time, sleep onset latency and wake time after sleep onset were used in this study in both the pre- and the post-test. The sleep efficiency is calculated by dividing the total sleep time by the total time spent in bed.
Intervention
In the treatment condition the participants received cognitive behavioral therapy for insomnia (CBT-I) in the form of relaxation exercises, sleep hygiene, sleep diary, psycho-education, and sleep restriction. The application presents these different exercises, adjust them to the needs of the participant, and will remind participants to complete the exercises. This feedback is shown immediately to the participant.
The sleep diary was filled out daily, including sleeping time, waking time, total time in bed, and how many times the participant woke up during the night. The participant rates the quality of sleep per night on a scale of 1 to 10.
The psycho-education module consists of information on sleep, sleep problems, and sleep hygiene. These were accessible at any time.
The sleep hygiene module consists of re-associating the bed with sleeping. Participants were instructed to only use the bed for sleeping and sex. Daytime naps should be avoided and participants should maintain a regular rising time. The sleep restriction module consists of restraining sleep time to improve the sleep efficiency. Guided by the results of the sleeping
11 diary, the sleep window is limited to actual sleep time. The minimum hours of sleep suggested is five hours a night. The sleep window was increased by 15-30 minutes a week when the sleep efficiency was over 90%.
Procedure
These CBT-I modules administered through the Sleepcare smartphone application required six weeks to complete, but participants got a timeframe of seven weeks. After being assigned to a condition, participants received a reminder in the form of an email after three days, after one week, and after two weeks as long as they have not yet registered for the application. After three weeks the participants in the treatment condition received a questionnaire about their experience with the application and expectations about the
application. After finishing the full seven weeks, participants in both conditions filled in the same questionnaire as in the beginning, including the same questions about demographic and general information, chronic insomnia, and dysfunctional beliefs about sleep (Insomnia Severity Index, DBAS). This same questionnaire was also answered by participants in the treatment condition after three months as a follow-up. The participants in the waitlist control condition received no follow-up.
Analysis
A mixed design ANOVA was used to analyze within and between group time effects on all three hypotheses. With an ANOVA we analyzed if CBT-I administered through the
Sleepcare smartphone application will reduce insomnia symptoms over time on the ISI scale, improve results over time in the sleep diary, and reduce dysfunctional beliefs and attitudes about sleep over time on the DBAS when compared to those without treatment. Assumptions
12 of sphericity and homogenous variance were checked with the Mauchly and Levene’s test. The Levene’s test was not significant and therefore homogeneous variances for all levels of the repeated-measures variables were assumed. All tests were two-tailed at aa significance level of p = .05. The effect sizes were expressed in Cohen’s d, with the within group effect calculated by d = M1 - M2 / σpooled , and using the mean difference to calculate the between
group effect. The pooled SD was calculated by .
The baselines of the two conditions were checked for differences by performing independent t-tests to see if certain variables differ significantly. Variables that associated with non-response and/or baseline differences were entered as a covariate in the analyses. Condition and age were detected as variables that predicted non-response, therefore age was taken into account as a covariate on the sleep diary tests because of the correlation with non-response. Randomization was checked with independent t-tests and x2, and detected no significant difference between any of the groups or dependent variables.
Results
The online questionnaire was started by 640 people. Of these participants 229 started the online diary, and the other participants were excluded due to various exclusion criteria as seen in Figure 1. Eventually, 151 participants were included in this study. Of these
participants, 57 were male and 94 were female. The mean age was 39.66 years (SD = 13.44) as can be seen in Table 1, together with other demographic baseline information.
