Efficacy of Group Cognitive Behavior Therapy Targeting Time Management for Adults with Attention Deficit/Hyperactivity Disorder in Japan: A Randomized Control Pilot Trial

Abstract

Objective:

Time-processing disorders in adults is a priority area for intervention. Time management program, which has been demonstrated to be effective in children with ADHD, has not been examined in adults. We anticipate the need for the development of specialized programs for adults. This is because it has been reported that time processing disorders have different patterns in childhood and adulthood. This study aimed to evaluate the therapeutic effect of a gCBT program focusing on time management for adults with ADHD.

Method:

Adults with ADHD were randomly assigned to gCBT (n = 24) or a treatment as usual group (n = 24). Outcome measures were masked clinically rated, self-reported, and family-reported ADHD symptoms.

Results:

The gCBT group significantly reduced ADHD symptoms on all measures.

Conclusion:

Interventions focused on time management have been shown to be effective not only in children with ADHD but also in adult patients.

Keywords

adult ADHD cognitive behavioral therapy time management

Introduction

Adults with Attention-Deficit/Hyperactivity Disorder (ADHD) have a pooled prevalence estimate of 2.5% (Simonet al., 2009). Adults with ADHD have higher rates of comorbidity within various mental disorders (Kessler et al., 2006); these include academic, social, and occupational dysfunctions, as well as some comorbidities such as smoking and substance abuse (Wilens & Dodson, 2004).

About 40% to 60% of people diagnosed with ADHD in childhood have symptoms that persist until adulthood (Barkley, 1998; Barkley et al., 2002; Pliszka, 2007; Simon et al., 2009; Turgay et al., 2012). Of the ADHD symptoms observed in childhood, hyperactivity decreases by about 50% and impulsivity by about 40% with age, but inattentiveness decreases by only about 20% (Wilens et al., 2002), and among these three symptoms of ADHD, inattentiveness is particularly likely to last until adulthood (Biederman et al., 2000).

One of the problems associated with ADHD-related inattentiveness is poor time management. In adults, this can entail, among other things, being late to work or being unable to meet deadlines and is thus a priority area for intervention. A lack of time management skills damages people’s social credibility, resulting in dismissals and poor interpersonal relationships, and it is directly linked to impairments in one’s social life as well as secondary disabilities such as depression. Time management is defined as “behaviours that aim at achieving an effective use of time while performing certain goal-directed activities” (Claessens et al., 2007). Furthermore, it is associated with work and academic achievement (Barling et al., 1996; Burt & Kemp, 1994) as well as with occupational satisfaction and stress (Macan et al., 1990).

Numerous studies have investigated aspects of time processing in ADHD using neuropsychological tests, and they report that such factors related to time processing deficits are prevalent in ADHD and make time management difficult in both childhood and adulthood (Barkley et al., 1997; Sonuga-Barke et al., 2010). Valko et al. (2010) reported that time-processing deficits were still present in both adult and child patients. In addition, Castellanos (2002) indicated that children with ADHD are characterized by smaller volumes in both the cerebellum and caudate compared to non-ADHD children. As children with ADHD grow into adulthood, the volume of the caudate, which is associated with inhibition, tends to catch up with that of non-ADHD patients, whereas the volume of the cerebellum, which is associated with time processing, does not.

Executive dysfunction and inhibitory control dysfunction are concepts similar to time management and time processing deficit. The executive function is a general term for cognitive systems that control thoughts and actions by maintaining and switching task rules and updating information when performing complex tasks, or those cognitive control functions (Miyake & Shah, 1999). Current research into this function has shown that it can be further divided into the following three elements: updating, shifting, and common executive function (common-EF) (Friedman et al., 2008).

Previously, there was a tendency to view inhibition control as a part of executive functioning (Miyake et al., 2000). Today, however, effectively biasing lower-level information processing, by maintaining common executive functions such as task goals and task-related information, is considered to relatively suppress response to other tasks—rather than kinetically suppressing reaction to a task. In other words, it can be said that the executive function is a result of the common-EF, which is a part of the executive function, rather than the inclusion of exhibit control.

In summary, time management is a part of executive functioning, and it is a function specialized in the execution of time. When supporting time management for those with time processing deficits, it is necessary to teach them to reference cerebellar information related to time processing while correcting that information in time logs and to perform inhibition control with common-EF, which is an executive function skill.

There are few psychosocial treatments for ADHD that are specialized for time management. Many of the treatments are multimodal, and the intervention effect of time management alone has not been examined. For adults with ADHD, psychopharmacology and multimodal group cognitive behavioral therapy (gCBT) programs targeting interpersonal, cognitive, emotional, problem solving, and time management aspects are also recommended by several international treatment guidelines (CADDRA, 2018; NICE, 2018). For example, Solanto et al. (2010) addressed time management, organizing, and planning; Emilsson et al. (2011) examined attention, memory, impulse control, planning, problem solving, emotional control, social skills, and critical thinking; Young et al. (2016) studied executive functioning, problem solving, emotional control, social skills, and critical thinking; and Safren et al. (2005) focused on planning using a notebook with a task list and a calendar system.

