Summary
Background
Medication non-adherence is a significant challenge in the management of schizophrenia, with non-adherence rates ranging from 30 to 70%. Digital interventions may address barriers to adherence and improve outcomes. We aimed to evaluate the efficacy of a narrative-based psychoeducational intervention in improving medication adherence, attitudes, psychological state, and quality of life in individuals with stable schizophrenia.
Methods
This multicentre, parallel-group randomised controlled trial with blinded outcome assessment was conducted at seven community rehabilitation centres. Eligible patients (aged 18–60 years) had a diagnosis of schizophrenia (ICD-10) in a clinically stable phase, had normal vision and hearing, and were able to use a smartphone independently. Upon enrolment, participants were randomly allocated (1:1) to either the intervention group or the control group. The intervention group received standard community rehabilitation plus a narrative-based psychoeducational intervention three times per week for 3 months, while the control group received standard community rehabilitation only. Co-primary outcomes were medication adherence and medication attitude, as assessed using validated self-report questionnaires: the Morisky Medication Adherence Scale-8 (MMAS-8) and the Drug Attitude Inventory (DAI-10), respectively, at: baseline (T0, prior to randomization), post-intervention (T1, after 3 months of intervention), 1-month follow-up (T2, 4 months from baseline), and 3-month follow-up (T3, 6 months from baseline). This trial is registered with ClinicalTrials.gov, NCT06175559.
Findings
Between Feb 1 and March 25, 2024, 72 participants were enrolled. Two participants withdrew (distance concerns). Data collection continued until October 30, 2024. Among the 70 participants who completed assessments (mean [SD] age, 44.20 [8.06] years; 27 women [38.57%], 43 men [61.43%]), significant between-group differences emerged over time. The psychoeducational intervention group reported greater positive changes than the control group in medication adherence (β = 0.75 [95% CI, 0.10–1.40]; P = 0.02) and medication attitude (β = 2.29 [0.42–4.15]; P = 0.02) at the T1, and the difference was sustained at 3-month follow-up for adherence (β = 1.65 [0.68–2.62]; P < 0.001) and attitude (β = 3.42 [1.38–5.45]; P = 0.001). Older patients showed significantly better outcomes in medication attitudes (β = 1.56 [0.03–3.10]; P = 0.05) and clinical symptoms (β = −2.43 [−4.83 to −0.04]; P = 0.05), while those with lower education levels had significantly better medication adherence (β = 0.93 [0.20–1.67]; P = 0.01) and social relationships (β = 0.87 [0.23–1.52]; P = 0.01).
Interpretation
Our findings suggest that narrative-based psychoeducational interventions can improve medication adherence in individuals with schizophrenia, with sustained effects. Future studies should investigate the long-term effectiveness and implementation of narrative-based psychoeducation interventions in larger and more diverse patient populations.
Funding
Shanghai Municipal Science and Technology Commission 2024 “Science and Technology Innovation Action Plan” Medical Innovation Research Field Program, the Shanghai Municipal Education Commission, Shanghai Jiao Tong University 2024 Medical-Industrial Intersection Research Fund, and Shanghai Jiao Tong University Medical-Industrial Interdisciplinary Youth Program.
Keywords: Schizophrenia, Narrative-based, Psychoeducation, Medication adherence, Cognitive reconstruction, Randomised controlled trial
Research in context.
Evidence before this study
On Feb 19, 2025, we conducted a comprehensive literature search of PubMed, EMBASE, Web of Science, CINAHL, PsycINFO, and the Cochrane Library, with no language restrictions. The search strategy combined subject headings and keywords, focusing on four core areas: (1) schizophrenia, (2) medication adherence, (3) psychoeducation, and (4) digital interventions. Existing studies have shown that psychoeducational interventions can improve medication adherence and functional recovery in individuals with schizophrenia, particularly when delivered by trained professionals. However, traditional psychoeducation often relies heavily on human resources and lacks interactivity and accessibility, limiting its scalability in community settings. Although recent studies have begun to explore digital health technologies as alternatives, few have systematically incorporated narrative psychology to examine its impact on health beliefs, treatment motivation, and behavioural change. To date, no randomised controlled trials have rigorously evaluated the effectiveness of narrative-based digital psychoeducational interventions for individuals with schizophrenia.
Added value of this study
This study is the first to combine narrative psychology principles with a digital psychoeducational intervention tailored for community use. We developed an interactive digital tool and evaluated its impact through a randomised controlled trial. Compared with standard community rehabilitation, the intervention significantly and sustainably improved medication adherence and attitudes among individuals with schizophrenia. Subgroup analyses further revealed that older patients and those with higher educational attainment benefited more from the intervention, suggesting strong adaptability and the potential for personalised applications.
Implications of all the available evidence
Our findings support the integration of narrative-based digital psychoeducation into community-based mental health services, particularly in settings with limited resources or professional staffing. By enhancing emotional engagement, role immersion, and self-reflection, this intervention helps patients better understand and accept antipsychotic treatment, thereby improving medication behaviour and clinical outcomes. Future studies should explore broader applications of this approach across diverse populations and cultural contexts, as well as strategies for personalised optimisation and clinical integration.
