Authors: Atsurou Yamada, Daisuke Nakanishi, Akane Nogimura, Yan Luo, Fujika Katsuki, Yoshinori Ito, Fuminobu Imai, Norio Watanabe, Msatsugu Sakata, Tatsuo Akechi, Masaru Horikoshi, Toshi A. Furukawa
Categories: Research, Autism spectrum disorder, Parents, Raising, Depression, Smartphone, Behavioral activation, Assertiveness training, Problem-solving therapy
Source: BMC Psychiatry
Authors: Atsurou Yamada, Daisuke Nakanishi, Akane Nogimura, Yan Luo, Fujika Katsuki, Yoshinori Ito, Fuminobu Imai, Norio Watanabe, Msatsugu Sakata, Tatsuo Akechi, Masaru Horikoshi, Toshi A. Furukawa
Parents of autistic children often experience high levels of stress and depressive symptoms. Although cognitive behavioral therapy (CBT) is effective in alleviating depression and reducing stress, its delivery requires trained personnel and considerable time, limiting accessibility. Smartphone applications may provide a feasible alternative. This study evaluated the efficacy of smartphone-delivered behavioral activation, assertiveness training, and problem-solving therapy in improving depressive symptoms and reducing stress among parents of autistic children.
This was an individually randomized, parallel-group, multicenter trial. Eligible participants were parents aged 24 to < 60 years caring for biological children aged 6 to < 16 years diagnosed with autism by child psychiatrists. Participants were randomly assigned (1:1) to a smartphone-based intervention or a control group that received psychoeducation only. Both groups continued treatment as usual. The intervention consisted of an 8-week smartphone application program delivering behavioral activation, assertiveness training, and problem-solving therapy. The primary endpoint was the Patient Health Questionnaire-9 (PHQ-9) score at Week 8. Secondary outcomes included Generalized Anxiety Disorder-7 (GAD-7) scores at Weeks 4 and 8, Cognitive Behavioral Therapy Skills Scale scores, presenteeism assessed with the Health and Work Performance Questionnaire (HPQ) at Week 8, and app usage.
A total of 63 parents were randomized to the intervention group (n = 31) or control group (n = 32). Changes in PHQ-9 scores from baseline to Week 8 did not differ significantly between groups. No significant between-group differences were observed for GAD-7, CBT Skills, or HPQ presenteeism at Week 8.
The smartphone-based intervention did not demonstrate additional benefits over psychoeducation alone in reducing depressive symptoms. Although symptoms improved in both groups, no significant between-group differences were observed, and the findings should be interpreted cautiously given the limited statistical power.
This study was retrospectively registered in UMINCTR (ID UMIN000052319) on October 1, 2023.
Autism spectrum disorder (ASD) is characterized by persistent deficits in social communication and interpersonal interactions, along with restricted, repetitive patterns of behavior, interest, or activities. The reported prevalence is 27.6 per 1,000 children aged 8 years [1], indicating that ASD is a common condition. In this manuscript, we use the term “autistic children” rather than “children with autism,” reflecting current preferences for identity-first and neurodiversity-affirming language.
Parents of autistic children experience substantial stress and are more likely to exhibit psychological symptoms [2–5]. A recent systematic review reported that approximately 31% of these parents met criteria for major depressive disorder and 33% had an anxiety disorder, rates substantially higher than in parents of children without developmental disorders [6]. Large-scale claims database studies provide further context. One study analyzed more than 42,000 autistic children aged 1–17 years and their parents, identifying depression diagnoses using ICD-9 codes. Both mothers (odds ratio [OR] 2.95, 95% confidence interval [CI] 2.81–3.09) and fathers (OR 2.41, 95% CI 2.25–2.58) were significantly more likely to have depression compared with parents of non-autistic children, although the temporal sequence of diagnoses could not be determined [7]. Another matched case–control study using U.S. commercial insurance data included 23,316 families (5,779 with an autistic child). Depression was identified through ICD-9/10 diagnostic codes among parents aged 27–62 years. Parents of autistic children had approximately twice the odds of depression (OR 1.91, 95% CI 1.73–2.10), with a markedly higher risk for mothers (OR 4.0, 95% CI 2.62–6.12). Parents with multiple autistic children were also at greater risk (OR 1.6, 95% CI 1.18–2.13) [8].
Various interventions have been developed to address this stress. In particular, a meta-analysis reported that cognitive behavioral therapy (CBT) improves the mental health of parents raising children with developmental disabilities [9]. Furthermore, another meta-analysis reported that CBT improves parenting confidence and the mental health of parents raising autistic children [10]. CBT encompasses multiple components, including psychoeducation, self-monitoring, cognitive restructuring, behavioral activation, assertiveness training, problem-solving therapy, relaxation techniques, and mindfulness. However, the specific cognitive and behavioral strategies that demonstrate the greatest efficacy in reducing depression and anxiety have yet to be conclusively identified [11].