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Table 1
Demographic Information of the Participants Included in the Study
Demographics Smartphone (n = 74) Waitlist (n = 77) p X2
Age (years), mean (SD) 38.51 (12.96)
40.75 (13.88)
.31 1.02a
Female (%) 60.8 63.6 .72 0.13
Living with partner (%) 67.6 63.6 .61 0.26
High educational level (%) 70.3 63.6 .78 1.10
Employed (%) 78.4 72.7 .42 0.65
Years with insomniab (%) - < 1 year - 1 - 5 years - > 5 - 10 years - > 10 years 13.04 39.13 18.84 28.99 11.77 55.88 14.71 17.65 .23 4.31
Use of sleep medication (%) 10.8 3.9 .10 2.67
a
t-test was performed instead of x2 b
total n = 137
Five outliers were found in the online pre-test of the sleep diary, of which three in the smartphone condition and two in the waitlist condition. Outliers of interest to this study were two outliers in the waitlist condition, the other three were on sub-tests not used in any of the hypotheses. One of the outliers was on Sleep Quality with a z-score of 4.03 and one on Sleep Onset Latency with a z-score of 5.01. Their scores were above z = 3.29 and were therefore deleted. Only the score on the specific sub-tests for which they scored above the z-value were deleted, both on the pre- and post-tests.
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Table 2
Means, Standard Deviations, and Effect Sizes (Cohen’s d) of the Questionnaires and Diary in the Smartphone and Waitlist Conditions
Pre test Post test Cohen d between Cohen d within n M (SD) n M (SD) group group Sleep Diary Sleep efficiency (SE) in % Smartphone 73 77.61 (7.33) 29 84.66 (6.81) 0.74** 1.00 Waitlist 75 77.07 (8.17) 46 77.90 (9.38) 0.09 Sleep onset latency (SOL) in minutes Smartphone 73 32.61 (20.31) 29 20.52 (13.25) 0.42 -0.71 Waitlist 75 32.88 (24.69) 46 30.47 (22.38) -0.10 Sleep quality (SQ) Smartphone 73 2.97 (0.41) 29 3.39 (0.51) 0.70** 0.91 Waitlist 75 2.93 (0.52) 46 3.09 (0.54) 0.30 Total sleep time (TST) in hours Smartphone 73 6.55 (0.87) 29 7.01 (0.99) -0.37 0.49 Waitlist 75 6.43 (0.82) 46 6.69 (1.01) 0.28 Wake after sleep onset (WASO) in minutes Smartphone 73 44.62 (31.72) 29 24.51 (17.16) -0.91*** 0.79 Waitlist 75 43.78 (29.97) 46 44.14 (31.72) 0.01 Questionnaire Dysfunctional beliefs and attitudes about sleep (DBAS) Smartphone 74 5.32 (1.30) 45 4.69 (1.35) -0.25 0.48*** Waitlist 77 5.18 (1.31) 62 4.88 (1.62) 0.20 Insomnia Severity Index (ISI) Smartphone 74 16.41 (3.05) 48 9.88 (4.87) -0.78*** 1.61*** Waitlist 77 16.35 (3.26) 62 13.40 (4.53) 0.75 * p = < .05, ** < .01, *** < .001
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Questionnaire results
There was a significant main effect in reducing insomnia symptoms on the ISI scale, F (1,108) = 144.36, p < .001. There was an interaction effect between the reduction of insomnia symptoms and condition, F (1,108) = 17.19, p <.001. Both of these effects were in the
expected direction, as can be seen in Table 2 and Figure 2. The between group effect size was d = -0.78, which can be considered a large effect.
Figure 2. The decline in both conditions in insomnia symptoms measured on the ISI scale, on the pre and
post-test.
There was a significant main effect in reducing dysfunctional beliefs and attitudes about sleep, F (1,105) = 22.03, p < .001 . There was no interaction effect between the DBAS scale and condition, F (1,105) = 1.69, p = .196. This finding was inconsistent with the
16 proposed third hypothesis. The direction of the means were in line with the predictions, but the effect size was small with d = -0.25.
Figure 3. The decline in both conditions in dysfunctional beliefs and attitudes about sleep measured on the
DBAS scale, on the pre and post-test.
Sleep diary results
There was no significant main effect of improvement of SE, F (1,72) = 2.02, p = .16. There was an interaction effect between the SE scale and condition, F (1,72) = 9.94, p = .002. The effect size was medium to large, with d = 0.74. These results were in line with the
17
Figure 4. The sleep efficiency in percentages of both conditions in the pre and post-diary.
