1 The Effectiveness of Mobile Health as Addition to Mental Health Interventions for
youth: A Meta-Analysis
Jasmijn Vogel
Forensic Child and Youth Care Sciences, University of Amsterdam, Amsterdam, The Netherlands
Student number: 10537201
2 Abstract
In recent years, mobile phones are used as an addition to therapy more and more. Until now, no meta-analysis about the effectiveness of mobile supported therapies was conducted. In this study a meta-analysis on K = 10 studies and #ES = 62 effect sizes, reporting on N = 1003 adolescents and young adults, was conducted to examine the effectiveness of mobile phone supported therapy on treatment outcomes in
adolescents and youth. A small significant and positive effect was found (d = .15). However, results should be interpreted with great care, because there were indications of publication bias. Moderator Analyses showed that mobile phone supported therapy seems the most efficient for weight management problems and improving treatment adherence. Furthermore, largest effects were found when therapy was based on the social cognitive learning theory of Bandura (1977). Interesting findings were the larger effects on mobile phone supported therapy of treatment intensity in months, whereby a shorter treatment resulted in better results. The current meta-analysis suggests that the mobile phone could be an enrichment to youth therapy.
3 Introduction
Mobile technology allows people these days to be in and out of contact with other people all day. Currently, in the Netherlands 98,2% of the young people, between 12 and 24 years old, have a mobile phone to access Internet (CBS, 2017). As this is such an important medium for young people, and even partly replaces in-person contact (Rafla, Carson, & DeJong, 2014), the idea occurred in the past years that
implementing mobile technology in youth mental health care might be an effective intervention or contribute to the effectiveness of youth psychological treatment (Gipson, Torous, & Maneta, 2017; Powell, Chen, & Thammachart, 2017; Rafla, Carson, & DeJong, 2014; Reid, et al. 2012).
Weisz and colleagues (2017) conducted a multi-level meta-analysis about youth psychological treatment and aimed to map the research outcomes of completed research in the past five decades. Significant positive treatment effects were found for anxiety (medium effect) and depression (small effect), but not for youth with multiple problems (Weisz et al., 2017). To enhance therapeutic effects for those youth with complex needs, it is thought to extend treatment into youth’s everyday lives and personalise treatment through the implementation of add-ons, like an additional drug, device, and/or therapy (Ng & Weisz, 2016; Weisz et al., 2017).
In this study we examine the effects of such an add-on, namely mobile phones, through a multi-level meta-analysis. This is also known as mobile health or mHealth, which can be defined as: “any psychological or mental health intervention that is delivered or supported by the use of mobile technology, by means of a (mobile) or (smartphone). In addition, to be considered a mHealth intervention, the technology should enhance the treatment or assessment, increase dissemination of interventions,
4 or provide clinicians and clients with greater choice for accessing treatment materials or activities.” (Clough & Casey, 2015, p. 1).
Seko and colleagues (2014) conducted a review about mobile health interventions via mobile phones. Advantages and challenges were identified, upon which recommendations for further research were made. Ubiquity was noted as the first advantage of mHealth interventions. As stated before, most people own a mobile phone. Clients are already familiar with the device, which makes it easier to use and implement in therapy. Another advantage was the cost-efficacy, because less contact with the therapist is needed. Furthermore, user-advantages were flexibility, rapid and timely communication, and interactivity.
Enriching therapy with mobile phone apps brings the learning progress to a real-life setting in everyday life (Gibson, Cartwright, Kerrisk, Campbell, & Seymour, 2016; Heron & Smyth, 2010). Data gathering in real-life settings is possible and even real-life intervention might be a possibility (Hetrick et al., 2018; Shrier, Rhoads, Burke, Walls, & Blood, 2014). The endless communication possibilities also have a down sight, as there are no clear boundaries to the extent of therapists’ responsibilities (Hetrick et al., 2018; Luxton, McCann, Bush, Mishkind, & Reger, 2011). The
question arises what a therapist is obligated to do if, for example, they receive disturbing or life-threatening messages during the night (Gipson, Torous, & Maneta, 2017). Besides, youth can have different expectations than the therapist can offer, which can result in disappointments (Hetrick et al., 2018; Luxton, June, & Kinn, 2011). At last, an advantage was noted that mobile phones could offer private space, so they have a ‘personal nature’. The use of mobile devices in public spaces is universally accepted. The three main challenges were confidentiality, privacy and technical difficulties.
5 Two reoccurring concepts in mobile health literature are efficacy and self-management (Kim, Logan, Young, & Sabee, 2015). Self-efficacy theory states that behavioural change can be achieved by increasing self-efficacy (Bandura, 1977; Stephens, Yager, & Allen, 2017). Self-efficacy refers to someone’s own beliefs about their ability to execute chosen behaviour in a specific context (Bandura, 1977). Ways to increase self-efficacy are: mastery experience, social modelling, verbal persuasion, and awareness of states of psychological arousal (Bandura & Adams, 1977). Self-management can be linked to self-efficacy as the behavioural outcome measure (Bandura, 1977; Dishman et al., 2008).
In youth attending therapy, a big problem remains missing appointments or even a premature termination of treatment (Gopalan et al., 2010), in particular in youth with complex needs (Miller, Southam-Gerow, & Allin Jr., 2008). It is thought that text message reminders could be helpful to engage youth in treatment and in this way increase treatment adherence (Branson, Clemmey, & Mukherjee, 2013; Clough & Casey, 2011; Copalan et al., 2010). Moreover, mobile phones could be a tool to improve homework adherence, which is an important tool in, for example, cognitive behavioural therapy (Aguilera & Muench, 2012). Mobile phones could be helpful in keeping track of homework, providing coaching and suggestions, stimulation of intrinsic motivation by motivational techniques, and facilitation of self-monitoring of progress in a real-life setting (Wilansky et al., 2016). Especially motivational
techniques might be effective in the treatment of substance abuse in youth (Shrier, Rhoads, Burke, Walls, & Blood, 2014)
Youth articulated that they liked to monitor their moods, and they considered a smartphone useful to help them cope when needed (Grist, Porter, & Stallard, 2018; Reid et al., 2012). The perspectives and preferences of youth themselves regarding the
6 usage of smartphone applications during treatment are important. Smith, Kerr, Fenner, and Straker (2014) used a focus group with overweight teens and found that they did not always respond to the text-messages in real-life, as prior research about the implementation of mobile phones in therapy for youth would suggest. That said, considerable research outlines that youth would be interested in the implementation of mobile phones into therapy (Gibson, Cartwright, Kerrisk, Campbell, & Seymour, 2016; Ranney et al., 2016; Smith, Kerr, Fenner, & Straker, 2014, Whitehouse et al., 2013). Youth are already familiar with the use of mobile phones and use this medium to communicate difficult emotions (Thorsen, Patena, Guthrie, Spirito, & Ranney, 2016).
Some researchers found that youth valued flexible available contact with their therapist (Gibson, Cartwright, Kerrisk, Campbell, & Seymour, 2016), according to which youth indicated to prefer asynchronous contact, such as text-messaging, above synchronous contact, for example in telephone calls (Seko, Kidd, Wiljer, &
McKenzie, 2014). A disadvantage to asynchronous contact, however, is expectations of response time, which might not always be met by the therapist, resulting in the feeling of lack of response or response at unfavourable times (Hetrick et al., 2018; Smith, Kerr, Fenner, & Straker, 2014).
Youth found it important that they felt in control of their choices, and could influence their own therapy trajectory by means of mobile phone use (Chapman et al., 2017; Gibson & Cartwright, 2013; Gibson, Cartwright, Kerrisk, Campbell, &
Seymour, 2016; Kenny, Dooley, & Fitzgerald, 2016). Next to control, privacy was highlighted as being of great significance (Grist, Porter, & Stallard, 2018; Whitehouse et al., 2013). Given that most discussed subjects are of sensitive nature, youth wanted to be sure that this information was being handled with care.
7 A content preference articulated by youth was mood monitoring (Grist, Porter, & Stallard, 2018; Matthews, Doherty, Sharry, & Fitzpatrick, 2008), because they felt this helped their therapist to better understand their thoughts and feeling (Reid et al., 2012). Better understanding of the youths’ feelings by the therapist might result in less judgment, which was considered important by youth (Whitehouse et al., 2013). Lastly, simplicity and personalization were two reoccurring concepts (Grist, Porter, & Stallard, 2018; Smith, Kerr, Fenner, & Straker, 2014). In other words, youth wanted the mobile application to be easy to use and valued the possibility of personalizing the mobile application in design and content.