Among these components, behavioral activation has been consistently demonstrated as an effective treatment for depression, with multiple randomized controlled trials (RCTs) and meta-analyses confirming its efficacy [12, 13]. Beyond depression, behavioral activation has also been reported to reduce stress-related symptoms and improve quality of life across various populations. Given that parents raising autistic children frequently experience elevated daily stress and restricted opportunities for self-care, behavioral activation may represent a particularly promising strategy for this group.
Assertiveness training, which focuses on developing constructive communication skills, has also been shown to support mood and coping in various contexts.
Parents raising autistic children often rely on emotion-focused coping strategies, which aim to regulate emotional distress rather than directly address the stressor. Such strategies may include avoidance, denial, venting emotions, behavioral disengagement, or mental disengagement [14–16]. However, they are generally regarded as ineffective and are often associated with poorer quality of life [14]. A systematic review further demonstrated that high parenting stress in parents of autistic children is linked to maladaptive coping strategies and reduced quality of life, whereas adaptive coping strategies are associated with better quality of life [14]. Indeed, parents of autistic children have been shown to use avoidance-based coping more frequently than parents of typically developing children [15]. Moreover, a study of mothers of autistic children demonstrated that greater reliance on avoidant coping predicted higher levels of depression and anxiety, whereas greater use of problem-focused coping predicted lower depression and better psychological well-being [17]. In addition to behavioral activation, problem-solving therapy is a practical form of psychotherapy that helps individuals alleviate psychological distress by applying structured problem-solving skills to everyday difficulties. An RCT of mothers of young children recently diagnosed with autism demonstrated that six sessions of problem-solving education significantly reduced parenting stress and lowered mean depressive symptom scores compared with usual care [18]. A subsequent pilot feasibility study of mothers of autistic children reported that eight sessions of problem-solving skills training led to substantial improvements in problem-solving ability as well as reductions in depressive symptoms, negative mood, and post-traumatic stress symptoms [19].
Although problem-solving therapy is a relatively simple treatment, it typically requires 10 to 12 in-person sessions of approximately 1 to 1.5 h each. It also necessitates trained personnel and adequate time for completion. Therefore, it cannot be considered widely popular at present [20]. The number of children diagnosed with ASD is increasing, and parents face a growing need to address their daily problems and anxiety regarding the future of their children. In light of this, we focused on utilizing smartphones as a tool for delivering problem-solving therapy through information and communication technology. In fact, computer- and smartphone-based CBT programs are being implemented in Australia, the United Kingdom, the Netherlands, Sweden, and other countries, with reported effectiveness [21, 22]. Given the small number of therapists compared with the large number of parents, developing a problem-solving therapy using smartphones may help improve the mood profiles of parents raising autistic children and reduce parenting stress. We have developed a smartphone-based CBT app and demonstrated that problem-solving therapy and behavioral activation applications are effective in reducing the fear of cancer recurrence and depression in breast cancer survivors [23, 24].
We hypothesized that a CBT-based intervention, including smartphone-delivered problem-solving therapy, would primarily improve depressive symptoms in parents of autistic children and secondarily reduce parental stress and enhance parenting confidence, thereby contributing to a better quality of life. Furthermore, because raising an autistic child often restricts leisure activities and complicates family communication, we incorporated behavioral activation and assertiveness training into our program. These components were intended to counteract emotional and avoidant coping strategies. We utilized these three cognitive–behavioral components (behavioral activation, assertiveness training, and problem-solving therapy) from an internet-based CBT program that has previously been developed and tested in several RCTs [25, 26].
The present randomized study aimed to examine the efficacy of smartphone-based behavioral activation, assertiveness training, and problem-solving therapy in improving depressive symptoms in parents raising autistic children.
This study was an individually randomized, parallel-group, multicenter trial (Fig. 1). An independent data center managed by TAF provided computer-generated random allocation sequences. The allocation sequences were maintained centrally, and participants were randomized to psychoeducation plus smartphone-based interventions (behavioral activation, assertiveness training, and problem-solving therapy) or to a waitlist control group receiving psychoeducation alone (Fig. 2).
Fig. 1CONSORT diagram
Fig. 2Screenshots from the application for smartphone-based behavioral activation, assertiveness training, and problem-solving therapy
The inclusion criteria for participants were as (1) parents aged 24–60 years raising a biological child diagnosed with ASD by experienced child psychiatrists; (2) the child was between 6 and < 16 years of age at the time of obtaining electronic informed consent (e-consent) and living with the parent; (3) ability to complete the electronic patient-reported outcome questionnaire using a smartphone; and (4) regular use of a smartphone or iPad with the ability to install apps and access the internet.
