European Child & Adolescent Psychiatry

, Volume 27, Issue 11, pp 1391–1412 | Cite as

What is the level of evidence for the use of currently available technologies in facilitating the self-management of difficulties associated with ADHD in children and young people? A systematic review

  • Lauren PowellEmail author
  • Jack Parker
  • Valerie Harpin
Open Access


A number of technologies to help self-manage attention deficit hyperactivity disorder (ADHD) in children and young people (YP) have been developed. This review will assess the level of evidence for the use of such technologies. The review was undertaken in accordance with the general principles recommended in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis. 7545 studies were screened. Fourteen studies of technology that aim to self-manage difficulties associated with ADHD in children and YP were included. Primary outcome measures were measures that assessed difficulties related to ADHD. Databases searched were MEDLINE, Web of Science (Core collection), CINAHL, the Cochrane Library, ProQuest ASSIA, PsycINFO and Scopus. The methodological quality of the studies was assessed. This review highlights the potential for the use of technology in paediatric ADHD management. However, it also demonstrates that current research lacks robustness; using small sample sizes, non-validated outcome measures and little psychoeducation component. Future research is required to investigate the value of technology in supporting children and YP with ADHD and a focus psychoeducation is needed.


Technology Intervention ADHD Psychoeducation 



Attention deficit hyperactivity disorder


ADHD Rating Scale


ADHD vragen lijst


Behaviour rating inventory of executive function


Corsi block tapping task


Conners continuous performance test


Cochrane risk of bias


Disruptive Behaviour Disorder Rating Scale


Delis–Kaplan function system


Home situations questionnaire


It is about time questionnaire


Intention to treat


Movement assessment battery for children


Minimally clinically important difference


Medical subject headings


Strengths and difficulties questionnaire


Sensitivity to punishment and sensitivity to reward questionnaire for children


Social skills rating system


Trail making test


Time management questionnaire


Randomised controlled trial


South Australian spelling test


Quantifying behaviour test


Weschler adult intelligence scale-revised as a neuropsychological instrument


Weschler abbreviated scale of intelligence


Weshler intelligence scale for children three


Wechsler preschool and primary scale of intelligence revised


Young people


Attention deficit hyperactivity disorder (ADHD) is a highly comorbid [1, 2, 3] neurodevelopmental disorder. It has a worldwide prevalence of 3–5% in school age children [4], 80–85% of these individuals continue to be effected by their ADHD into adolescence [5, 6, 7, 8] and 60% into adulthood [9].

Due to the symptoms and complexity of the condition, there are a number of important long-term difficulties associated with ADHD. These include low academic attainment [10, 11], which can persist into adulthood [12], poor executive functioning [13], poor social relationships, strained parent/child/sibling relationships [14] and problems with social interactions with peers [15]. This results in poorer quality of life and self-esteem in children and YP with ADHD [16, 17].

Children and YP with ADHD are reliant on clinicians and parents to help them to manage their condition. However, as they transit into adulthood, the support is not as readily available or indeed wanted by the individual [18]. It is, therefore, imperative that children and YP learn to self-manage their condition and indeed be educated about their condition and how to manage it [19, 20, 21, 22, 23]. Individuals with ADHD often experience crises and access to their usual services may not be immediately available. However, the increasing sophistication and usage of technology may provide valuable resources to facilitate the self-management of ADHD for children and YP.

Over recent years, technological advances have meant that technology is more widely available and has become more popular and integrated into many lives. Society is also better connected with an estimated 46% of the worldwide population having an internet connection compared with 1% in 1995 [24]. As a result of this, a number of attempts have been made to harness technology to help manage ADHD in children and YP such as eye tracking [25], brain computer interface [26, 27] and a computerised test that quantifies ADHD core symptoms; the QbTest [28]. Technology has also been used for cognitive training in children and YP with ADHD [29]. However, these technologies are reliant on an administrator or a therapist. Other technologies have been developed to self-manage ADHD-related difficulties in children and YP that can be used independently of a therapist which, therefore, reduces the reliance on services. These include a handheld organisation device [30], a device to self-monitor ADHD symptoms [31], computer software to improve reading speed [32], and computer games that focus on mathematical ability [33] and the promotion of behavoural learning and organisation [34]. Although these studies report that technology has the potential to self-manage ADHD-related difficulties in children and YP, little is known about the level of evidence for these technologies. Therefore, this review will assess the level of evidence for currently available technologies for self-managing ADHD and related difficulties in children and YP.


The review protocol was registered with PROSPERO (CRD42017057715). The review was undertaken in accordance with the general principles recommended in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis [35].

Search methods

The following databases were searched in February 2017 from the last 5 years: MEDLINE, Web of Science (Core collection), CINAHL, the Cochrane Library, ProQuest ASSIA, PsycINFO and Scopus. Medical Subject Headings (MeSH) keywords used were attention deficit disorder with hyperactivity, hyperkinesis, attention deficit and disruptive behaviour disorders, conduct disorder, child behaviour disorders, disruptive impulse control and conduct disorders, adolescent, young adult, educational technology, technology, self-help devices, video games, internet, software, social media, mobile applications, self care and social support. Text terms used were attention deficit and disruptive behaviour disorders, attention deficit hyperactivity disorder, ADHD, ADDH, ADHS, hkd, attention, behaviour, dysfunctional, disorder, disrupt, defiant, impulsive, inattentive, inattention, hyperkinesis, damage, hyperactive, conduct, child, boy, girl, young person, YP, young people, adolescent, teen, youth, technology, assistive technology, self-help devices, game, website, download, forum, email, mobile app, condition management, manage, self-manage, support and support network. Terms were combined using Boolean logic (“AND”, “OR”). MeSH is specific recognised terms used for the purpose of identifying journal articles and books in electronic databases. Free text terms and synonyms are specific words that the search strategy looks for in the title and abstract.

A copy of the MEDLINE search strategy is presented in Appendix 1. Electronic citations were downloaded to Endnote software. The inclusion criteria are described in Table 1. Studies included in this review were from 2014 to 2016.
Table 1

Inclusion and exclusion criteria for this review

Inclusion criteria

Exclusion criteria

English language articles

Studies where intervention is not clearly defined

Studies recruiting individuals under the age of 18 years

Studies including individuals over the age of 18 years

Evaluating technologies that can be used independently of a therapist

Non-interventional studies

Participants reported to have ADHD diagnosis

Interventions that are led by anybody other than the child/YP with ADHD (e.g. clinician led interventions)


Participants without reported ADHD diagnosis (e.g. parent or teacher reported)

Validated outcome measures assessing ADHD-related difficulties

Outcome measures that do not assess ADHD-related difficulties or are not validated

ADHD attention deficit hyperactivity disorder

Due to the infancy of this research topic, any study design was accepted as appropriate to answer the research question. The research question is “What is the level of evidence that current technology that aims to self-manage difficulties associated with ADHD in children and young people is helpful?” The primary outcome measures (see Table 2) of this review are measures that assess ADHD related difficulties. 
Table 2

List of included outcome measures

Observational checklist for observations and recording behaviours

Conners parent scale (brief version)

Corsi Block Tapping Task (CBTT)

Chart to track each students appropriate behaviour

Connors teacher rating scale

Digit span subtest from the Weschler Intelligence Scale for Children (WISC-III)

Guided reading packet

5 subtests from the Movement Assessment Battery for Children (MABC-2-NL)

Disruptive Behaviour Disorders Rating Scale (DBDRS; parent and teacher versions)

X 2 outcome measures—multiple choice, fill in the blanks and short answer response

Behaviour Rating Inventory of Executive Function (BRIEF): plan/organise

Sensitivity to punishment and sensitivity to reward questionnaire for children (SPRSQ-C)

Total time to complete reading

BRIEF: working memory subscale—parent

Paediatric quality of life inventory (PedsQL; parent and child versions)