Finally, researchers might want to take into consideration to involve youth in the development of the mobile phone enhanced therapy. It offers the possibility to integrate youths’ language (Thorsen, Patena, Guthrie, Spirito, & Ranney, 2016), engage youth more and give them a voice (Chapman et al., 2017; Hetrick et al., 2018; Kim, Logan, Young, & Sabee, 2015).
A first important moderator of mobile phone effectiveness could be intensity of the application. There is a big difference between three messages a day or one per week. Too many messages might feel intrusive and too less might have no effect. In the study of Gonzales, Anglin, and Glik (2014), most young people (80%) indicated that one message a day was the amount of messages they wanted to receive.
Contradicting were the findings by Smith, Kerr, Fenner, and Straker (2014), where the focus group indicated that three messages a week was too frequent.
Secondly, duration of the intervention could be a moderator, as in recent years the idea occurred that interventions have to be more efficient in a shorter time frame. Maybe the implementation of mobile technology in therapy can result in the same
8 effects in a shorter period of time. Notably, Gibson et al. (2016) assumed that using smartphones in therapy might decrease the number of needed in-person sessions.
At last, the utilization of mobile phones in aftercare could be a moderator. It is possible that mobile health interventions help people continue practicing their learned skills, keep them motivated to maintain the achieved goals, and monitor the
adolescent’s thoughts and behaviours (Dennis, Scott, Funk, & Nicholson, 2015; Rafla et al., 2014). Furber et al. (2011) indicated that text-messages could be used as
reminders. Recovery monitoring is thought be an effective way to reduce relapse during aftercare. EMI within the hour after an EMA resulted in reduction of the substance use rate in the following week (Dennis et al., 2015).
Until now there was no meta-analysis available on the usage of mobile phones in youth therapy. The current meta-analysis focuses solely on the use of mobile phones during therapy and/or as an aftercare intervention. Lots of applications are available on the Internet for different kinds of mental health problems. These ‘self-help’ applications could be helpful, but the users do not have access to guidance from a professional.
In the area of more severe mental health problems, lack of guidance might produce negative effects. In previous research youth indicated the need for some level of support with mobile-based interventions (Mitchell & Gordon, 2007) and the
preference for personal contact (Grist, Porter, & Stallard, 2018; Hollis et al., 2017; Smith, Kerr, Fenner, & Straker, 2014). So mobile health intervention supported by a mobile phone seems a great solution. Youth always carry their mobile phones around, what not applies to a computer and/or tablet. Because of the growing interest and use of mobile health applications, this meta-analysis seems timely.
9 Method
Search strategy
All studies published in English or Dutch before 2018 addressing mental health interventions supported by mobile phones with youth were included. In our search, we first set out to identify all studies on mobile health with adolescents with mental health problems, including weight management problems and treatment attendance. Within these studies, we then selected all studies focusing on mental health interventions with youth supported by mobile phones.
Multiple electronic databases were searched to identify relevant studies: PsycINFO, Medline, and Web of Science. The main categories of the search were: “mobile”, “therapy”, “adolescents and young adults”, and “mental health”. Weight management problems, like obesity and overweight, were included in the “mental health” category. The complete search conducted in this meta-analysis can be found in Appendix A. In addition, we searched the reference lists of related meta-analyses for relevant studies. Figure 1 shows the flow-chart for our search, for which the model of Moher, Liberati, Tetzlaff, and Altman (2009) was used.
To analyse the articles an online program called Rayyan (Ouzzani, Hammady, Fedorowicz, & Elmagarmind, 2016) was used. In the first round of selection articles were labelled ‘exclude’ or ‘not sure’. In the second round of selection articles were analysed more deeply and then labelled ‘for sure’ or ‘exclude’. The articles excluded in the second round had two labels, because of which a re-check was made possible. Inclusion criteria
To be included in the current meta-analysis, studies had to meet de following criteria: 1) mobile device as an addition to mental health therapy, 2) participants had to be aged between 12 – 27 years, 3) articles had to be published, 4) publishing
10 language had to be English or Dutch, 4) randomized-controlled trial design or quasi-experimental design. The decision to include both randomized-controlled trial designs and quasi-experimental designs was made for reasons of internal (causal) conclusion validity. Studies focusing on mHealth without face-to-face contact between therapist and client were excluded. Furthermore, studies targeting other devices, like computer and/or tablet, besides mobile phone and/or smartphone were excluded. The search yielded K = 10 studies, #ES = 62, reporting on N = 1003 youth.
Coding the studies
Each study was coded using a detailed coding system for recording outcomes and moderators following the guideline of Lipsey and Wilson (2001). The outcome was the effectiveness of mobile phone supported therapy for youth, defined as any positive treatment outcome for youth.
Moderators
Several study, sample, treatment, publication, and outcome characteristics were coded as potential moderators. Outcome measure of mental health domain was divided into six categories, namely mental health, therapy attendance, self-image, weight management, deviant behavior, and youth satisfaction.
Study characteristics were dichotomously coded for whether the study used random (versus quasi-experimental) allocation to condition (i.e., study design), used a clinical sample (versus a community sample), controlled for pre-existing differences between groups, used intention-to-treat (versus completer analysis), and compared mobile phone supported mental health treatment to alternative treatment (versus no treatment/placebo control group). There were no continuous moderators coded as study characteristics.
11 A publication characteristic was coded for whether the origin of the study was in Northern America (versus Australia and versus Europe). Publication year and journal impact factor were publication characteristics included as continuous moderators.
Sample characteristics that were coded as potential moderators were age, proportion of males, and risk-group. Age and proportion of male participants were included as continuous moderators. Risk was coded as a dichotomous variable: high-risk versus low-high-risk.
Outcome-specific characteristics were continuous coded for the duration of follow-up and percentage of non-response. Time of measurement was coded as a dichotomous variable: intermediate- and post-measurement versus follow-up measurement.
Several treatment characteristics were coded as potential moderators. We coded whether the application intensity was high (versus low) in amount of contact moments through phone per week, what type of therapy was used (i.e., cognitive behavioral therapy or behavioral therapy) that was supported by mobile phones, what theoretical framework (i.e., cognitive behavioral approach or social cognitive therapy) was grounded in therapy, what type of mobile health (i.e., texting or automated text messages) was used next to the mental health treatment, whether mobile phones supported aftercare treatment (versus no aftercare), what type of assessment (i.e., questionnaire or objective data) was used during therapy, and who was the informant (i.e., youth or court) of the data. The only treatment characteristic coded as a
12 Calculation and analysis
For each study outcome we calculated an effect size of Cohen’s d, using formulas from Lipsey and Wilson (2001), and Wilson (2010), with a positive effect size indicating better results for the mobile health group. Based on Cohen’s (1988) guidelines, an effect size of d = .20 was considered small, an effect size of d = .50 was considered medium, and an effect size of d = .80 was considered large. To control for pre-treatment differences on the outcome measure, we calculated effect sizes for both pre-treatment and post-treatment, and then subtracted the pre-treatment effect from the post-treatment effect whenever possible. When outcome effects were reported to be non-significant without reporting statistics to be able to calculate an effect size, we conservatively estimated the effect size to be zero (Lipsey & Wilson, 2001).
Several steps were taken to prepare the data for data-analysis. Continuous moderators were centered around their mean, categorical moderators were dummy-coded, and the standard errors and sampling variance were calculated using formulas by Lipsey and Wilson (2001).
In traditional meta-analysis, effect sizes and effect size characteristics are pooled within studies, because only one effect size per study can be included in the analysis, which generally results in a loss of information and power. To retain maximum information and power, and to be able to conduct comprehensive
moderator analyses, we conducted a multi-level meta-analysis following the approach suggested by Van den Noortgate and Onghena (2003).
The meta-analysis was conducted in R (version 3.4.1) with the
metafor-package, using a 3-level random effects model to account for sampling variance (level 1), variance between effect sizes within studies (level 2), and variance between
13 when multiple effect sizes per study are included (Assink & Wibbelink, 2016;
Houben, Van den Noortgate, & Kuppens, 2015; Van den Bussche, Van den Noortgate, & Reynvoet, 2009; Viechtbauer, 2010).