Eligibility criteria did not require parents to have depressive symptoms or high levels of stress. Parents of autistic children were eligible regardless of their current mental health status, meaning that families coping well were also included.
The exclusion criteria for participants were as (1) inability to understand Japanese; (2) severe uncontrolled mental illness (e.g., schizophrenia, bipolar disorder, severe depression) that rendered them unsuitable for participation; (3) currently taking psychosomatic medicine or seeing a psychiatric care, where the treating psychiatrist determined that participation was inappropriate; (4) prior receipt of structured problem-solving therapy, behavioral activation therapy, or CBT; and (5) having a child diagnosed with ASD aged 6–16 years for whom the treating physician judged study participation to be inappropriate. In these cases, the determination was not based on predefined diagnostic thresholds but rather on the treating psychiatrist’s overall clinical judgment regarding whether participation might adversely affect the parent’s or child’s health. Participants were advised to consult their psychiatrist prior to enrollment, and enrollment was not permitted if the psychiatrist advised against participation.
This study was performed in accordance with the Declaration of Helsinki, and the protocol was approved by the Nagoya City University Certified Review Board on April 9, 2019 (ID: 46-18-0016).
Information about the study was provided to parents of children receiving treatment at the Child Psychiatry Outpatient Department of Nagoya City University Hospital Mental Health Center or the Mie Prefectural Children’s Mental and Physical Development Medical Center. If a child met the inclusion criteria, the attending physician invited the parents to participate in the study. Those interested in participating could apply through a website.
The study website also provided information about the trial. After providing e-consent and completing a baseline survey (electronic patient-reported results) at Week 0, participants were randomly assigned in a 1 ratio to the smartphone-based intervention (behavioral activation, assertiveness training, and problem-solving therapy) or to the waitlist control group using the web-based data capture system at the data management center. Participants were stratified according to their PHQ-9 scores at Week one group with scores ≤ 9 and another with scores ≥ 10. Assessments were conducted at baseline and at Weeks 2, 4, 8, 12, and 16, consistent with previous smartphone-based CBT trials designed to capture short-term and longer-term changes. In addition to outcome measures, we collected data on parental personality and autism traits to explore whether these baseline characteristics were associated with the ease or difficulty of improving depressive symptoms in response to the intervention. Participants received cash voucher rewards of up to 2,000 yen, depending on the number of completed assessments.
The study period was from April 1, 2022, to March 31, 2023. The follow-up was completed on September 15, 2023. This study was based on a protocol approved on April 19, 2019, by the Nagoya City University Certified Review Board (46-18-0016).
The intervention utilized a smartphone application named “Watashi no kokoro kea Rejitore!” (“My Mind Care: RegiTre!”), which provided psychoeducation, behavioral activation, assertiveness training, and problem-solving therapy. This application was not specifically tailored for parents of autistic children but was developed from previously validated CBT-based programs. Participants in the intervention group received these components sequentially over 8 weeks. Behavioral activation included psychoeducation on the importance of engaging in rewarding activities and worksheets for behavioral experiments. Assertiveness training focused on developing assertive communication strategies, and problem-solving therapy consisted of nine sessions following the five standard problem-solving steps. All modules were delivered in a dialogue-based, self-learning format via smartphone, with each session requiring approximately 30 min per week. The program was typically completed in 8 weeks, although completion in as few as 2 weeks was possible. To encourage adherence, participants received supportive reminder e-mails every 2 weeks during the intervention period. The psychoeducation module provided an introduction to stress and the CBT model, with an emphasis on the importance of weekly self-assessments using the PHQ-9. The behavioral activation program, a key feature of the internet-based CBT intervention developed in earlier studies [24–27], encouraged participants to engage in pleasurable activities under the principle of positive behavioral reinforcement. It incorporated tools such as a personal experiment worksheet to explore new activities and a gamified “action marathon” to foster active participation.
The assertiveness training module centered on teaching assertive communication skills, enabling participants to express their feelings and needs effectively while avoiding aggressive or passive communication. This module, also integrated into the internet-based CBT framework from prior research [25], was designed to enhance interpersonal interactions and communication skills.
The problem-solving therapy component focused on systematic problem analysis and goal setting with specific, attainable objectives, followed by brainstorming potential solutions, evaluating their pros and cons, and selecting the most appropriate course of action. Similar to the behavioral activation and assertiveness training modules, problem-solving therapy formed a core element of the previously developed internet-based CBT [24–27] and has been demonstrated to effectively reduce depressive symptoms [28].
First, all participants were required to complete the psychoeducation component within 2 weeks of providing consent. Participants assigned to the intervention group received an e-mail every 2 weeks during the 8-week period reminding them to complete lessons using the behavioral activation, assertiveness training, and problem-solving therapy app. The research team checked the participants’ progress (number of times and duration of use of each application) using Google Analytics. The control group was also e-mailed every 2 weeks for 8 weeks, reminding them to self-check. Owing to clerical errors, the frequency of these e-mails was lower than that originally specified in the protocol.