Introduction
Schizophrenia is a severe and chronic psychiatric disorder characterised by symptoms such as delusions, hallucinations, disorganised thinking, and cognitive impairment.1,2 Long-term adherence to antipsychotic medication is critical for symptom management and relapse prevention.3,4 However, many patients struggle to maintain medication adherence due to side effects, cognitive deficits, and lack of motivation.5, 6, 7 Furthermore, medication adherence tends to decline as symptoms stabilise, increasing the risk of relapse and hospitalisation, which poses significant challenges to both patients and healthcare systems.8 Consequently, improving medication adherence remains a central challenge in community rehabilitation efforts.9
Interventions aimed at reducing medication non-adherence in schizophrenia primarily include psychoeducation, which has been shown to be effective in supporting symptom recognition, self-management, and functional recovery and is widely implemented in schizophrenia care.10,11 However, such interventions often rely on intensive professional support, posing considerable challenges for implementation in resource-limited community settings.12 Traditional psychoeducation is typically delivered by therapists in a structured format with limited opportunities for patient interaction, making it less suitable for individuals with impairments in attention, memory, or metacognitive abilities.13
Recent advances in digital health technologies have provided novel solutions for community rehabilitation in patients with schizophrenia.14 These technologies not only enhance access to health information and increase user engagement and behavioural follow-up to compensate for limitations of conventional rehabilitation approaches,15 but also offer highly interactive, contextualised, and personalised feedback. A particularly promising development is narrative-based digital psychoeducation, which has demonstrated unique potential to reshape health beliefs, evoke emotional resonance, and enhance treatment motivation.16 Rooted in narrative psychology, this approach emphasises that when individuals emotionally and cognitively engage with realistic, affect-rich scenarios and roles, their health beliefs, illness attitudes, and behavioural intentions can be reconstructed through simulated experiences and observational learning. However, few studies have integrated narrative techniques with digital behavioural mechanisms to assess their impact on medication adherence among patients with schizophrenia.17
Based on these considerations, we developed a narrative-based digital psychoeducational intervention that combines graphic narrative structures with interactive methods, guiding patients to adopt contextualised roles and reconstruct their understanding and acceptance of medication adherence through virtual interactions. This randomised controlled trial (RCT) evaluated the intervention's effects on medication adherence and attitudes compared with standard community rehabilitation. The intervention lasted for 3 months, followed by follow-up assessments at 1 month and 3 months post-intervention. We hypothesised that participants receiving the narrative-based digital psychoeducational intervention would exhibit significantly greater improvements in medication adherence and medication attitudes compared with those receiving standard community rehabilitation. We also explored secondary outcomes including anxiety, depression, quality of life domains, and clinical symptoms across the follow-up period.
Methods
Study design and ethics
This multicentre, assessor-blinded RCT employed a 1:1 allocation ratio, with a 3-month intervention and a subsequent 3-month follow-up period. All diagnoses were made by psychiatrists at the Shanghai Mental Health Centre, while rehabilitation services were delivered at community-based recovery centres located in the patients' residential neighbourhoods. Seven community rehabilitation centres across different subdistricts participated in the study. The recruitment period was from Feb 1 to March 25, 2024, the primary intervention phase took place from March 28 to July 1, 2024, and data collection continued until Oct 30, 2024. Patients with stable-phase schizophrenia were randomly assigned to either community standard rehabilitation or narrative-based psychoeducational intervention. The standard group received routine rehabilitation and medication education, while the intervention group participated in 3–4 narrative-based psychoeducation sessions per week, each lasting 15–20 min, at community centres, assisted by therapists (Fig. 1).
Fig. 1.
Experimental flow.
This study was approved by the university Ethics Review Committee (H20230207I), with no significant protocol changes between the trial initiation and registration confirmation. Eligible patients were enrolled after signing a written informed consent form. The protocol details were presented in Appendix File 1.
Participants
This study recruited patients diagnosed with schizophrenia who were registered at the Shanghai Mental Health Centre and required ongoing pharmacological treatment. Trained therapists conducted monthly assessments using the PANSS (Positive and Negative Syndrome Scale, PANSS), with a minimum evaluation period of one year. Patients were considered stable if their PANSS score did not change by more than 3 points and if there were no changes in medication for at least 6 months prior to the assessment. The recruitment period was from February 1 to March 25, 2024, and data collection continued until October 30, 2024.
The inclusion criteria for this study were: (1) enrolled in the Shanghai Mental Health Information Management System; (2) diagnosis of schizophrenia (ICD-10) with all participants in a clinically stable phase, as determined by trained therapists.; (3) aged 18–60 years with at least primary school education; (4) normal vision and hearing, or corrected vision and hearing within normal limits; (5) ownership and independent use of a smartphone or other electronic devices; (6) requiring maintenance treatment with second-generation antipsychotic medications; (7) Written informed consent was provided by each patient or, if the patient was illiterate or unable to sign, by a legally authorised family member or guardian.
The exclusion criteria for this study were: (1) planning to relocate outside of Shanghai; (2) presence of severe physical diseases or organic brain disorders; (3) comorbid other psychotic disorders; (4) participation in any treatment or intervention other than medication or basic public health services in the past 6 months.
Procedures
Participants were recruited from psychiatric hospitals and community mental health centres, targeting individuals with schizophrenia on long-term antipsychotic medication. After informed consent, participants were assessed for medication adherence, demographic and medical information. Data was collected at four follow-up timepoints: T0 (Baseline, prior to randomisation): initial assessments of medication adherence, mental status, and relevant social factors were recorded; T1 (Post-intervention, Month 3): upon completion of the 3-month intervention, immediate changes in medication adherence, mental health status, social functioning, and user feedback were evaluated; T2 (1-month follow-up, Month 4): 1 month after the intervention ended, follow-up assessments were conducted to examine the short-term sustainability of the intervention's effects; and T3 (3-month follow-up, Month 6): 3 months after the intervention, long-term outcomes regarding medication adherence, mental status, and maintenance of behavioural improvements were assessed.
All follow-up assessments were paper based, with participants receiving $30 USD per completed assessment.
Patients in the intervention group received a digital psychoeducational intervention, in addition to the standard community-based rehabilitation care provided by physicians at community rehabilitation centres. This intervention is a graphics-based digital psychoeducational program designed to enhance medication adherence and treatment motivation among patients with chronic schizophrenia (Appendix File 2). It integrates narrative psychology and cognitive-behavioural principles through three core modules: interactive storylines, cognitive training games, and self-monitoring logs. Set in realistic life scenarios, the application enables users to engage in third-person role-play, promoting emotional involvement and self-reflection. Narrative paths are open-ended, supporting personalised experiences. Cognitive games simulate daily tasks with adaptive difficulty, strengthening executive function and facilitating real-world skill transfer. All interactions are tracked in real time for behavioural analysis and feedback. The digital psychoeducational intervention was delivered 2–3 times per week, with each session lasting 20 min.
