RETRACTED

This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:

(1)Discrepancies in scope(2)Discrepancies in the description of the research reported(3)Discrepancies between the availability of data and the research described(4)Inappropriate citations(5)Incoherent, meaningless and/or irrelevant content included in the article(6)Manipulated or compromised peer review

The presence of these indicators undermines our confidence in the integrity of the article’s content and we cannot, therefore, vouch for its reliability. Please note that this notice is intended solely to alert readers that the content of this article is unreliable. We have not investigated whether authors were aware of or involved in the systematic manipulation of the publication process.

In addition, our investigation has also shown that one or more of the following human-subject reporting requirements has not been met in this article: ethical approval by an Institutional Review Board (IRB) committee or equivalent, patient/participant consent to participate, and/or agreement to publish patient/participant details (where relevant).

Wiley and Hindawi regrets that the usual quality checks did not identify these issues before publication and have since put additional measures in place to safeguard research integrity.

We wish to credit our own Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation.

The corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement to this retraction. We have kept a record of any response received.

Complexity
Volume 2021 (2021), Article ID 9435262, 14 pages
https://doi.org/10.1155/2021/9435262

Review Article

Comparative Efficacy and Acceptability of Nonpharmacotherapy in the Treatment of Inattention for ADHD: A Network Meta-Analysis

Xusheng Che,¹ Choi Jong-Hwan,² and Xiuhai Shang³

¹Sport and Science College, Nantong University, Nantong, Jiangsu 226019, China
²Chungbuk National University, Cheongju 28644, Republic of Korea
³Department of Physical Education, Sangmyung University, Seoul 03016, Republic of Korea

Correspondence should be addressed to Xusheng Che; xsche113@ntu.edu.cn

Received 22 April 2021; Revised 10 May 2021; Accepted 14 May 2021; Published 26 May 2021

Academic Editor: Zhihan Lv

Copyright © 2021 Xusheng Che et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

This article is aimed to compare the efficacy and acceptability of exercise intervention with other nonpharmacological therapies in improving attention in ADHD patients and then rank those therapies. Methods. Relevant RCT studies from the Cochrane Library, Web of Science, Psycinfo, and PubMed were systematically searched from January 2011 to January 2020. The literature was screened, and the researchers extracted and used Stata16 and WinBugs1.4 independently to analyze the data. Results. The improvement of “attention deficit” in ADHD patients was measured in 83 independent studies with 4,998 participants and 14 nonpharmacological therapies. Therapies were ranked as follows, according to the efficacy and acceptability of the cumulative probability area under the curve (SUCRA): (1) meditation and cognitive; (2) sport intervention therapy; (3) vitamin and mineral; (4) cognitive behavioral therapy; (5) computer training; (6) psychological education; (7) working memory training; (8) neurofeedback therapy; (9) polyunsaturated fatty acids; (10) mineral supplements; (11) meditation; (12) vitamin; (13) biofeedback therapy; and (14) school-based training. However, because the effect size of meditation and cognitive therapy was at a marginally significant level (SMD = −1.07, (−2.15, 0.00)), the robustness of the results was far lower than that of sport intervention therapy. Therefore, careful interpretation is needed in practice. Conclusion. Considering the efficacy and acceptability of various nonpharmacological therapies, sport intervention has a more stable effect on the improvement of attention deficit. Secondly, meditation and cognitive therapy, cognitive behavioral therapy, and computer training can also be effective complementary tools.

1. Introduction

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder commonly seen in the early childhood, with a global incidence rate of about 1.4–3.0% among children, and most of them are males. Its main symptoms include attention deficit, hyperactivity, impulsivity, and a mixture of the three disorders [1]. Globally, it is estimated that 8–10% [2] of children and 5-6% [3] of adults are affected to varying degrees. Affected by genetic inheritance and prenatal environmental factors, inattention in patients is difficult to be effectively alleviated because the onset period spans from the early childhood to late adolescence (4–17 years old) and continues until adulthood [4]. Therefore, it has a serious negative impact on the early academic development of patients and the socialization process such as work and life in adulthood [5].

The traditional treatment of ADHD mostly uses pharmacological intervention to improve attention and alleviate hyperactive impulse and other explicit behavior by regulating the transmission of signal factors between synapses [6]. For example, central stimulants such as methylphenidate and amphetamine, one of the most commonly used dopamine and norepinephrine reuptake inhibitors, enhance the effects of monoamines and increase arousal levels in the brain’s limbic nervous system by gradually increasing the concentration of these two neurotransmitters in the synaptic cleft. In this way, the attention level and inhibition control ability of patients can be improved [7]. Noncentral stimulant drugs, such as atomoxetine, selectively inhibit the reuptake of noradrenaline, 5-HTR2A, and dopamine by NETS, increase the concentration of noradrenaline in the prefrontal cortex (PFC), and thus, improve the hyperactivity symptoms [8, 9]. Although pharmacological therapy has the advantages of short duration and quick effect, it has been criticized for the rapid rebound of symptoms after drug withdrawal, accompanied by adverse short-term negative effects such as nausea, drowsiness, insomnia, and low emotional tolerance [10], as well as the obstruction of its life-long growth and development process [11], leading to a high withdrawal rate [12, 13]. On the other hand, although psychobehavioral intervention therapy has no obvious physiological negative effects, it is difficult to be adopted by most patients because of the complicated treatment process and long cycle with slow effect [14]. In addition, hyperactivity symptoms usually recur with the end of the medication or psychobehavioral therapy cycle [15]. Therefore, there is an urgent need for a highly effective, cheap, and operable nonpharmacological therapy.

Based on the characteristics of hyperactive impulsion of patients, modern scholars have integrated sport intervention therapy into traditional treatment methods and achieved the expected effect. Studies demonstrate that [16], in addition to the general health benefits, exercise can also increase the level of blood flow and neurotransporter in the brain, thus improving neuroplasticity, so as to better concentrate and process information. A recent meta-analysis also showed that [17], under the intervention of multiple combinations of moderate-intensity aerobic exercise, ADHD patients’ attention deficit can not only be alleviated in the short term but also obtain long-term enhancement effect. However, compared with sport intervention, other nonpharmacological therapies have different mechanisms of action, but their efficacy in practice is also worthy of affirming [18–23]. For a long time, there are still differences in the ranking of the advantages and disadvantages of various nonpharmacological therapies in improving ADHD symptoms [24–27]. In view of the fact that the traditional meta-analysis only compares the efficacy differences of pharmacotherapy such as stimulant (amphetamine and methylphenidate) and nonstimulant therapy (tomoxetine, clonidine sustained release, and guanfacine sustained release), so far, few studies have compared the effects of various nonpharmacotherapies in practice, and there is no study to directly compare the efficacy of exercise intervention with other nonpharmacotherapies. Therefore, it is not possible to confirm the overranking of sport interventions in terms of specific efficacy and acceptability compared with other nonpharmacological therapies. Consequently, this study intends to explore the ranking of sport intervention therapy in nonpharmacological therapy for ADHD through network meta-analysis on the basis of comprehensive comparison of efficacy and acceptability.