Participants were assessed weekly during the study period (Weeks 0–8), and follow-up assessments were conducted at Weeks 12 and 16 via smartphone. The primary outcome was the Patient Health Questionnaire-9 (PHQ-9) depression module at Week 8. The secondary outcome included the difference in PHQ-9 at Weeks 1, 2, 3, 4, 5, 6, 7, 12, and 16; Generalized Anxiety Disorder-7 (GAD-7) at Weeks 4 and 8; CBT skills at Week 8; the presenteeism scale at Week 8; and the usage status of smartphone apps.
The background characteristics measured at baseline included demographic data, personality traits assessed using the short form of the Big Five Scale of Personality Trait Adjectives [29, 30], autism traits assessed using the short form of the Autism Spectrum Quotient (AQ) [31, 32], social support evaluated using the Social Support Questionnaire (SSQ) [33, 34], and cognitive and behavioral skills assessed using the Cognitive and Behavioral Skills Scores [11, 35–40].
The PHQ-9 assesses symptoms for major depressive episodes as defined by the DSM-IV and DSM-5. Each item is scored on a 4-point Likert scale ranging from 0 (not at all) to 3 (approximately every day), resulting in a total score ranging from 0 to 27. Scores of 4 or below indicate a relatively calm state, scores between 5 and 9 suggest mild depressive symptoms, and scores of 10 or above indicate significant depressive symptoms. The reliability and validity of the original and Japanese versions have been thoroughly validated [41, 42].
The GAD-7 is a seven-item instrument designed to assess general anxiety, focusing on symptoms such as nervousness, tension, and excessive worry. Each item is rated on a scale from 0 (not at all) to 3 (approximately every day), yielding a total score ranging from 0 to 21. The reliability and validity of the GAD-7 have been confirmed [43]. The PHQ-9, alongside the GAD-7, is widely recommended for evaluating outcomes related to depression and anxiety [44].
We utilized three items from the HPQ presenteeism subscale, which has demonstrated strong reliability and validity [45, 46]. The Japanese version, used in the World Mental Health Survey in Japan [47], employs a 0–10 rating scale. In Q1, participants rate the performance of a typical worker in a similar role. In Q2, they assess their usual job performance over the past 1–2 years, and in Q3, their overall job performance on days worked during the past 4 weeks. Presenteeism was measured using two absolute presenteeism, calculated by multiplying the Q3 score by 10, and relative presenteeism, defined as the Q3/Q1 ratio.
This study employed the short form of the Big Five Scale of Personality Trait Adjectives to evaluate five core personality neuroticism, extraversion, openness, agreeableness, and conscientiousness. Each trait is measured using 5–7 descriptive adjectives rated on a 5-point Likert scale, ranging from 0 (untrue of me) to 4 (true of me). The reliability and validity of the abbreviated version have been thoroughly validated [29, 30].
The AQ, a brief instrument developed by Baron-Cohen et al. [31], was used to assess autistic traits in participants. The reliability and validity of the Japanese version and its abbreviated 10-item form have been well established, with the shortened version utilized in this study [32].
Social support was evaluated using the short form of the SSQ [31], which assesses the number of support providers and the level of satisfaction with the received support across six domains. The reliability and validity of the original and Japanese versions have been demonstrated to be robust [33, 34].
The CBTSS assesses five core cognitive and behavioral self-monitoring, cognitive restructuring, behavioral activation, assertiveness training, and PS. Designed specifically for Japanese university students, the scale has shown strong construct validity, internal consistency, and content validity [35]. Each item is scored on a 4-point Likert scale ranging from 0 (very untrue) to 3 (very true).
Among smartphone-based CBT, a combination of self-monitoring, cognitive restructuring, and behavioral activation has demonstrated a statistically significant effect with an effect size of 0.3 to 0.4 in 164 patients with major depression [26]. In this study, we assumed an effect size of 0.3 and estimated that a total sample size of 64 people would be required for detection at alpha = 0.05 and beta = 0.20. Given a dropout rate of approximately 20%, the target number of registered cases was set at 80.
We assessed treatment effects in the primary analysis set following the intention-to-treat principle. Primary outcomes were analyzed using a generalized linear model with unstructured covariance and robust standard errors. Fixed effects included baseline PHQ-9 score, treatment allocation, time, and treatment-by-time interaction. The primary outcome was the difference in PHQ-9 scores between groups at Week 8, with statistical significance set at p < 0.05. Secondary outcomes were analyzed using similar models.