In the control group, standard community care was provided by trained therapists, during which traditional psychoeducation was delivered. These sessions included face-to-face group or individual education covering topics such as illness awareness, the importance of long-term medication adherence, common antipsychotic medications and their side effects, strategies to improve adherence (e.g., using pillboxes, incorporating medication into daily routines), family involvement in medication monitoring, and early warning signs of relapse. These sessions were conducted 2–3 times per week, each lasting approximately 20 min.
Outcomes
Co-primary outcomes
The co-primary outcomes were medication adherence and medication attitude. Medication Adherence was assessed via 8-item Morisky Medication Adherence Scale (MMAS-8).18 This self-reported questionnaire evaluates behaviors associated with medication-taking, with a total score ranging from 0 to 8. Higher scores indicate better adherence. Scores can also be interpreted categorically: 8 = high adherence, 6–7 = medium adherence, and <6 = low adherence. Medication attitude was assessed via the Drug Attitude Inventory (DAI-10)19: This scale evaluates patients' attitudes toward medication, with higher scores indicating better attitudes.
Secondary outcomes
Secondary outcomes were anxiety, depression, quality of life, social functioning, and clinical symptoms. Anxiety was assessed via Generalized Anxiety Disorder 7 (GAD-7)20: This scale measures the level of anxiety in individuals, categorising the severity into mild, moderate, and severe levels. Depression was assessed via Patient Health Questionnaire-9 (PHQ-9)21: This scale assesses the severity of depressive symptoms, categorising the level of depression from mild to severe. Quality of Life was assessed via World Health Organization Quality of Life-BREF (WHOQOL-BREF)22: This scale evaluates overall quality of life and health status, with a focus on physical, psychological, social, and environmental domains. Social Functioning was assessed via Social Disability Screening Schedule (SDSS)23: This scale assesses the degree of social disability, with higher scores indicating more severe social dysfunction. Clinical Symptoms was assessed via Brief Psychiatric Rating Scale (BPRS)24: This scale evaluates clinical symptoms across five factors, including anxiety-depression, lack of vitality, thought disturbances, activation, and hostile suspicion.
Sample size calculation
The primary outcome of this study is the medication adherence score based on literature review.25 Sample size was calculated using G∗Power 3.1 software for a two-tailed independent-samples t-test. Based on an expected between-group mean difference of 0.78 (group 1: 0.88; group 2: 0.10) and a pooled standard deviation of 0.93 (SD1 = 1.01, SD2 = 0.85), the calculated effect size (Cohen's d) was approximately 0.83. With a significance level of α = .05 and a statistical power of 90%, the minimum required sample size was 31 participants per group. Considering a 1:1 randomisation ratio and a 20% loss to follow-up, at least 76 participants were needed (38 per group). The sample size for each district was determined based on the proportion of schizophrenia patients in each area. Finally, 82 participants were recruited, and after screening and assessment, 70 eligible patients were included in the study.
Randomisation, allocation concealment, and blinding
Outcome assessors were blinded to group allocation throughout the study. Data analysts remained blinded to group assignment until completion of all primary analyses. Due to the nature of the intervention, neither participants nor therapists could be blinded. After participants signed the informed consent form, they were randomly allocated to either the community rehabilitation group or the intervention group in a 1:1 ratio. The randomisation process was conducted using a computer-generated random number table to ensure that baseline characteristics were evenly distributed across the two groups. The study included four assessment points: baseline, post-intervention, 1-month follow-up, and 3-month follow-up, to evaluate the impact of the gamified intervention model on medication adherence and other health outcomes.
Statistical analysis
All randomised participants were included in the analysis, which was conducted according to the intention-to-treat (ITT) principle. Baseline characteristics between the two study groups were compared using independent-sample t tests for continuous variables and χ2 tests or Fisher exact tests, as appropriate, for categorical variables.
Within-group changes over time in medication attitudes, adherence, and other secondary outcomes were assessed using one-way repeated-measures ANOVA. Between-group differences in outcome changes across timepoints were evaluated using generalised estimating equation (GEE) models with interaction terms, adjusting for covariates. Baseline variables with P < 0.10 for between-group differences were included as covariates. Mean differences in score changes from baseline to each timepoint between groups were calculated, and effect sizes (Cohen d) were reported.
Subgroup analyses were conducted by adding interaction terms between group allocation and demographic variables (sex, age group, education level) in the GEE models to explore potential effect modifiers. Sensitivity analysis was inherently addressed through the use of GEE, which provides robust standard errors and is less sensitive to missing data and deviations from model assumptions. Given that only one participant was lost to follow-up, no additional per-protocol analysis was performed. All statistical analyses were performed using SPSS software, version 26.0 (IBM Corp., Armonk, NY, USA). Two-sided P values < 0.05 were considered statistically significant.
Role of the funding source
The funder did not contribute to the trial design, patient recruitment, data collection, data analysis, data interpretation, or writing of the article.
Results
Between Feb 1 and March 25, 2024, 82 individuals with schizophrenia were recruited from Shanghai community rehabilitation centres. After excluding 10 individuals (3 not meeting criteria, 7 transitioning to long-acting injectables), 72 were eligible. Two withdrew due to distance concerns, leaving 70 participants (35 per group) who completed baseline assessments. No dropouts occurred during the intervention, and all 70 completed the post-intervention assessment. At the one-month follow-up, one participant withdrew due to hospitalisation. By the three-month follow-up, all 69 remaining participants completed assessments. The dropout rate was 1.43%, with no adverse reactions reported.