Sustained attention dots task version 02 k

Shape school

Counting span task

Calculating time for distractions


Connors Continuous Performance Test (CPT II)

Time calculation



Barkley School Situations Questionnaire


Social Skills Rating Scale (SRRS) self-control subscale

Go/no-go task (not QbTest)

BRIEF—control initiate

SRRS total

Time management questionnaire—parent and teacher completion

BRIEF—organisation of materials

ADHD VragenLijst (AVL)

It’s About time Questionnaire (IATQ)—parent version


SSRS—teacher version

Self efficacy questionnaire

BRIEF—metacognition index

Stop task

Knox cubes LDT

Weshler Preschool and Primary Scale of Intelligence Revised translated in Dutch (WPPSI-R NL)

ADHD Rating Scale 1 (ADHD-RS-1)

Action detector

Subscale of Cooperation of the SRRS (parent version)

Stroop (and day/night version)

Duration of arbitrary standing

SRRS: subscales Responsibility

The home situations questionnaire (HSQ)

Disruptive Behaviour Disorder Rating Scale (DBDRS)

SRRS: assertiveness subscale

Raven coloured progressive matrices (full and shortened version)

Improvement index during training

Three subtests in Mandarin Literacy Assessment

ADHD Rating Scale (ARS-IV)

Strengths and Difficulties Questionnaire (SDQ)

Trail Making Test (TMT) of the Delis-Kaplan Function System (D-KEFS)

Weshler Abbreviated Scale of Intelligence (WASI)

South Australian Spelling Test (SAST)


Quality Assessment

Methodological quality of included studies was assessed using the Cochrane Risk of Bias tool (CRoB) [36] for RCT designs and the Downs and Black Instrument [37] for non-RCT designs. This CRoB tool addresses specific domains, namely, sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, and selective outcome reporting. The Downs and Black Instrument provides an individual score for each study with a maximum score of 32 [37] and assesses the way in which the studies report their findings, external validity, internal validity bias and selection bias.

Data extraction

Retrieved titles, abstracts, and/or papers were screened independently by 2 review authors (LP, JP) to identify studies that met the inclusion criteria. Disagreements were resolved between reviewers through discussion. A standardised form was used for data extraction using Microsoft Excel. Details of the study characteristics, including participants, the intervention, and comparator (where applicable) were recorded. Data extraction was carried out by reviewer LP and checked for accuracy by reviewer JP.

Outcome measurement assessment

It is vital that when undertaking a systematic review, the quality of the outcome measures used in each of the included studies is assessed. This is to ensure the validity and reliability of their results. To complete the outcome measure quality assessment, where possible, three domains should be considered for each outcome measure [38], (1) whether the psychometric properties of the scale have been assessed previously [39], (2) whether the clinimetric properties of the outcome measure have been thought through [40, 41, 42, 43, 44], specifically the Minimally Clinically Important Difference (MCID) [43], and (3) whether the design and analysis of the outcome measure satisfies the requirements of measurement theory [45, 46, 47]. We identified all outcome measures (N = 58) used across the 14 studies and reviewed each of them individually to assess whether they fulfilled the first domain described above. The MCID was not assessed for the included outcome measures and was, therefore, not assessed. The 58 included outcome measures are listed in Table 2.

Literature for each outcome measure, where applicable, was reviewed. We then examined each outcome measure to ascertain how the data were scored, collected and analysed within the results section of each study.

In line with the literature, all 58 outcome measures included were measures of difficulties related to ADHD.

Data synthesis

We have presented a narrative overview of the included studies with supporting evidence tables and text. A meta-analysis was not undertaken.

Appraisal of evidence

The results of the search varied from case studies to Randomised Controlled Trials (RCTs). The studies identified were appraised using the levels of evidence [48] to locate the best available evidence that involves the application of systematic, robust, transparent and explicit methodology [49]. The grading system (see Table 3) was created to highlight that varying study designs and methodologies are at risk of bias in their results. This is crucial as the study design may affect the validity and reliability of results due to the research method used. For example, when evaluating the effectiveness of an intervention, it is often considered that RCT evidence is the “gold standard”, the most reliable form of evidence due to the measures they take to reduce the influence confounding variables could potentially have on the results [50].
Table 3

Levels of evidence outlined by Weiss et al. [48]

Level of evidence


Group research

Outcome research

Single participant research


Randomised controlled trial

N-or-1 randomised controlled trial


Non-randomised control trial

Prospective cohort study with concurrent control group

Analytic survey

ABABA design

Alternating treatments. Multiple baseline across participants


Case–control study. Cohort study with historical control group

ABA design



Before and after case series without control group

AB design


Descriptive case series


Expert opinion


Common sense



Search results

The electronic searches identified 7391 citations following de-duplication, including 9 additional citations that were identified through reference searches/other sources. We excluded 7331 citations at the title and abstract stages as they did not fit the inclusion criteria. We then obtained 60 citations as full-text articles. Of these, 50 were excluded at the full-text stage; details of these excluded studies with the reason for exclusion are shown in Appendix 2. 14 studies reported across 14 publications were included in the review (see Fig. 1). Four of these publications were obtained from a recent meta analysis [29], which examined the effects of cognitive training on ADHD symptoms, neuropsychological deficits, and academic skills in children and YP with ADHD [51, 52, 53, 54].
Fig. 1

Studies included in this review

Quality assessment

Full details from the Cochrane risk of bias assessment are presented in Appendix 3. A summary of the RCT risk of bias assessment is presented in Table 4, non-RCT risk assessment in Table 5 and a summary of the outcome measurement quality assessment can be found in Appendix 5.
Table 4

RCT risk of bias summary

Table 5

non-RCT risk of bias summary

*Item modified (see Appendix 4 for further details)

One of the eight included RCTs, one was considered to be at overall high risk of bias [34], five RCTs were judged as being at overall low risk [51, 52, 54, 55, 56] and two RCTs were considered to be at overall unclear risk of bias [53, 57]. All included RCTs were considered to be at a low risk of bias for selective reporting [34, 51, 52, 53, 54, 55, 56, 57].

Non-RCT study designs were assessed using the Downs and Black Scale, as they were mainly exploratory interventional studies. Overall, the non-RCT studies obtained low scores on items covering external and internal validity, selection bias and statistical power. Studies obtained higher scores for the items covering reporting of results and study procedures. The maximum total score that could be obtained is 32. Of the six included non-RCT studies in this review, the lowest score was 6 [58] and the highest score was 11 [59, 60].

Quality assessment of measurement scales

Ten of the fourteen included studies [34, 51, 52, 53, 54, 55, 56, 57, 58, 63] used ordinal scales of measurement all with established psychometric properties. Twelve of the included studies [34, 51, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62] used scales of measurement that did not have established psychometric properties. Four of the fourteen studies [58, 59, 60, 63] did not perform any formal statistical analysis. The sample size for these four studies ranged from one to eight. Five of the fourteen studies [34, 52, 53, 56, 57] aggregated data used with ordinal scales, which may put findings at risk. Further details of the outcome measurement quality assessment can be found in Appendix 5. Description of the studies can be found in Table 6.
Table 6

Summary of study, participant and intervention characteristics and results

Authors, year, country, study design, level of evidence

Number recruited (N), final follow-up (n) overall and between groups

Gender, mean age (years), how ADHD diagnosis confirmed

Intervention and length/frequency/groups in study

Outcome measures used and ADHD-related difficulty domain assessed*

ADHD-related difficulty results and reported p values

Bamford, 2016, USA, single subject design (ABAB), level III [59]

N = 4

2 males, 2 females, 16.5, medical diagnosis (N = 4)

iPad choice works app, 20 min (baseline observation), use app for 8 days, single participant group

Guided reading packet, multiple choice, observation checklist, education*

A trend towards improvements in on task behaviour was reported when participants used the iPad

Pinna, 2015, USA, case series, level IV [60]