Despite deviations from the exclusion criteria outlined in the original protocol, all participants were included in the analysis. The participant flow is depicted in Fig. 1. Informed consent was obtained from 73 individuals who were initially screened and considered eligible. Of these, three could not be contacted after providing consent, and seven did not proceed to randomization for other reasons. Consequently, 63 participants were randomized, with 31 assigned to the intervention group and 32 to the control group. All participants completed the study, and primary outcome data at Week 8 were collected from all participants. In the intervention group, 26 of 31 participants (84%) completed the follow-up assessment at Week 16.
Baseline demographic and clinical characteristics, shown in Table 1, were well-balanced between groups. Most participants were mothers, with a small number of fathers included. In the intervention group, 29% had siblings diagnosed with ASD, compared to 6.3% in the control group. Baseline PHQ-9 scores were slightly higher in the control group than in the intervention group (9.3 [SD = 5.3] vs. 8.9 [SD = 6.3]).
Table 1Baseline characteristics of participants by groupBaseline characteristicsIntervention Group(n = 31)Control Group(n = 32)Total(n = 63)Age (year), mean (SD)41.7 (5.5)45.9 (4.2)43.8 (5.3)Sex, n (%) Female30 (96.8%)30 (93.8%)60 (95.2%) Male1 (3.2%)2 (6.3%)3 (4.8%)Marital status, n (%) Single0 (0%)1 (3.1%)1 (1.6%) Married or cohabiting26 (83.9%)28 (87.5%)54 (85.7%) Divorced5 (16.1%)3 (9.4%)8 (12.7%)Employment status, n (%) Homemaker8 (25.8%)10 (31.3%)18 (28.6%) Part-time < 40 h/week10 (32.3%)12 (37.5%)22 (34.9%) Part-time > 40 h/week or full-time13 (41.9%)10 (31.3%)23 (36.5%)Length of education (year), mean (SD)15.2 (4.2)15.1 (4.7)15.2 (4.4)Number of family members, mean (SD)4.6 (1.4)3.8 (0.7)4.2 (1.2)Number of children, mean (SD)2.3 (1.2)2.0 (0.8)2.1 (1.0)Number of children with ASD, n (%) 122 (71.0%)30 (93.8%)52 (82.5%) 28 (25.8%)2 (6.3%)10 (15.9%) 3 or more1 (3.2%)0 (0%)1 (1.6%)PHQ-9 scores, mean (SD)8.9 (6.3)9.3 (5.3)9.1 (5.8)GAD-7 scores, mean (SD)9.2 (5.0)8.9 (4.9)9.0 (4.9)CBTSS, mean (SD) Assertion8.1 (3.4)9.2 (3.9)8.7 (3.7) Behavioral activation12.6 (4.8)14.1 (3.7)13.4 (4.3) Cognitive restructuring7.6 (3.7)8.3 (3.9)8.0 (3.8) Problem solving10.3 (3.1)10.1 (3.4)10.2 (3.2) Self-monitoring9.4 (4.0)9.1 (4.4)9.2 (4.2)Big-Five scores, mean (SD) Conscientiousness3.1 (0.6)3.2 (0.6)3.2 (0.6) Extraversion2.9 (0.9)3.0 (0.6)2.9 (0.8) Agreeableness2.9 (0.6)3.1 (0.5)3.0 (0.5) Openness2.5 (0.5)2.7 (0.5)2.6 (0.5) Neuroticism3.2 (0.7)3.0 (0.5)3.1 (0.6)Social Support Questionnaire, mean (SD) Number2.3 (1.0)2.5 (0.8)2.4 (0.9) Satisfaction3.5 (1.0)3.5 (0.9)3.5 (1.0)Presenteeism scale from the HPQ scores, mean (SD) Absolute presenteeism55.8 (23.5)61.3 (20.4)58.6 (22.0) Relative presenteeism1.0 (0.5)1.0 (0.4)1.0 (0.4)AQ scores, mean (SD)3.1 (2.5)3.3 (2.1)3.2 (2.3)History of psychiatric diseases, n (%) None21 (67.7%)22 (68.8%)43 (68.3%) Previously treated5 (16.1%)6 (18.8%)11 (17.5%) Currently on treatment5 (16.1%)4 (12.5%)9 (14.3%)Smoking, n (%) Yes6 (19.4%)2 (6.3%)8 (12.7%) No25 (8.1%)30 (93.8%)55 (87.3%)Drinking alcohol, n (%) None22 (71.0%)21 (65.6%)43 (68.3%) Average daily amount of pure alcohol within 22 g9 (29.0%)9 (28.1%)18 (28.6%) Average daily amount of pure alcohol 22 g or more0 (0%)1 (3.1%)1 (1.6%) Problem behavior0 (0%)1 (3.1%)1 (1.6%)Exercise, n (%) No exercise17 (54.8%)12 (37.5%)29 (46.0%) Sometimes13 (41.9%)15 (46.9%)28 (44.4%) Everyday1 (3.2%)5 (15.6%)6 (9.5%)The following are the characteristics of children diagnosed with ASDBaseline characteristicsIntervention Group(n = 41)Control Group(n = 34)Total(n = 75)Age (year), mean (SD)10.9 (2.6)11.4 (2.4)11.1 (2.5)Sex, n (%) Female12 (29.3%)9 (26.5%)21 (28.0%) Male29 (70.7%)25 (73.5%)54 (72.0%)Age of diagnosis (year), mean (SD)6.0 (2.9)6.4 (3.2)6.2 (3.1)Treatment condition Never treated6 (14.6%)3 (8.8%)9 (12.0%) Previously treated0 (0%)1 (2.9%)1 (1.3%) Currently on treatment35 (85.4%)30 (88.2%)65 (86.7%)Abbreviations: AQ, Autism-spectrum Quotient; ASD, autism spectrum disorder; Big Five, Big Five Scale of Personality Trait Adjectives; CBTSS, Cognitive Behavioral Therapy Skills Scale; GAD-7, Generalized Anxiety Disorder-7; HPQ, Health and Work Performance Questionnaire; PHQ-9, Personal Health Questionnaire-9; SD, standard deviation
Table 2 presents the number of participants in the intervention group who completed each session. No adverse events occurred during the trial.