It was hypothesized that enhancing patients’ time management capacity by teaching them how to overcome time processing obstacles, to estimate time, to use time (including planning), and to prevent procrastination will be effective in improving time management in adults with ADHD. For reference, an intervention study focusing on time management in children was already conducted by Wennberg et al. (2018), which was fundamentally focused on time processing deficits. To reduce inaccuracies in patients’ sense of time, the children were taught to structure their life scenes based on time logs and to improve their poor working memory, and the researchers helped them develop skills such as using reminders to remember what to do. In addition, regarding insufficient monitoring of the passage of time during work, the children were taught skills such as using alarms to recognize the passage of time.

On the other hand, Valko et al. (2010) stated that in their sample of adults with ADHD, they were not able to completely separate processing deficits from executive functions and inhibitory control, which appeared to “interact on a more subtle level than in children.” This study indicates that time management in adults requires not only basic cerebellum time processing, but also the mobilization of multiple skills related to executive function in the prefrontal cortex, such as drawing up a plan and maintaining focus until its completion by utilizing inhibitory control, since social demands for time management are more difficult and complex in adulthood than in childhood. Regarding executive function skills related to time management, Imura et al. (2016) reported that time management in real life is affected by factors such as the estimation of the time required for tasks, utilization of time for setting goals, priorities, planning, and procrastination. Knouse et al. (2017) likewise recounted that “overly positive” or optimistic cognitions of ADHD in adults may contribute to impairment of time management. Therefore, to improve time management of adults with ADHD, it was assumed that it would be necessary to learn not only how to supplement time processing problems, but also how to estimate time, utilize time including planning, and prevent procrastination.

Anticipating the need for the development of specialized programs to treat time processing disorders in adults, we developed a gCBT program targeting only time management, with reference to Solanto et al. (2010). The program consisted of scenarios in the morning, noon, evening, and night. The goal was to specifically acquire the time management, executive function, and inhibition control skills that are necessary to carry out time management in such situations. For example, the morning scenario was configured so participants would learn time management by logging their dressing and breakfast times (correction of time processing problems), making efficient morning plans based on those time logs (acquisition of executive function skill), and organizing their environment to prevent derailment from said plans by such things as television (acquisition of inhibition control skill). Once the participants studied time management-related skills (correction of time processing disorders, executive function skills, and inhibition control skills), we also asked them to discuss with one another ideas for specific management methods for the scenarios. Preliminary data on the effectiveness of the program for ADHD symptoms in inattentive-memory symptoms was published previously (Nakashima et al., 2019).

The purpose of this study was to evaluate the therapeutic effects of a newly developed program that focuses on time management, conducted as gCBT for adults with ADHD. It was hypothesized that the gCBT group would show significantly greater improvement compared to the control group (TAU). The Conners’ Adult ADHD Rating Scale (CAARS) inattention memory scale (both self-reported and family reported), the Clinical Global Impression assessment of illness severity, and functional improvements in work (job responsibilities and house chores) were used to assess improvement.

Methods

Trial Design

The study was a randomized, controlled trial. The study protocol was registered at the UMIN Clinical Trials Registry (UMIN-CTR) (ID = UMIN000026916).

Participants

Participants were outpatients at 40 mental health clinics across Japan. They were recruited through posters in psychiatry clinics, medical news on the Internet, online columns, and e-mail magazines. All participants applied by way of an application page on a dedicated website. Participants attended the gCBT at Kyushu University Hospital, Fukuoka, Japan, and this treatment was conducted for research purposes only, independent of usual clinical practice.

Eligibility Criteria for Participants

Inclusion criteria

Participants were required to be between 20 and 65 years old according to the DSM-IV (American Psychiatric Association, 1994) diagnosis of ADHD. Participants received a two-step screening. The first screening consisted of each participant completing the Adult ADHD Self-Report Scale-V1.1 online and sending their doctor’s consent form to the research secretary. In the second screening, those who completed the first step received individual diagnosis interviews, in which they were required the following: to meet the DSM-IV diagnosis of ADHD based on the Diagnostisch Interview voor ADHD bij volwassenen (DIVA-2; Kooij, 2012); to score at least 66 (93rd percentile) on the Inattentive/Memory subscale, DSM-IV Inattentive subscale, or DSM-IV Total ADHD Symptom of the CAARS-Self Report: Long Version; and to have received TAU for at least a month by the beginning of the intervention. Participants were not requested to maintain their medication during the study; their doctors were able to adjust dosage as necessary, and approximating TAU was permitted for factors not targeted by gCBT.

Exclusion criteria

Exclusion criteria included psychiatric hospitalization within 30 days of application and difficulty participating in the protocol treatment for 8 months. We also excluded participants with schizophrenia, bipolar disorder, and substance-related disorder with a semi-structured interview, using the Structured Interview for DSM-IV (SCID; First et al., 1997) (the validity of the Japanese version of the scale is well confirmed; Takahashi et al., 2003), as well as those with estimated IQ < 80 and major neurocognitive disorders, based on information provided by their doctors.