8-item Morisky Medication Adherence Scale (MMAS-8), Drug Attitude Inventory (DAI-10), Generalized Anxiety Disorder 7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), World Health Organization Quality of Life-BREF (WHOQOL-BREF), Social Disability Screening Schedule (SDSS) and Brief Psychiatric Rating Scale (BPRS).
70 participants (43 males, 27 females; mean age 44.20 ± 8.06 years) were randomly assigned to the intervention and control groups. Baseline comparisons showed no significant differences between groups in age, gender, education, marital status, or living situation (P > 0.05) (Table 1).
Table 1.
Demographic characteristics of participants.
| Total (N = 70) | Control group (N = 35) | Intervention group (N = 35) | χ2/t | P | |
|---|---|---|---|---|---|
| Gender | |||||
| Male | 43 (61.43%) | 20 (57.14%) | 23 (65.71%) | 0.54 | 0.46 |
| Female | 27 (38.57%) | 15 (42.86%) | 12 (34.29%) | ||
| Age | 44.20 ± 8.06 | 44.29 ± 9.10 | 44.11 ± 6.99 | 0.09 | 0.93 |
| Education level | |||||
| Primary School and below | 2 (2.86%) | 0 (0%) | 2 (2.86%) | 3.29 | 0.19 |
| Secondary school | 45 (64.29%) | 21 (60.00%) | 24 (68.57%) | ||
| University and above | 23 (32.86%) | 14 (40.00%) | 9 (25.71%) | ||
| Marital status | |||||
| Married | 11 (15.71%) | 5 (14.29%) | 6 (17.14%) | 0.51 | 0.77 |
| Widowed | 0 (0%) | 0 (0%) | 0 (0%) | ||
| Single | 49 (70%) | 24 (68.57%) | 25 (71.43%) | ||
| Living situation | |||||
| Living Alone | 5 (7.14%) | 3 (8.57%) | 2 (5.71%) | 0.22 | 0.64 |
| Living with Family | 65 (92.86%) | 32 (91.43%) | 33 (94.29%) | ||
| Medication adherence | |||||
| Medication adherence | 7.07 ± 1.45 | 7.23 ± 1.22 | 6.91 ± 1.66 | 0.92 | 0.36 |
| Medication attitude | 5.40 ± 3.39 | 5.83 ± 3.12 | 4.97 ± 3.64 | 1.06 | 0.29 |
| Psychological state | |||||
| Anxiety | 3.10 ± 3.28 | 2.69 ± 3.01 | 3.51 ± 3.52 | −1.06 | 0.29 |
| Depression | 3.81 ± 3.77 | 3.46 ± 3.10 | 4.17 ± 4.36 | −0.79 | 0.43 |
| Quality of life | |||||
| Physical domain | 12.02 ± 2.13 | 12.08 ± 2.37 | 11.97 ± 1.89 | 0.22 | 0.82 |
| Psychological domain | 14.13 ± 2.35 | 13.96 ± 2.56 | 14.31 ± 2.15 | −0.61 | 0.57 |
| Social relationships domain | 13.11 ± 4.25 | 12.78 ± 5.22 | 13.45 ± 3.04 | −0.65 | 0.52 |
| Environmental relationships domain | 14.01 ± 2.37 | 14.21 ± 2.61 | 13.80 ± 2.12 | 0.73 | 0.47 |
| Social functioning | 2.20 ± 2.79 | 2.20 ± 2.80 | 2.20 ± 2.83 | <0.001 | 0.99 |
| Clinical symptoms | 27.07 ± 1.64 | 25.77 ± 11.08 | 28.37 ± 16.04 | −0.79 | 0.43 |
One-way repeated measures ANOVA indicated that participants in the intervention group showed significant within-group improvements across all follow-up timepoints in medication attitudes (F = 16.68; P < 0.01) and medication adherence (F = 5.77; P < 0.01) (Appendix File 3). Similarly, the control group exhibited significant within-group improvements only in medication attitudes (F = 3.16; P = 0.03) and medication adherence (F = 2.72; P = 0.05). All outcome trend graphs are presented in Appendix File 4.
Generalised estimating equation analyses (Table 2) further showed that, compared with the control group, participants in the intervention group exhibited significantly greater improvements in medication attitudes from T0 to T1 (β = 2.29; 95% CI, 0.42–4.15; P = 0.02), with effects sustained at T2 (β = 2.67; 95% CI, 0.593–4.75; P = 0.01) and T3 (β = 3.42; 95% CI, 1.38–5.45; P = 0.001). Similarly, medication adherence improved significantly between groups from T0 to T1 (β = 0.75; 95% CI, 0.10–1.40; P = 0.02) and remained significant at T2 (β = 1.25; 95% CI, 0.66–1.85; P < 0.001) and T3 (β = 1.65; 95% CI, 0.68–2.62; P < 0.001). The effect sizes (Cohen's d) ranged from 0.37 to 0.84 for medication attitudes and 0.29 to 0.80 for adherence, indicating medium to large effects.
Table 2.
GEE-based between-group comparisons of main outcomes across timepoints.