To examine heterogeneity of the effect size distribution, we tested significant variance at level 2 and 3 using likelihood ratio tests comparing the full model to models excluding the variance parameters of level 2 and 3, respectively (Assink & Wibbelink, 2016). If there occurred significant variance at one of the two or both levels, the effect size distribution was considered to be heterogeneous, which means that the overall mean effect size cannot be treated as an estimate of a common effect size. If this was the case, heterogeneity among (within or between) study effect sizes was examined by including study, sample, outcome, and treatment characteristics. File drawer analysis
A common threat to the generalizability of meta-analytic outcomes is publication or file drawer bias (Rosenthal, 1995). Because studies with non-significant or unfavorable outcomes are published less often, studies included in a meta-analysis may not be an adequate representation of all existing studies, and may therefore provide a too optimistic image of actual treatment effects. We conducted a funnel plot analysis and trim and fill procedure (Duval & Tweedie, 2000a; Duval & Tweedie, 2000b) to examine the influence of (correcting for) funnel plot asymmetry. The trim and fill procedure estimates missing effect sizes based on the existing effect size distribution. If the trim and fill procedure leads to the estimation of missing effect sizes at the left side of the funnel plot, this might indicate publication bias. In that case, effect sizes can be imputed by means of a trim and fill procedure, which yields an overall effect size corrected for publication bias.
14 Results
The current meta-analysis consist of K = 10 studies, #ES = 62, reporting N = 1003 adolescents and young adults.
Overall effects
Table 1 summarizes the overall effects for offending, externalizing problems, social skills, and internalizing problems.
Mental health outcomes were reported on N = 1003 adolescents and young adults, including n = 516 adolescents and young adults who received mobile phone supported therapy. A small significant overall effect was found (d = .15). After mobile phone supported therapy, adolescents and young adults showed less mental health problems than adolescents and young adults in the control group.
There was no significant variance between effect sizes within studies (σ2 level2 =
.010, χ2 (1) = 1.52, p = .22), but there was significant variance between studies (σ2 level3
= .028, χ2 (1) = 15.08, p < .001), accounting for 37% of the total variance among
effect sizes.
However, the funnel (see Figure 2) plot showed that small studies with outcomes unfavorable to mobile phone supported mental health therapy were less often reported. After a trim and fill procedure to correct for this asymmetry (Duval & Tweedie, 2000a; 2000b), the overall effect size was no longer significant (k = 14, #ES = 77, d = -.04, t = -0.40, p = .69).
Moderator analysis
Table 2 shows the results of the moderator analysis for mobile health in therapeutic setting.
15 The mental health outcome domain had a significant moderating effect on the effects of mobile supported therapy, whereby therapy attendance and weight
management showed the strongest effects.
For study characteristics, no moderating effects were found. Type of control condition (alternative intervention versus no treatment), type of sample (clinical versus community), control for pre-test (yes versus no), type of design (quasi-experimental versus random control trial), and type of analysis (intention-to-treat versus completer) did not have moderating effects on mobile phone supported therapy outcomes. Moreover, no differences were found for publication characteristics:
mobile phone supported therapy effects were similar regardless of publication year, journal impact factor, and origin of the publication.
For sample characteristics, only one significant moderating effect was found for age. The effects of the intervention became larger with increasing age. There were no significant moderator effects for gender and risk group (low-risk versus high-risk). Moreover, a trend was found for time of measurement, whereby larger effects were found at the follow-up measurement than at intermediate- and post measurement. For the outcome characteristics, duration of follow-up and percentage non response were not significant. Mobile phone supported therapy effects were similar regardless of follow-up duration.
Of all treatment characteristics, two significant moderating effects were found and four trends. First, treatment effects were largest when therapy was supported by the type of mobile health “automated text-messages”, but there was also a trend for text-messages sent by therapists. Second, treatments effects were largest when the therapy was grounded in the theoretical framework “social-cognitive therapy”. For the “motivating” theoretical framework, treatment effects were smaller, but still
16 significant. Third, a trend was found for program duration in months, indicating that the effects of mobile phone supported therapy were larger with a shorter duration of the intervention. Fourth, a trend was found for aftercare, which indicated that the effects of mobile phone supported therapy were larger for aftercare programs. Fifth, a trend was found on assessment type, indicating that the effects of mobile phone supported therapy were larger when different forms of assessment were used. Lastly, a trend was found for informant. The effects of mobile phone supported therapy were larger when the therapist was the informant. Application intensity per week (i.e., amount of mobile phone contacts per week) and type of therapy showed no moderating effects.
Discussion
A meta-analysis was conducted to examine the effectiveness of mental health treatment supported by mobile phones on mental health outcomes in youth. A small overall effect was found, which is promising for the growing research and
development of mobile health in addition to therapy. A trim and fill procedure, however, showed that it was likely that the overall effect size was affected by
publication bias. This might indicate that studies with no treatment effects or negative treatment effects were less likely to be reported, and that the available research base may overestimate the actual effects of mental health supported by mobile phones on mental health outcomes in youth.
Moderator analyses for mental health domain revealed larger effects for therapy attendance and weight management. These findings can be clarified with the proposed working mechanism of mobile health. Two of these working mechanisms were self-efficacy (Bandura, 1977) and self-management. To maintain a healthy weight youth have to manage their thoughts, diet, and exercise pattern, but most of all
17 believe they can execute those behaviours to which they need self-efficacy skills and self-management skills. In line with these findings, we found an effect on social cognitive theory, which is the umbrella concept of self-efficacy (Bandura, 1991). According to social cognitive theory, self-control results in behavioural change (Bandura, 1991; Luszczynska & Schwarzer, 2005). Also, as expected, a significant effect was found for ‘motivation’.
Surprisingly, no effect was found for CBT, which is a commonly used therapy for youth (Bennet et al., 2013; Compton et al., 2004; Silverman, Pina, & Viswesvaran, 2008). The use of motivation techniques to improve intrinsic motivation is an
important part of CBT (Wilansky et al., 2016), but this might indicate that
motivational text messaging only increases extrinsic motivation or that the effects are low because motivation already plays a big role in CBT. Another reason that no effects were found for CBT could be that, in the current meta-analysis, no studies focussing on mood-monitoring were included. Mood-monitoring is thought to be an important component of CBT (Matthews, Doherty, Sharry, & Fitzgerald, 2008).
The treatment characteristic “type of mobile health” proved to be a significant moderator. The effect was largest for automated text messaging, which are
computerized responses to youths’ texts. Furthermore, an effect was found for text messaging, in other words reciprocal contact between therapist and adolescent or young adult through texting. Automated text messaging could resolve the problem of unreached expectations by youth of their therapists (Hetrick et al., 2018; Luxton, June, & Kinn, 2011), because the responses are automated and not depended on work hours of the therapist. Still this is no solution for the problem of therapists’
responsibilities and where boundaries have to be set (Hetrick et al., 2018; Luxton, McCann, Bush, Mishkind, & Reger, 2011).
18 For different treatment characteristics a trend was found, namely, aftercare, informant, duration of the intervention, and assessment type. The trend for aftercare could indicate that the implementation of a mobile phone into therapy is more useful for aftercare treatment. Mostly during therapy the frequency of in-person therapeutic contact is higher than in aftercare. The addition of the mobile phone might fill this gap of less in-person contact, and in this way make the aftercare program more efficient. The trend found for informant, with the largest treatment effects for therapist report, could indicate the presence of confirmation bias. In other words, the therapist expects good results and believes the treatment to result in positive effects. Therapists might therefore assess the outcomes as more favourable than other informants (i.e., youth-report). Furthermore, there was a trend indicating that shorter interventions showed larger effects, which concurs with the ‘less is more principle’
(Bakermans-Kranenburg, Van IJzendoorn, & Juffer, 2003), and supports new policies that interventions need to be shorter, and more efficient. Lastly, even though youth indicated that they preferred the contact over mobile phone not to be too frequent (Gonzales, Anglin, & Glik, 2014, & Smith, Kerr, Fenner, & Straker, 2014), no significant moderating effect was found for intensity of the application
The only significant sample characteristic that significantly moderated the overall mean effect size was age, which suggests that for older youth the support of mobile phones in therapy may be more effective. No effects were found for gender or risk-group.
Finally, effects were larger at follow-up measurements than at post-treatment. A possible explanation for these findings could be that adolescents are better faced against challenges after treatment if they succeed in increasing their self-management
19 and self-efficacy skills. Additionally, no significant moderator effects were found for the duration of follow-up.
