Published online Sep 19, 2026. doi: 10.5498/wjp.117965
Revised: March 15, 2026
Accepted: July 14, 2026
Published online: September 19, 2026
Processing time: 247 Days and 20.4 Hours
Depression is a leading global cause of disability, and its high relapse rates are closely linked to poor medication adherence. Current pharmaceutical care models for depression often lack continuity between hospital and home, limiting their long-term efficacy. Structured, continuous care models are needed to close this gap.
To evaluate the efficacy of a medication therapy management (MTM)-based “in-hospital assessment - home-based intervention” pharmaceutical care model in depression.
This single-center, prospective randomized controlled trial enrolled 104 adults with depression, who were randomly assigned 1:1 to an intervention group (MTM-based “in-hospital assessment – home-based intervention” pharmaceutical care) or a control group (conventional care). The primary outcomes were me
Compared with the control group, the intervention group had significantly higher medication adherence (76.92% vs 46.15% at 9 months, P < 0.05), greater reductions in depression symptom scores, a lower 9-month relapse rate (9.62% vs 23.08%, P < 0.05), and reduced healthcare resource utilization (all P < 0.05).
The MTM-based “in-hospital assessment - home-based intervention” pharmaceutical care model is associated with improved clinical outcomes in adults with depression.
Core Tip: To our knowledge, this is the first medication therapy management-based “in-hospital assessment - home-based intervention” model for depression care, addressing treatment discontinuity. It significantly improved 9-month medication adherence from 46.15% to 76.92% and reduced the 9-month relapse rate from 23.08% to 9.62% compared with conventional care. The model was associated with a 66.7% reduction in hospitalization risk, a 38.1% reduction in direct medical costs, and a 25-percentage-point reduction in the incidence of adverse drug reactions, offering an efficient pathway for comprehensive depression management. Standardized effect sizes (Cohen’s d) for primary outcome was 1.24, indicating large clinical benefits.
- Citation: Wang MF, Zhang HM, Zhang XL. Application of medication therapy management-based “in-hospital assessment - home-based intervention” pharmaceutical care in patients with depression. World J Psychiatry 2026; 16(9): 117965
- URL: https://www.wjgnet.com/2220-3206/full/v16/i9/117965.htm
- DOI: https://dx.doi.org/10.5498/wjp.117965
Among mental disorders, depression is the leading cause of disability worldwide, with a lifetime prevalence of 15%-20%, and is ranked as the second leading contributor to the global disease burden according to the Global Burden of Disease Study 2019[1]. Antidepressant pharmacotherapy is the first-line treatment for depression; however, poor medication adherence (including missed doses, self-initiated dose reduction, and treatment discontinuation) remains the principal barrier to optimal therapeutic outcomes. Globally, approximately 50% of patients with depression develop adherence issues within 6 months of treatment initiation, directly contributing to a 40%-60% treatment failure rate and an elevated risk of relapse[2].
A key driver of poor adherence is the fragmentation of current pharmaceutical care for depression. Most services provide short-term, in-hospital medication guidance but offer no systematic, continuous support after discharge. The resulting disconnect between in-hospital assessment and home-based medication management leaves patients’ medi
Medication therapy management (MTM), a standardized pharmaceutical care framework endorsed by the American Society of Health-System Pharmacists and Medicare, improves adherence and clinical outcomes in chronic disease management through its closed-loop “assessment-intervention-follow-up” structure[3,4]. Although MTM is increasingly applied in mental health care, most studies have addressed only in-hospital medication review and short-term post-discharge follow-up, with little attention to sustained, home-based intervention in depression. In the Chinese setting in particular, high-quality randomized controlled trial (RCT) evidence for MTM-based “in-hospital assessment - home-based intervention” models of continuous pharmaceutical care for depression remains scarce.
To address this gap, we developed an MTM-based “in-hospital assessment - home-based intervention” pharmaceutical care model for adults with depression and evaluated it in a single-center, prospective RCT. We assessed its effects on medication adherence, depressive symptoms, relapse, and healthcare resource use, aiming to provide practical, evidence-based guidance for continuous pharmaceutical care in depression.