| Time | Intervention group | Control group | Time effect |
Group effect |
Interaction effect |
Effect size | |||
|---|---|---|---|---|---|---|---|---|---|
| β (95% CI) | P value | β (95% CI) | P value | β (95% CI) | P value | ||||
| Medication attitude | |||||||||
| T0 | 4.97 ± 3.64 | 5.83 ± 3.12 | |||||||
| T1 | 5.94 ± 3.27 | 4.51 ± 4.35 | −1.31 (−2.56 to −0.07) | 0.04 | −0.86 (−2.42 to 0.71) | 0.28 | 2.29 (0.42–4.15) | 0.02 | 0.37 |
| T2 | 8.34 ± 2.59 | 6.53 ± 4.93 | 0.70 (−0.82 to 2.22) | 0.37 | 2.67 (0.59–4.75) | 0.01 | 0.46 | ||
| T3 | 8.91 ± 1.90 | 6.35 ± 3.89 | 0.52 (−0.96 to 2.01) | 0.49 | 3.42 (1.38–5.45) | 0.001 | 0.84 | ||
| Medication adherence | |||||||||
| T0 | 6.91 ± 1.66 | 7.23 ± 1.22 | |||||||
| T1 | 7.44 ± 1.02 | 7.01 ± 1.81 | −0.21 (−0.76 to 0.33) | 0.44 | −0.32 (−0.99 to 0.35) | 0.35 | 0.75 (0.10–1.40) | 0.02 | 0.29 |
| T2 | 7.64 ± 1.17 | 6.68 ± 1.95 | −0.52 (−1.02 to −0.03) | 0.04 | 1.25 (0.66–1.85) | <0.001 | 0.60 | ||
| T3 | 7.66 ± 0.49 | 6.68 ± 2.05 | −0.90 (−1.67 to −0.12) | 0.02 | 1.65 (0.68–2.62) | <0.001 | 0.80 | ||
The variable ‘time’ represents within-participant changes over time; ‘group’ captures between-group differences at baseline; and ‘time × group’ indicates whether the change over time differs by group, reflecting the effect of the intervention. P < 0.05 considered statistically significant. 95% CI: 95% confidence intervals.
Within-group analyses revealed that the intervention group also showed significant improvements across follow-up timepoints in anxiety (F = 4.80; P < 0.01), depression (F = 4.40; P < 0.01), the physical health domain (F = 19.55; P < 0.01), environmental domain (F = 3.83; P = 0.01), social functioning (F = 3.15; P = 0.03), and clinical symptoms (F = 4.81; P < 0.01).
Additionally, generalised estimating equation analyses (Table 3) further demonstrated that, compared with the control group, participants in the intervention group exhibited significantly greater improvements in multiple domains. Specifically, the Environmental Relationships Domain improved significantly between groups from T0 to T1 (β = 1.01; 95% CI, 0.11–1.92; P = 0.03) and this effect persisted at T2 (β = 1.15; 95% CI, 0.31–1.99; P = 0.01). Social Functioning also showed significant between-group improvements from T0 to T1 (β = −2.02; 95% CI, −3.66 to −0.74; P = 0.003) and remained significant at T3 (β = −1.88; 95% CI, −3.51 to −0.24; P = 0.03). Moreover, Clinical Symptoms significantly decreased in the intervention group compared to controls from T0 to T1 (β = −7.11; 95% CI, −14.07 to −0.16; P = 0.05), with sustained improvements observed at T2 (β = −7.84; 95% CI, −13.62 to −2.06; P = 0.01) and T3 (β = −7.81; 95% CI, −14.33 to −1.29; P = 0.02), with corresponding medium to large effect sizes.
Table 3.
GEE-based between-group comparisons of secondary outcomes across timepoints.
| Time | Intervention group | Control group | Time effect |
Group effect |
Interaction effect |
Effect size | |||
|---|---|---|---|---|---|---|---|---|---|
| β (95% CI) | P value | β (95% CI) | P value | β (95% CI) | P value | ||||
| Anxiety | |||||||||
| T0 | 3.51 ± 3.52 | 2.69 ± 3.01 | |||||||
| T1 | 2.31 ± 2.88 | 3.97 ± 4.86 | 1.29 (−0.15 to 2.72) | 0.08 | 0.83 (−0.68 to 2.34) | 0.28 | −2.49 (−4.32 to −0.66) | 0.01 | 0.27 |
| T2 | 1.49 ± 2.28 | 4.26 ± 4.64 | 1.53 (0.11–2.96) | 0.04 | −3.56 (−5.38 to −1.75) | <0.001 | 0.42 | ||
| T3 | 1.57 ± 2.15 | 4.56 ± 5.14 | 1.86 (−0.01 to 3.72) | 0.05 | −3.80 (−6.15 to −1.45) | 0.002 | 0.33 | ||
| Depression | |||||||||
| T0 | 4.17 ± 4.36 | 3.46 ± 3.10 | |||||||
| T1 | 3.17 ± 2.49 | 4.91 ± 5.48 | 1.46 (−0.10 to 3.01) | 0.07 | 0.71 (−1.03 to 2.46) | 0.42 | −2.46 (−4.50 to −0.42) | 0.02 | 0.42 |
| T2 | 2.54 ± 2.97 | 5.06 ± 4.92 | 1.52 (0.09–2.96) | 0.04 | −3.15 (−5.13 to −1.17) | 0.002 | 0.76 | ||
| T3 | 1.94 ± 1.98 | 5.06 ± 5.75 | 1.56 (−0.49 to 3.61) | 0.14 | −3.79 (−6.40 to −1.18) | 0.01 | 0.76 | ||
| Physical domain | |||||||||
| T0 | 11.97 ± 1.89 | 12.08 ± 2.37 | |||||||