This study needs to be interpreted in light of some limitations. First, so far just little effectiveness studies were conducted on mobile phone supported therapy. The current meta-analysis synthesizes the results of the effectiveness studies done so far. Gaps in literature were highlighted and directions for further research can be given. In future research the documentation of the results is highly important, whereby a clear description of the type of mobile phone use and the theoretical framework is
necessary. Lastly, as with every meta-analytic study, we depended on the quality and elaborateness of reporting in the included studies. There was lack of reporting on ethnicity of the participants in several studies. Besides, most times there was no information on the theoretical framework.
To our knowledge, the present study is the first to examine mobile phone supported therapy effects for adolescents and youth. Even so the current meta-analysis gives a clear overview of the possible effects of mobile phone supported therapy and suggests that the mobile phone could be an enrichment to youth therapy. More effectiveness studies on different mobile phone strategies and different mental health outcomes have to be conducted.
20 References
Aguilera, A., & Muench, F. (2012). There is an app for that: Information technology applications for cognitive behavioural practitioners. The Behavioural
Therapist, 35, 65-73.
Assink, M., & Wibbelink, C. J. M. (2016). Fitting three-level meta0analytic models in R: A step-by-step tutorial. The Quantitative Methods for Psychology, 12, 154-174. doi:10.20982/tqmp.12.3.p154
Bakermans-Kranenburg, M. J., van IJzendoorn, M. H., & Juffer, F. (2003). Less is more: Meta-analyses of sensitivity and attachment interventions in early childhood. Psychological Bulletin, 129, 195-215.
doi:10.1037/0033-2909.129.2.195
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioural change.
Advances in Behaviour Research and Therapy, 84, 191-215.
doi:10.1016/0146-6402(78)90002-4
Bandura, A, & Adams, N. E. (1977). Analysis of self-efficacy theory of behavioural change. Cognitive Therapy and Research. 1, 287-310.
doi:10.1007/BF01663995
Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational
behavior and human decision progress, 50, 248-287.
doi:10.1016/0749-5978(91)90022-L
Bennett, K., Manassis, K., Walter, S. D., Cheung, A., Wilansky-Traynor, P., Diaz- Granados, N., Duda, S., Rice, M., Baer, S., Barrett, P., Bodden, D., Cobham, V. E., Dadds, M. R., Flannery-Schroeder, E., Ginsburg, G., Heyne, D., Hudson, J. L., Kendall, P. C., Liber, J., Warner, C. M., Mendlowitz, S., Nauta, M. H., Rapee, R. M., Silverman, W., Siqueland, L., Spence, S. H.,
21 Utens, E., & Wood, J. J. (2013) Cognitive behavioural therapy age effects in and adolescents anxiety: An individual patient data meta-analysis. Depression
and Anxiety, 30, 829-841. doi:10.1002/da.22099
Branson, C. E., Clemmey, P., Mukherjee, P. (2013). Text message reminders to improve outpatient therapy attendance among adolescents. Psychological
Services, 10, 298-303. doi:10.1037a0026693
Centraal Bureau voor de Statistiek (CBS) (2018, 8 maart). Internet; toegang, gebruik
en faciliteiten [Dataset]. Retrieved of
http://statline.cbs.nl/Statweb/publication/?DM=SLNL&PA=83429NED&D1= 0,2-5&D2=0,3-6&D3=0&D4=a&HDR=T&STB=G1,G2,G3&VW=T
Chapman, L. Edbrooke-Childs, J., Martin, K., Webber, H., Craven, M. P., Hollis, C., Deighton, J., Law, R., Fonagy, P., Wolpert, M. (2017). A mobile phone app to support young people in making shared decisions in therapy (Power Up): Study protocol. JMIR Research Protocols, 6, e206. doi:10.2196/resprot.7694 Clough, B. A., & Casey L. M. (2015). The smart therapist: A look to the future of
smartphones and mHealth technologies in psychotherapy. Professional
Psychology: Research and Practice, 46, 147-153. doi:10.1037/pro0000011
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum. doi:10.1016/C2013-0-10517-X
Compton, S. N., March, J. S., Brent, D., Albano, A. M., Weersing, R., & Curry, J. (2004). Cognitive-behavioural psychotherapy for anxiety and depressive disorders in children and adolescents: An evidence-based medicine review.
Journal of the American Academy of Child and Adolescent Psychiatry, 43,
930-959. doi:10.1097/01.chi.0000127589.57468.bf
22 examine the feasibility and potential effectiveness of using smartphones to provide recovery support for adolescents. Substance Abuse, 36, 486-492. doi:10.1080/08897077.2014.970323
Dishman, R. K., Motl, R. W., Sallis, J. F., Dunn, A. L., Birnbaum, A. S., Welk, G. J., Bedimo-Rung, A. L., Voorhees, C. C., & Jobe, J. B. (2008). Self-management skills mediate self-efficacy and physical activity. American Journal of
Preventive Medicine, 29, 10-18. doi:10.1016/j.amepre.2005.03.012
Duval, S., & Tweedie, R. (2000a). A nonparametric "trim and fill" method of accounting for publication bias in meta-analysis. Journal of the American
Statistical Association, 95, 89-98. doi:10.2307/2669529
Duval, S., & Tweedie, R. (2000b). Trim and fill: A simple funnel-plot based method of testing and adjusting for publication bias in meta-analysis. Biometrics, 56, 455-463. doi:10.1111/j.0006-341X.2000.00455.x
Furber, G. V., Crago, A. E., Meehan, K., Sheppard, T. D., Hooper, K., Abbot, D. T., Allison, S., & Skene, C. (2011). How adolescents use SMS (Short Message Service) to micro-coordinate contact with youth mental health outreach services. Journal of Adolescents Health, 48, 113-115.
doi:10.1016/j.jadohealth.2010.05.022
Gibson, K., & Cartwright, C. (2013). Agency in young clients’ narratives of counselling: “It’s whatever you want to make of it”. Journal of Counselling
Psychology, 60, 340-352. doi:10.1037/a0033110
Gibson, K., Cartwright, C., Kerrisk, K., Campbell, J., & Seymour, F. (2016). What young people want: A qualitative study of adolescents’ priorities for
engagement across psychological services. Journal of Child and Family
23 Gipson, S. Y. T., Torous, J., & Matena, E. (2017). Mobile technologies in child and
adolescent psychiatry: Pushing for further awareness and research. Child and
Adolescent Psychiatry, 25, 191-193. doi:10.1097/HRP.0000000000000144
Gonzales, R., Anglin, M. D., & Glik, D. C. (2014). Exploring the feasibility of text messaging to support substance abuse recovery among youth in treatment.
Health Education Research, 29, 13-22. doi:10.1093/her/cyt094
Gopalan, G., Goldstein, L., Klingenstein, K., Sicher, C. Blake, C., & McKay, M. M. (2010). Engaging families into child mental health treatment: Updates and special considerations. Journal of Canadian Academy of Child and Adolescent
Psychiatry, 19, 182-196.
Grist, R., Porter, J., & Stallard, P. (2018) Acceptability, use, and safety of a mobile phone app (BlueIce) for young people who self-harm: Qualitative study of service users’ experience. JMIR Mental Health, 5, e16.
doi:10.2196/mental.8779
Heron, K. E., & Smyth, J. M. (2010). Ecological momentary intervention:
Incorporating mobile technology into psychosocial and health behavioural treatments. British Journal of Health Psychology, 15, 1-39.
doi:10.1348/135910709X466063
Hetrick, S. E., Robinson, J., Burge, E., Blandon, R., Mobilio, B., Rihebce, S. M., Simmons, M. B., Alvarez-Jimenez, M., Goodrich, S., & Davey, C. G. (2018). Youth codesign of a mobile phone app to facilitate self-monitoring and management of mood symptoms in young people with major depression, suicidal ideation, and self-harm. JMIR Mental Health, 5, e9.
doi:10.2196/mental.9041
24 C., & Davies, B. (2017). Annual research review: Digital health interventions for children and young people with mental health problems – a systematic and meta-review. The Journal of child Psychology and Psychiatry, 58, 474-503. doi:10.1111/jcpp.12663
Houben, M., Van den Noortgate, W., & Kuppens, P. (2015). The relation between short-term emotion dynamics and psychological well-being: A meta-analysis.