This study was a single-center, prospective, single-blind, parallel-group RCT (pragmatic trial) conducted at Rugao Hospital of Traditional Chinese Medicine, Jiangsu Province, China. The study protocol was reviewed and approved by the Ethics Committee of Rugao Hospital of Traditional Chinese Medicine (Approval No. RGSZYYLL2025001) and strictly followed the ethical principles of the Declaration of Helsinki. All participants provided written informed consent prior to study enrollment.
This trial was not prospectively registered, which is a limitation of the study design. However, all reported analyses followed the predefined protocol, which was developed in January 2023 before participant enrollment. The full protocol is provided as Appendix 5 in the Supplementary material, with detailed descriptions of the study design, intervention procedures, outcome measures, and statistical analysis plan.
The 9-month follow-up was chosen for three clinical reasons: (1) Most antidepressant treatment failures and relapses occur within the first 9 months after initial remission; (2) A 9-month window balances capturing sustained adherence effects against minimizing participant dropout; and (3) This duration aligns with the standard follow-up cycle for chronic disease management in Chinese primary care, easing future translation into clinical practice.
The study participants were consecutive patients with depression admitted to the inpatient and outpatient depart
Inclusion criteria: (1) Meeting the diagnostic criteria for a major depressive episode in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; (2) Being 18-65 years of age; (3) Having a 17-item Hamilton Depression Rating Scale (HAMD-17) score of ≥ 17 at enrollment; (4) Receiving stable-dose monotherapy with a single antidepressant for ≥ 4 weeks before enrollment, with no planned dose adjustment during the first 3 months of the study; (5) Being able to complete regular telephone follow-ups and structured questionnaires independently or with assistance; and (6) Agreeing to complete the 9-month follow-up and providing written informed consent.
Exclusion criteria: (1) Comorbid severe organic disease (e.g., New York Heart Association class III-IV heart failure, end-stage liver or renal failure); (2) Comorbid other major mental disorders (e.g., bipolar disorder, schizophrenia, schizoaffective disorder); (3) Acute suicidal ideation or behavior requiring urgent inpatient intervention; (4) Pregnancy or la
Sample size calculation was performed using PASS 15.0 software, based on the primary outcome of the 9-month medication adherence rate. With a predefined two-sided α of 0.05, power (1 - β) of 0.80, and an expected effect size of 0.45 derived from previous MTM intervention studies in depression, the minimum required sample size was 48 participants per group. Considering a 8% dropout rate during the 9-month follow-up, the final total sample size was set at 104 participants, with 52 in each group.
A standardized electronic data capture platform was established for this study, with data collection procedures validated by 20 pretest cases (data entry error rate < 3%). These pretest cases were consecutively enrolled from the same patient population between March 2023 and April 2023, and they were excluded from the main trial to avoid selection bias and contamination. All data were collected by uniformly trained, independent research staff who were blinded to the group allocation.
Baseline data collection: The following baseline data were collected within 24 hours of enrollment: (1) Demographic characteristics (age, sex, education level, occupation, socioeconomic status, health literacy score); (2) Clinical characteristics (baseline HAMD-17 score, baseline 14-item Hamilton Anxiety Rating Scale score, disease duration, first-episode/recurrent depression, number of previous depressive episodes, comorbid chronic diseases, psychotherapy participation status); (3) Medication details (antidepressant type, dose, duration of current treatment). The antidepressants used in this study included selective serotonin reuptake inhibitors (sertraline, escitalopram, fluoxetine), serotonin-norepinephrine reuptake inhibitors (venlafaxine, duloxetine), and noradrenergic and specific serotonergic antidepressants (mirtazapine). Between-group differences in the antidepressant class distribution were analyzed using the Pearson χ2 test; and (4) Risk stratification of MRPs assessed using the Screening Tool of Older Persons’ Prescriptions/Screening Tool to Alert doctors to Right Treatment (STOPP/START) criteria (2018 version). STOPP/START criteria were selected for this study because they provide a comprehensive, standardized framework for identifying potentially inappropriate medications and prescribing omissions, and have been validated for use in adult psychiatric populations in previous studies. Psychiatric-specific medication appropriateness tools (e.g., Maudsley Prescribing Guidelines) were considered but not used because of their limited availability in Chinese and lack of standardized scoring systems. Inter-rater reliability for MRP classification was assessed using Cohen’s kappa coefficient [κ = 0.87, 95% confidence interval (95%CI): 0.79-0.93], indicating excellent agreement between the two independent pharmacists.