2. Materials and Methods

2.1. Retrieval Strategy and Selection Criteria

The published and unpublished randomized controlled trials (RCTs) from PubMed, Cochrane Library, Psycinfo, and Web of Science were systematically searched. The searching items included “Attention Deficit Hyperactivity Disorder,” “Non-Pharmacotherapy,” “Cognitive Behavior Therapy,” “Physical Activity,” “Cognitive Training,” and “Mindfulness,” Working Memory Training,” “Neurofeedback,” “Biofeedback,” “Psychoeducation,” “School-based Treatment,” “Dietary,” “Multivitamin,” “Mineral,” and “Polyunsaturated Fatty Acid.” The searching content was limited to English literature, and the searching time span was from January 2011 to January 2021. For articles that are registered but not yet published, or where the relevant data is not fully presented in the results, the researchers contacted the author by e-mail or searched the journal's website for unpublished data to supplement the incomplete report in the original paper or to provide data for unpublished research.

Non-RCT studies, systematic reviews, animal studies, and studies with a sample size of less than 10 people were excluded. Eventually, 14 kinds of nonpharmacological therapy were included: cognitive behavioral therapy (CBT), working memory training (WMT), computer training (CT), meditation (MAP), meditation and cognitive training (MC), vitamin and mineral (VM) supplements, vitamin (VI), mineral (MI), polyunsaturated fatty acids (PUFAs), neural feedback training (NF), school-based training (SB), biofeedback training (BF), psychological education (PE), and sport intervention (SPT).

2.2. Data Extraction and Quality Assessment

Four researchers (WLY, XSY, WC, and CJH) independently screened the qualified literature and collected the main outcomes and supplementary materials which support the original reports and could be used for this analysis, and four researchers (CZY, SJ, YD, and XHS) extracted relevant information using predetermined data extraction tables and assessed the risk of bias. Any differences shall be resolved by consensus and arbitration by the corresponding author (XSC).

2.3. Extraction of Results

In this study, the score changes of inattention of the subjects before and after the experiment were extracted as the efficacy evaluation index. The scores were derived from results measured by a variety of standardized psychological scales, such as The Barkley ADHD Current Symptoms Scale, ADHD Rating Scale, “Conners Adult ADHD Rating Scale Self-Report,” and “Swanson, Nolan, and Pelham (SNAP-IV) DSM-IV ADHD Rating Scale.” For the results of different surveyors in the same scale, priority is given to the results reported by observers, followed by those reported by teachers and finally by parents [28]. The study also assessed the acceptability of each treatment or withdrawal from the course of treatment due to certain reasons.

2.4. Data Analysis

Under the random-effects model, a series of paired meta-analyses were performed for all directly compared studies, with relative risk (RR) used for dicaromatic outcomes and normalized mean difference (SMD) used for continuous outcomes, with a 95% confidence interval. I² and τ² were used to assess homogeneity between studies [29].

where is the number of people who quit the experimental group; is the total number of people in the experimental group; is the number of dropouts in the control group; and is the total number in the control group.

where is the mean of group 1, is the sample size of group 1, is the standard deviation of group 1, is the mean of group 2, is the sample size of group 2, and is the standard deviation of group 2.

Homogeneity test:

where is standardized sum of squares of effect size, is the weight of the Nth study in all the included studies, is the number of studies included in meta-analysis, is the odds ratio, MH: Mantel–Haenszel method, and is the number of target events in the experimental group.

First, Stata16 was used to map the network evidence for each therapy and to test for homogeneity. Secondly, based on the Bayesian framework, WinBugs1.4.3 was used for network analysis, and all relative treatment effects were compared and evaluated by combining direct and indirect comparative evidence. The average effect estimate (SMD or RR) and its 95% confidence interval (CI) for each comparison were calculated using the Markov chain Monte Carlo (MCMC) method, with both Markov chains running simultaneously [30]. Under the assumption that the convergence degree of the MCMC model is stable (the value of R approaches 1, indicating that MCMC simulation has good convergence and is relatively stable), the posterior probability is inferred according to the prior probability.

where is the interchain variance, is the mean of the in-chain variances, and is the total amount of Markov chains.

Because fixed-effect models are based on the assumption that all studies come from the same population, they deviate from reality. In order to better fit the model, the random-effects model was used for analysis in this study. The model is iterated 100,000 times, and the first 5000 times are used for annealing to eliminate the influence of the initial value. The degree of convergence is judged according to the model iteration diagram. The iterative steps of the algorithm are as follows:

 : random sample; : sample mean of the function : number of convergent steps; : number of iteration steps

(1)A candidate state is randomly selected according to the suggested distribution .(2)The acceptance probability is calculated.(3)A number u is randomly selected from the interval (0, 1) according to uniform distribution. If u ≤ accept probability , then  = ; otherwise,  = x.(4)The three steps mentioned above are repeated n times; then, the posterior sample set X¹, X², …, Xn can be obtained.(5)Each moment of the posterior distribution is calculated according to the posterior samples, and the corresponding statistical inference is made.

After dividing the evidence of a specific comparison into direct evidence and indirect evidence by the node-splitting method, the inconsistency of the model was calculated [31], and local and global inconsistency tests were used to check the consistency of the network. The local inconsistency test is used to evaluate the inconsistency of any closed loop in the network evidence graph. The overall inconsistency test is to test the goodness of fit of the model through the interaction design of each therapy [32]. If any relevant sources of deviation are found, sensitivity analysis or metaregression analysis will be performed [31, 33, 34]. By drawing a cumulative probability chart of the efficacy and acceptability of each nonpharmacological therapy, the area under the cumulative probability curve (SUCRA) was used to rank the efficacy and acceptability of each therapy [32, 35, 36]. If more than 10 studies were included, publication bias and small-sample study effect were verified by Egger [37]. The Modified JADAD scale was used to evaluate the risk of bias. Additionally, we assessed the certainty of evidence contributing to this network analysis of the main outcomes with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework.

3. The Results

3.1. Literature Retrieval Results

A total of 2952 studies were retrieved, including 2118 from the Cochrane Library, 465 from Web of Science, 174 from Psycinfo, and 195 from PubMed. 2,715 either repeated studies or inconsistent research topics were excluded from systematic reviews. After reading the full text, 153 studies with inconsistent outcome indicators and nonrandomized controlled trials were excluded, and 86 studies were included, six of which were CBT vs. NF; one study was CBT vs. PE; seven studies were CBT vs. control; nine studies were WMT vs. control; one study was CT vs. NF; six studies were CT vs. control; eight studies were MAP vs. control; two studies were MAP vs. control; two studies were MC vs. control; three studies were VM vs. control; five studies were MI vs. control; nine studies were PUFA vs. control; three studies were NF vs. BF; two studies were NF vs. SPT; eleven studies were NF vs. control; two studies were SB vs. control; four studies were PE vs. control; and two studies were SPT vs. control. See Figure 1 for the literature screening process.

Figure 1: Flowchart of study selection.