Psychological Bulletin, 141, 901-930. doi:10.1037/a0038822
Kenny, R., Dooley, B., & Fitzgerald, A. (2016). Developing mental health mobile apps: exploring adolescents perspectives. Health Informatics Journal, 22, 265-275. doi:10.1177/1460458214555041
Kim, K. K., Logan, H. C., Young, E., & Sabee, C. M. (2015). Youth-centered design and usage results of the iN Touch mobile self-management program for overweight/obesity. Personal and ubiquitous computing, 19, 59-68. doi:10.1007/s00779-014-0808-x
Kulendran, M., King, D., Schmidtke, K. A., Curtis, C., Gately, P., Darzi, A., & Vlaev, (2016). The use of commitment techniques to support weight loss maintenance in obese adolescents. Psychology & Health, 31, 1332-1341.
doi:10.1080/08870446.2016.1204452
Lipsey, M. W., & Wilson, D. B. (2001). In Bickman L., Rog D. J. (Eds.), Practical
meta-analysis. Thousand Oaks, CA: SAGE Publications, Incorporated.
Luszczynska, A., & Schwarzer, R. (2005). Social cognitive theory. In: Connor, M., & Norman, P. (Eds.), Predicting Health Behaviour (pp. 127-169). New York, NY: Open University Press
25 prevention: current applications prevention: Current applications and further directions. Telemedicine and e-Health, 17, 50-54. doi:10.1089/tmj.2010.0091 Luxton, D. D., McCann, R. A., Bush, N. E., Mishkind, M. C., & Reger, G. M. (2011).
mHealth for mental health: Integrating smartphone technology in behavioral healthcare. Professional Psychology: Research and Practice, 42, 505-12. doi:10.1037/a0024485
Matthews, M., Doherty, G., Sharry, J., & Fitzpatrick, C. (2008). Mobile phone mood charting for adolescents. Britisch Journal of Guidance & Counseling, 36, 113- 129. doi:10.1080/03069880801926400
Miller, L. M., Southam-Gerow, M. A., & Allin Jr., R. B. (2008). Who says in treatment? Child and family predictors of youth client retention in a public mental health agency. Child Youth Care Forum, 37, 153-170.
doi:10.1007/s10566-008-9058-2
Mitchell, N., & Gordon, P. K. (2007). Attitudes towards computerized CBT for depression amongst a student population. Behavioural and Cognitive
Psychotherapy, 35, 421-430. doi:10.1017/S1352465807003700
Moher, D., Liberati, A., Tetzlaff, J., Attman, D. G., & The PRISMA Group (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLOS Medicine, 6, e1000097.
doi:10-1371/journal.pmed1000097
Ng, M. Y., & Weisz, J. R. (2016). Annual research review: Building a science of personalized intervention for youth mental health. The Journal of Child
Psychology and Psychiatry, 57, 216-236. doi:10.1111/jcpp.12470
26 web and mobile app for systematic reviews. Systematic Reviews, 5 .
doi:10.1186/s13643-016-0384-4
Powell, A. C., Chen, M., & Thammachart, C. (2017) The economical benefits of mobile apps for mental health and telepsychiatry services when used by adolescents. Child and adolescent psychiatric clinics of North America, 26, 125-133. doi:10.1016/j.chc.2016.07.013
Rafla, M., Carson, N. J., & DeJong, S. M. (2014). Adolescents and the internet: What mental health clinicians need to know. Current Psychiatry Reports, 16: 472. doi:10.1007/s11920-014-0472-x
Ranney, M. L., Choo, E. K., Cunnigham, R. M., Spirito, A., Thorsen, M., Mello, M. J., & Morrow, K (2016) Acceptability, language, and structure of text
message-based interventions for high-risk adolescent females: A quantitative study. Journal of Adolescent Health, 55, 33-40. doi:10.1016/j.jadohealth.2013.12.017
Reid, S. C., Kauer, S. D., Khor, A. S., Hearps, S. J. C., Sanci, L. A., Kennedy, A. D., & Patton, G. C. (2012). Using a mobile phone application in youth mental health: An evaluation study. Australian Family Physician, 41, 711-714.
Rosenthal, R. (1995). Writing meta-analytic reviews. Psychological Bulletin, 118, 183-192. doi:10.1037/0033-2909.118.2.183
Seko, Y., Kidd, S., Wiljer, D., McKenzie, K. (2014). Youth mental health interventions via mobile phones: A scoping review. Cyberpsychology,
Behavior, and Social Networking, 17, 591-602. doi:10.1089/cyber.2014.0078
Shingleton, R. M., Pratt, E. M., Gorman, B., Barlow, D. H., Palfai, T. P., Thompson- Brenner, H. (2016) Motivational text message intervention for eating
27 disorders: A single-case alternating treatment design using ecological
momentary assessment. Behavior Therapy, 47, 325-338. doi:10.1016/j.beth.2016.01.005
Shrier, L. A., Rhoads, A., Burke, P., Walls, C., & Blood, E. A. (2014). Real-time, contextual intervention using mobile technology to reduce marijuana use among youth: A pilot study. Addictive Behaviors, 39, 173-180.
doi:10.1016/j.addbeh.2013.09.028
Silverman, W. K., Pina, A. A., Viswesvaran, C. (2008). Evidence-based psychosocial treatments for phobic and anxiety disorders in children and adolescents.
Journal of Clinical Child & Adolescent Psychology, 37, 105-130.
doi:10.1080/15374410701817907
Smith, K. L., Kerr, D. A., Fenner, A. A., & Straker L. M. (2014) Adolescents just do not know what they want: A qualitative study to describe obese adolescents’ experiences of text messaging to support behavior change maintenance post intervention. Journal of Medical Internet Research, 16, e103.
doi:10.2196/jmir.3113
Stephens, J. D., Yager, A. M., & Allen, J. (2017). Smartphone technology and text messaging for weight loss in young adults: A randomized controlled trial.
Journal of Cardiovascular Nursing, 32, 39-46.
doi:10.1097/JCN.0000000000000307
Thorsen, M. M., Patena, J. V., Guthrie, K. M., Spirito, A., & Ranney, L. R. (2016). Using high-risk adolescents’ voices to develop a comprehensible cognitive behavioral therapy-based text-message program. Behavioral Medicine, 44, 89-99. doi:10.1080/08964289.2016.1223597
28 masked priming: A meta-analysis. Psychological Bulletin, 135, 452-477. doi:10.1037/a0015329
Van den Noortgate, W., & Onghena, P. (2003). Multilevel meta-analysis: A comparison with traditional meta-analytical procedures. Educational and
Psychological Measurement, 63, 765-790. doi:10.1177/0013164403251027
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package.
Journal of Statistical Software, 36, 1-48. doi:10.18637/jss.v036.i03
Weisz, J. R., Kuppens, S., Ng, M. Y., Eckshtain, D., Ugueto, A. M., Vaughn- Coaxum, R., Jensen-Doss, A., Hawley, K. M., Krumholz Marchette, L. S., Chu, B. C., Weersing, V. R., & Fordwood, S. R. (2017). What five decades of research tells us about the effects of youth psychological therapy: A multilevel meta-analysis and implications for science and practice. American
Psychologist, 72, 79-117. doi:10.1037/a0040360
Whitehouse, S. R., Lam, P., Balka, E., McLellan, S., Deevska, M., Penn, D., Issenman, R., & Paone, M. (2013). Co-creation with TickiT: Designing and evaluating a clinical eHealth platform for youth. JMIR Research Protocols, 2, e42. doi:10.2196/resprot.2865
Wilansky, P., Eklund, J. M., Milner, T., Kreindler, D., Cheung, A., Kovacs, T., Shooshtari, S., Astell, A., Ohinmaa, A., Henderson, J., Strauss, J., & Mills, R. S. L. (2016). Cognitive behavior therapy for anxious and depressed youth: Improving homework adherence through mobile technology. JMIR Research
Protocols, 5, e209. doi:10.2196/resprot.5841
Wilson, D. B. (2010). Practical meta-analysis effect size calculator. Retrieved from http://gemini.gmu.edu/cebcp/EffectSizeCalculator/
29 Table 1
Overall Effect of Caregiver Involvement in TF-CBT on Youth Outcomes Outcome k #ES M d 95% CI p σ2level 2 σ2level 3 % Var.
Level 1 Level 2 % Var. Level 3 % Var. Youth-
outcomes 10 62 .15 0.01; 0.29 < .05 .010 .028*** 49.90 12.95 37.16
Note. Youth outcomes = social-emotional, physical health, psychosocial
problems; k = number of studies; #ES = number of effect sizes; mean r = mean effect size (r); CI = confidence interval; σ2level 2 = variance between effect sizes extracted from the same study; σ2level 3 = variance between studies; % Var = percentage of variance distributed.