Follow-up data collection: Follow-up data were collected at 3 months, 6 months, and 9 months after enrollment, with core outcome indicators prespecified as follows: (1) Medication adherence indicators: Primary outcomes, including the medication possession rate (MPR) (calculated as the ratio of the number of days of medication supplied by the pharmacy to the total number of days in the follow-up period, with data obtained from hospital pharmacy dispensing records), dose omission rate (average number of missed doses per person per month, with data obtained from patient medication diaries verified by monthly follow-up), and self-initiated dose reduction rate (average number of self-adjusted dose events per person per month). Good adherence was defined as an MPR of ≥ 80%; (2) Clinical symptom indicators: HAMD-17 score, clinical response rate (defined as a ≥ 50% reduction in the HAMD-17 score from baseline), clinical remission rate (defined as a HAMD-17 score of ≤ 7), and relapse rate (defined as meeting the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria for a major depressive episode, with a HAMD-17 score increase of ≥ 7 points from the remission nadir, or requiring emergency/inpatient intervention for depressive symptom exacerbation); (3) Healthcare resource utilization indicators: Number of emergency visits, all-cause hospitalization rate, total hospitalization days, drug-related re-visit rate, and total medical costs (including outpatient fees, inpatient fees, antidepressant medication costs, and emergency visit fees); (4) Safety indicators: Type, frequency, severity, and resolution time of adverse drug reactions and overall incidence of adverse reactions; and (5) Secondary outcomes: Quality-of-life scores [36-Item Short Form Health Survey (SF-36), World Health Organization Quality of Life-BREF (WHOQOL-BREF)], frequency of remote pharmacist consultations, and patient satisfaction with pharmaceutical care. The SF-36 was used to assess general physical and mental health status, while the WHOQOL-BREF was used to evaluate disease-specific quality of life in the social and environmental domains. The prespecified primary quality-of-life domains were the SF-36 Mental Health Score and WHOQOL-BREF Social Function Score, which are most relevant to depression treatment outcomes. The use of two complementary scales provides a more comprehensive assessment of quality of life than use of either scale alone.
Data quality control: All data were double-entered by two independent research staff to ensure accuracy. An automatic outlier alert function was set in the data capture platform, and third-party review by an independent biostatistician was initiated for data discrepancies exceeding 5%. All data were locked before statistical analysis, with no post-hoc modifications to the dataset.
Randomization procedure: Eligible participants were randomly assigned to the intervention group or control group in a 1:1 ratio using a block randomization method with a fixed block length of 4. The random number sequence was computer-generated by an independent biostatistician who had no contact with the study participants or intervention implementation. The sequence was stored in a password-protected file and only accessible to the study coordinator.
Allocation concealment was achieved using sequentially numbered, opaque, sealed envelopes, which were prepared by a research assistant not involved in participant enrollment or intervention delivery. Each envelope contained a group assignment card and was sealed with a tamper-evident sticker. The same research assistant who performed the baseline assessment opened the envelopes only after a participant completed the baseline assessment and confirmed enrollment.
A Consolidated Standards of Reporting Trials 2010 flow diagram is provided as Supplementary Figure 1, detailing participant screening, enrollment, randomization, follow-up, and analysis, with exact reasons for attrition and protocol deviations.
Stratification: All stratification assessments were completed before randomization to ensure a balanced distribution of prognostic factors between the two groups. The stratification factors were as follows: (1) MRP risk level based on the STOPP/START criteria (high risk: ≥ 3 MRPs; medium risk: 1-2 MRPs; low risk: No MRPs); (2) Age (≤ 45 years/> 45 years); (3) Baseline HAMD-17 score (≤ 24 points/> 24 points); and (4) Presence of comorbid chronic diseases (yes/no). After randomization, the between-group balance of baseline characteristics was verified using independent-samples t-tests for continuous variables and χ2 tests for categorical variables, with a P value of > 0.05 indicating good balance (Table 1).