There was no significant main effect of improvement of sleep onset latency, F(1,72) = 0.235, p = .629. There was no interaction effect between the SOL scale and condition, F(1,72) = 3.283, p = .074. This was not in line with the predictions, where a significant interaction was expected. The effect size was medium with d = 0.42
18
Figure 5. The sleep onset latency in minutes on both conditions in the pre and post-diary.
There was no significant main effect of improvement of sleep quality, F(1,72) = 3.40, p = .069. There was an interaction effect between the SQ scale and condition, F(1,72) = 8.68, p = .004 This was in line with the predictions, where a significant interaction was expected. The effect size was d = 0.70, which is a medium effect.
19
Figure 6. The sleep quality of both conditions in the pre and post-diary.
There was no significant main effect of improvement of total sleep time, F(1,72) = 2.092, p = .152. There was no interaction effect between the TST scale and condition, F(1,72) = 2.481, p = .12. This was not in line with the predictions, where a significant interaction was expected. The effect size was small to medium with d = -0.37.
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Figure 7. Total sleep time in hours in both conditions, in the pre and post-test.
There was no significant main effect on improvement of wake time after sleep onset, F(1,72) = 0.03, p = .88. An interaction was found between the WASO and condition, F(1,72) = 16.97, p < .001. The effect size was large, d = -0.91.
21
Figure 8. The WASO scores in minutes of both conditions in the pre and post-test.
Discussion
This is the first study to demonstrate the efficacy of CBT-I administered through a smartphone application. The first hypothesis that CBT-I administered through the Sleepcare smartphone application will reduce insomnia symptoms over time on the ISI scale, when compared to those in the waitlist control condition, is supported by the findings in the ANOVA. This indicates that the Sleepcare smartphone application has significantly reduced the insomnia symptoms over time, and also in comparison to the waitlist control condition. The second hypothesis that the CBT-I administered through the Sleepcare smartphone application will improve sleep quality, quantity, and efficiency in comparison to a waitlist, is also supported by the these findings. The sleep efficiency, sleep quality and wake onset after
22 sleep time improved significantly more in the smartphone condition than in the waitlist
control condition. This interaction indicates that the results on the post-test in the smartphone condition were significantly greater than the results on the waitlist control condition. Sleep onset latency and total sleep time did not show this significant improvement, but the means were in the expected direction which means they declined in the sleep onset latency and improved on the total sleep time. The third hypothesis, that dysfunctional beliefs and attitudes about sleep will be reduced in the smartphone condition when compared to a waitlist control condition over time, is not supported by the previous findings. This can be explained by the fact that there was no specific segment in the smartphone application addressing such beliefs, which makes the small change between the pre- and post-test logical and expected. In further research, a segment could be added to the Sleepcare application to target these beliefs and attitudes about sleep specifically.
The observed between group effects were very similar to those found in recent studies that use similar interventions delivering CBT-I online (Cheng & Dizon, 2012; Lancee et al., 2012; Lancee et al., 2013; van Straten et al., 2013) as can be seen in Table 3.
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Table 3
Effect Sizes Reported in Cohen’s d
Current study Cheng & Dizon (2012) Lancee et al. (2012) Zachariae et al. (2015)* Questionnaire Insomnia Severity Index -0.78 -0.86 -1.44 1.09 Sleep Diary Sleep Efficiency 0.74 0.40 -0.95 0.58 Sleep Quality 0.70 0.41 - 0.49 Sleep Onset Latency 0.42 -0.55 0.50 0.41 Total Sleep Time -0.37 0.22 -0.47 0.29 Wake time After Sleep Onset -0.91 -0.18 0.74 0.45 *
Effect sizes reported in Hedges’ g
The Insomnia Severity Index scale showed a Cohen’s d of -0.78 in this study, which is a large effect. This is comparable to the effect sizes found in the other studies as seen in Table 3, but not as big as for instance the effect size found in the research by Lancee et al. (2012), d = 1.44. The effect sizes of the sleep diary indices vary, but are also comparable to the effect sizes found in the other studies. These comparable effect sizes show that the smartphone
24 application works almost as good as the internet delivered CBT-I and is therefore a good alternative to face-to-face or internet delivered CBT-I.