| T1 | 13.36 ± 1.91 | 13.10 ± 1.88 | 1.02 (0.19–1.85) | 0.02 | −0.11 (−1.10 to 0.87) | 0.82 | 0.38 (−0.69 to 1.44) | 0.49 | 0.77 |
| T2 | 14.53 ± 1.57 | 14.12 ± 2.31 | 2.01 (1.05–3.00) | <0.001 | 0.59 (−0.67 to 1.79) | 0.37 | 0.43 | ||
| T3 | 14.71 ± 1.89 | 13.50 ± 2.22 | 1.42 (0.28–2.57) | 0.02 | 1.39 (−0.09 to 2.73) | 0.07 | 0.62 | ||
| Psychological domain | |||||||||
| T0 | 14.31 ± 2.15 | 13.96 ± 2.56 | |||||||
| T1 | 14.34 ± 1.81 | 13.75 ± 1.99 | −0.21 (−0.96 to 0.54) | 0.58 | 0.34 (−0.75 to 1.43) | 0.54 | 0.24 (−0.77 to 1.27) | 0.63 | 0.54 |
| T2 | 15.01 ± 1.56 | 13.51 ± 1.58 | −0.47 (−1.12 to 0.18) | 0.16 | 1.18 (0.28–2.07) | 0.01 | 0.47 | ||
| T3 | 14.75 ± 1.75 | 14.06 ± 2.02 | 0.09 (−1.08 to 1.26) | 0.88 | 0.36 (−1.12 to 1.83) | 0.64 | 0.21 | ||
| Social relationships domain | |||||||||
| T0 | 13.45 ± 3.04 | 12.78 ± 5.22 | |||||||
| T1 | 14.53 ± 2.54 | 13.03 ± 3.04 | 0.25 (−1.56 to 2.06) | 0.79 | 0.67 (−1.31 to 2.64) | 0.51 | 0.84 (−1.22 to 2.89) | 0.42 | 0.14 |
| T2 | 14.86 ± 2.29 | 13.69 ± 2.72 | 0.82 (−0.78 to 2.43) | 0.32 | 0.59 (−1.32 to 2.49) | 0.55 | 0.21 | ||
| T3 | 14.08 ± 2.69 | 13.45 ± 3.23 | 0.68 (−1.48 to 2.83) | 0.54 | −0.05 (−2.51 to 2.41) | 0.97 | 0.59 | ||
| Environmental relationships domain | |||||||||
| T0 | 13.80 ± 2.12 | 14.21 ± 2.61 | |||||||
| T1 | 14.61 ± 2.00 | 14.01 ± 2.37 | −0.20 (−0.84 to 0.44) | 0.54 | −0.41 (−1.51 to 0.68) | 0.46 | 1.01 (0.11–1.92) | 0.03 | 0.31 |
| T2 | 14.86 ± 1.90 | 14.07 ± 1.82 | −0.09 (−0.68 to 0.49) | 0.75 | 1.15 (0.31–1.99) | 0.01 | 0.96 | ||
| T3 | 15.03 ± 1.93 | 14.35 ± 2.19 | 0.14 (−1.01 to 1.30) | 0.81 | 1.08 (−0.37 to 2.54) | 0.15 | 0.37 | ||
| Social functioning | |||||||||
| T0 | 2.20 ± 2.83 | 2.20 ± 2.80 | |||||||
| T1 | 1.34 ± 2.38 | 3.54 ± 3.26 | 1.34 (0.20–2.49) | 0.02 | 0 (−1.30 to 1.30) | 1.00 | −2.20 (−3.66 to −0.74) | 0.003 | 0.41 |
| T2 | 1.97 ± 2.57 | 3.06 ± 2.53 | 0.91 (−0.04 to 1.85) | 0.06 | −1.14 (−2.56 to 0.29) | 0.12 | 0.62 | ||
| T3 | 1.06 ± 1.97 | 2.88 ± 3.68 | 0.73 (−0.61 to 2.08) | 0.29 | −1.88 (−3.51 to −0.24) | 0.03 | 0.73 | ||
| Clinical symptoms | |||||||||
| T0 | 28.37 ± 16.04 | 25.77 ± 11.08 | |||||||
| T1 | 24.69 ± 6.43 | 29.20 ± 16.83 | 3.43 (−1.17 to 8.03) | 0.14 | 2.60 (−3.77 to 8.97) | 0.42 | −7.11 (−14.07 to −0.16) | 0.05 | 0.35 |
| T2 | 22.06 ± 3.56 | 27.41 ± 11.26 | 1.53 (−1.21 to 4.26) | 0.27 | −7.84 (−13.62 to −2.06) | 0.01 | 0.65 | ||
| T3 | 21.37 ± 3.49 | 26.68 ± 8.48 | 0.81 (−2.43 to 4.04) | 0.63 | −7.81 (−14.33 to −1.29) | 0.02 | 0.82 | ||
The variable ‘time’ represents within-participant changes over time; ‘group’ captures between-group differences at baseline; and ‘time × group’ indicates whether the change over time differs by group, reflecting the effect of the intervention. P < 0.05 considered statistically significant. 95% CI: 95% confidence intervals.
In exploratory subgroup analyses, the intervention effects were examined separately by gender, age, and education level. Patients were categorised by age based on the World Health Organization (WHO) classification, with individuals under 45 years defined as younger adults and those aged 45 years and above as older adults.26,27 Educational attainment was classified into two levels: high school education or below, and college education or above. Among older participants, the digital psychoeducation intervention demonstrated significantly greater improvements in medication attitudes (β = 1.56; 95% CI, 0.03–3.10; P = 0.05) and clinical symptoms (β = −2.43; 95% CI, −4.83 to −0.04; P = 0.05) compared to standard care. In contrast, participants with lower educational attainment showed greater improvements in medication adherence (β = 0.93; 95% CI, 0.20–1.67; P = 0.01) and social functioning (β = 0.87; 95% CI, 0.23–1.52; P = 0.01) when receiving the digital intervention. However, due to the limited sample size, the robustness of some subgroup effects may be limited and should be interpreted with caution (Table 4).
Table 4.