N = 9

7 males, 2 females, 13, diagnosed by psychiatrist, psychologist or physician

Tablet-based reading app designed to help reading, answer questions about the text and record the answer. Participants read at same time on 2 separate days, day 1 reading a book, day 2 reading text on app, single participant group

Total time to complete reading, calculating time for distractions, time calculation, ADHD symptoms*

No significant differences were found in student’s ability to recall information from a story were observed when they read from the application or a book

Ruiz-Manrique et al., 2016, Spain, case study, level V [58]

N = 1

Male, 10 years, DSM V criteria

App “ADHD Trainer”, every day at the same time, no more than 4 h daily for first month (average 1 h per day), at least 10 min per day for following month, single participant group

Conners parent Scale (brief version), Connors teacher rating scale, Barkley School Situations Questionnaire, ADHD symptoms*

ADHD symptoms improved following training. BSSQ was 70 pre and 66 post training. Conners scores were 19 for teachers and 20 for parents pre training, 15 for teachers and 16 for parents post training. Authors report that findings demonstrate that “cognitive computerised training” may improve some ADHD cognitive symptoms

Weerdemeester et al., 2016, The Netherlands, 2 arm feasibility RCT, adventurous dreaming highflying dragon computer game (ADHD group), computer game without ADHD focussed training components (control group), level II [56]

N(n) = 73(66), intervention; 37(32), control; 36(34)

58 males, 15 females, 9, formal diagnosis (N = 39), elevated symptoms (no diagnosis or other diagnosis; (N = 26), comorbid disorder (N = 8)

Computer games: “Adventurous Dreaming Highflying Dragon” (intervention), comparable intervention without ADHD focussed training components (control), 6 15-min sessions over 3 weeks, intervention and control group

ADHD VragenLijst (AVL), Go/no-go task, MABC-2-NL, ADHD symptoms* and motor skills*

Total and hyperactivity sections of AVL and the Go/No-go outcomes demonstrated improvements (p ≤ 0.05). The impulsivity section of the AVL and the fine motor skills also showed improvements (p ≤ 0.10). Teacher-rated ADHD symptoms improved in the intervention compared to control group (p ≤ 0.05)

Bul et al., 2016, The Netherlands, RCT crossover trial, serious game intervention (intervention), treatment as usual (control), level 1 [34]

N(n) = 170(139), intervention; 88(68), control; 82(71)

137 males, 33 females, 9, DSM IV criteria

Serious game intervention (programmed so can not play more than 65 min in 24-h period) called “Plan-It Commander”, or treatment as usual, 10 weeks, intervention and control crossover design

Time management questionnaire—parent and teacher completion, subscale Plan/Organise and working memory of BRIEF, Subscale of cooperation, responsibility, assertiveness, self control and total SRRS of SRRS, IATQ, SRRS teacher version, self efficacy questionnaire, social skills*, self efficacy* and executive function*

Group 1 participants significantly improved time management skills compared to group 2 (parent reported; p = .02). Parents and teachers reported total social skills improved within groups effects on total social skills and teacher reported planning/organising skills were non-significant between groups. Group 1 positive effects were maintained and improved in last 10 weeks of study

Shih et al., 2014, Taiwan, before and after case series, level IV [61]

N = 2

2 males, 8.5, diagnosed, details not reported

Wii remote controller and control system, used to detect activity in students and giving them reminders when they are standing (rather than sitting) in the classroom, 40-min sessions, 3–5 times per week, single participant group

Action detector and duration of arbitrary standing, ADHD symptoms*

Both participants improved hyperactive behaviour during intervention phase (p < 0.01). Effects maintained at maintenance phase (1 week later)

van der Oord et al., 2014, The Netherlands, 2 arm RCT, Executive function training (intervention) or wait list (control), level I [57]

N(n) = 43(40), treatment 21(18), wait list 22(22).

33 males, 7 females, 9.75, DSM IV diagnosis (N = 77)

Computer game (macintosh computer installed in participant’s homes), EF training group consisted of 25 40 min over 5 weeks. Sessions covered inhibition, cognitive flexibility and working memory, intervention and wait-list conditions

All subscales of BRIEF, DBDRS, ADHD symptoms*, executive function*

Participants in the EF training showed improvements compared to those in wait-list condition on parent rated EF and ADHD behaviour in total sample and subsample (those treated with methylphenidate). Effects maintained at follow-up. Between group differences suggested for ODD subscale of DBDRS

Dovis et al., 2015, The Netherlands, 4 arm RCT, full active condition (visuospatial WM, inhibition and cognitive flexibility trained), partially active condition (inhibition and cognitive flexibility trained), WM training task presented in placebo mode and a full placebo condition, level I [55]

N(n) = 89(57), full active training 31(20), partially active training 28(21), placebo training 30(16)

71 males, 18 females, 10, DSM IV diagnosis

Computer game “Braingame Brian”, 5 weeks, weekly phone calls from research team, four groups: “full active” (visuospatial WM, inhibition, cognitive flexibility training), “partially active” (inhibition and cognitive flexibility), WM training task presented in placebo mode and full placebo condition

Improvement index during training, Stop task Stroop, CBTT, WISC-III, TMT of D-KFES, Raven, DBDRS, BRIEF, SPRSQ-C, PedsQL, Home situations Questionnaire, executive function*, ADHD symptoms*, QoL*, social skills*

Improvements were observed in visual spatial STM and WM, inhibitory performance and interference control

Lin et al., 2016, Taiwan, single case (ABA), level III [62]

N = 2

2 males, 11, diagnosed, details not reported

App, Mobile Augmented Reality (MAR), data collected over 3 months, single participant group

Three subtests in Mandarin Literacy Assessment (MLA), education*

MLA scores increased during intervention and maintenance phases( p < 0.05)

Rijo et al., 2015, Portugal, before and after case series, level III [63]

Study 1: N = 8, study 2: N = 12 (N = 6 ADHD, N = 6 not ADHD)

Study 1: 2 males, 2 females, 7, not diagnosed. Study 2:gender not reported, 7, N = 6 diagnosed, details not given

Computer serious game involving a treasure hunt find things like letters and words, 3 months, daily use monitored by the research team, two studies, single participant groups in each

CPT II, WISC III, ADHD symptoms*, executive function*

A trend towards improved CPT II and WISC III scores post intervention was reported

Van Dongen-Boomsma et al., 2014, The Netherlands, randomised placebo control trial, level I [51]

N(n) = 51(47), intervention; 27(26), control 24(21)

34 males, 17 females, intervention; 6.5 ± 0.6, control; 6.6 ± 0.7, DSM IV criteria

CogMed Working Memory Training (CMWT; computer software). 25 sessions of 15 min, 5 days a week, sessions. Both conditions included 7 visuo spatial working memory tasks. In intervention group, software adjusted task difficulty based on child’s performance. Control group same as intervention, exec items to memorise did not exceed starting level, intervention and control

ADHD-RS-1, BRIEF (parent and teacher Dutch versions), BRIEF-P, Adapted didgit span from WISC-III, Knox cubes LDT, Sentences from WPPSI-RN, Shortened Ravens progressive matricies, Day night stroop task, Sustained attention dots task version 02 k, Shaoe school

Does not provide evidence in favour of CMWT. Significant improvement of active condition found on verbal working memory task (p = .041; adapted Digit Span WISC-III..No significant treatment effect on any other outcome measurements. No significant differences found in ADHD-RS and Behavior Rating Inventory of Executive Function

Egeland et al., 2013, Norway, RCT, level I [52]

N(n) = 75(67), intervention 38(33), control 37(34)

49 males, 18 females, 10.4, ICD-10 criteria

CogMed roboMemo program performed daily at school for 5-7 weeks, 30–45 min in length, consists of 13 adaptice exercises, difficulty level altered based on child’s performance. Tasks taxed working memory capacity, these included tasks such as letter and digit span tasks. Two groups; intervention and control

ARS-IV ADHD rating scale, SDQ, BRIEF metacognition index and general executive composite