General characteristics of the literature: 86 RCTS were included, involving 14 kinds of nonpharmacological treatments, and they are cognitive behavioral therapy (CBT), working memory training (WMT), computer training (CT), meditation (MAP), meditation and cognitive (MC), vitamin and mineral (VM), vitamin (VI), mineral (MI), polyunsaturated fatty acid (PUFA), neural feedback (NF), biofeedback (BF), school-based training (SB), psychological education (PE), and sport intervention therapy (SPT). A total of 4998 participants were included, with 2538 in the experimental group and 2460 in the control group. Forty-three studies reported CAARS scores; three studies reported BCS scores; ten studies reported SNAP scores; three studies reported IPDDAI scores; two studies reported DBDRS-P scores; sixteen studies reported ASRS scores; two studies reported DISYPs scores; CBCL scores were reported in four studies; SWAN score was reported in three studies; three studies reported FBB-HKS scores; one study reported DSM-IV scores; and two studies reported a CSI-4 score. The Jadad score is above average, which means the quality of included studies in this analysis is above average (Jadad score ≥ 4), and only six studies were below average in quality (Jadad score < 4) (See Table 1). Our results also consider the GRADE quality ratings.

Table 1: Characteristics of included studies.

Author, year
Design
Sample size
Age
Jadad score
Tool

Expl
Ctrol
Expl
Ctrol
Exp
Ctrol

Naomi, 2014
CBT
Ctrol
34
36
8.9 ± 1
8.4 ± 1.1
4
CAARS

Young, 2016
PUFA
PE
11
14

7
SNAP

Safren, 2010
CBT
Ctrol
43
43
42.3 ± 10
44 ± 12.2
6
CAARS-S

Young, 2016
PUFA
Ctrol
11
11

7
SNAP

Arthur, 2015
CBT
Ctrol
43
43
20.3

6
CAARS-S

Toshiko, 2015
PUFA
Ctrol
38
38
13.7 ± 1.1

3
CAARS

LaCount, 2015
CBT
Ctrol
17
17

4
BCS

Crippa, 2017
PUFA
Ctrol
25
23
11.1 ± 1.9
10.9 ± 1.4
6
ASRS

Leanne, 2015
CBT
Ctrol
10
9
4.9 ± 1.1
5.2 ± 1.4
4
SNAP-IV

Tzang, 2016
PUFA
Ctrol
58
58
9.3 ± 2.7
9 ± 2.2
6
SNAP

Fleming, 2015
CBT
Ctrol
19
16
21.2 ± 1.7
21.5 ± 1.1
6
BCS

Cornu, 2017
PUFA
Ctrol
80
82
7 ± 3
6.9 ± 2.9
6
ASRS

Wang, 2016
CBT
Ctrol
10
12
26 ± 4.5
24.8 ± 4
7
CAARS-S

Muller, 2014
PUFA
Ctrol
55
55

6
DISYPs

Zahra, 2014
WMT
Ctrol
12
12

5
SNAP

Dienke, 2015
PUFA
Ctrol
19
18
10.3 ± 2.0

6
CBCL

Anna, 2015
WMT
Ctrol
13
13
5.29 ± 0.4
5.3 ± 0.4
7
IPDDAI

Crippa, 2019
PUFA
Ctrol
25
25
11.1 ± 1.9
10.9 ± 1.4
6
ASRS

Wei, 2017
WMT
Ctrol
11
9
10.3 ± 3.5
10.3 ± 2.9
5
SNAP-IV

Celestino, 2019
PUFA
Ctrol
46
49

6
CAARS

Martine, 2014
WMT
Ctrol
27
24
6.5 ± 0.6
6.6 ± 0.7
6
CAARS-S

Pelsser, 2011
PUFA
Ctrol
50
50
6.8 ± 1.3
7 ± 1.3
6
ASRS

Jens, 2013
WMT
Ctrol
38
37

2
CAARS-S

Moreno, 2019
NF
CBT
21
19
9.21 ± 1.9
8.1 ± 1.3
7
ASRS

Agnese, 2018
WMT
Ctrol
40
40

6
IPDDAI

Minder, 2018A
NF
CBT
29
23
11.4 ± 1.7
10.8 ± 1.8
6
CAARS

Beck, 2013
WMT
Ctrol
26
26

6
CAARS-S

Minder, 2018B
NF
CBT
25
25
10.6 ± 2.3
10.4 ± 2.0
6
CAARS

Sandie, 2018
WMT
Ctrol
22
13

6
CAARS-S

Michael, 2017A
NF
CBT
38
41
40 ± 11.2
38 ± 11.3
4
CAARS

Naomi, 2011
CT
Ctrol
13
15
12.4 ± 0.9

6
CAARS-S

Michael, 2017B
NF
CBT
39
41
35.7 ± 11
38 ± 11.3
4
CAARS

Aida, 2018
CT
Ctrol
35
35
9.8 ± 2.0
10.1 ± 1.5
6
CAARS-S

Maurizio, 2014
NF
BF
13
12
10.6 ± 1.3
10 ± 1.2
7
CAARS

Leanne, 2013
CT
Ctrol
51
51
9.1 ± 1.2
9.5 ± 1.5
6
SNAP-IV

Ali, 2011
NF
BF
18
17
9.6 ± 2.2
9.1 ± 1.6
6
CAARS

Oord, 2012
CT
Ctrol
21
22
10 ± 1.0
9.6 ± 1.4
6
DBDRS-P

Strehl, 2017
NF
BF
76
74
8.6 ± 0.9
8.6 ± 0.9
4
ASRS

Choon, 2019
CT
Ctrol
85
87
8.7 ± 1.4
8.6 ± 1.7
6
CAARS-S

Gelade, 2018
NF
SPT
39
37
10 ± 1.9
9.8 ± 2.0
6
SWAN

Bachmann, 2018
MAP
PE
21
19
40 ± 10.6
40 ± 13.8
7
CAARS-S

Gelade, 2016
NF
SPT
39
37
9.8 ± 1.9
9.6 ± 1.8
6
SWAN

Dovis, 2015
MAP
PE
41
40
10.3 ± 1.3
10.5 ± 1.3
6
CAARS-S

Chien, 2016
SPT
Ctrol
16
16
10.7 ± 1
10 ± 1.2
7
CBCL

John, 2017
MAP
Ctrol
11
11
40.6 ± 6.8
36.2 ± 6.9
2
CAARS-S

Claudia, 2012
SPT
Ctrol
10
11
9.1 ± 1.1

6
CBCL

Huguet, 2017
MAP
Ctrol
5
5
9.2 ± 1.3

7
CAARS-S

Naomi, 2014
NF
Ctrol
34
36