Subgroup analysis in outcomes.
| Age—young |
Age—old |
|||
|---|---|---|---|---|
| β (95% CI) | P value | β (95% CI) | P value | |
| Medication Attitude | 0.68 (−0.864 to 2.224) | 0.39 | 1.56 (0.03–3.10) | 0.05 |
| Medication Adherence | −9.42 (−23.27 to 4.44) | 0.18 | 38.23 (15.01–61.44) | 0.001 |
| Anxiety | 0.84 (−0.05 to 1.72) | 0.07 | 0.55 (−0.33 to 1.43) | 0.24 |
| Depression | −0.29 (−1.81 to 1.24) | 0.71 | −1.47 (−3.54 to 0.59) | 0.16 |
| Physical Domain | 0.39 (−1.01 to 1.78) | 0.59 | −1.80 (−3.17 to −0.43) | 0.01 |
| Psychological Domain | 0.24 (−1.02 to 1.50) | 0.71 | 1.77 (0.67–2.87) | 0.002 |
| Social Relationships Domain | 0.04 (−0.71 to 0.80) | 0.92 | 1.12 (0.31–1.93) | 0.01 |
| Environmental Relationships Domain | 0.80 (0.01–1.59) | 0.05 | 0.89 (0.13–1.64) | 0.02 |
| Social Functioning | 0.04 (−0.71 to 0.79) | 0.92 | 1.12 (0.31–1.93) | 0.01 |
| Clinical Symptoms | −1.00 (−37.70 to 35.71) | 0.96 | −2.43 (−4.83 to −0.04) | 0.05 |
| Education-low |
Education-high |
|||
|---|---|---|---|---|
| β (95% CI) | P value | β (95% CI) | P value | |
| Medication Attitude | −1.95 (−3.75 to −0.14) | 0.04 | 0.16 (−1.51 to 1.82) | 0.86 |
| Medication Adherence | 0.934 (0.20–1.67) | 0.01 | 0.66 (−0.70 to 2.03) | 0.34 |
| Anxiety | −1.31 (−2.77 to 0.15) | 0.08 | 60.84 (27.68–94.00) | <0.001 |
| Depression | −1.04 (−2.32 to 0.24) | 0.11 | −0.61 (−14.18 to 12.96) | 0.93 |
| Physical Domain | 0.99 (−0.13 to 2.12) | 0.08 | −1.66 (−2.98 to −0.34) | 0.01 |
| Psychological Domain | 0.44 (−0.23 to 1.12) | 0.20 | −0.83 (−5.566 to 3.91) | 0.73 |
| Social Relationships Domain | 0.87 (0.23–1.52) | 0.01 | 0.75 (−0.224 to 1.72) | 0.13 |
| Environmental Relationships Domain | 0.44 (−0.23 to 1.12) | 0.20 | −34.13 (−63.77 to −4.50) | 0.02 |
| Social Functioning | −1.50 (−3.26 to 0.25) | 0.09 | 0.75 (−0.22 to 1.72) | 0.13 |
| Clinical Symptoms | −1.86 (−6.56 to 2.83) | 0.44 | −10.60 (−34.82 to 13.62) | 0.39 |
Age—old: ≥45 years, Age—young: <45 years. Education-high: college education or above, Education-low: high school education or below. P < 0.05 considered statistically significant. 95% CI: 95% confidence intervals.
Discussion
Compared with traditional psychoeducation, the narrative-based digital psychoeducational intervention demonstrated more sustained improvements in medication adherence and medication attitudes among patients with schizophrenia. Patients in the intervention group showed higher adherence scores following the three-month intervention, with adherence continuing to increase during follow-up, whereas scores in the control group slightly declined. This suggests that narrative digital interventions may establish a positive feedback loop for medication-taking behavior through reinforcement learning and behavioural feedback mechanisms.28 The sustained improvements likely resulted from task–driven activities, contextual simulations, and immediate feedback incorporated in the intervention, which systematically enhanced patients’ knowledge about medication and facilitated the internalisation of medication-taking as part of their self-management routines, thereby increasing behavioural stability and adherence motivation.29 Concurrently, positive changes in medication attitudes provided a psychological foundation for improved adherence. Story contexts and role-playing elements helped patients adopt roles with reduced psychological defences, alleviating resistance and shame, and enhancing their understanding of illness and treatment. This dual cognitive-emotional engagement enabled patients not only to understand the importance of medication adherence, but also to develop an intrinsic motivation to take medication, leading to greater acceptance and trust in treatment.30,31 These encouraging findings indicate that narrative psychoeducation can improve medication adherence and foster more positive medication attitudes through cognitive and affective pathways.
Patients in the intervention group exhibited sustained reductions in anxiety and depressive symptoms during follow-up. Although the intervention did not include dedicated mood management modules, embedded narrative scenarios, task progression, and interactive feedback likely played an indirect regulatory role. Moreover, the superior medication adherence observed in the intervention group may have also contributed to the alleviation of anxiety and depressive symptoms. The scenario-based exposure and virtual coping mechanisms provided patients with rehearsal opportunities that strengthened cognitive preparedness and self-regulation when facing negative emotions. Prior studies have shown that engaging digital interactive activities can promote dopamine release, enhancing positive affect and self-efficacy, particularly when combined with appropriately challenging tasks and reward feedback.32,33 Additionally, improvements in patients’ understanding of their illness and medication may have increased their perceived control over treatment, thereby alleviating anxiety related to illness uncertainty.34
Improvements in social functioning were also evident in the intervention group, particularly with progressive enhancement during follow-up. The narrative story reconstructed patients' daily life and interpersonal contexts, offering a low-risk environment for practicing social skills, decision-making, and coping strategies. The transfer of these virtual experiences to real-world social interactions likely manifests gradually, especially in everyday communication, task completion, and role awareness.35,36 While some patients did not demonstrate immediate gains in social functioning post-intervention, positive changes in rehabilitation motivation and real-life behaviors emerged over time, consistent with previous research on delayed transfer effects from virtual training.37 Furthermore, significant improvements in clinical psychiatric symptoms were observed in the intervention group. This outcome is likely attributable to enhanced medication adherence, as regular antipsychotic treatment is a prerequisite for symptom control. Thus, clinical symptom improvement indirectly supports the intervention's effectiveness in establishing stable treatment adherence through multimodal engagement.