Intervention

Treatment as usual group

Participants received usual treatment, both pharmacological and non-pharmacological, at the clinics where they were patients. A total of 68.8% of participants received ADHD medication. The most popular non-pharmacological interventions comprised of psychoeducation on ADHD, which was provided to 38.3% of participants.

gCBT group

Participants assigned to the intervention group were required to participate in gCBT to learn time management skills in addition to TAU. They attended 120-minute group sessions once a week for a total of eight times, from October 2017, at Kyushu University Hospital, Fukuoka, Japan. The session groups consisted of up to eight participants each. A clinical psychologist facilitated every session group as the group leader, along with a social worker or a nurse as co-leader. The intervention program involved time management, and it was structured in three stages, as shown in Table 1. In the first stage of the program (sessions 1-4), the goal was to enhance time management skills within each segment of the participants’ daily activities. This was addressed through discussions about the necessary skills required for improving functioning in daily scenarios. Each scenario and the required skills varied depending on the time of the day. In the second stage (session 5), the discussions on time management skills were expanded to include management of the entire 24-hour day. The last stage (sessions 6-8) targeted overall time management skills. We established the following time management skills for this program: managing schedules in the form of checking one’s planner, receiving psychoeducation on sleep, planning based on a time log, measuring progress in small steps, and overcoming procrastination. Further details of the program can be found in Nakashima and Inada (2017). In every session, the therapist assigned various behavioral homework activities to practice time management skills that were discussed in that session. These activities were based on the daily lives of the participants (e.g., recording the time log, creating schedules based on those time logs, and carrying out activities they had been previously avoiding). Participants submitted the homework by scanning their time logs and the recorded matter and then e-mailing the images to the facilitator 2 days before the next session. Images of the submitted homework were projected on the screen at the beginning of the next session and shared. When participants did not submit homework by the deadline, the group leader lowered the difficulty of the homework and encouraged its completion by e-mail. The group leader emphasized motivation by giving participants successful experiences of achieving their homework, even going so far as to reducing the difficulty of the task, and this did not prevent the achievement of treatment goals.

Table 1.

Time Management Program Sequence.

Session	Theme	Targeted time management skills
First step (time management for several hours)
1	Understand why people with ADHD have trouble managing time	Psychoeducation/Setting a goal
2	Stop staying up late Learn how to motivate by myself	Pros and cons of night owls/contingent self-reward
3	Complete morning preparations by a set time	Time estimate/to get ready for the morning modification of living environment
4	Completing your evening’s tasks	Create a to-do list/organization/planning for a short time
Second step (time management for 24 hours)
5	Efficiently completing your day’s activities	Prioritize things to do/utilize gap time 24-hour planning
Third step (time management for 1 week)
6	Decomposition of big jobs	Planned for several days · disassembled into small steps environment setting
7	Overcoming procrastination	Overcoming procrastination/planning for 1-week action plan
8	How to live with yourself in the future	Goal arrival confirmation/prioritization based on values

Group Leaders and Training

Three psychologists, each with over 10 years of clinical experience in assessment and treatment of ADHD, led the three gCBT groups. A social worker or a nurse supported each group as co-leader. The psychologists all attended 6 hours of lectures on ADHD and 6 hours of exercises on gCBT before starting the intervention. While the group sessions were in progress, these three group leaders reported on the progress of their respective groups once a week and conducted peer SVs to further learn practical skills for gCBT facilitation.

Fidelity Rating

Each group leader evaluated their own gCBT performance using a checklist of the agenda, and interventions were delivered as planned. All sessions were recorded, and videos were rated by a Group–Cognitive Therapy Scale (G-CTS; Nakashima et al., 2017). This was developed for the gCBT evaluation of the group leaders’ clinical competencies and based on the Cognitive Therapy Scale (CTS, or CTRS: Cognitive Therapy Rating Scale; www.beckinstitute.org), which is the most prominent existing tool for assessing group leaders’ individual session performance. Twenty-four videos of sessions were independently assessed using the G-CTS by two therapists familiar with gCBT, and this demonstrated that the group leaders’ skills reached high standards. Comparison of ratings yielded no differences between the groups in mean rated group leader competence.

Outcome

Clinical Global Impression-Severity Scale

Participants were evaluated at pre-treatment and at 2-, 4-, and 8-month follow-ups using the Clinical Global Impression-Severity scale (CGI-S; Guy, 1976), in addition to a half-day behavioral observation during several assessments of this study by two independent (masked) evaluators who were clinical psychologists with clinical experience of 15 or more years. The half-day observation was made on the participants’ time management skills and based on the following criteria: gather at the meeting place on time; remember to bring the documents they were instructed to bring; undergo various tests at fixed times; and eat, take breaks, and answer questionnaires within the set time limit. The participants were assessed by these two evaluators using the 7-point CGI-S rating scale (1 = Normal, not at all to 7 = Among the most extremely ill patients), based on their experience of treating patients suffering from the same conditions as those in this study.

Inattention/Memory Subscale of the Conners’ Adult ADHD Rating Scale–Self-Report: Long Version

The CAARS–self-report–Inattention/Memory scale (CAARS-S-IN; Conners et al., 1999; Erhardt et al., 1999) served as the other outcome measure. The CAARS is a quantitative measure of ADHD symptoms based on the DSM-IV. It consists of 66 items, which are rated on a 4-point Likert-type scale (0 = not at all, never to 3 = very much, very frequently), with higher scores indicating more severe symptoms. Results are represented by T-scores for each of the eight subscales. Scores over 65 points are considered to be in the clinical range. The validity of the Japanese version has been well confirmed (Nakamura et al., 2012).