Significant training effect in psychomotor speed, but not to any other neuropsychological measures. No training induced changes in symptom rating scales at home or school

Klingberg et al., 2005, Sweden, double blind RCT, Level I [54]

N(n) = 53(46), intervention 27(20), control 26(24)

36 males, 8 females, intervention 9.8, control 9.9, reports ADHD diagnosis confirmed

Working memory tasks in a computer program “RoboMemo® Cogmed Cognitive Medical systems AB”, provided on a CD, used by child on personal computer at home or school. Included visuo spatial working memory tasks. Children performed 90 trials on each day, around 40 min per day, difficulty level adjusted based on child’s performance

WAIS-RNR, Digit span from WISC III, Stroop interference, Raven’s coloured progressive matrices, Connors for parents and teachers

Significant effects for verbal WM; Digit span p = .01 post intervention/p = .03 at follow-up, Stroop (accuracy) p = .004 post intervention/p = .44 follow-up response inhibition, and complex reason in measures. Parent ratings showed significant reduction insymptoms of inattention (post intervention: p = .002; follow-up: p = .04) and hyperactivity/impulsivity (post intervention/follow-up: p = .03), both post intervention and at follow-up

Johnstone et al., 2012, Australia, randomised waitlist control, Level I [53]

N(n) 151(128), waitlist (WL) ADHD group n = 20, Software (SW) ADHD group n = 22, software with attention monitoring (SWAM)ADHD group n = 18, non ADHD WL n = 25, non ADHD SW group n = 23, non ADHD SWAM group n = 20. Drop out rate not reported therefore post training assessment n not available

96 males, 55 females, ADHD WL 19 male, 1 female, SW ADHD group 19 males, 3 females, SWAM ADHD group 16 males, 2 females, non ADHD WL group 15 males, 10 females, non ADHD SW group 15 males, 8 females, non ADHD SW + AM group 12 males, 8 females

Three conditions; waitlist, working memory and inhibitory control with attention monitoring or working memory and inhibitory control without attention monitoring. Reported as ADHD diagnosis confirmed

WASI, South Australian Selling Test, Counting span task

Non-significant post training improvements in spatial working memory (p = .066), ignoring distracting stimuli, and sustained attention reported for children with ADHD and without. Improvements for both groups maintain 6-weeks after training. Results suggest combined

training can result in improved behavioural control for children with and without ADHD


This review set out to answer the question “What is the level of evidence that current technology that aims to self-manage difficulties associated with ADHD in children and young people is helpful?” The review found that the evidence demonstrates that technology shows promise in self-managing difficulties related to ADHD in children and YP. However, this claim is based on evidence that often consists of small sample sizes, use a wide variety of outcome measures (many of which are not validated) and provide little support for the importance of the role of psychoeducation in children and YP with ADHD that has been so widely reported and encouraged elsewhere [19, 20, 21, 22, 23, 64]. For example, the European Guidelines suggest psychoeducation for parent/carer and child with ADHD as a first step to treatment [23]. One systematic review even stated that psychoeducation for YP with ADHD and their families could provide an expert understanding of their condition could lead to more positive individual choices [20].

Of the fourteen included studies in this review, the interventions assessed include two tablet devices [59, 60] two mobile applications [58, 62], the use of a Wii remote control [61], computer software [51, 54] and computer games [34, 52, 53, 55, 56, 57, 63]. Following exclusions, outcome measure assessment (Appendix 5) and quality assessment of the fourteen included studies was conducted (appendices 3 and 4).

Only four of the sixteen papers included in the Cortese et al. [29] meta analysis were included in this review [51, 52, 53, 54] and one additional paper resulted from our search strategy before the Cortese review was screened for studies to include in this review [57]. Two of the unincluded papers presented in this meta analysis [29] did not report technological interventions [65, 66], one did not use validated outcome measures of ADHD-related difficulties [67], four reported technological interventions that were not for independent use [68, 69, 70, 71], two reported on participants who did not have a primary diagnosis of ADHD [72, 73] and two of the studies did not report including participants who had an official clinical diagnosis of ADHD [74, 75]. One of these papers reported participants who had a parent-reported ADHD diagnosis [75]. Papers included in this review all report on participants who have obtained a formal ADHD diagnosis. This is crucial to ensure that comparisons can be made across studies. Parent-reported diagnoses may not be as reliable as clinically reported diagnoses and therefore do not enable comparisons to be made and therefore the results from such studies should be interpreted with caution.

The interventions used in ten [34, 51, 52, 53, 54, 55, 56, 57, 61, 62] of the fourteen included studies identified statistically significant results for some, not all, primary outcome measures included in this review. Statistically significant improvements included improved ADHD symptoms [54, 56, 57, 61], social skills [34], executive functioning [51, 52, 54, 55, 57] and educational outcomes [62]. Statistical significance was not observed for the quality of life [55] or self-efficacy [34] measures which interestingly, only featured in two of the included RCTs [34, 55]. Although a trend towards improved symptoms [58, 61] and executive functioning [63] was observed in three of the included studies [58, 61, 63], no formal statistical analysis was undertaken and the sample sizes were small ranging from one to eight participants. Therefore, these findings should be interpreted with caution.

As described fully in the quality assessment, one of the eight included RCTs were considered to be at overall high risk of bias [34], two were considered as having an unclear rick of bias [53, 57] and five were considered as having a low risk of bias [51, 52, 54, 55, 56]. This does not mean that interventions were not successful in improving ADHD-related difficulties. A number of conclusions could be drawn from this including the difficulty of blinding participants to an intervention as it is often impossible to conceal which arm participants are randomised to. It is also difficult to stop a potentially impulsive and hyperactive population to withhold their randomization allocation to an outcome measure assessor. Overall, the included non-RCTs obtained low scores on the Downs and Black scale. Out of a maximum score of 32, two studies scored eleven [59, 60], three scored nine [61, 62, 63] and one obtained a score of six [58]. A number of conclusions could be drawn from this including low sample sizes and the non-RCT nature of the studies (thus obtaining low scores on items that assess whether or not participants and research staff are blinded).

Of the fourteen included studies in this review, five [34, 52, 53, 56, 57] aggregated data with ordinal scales, four used no formal statistical analysis [58, 59, 60, 63] and two carried out statistical analysis when their sample sizes only consisted of two participants each [61, 62]. Clinimetric properties were not described for any of the primary outcome measures of this review. The lack of statistical significance across a number of outcome measures in this review could be a result of lack of statistical power due to small sample sizes and the inability to ascertain a clinically meaningful result.

The results from this systematic review should be generalised to a wider population of children and YP with ADHD with caution due to the low recruitment figures for five of included studies where n ranged from one to twelve [58, 59, 60, 61, 62] and only two of the included RCTs had a sample size of more than one hundred [34, 53]. Observations of the lack statistical significance should also be interpreted with caution, given the level of evidence provided and the methodological quality of the existing evidence base.

This review included a small number of papers including 1040 participants overall with 170 being from one study alone [34]. Six of the selected studies recruited fewer than 20 participants [51, 58, 61, 62, 63]. This could be for a number of reasons. It may have been difficult to engage with and recruit YP with ADHD to a research study, although this has not been our personal experience.

Additionally, ADHD severity and the presence of comorbidity can affect the level of impairment experienced by the individual, which can affect the way in which they respond to interventions. The included studies did not report the severity of the ADHD in their participants. However, one study [52] reported that their participants were participants were diagnosed with hyperkinetic disorder according to the International Statistical Classification of Diseases and Related Health Problems 10 (ICD-10) [76]. This diagnosis would have been based on narrower criteria than the DSV-IV as in the ICD-10 ADHD is diagnosed based on a minimum number of symptoms in all three dimensions (inattention, impulsivity and hyperactivity) [76] whereas the DSM-IV requires a minimum number of symptoms in one dimension [77]. This means that it is difficult to inter a significant improvement of ADHD-related difficulties.