This study also revealed a novel interaction between age and the intervention group in medication-related behaviors. Specifically, compared to younger participants in the control group, older patients who received the narrative psychoeducational intervention demonstrated greater improvements in medication adherence and clinical symptoms. This finding may be associated with the higher levels of psychological reactance and nonadherence commonly reported among younger patients. Several large-scale, validated studies support this observation, indicating that younger populations generally exhibit greater resistance to medical advice.38,39
In addition, differences in intervention effects were observed across educational levels. Patients with lower educational attainment showed significantly greater improvements in medication adherence and social relationships than those with higher education following the narrative psychoeducational intervention. This finding contrasts with previous research that has emphasised the positive role of higher education in psychosocial rehabilitation.40,41 Compared to mainstream evidence-based psychosocial interventions (e.g., traditional psychoeducation), the narrative-based digital intervention in this study provided a more concrete and contextualised approach for individuals with limited education or cognitive capacity.42 For example, cognitive remediation (CR) or conventional psychoeducation typically relies on highly structured cognitive strategies and abstract reasoning, whereas digital narrative-based interventions employ vivid, story-driven mechanisms that more effectively stimulate emotional engagement, facilitate identity reconstruction, and promote behavioural transformation.43 Furthermore, in terms of social functioning, patients with lower education levels were more likely to benefit from the simulated social scenarios embedded in the intervention, which offered opportunities for practicing social roles and adjusting interpersonal strategies in real-life contexts. Compared to traditional interventions such as social skills training (SST), which require frequent in-person sessions, narrative interventions offer a lower-cost and lower-risk alternative with greater accessibility for patients.42 By contrast, individuals with higher education may derive relatively limited benefits due to their greater social experience and stronger cognitive foundation in understanding the intervention content.
Although the intervention in this study did not directly target positive symptoms, it may have indirectly improved clinical outcomes by enhancing treatment adherence, consistent with the mechanism of traditional psychoeducation. The findings also revealed differential effects among specific demographic groups, such as individuals with lower educational attainment or older age, highlighting the need to further investigate the moderating role of individual characteristics on intervention efficacy. Future studies should validate these findings using larger, multicentre samples, assess long-term sustainability, and systematically compare the intervention with other evidence-based approaches—particularly with regard to feasibility and cost-effectiveness in resource-limited settings. From a practical perspective, the low cost, digital format, and favorable user experience of the intervention suggest its suitability for community-based psychiatric rehabilitation services. From a policy perspective, this study supports the integration of digital narrative-based interventions into routine care pathways for schizophrenia to address the current gaps in the provision of psychosocial interventions within primary care systems.
This study provides valuable insights into the application of narrative-based psychoeducation among individuals with schizophrenia; however, several limitations should be acknowledged. First, medication adherence was assessed using validated self-reported scales rather than objective real-world indicators. This reliance on subjective measures may introduce recall or social desirability bias and limits the ability to fully capture actual adherence behaviours. Future studies could incorporate objective records, such as electronic pill monitoring or prescription refill rates, to validate the findings. Second, the sample was recruited solely from urban areas in Shanghai, which may limit the generalisability of the results to rural populations or those in different cultural and race contexts. Third, although the sample size was adequate to support primary outcomes, it may have limited power to detect subgroup effects and constrain the statistical generalisability. Lastly, while improvements were observed over the 3-month follow-up period, the durability of these effects beyond this timeframe remains unknown. Long-term follow-up is needed to assess sustained behaviour change and relapse prevention.
This study demonstrated that narrative-based psychoeducational interventions significantly improved medication adherence, medication attitudes, and clinical symptoms among patients with schizophrenia, with effects sustained over time. The intervention effects varied by age and educational level, suggesting that future intervention designs should account for individual-level heterogeneity.
Contributors
LZ: conceptualisation, funding acquisition, project administration, resources, supervision and writing–original draft. ZD: formal analysis, visualisation, investigation and writing–original draft. ZZW: data curation, formal analysis, methodology and visualisation. LYL: data curation, validation, and investigation. YHY: validation, methodology and software. CFY: funding acquisition and writing–review & editing. ZWB: funding acquisition, project administration, resources, supervision and writing–review & editing. CJ: funding acquisition, project administration, resources, supervision, writing–review & editing. ZZW and ZD have accessed and verified the underlying data. All authors approved the final version of the manuscript and were responsible for the decision to submit the manuscript. All authors take responsibility for all aspects of the randomised controlled trial.
Data sharing statement
Intervention materials and datasets for this study are available upon reasonable request by contacting the corresponding author.
Declaration of interests
We declare no competing interests.
Acknowledgements
This research was funded by Shanghai Municipal Science and Technology Commission 2024 “Science and Technology Innovation Action Plan” Medical Innovation Research Field Program (24Y22800501, 24Y22800502, 24Y22800503), the Shanghai Municipal Education Commission (2024AlYB014), Shanghai Jiao Tong University 2024 Medical-Industrial Intersection Research Fund (YG2024ZD24) and Shanghai Jiao Tong University Medical-Industrial Interdisciplinary Youth Program (YG2025QNA11). We would like to thank all participants and collaborators who contributed to this study. We also confirm that all potential conflicts of interest have been disclosed accurately, completely, and in the most up-to-date manner, in accordance with the authorship disclosure section of the journal. The seven participating community rehabilitation centres were located in the following subdistricts: Gumei Subdistrict in Minhang District, Xujiahui Subdistrict in Xuhui District, Xinzhuang Subdistrict in Minhang District, Lingyun Subdistrict in Xuhui District, Langxia Subdistrict in Jinshan District, Jinshanwei Subdistrict in Jinshan District, and Liangcheng Subdistrict in Hongkou District. The authors declare that the language of this manuscript was refined using an artificial intelligence tool, ChatGPT 4o. The tool was used to improve syntax and grammar for enhanced clarity and readability.
Footnotes
Supplementary data related to this article can be found at https://doi.org/10.1016/j.eclinm.2025.103483.
Contributor Information
Zhao Liu, Email: hotlz@sjtu.edu.cn.
Weibo Zhang, Email: zhangweibo600@163.com.
Jun Cai, Email: caijun533@163.com.
Appendix A. Supplementary data
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