Table 2Treatment receivedNumber of participants who completed each sessionIntervention Group (n = 31)Psychoeducation31 (100%)Behavioral activation30 (96.8%)Assertiveness training27 (87.1%)Problem- solving therapy22 (71.0%)Epilogue10 (32.3%)
Outcome data for both groups are summarized in Table 3. No significant differences in PHQ-9 change scores at Week 8 were observed between the groups. Both groups showed significant reductions in PHQ-9 scores from baseline to Week -2.52 (-4.08 to -0.97) in the intervention group and − 2.48 (-4.01 to -0.95) in the control group (Fig. 3).
Table 3PHQ-9 scores for two groups each weekOutcome, time pointIntervention Group (n = 31)Control Group (n = 32)Comparison between groupsNo.Mean (SD)^a^ LS mean changes (95% CI)No.Mean (SD)^a^ LS mean changes (95% CI)^a^ Difference in LS mean changes (95% CI)P-value^b^ Standardized mean difference (95% CI)PHQ-9 scoreBaseline318.94 (6.29)NA329.25 (5.34)NANANAWeek 1318.13 (6.12)-0.85 (-1.87 to 0.18)327.06 (4.72)-2.14 (-3.14 to -1.13)1.29 (-0.14 to 2.73)0.0770.23 (-0.03 to 0.49)Week 2317.29 (5.11)-1.68 (-2.75 to -0.62)325.88 (3.54)-3.33 (-4.37 to -2.28)1.64 (0.15 to 3.13)0.0310.37 (0.03 to 0.71)Week 3307.10 (5.44)-1.90 (-3.00 to -0.80)315.97 (4.10)-3.20 (-4.29 to -2.12)1.30 (-0.24 to 2.85)0.0990.27 (-0.05 to 0.59)Week 4296.90 (5.99)-2.11 (-3.31 to -0.92)327.47 (4.68)-1.73 (-2.89 to -0.58)-0.38 (-2.04 to 1.28)0.65-0.07 (-0.38 to 0.24)Week 5297.45 (6.17)-1.53 (-3.04 to -0.03)326.03 (3.85)-3.17 (-4.63 to -1.71)1.64 (-0.46 to 3.73)0.1260.32 (-0.09 to 0.72)Week 6286.32 (5.73)-2.40 (-3.75 to -1.06)325.91 (4.08)-3.29 (-4.61 to -1.98)0.89 (-0.99 to 2.77)0.3530.18 (-0.20 to 0.56)Week 7296.21 (5.81)-2.83 (-4.33 to -1.32)326.56 (4.11)-2.64 (-4.11 to -1.17)-0.19 (-2.29 to 1.92)0.861-0.04 (-0.45 to 0.38)Week 8316.45 (6.00)-2.52 (-4.08 to -0.97)326.72 (4.85)-2.48 (-4.01 to -0.95)-0.04 (-2.22 to 2.14)0.971-0.01 (-0.40 to 0.39)Abbreviations: CI, confidence interval; PHQ-9, Patient Health Questionnaire-9; LS, least squares; SD, standard deviation.^a^ Estimated from the MMRM model where group, week, group×week, and baseline PHQ-9 score are included as covariates.^b^ The standardized mean difference (SMD) was calculated by dividing the mean difference (MD) between two groups by the pooled standard deviation (SD). The MD was obtained from the difference in least squares (LS) mean changes, and the pooled SD was calculated from the SDs of the change scores between baseline and each time point in two groups. As the sample size is small, the SMD is corrected using the formula Hedges’s g = {1–3/[4(n1 + n2)-9]}*SMD.