There are a number of reasons evaluating a complex intervention with this population could remain challenging. For example, no ADHD diagnosis is the same. ADHD is a highly comorbid condition with a large number of potential-related difficulties. The extent to which each individual is impaired by their ADHD symptoms and related difficulties are also highly variable. In this review, four studies excluded participants who had specific comorbid diagnosis [51, 52, 53, 55]; one study excluded participants with autism spectrum disorder and conduct disorder [55], one study excluded participants diagnosed with pervasive developmental disorders, Tourette’s disorder and those who show evidence of bipolar disorder and conduct disorder [52]. One study excluded those diagnosed with pervasive developmental disorder [51] and another study excluded those with any “clinically significant comorbid condition” [53]. These findings should be interpreted with caution as at least 65% of children and YP diagnosed with ADHD have a comorbid condition [1] therefore these participant groups are not representative of the wider ADHD population. These factors coupled with evaluating an intervention make it very difficult to ascertain a clinically significant improvement in this population following the use of an investigative intervention. It also means that it is difficult to control each arm of an RCT study design. It has therefore been suggested [78] that the integration of realist evaluation within an RCT design may be more appropriate for evidence-based medicine whereby “statistically significant benefits may be marginal in clinical practice” [79].

The results of the included studies were not combined for a meta-analysis due to the variety of the types and quality of data collected for the primary outcome measures. It would also be difficult to compare primary outcomes across the included studies accurately as there was a wide variety of measures assessing ADHD-related difficulties used, many of which lacked validity as a measure of ADHD-related difficulties in children and YP.

Despite the wide variety of outcome measures included in this review, none of them assessed ADHD knowledge and understanding. To self-manage ADHD, the Chronic Illness Model [80] states that psychoeducation with a collaborative care model enhances health outcomes [64]. Similarly, the Health Belief Model states that people are more likely to seek treatment if they have knowledge and understanding of their condition [81, 82]. It is important that ADHD psychoeducation delivery is conveyed to the individual and their parents in a culturally appropriate manner, via a reputable website and written and updated by reputable experts [64]. It has been suggested that psychoeducaiton for parents and the YP with ADHD is the first step to treatment [23]. A systematic review has emphasised the value of psychoeducation for children and YP with ADHD can lead to an expert understanding of their condition and lead them to making more positive individual choices [20]. Public Heath England [19] and the Mental Health Taskforce’s Five Year Forward View for Mental Health [22] states that early intervention avoids YP falling into crisis and expensive and longer-term interventions into adulthood. Therefore, it is vital that ADHD psychoeducation begins as early as possible following an ADHD diagnosis so that the YP can learn to accept and self-manage their condition in preparation for transition into adulthood.

Transition periods are particularly challenging for somebody diagnosed with ADHD and they present frequently throughout the course of a young person’s life. For example YP move to secondary education, undertake regular exams, have to navigate through puberty, sometimes move house and many transfer adult ADHD services. The latter is particularly challenging due to the nature of child and adult ADHD services being very different and providing support in very different ways. Child services provide more in person support and may involve more frequent appointments than adult services. Therefore, a smooth transition between services is vital for a YP with ADHD to minimise disruption [83]. Despite this evidence, none of the included studies provided psychoeducation as part of their interventions.

Future research should focus on the development and co-collaboration of an evidence-based intervention that may focus on psychoeducation for this population. Due to the majority of the included interventions in this review taking the form of computer games, perhaps an ADHD technological intervention with a psychoeducation focus should take a different form such as a website. Evidence suggests that to engage with this population, technological interventions should be interactive [84, 85]. Research in this area should also consider larger sample sizes and ADHD severity and the presence of comorbid conditions should be reported for participants and accounted for during analysis.

Outcome measures for all interventions for ADHD need to be carefully planned. They should include core symptoms but it is likely that these are not the realistic targets of this type of intervention and goal-orientated outcomes agreed with YP and families may be more relevant. Functional and quality of life outcomes need longer follow-up but in a chronic disorder have far more significance. Finally, advancements in technology and improvements of the suitability of interventions specifically designed for independent use to facilitate self-management could involve a psychoeducational component. Such technologies should be co-designed with stakeholders including children and YP with ADHD adopting a user-centred design methodology to ensure the technology is suitable for this population.



In collaboration with Sheffield Children’s NHS Foundation Trust, this research was funded/supported by the University of Sheffield’s School of Health and Related Research (ScHARR) Research Stimulation Prize and the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care Yorkshire and Humber (NIHR CLAHRC YH). The views and opinions expressed are those of the authors and are not necessarily those of the National Health Service, the National Institute for Health Research, or the UK’s Department of Health (DoH).

Compliance with ethical standards

Conflict of interest

The authors declare that thay have no competing interest.