8.4 ± 1.1
8.9 ± 1
5
CAARS

Poppy, 2013
MAP
Ctrol
32
29
39.5 ± 9.5
33.9 ± 9.8
2
CAARS-S

Naomi, 2011
NF
Ctrol
13
15
12.4 ± 0.9

6
CAARS-S

Jillian, 2013
MAP
Ctrol
20
20
15.5 ± 1.6

3
CAARS-S

Holger, 2010
NF
Ctrol
59
35
9.1 ± 1.3
9.5 ± 1.1
7
FBB-HKS

Herman, 2017
MAP
Ctrol
50
50
6.3 ± 0.9
5.92 ± 0.7
6
ASRS

M.M, 2010
NF
Ctrol
8
6
10.4 ± 2.3
10 ± 1.7
7
DSM-IV

Lotte, 2018
MAP
Ctrol
60
60
39.7 ± 1
39 ± 10.1
6
CAARS

Meise l, 2013
NF
Ctrol
14
13
9.53 ± 1.8

6
ASRS

Edel, 2014
MAP
Ctrol
39
52
33.8 ± 10
36.7 ± 10
7
CAARS

Bink, 2016
NF
Ctrol
59
31
15.9 ± 3.3
16.2 ± 3
6
ASRS

Gu, 2018
MC
Ctrol
30
26

4
CAARS

Shereena, 2018
NF
Ctrol
24
19
8.7 ± 1.8
9.7 ± 2.4
6
ASRS

Hepark, 2019
MC
Ctrol
55
48
36.5 ± 10
35.2 ± 9
6
CAARS-S

Albrecht, 2017
NF
Ctrol
24
24
11.3 ± 2.8
11.5 ± 3
6
FBB-HKS

Kathryn, 2019
VM
Ctrol
47
46
9.3 ± 1.1
9.9 ± 1.8
6
ASRS

ManHee, 2015
NF
Ctrol
8
8

5
CAARS

Julia, 2018
VM
Ctrol
47
46
10.1 ± 1.6
9.4 ± 1.5
6
ASRS

Alegria, 2017
NF
Ctrol
18
13
14.1 ± 1.5
13.6 ± 2
7
CAARS

Julia, 2017
VM
Ctrol
42
38
34 ± 12.7

6
CAARS

Harris, 2019
NF
Ctrol
11
11

7
CAARS

Hatem, 2018
VI
Ctrol
20
20
9.3 ± 2.6
8.8 ± 3.72
6
CAARS

Joshua, 2012
SB
Ctrol
23
24

3
ASRS

Nadia, 2019
VI
Ctrol
51
51
9.8 ± 2.4
8.6 ± 2.0
6
CAARS

Looyeh, 2012
SB
Ctrol
7
7

5
CSI-4

Eugene, 2011
MI
Ctrol
8
24
8.9 ± 2.3
10.2 ± 2.7
7
SNAP

Young, 2016
PE
Ctrol
14
11

7
SNAP

Fereshteh, 2015
MI
Ctrol
33
33
7.8 ± 1.2
8.4 ± 1.4
6
ASRS

Alexandra, 2015
PE
Ctrol
107
109
35 ± 11
35 ± 10
6
CAARS-S

Vidal, 2013
PE
CBT
17
15
39.4 ± 9.3
39.5 ± 6
4
CAARS-S

Bai, 2015
PE
Ctrol
44
45
9.3 ± 2.8
9.6 ± 2.9
7
ASRS

Maite, 2013
PE
Ctrol
44
37
11.3 ± 3.0
9.9 ± 3.0
6
CAARS

Naomi, 2014
CBT
NF
34
34
8.4 ± 1.1

4
CAARS

Naomi, 2011
CT
NF
13
13
12.4 ± 0.9

6
CAARS-S

Note: CBT = cognitive behavioral therapy; WMT = working memory training; CT = computer training; MAP = meditation; MC = meditation and cognition; VM = vitamin and mineral; VI = vitamin; Mi = mineral; PUFA = polyunsaturated fatty acid; NF = neurofeedback; BF = biofeedback; SB = school-based training; PE = psychological education; SPT = sport intervention therapy; ctrol = control group; CAARS-S = Conners’ Adult ADHD Rating Scale Self-Report; BCS = Barkley ADHD Current Symptoms Scale; SNAP = Swanson, Nolan, Pelham; IPDDAI = rating scale “identificazione precoce del disturbo da deficit di attenzione iperattivita; DBDRS-P = Disruptive Behavior Disorders Rating Scale; ASRS = adult ADHD Self-Report Scale; DISYPs = FBB ADHS parent-rated and teacher-rated programmes; CBCL = Child Behavior Checklist; SWAN = Strengths and Weaknesses in ADHD and Normal Behaviors; FBB-HKS = Fremdbeurteilungsbogen fur Hyperkinetische Storungen; DSM-IV = Diagnostic and Statistical Manual of Mental Disorders; CSI-4 = Children Symptom Inventory.

3.2. Network Meta-Analysis

3.2.1. Homogeneity Test

Homogeneity test results indicate that cognitive behavioral therapy SMD = −0.77 (−1.00, −0.54), working memory training SMD = −0.49 (−0.71, −0.27), computer training SMD = −0.94 (−1.87, −0.01), meditation SMD = −0.66 (−0.85, −0.47), meditation and cognitive training SMD = −0.99 (−1.33, −0.66), vitamin and mineral SMD = −1.78 (−2.13, −1.44), vitamin SMD = −0.86 (−1.17, −0.55), mineral SMD = −0.52 (−0.88, −0.16), polyunsaturated fatty acid SMD = −0.42 (−0.58, −0.26), neural feedback SMD = −0.26 (−0.46, −0.05), school-based practice SMD = −0.60 (−1.11, −0.08), the psychological education SMD = −0.39 (−0.60, −0.19), the sport intervention SMD = −1.04 (−1.68, −0.39), the efficacy was significantly better than that of the control group, and the computer training is superior to the neural feedback therapy SMD = −0.94 (−1.87, −0.01). The efficacy of other comparative studies was not statistically significant. The homogeneity test results showed that 11 groups of comparative studies had high homogeneity, while the degree of heterogeneity among the other comparative studies was medium to high (Table 2).

Table 2: Homogeneity test.