THE EFFECTIVENESS OF MOBILE HEALTH INTERVENTIONS FOR YOUTH
30 30 Table 2
Moderator effects for support of mobile phones to treatment outcomes in youth
Moderator variable k #ES B0/ d t0 B1 t1 F(df1, df2)
Outcome measures
Mental health outcome 10 62 F(5, 56) = 3.412**
Mental health (RC) 4 6 0.00 -0.027 Therapy attendance 3 13 0.37 3.085** 0.37 2.411* Self-image 3 8 0.05 0.496 0.05 0.492 Weight management 3 11 0.29 2.707** 0.29 2.552* Deviant behaviour 4 18 0.16 1.528 0.16 1.131 Youth satisfaction 1 6 -0.15 -1.154 -0.14 -1.383 Sample characteristics
Percentage male (gender) 10 62 0.15 2.054* -0.00 -0.083 F(1, 60) = 0.007
Age 10 62 0.12 2.206* 0.05 2.174* F(1, 60) =4.726*
Risk group 10 62 F(1, 60) = 0.106
Low to moderate risk 5 30 0.13 1.211
High risk 5 32 0.17 1.718+ 0.05 0.325
Treatment characteristics
Type of mobile health 10 62 F(2, 59) = 7.179**
Texting (RC) 4 9 0.19 1.945+
Automated text messages 1 10 0.43 4.244*** 0.23 1.651 Other mobile health 5 43 0.02 0.362 -0.18 -1.595 Application intensity per
week 9 61 0.15 1.841+ 0.00 -0.104 F(1, 59) = 0.011
Program duration in months 9 61 F(1, 59) = 3.840+
Low 6 38 0.24 2.893** High 3 23 -0.03 -0.239 -0.26 -1.960+ Type of therapy 62 10 F(3, 58) = 0.111 Cognitive behavioural therapy 3 15 0.18 1.293 Behavioural therapy 3 28 0.10 0.796 -0.08 -0.400 HIIT 1 1 0.31 0.693 0.13 0.273 Mixed therapy 3 18 0.18 1.188 0.00 0.002 Theoretical Framework 9 46 F(4, 42) = 13.569*** Cognitive behavioural approach 1 18 -0.05 -1.325 Character development model 2 5 -0.02 -0.267 0.03 0.325
Social cognitive theory 3 15 0.40 6.427*** 0.45 6.157***
Motivating 3 8 0.17 2.067* 0.22 2.431*
Aftercare 10 62 F(1, 60) = 3.414+
No Aftercare 7 47 0.09 1.216
Aftercare 3 15 0.25 2.842** 0.16 1.848+
THE EFFECTIVENESS OF MOBILE HEALTH INTERVENTIONS FOR YOUTH
31 31
Moderator variable k #ES B0/ d t0 B1 t1 F(df1, df2)
Assessment type 10 62 F(2, 59) = 2.782+ Questionnaire (RC) 5 44 0.13 1.571 Objective Data 6 12 0.14 1.487 0.01 0.090 Composite 2 5 0.36 2.945** 0.23 2.359* Informant 10 62 F(3, 58) = 2.504+ Youth Report (RC) 5 44 0.13 1.531 Court 2 5 0.00 0.014 -0.12 -0.682 Therapist Report 5 8 0.35 3.422** 0.22 2.546* Mixed report 1 5 0.13 0.838 0.00 0.006 Outcome characteristic Time of measurement 10 62 F(1,60) = 3.117+ Intermediate_Post (RC) 7 39 0.09 1.123 Follow-up 4 23 0.24 2.779** 0.15 1.766+ Time of measurement follow-up 4 23 0.19 1.458 -0.01 -0.394 F(1, 21) = 0.155 Percentage non response 10 62 0.12 1.728+ -0.01 -1.048 F(1, 60) = 1.098 Study characteristics Control condition 10 62 F(1, 60) = 0.699 No treatment (RC) 1 4 0.32 1.476 Alternative treatment 9 58 0.13 1.762+ -0.19 -0.836 Type of sample 10 62 F(1, 60) = 0.182 Community (RC) 4 29 0.11 0.998 Clinical 6 33 0.18 1.881+ 0.06 0.426 Multivariate 10 62 F(1,60) = 0.348 Univariate (RC) 10 56 0.16 2.158* Multivariate 2 6 0.08 0.540 -0.08 -0.590
Control for pre-test 10 62 F(1,60) = 0.004
No control pre-test (RC) 5 6 0.16 1.188 -0.01 -0.061 Control pre-test 6 56 0.15 1.737+ Design 10 62 F(1,60) = 0.517 Quasi-experimental (RC) 2 4 0.30 1.368 RCT 8 58 0.13 1.770+ -0.17 -0.719 Intention to treat 10 62 F(1,60) = 0.104 Completer (RC) 4 10 0.18 1.455 Intention to treat 6 52 0.13 1.511 -0.05 -0.322 Publication Characteristics Publication year 10 62 0.12 1.864+ 0.03 1.209 F(1, 60) = 1.463 Impact factor 10 62 0.16 2.111* 0.03 1.351 F(1, 60) = 1.825 Origin 10 62 F(2, 59) = 1.755 Northern America (RC) 6 26 0.22 2.772** Australia 2 2 -0.02 -0.153 -0.24 -1.829+ Europe 2 34 0.22 1.076 -0.01 -0.024
Note. k = number of independent studies; #ES = number of effect sizes; B0/ mean r =
intercept/ mean effect size (d); t0 =difference in mean d with zero; B1 =estimated
regression coefficient; t1 = difference in mean d with reference category; F(df1, df2) =
omnibus test.
THE EFFECTIVENESS OF MOBILE HEALTH INTERVENTIONS FOR YOUTH
32 32
Figure 1
Flow-chart for the meta-analytic search
Records identified through database searching (n = 5816) Sc reen in g Inc lude d Eli gib ilit y Ide nti fic at io n
Additional records identified through other sources
(n = 1)
Records after duplicates removed (n = 1869)
Records screened
(n = 1869) Records excluded (n = 1722)
Full-text articles assessed for eligibility
(n = 147)
Full-text articles excluded, with reasons (n = 137) Studies included in qualitative synthesis (n = 10) Studies included in quantitative synthesis (meta-analysis) (n = 10)
THE EFFECTIVENESS OF MOBILE HEALTH INTERVENTIONS FOR YOUTH
33 33 Figure 2
THE EFFECTIVENESS OF MOBILE HEALTH INTERVENTIONS FOR YOUTH
34 34 APPENDIX A
The systematic literature search in different databases
PsycINFO
Ovid
#1 Mobile (Health)
cellular phones/ OR text messaging/ OR (mhealth OR mmental OR mobile assessment* OR mobile intervention* OR cell* phone* OR cell* telephone* OR phone* cell* OR telephone* cell* OR mobile phone* OR mobile telephone* OR phone* mobile* OR telephone* mobile* OR smart phone* OR smartphone* OR blackberr* OR iphone* OR android* OR mobile app* OR phone app* OR textmessag* OR text messag* OR SMS* OR short messag* OR shortmessag* service* OR message* text* OR texting*).ti,ab,id.
#2 Therapy
intervention/ OR treatment/ OR computer assisted therapy/ OR online therapy/ OR exp cognitive behavior therapy/ OR cognitive therapy/ OR drug therapy/ OR psychotherapy/ OR exp behavior therapy/ OR dialectical behavior therapy/ OR schema therapy/ OR adolescent psychotherapy/ OR (therap* OR psychotherap* OR CBT* OR treatment* OR intervention* OR program* OR recovery support* OR being treated OR outpatient OR attending specialist* OR clinic based OR
counseling).ti,ab,id.