  1. 1.
    Biederman J, Newcorn J, Sprich S (1991) Comorbidity of attention deficit hyperactivity disorder. Am J Psychiatry 148(5):564–577PubMedGoogle Scholar
  2. 2.
    Kadesjö B, Gillberg C (2001) The comorbidity of ADHD in the general population of Swedish school-age children. J Child Psychol Psychiatry 42(4):487–492PubMedGoogle Scholar
  3. 3.
    Yoshimasu K et al (2012) Childhood ADHD is strongly associated with a broad range of psychiatric disorders during adolescence: a population-based birth cohort study. J Child Psychol Psychiatry 53(10):1036–1043PubMedPubMedCentralGoogle Scholar
  4. 4.
    Polanczyk G et al (2007) The worldwide prevalence of ADHD: a systematic review and metaregression analysis. Am J Psychiatry 164(6):942–948Google Scholar
  5. 5.
    Barkley RA et al (1990) The adolescent outcome of hyperactive children diagnosed by research criteria: I. An 8-year prospective follow-up study. J Am Acad Child Adolesc Psychiatry 29(4):546–557PubMedGoogle Scholar
  6. 6.
    Biederman J et al (1996) Predictors of persistence and remission of ADHD into adolescence: results from a four-year prospective follow-up study. J Am Acad Child Adolesc Psychiatry 35(3):343–351PubMedGoogle Scholar
  7. 7.
    Gittelman R et al (1985) Hyperactive boys almost grown up: I. Psychiatric status. Arch Gen Psychiatry 42(10):937–947PubMedGoogle Scholar
  8. 8.
    Weiss G et al (1985) Psychiatric status of hyperactives as adults: a controlled prospective 15-year follow-up of 63 hyperactive children. J Am Acad Child Psychiatry 24(2):211–220PubMedGoogle Scholar
  9. 9.
    Faraone SV, Biederman J, Mick E (2006) The age-dependent decline of attention deficit hyperactivity disorder: a meta-analysis of follow-up studies. Psychol Med 36(02):159–165PubMedGoogle Scholar
  10. 10.
    Harpin VA (2005) The effect of ADHD on the life of an individual, their family, and community from preschool to adult life. Arch Dis Child 90(suppl 1):i2–i7PubMedPubMedCentralGoogle Scholar
  11. 11.
    Mannuzza S, Klein RG (2000) Long-term prognosis in attention-deficit/hyperactivity disorder. Child and adolescent psychiatric clinics of North AmericaGoogle Scholar
  12. 12.
    Kuriyan AB et al (2013) Young adult educational and vocational outcomes of children diagnosed with ADHD. J Abnorm Child Psychol 41(1):27–41PubMedPubMedCentralGoogle Scholar
  13. 13.
    Barkley RA (1997) Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychol Bull 121(1):65Google Scholar
  14. 14.
    Johnston C, Mash EJ (2001) Families of children with attention-deficit/hyperactivity disorder: review and recommendations for future research. Clin Child Fam Psychol Rev 4(3):183–207PubMedGoogle Scholar
  15. 15.
    de Boo GM, Prins PJ (2007) Social incompetence in children with ADHD: possible moderators and mediators in social-skills training. Clin Psychol Rev 27(1):78–97PubMedGoogle Scholar
  16. 16.
    Klassen AF, Miller A, Fine S (2004) Health-related quality of life in children and adolescents who have a diagnosis of attention-deficit/hyperactivity disorder. Pediatrics 114(5):e541–e547PubMedGoogle Scholar
  17. 17.
    Peasgood T et al (2016) The impact of ADHD on the health and well-being of ADHD children and their siblings. Eur Child Adolesc Psychiatry 25(11):1217–1231PubMedPubMedCentralGoogle Scholar
  18. 18.
    Young S, Murphy CM, Coghill D (2011) Avoiding the ‘twilight zone’: recommendations for the transition of services from adolescence to adulthood for young people with ADHD. BMC Psychiatry 11(1):174PubMedPubMedCentralGoogle Scholar
  19. 19.
    Public Health England (2016) The mental health of children and young people in England. Cited 30th May 2017
  20. 20.
    Ferrin M, Taylor E (2011) Child and caregiver issues in the treatment of attention deficit–hyperactivity disorder: education, adherence and treatment choice. Fut Neurol 6(3):399–413Google Scholar
  21. 21.
    The Health Foundation (2011) Helping people help themselves. Cited 30th May 2017
  22. 22.
    The Mental Health Taskforce (2016) The five year forward view for mental health. Cited 30th May 2017
  23. 23.
    Taylor E et al (2004) European clinical guidelines for hyperkinetic disorder–first upgrade. Eur Child Adolesc Psychiatry 13(1):i7–i30PubMedGoogle Scholar
  24. 24.
    Internet Live Stats (2016) Number of internet users. Cited 16th Jan 2017
  25. 25.
    Garcia-Zapirain B, de la Torre Díez I, López-Coronado M (2017) Dual system for enhancing cognitive abilities of children with ADHD using leap motion and eye-tracking technologies. J Med Syst 41(7):111PubMedGoogle Scholar
  26. 26.
    Blandón DZ et al (2016) Influence of a BCI neurofeedback videogame in children with ADHD. Quantifying the brain activity through an EEG signal processing dedicated toolbox. In: Computing Conference (CCC), 2016 IEEE 11th Colombian. IEEEGoogle Scholar
  27. 27.
    Lim CG et al (2017) Effectiveness of a brain-computer interface based programme for the treatment of ADHD: a pilot study. Psychopharmacol Bull 43(1):73–82Google Scholar
  28. 28.
    Edebol H, Helldin L, Norlander T (2013) Measuring adult attention deficit hyperactivity disorder using the quantified behavior test plus. PsyCh J 2(1):48–62PubMedGoogle Scholar
  29. 29.
    Cortese S et al (2015) Cognitive training for attention-deficit/hyperactivity disorder: meta-analysis of clinical and neuropsychological outcomes from randomized controlled trials. J Am Acad Child Adolesc Psychiatry 54(3):164–174PubMedPubMedCentralGoogle Scholar
  30. 30.
    Shrieber B, Seifert T (2009) College students with learning disabilities and/or ADHD use of a handheld computer compared to conventional planners. In: Proceedings of the chairs conference on instructional technologies researchGoogle Scholar
  31. 31.
    Gulchak DJ (2008) Using a mobile handheld computer to teach a student with an emotional and behavioral disorder to self-monitor attention. Educ Treat Child 31(4):567–581Google Scholar
  32. 32.
    Hecker L et al (2002) Benefits of assistive reading software for students with attention disorders. Ann Dyslexia 52(1):243–272Google Scholar
  33. 33.
    Ota KR, DuPaul GJ (2002) Task engagement and mathematics performance in children with attention-deficit hyperactivity disorder: effects of supplemental computer instruction. Sch Psychol Q 17(3):242Google Scholar
  34. 34.
    Bul KC et al (2016) Behavioral outcome effects of serious gaming as an adjunct to treatment for children with attention-deficit/hyperactivity disorder: a randomized controlled trial. J Med Internet Res 18(2):e26PubMedPubMedCentralGoogle Scholar
  35. 35.
    Moher D et al (2010) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Int J Surg 8(5):336–341PubMedGoogle Scholar
  36. 36.
    Higgins JP, Green S (2011) Cochrane handbook for systematic reviews of interventions, vol 4. Wiley, New YorkGoogle Scholar
  37. 37.
    Downs SH, Black N (1998) The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Community Health 52(6):377–384PubMedPubMedCentralGoogle Scholar
  38. 38.
    Roseman F et al (2013) Implementation of the INTERGROWTH-21st project in the UK. BJOG Int J Obstet Gynaecol 120(Suppl 2):117–122Google Scholar
  39. 39.
    Tyson S, Connell L (2009) The psychometric properties and clinical utility of measures of walking and mobility in neurological conditions: a systematic review. Clin Rehabil 23(11):1018–1033PubMedGoogle Scholar
  40. 40.
    Altaim T (2015) Developing a functional outcome measure for individuals with low back pain within a Jordanian physiotherapy service. University of SheffieldGoogle Scholar
  41. 41.
    de Vet HC, Terwee CB, Bouter LM (2003) Current challenges in clinimetrics. J Clin Epidemiol 56(12):1137–1141PubMedGoogle Scholar
  42. 42.
    Feinstein AR (1983) An additional basic science for clinical medicine: IV. The development of clinimetrics. Ann Intern Med 99(6):843–848PubMedGoogle Scholar
  43. 43.
    Kirshner B, Guyatt G (1985) A methodological framework for assessing health indices. J Chronic Dis 38(1):27–36PubMedGoogle Scholar
  44. 44.
    Okasheh ROA (2011) Clinical measurement of functional outcomes of pulmonary rehabilitation. Sheffield Hallam UniversityGoogle Scholar
  45. 45.
    Mawson SJ (1993) Measuring physiotherapy outcome in stroke rehabilitation. Physiotherapy 79(11):762–765Google Scholar
  46. 46.
    Michels E (1983) Measurement in physical therapy. On the rules for assigning numerals to observations. Phys Ther 63(2):209–215PubMedGoogle Scholar
  47. 47.