Fig. 3Changes from baseline in the overall PHQ-9. Figure. 3 presents the mean change in the PHQ-9 score from baseline. The overall possible score range is 0–27, with higher scores indicating a more depressed state. The bars indicate standard errors
The changes in GAD-7 scores from baseline to Week 8 were − 2.58 (-3.89 to -1.27) in the intervention group and − 2.48 (-3.77 to -1.20) in the control group, indicating a significant decrease in both groups. However, no significant differences were observed between the groups (Table 4).
Table 4GAD-7 scores for two groups each weekOutcome, time pointIntervention Group (n = 31)Control Group (n = 32)Comparison between groupsNo.Mean (SD)^a^ LS mean changes (95% CI)No.Mean (SD)^a^ LS mean changes (95% CI)^a^ Difference in LS mean changes (95% CI)P-value^b^ Standardized mean difference (95% CI)GAD-7 scoreBaseline319.16 (4.99)NA328.88 (4.94)NANANAWeek 4296.55(5.59)-2.43 (-3.75 to -1.10)316.45 (3.98)-2.54 (-3.83 to -1.25)0.12 (-1.74 to 1.97)0.9020.02 (-0.11 to 0.15)Week 8316.52 (4.79)-2.58 (-3.89 to -1.27)326.44 (4.58)-2.48 (-3.77 to -1.20)-0.10 (-1.93 to 1.74)0.917-0.02 (-0.14 to 0.10)Abbreviations: CI, confidence interval; GAD-7, Generalized Anxiety Disorder-7; LS, least squares; SD, standard deviation.^a^ Estimated from the MMRM model where group, week, group×week, and baseline GAD-7 score are included as covariates.^b^ The standardized mean difference (SMD) was calculated by dividing the mean difference (MD) between two groups by the pooled standard deviation (SD). The MD was obtained from the difference in LS mean changes, and the pooled SD was calculated from the SDs of the change scores between baseline and each time point in two groups. As the sample size is small, the SMD is corrected using the formula Hedges’s g = {1–3/[4(n1 + n2)-9]}*SMD.
Table 5 presents changes in CBT skills and presenteeism scores from baseline to Week 8. No significant differences were found between the intervention and control groups.
Table 5CBTSS scores and presenteeism scale from the HPQ scores at baseline and week 8Outcome, time pointIntervention Group (n = 31)Control Group (n = 32)Comparison between groupsNo.Mean (SD)Mean change from baseline (SD)No.Mean (SD)Mean change from baseline (SD)^a^ Difference in mean changes (95% CI)P-valueCBTSS - Assertiveness TrainingBaseline318.06 (3.43)NA329.22 (3.92)NANAWeek 8317.77 (4.17)-0.29 (3.26)328.59 (4.56)-0.625 (3.85)0.03 (-1.74 to 1.79)0.976CBTSS - Behavioral ActivationBaseline3112.65 (4.76)NA3214.09 (3.66)NANAWeek 83112.48 (5.12)-0.16 (4.01)3212.97(4.95)-1.13 (4.37)0.55 (-1.52 to 2.62)0.597CBTSS - Cognitive RestructuringBaseline317.65 (3.74)NA328.28 (3.94)NANAWeek 8318.71 (3.98)1.06 (3.69)327.78 (4.29)-0.5 (3.71)1.32 (-0.41 to 3.05)0.132CBTSS - Problem Solving;Baseline3110.26 (3.07)NA3210.09 (3.36)NANAWeek 8319.35 (3.17)-0.90 (3.29)329.59 (3.25)-0.50 (2.77)-0.33 (-1.69 to 1.03)0.629CBTSS - SMBaseline319.42 (4.01)NA329.06 (4.38)NANAWeek 8318.90 (4.76)-0.52 (3.06)329.44 (3.60)0.38 (2.98)-0.80 (-2.24 to 0.64)0.270Presenteeism scale from the HPQ scores (absolute)Baseline3155.81 (23.49)NA3261.25 (20.44)NANAWeek 83159.03 (17.77)3.23 (25.61)3262.5 (18.32)1.25 (18.79)-1.74 (-10.26 to 6.79)0.685Presenteeism scale from the HPQ scores (relative)Baseline311.00 (0.49)NA321.03 (0.38)NANAWeek 8310.97 (0.38)-0.03 (0.57)321.07 (0.47)0.04 (0.43)-0.09 (-0.30 to 0.11)0.375^a^ Estimated from the ANCOVA model, where the baseline score of the outcome is included as a covariateAbbreviations: CI, confidence interval; CBTSS, Cognitive Behavioral Therapy Skills Scale; HPQ, Health and Work Performance Questionnaire; SD, standard deviation; SM: self-monitoring.