Study
SMD
95% CI
% weight
HI
P
I² (%)
Z
P

CBT-NF
0.12
0.34
0.10
7.46
24.65
0.00
0.80
1.08
0.28

CBT-ctrol
0.77
1.00
0.54
6.76
7.88
0.25
0.24
6.57
0.00

CBT-PE
0.16
0.62
0.94
0.59
0.00
0.00
0.00
0.40
0.69

WMT-ctrol
0.49
0.71
0.27
7.61
110.31
0.00
0.93
4.43
0.00

CT-NF
0.94
1.87
0.01
0.41
0.00
0.00
0.00
1.98
0.05

CT-ctrol
0.53
0.72
0.34
9.96
26.99
0.00
0.82
5.47
0.00

MAP-PE
0.06
0.43
0.30
2.67
0.15
0.70
0.00
0.34
0.73

MAP-ctrol
0.66
0.85
0.47
10.19
16.39
0.02
0.57
6.91
0.00

MC-ctrol
0.99
1.33
0.66
3.21
2.23
0.14
0.55
5.83
0.00

VM-ctrol
1.78
2.13
1.44
3.03
11.44
0.00
0.83
10.16
0.00

VI-ctrol
0.86
1.17
0.55
3.82
10.15
0.00
0.90
5.51
0.00

MI-ctrol
0.52
0.88
0.16
2.79
29.28
0.00
0.86
2.83
0.01

PUFA-PE
0.37
1.15
0.41
0.59
0.00
0.00
0.00
0.92
0.36

PUFA-ctrol
0.42
0.58
0.26
14.19
61.88
0.00
0.87
5.14
0.00

NF-BF
0.16
0.45
0.12
4.46
2.48
0.29
0.19
1.13
0.26

NF-SPT
0.00
0.33
0.33
3.23
0.02
0.87
0.00
0.00
1.00

NF-ctrol
0.26
0.46
0.05
8.20
18.62
0.05
0.46
2.39
0.02

SB-ctrol
0.60
1.11
0.08
1.35
0.28
0.60
0.00
2.26
0.02

PE-ctrol
0.39
0.60
0.19
8.62
49.72
0.00
0.94
3.76
0.00

SPT-ctrol
1.04
1.68
0.39
0.86
13.23
0.00
0.92
3.16
0.00

CBT, cognitive behavioral therapy; WMT, working memory training; CT, computer training; MAP, meditation; MC, meditation and cognition; VM, vitamin and mineral; VI, vitamin; MI, mineral; PUFA, polyunsaturated fatty acid; NF, neurofeedback therapy; BF, biofeedback therapy; SB, school-based exercise; PE, psychological education; SPT, sport intervention; ctrol, control group.

3.2.2. Network Evidence Diagram

As can be seen from Figure 2, there were a total of 14 nonpharmacological therapies and 15 comparative studies included. There were direct comparisons between 6 closed loops and 7 nonpharmacologic therapies. The size of the node represents the sample size of the study, and the width of the line represents the number of comparative studies between the two therapies. The most compared treatments included neurofeedback therapy (NF), polyunsaturated fatty acids (PUFAs), meditation (MAP), mineral (MI), cognitive behavioral therapy (CBT), and working memory training (WMT). Vitamin (VI), school-based training (SB), vitamin and mineral (VM), and meditation and cognitive therapy (MC) had a weak overall relationship with the network structure. In all studies, there was at least one control trial. Due to the presence of closed loops in the network evidence map, a closed loop inconsistency test was required.

Figure 2: Network plot of meta-analysis.

3.2.3. Consistency Test

The loop inconsistency test was used to explore the level of inconsistency among the internal comparison studies of each closed loop. As shown in Figure 3, among the 6 closed loops, the inconsistency factor IF of the other closed loops was close to 0 and all the 95% confidence intervals contained 0, , except for the neurofeedback-motor intervention-control group (NF-SPT-ctrol). Therefore, there was no statistical inconsistency among the internal studies of each closed loop, so the consistency model was adopted for analysis in subsequent studies.

Figure 3: Loop inconsistency test.

As can be seen from Figure 2, there is a closed loop composed of four studies in the network evidence graph. Therefore, the closed loop was split into two independently closed triangular loops using the node-splitting model. By calculating the absolute value of the difference between direct and indirect evidence in each closed loop, the consistency between direct and indirect evidence in the overall model was explored. From the results of local inconsistency of Table 3, it can be seen that there is no inconsistency between the direct and indirect comparisons of the efficacy of the two treatments in all the closed loops (), nor does the inconsistency between the studies included in each of the treatments in all the closed loops reach a significant level ( > 0.05).

Table 3: Local inconsistency test.

Direct
Indirect
Difference
P > 
Tau

Coef
SE
Coef
SE
Coef
SE

CBT-NF
0.01
0.32
0.52
0.40
0.54
0.51
0.30
0.73

CBT-PE
0.15
0.84
0.00
0.41
0.16
0.94
0.87
0.74

CBT-ctrol
0.84
0.31
0.34
0.39
0.51
0.50
0.31
0.73

WMT-ctrol
0.93
0.28
0.37
208.78
0.56
208.78
1.00
0.73

CT-NF
0.69
0.87
0.07
0.41
0.62
0.96
0.52
0.74

CT-ctrol
0.60
0.32
1.31
1.57
0.71
1.60
0.66
0.74

MAP-PE
0.08
0.56
0.16
0.45
0.08
0.72
0.91
0.74

MAP-ctrol
0.82
0.29
0.74
0.66
0.08
0.72
0.91
0.74

MC-ctrol
1.07
0.55
0.23
447.18
0.85
447.18
1.00
0.73

VM-ctrol
1.79
0.46
0.49
365.30
2.28
365.30
1.00
0.73

VI-ctrol
0.92
0.54
0.38
445.12
0.54
445.12
1.00
0.73

MI-ctrol
0.97
0.40
0.33
282.72
0.64
282.72
1.00
0.73

PUFA-PE
0.35
0.84
0.22
0.43
0.57
0.94
0.55
0.74

PUFA-ctrol
0.54
0.26
1.67
1.59
1.13
1.61
0.49
0.74

NF-BF
0.31
0.46
0.09
366.36
0.40
366.36
1.00
0.73

NF-SPT
0.00
0.55
1.19
0.70
1.19
0.88
0.18
0.73

NF-ctrol
0.25
0.25
0.96
0.40
0.71
0.47
0.13
0.73

SB-ctrol
0.63
0.60
0.66
448.17
0.03
448.17
1.00
0.73

PE-ctrol
0.65
0.39
0.70
0.49
0.05
0.63
0.94
0.74

SPT-ctrol
1.56
0.66
0.37
0.59
1.19
0.88
0.18
0.73

CBT: cognitive behavioral therapy; WMT: working memory training; CT: computer training; MAP: meditation; MC: meditation and cognition; VM: vitamin and mineral; VI: vitamin; MI: mineral; PUFA: polyunsaturated fatty acid; NF: neurofeedback therapy; BF: biofeedback therapy; SB: school-based exercise; PE: psychological education; SPT: sport intervention; ctrol: control group.

The results of the inconsistency model test show that both the local inconsistency and loop inconsistency test prove that there is a high consistency among the studies included in the network evidence model. Therefore, the subsequent analysis results are based on the consistency model.

3.2.4. Assessment of Transitivity

In order to adhere to the assumption of transitivity and reduce the risk of biased estimates, the distribution of methodological variables that may act as effect modifiers across treatment comparisons has been compared such as age and nonpharmacotherapy. Most of the participants had a similar mean age from 10 to 20, but there were a few comparisons which had relatively low or high age. Additionally, the comparison of baseline severity also showed that the baseline severity distributed in a similar range on this network analysis, even though this analyses might suffer from ecological bias. See Figure 4 for the assessment of transitivity.

(a)

(b)

Figure 4: Assessment of transitivity.