#3 Adolescents and young adults
(adolescence 13 17 yrs).ag. OR adolescent psychiatry/ OR adolescent
psychopathology/ OR adolescent psychology/ OR students/ OR (prepubescen* OR prepuberty* OR puberty OR pubescen* OR teen* OR young* OR youth* OR minors* OR under ag* OR underag* OR juvenile* OR girl* OR boy* OR preadolesc* OR adolesc* OR highschool* OR high school* OR college OR university OR
student*).ti,ab,id. #4 mental health
psychopathology/ OR (mental disorder* OR psychiatric* OR mental illness* OR mental health OR DSM*).ti,ab,id. OR exp attention deficit disorder/ OR exp behavior disorders/ OR conduct disorder/ OR emotional trauma/ OR exp feeding disorders/ OR exp mental disorders/ OR neurodevelopmental disorders/ OR oppositional defiant disorder/ OR exp sexual function disturbances/ OR exp sleep disorders/ OR exp somatoform disorders/ OR intellectual development disorder/ OR exp learning disorders/ OR exp language disorders/ OR communication disorders/ OR stuttering/ OR rett syndrome/ OR tics/ OR tourette syndrome/ OR exp attachment disorders/ OR trichotillomania/ OR child psychiatry/ OR orthopsychiatry/ OR child
psychopathology/ OR adolescent psychiatry/ OR adolescent psychopathology/ OR geriatric psychiatry/ OR delirium/ OR exp memory disorders/ OR catatonia/ OR personality change/ OR delusions/ OR transgender/ OR kleptomania/ OR pyromania/ OR exp speech disorders/ OR restless leg syndrome/ OR (mental* retard* OR
reading disorder* OR mathematic* disorder* OR learning disorder* OR disorder of written expression OR dysle* OR developmental coordination disorder* OR dyspraxi* OR language disorder* OR phonological disorder* OR stuttering OR communication disorder* OR motor skills disorder* OR pervasive developmental disorder* OR autis* OR ASD OR asperger* OR rett* disorder* OR rett syndrome* OR childhood
disintegrative disorder* OR ADHD OR hyperactiv* disorder* OR attention deficit disorder* OR conduct disorder* OR oppositional defiant disorder* OR ODD OR
THE EFFECTIVENESS OF MOBILE HEALTH INTERVENTIONS FOR YOUTH
35 35
disruptive behavior disorder* OR feeding disorder* OR eating disorder* OR anorexi* OR bulimi* OR (food ADJ2 disorder*) OR binge OR purging OR pica OR rumination disorder* OR tic disorder* OR tourette* OR elimination disorder* OR encopresis OR ((constipation OR overflow) ADJ3 disorder) OR enuresis OR selective mutism OR attachment disorder* OR stereotypic movement disorder* OR (disorder ADJ1 (infancy OR child* OR adolesc*)) OR deliri* OR dement* OR amnestic OR amnesia OR alzheimer* OR cognitive disorder* OR mental disorder* OR catatoni* OR personality change OR ((substance OR alcohol OR caffein* OR cannabis OR cocaine OR nicotin* OR tobacco OR marijuana) ADJ2 (disorder* OR abuse OR misuse OR addict* OR intoxication OR withdrawal OR dependence OR abstinen* OR abstain* OR "use")) OR psychotic* OR schizo* OR paranoid* OR delusion* OR mood disorder* OR depress* OR dysthymic* OR bipolar* OR hypomanic* OR manic* OR mania OR cyclothymic* OR panic disorder* OR anxiety disorder* OR agoraphobi* OR phobi* OR obsessive compulsive OR OCD OR stress disorder* OR PTSD OR ((trauma OR stressor) ADJ2 disorder*) OR somatoform OR somatization OR somatic symptom disorder* OR psychosomatic disorder* OR conversion disorder* OR pain disorder* OR hypochondri* OR body dysmorph* OR factitious disorder* OR
munchausen OR dissociative OR depersonali#ation disorder* OR identity disorder* OR (sexual ADJ3 (dysfunction* OR disorder*)) OR erectile disorder* OR orgasmic disorder* OR premature ejaculation OR dyspareunia OR vaginismus OR paraphili* OR exhibitionis* OR fetish* OR frotteuris* OR pedophil* OR masochis* OR sadis* OR transvest* OR transgender* OR voyeuris* OR gender dysphori* OR penetration disorder* OR (sleep* ADJ2 disorder*) OR dyssomnia* OR somni* OR hypersomn* OR insomni* OR narcolep* OR parasomn* OR nightmare OR sleep terror OR sleep walking OR sleepwalking OR impulse control disorder* OR explosive disorder* OR kleptomani* OR pyroman* OR pathologic* gambling OR trichotillomani* OR
adjustment disorder* OR personality disorder* OR borderline OR narcis* OR neurodevelopmental OR intellectual disability* OR speech sound disorder* OR fluency disorder OR hoarding disorder* OR excoriation disorder* OR skin-picking disorder* OR hair-pulling disorder* OR social engagement disorder* OR derealization disorder* OR restless leg* syndrom* OR neurocognitive disorder*).ti,ab,id. OR
acting out/ OR aggressive behavior/ OR anger control/ OR antisocial behavior/ OR anxiety/ OR behavioral inhibition/ OR behavior problems/ OR coping behavior/ OR "depression (emotion)"/ OR emotional control/ OR emotional regulation/ OR externalization/ OR separation anxiety/ OR social anxiety/ OR rebelliousness/ OR tantrums/ OR timidity/ OR juvenile delinquency/ OR
(acting out OR affect regulation OR aggress* OR anger OR angry OR antisocial OR anxiety OR anxious* OR behavi* difficult* OR ((coping OR defiant OR disruptive OR dysfunctional* OR explosiv* OR maladaptiv* OR problem*) ADJ3 behavio*) OR depress* OR emotion* regulation OR emotional restraint OR externali* OR
hyperactiv* OR inhibit* OR internali* OR misbehavio* OR misconduct OR sad OR sadness OR self control OR self-harm* OR selfharm* OR selfcontrol OR self-regulat* OR selfregulat* OR self-image OR shy* OR tantrum* OR personality trait* OR timid* OR obesity OR insomni* OR sleeplessnes* OR smoking OR grief OR mood* OR stress OR trauma).ti,ab,id.
THE EFFECTIVENESS OF MOBILE HEALTH INTERVENTIONS FOR YOUTH
36 36
Medline
Ovid, Epub Ahead of Print, In-Process & Other Non-Indexed Citations, Ovid MEDLINE(R) Daily and Ovid MEDLINE(R) 1946 to Present
#1 Mobile (Health)
cell phone/ OR text messaging/ OR (mhealth OR mmental OR mobile assessment* OR mobile intervention* OR cell* phone* OR cell* telephone* OR phone* cell* OR telephone* cell* OR mobile phone* OR mobile telephone* OR phone* mobile* OR telephone* mobile* OR smart phone* OR smartphone* OR blackberr* OR iphone* OR android* OR mobile app* OR phone app* OR textmessag* OR text messag* OR SMS* OR short messag* OR shortmessag* service* OR message* text* OR
texting*).ti,ab,kf. #2 Therapy
therapy, computer-assisted/ OR online therapy/ OR cognitive therapy/ OR psychotherapy/ OR behavior therapy/ OR anger management therapy/ OR (therap* OR psychotherap* OR CBT* OR treatment* OR intervention* OR program* OR recovery support* OR being treated OR outpatient OR attending specialist* OR clinic based OR counseling).ti,ab,kf.