The Inattention/Memory subscale of the Conners’ Adult ADD Rating Scale–Observer Report: Long Version

The CAARS–Observer Report–Inattention/Memory scale (CAARS-Ob-IN) is a quantitative measure of ADHD symptoms based on DSM-IV. It consists of 66 items, which are rated on a 4-point Likert-type scale (0 = not at all, never to 3 = very much, very frequently), with higher scores indicating more severe symptoms. Results are represented by T-scores for each of the eight sub-symptoms. Scores over 65 points are considered to be in the clinical range. The validity of the Japanese version has been well confirmed (Nakamura et al., 2012). The CAARS-Ob-IN, Long Version was completed pre- and post-treatment by a spouse, partner, family member, or close friend of each participant, with said participant’s consent.

Sheehan Disability Scale

Participants’ appraisal of functional impairment or disability as related to familial, social, and vocational aspects of life was assessed using the Sheehan Disability Scale (Leon et al., 1997; Sheehan, 1983), which is scored on a visual analog scale from 0 (not at all) to 10 (extremely). The validity of the Japanese version has been well confirmed (Yoshida et al., 2004). The Sheehan Disability Scale is one that measures functional impairment in the following three areas: work/school, social life, and home life/family responsibilities. The target of the program in this study was learning time management skills to complete work (job responsibilities and house chores) by the deadline. Therefore, the Work/School subscale, which falls under the category of job responsibilities and house chores, was adopted as an index.

Demographic Characteristics

Demographic data, such as age, gender, marital status, occupation, and education were also collected.

Sample Size

A systematic review of psychological treatments, mostly CBT for adult ADHD, yielded a Cohen’s d of 0.76 (95% confidence interval (CI) [0.21, 1.31]) at post-test (Young et al., 2016). To detect an effect size of 0.76 or greater at an alpha error rate of 0.05 and a beta error rate of 0.10, the estimated sample size was 48 participants per arm. With an anticipated dropout rate of 30%, the necessary sample size was 69 participants per arm. The statistical analysis was conducted using the G*Power 3 program (Faul et al., 2007). Since this study was part of a preliminary study for large-scale randomized controlled trail (RCT) targeting more than 100 such subjects, the target number of cases was set to 48 people, about one-third of those calculated.

Randomization

Recruitment of participants began in April 2017. In September 2017, participants satisfying the subject criteria in the web screening and pre-individual diagnostic interview were assigned to either the gCBT or TAU group using a random number table (using minimization methods with an assignment factor for severity of ADHD inattentive symptoms) by an independent individual who was aware of the hypothesis but only given the participants’ ID numbers; this individual then randomized the information using a computer program. Allocation was not concealed from the researchers and participants but masked from the evaluators on the CGI-S.

Analytical Method

Linear mixed effects models were run separately for each outcome as a dependent variable and performed with three base factors: group (0 = TAU, 1 = gCBT), time (1 = baseline, 2 = 2-month follow-up, 3 = 4-month follow-up, 4 = 8-month follow-up), and the interaction of group and time (group × time). Intention-to-treat analysis was conducted. The Linear Mixed Model in SPSS Statistics 25 (IBM Corp., USA) was used. Effect sizes and 95% CI were calculated using Cohen’s d among those who completed the questionnaire at baseline and at a follow-up. The values of 0.2, 0.5, and 0.8 are generally interpreted as being suggestive of small, medium, and large effects, respectively (Cohen, 1992).

Ethics

The Research Ethics Review Board of National Hospital Organization Hizen Psychiatric Medical Center (No. 29-1) approved the study procedures. We explained the study to the participants verbally as well as in writing. Participants each submitted a consent form after the research was explained, once they had an opportunity to consider their decisions. They were also informed that their consent could be withdrawn at any time during the study.

Changes to the Protocol

A major change to the protocol was made in the statistical analysis method. Originally, a per-protocol analysis was planned, but a mixed-model for repeated measures conditional growth model analysis was conducted in the present study in consideration for the influence of time elapsed.

Results

Participant Flowchart

The participant flowchart is shown in Figure 1. Ninety-one participants applied via the website; 91 (100%) of them passed ASRS screening, and 90 (98.9%) fulfilled the forementioned eligibility criteria for participants (IQ ≥ 80). Out of these, 35 declined to participate; 11 did not respond to e-mail, 20 lived far from Fukuoka, three were hospitalized, and one became unable to join the study because of worsening social phobia. Fifty-five (60.4%) reserved diagnostic interviews, but one did not appear. Out of these 55, six were excluded for the following reasons: one was not diagnosed with ADHD per the DSM-IV; three had T-scores below 65 on the CAARS-S-IN; one was diagnosed with schizophrenia, bipolar disorder, or substance use disorder; and one relocated. The remaining 48 (52.7%) participants were randomly allocated to an intervention or control group (n = 24 for each).

Figure 1.

Flowchart.

At post-intervention, 23 (95.8%) participants in the intervention group and 18 (75.0%) in the control group completed the survey. In the 2-month follow-up, 23 (95.8%) participants in the intervention group and 20 (83.3%) in the control group completed the survey. In the 8-month follow-up, 23 (95.8%) participants in the intervention group and 18 (75.0%) in the control group completed the survey. At each follow-up, the response rate of the control group was higher when compared to that of the intervention group. Reasons for dropping out were not assessed in this study.