    Stevens SS (1946) On the theory of scales of measurement. Science 103(2684):677–680PubMedGoogle Scholar
  48. 48.
    Weiss PL et al (2009) Video capture virtual reality: a decade of rehabilitation assessment and intervention. Phys Ther Rev 14(5):307–321Google Scholar
  49. 49.
    Nelson AM (2008) Addressing the threat of evidence-based practice to qualitative inquiry through increasing attention to quality: a discussion paper. Int J Nurs Stud 45(2):316–322PubMedGoogle Scholar
  50. 50.
    Sackett DL et al (2002) Evidence-based medicine: how to practice and teach EBM, 2nd edn. Churchill Livingstone, New YorkGoogle Scholar
  51. 51.
    Dongen-Boomsma M et al (2014) Working memory training in young children with ADHD: a randomized placebo-controlled trial. J Child Psychol Psychiatry 55(8):886–896PubMedGoogle Scholar
  52. 52.
    Egeland J, Aarlien AK, Saunes B-K (2013) Few effects of far transfer of working memory training in ADHD: a randomized controlled trial. PLoS One 8(10):e75660PubMedPubMedCentralGoogle Scholar
  53. 53.
    Johnstone SJ et al (2012) Neurocognitive training for children with and without AD/HD. ADHD Atten Deficit Hyperact Disord 4(1):11–23Google Scholar
  54. 54.
    Klingberg T et al (2005) Computerized training of working memory in children with ADHD-a randomized, controlled trial. J Am Acad Child Adolesc Psychiatry 44(2):177–186PubMedGoogle Scholar
  55. 55.
    Dovis S et al (2015) Improving executive functioning in children with ADHD: Training multiple executive functions within the context of a computer game. A randomized double-blind placebo controlled trial. PLoS One 10(4):e0121651PubMedPubMedCentralGoogle Scholar
  56. 56.
    Weerdmeester J et al (2016) A feasibility study on the effectiveness of a full-body videogame intervention for decreasing attention deficit hyperactivity disorder symptoms. Games Health J 5(4):258–269PubMedGoogle Scholar
  57. 57.
    van der Oord S et al (2014) A pilot study of the efficacy of a computerized executive functioning remediation training with game elements for children with ADHD in an outpatient setting: outcome on parent- and teacher-rated executive functioning and ADHD behavior. J Attent Disord 18(8):699–712Google Scholar
  58. 58.
    Ruiz-Manrique G, Tajima-Pozo K, Montañes-Rada F (2014) Case report: “ADHD Trainer”: the mobile application that enhances cognitive skills in ADHD patients. F1000Research 3Google Scholar
  59. 59.
    Bamford C (2016) The effects of self-monitoring using an iPad to increase on-task behaviors of high school students with Attention Deficit/Hyperactivity Disorder (ADHD). Rowan UniversityGoogle Scholar
  60. 60.
    Pinna JE (2015) Development and preliminary testing of tablet application to increase reading motivation and summarization for adolescent students with ADHD. University of BaltimoreGoogle Scholar
  61. 61.
    Shih CH, Wang SH, Wang YT (2014) Assisting children with attention deficit hyperactivity disorder to reduce the hyperactive behavior of arbitrary standing in class with a Nintendo Wii remote controller through an active reminder and preferred reward stimulation. Res Dev Disabil 35(9):2069–2076PubMedGoogle Scholar
  62. 62.
    Lin C-Y et al (2016) The effect of literacy learning via mobile augmented reality for the students with ADHD and reading disabilities, in universal access in human–computer interaction: users and context diversity, Pt III. In: Antona M, Stephanidis C (eds.) 103–111Google Scholar
  63. 63.
    Rijo R et al (2015) Mysterious bones unearthed: development of an online therapeutic serious game for children with attention deficit-hyperactivity disorder. In: CruzCunha MM et al (eds) Conference on Enterprise Information Systems/International Conference on Project Management/Conference on Health and Social Care Information Systems and Technologies, Centeris/Projman/Hcist. pp 1208–1216Google Scholar
  64. 64.
    Bussing R et al (2012) ADHD knowledge, perceptions, and information sources: perspectives from a community sample of adolescents and their parents. J Adolesc Health 51(6):593–600PubMedPubMedCentralGoogle Scholar
  65. 65.
    Tamm L et al (2013) Preliminary data suggesting the efficacy of attention training for school-aged children with ADHD. Dev Cogn Neurosci 4:16–28PubMedGoogle Scholar
  66. 66.
    Tucha O et al (2011) Training of attention functions in children with attention deficit hyperactivity disorder. ADHD Attent Deficit Hyperact Disord 3(3):271–283Google Scholar
  67. 67.
    Johnstone SJ et al (2010) A pilot study of combined working memory and inhibition training for children with AD/HD. ADHD Attent Deficit Hyperact Disord 2(1):31–42Google Scholar
  68. 68.
    Hovik KT et al (2013) RCT of working memory training in ADHD: long-term near-transfer effects. PLoS One 8(12):e80561PubMedPubMedCentralGoogle Scholar
  69. 69.
    Shalev L, Tsal Y, Mevorach C (2007) Computerized progressive attentional training (CPAT) program: effective direct intervention for children with ADHD. Child Neuropsychol 13(4):382–388PubMedGoogle Scholar
  70. 70.
    Steiner NJ et al (2014) Neurofeedback and cognitive attention training for children with attention-deficit hyperactivity disorder in schools. J Dev Behav Pediatr 35(1):18–27PubMedGoogle Scholar
  71. 71.
    Steiner NJ et al (2011) Computer-based attention training in the schools for children with attention deficit/hyperactivity disorder: a preliminary trial. Clin Pediatr 50(7):615–622Google Scholar
  72. 72.
    Gray S et al (2012) Effects of a computerized working memory training program on working memory, attention, and academics in adolescents with severe LD and comorbid ADHD: a randomized controlled trial. J Child Psychol Psychiatry 53(12):1277–1284PubMedGoogle Scholar
  73. 73.
    Green CT et al (2012) Will working memory training generalize to improve off-task behavior in children with attention-deficit/hyperactivity disorder? Neurotherapeutics 9(3):639–648PubMedPubMedCentralGoogle Scholar
  74. 74.
    Chacko A et al (2014) A randomized clinical trial of Cogmed working memory training in school-age children with ADHD: a replication in a diverse sample using a control condition. J Child Psychol Psychiatry 55(3):247–255PubMedGoogle Scholar
  75. 75.
    Rabiner DL et al (2010) A randomized trial of two promising computer-based interventions for students with attention difficulties. J Abnorm Child Psychol 38(1):131–142PubMedGoogle Scholar
  76. 76.
    World Health Organization (2017) The ICD-10 classification of mental and behavioural disorders: clinical descriptions and diagnostic guidelines. Cited 22nd Sep 2017.
  77. 77.
    National Institute for Health and Care Excellence (2017) Attention deficit hyperactivity disorder: the MICE guideline on diagnosis and management of ADHD in children, young people and adults. Cited 22nd Sep 2017.Google Scholar
  78. 78.
    Bonell C et al (2012) Realist randomised controlled trials: a new approach to evaluating complex public health interventions. Soc Sci Med 75(12):2299–2306PubMedGoogle Scholar
  79. 79.
    Greenhalgh T, Howick J, Maskrey N (2014) Evidence based medicine: a movement in crisis? BMJ 348:g3725PubMedPubMedCentralGoogle Scholar
  80. 80.
    Subcommittee on Attention deficit hyperactivity disorder steering committee on quality improvement and management: clinical practice guideline for the diagnosis, evaluation, and treatment of attention-deficit/hyperactivity disorder in children and adolescents. Pediatrics, 2011: 2011-2654Google Scholar
  81. 81.
    Janz NK, Becker MH (1984) The health belief model: a decade later. Health Educ Q 11(1):1–47PubMedGoogle Scholar
  82. 82.
    Rosenstock IM (1974) Historical origins of the health belief model. Health Educ Monogr 2(4):328–335Google Scholar
  83. 83.
    Swift KD et al (2013) Transition to adult mental health services for young people with Attention Deficit/Hyperactivity Disorder (ADHD): a qualitative analysis of their experiences. BMC Psychiatry 13(1):74PubMedPubMedCentralGoogle Scholar
  84. 84.
    Powell L, Parker J, Robertson N, Harpin V (2017) Attention deficit hyperactivity disorder: is there an app for that? Suitability assessment of apps for children and young people with ADHD. JMIR Mhealth Uhealth 5(10):e145PubMedPubMedCentralGoogle Scholar
  85. 85.
    Powell L, Parker J, Harpin V (2017) ADHD: is there an app for that? A suitability assessment of apps for the parents of children and young people with ADHD. JMIR Mhealth Uhealth 5(10):e149PubMedPubMedCentralGoogle Scholar