To the best of our knowledge, this is the first study to investigate the efficacy of smartphone-based psychological therapy for depressive symptoms in parents raising autistic children. At the outset, it is important to note that although the sample size calculation followed the original protocol and was based on previously reported effect sizes for smartphone-based CBT (0.3–0.4), the present trial may have been underpowered to detect smaller between-group differences. Moreover, because the control condition involved psychoeducation—a form of active intervention—the expected difference between the groups was likely attenuated. This context should be kept in mind when interpreting the nonsignificant between-group findings presented in this study.
No evidence was found that app use effectively reduced depression, as there was no significant difference in PHQ-9 scores at Week 8 between the intervention and control groups. PHQ-9 scores decreased similarly over time in both groups. It is possible that the effects of the psychoeducation, self-evaluation, and encouraging e-mails administered to both groups were robust, whereas the additional impact of the intervention components was minimal. Participants assigned to the intervention group may have needed more time and effort than those in the control group, potentially because of other influencing factors.
In our previous study, smartphone-based problem-solving therapy and behavioral activation applications were effective in treating fear of cancer recurrence and depression in breast cancer survivors [22, 25]. However, these approaches appear to have limited effects on the stress associated with raising autistic children. By contrast, a previous RCT demonstrated positive effects of problem-solving therapy in reducing parenting stress and depressive symptoms during the critical post-diagnosis period [18]. In that study, problem-solving therapy was individually administered by a trained interventionist to address a single, measurable problem. The participants were mothers of children with a mean age of 34 months who had recently been diagnosed with ASD. Another study revealed that among the primary caregivers of children aged 2 to 5 diagnosed with some form of ASD within the past 4 to 24 weeks, problem-solving therapy significantly decreased negative emotions and enhanced their coping skills [19]. In this study, problem-solving therapy involved eight 1-hour sessions conducted by extensively trained facilitators. However, it is important to note that this was a pilot feasibility study involving only 24 participants.
In our study, the problem-solving therapy was conducted via smartphone, and participants were not directly guided by facilitators, requiring them to independently identify their target problems. Additionally, since the participants were parents of elementary and junior high school students with ASD, the issues they faced might not have been solely related to the diagnosis but could encompass a range of ongoing parenting challenges, making it difficult to pinpoint a specific problem. Furthermore, because the participants were parents whose children were visiting the hospital, they had already been able to consult their physicians, which may have made the application less effective.
Another reason why no significant difference was observed between the intervention and control groups was that the participants were not limited to baseline PHQ-9 scores. This study included participants who had low baseline PHQ-9 scores and whose scores did not improve after the intervention. The intervention itself may not have been sufficiently effective because of factors other than participant selection and CBT using the app. The participants were encouraged to participate by their physicians, resulting in their participation despite having relatively low intrinsic motivation. The e-mails were infrequent, once every 2 weeks, and were sent by a third party to the participants; therefore, their encouraging effect was weak. The overall load, including assertiveness training and PS, was considerably high.
Given these considerations, the present findings should be interpreted as preliminary and may serve to inform the design of future trials with sufficient statistical power. The strength of our study lies in the perfect follow-up rate of outcome assessment. We achieved a high follow-up rate, with Week 8 results available for all participants.
This study has some limitations. First, although the sample size was determined according to the protocol and based on prior effect-size estimates for smartphone-based CBT, the power may still have been insufficient to detect smaller between-group effects, particularly given that the control condition functioned as an active comparator. Therefore, the possibility that the nonsignificant group differences were attributable to limited statistical power cannot be excluded. Second, the intervention was not specifically adapted to the unique caregiving context of parents of autistic children. Although problem-solving therapy has demonstrated efficacy in this population, and behavioral activation and assertiveness training have shown benefits for depression in general populations, the lack of tailoring to the dual demands of caregiving and self-care, as well as the use of multiple combined components (behavioral activation, assertiveness training, and problem-solving therapy), may have limited effectiveness. Third, participants were limited to parents of outpatients, and their regular clinical visits may already have alleviated some psychological burden. Fourth, the participating children varied widely in age and time since diagnosis, which may have influenced parental stress in heterogeneous ways. Finally, because the intervention was delivered via smartphone, it was unclear how deeply participants understood and engaged with the program despite ongoing participation.
Future research should consider developing and testing app-based programs specifically adapted to the daily challenges of parents of autistic children, for example by integrating flexible scheduling, caregiver–child joint activities, or modules addressing advocacy and family communication. Trials should also consider assigning participants to a manageable number of components to promote continuity and engagement. In addition, narrowing the target population according to child age and autism severity, and tailoring components to individual needs, may enhance intervention efficacy.
This trial did not detect significant differences between the smartphone-based intervention and psychoeducation alone in reducing depressive symptoms among parents raising autistic children. Although symptoms improved in both groups, these findings should be interpreted cautiously given the limited statistical power and the use of an active control condition. Further adequately powered studies are needed.