Baseline Characteristics

Demographic characteristics are presented in Table 2. There were no significant differences in demographic characteristics between the intervention and the control groups. In both groups, the majority (88.0%) of the participants were females with a mean age of 39.11 years (SD = 9.62). Thirty (62.5%) participants had jobs. The mean IQ score was 105.67 in the WAIS-III, with a mean of 15.83 years (SD = 1.94) of education. Thirty-three (68.8%) participants were taking medication with methylphenidate or atomoxetine. In this study, we did not control for pharmacotherapy, but no significant changes were made during the research period according to the medical information from the participants’ respective clinics.

Table 2.

Demographic and Clinical Characteristics.

	All (N = 48)		Group CBT (N = 24)		TAU (N = 24)		χ ²
	n	%	n	%	n	%	χ ²
Gender
Males	6	12.5	3	12.5	3	12.5	0.000
Females	42	87.5	21	87.5	21	87.5
Marital status
Married	21	43.8	10	41.7	11	45.6	0.762
Never married	21	43.8	10	41.7	11	45.6
Divorced	6	12.5	4	16.6	2	8.3
Employed (any)
Employed full time	17	35.4	8	33.3	9	37.5	12.002^†
Medication status
Medication	32	66.7	18	75.0	14	58.3	1.500
No medication	16	33.3	6	25.0	10	41.7
ADHD subtype
Combined	24	50.0	12	50.0	12	50.0	0.083
Inattentive	24	50.0	13	54.2	11	45.8
	M	(SD)	M	(SD)	M	(SD)	t
Age	39.11	(9.62)	39.67	(9.79)	38.54	(9.45)	0.405
Years of education	15.83	(1.94)	15.41	(2.12)	16.25	(1.75)	1.483
AQ	25.13	(5.30)	24.25	(4.71)	26.00	(5.88)	0.686
WAIS-Ⅲ Abbreviated IQ	105.67	(19.20)	105.50	(15.61)	105.83	(22.78)	0.033
CAARS-Self-IN	78.69	(9.30)	80.00	(9.03)	77.38	(9.57)	0.977
CAARS-Ob-IN	65.66	(13.53)	66.67	(13.78)	64.65	(13.29)	0.511
CGI-S	3.75	(1.05)	3.62	(0.74)	3.88	(1.36)	0.729

†

p < .10.

Note: CAARS-Self-IN = The Inattention/Memory subscale of the Conners Adult ADD Rating Scale – Self-Report: Long Version, CAARS-Ob-IN = The Inattention/Memory subscale of the Conners Adult ADD Rating Scale – observer Report: Long Version, CGI-S = The Clinical Global Impression-Severity scale, T1 = baseline. T2 = 2-month follow up, T3 = 4-month follow up, T4 = 8-month follow up.

Recruitment

Recruitment and the baseline survey were conducted from April to July 2017. The intervention and control groups were assessed at approximately 2, 4, and 8 months after the baseline survey.

Feasibility

Throughout the research period, from the start of participation to the end of the intervention 6 months later, one person from the gCBT group (intervention group) and six from the TAU group (control group) dropped out. We examined by one-way analysis of variance whether there was any difference between the outcome measures and the demographic data at the time of T1 (pre-intervention) between these seven who dropped out and the other 31 subjects who remained in the program. Of those who dropped out, five (71.4%) of the subjects were not taking ADHD medication, and this rate was significantly lower than that of participants who stayed on to complete the program (χ²(1) = 5.35, p = .02). There were no differences in the other indicators. Participants in gCBT attended 94.8% of scheduled sessions. The homework completion rate was 94.79% (SD = 13.25).

Effects of the GCBT Program on Each Outcome Variable

Effects of gCBT on the CAARS-S-IN

Table 3 shows the means and SDs of the outcome variables at baseline and in the 2-, 4-, and 8-month follow-ups in the intervention and control groups. Tables 4 and 5 shows the estimated effects of the gCBT program on the outcome variables based on mixed-model analyses as well as the effect sizes (Cohen’s d). The gCBT program showed a significant effect on the CAARS-S-IN (t = 33.51, p < .05). The effect size was medium to large in the 2-, 4-, and 8-month follow-ups (Table 3). Furthermore, when the rates of participants, whose variations from pre-intervention to the 2-/4-/8-month follow-ups decreased by 30% or more, were compared between groups, the improvement rates were significantly higher only in the 2-month follow-ups in the intervention group (χ² (1) = 6.86, p < .01).

Table 3.

Means and Standard Deviations at Outcomes.

Outcome measures	Group CBT (N = 24)								TAU (N = 24)
	Pre intervention (T1) M(SD)		2-Month follow up (T2) M(SD)		4-Month follow up (T3) M(SD)		8-Month follow up (T4) M(SD)		Pre intervention (T1) M(SD)		2- Month follow up (T2) M(SD)		4-Month follow up (T3) M(SD)		8-Month follow up (T4) M(SD)
	n = 24		n = 23		n = 23		n = 23		n = 24		n = 18		n = 21		n = 19
CAARS-Self-IN	79.27	(9.09)	71.61	(9.77)	68.96	(9.90)	68.43	(12.35)	76.80	(10.66)	75.61	(11.16)	75.10	(11.37)	75.89	(10.95)
CAARS-Ob-IN	66.27	(13.47)	61.13	(13.68)	55.00	(11.05)	57.77	(13.88)	62.00	(14.36)	62.67	(14.78)	62.38	(16.32)	62.63	(16.26)
Clinical global impressions-severity of illness scale	4.13	(0.71)	2.52	(0.73)	1.91	(0.83)	2.00	(0.95)	3.76	(0.66)	3.95	(0.69)	3.29	(0.87)	3.55	(0.76)
Sheehan disabilty scale work	6.32	(2.10)	4.57	(2.33)	5.13	(2.62)	4.70	(2.18)	5.47	(1.64)	6.00	(2.52)	5.62	(1.94)	6.00	(2.45)