#3 Adolescents and young adults (13-24 yo)
child behavior/ OR child development/ OR child psychiatry/ OR child psychology/ OR child behavior disorders/ OR child/ OR puberty/ OR adolescent/ OR adolescent behavior/ OR adolescent development/ OR adolescent psychiatry/ OR adolescent psychology/ OR young adult/ OR students/ OR (prepubescen* OR prepuberty* OR puberty OR pubescen* OR teen* OR young* OR youth* OR minors* OR under ag* OR underag* OR juvenile* OR girl* OR boy* OR preadolesc* OR adolesc* OR highschool* OR high school* OR college OR university OR student*).ti,ab,kf. #4 mental health
psychopathology/ OR mental health/ OR (mental disorder* OR psychiatric* OR mental illness* OR mental health OR DSM*).ti,ab,kf. OR exp intellectual disability/ OR exp mental disorders/ OR exp psychomotor disorders/ OR (mental* retard* OR reading disorder* OR mathematic* disorder* OR learning disorder* OR disorder of written expression OR dysle* OR developmental coordination disorder* OR dyspraxi* OR language disorder* OR phonological disorder* OR stuttering OR communication disorder* OR motor skills disorder* OR pervasive developmental disorder* OR autis* OR ASD OR asperger* OR rett* disorder* OR rett syndrome* OR childhood
disintegrative disorder* OR ADHD OR hyperactiv* disorder* OR attention deficit disorder* OR conduct disorder* OR oppositional defiant disorder* OR ODD OR disruptive behavior disorder* OR feeding disorder* OR eating disorder* OR anorexi* OR bulimi* OR (food ADJ2 disorder*) OR binge OR purging OR pica OR rumination disorder* OR tic disorder* OR tourette* OR elimination disorder* OR encopresis OR ((constipation OR overflow) ADJ3 disorder) OR enuresis OR selective mutism OR attachment disorder* OR stereotypic movement disorder* OR (disorder ADJ1 (infancy OR child* OR adolesc*)) OR deliri* OR dement* OR amnestic OR amnesia OR alzheimer* OR cognitive disorder* OR mental disorder* OR catatoni* OR personality change OR ((substance OR alcohol OR caffein* OR cannabis OR cocaine OR nicotin* OR tobacco OR marijuana) ADJ2 (disorder* OR abuse OR misuse OR addict* OR intoxication OR withdrawal OR dependence OR abstinen* OR abstain* OR "use")) OR psychotic* OR schizo* OR paranoid* OR delusion* OR mood disorder* OR depress* OR dysthymic* OR bipolar* OR hypomanic* OR manic* OR mania OR cyclothymic* OR panic disorder* OR anxiety disorder* OR agoraphobi* OR phobi* OR obsessive compulsive OR OCD OR stress disorder* OR PTSD OR
THE EFFECTIVENESS OF MOBILE HEALTH INTERVENTIONS FOR YOUTH
37 37
((trauma OR stressor) ADJ2 disorder*) OR somatoform OR somatization OR somatic symptom disorder* OR psychosomatic disorder* OR conversion disorder* OR pain disorder* OR hypochondri* OR body dysmorph* OR factitious disorder* OR
munchausen OR dissociative OR depersonali#ation disorder* OR identity disorder* OR (sexual ADJ3 (dysfunction* OR disorder*)) OR erectile disorder* OR orgasmic disorder* OR premature ejaculation OR dyspareunia OR vaginismus OR paraphili* OR exhibitionis* OR fetish* OR frotteuris* OR pedophil* OR masochis* OR sadis* OR transvest* OR transgender* OR voyeuris* OR gender dysphori* OR penetration disorder* OR (sleep* ADJ2 disorder*) OR dyssomnia* OR somni* OR hypersomn* OR insomni* OR narcolep* OR parasomn* OR nightmare OR sleep terror OR sleep walking OR sleepwalking OR impulse control disorder* OR explosive disorder* OR kleptomani* OR pyroman* OR pathologic* gambling OR trichotillomani* OR
adjustment disorder* OR personality disorder* OR borderline OR narcis* OR neurodevelopmental OR intellectual disability* OR speech sound disorder* OR fluency disorder OR hoarding disorder* OR excoriation disorder* OR skin-picking disorder* OR hair-pulling disorder* OR social engagement disorder* OR derealization disorder* OR restless leg* syndrom* OR neurocognitive disorder*).ti,ab,kf.
OR acting out/ OR aggression/ OR anger/ OR social behavior disorders/ OR juvenile delinquency/ OR anxiety/ OR anxiety, separation/ OR "Inhibition (Psychology)"/ OR problem behavior/ OR adaptation, psychological/ OR stress, psychological/ OR anxiety/ OR marijuana abuse/ OR substance-related disorders/ OR alcoholism/ OR smoking cessation/ OR shyness/ OR
(acting out OR affect regulation OR aggress* OR anger OR angry OR antisocial OR anxiety OR anxious* OR behavi* difficult* OR ((coping OR defiant OR disruptive OR dysfunctional* OR explosiv* OR maladaptiv* OR problem*) ADJ3 behavio*) OR depress* OR emotion* regulation OR emotional restraint OR externali* OR
hyperactiv* OR inhibit* OR internali* OR misbehavio* OR misconduct OR sad OR sadness OR self control OR self-harm* OR selfharm* OR selfcontrol OR self-regulat* OR selfregulat* OR self-image OR shy* OR tantrum* OR personality trait* OR timid* OR obesity OR insomni* OR sleeplessnes* OR smoking OR grief OR mood* OR stress OR trauma).ti,ab,kf.
THE EFFECTIVENESS OF MOBILE HEALTH INTERVENTIONS FOR YOUTH
38 38
Web of Science
Thompson Reuters, Web of Science Core Collection
#1 Mobile (Health)
TS=("mhealth" OR "mmental" OR "mobile assessment*" OR "mobile intervention*" OR "cell* phone*" OR "cell* telephone*" OR "phone* cell*" OR "telephone* cell*" OR "mobile phone*" OR "mobile telephone*" OR "phone* mobile*" OR "telephone* mobile*" OR "smart phone*" OR "smartphone*" OR "blackberr*" OR "iphone*" OR "android*" OR "mobile app*" OR "phone app*" OR "textmessag*" OR "text messag*" OR "SMS*" OR "short messag*" OR "shortmessag* service*" OR "message* text*" OR "texting*")
#2 Therapy
TS=("therap*" OR "psychotherap*" OR "CBT*" OR "treatment*" OR "intervention*" OR "program*" OR "recovery support*" OR "being treated" OR "outpatient" OR "attending specialist*" OR "clinic based" OR "counseling")
#3 Adolescents and young adults
TS=("prepubescen*" OR "prepuberty*" OR "puberty" OR "pubescen*" OR "teen*" OR "young*" OR "youth*" OR "minors*" OR "under ag*" OR "underag*" OR "juvenile*" OR "girl*" OR "boy*" OR "preadolesc*" OR "adolesc*" OR "highschool*" OR "high school*" OR "college" OR "university" OR "student*")
#4 mental health
TS=("mental disorder*" OR "psychiatric*" OR "mental illness*" OR "mental health" OR "DSM*" OR "mental* retard*" OR "reading disorder*" OR "mathematic* disorder*" OR "learning disorder*" OR "disorder of written expression" OR "dysle*" OR
"developmental coordination disorder*" OR "dyspraxi*" OR "language disorder*" OR "phonological disorder*" OR "stuttering" OR "communication disorder*" OR "motor skills disorder*" OR "pervasive developmental disorder*" OR "autis*" OR "ASD" OR "asperger*" OR "rett* disorder*" OR "rett syndrome*" OR "childhood disintegrative disorder*" OR "ADHD" OR "hyperactiv* disorder*" OR "attention deficit disorder*" OR "conduct disorder*" OR "oppositional defiant disorder*" OR "ODD" OR "disruptive behavior disorder*" OR "feeding disorder*" OR "eating disorder*" OR "anorexi*" OR "bulimi*" OR ("food" NEAR/2 "disorder*") OR "binge" OR "purging" OR "pica" OR "rumination disorder*" OR "tic disorder*" OR "tourette*" OR "elimination disorder*" OR "encopresis" OR (("constipation" OR "overflow") NEAR/2 "disorder") OR "enuresis" OR "selective mutism" OR "attachment disorder*" OR "stereotypic movement disorder*" OR ("disorder" NEAR/0 ("infancy" OR "child*" OR "adolesc*")) OR "deliri*" OR "dement*" OR "amnestic" OR "amnesia" OR "alzheimer*" OR "cognitive disorder*" OR "mental disorder*" OR "catatoni*" OR "personality change" OR (("substance" OR "alcohol" OR "caffein*" OR "cannabis" OR "cocaine" OR "nicotin*" OR "tobacco" OR "marijuana") NEAR/2 ("disorder*" OR "abuse" OR "misuse" OR "addict*" OR "intoxication" OR "withdrawal" OR "dependence" OR "abstinen*" OR "abstain*" OR "use")) OR "psychotic*" OR "schizo*" OR "paranoid*" OR "delusion*" OR "mood disorder*" OR "depress*" OR "dysthymic*" OR "bipolar*" OR "hypomanic*" OR "manic*" OR "mania" OR "cyclothymic*" OR "panic disorder*" OR "anxiety disorder*" OR "agoraphobi*" OR "phobi*" OR "obsessive compulsive" OR "OCD" OR "stress disorder*" OR "PTSD" OR (("trauma" OR "stressor") NEAR/2 "disorder*") OR "somatoform" OR "somatization" OR "somatic symptom disorder*" OR "psychosomatic disorder*" OR "conversion disorder*" OR "pain disorder*" OR "hypochondri*" OR "body dysmorph*" OR "factitious disorder*" OR "munchausen" OR "dissociative" OR "depersonali?ation disorder*" OR "identity disorder*" OR ("sexual" NEAR/2 ("dysfunction*" OR "disorder*")) OR "erectile disorder*" OR