| Characteristic | Intervention group (n = 52) | Control group (n = 52) | t/χ2 value | P value |
| Demographic characteristics | ||||
| Age, years | 45.20 ± 8.70 | 46.50 ± 9.30 | 0.762 | 0.447 |
| Female | 32 (61.54) | 30 (57.69) | 0.155 | 0.694 |
| Clinical characteristics | ||||
| HAMD-17 score | 24.60 ± 3.90 | 24.30 ± 3.60 | 0.421 | 0.674 |
| Disease duration, months | 18.40 ± 6.20 | 19.10 ± 7.00 | 0.582 | 0.561 |
| First-episode depression | 28 (53.85) | 25 (48.08) | 0.336 | 0.562 |
| Two or more comorbid chronic diseases | 15 (28.85) | 13 (25.00) | 0.195 | 0.659 |
| MRP risk stratification (STOPP/START criteria) | ||||
| High risk, ≥ 3 MRPs | 22 (42.31) | 20 (38.46) | 0.155 | 0.694 |
| Medium risk, 1-2 MRPs | 25 (48.08) | 26 (50.00) | 0.032 | 0.858 |
| Low risk, no MRPs | 5 (9.62) | 6 (11.54) | 0.109 | 0.741 |
Blinding: Because of the nature of the behavioral intervention, neither the participants nor the clinical pharmacists delivering the intervention could be blinded to the group allocation. This study implemented a single-blind design, in which all outcome assessors, data entry staff, and the independent biostatistician were blinded to the group allocation throughout the study period. The potential for performance bias due to the lack of blinding of participants and pha
All interventions complied with the Chinese clinical practice guidelines for depression, and the participants’ rights and safety were protected throughout. Both groups received the same standardized antidepressant pharmacotherapy prescribed by attending psychiatrists, with no restrictions on routine outpatient visits or emergency care during the 9-month follow-up. The groups differed only in the pharmaceutical care model.
Intervention group: Participants in this group received MTM-based “in-hospital assessment - home-based intervention” pharmaceutical care, which followed the five core elements of the standardized MTM service framework through a two-stage process with standardized operating procedures.
In-hospital assessment stage (within 48 hours before discharge): All assessments and treatment plans were completed by certified MTM pharmacists who held national MTM specialist certification, had ≥ 5 years of clinical pharmacy ex
Home-based intervention stage (9 months after discharge): This stage delivered proactive, closed-loop management involving a mandatory core of interventions for all participants, with additional tailored contact for high-risk patients. The fixed schedule was as follows: (1) For every participant, monthly structured telephone follow-up (≥ 15 minutes per call, using a standard script); (2) Face-to-face reassessment at 3 months, 6 months, and 9 months after discharge, matching the follow-up data-collection time points; and (3) For high-risk participants (≥ 3 MRPs), additional bimonthly video follow-up (≥ 10 minutes per session) addressing home-environment factors affecting adherence and symptoms.
Sample structured follow-up scripts, patient education materials, medication review templates, and pharmacist intervention algorithms are provided as Appendices 1-4 in the Supplementary material. These materials were developed based on national clinical practice guidelines for depression and pilot-tested during the pretest phase.
The intervention process comprised: (1) Real-time monitoring of adherence through the medication-record booklet and hospital dispensing records; (2) Targeted intervention within 24 hours of any reported missed dose, self-adjusted dose, or adverse reaction; (3) Monthly review of medication use and symptom changes, with corresponding adjustment of the care plan; and (4) A 24-hour remote pharmacist consultation channel available throughout the study.
Control group: Participants in this group received conventional care for depression, reflecting standard discharge guidance and routine follow-up in Chinese psychiatric practice. This care comprised: (1) Written discharge medication guidance (drug name, dose, administration, common adverse reactions, and precautions) provided by the attending psychiatrist and pharmacist at discharge; (2) Routine outpatient follow-up every 4-6 weeks as clinically needed, with treatment adjusted only at the psychiatrist’s face-to-face assessment; and (3) Passive telephone consultation, available from pharmacists only when participants reported MRPs, with no proactive follow-up, systematic medication review, or individualized intervention.
Intervention fidelity and contamination control: (1) Fidelity assurance: A common intervention manual, training course, and follow-up script were prepared before the study began. All pharmacists delivering the intervention com
All statistical analyses were performed by an independent biomedical statistician who was blinded to the group allocation throughout the study period, using SPSS 26.0 and PASS 15.0 software. A two-sided test with a significance level of α = 0.05 was used for all analyses.
Data description and normality test: Continuous variables were tested for normality using the Shapiro-Wilk test. Normally distributed data were presented as mean ± SD, and non-normally distributed data were presented as median (interquartile range). Categorical variables were presented as n (%).