Note: CAARS-Self-IN = The Inattention/Memory subscale of the Conners Adult ADD Rating Scale– Self-Report: Long Version, CAARS-Ob-IN = The Inattention/Memory subscale of the Conners Adult ADD Rating Scale– observer Report: Long Version, CGI-S = The Clinical Global Impression-Severity scale, T1 = baseline. T2 = 2-month follow up, T3 = 4-month follow up, T4 = 8-month follow up.

Table 4.

Effect of the Group Cognitive Behavior Therapy Program on Outcome Variables for the Whole Sample.

	Estimate of fixed effects						Cohen’s d
	Effect	SE	df	t	p	95% CL	T2–T1	95% CI	T3–T1	95% CI	T4–T1	95% CI
CAARS-self-IN	76.20	2.27	97.07	33.51	.00	71.70 to 80.73	0.64	0.00 to 1.27	0.84	0.22 to 1.45	0.95	0.30 to 1.57
CAARS-Ob-IN	58.74	3.45	88.17	17.01	.00	51.88 to 65.61	0.30	–0.32 to 0.92	0.23	–0.37 to 0.82	0.39	0.22 to 1.00
CGI-S	3.64	0.18	119.31	20.86	.00	3.30 to 3.99	2.57	1.72 to 3.40	2.28	1.51 to 3.04	2.47	1.65 to 3.27
Sheehan disabilty scale work	6.04	0.05	117.92	12.67	.00	5.92 to 6.98	1.06	0.39 to 1.71	0.64	0.03 to 1.24	1.02	0.37 to 1.66

Note: CAARS-Self-IN = The Inattention/Memory subscale of the Conners Adult ADD Rating Scale– Self-Report: Long Version, CAARS-Ob-IN = The Inattention/Memory subscale of the Conners Adult ADD Rating Scale–Observer Report: Long Version, CGI-S = The Clinical Global Impression-Severity scale, T1 = baseline. T2 = 2-month follow up, T3 = 4-month follow up, T4 = 8-month follow up.

Table 5.

Parameter Estimates of Group CBT and TAU.

	Group CBT				TAU
	M	SD	df	95% CL	M	SD	df	95% CL
CAARS-self-IN	72.41	1.81	45.30	68.76 to 76.06	76.22	1.85	48.17	72.51 to 79.94
CAARS-Ob-IN	57.87	3.82	38.99	50.15 to 65.60	65.77	4.19	40.71	57.32 to 74.23
CGI-S	2.66	0.13	42.35	2.41 to 2.92	3.69	0.13	45.06	3.43 to 3.95
Sheehan disabilty scale work	5.31	0.35	45.75	4.61 to 6.01	5.70	0.36	48.06	4.98 to 6.41

Note: CAARS-self-IN = The Inattention/Memory subscale of the Conners Adult ADD Rating Scale – Self-Report: Long Version, CAARS-Ob-IN = The Inattention/Memory subscale of the Conners Adult ADD Rating Scale– Observer Report: Long Version, CGI-S = The Clinical Global Impression-Severity scale.

Effect of gCBT on the CAARS-Ob-IN

The gCBT program showed a significant effect on the CAARS-Ob-IN (t = 17.01, p < .05). The effect size was small in the 2-, 4-, and 8-month follow-ups (Table 3).

Effect of gCBT on the CGI-S

The gCBT program showed a significant effect on the CGI-S (t = 20.86, p < .01). The effect size was large in the 2-, 4-, and 8-month follow-ups (Table 3).

Effect of gCBT on the Sheehan Disability Scale WORK

The gCBT program showed a significant effect on the Sheehan disability scale (t = 12.67, p < .01). The effect size was medium to large in the 2-, 4-, and 8-month follow-ups (Table 3).

Discussion

In this RCT study, the effects of a newly developed gCBT program focusing on time management for improving the ADHD symptoms and other outcomes among adults with ADHD were examined in the 2-, 4-, and 8-month follow-ups. The gCBT program showed a significant intervention effect on ADHD, as measured by the CAARS–inattentive/memory scale; self-reported with an effect size of 0.95, and family-reported with an effect size of 0.39 in the 8-month follow-up. The gCBT program also showed a significant intervention efficacy on the clinical severity of patient illness; the CGI-S masked evaluator rating in the 8-month follow-up showed significant intervention effects on improving functional impairment, with an effect size of 2.47. Interventions focused on time management have been shown to be effective not only in children with ADHD but also in adult patients.