Limitations
In this study there were a few limitations, which could have affected the end results. First, 190 participants were needed in this study, with a power of 0.8 and 50% dropout rate. Because of high non-response rate on the post-tests, this number was not obtained. The highest non-response rate was in the smartphone condition, where only 40.5% of the
respondents filled in the post-test of the sleep diary. To be able to generalize the findings of tis research from a small sample to a larger population, it is important to have larger group of participants since this leads to more power, and therefore a significant result.
Second, age correlated with sleep diary indices, which means it had to be taken into account as a covariate for those ANOVA’s. Older participants seemed to be more compliant to fill in the post-test than younger participants. This may be a problem in the generalization of the smartphone application results to the entire population, since the younger participants are less represented in the post-test. However, this is was fixed by making age a covariate.
A third limitation in this study was that not all participants in the smartphone condition were offered sleep restriction, while the sleep restriction was supposed to be the main
ingredient of this application. The sleep restriction was only offered when the participants’ sleep efficiency was under 85%, which was obtained by a lot of participants and therefore they received no sleep restriction. One of the criteria to be included in this study was an average of 5 hours of sleep a night. This could have negatively affected the amount of participants with sleep efficiencies lower than 85%. In further research it would be recommended to include participants who sleep less than five hours a night. People with insomnia will sleep less, so including participants that sleep less than five hours a night would
25 add to the generalization of the results. This would create the full-scale picture of how the application would work on various kinds of insomnia patients.
A weakness of this study that ties into creating a more full-scale picture, is that it excluded participants with comorbidities, even though insomnia has a high rate of
comorbidity (Baglioni et al., 2011; Taylor, Lichstein, Durrence, Reidel, & Bush, 2005). To create this full-scale picture, people with comorbidities should be included in further research. That way the results will be more applicable to the general population.
A second weakness of this study was that the dysfunctional beliefs and attitudes about sleep were not reduced. This is expected, since there was no segment that targeted these attitudes specifically. In a next version of the application, a segment could be added to address the dysfunctional beliefs and attitudes about sleep. Addressing the beliefs and attitudes about sleep could also have a positive effect on the sleep itself.
The last weakness of this study is that only participants with an Android phone could participate. This excluded all the participants who had iPhones, which means it is not available for everyone. A strength of this application is that it is a convenient tool for everyone to use, so by only making it available for Android this statement is already contradicted.
Implications
The idea of using a smartphone application to reduce insomnia symptoms appears to have great potential; significant interactions were shown in the results in both the ISI scale and the sleep diary indices. The effect sizes were very comparable to effect sizes seen in other internet delivered CBT-I studies. Therefore, this smartphone application can be considered a plausible substitute for face-to-face or regular internet based CBT-I, but could still use some improvement on for instance the sleep diary indices and the sleep restriction. On the other
26 hand, at this moment this application could definitely be offered to patients who are on a waitlist to receive treatment. This way these patients can already improve their sleep pattern by working with the application, which eventually means they will need to spend less time and money on seeing a therapist face-to-face. It could also help these patients through other segments of the application, for instance the relaxation and sleep hygiene could improve the patients’ sleep complaints before seeing the therapist. This could also result in shorter face-to-face therapy and therefore less costly.
The application could also be used by people who do not suffer from insomnia
necessarily, but who would like to improve their sleep schedule on their own. Another way of implementing this application could be with patients who are not (physically/mentally) able to go see a therapist, but do want to improve their insomnia complaints. It could also be
combined with a version of internet delivered CBT-I which means patients will interact with a real therapist, just not face-to-face. Adding the email system with a real therapist as in internet delivered CBT-I could add to this application if participants have questions or need more support than just the virtual coach that is already implemented in the application.
The Sleepcare smartphone application is a non-invasive tool that can be used any time of the day in the privacy of the home or any other convenient place. Due to the innovative approach Sleepcare takes to tackle insomnia, further research should focus on developing this application.
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