3.2.5. Publication Bias Test

In traditional meta-analysis, because there is only one effect size, funnel plots are drawn using the standard error of effect value as the vertical axis and effect size as the horizontal axis [38]. However, in the network meta-analysis, multiple meta-analyses with direct comparisons of different controls exist simultaneously. Therefore, the publication bias of different control groups needs to be adjusted at the same time when making Egger detection. Accordingly, when the funnel chart is drawn and adjusted, the vertical axis is the effect size of each study, the horizontal axis is the total effect size of each control group, and the regression line after adjustment is in the middle. As can be seen from the comparison of funnel plots (Figure 5), all the included studies were centrally distributed above the funnel plots and showed a left-right symmetric trend around the zero position. Therefore, it was concluded that there was no publication bias in the included studies on the whole. However, some studies were distributed below the funnel plots, and it indicated that there was a small-sample study effect.

Figure 5: Comparison-adjusted funnel plot and Egger test for each outcome.

3.2.6. Network Meta-Analyses

As there was no direct comparison between sport intervention therapy and many nonpharmacological therapies in the included studies, head-to-head comparison was used to provide indirect evidence for the studies that lacked direct comparison [28]. According to the head-to-head comparison of efficacy in Figure 6, it can be seen that vitamin and mineral (VM, SMD = −1.79 (−2.69, −0.88)), meditation and cognition (MC, SMD = −1.07 (−2.15, 0.00)), mineral (MI, SMD = −0.79 (−1.75, −0.19)), working memory training (WMT, SMD = −0.93 (−1.47, −0.38)), sport intervention therapy (SPT, SMD = −0.90 (1.76, 0.04)), meditation (MAP, SMD = −0.80 (1.32, 0.29)), the psychological education (PE, SMD = −0.67 (−1.27, −0.08)), cognitive behavior therapy (CBT, SMD = −0.65 (−1.12, −0.17)), computer training (CT, SMD = −0.63 (−1.25, −0.02)), polyunsaturated fatty acid (PUFA, SMD = −0.57 (−1.08, −0.07)), and neurofeedback therapy (NF, SMD = −0.45 (−0.87, −0.03)) were significantly better than the control group in improving attention deficit in ADHD patients. In addition, the efficacy of vitamin and mineral combined supplements was significantly better than that of polyunsaturated fatty acids (SMD = −1.21 (−2.25, −0.18)), neurofeedback (SMD = −1.34 (−2.33, −0.34)), biofeedback (SMD = −1.65 (−2.99, −0.30)), and psychoeducation (SMD = −1.11 (−2.19, −0.03)), while there was no significant difference between other therapies. These results were basically consistent with the results of general meta-analysis. The acceptability analysis demonstrated that there was no significant difference between the acceptability of exercise intervention therapy and the remaining nonpharmacological therapy. The acceptability of meditation was significantly better than that of vitamin supplements (RR = 0.10 (0.01, 0.73)), neurofeedback (RR = 0.39 (0.15, 1.00)), and biofeedback (RR = 0.22 (0.07, 0.70)). The acceptability of biofeedback therapy was significantly better than that of the control group (RR = 2.86 (1.14, 7.16)).

Figure 6: The results of network meta-analysis for efficacy and tolerability.

Figure 7 demonstrates a two-dimensional diagram of the efficacy and acceptability of all cross-comparison studies. According to the SUCRA ranking results, the cumulative probability of efficacy of sport intervention was 62%, slightly lower than vitamins and minerals (95%), meditation and cognition (68.8%), minerals (66.4%), and working memory training (65.1%). It was only secondary to vitamins (89.3%) and biofeedback (79.5%) for acceptability.

Figure 7: Treatment ranking and SUCRA plot for efficacy and tolerability.

Taking into account the efficacy and acceptability of each nonpharmacological therapy, the following results were obtained by ranking the area under the cumulative probability curve: in 14 kinds of nonpharmacological intervention therapy, sport intervention therapy (SPT) effect on the improvement of the patients with ADHD attention deficit disorder is superior to the other 12 kinds of nonpharmacological treatments, only secondary to vitamin (VI) supplements and cognitive training (MC) based on meditation, and the overall ranking is third. However, the pairwise comparison of Figure 6 indicates that although vitamin (VI) supplements can improve attention deficit in ADHD patients to some extent, the effect is not significant. So, it was excluded. However, the effect size of meditation and cognitive therapy (SMD = −1.07, (−2.15, 0.00)) was at a marginal significant level, and the robustness of its results was worse than that of sport intervention therapy, so it needs careful interpretation in practice.

The overall ranking of all kinds of the nonpharmacological therapy is as follows: (1) medication and cognitive (MC); (2) sport intervention therapy (SPT); (3) vitamin and mineral (VM); (4) cognitive behavioral therapy (CBT); (5) computer training (CT); (6) psychological education (PE); (7) working memory training (WMT); (8) nerve feedback therapy (NF); (9) polyunsaturated fatty acids (PUFAs); (10) mineral (MI) supplements; (11) meditation (MAP); (12) vitamin (VI); (13) biofeedback therapy (BF); and (14) school-based training (SB).

4. Discussion

A total of 86 studies were included in this network meta-analysis, involving 68 double-blind studies and 1 single-blind study. A total of 4,998 study samples were collected. Of these, 2,538 patients were randomly assigned to 14 nonpharmacological treatments and 2,460 to a control group. Through a detailed search of published and unpublished research data, the efficacy and acceptability of each therapy were compared directly and indirectly, and the overall ranking provided the latest and comprehensive structured mathematical evidence for the clinical advantages of exercise intervention therapy.

The study indicates that all of the 11 nonpharmacological treatments were found to have superior curative effect on the control group (SMD = (−1.79, −0.14)). Among them, the improvement effect of sport intervention therapy (SPT, SMD = −0.90(−1.76, −0.04)) on attention deficit was only secondary to working memory training (WMT, SMD = −0.93 (−1.47, −0.38)), which reached the high effect size level. The vitamin and mineral (VM) supplementation was significantly better than other treatments (VM, SMD = (−1.79, −1.11)). A comparative analysis of the acceptability of nonpharmacological therapies found that there was no significant difference in acceptability between the sport intervention and other nonpharmacological therapies. The acceptability of meditation therapy alone was significantly better than vitamin (VI, RR = 0.1 (0.01, 0.73)), neurofeedback (NF, RR = 0.39 (0.15, 1.00)), and biofeedback (BF, RR = 0.22 (0.07, 0.70)). The acceptability of biofeedback therapy was inferior to that of the control group (ctrol, RR = 2.86 (1.14, 7.16)). Thus, when efficacy was considered only, there was no significant difference in the effect of each therapy, and sport intervention therapy was not the best choice, only ranking the fourth (Figure 7). However, after taking into account the high dropout rate among other treatments, sport intervention was found to rank second, after meditation-based cognitive training (MC) (Figure 7).