In the intervention group, CAARS–inattentive/memory self-reported scores improved in the 2-, 4-, and 8-month follow-ups with moderate to large effect sizes. In CAARS, the scores are percentile-converted so that each subject’s position in the group by age and gender can be understood; in this present study, the higher the percentile of a participant, the more severe their ADHD symptoms were. In the gCBT group, participants who at pre-intervention were in the 98th percentile and above and had severity far greater than the average, were located in the 95th to 98th percentiles by the 8-month follow-up and were found to have reduced symptoms. Their scores also improved above 65 (T-score), divided the clinical area at the end of the program. The improvement rate in the gCBT group were higher in the 2-month follow-up compared to the control group. The efficacy on ADHD symptoms was still comparable to those of previous studies (Schoenberg et al., 2014; Solanto et al., 2008, 2018). In comparison with the TAU group, a statistically significant difference was observed in the 2- and 4-month follow-ups, although a smaller but significant trend was observed in the 8-month follow-up. One reason for the smaller difference between the two groups in the 8-month follow-up could be the self-help efforts of participants in the TAU group. According to comments by many participants in the TAU group, they coped with inattentive symptoms of ADHD through self-help methods, such as the application of time management. During the intervention, the high rates of attendance and submission of homework may have resulted in a large effect size in this study. Participants in gCBT were able to obtain immediate feedback from their group, which motivated them to use their time management skills. However, after the intervention, they were no longer able to obtain feedback from their peers. Continuing to use learned skills would be critical to maintaining the program effect after completion of the intervention. As Lopez et al. (2018) also pointed out the lack of research on long-term effects in their Cochrane review, further studies are needed on long-term effects of gCBT for adults with ADHD.

The effect of the intervention program on the CAARS–inattentive/memory family-reported subscale was significant compared to the TAU group at all measurement points. However, the effect sizes were small, and improvement seemed to be limited when compared with the self-reported score. This is presumed to be due to a floor effect because, at pre-intervention, the families evaluated the ADHD severity of participants in the 86th to 94th percentiles (above the average but below the clinical threshold). Furthermore, at the 8-month follow-up, the family ratings were located in the 27th to 37th percentiles (average and below the clinical threshold). Compared to the same age group (pre-intervention: 71.40, post intervention: 66.64) in a previous study (Solanto et al., 2018), family evaluation of participants in our study was milder at both pre-intervention and post-intervention. Comparing the amount of change pre- and post-intervention, the efficacy on family-reported ADHD symptoms was comparable to that in Solanto et al. (2018).

In the intervention group, CGI-S scores improved in the 2-, 4-, and 8-month follow-ups, with large effect sizes. There were significant differences compared to the TAU group at all measurement points. The treatment effect of CBT on CGI-S increased over time, similar to Emilsson et al. (2011). In addition, compared with previous studies by Young et al. (2017) (pre-intervention: 3.96, post-intervention: 3.03, 3 months after intervention: 3.14) and Emilsson et al. (2011) (intervention: 4.00, post-intervention: 3.18, 3 months after intervention: 3.00), the participants of this study showed more severe symptoms at pre-intervention and in the 2- and 4-month follow-ups, with a clinician evaluation of “Moderately ill.” They showed milder improvement at all time points and were rated as being “Borderline mentally ill.” In other words, the gCBT program of this study showed superior effects in the clinician evaluation than in previous studies.

In the intervention group, the work (job responsibility and house chores) scores on the Sheehan disability scale improved in the 2- and 8-month follow-ups with large effect sizes, with a moderate effect size in the 4-month follow-up. There were significant differences compared to the TAU group at all measurement points. Compared with pre-intervention (6.23 in the same effect index of a previous study; Hirvikoski et al., 2011), participants in this study were severely impaired with dysfunctions in job responsibilities and house chores. After the intervention program ended, we were unable to compare scores to those in Hirvikoski et al. (2011) because that study did not report this information. However, in our study, improvement of dysfunction was maintained until the 8-month follow-up, and subjects had almost no impairment in daily life.

The time management program developed in this research can help address the sense of embarrassment experienced by adult ADHD patients and help them cope with the social demands of life situations, such as work that requires strict adherence to deadlines.

Limitations

This study has several limitations. First, we were unable to control for medication, meaning that the effect of medication on the program could not be examined. Second, participants in this study had higher intellectual levels than those in previous studies, so it is highly possible that effective learning has progressed. Third, since this study was influenced by Japanese culture, where punctuality is emphasized, generalization may be limited and therefore, the findings of this study must be examined for generalizability in other cultures. In Japan, punctuality is often seen as a characteristic that is preferred by workers of their leadership (Fukushige & Spicer, 2007); for example, Hong and Proverbs (2002) reported Japanese contractors achieve higher levels of time certainty than their UK and US counterparts. Punctuality is viewed in Japanese culture to be of such importance that participants with functional impairments due to lack of time management skills may have been doubly motivated to participate in the intervention program. Lastly, generalization is limited in this present study because many of the participants were women who did not have full time jobs, since the program was conducted during the day on weekdays.

Conclusion and Future Prospects

This research provided new findings that interventions utilizing time management skills were effective not only in childhood but also in adulthood. An effective program with a low dropout rate for ADHD patients was also developed by focusing on time management. Importantly, despite the simple nature of our program, its efficacy on ADHD symptoms and functional impairments in work (job responsibilities as well as house chores) was still comparable to those of previous studies.