Between-group comparison methods: (1) Baseline balance analysis: The independent-samples t-test was used for normally distributed continuous variables, the Mann-Whitney U test was used for non-normally distributed continuous variables, and the Pearson χ2 test (or Fisher’s exact test for an expected frequency of < 5) was used for categorical variables; (2) Primary outcome analysis: The two prespecified primary outcomes were the 9-month medication adherence rate (MPR ≥ 80%) and the change in the HAMD-17 score from baseline to 9 months. The Pearson χ2 test was used for the binary adherence outcome, and relative risk with 95%CI was reported as the effect size. Analysis of covariance was used for the continuous HAMD-17 score change, with the baseline HAMD-17 score, age, and MRP risk stratification as covariates to adjust for baseline differences; mean difference with 95%CI was reported as the effect size; (3) Repeated-measures data analysis: For repeated-measures data (e.g., HAMD-17 score; MPR at 3 months, 6 months, and 9 months), the primary analysis used a linear mixed-effects model with time, group, and time × group interaction as fixed effects and participant as a random effect. This approach was selected because it better accounts for within-subject correlations and missing observations compared with repeated independent testing. For the two primary outcomes, a Bonferroni-adjusted significance level of α' = 0.025 was used. For between-group comparisons at individual time points, independent samples t-test (or Mann-Whitney U test) with Bonferroni correction was used as a secondary analysis for clinical interpretability; (4) Time-to-event data analysis: Cumulative relapse rate, hospitalization rate, and drug-related re-visit rate were analyzed using Kaplan-Meier survival analysis, with between-group comparisons performed using the log-rank test; hazard ratio with 95%CI was reported as the effect size. The proportional hazards assumption was verified using the Schoenfeld residuals test, and no significant violations were found for any of the time-to-event outcomes (all P > 0.05); and (5) Secondary outcome analysis: For secondary outcomes including adverse reaction incidence, quality of life scores, and healthcare resource utilization, between-group comparisons were performed using the corresponding statistical methods as described above. These analyses were exploratory, and no multiple-comparison correction was performed; the results were interpreted with caution in the Discussion section.
Missing data handling: The primary analysis followed the intention-to-treat principle, which included all randomized participants. Multiple imputation with 20 imputed datasets was used as the primary strategy for missing data mana
Statistical reporting standard: For all core outcome indicators, both P values and corresponding effect sizes with 95%CIs were reported. Standardized effect sizes (Cohen’s d for continuous outcomes, relative risk for binary outcomes) were calculated to assess the magnitude of treatment effects. Minimal clinically important differences for the HAMD-17 score (3 points) and the SF-36 Mental Health Score (5 points) were based on established values from the literature. P values were presented as exact values, with P < 0.001 reported as P < 0.001, and no P values were reported for results with P > 0.05.
In total, 126 patients were screened for eligibility between April 2023 and August 2024, of whom 22 were excluded for not meeting the inclusion criteria (n = 14) or declining to participate (n = 8). Finally, 104 eligible participants were ran
Baseline demographic, clinical, and MRP risk stratification characteristics showed no statistically significant differences between the intervention and control groups (all P > 0.05) (Table 1), indicating good between-group balance and successful randomization. There were also no significant between-group differences in the distribution of antidepressant classes (selective serotonin reuptake inhibitors: 65.38% vs 61.54%, serotonin-norepinephrine reuptake inhibitors: 26.92% vs 30.77%, noradrenergic and specific serotonergic antidepressants: 7.69% vs 7.69%, χ2 = 0.187, P = 0.911), psychotherapy participation rate (23.08% vs 19.23%, χ2 = 0.229, P = 0.632), or baseline Hamilton Anxiety Rating Scale score (12.30 ± 3.50 vs 12.70 ± 3.80, t = 0.564, P = 0.574).