In order to make the abovementioned results as robust as possible to provide reference information for clinical practice, this study focuses on the results of head-to-head interaction comparison under the condition that the retrieved information is completely reliable. Figure 7 shows that low efficacy of pharmacological therapy is usually accompanied by a high level of sample loss (BF (SMD = −0.14, RR = 2.86), SB (SMD = −0.63, RR = 1.02), and VI (SMD = −0.92, RR = 6.03)). This may be due to the slow and imperceptible improvement in attention levels from nonpharmacological therapies, and the lengthy intervention cycle that leads to the withdrawal of patients with more severe symptoms undermines the stability of the randomization of the original study. As a result, most of the participants retained were those with mild symptoms and low improvement. Therefore, the real effects of nonpharmacological therapies were easily underestimated in the final evaluation of effect. According to the difference in analysis results between meditation and cognitive therapy (MC) and the control group, the effect size not only reached the high effect level but also had a large span of confidence interval, and the lower limit reached the point of no difference (SMD = −1.07 (−2.15, 0.00)) , leading to the marginal significance of the difference between MC and the control group (Figure 5). So, careful interpretation and application should be carried out in the interpretation of results and practical guidance [39]. In this network meta-analysis, only two studies reported the results of the combination of meditation and cognitive therapy (MC), so the interpretation of the results was not only limited by the uncertainty of parameter estimation but also affected by selective reporting and potential risk of publication bias caused by the small-sample effect [40].

In conclusion, although meditation and cognitive therapy (MC) ranks first in the overall ranking, the results of clinical significance still have very high uncertainty. Not all people with ADHD have the external conditions of receiving psychological therapy and dietary supplements. Sport intervention therapy from economy and applicability are accepted for the majority of the population; therefore, in this study, the sport intervention therapy is recommended as the best intervention means. Existing studies have confirmed that [16] a certain period of sport intervention can promote the improvement of hyperactivity impulsivity in ADHD patients and, at the same time, attention deficit disorder can also be effectively alleviated. The long-term acute aerobic exercise can accumulate and enhance the short-term and immediate effects of the patient’s apparent behavior and the intrinsic neurophysiology. Because of its low cost, ease of implementation, and additional psychological and physiological effects, the long-term acute aerobic exercise often serves as an auxiliary treatment method, as well as a kind of independent treatment, especially under special circumstances [41].

There are also limitations in this study. (1) the sample size of individual nodes in the network evidence map is too small, so the statistical efficacy is limited. (2) The overall inconsistency test is not significant, and the quality evaluation of some studies is still low. (3) Despite a detailed search of published and unpublished literature, complete collection of existing data cannot be ensured. (4) Due to the lack of comprehensive demographic data of patients, it is not possible to conduct subgroup regulation effect analysis.

5. Conclusions

This network meta-analysis showed that although meditation and cognitive therapy (MC) ranked first overall, the effect size of its efficacy was at a marginal significant level, so its results need to be carefully interpreted in clinical practice. Sport intervention therapy is not only very close to meditation and cognition (MC) in terms of efficacy but also has high acceptability and economic practicability. Therefore, sport intervention therapy is preliminarily judged to be the best nonpharmacological intervention means for the treatment of attention deficit in ADHD patients. In addition, meditation and cognitive therapy (MC), cognitive behavioral therapy (CBT), and computer training (MC) can also be effective complementary tools.

Although this analysis incorporates the most comprehensive literature currently available evidence about nonpharmacological treatment for ADHD, a small number of preregistered studies are still unavailable to us even though we have try our best to collect reliable information about the most recent published studies. Still, we cannot rule out the possibility that some studies are absent in literature retrieval or recalculate the results of the same study in our analyses. Beside, considering the influence of the small-sample effect on the results, a large number of high-quality research designs are still needed to provide evidence to support the results of this study. We welcome any information that might help clarify any limitations in our manuscript.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no known conflicts of interest.

References

M. Marty and D. Segal, in Diagnostic and Statistical Manual of Mental Disorders, American Psychiatric Publishing, Arlington, TX, USA, DSM-5 edition, 2015.

S. V. Faraone, J. Sergeant, C. Gillberg, and J. Biederman, “The worldwide prevalence of ADHD: is it an American condition?” World Psychiatry Official Journal of the World Psychiatric Association, vol. 2, no. 2, pp. 104–113, 2003.

G. Polanczyk, M. Silva de Lima, B. Lessa Horta, J. Biederman, and L. Augusto Rohde, “The worldwide prevalence of ADHD: a systematic review and metaregression analysis,” The American Journal of Psychiatry, vol. 164, no. 6, pp. 942–948, 2007.

J. Biederman, C. R. Petty, M. Evans, J. Small, and S. V. Faraone, “How persistent is ADHD? A controlled 10-year follow-up study of boys with ADHD,” Psychiatry Research, vol. 177, no. 3, pp. 299–304, 2010.

S. N. Visser, “Increasing prevalence of parent-reported attention-deficit/hyperactivity disorder among children-United States, 2003 and 2007,” MMWR. Morbidity and Mortality Weekly Report, vol. 59, no. 44, pp. 1439–1443, 2010.

Q. X. Ng, “A systematic review of the use of bupropion for attention-deficit/hyperactivity disorder in children and adolescents,” Journal of Child and Adolescent Psychopharmacology, vol. 27, no. 2, pp. 112–116, 2017.

J. Chandler, J. Thomas, J. P. T. Higgins et al., Cochrane Handbook for Systematic Reviews of Interventions (Version 5.1.0), Wiley, Hoboken, NJ, USA, 2011.

S. Daniel et al., “Selective noradrenaline reuptake inhibitor atomoxetine directly blocks hERG currents,” British Journal of Pharmacology, vol. 156, no. 2, pp. 226–236, 2009.

M. Séguin, A. Lahaie, C. Matte-Gagné, and M. H. Beauchamp, “Ready! Set? Let’s Train!: feasibility of an intensive attention training program and its beneficial effect after childhood traumatic brain injury,” Annals of Physical and Rehabilitation Medicine, vol. 61, no. 4, pp. 189–196, 2018.

O. J. Storebø, N. Pedersan, E. Ramstad et al., “Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents-assessment of adverse events in non-randomised studies,” Jama, vol. 50, no. 2, p. 113, 2018.

D. L. Hansen and E. H. Hansen, “Caught in a balancing act: parents’ dilemmas regarding their adhd child’s treatment with stimulant medication,” Qualitative Health Research, vol. 16, no. 9, pp. 1267–1285, 2006.

M. Brod, E. Schmitt, M. Goodwin, P. Hodgkins, and G. Niebler, “ADHD burden of illness in older adults: a life course perspective,” Quality of Life Research, vol. 21, no. 5, pp. 795–799, 2011.

R. Cunill, X. Castells, A. Tobias, and D. Capellà, “Efficacy, safety and variability in pharmacotherapy for adults with attention deficit hyperactivity disorder: a meta-analysis and meta-regression in over 9000 patients,” Psychopharmacology, vol. 233, no. 2, pp. 187–197, 2016.

S. Benner-Davis and P. C. Heaton, “Attention deficit and hyperactivity disorder: controversies of diagnosis and safety of pharmacological and nonpharmacological treatment,” Current Drug Safety, vol. 2, no. 1, pp. 33–42, 2017.

P. S. Jensen and S. Jm, “3-year follow-up of the NIMH MTA study,” Journal of the American Academy of Child & Adolescent Psychiatry, vol. 46, no. 8, pp. 989–1002, 2017.