Focusing on time management out of the various difficulties of adult ADHD patients made it possible to clearly communicate the purpose of the program to participants at the time of recruitment. As a result, it was possible to recruit individuals with participation motives that matched the purpose of the program, and this contributed to reducing the dropout rate.

In the future, using this method of aggregation, it may be possible to create programs that address other problems in adult ADHD patients. These could include issues such as money management and interpersonal relationships. Cultural perspectives should be taken into account when choosing the ADHD symptom for the focus of the intervention. Regarding regional disparities seen in the international comparative study of ADHD in adulthood (Fayyad et al., 2007), the DSM-5 stated that these were due to differences in diagnosis and methodology: “It shows that the action is related to the assessment of attention deficit/hyperactivity disorder.” For example, money management would be a more important skill in a country with an inadequate social security system, and anger management would be an important skill in a culture where calmness is required rather than emotional behavior. In the future, it will be necessary to consolidate programs for group cognitive behavioral therapy of ADHD in adulthood, taking culture into consideration.

The program in this study can also be used as a preparation module for patients to get used to the structure of cognitive behavioral therapy. In other words, by using this program to teach patients time management skills before implementing a certain cognitive behavioral therapy program, it would be easier for patients to follow the structure of cognitive behavioral therapy, which would enhance the effectiveness of the subsequent program. The method used in this study, that is, developing a relatively short-term program focused on a single technique, was demonstrated to reduce the dropout rate and enhance the treatment’s effectiveness. This method can be used to develop programs for conditions other than ADHD that have higher dropout rates. It is possible that this could be applied to the development of gCBT programs targeting other psychiatric disorders (e.g., depression).

The group leaders and co-leaders who facilitated this study each had more than 10 years of clinical experience related to ADHD, but their training dedicated to this program was minimal. Despite this, the peer SVs devised many effective inventions to assist the operation of the program. In the future, subsequent studies will require a well-developed training program for facilitators.

Footnotes

Acknowledgements

We are grateful to Dr. Yasuyuki Okumura (Initiative for Clinical Epidemiological Research, Tokyo) for his helpful advice. We thank Prof. Shigenobu Kanba (Kyushu University Hospital, Fukuoka, Japan) and Fumiaki Takanashi (Nolty Co., Tokyo, Japan) for their generous supports.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by JSPS KAKENHI Grant Number JP17K13959.

ORCID iDs

Misuzu Nakashima

Takefumi Ueno

Author Biographies

Misuzu Nakashima, PhD, is a clinical psychologist and academic research fellow at Department of Clinical Psychology, Graduate School of Human Environment Studies, Kyushu University, Fukuoka, Japan, and Clinical Research Division, National Hospital Organization Hizen Psychiatric Medical Center, Saga, Japan.

Naoko Inada, PhD, is a lecturer and a clinical psychologist at Teikyo University, Tokyo, Japan. She is interested in clinical interventions and developmental trajectory of neurodevelopmental disorders, including ADHD.

Yoshie Tanigawa is a clinical psychologist at Fukuoka Prefecture Government, Fukuoka, Japan.

Masako Yamashita is a clinical psychologist at Ohori Psychiatric Clinic, Fukuoka, Japan.

Emi Maeda, RN, provides return to work program for adults with ADHD at Kaname Clinic, Fukuoka, Japan.

Megumi Kouguchi is a psychiatric social worker and assistant professor at Seinan Jo Gakuin University, Fukuoka, Japan.

Yoko Sarada, PhD, is a psychologist and director of Ropponmatsu Psychological Education Clinical Office, Fukuoka, Japan.

Hiroyuki Yano is a clinical psychologist and academic research fellow at Department of Clinical Psychology, Graduate School of Human Environment Studies, Kyushu University, Fukuoka, Japan.

Keisuke Ikari, MD, PhD, is a psychiatric doctor at liaison psychiatry department of Iizuka Hospital, Fukuoka, Japan, and a researcher at Kyushu University, Fukuoka, Japan.

Hironori Kuga, MD, PhD, MPH, is a psychiatrist and researcher at Kyushu University, Fukuoka, Japan, and National Hospital Organization Hizen Psychiatric Medical Center, Saga, Japan.

Naoya Oribe, MD, PhD, is a psychiatric doctor at Hizen Psychiatric Medical Center, Saga, Japan and a researcher at Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.

Hitoshi Kaname, MD, PhD, is a psychiatrist and director of Kaname Clinic, Fukuoka, Japan.

Tsuyoshi Harada, MD, is child psychiaterist and president of Parkside Mental Development Clinic, Fukuoka, Japan, representative of The Japanese Society for Child and Adolescent Psychiatry, director of Japan Association of Parent Training, and director of Japanese Society for Child and Adolescent Psychiatry Clinic.

Takefumi Ueno, MD, PhD, is director of clinical research division in Hizen Psychiatric Medical Center, and a clinical professor of psychiatry in Graduate School of Medicine at Kyushu University and also a clinical professor of psychiatry at Saga University. Hizen Psychiatric Medical Center and Kyushu University are branches of CBT training Center in Japan.

Toshihide Kuroki, MD, PhD, is a professor of Clinical Psychology at Graduate School of Human Environment Studies, Kyushu University, Fukuoka, Japan, and an adviser to Clinical Research Division of National Hospital Organization Hizen Psychiatric Medical Center, Saga, Japan.

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