  86. 86.
    Barnett B, Corkum P, Elik N (2012) A web-based intervention for elementary school teachers of students with attention-deficit/hyperactivity disorder (ADHD). Psychol Serv 9(2):227–230PubMedGoogle Scholar
  87. 87.
    Benyakorn S et al (2016) Current state and model for development of technology-based care for attention deficit hyperactivity disorder. Telemed E-Health 22(9):761–768Google Scholar
  88. 88.
    Bishop J (2014) Gamification for human factors integration: Social, education, and psychological issues xxiv, 336 pp Hershey. Information Science Reference/IGI Global; US, PA, US, p 2014Google Scholar
  89. 89.
    Bonarini A et al (2016) A huggable, mobile robot for developmental disorder interventions in a multi-modal interaction space. In: 2016 25th IEEE International Symposium on Robot and Human Interactive Communication. p 823–830Google Scholar
  90. 90.
    Bul KCM et al (2015) Development and user satisfaction of “plan-it commander’’, a serious game for children with ADHD. Games Health J 4(6):502–512PubMedGoogle Scholar
  91. 91.
    Chan S, Parish M, Yellowlees P (2015) Telepsychiatry today. Curr Psychiatry Rep 17(11):89PubMedGoogle Scholar
  92. 92.
    Chen YL, Chen SH, Gau SS (2015) ADHD and autistic traits, family function, parenting style, and social adjustment for Internet addiction among children and adolescents in Taiwan: a longitudinal study. Res Dev Disabil 39:20–31PubMedGoogle Scholar
  93. 93.
    Christiansen H et al (2014) Slow cortical potential neurofeedback and self-management training in outpatient care for children with ADHD: study protocol and first preliminary results of a randomized controlled trial. Front Hum Neurosci 8:943PubMedPubMedCentralGoogle Scholar
  94. 94.
    Dale O, Grut L (2015) Mainstream ICT Can support children and adolescents with ADHD and/or autism in their everyday activities. Stud Health Technol Inform 217:679–684PubMedGoogle Scholar
  95. 95.
    Duffy LV (2013) Testing the efficacy of the creating opportunities for parent empowerment (COPE) intervention during hospital to home transition: empowering parents of children with epilepsy and other neurological conditions. Boston College 204Google Scholar
  96. 96.
    Enebrink P et al (2012) Internet-based parent management training: a randomized controlled study. Behav Res Ther 50(4):240–249PubMedGoogle Scholar
  97. 97.
    Epstein JN et al (2013) The myADHDportal.Com Improvement Program: an innovative quality improvement intervention for improving the quality of ADHD care among community-based pediatricians. Clinical Practice in Pediatric. Psychology 1(1):55–67Google Scholar
  98. 98.
    Fiellin LE, Hieftje KD, Duncan LR (2014) Videogames, here for good. Pediatrics 134(5):849–851PubMedPubMedCentralGoogle Scholar
  99. 99.
    Frutos-Pascual M, Garcia Zapirain B, Mendez Zorrilla A (2014) Adaptive tele-therapies based on serious games for health for people with time-management and organisational problems: preliminary results. Int J Environ Res Public Health 11(1):749–772PubMedPubMedCentralGoogle Scholar
  100. 100.
    Frutos-Pascual M, Garcia-Zapirain B (2015) Assessing visual attention using eye tracking sensors in intelligent cognitive therapies based on serious games. Sensors 15(5):11092–11117PubMedGoogle Scholar
  101. 101.
    Halperin JM et al (2013) Training executive, attention, and motor skills: a proof-of-concept study in preschool children With ADHD. J Attent Disord 17(8):711–721Google Scholar
  102. 102.
    Janeslatt G, Kottorp A, Granlund M (2014) Evaluating intervention using time aids in children with disabilities. Scand J Occup Ther 21(3):181–190PubMedGoogle Scholar
  103. 103.
    Kim J-W, Sharma V, Ryan ND (2015) Predicting methylphenidate response in ADHD using machine learning approaches. Int J Neuropsychopharmacol 18(11):1–7Google Scholar
  104. 104.
    Lim CG et al (2012) A brain-computer interface based attention training program for treating attention deficit hyperactivity disorder. PLoS One 7(10):e46692 [Electronic Resource] PubMedPubMedCentralGoogle Scholar
  105. 105.
    Mazurek MO, Engelhardt CR (2013) Video game use in boys with autism spectrum disorder, ADHD, or typical development. Pediatrics 132(2):260–266PubMedGoogle Scholar
  106. 106.
    Myers K, Stoep AV, Lobdell C (2013) Feasibility of conducting a randomized controlled trial of telemental health with children diagnosed with attention-deficit/hyperactivity disorder in underserved communities. J Child Adolesc Psychopharmacol 23(6):372–378PubMedPubMedCentralGoogle Scholar
  107. 107.
    Nie J et al (2016) Impaired inhibition and working memory in response to internet-related words among adolescents with internet addiction: a comparison with attention-deficit/hyperactivity disorder. Psychiatry Res 236:28–34PubMedGoogle Scholar
  108. 108.
    Pandria N et al (2015) The future of mobile health ADHD applications projecting WHAAM application on future mobile health. In: Proceedings of 2015 International Conference on Interactive Mobile Communication Technologies and Learning (Imcl), pp 279–282Google Scholar
  109. 109.
    Rohani DA, Sorensen HB, Puthusserypady S (2014) Brain–computer interface using P300 and virtual reality: a gaming approach for treating ADHD. In: Conference Proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 3606–9Google Scholar
  110. 110.
    Rosch KS, Mostofsky SH (2016) Increased delay discounting on a novel real-time task among girls, but not boys, with ADHD. J Int Neuropsychol Soc 22(1):12–23PubMedGoogle Scholar
  111. 111.
    Schafer EC et al (2014) Use of wireless technology for children with auditory processing disorders, attention-deficit hyperactivity disorder, and language disorders. Semin Hear 35(3):193–205Google Scholar
  112. 112.
    Schuck S et al (2016) Designing an iPad app to monitor and improve classroom behavior for children with ADHD: iSelfControl feasibility and pilot studies. Plos One 11(10):e0164229PubMedPubMedCentralGoogle Scholar
  113. 113.
    Silva AP et al (2015) Measurement of the effect of physical exercise on the concentration of individuals with ADHD. PLoS One 10(3):e0122119 [Electronic Resource] PubMedPubMedCentralGoogle Scholar
  114. 114.
    Stephenson J (2016) Using the Choiceboard Creator™ app on an iPad© to teach choice making to a student with severe disabilities. AAC Augment Altern Commun 32(1):49–57PubMedGoogle Scholar
  115. 115.
    Tse YJ et al (2015) Teletherapy delivery of caregiver behavior training for children with attention-deficit hyperactivity disorder. Telemed E-Health 21(6):451–458Google Scholar
  116. 116.
    Stoep AV, Myers K (2013) Methodology for conducting the children’s attention-deficit hyperactivity disorder telemental health treatment study in multiple underserved communities. Clin Trials 10(6):949–958PubMedCentralGoogle Scholar
  117. 117.
    Wallace C et al (2016) Attention-deficit/hyperactivity disorder, behavior regulation and virtual school support. J Dev Behav Pediatr 37(3):254–256PubMedGoogle Scholar
  118. 118.
    Wehmeier PM et al (2012) Does atomoxetine improve executive function, inhibitory control, and hyperactivity? Results from a placebo-controlled trial using quantitative measurement technology. J Clin Psychopharmacol 32(5):653–660PubMedGoogle Scholar
  119. 119.
    Wehmeier PM et al (2014) Does stimulant pretreatment modify atomoxetine effects on core symptoms of ADHD in children assessed by quantitative measurement technology? J Attent Disord 18(2):105–116Google Scholar
  120. 120.
    Weinstein A, Weizman A (2012) Emerging association between addictive gaming and attention-deficit/hyperactivity disorder. Curr Psychiatry Rep 14(5):590–597PubMedGoogle Scholar
  121. 121.
    Wronska N, Garcia-Zapirain B, Mendez-Zorrilla A (2015) An iPad-Based tool for improving the skills of children with attention deficit disorder. Int J Environ Res Public Health 12(6):6261–6280 [Electronic Resource] PubMedPubMedCentralGoogle Scholar
  122. 122.
    Wronska N, Garcia-Zapirain B, Mendez-Zorrilla A (2015) An iPad-Based tool for improving the skills of children with attention deficit disorder. Int J Environ Res Public Health 12(6):6261–6280PubMedPubMedCentralGoogle Scholar

Copyright information

© The Author(s) 2017

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.School of Health and Related ResearchSheffieldUK
  2. 2.Ryegate Children’s CentreSheffieldUK

Personalised recommendations