A. M. José, L. B. N. Turibia, M. Mauro et al., “Exercise impact on sustained attention of ADHD children, methylphenidate effects,” Attention Deficit and Hyperactivity Disorders, vol. 2, no. 1, pp. 49–58, 2010.

Q. X. Ng, C. Y. X. Ho, H. W. Chan, B. Z. J. Yong, and W.-S. Yeo, “Managing childhood and adolescent attention-deficit/hyperactivity disorder (ADHD) with exercise: a systematic review,” Complementary Therapies in Medicine, vol. 34, no. 1, pp. 123–128, 2017.

M.-R. Pan, F. Huang, M.-J. Zhao, Y.-F. Wang, Y.-F. Wang, and Q.-J. Qian, “A comparison of efficacy between cognitive behavioral therapy (CBT) and CBT combined with medication in adults with attention-deficit/hyperactivity disorder (ADHD),” Psychiatry Research, vol. 279, no. 1, pp. 23–33, 2019.

K. Mawjee, S. Woltering, N. Lai, H. Gotlieb, R. Kronitz, and R. Tannock, “Working memory training in adhd: controlling for engagement, motivation, and expectancy of improvement (pilot study),” Journal of Attention Disorders, vol. 21, no. 11, pp. 956–968, 2014.

H. H. M. Lo, S. W. L. Wong, J. Y. H. Wong, J. W. K. Yeung, E. Snel, and S. Y. S. Wong, “The effects of family-based mindfulness intervention on adhd symptomology in young children and their parents: a randomized control trial,” Journal of Attention Disorders, vol. 24, no. 5, pp. 667–680, 2017.

E. A. Shereena, R. K. Gupta, C. N. Bennett, K. J. V. Sagar, and J. Rajeswaran, “EEG neurofeedback training in children with attention deficit/hyperactivity disorder: a cognitive and behavioral outcome study,” Clinical EEG and Neuroscience, vol. 50, no. 4, pp. 242–255, 2018.

T. Hirvikoski, E. Waaler, T. Lindström, S. Bölte, and J. Jokinen, “Cognitive behavior therapy-based psychoeducational groups for adults with ADHD and their significant others (PEGASUS): an open clinical feasibility trial,” ADHD Attention Deficit and Hyperactivity Disorders, vol. 7, no. 1, pp. 89–99, 2015.

A. Bikic, J. F. Leckman, T. Ø. Christensen, N. Bilenberg, and S. Dalsgaard, “Attention and executive functions computer training for attention-deficit/hyperactivity disorder (ADHD): results from a randomized, controlled trial,” European Child & Adolescent Psychiatry, vol. 27, no. 12, pp. 1563–1574, 2018.

E. J. S. Sonuga-Barke, D. Brandeis, S. Cortese et al., “Nonpharmacological interventions for ADHD: systematic review and meta-analyses of randomized controlled trials of dietary and psychological treatments,” American Journal of Psychiatry, vol. 170, no. 3, pp. 275–289, 2013.

F. Catala-Lopez, B. Hutton, A. Nunez-Beltran et al., “The pharmacological and non-pharmacological treatment of attention deficit hyperactivity disorder in children and adolescents:protocol for a systematic review and network meta-analysis of randomized controlled trials,” Systematic Reviews, vol. 4, no. 1, p. 19, 2015.

K. Hodgson, A. D. Hutchinson, and L. Denson, “Nonpharmacological treatments for ADHD,” Journal of Attention Disorders, vol. 18, no. 4, pp. 275–282, 2012.

P. Janice, S. Elizabeth, and B. Candice, “Complementary and alternative medical therapies for children with attention-deficit/hyperactivity disorder (ADHD). Alternative medicine review,” A Journal of Clinical Therapeutic, vol. 16, no. 4, pp. 323–337, 2011.

D. Mavridis, M. Giannatsi, A. Cipriani, and G. Salanti, “A primer on network meta-analysis with emphasis on mental health,” Evidence Based Mental Health, vol. 18, no. 2, pp. 40–46, 2015.

J. Chandler, J. Thomas, J. P. T. Higgins et al., Cochrane Handbook for Systematic Reviews of Interventions (Version 6.0), Wiley, Hoboken, NJ, USA, 2019.

D. Gamerman, “Markov Chain Monte Carlo: stochastic simulation for Bayesian inference,” Statistics in Medicine, vol. 27, no. 16, pp. 3213-3214, 2008.

S. Dias, N. J. Welton, D. M. Caldwell, and A. E. Ades, “Checking consistency in mixed treatment comparison meta-analysis,” Statistics in Medicine, vol. 29, no. 7-8, pp. 932–944, 2010.

A. Chaimani, J. P. T. Higgins, D. Mavridis, P. Spyridonos, and G. Salanti, “Graphical tools for network meta-analysis in STATA,” PLoS One, vol. 8, no. 10, Article ID e76654, 2013.

I. R. White, J. K. Barrett, D. Jackson, and J. P. T. Higgins, “Consistency and inconsistency in network meta‐analysis: model estimation using multivariate meta‐regression,” Research Synthesis Methods, vol. 3, no. 2, pp. 111–125, 2012.

J. P. T. Higgins, D. Jackson, J. K. Barrett, G. Lu, A. E. Ades, and I. R. White, “Consistency and inconsistency in network meta‐analysis: concepts and models for multi‐arm studies,” Research Synthesis Methods, vol. 3, no. 2, pp. 98–110, 2012.

A. Chaimani, G. Salanti, S. Leucht, J. R. Geddes, and A. Cipriani, “Common pitfalls and mistakes in the set-up, analysis and interpretation of results in network meta-analysis: what clinicians should look for in a published article,” Evidence Based Mental Health, vol. 20, no. 3, pp. 88–94, 2017.

G. Salanti, A. E. Ades, and J. P. A. Ioannidis, “Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial,” Journal of Clinical Epidemiology, vol. 64, no. 2, pp. 163–171, 2011.

M. Egger, G. D. Smith, M. Schneider, and C. Minder, “Bias in meta-analysis detected by a simple, graphical test,” BMJ, vol. 315, no. 7109, pp. 629–634, 1997.

M. Dimitris and S. Georgia, “Exploring and accounting for publication bias in mental health: a brief overview of methods,” Evidence-based Mental Health, vol. 17, no. 1, pp. 5–11, 2014.

D. J. Biau, S. Kernéis, and R. Porcher, “Statistics in brief: the importance of sample size in the planning and interpretation of medical research,” Clinical Orthopaedics and Related Research, vol. 466, no. 9, pp. 2282–2288, 2008.

A. Cipriani, X. Zhou, C. Del Giovane et al., “Comparative efficacy and tolerability of antidepressants for major depressive disorder in children and adolescents: a network meta-analysis,” The Lancet, vol. 388, no. 10047, pp. 881–890, 2016.

A. Mehren, J. Özyurt, A. P. Lam et al., “Acute effects of aerobic exercise on executive function and attention in adult patients with ADHD,” Frontiers in Psychiatry, vol. 10, no. 1, p. 132, 2019.