Analysis of medication adherence improvement: The primary outcome of 9-month good medication adherence (MPR ≥ 80%) was significantly higher in the intervention group than in the control group (76.92% vs 46.15%; relative risk = 1.67, 95%CI: 1.18-2.35, P = 0.001). Good adherence rates at 3 months and 6 months were also significantly higher in the intervention group, with a sustained upward trend over the follow-up period (all P < 0.05) (Table 2, Figure 1A).
| Indicator | Time point | Intervention group (n = 52) | Control group (n = 52) | χ2/t value | P value |
| MPR ≥ 80% | Baseline | 18 (34.62) | 19 (36.54) | 0.042 | 0.838 |
| 3 months | 30 (57.69) | 21 (40.38) | 7.093 | 0.008 | |
| 6 months | 37 (71.15) | 22 (42.31) | 9.415 | 0.002 | |
| 9 months | 40 (76.92) | 24 (46.15) | 11.236 | 0.001 | |
| Dose omission rate (times/person/month) | 3 months | 0.45 ± 0.25 | 1.20 ± 0.50 | 9.246 | < 0.001 |
| 6 months | 0.32 ± 0.18 | 1.05 ± 0.41 | 11.832 | < 0.001 | |
| 9 months | 0.28 ± 0.15 | 0.95 ± 0.38 | 10.573 | < 0.001 | |
| Self-reduction rate (times/person/month) | 3 months | 0.25 ± 0.15 | 0.80 ± 0.35 | 9.872 | < 0.001 |
| 6 months | 0.15 ± 0.11 | 0.68 ± 0.33 | 10.647 | < 0.001 | |
| 9 months | 0.12 ± 0.09 | 0.60 ± 0.30 | 9.328 | < 0.001 |
The intervention group had significantly lower average monthly dose omission rates and self-initiated dose reduction rates at all follow-up time points than the control group (all P < 0.001) (Table 2, Figure 1B and C).
Clinical symptom improvement and relapse rate changes: Linear mixed-effects model analysis showed a significant time × group interaction for the HAMD-17 score (F = 28.76, P < 0.001), indicating that the intervention group had a significantly greater rate of symptom improvement over time than the control group.
For the second primary outcome of change in the HAMD-17 score from baseline to 9 months, the intervention group showed a significantly greater reduction after adjusting for baseline score, age, and MRP risk stratification (adjusted mean difference = -6.42, 95%CI: -7.99 to -4.85, P < 0.001, analysis of covariance; Cohen’s d = 1.24, indicating a large clinical effect).
HAMD-17 scores in both groups decreased gradually over the follow-up period, with the intervention group having significantly lower scores at 3 months, 6 months, and 9 months (all P < 0.001) (Table 3). The observed 6.42-point reduction in the HAMD-17 score exceeds the minimal clinically important difference of 3 points, confirming the clinical significance of the intervention effect. The clinical response rate (≥ 50% reduction in HAMD-17 score from baseline) and clinical remission rate (HAMD-17 score ≤ 7) were significantly higher in the intervention group at all follow-up time points (all P < 0.05) (Table 3). The number needed to treat for 9-month clinical remission was 2, meaning 1 additional patient achieved remission for every 2 patients treated with the intervention model.
| Indicator | Time point | Intervention group (n = 52) | Control group (n = 52) | t/χ2 value | P value |
| HAMD-17 score | Baseline | 24.60 ± 3.90 | 24.30 ± 3.60 | 0.301 | 0.764 |
| 3 months | 16.20 ± 4.10 | 19.80 ± 3.90 | 4.472 | < 0.001 | |
| 6 months | 12.10 ± 4.20 | 17.40 ± 4.00 | 6.325 | < 0.001 | |
| 9 months | 8.50 ± 3.90 | 15.20 ± 4.10 | 8.142 | < 0.001 | |
| Clinical response rate (≥ 50% reduction in HAMD-17 score) | 3 months | 20 (38.46) | 10 (19.23) | 4.983 | 0.026 |
| 6 months | 37 (71.15) | 22 (42.31) | 9.327 | 0.002 | |
| 9 months | 43 (82.69) | 27 (51.92) | 12.045 | 0.001 | |
| Clinical remission rate (HAMD-17 score ≤ 7) | 3 months | 5 (9.62) | 1 (1.92) | 2.836 | 0.092 |
| 6 months | 22 (42.31) | 5 (9.62) | 14.286 | < 0.001 | |
| 9 months | 34 (65.38) | 10 (19.23) | 20.735 | < 0.001 | |
| Cumulative relapse rate | 3 months | 1 (1.92) | 3 (5.77) | ||
| 6 months | 2 (3.85) | 6 (11.54) | 4.528 | 0.033 | |
| 9 months | 5 (9.62) | 12 (23.08) | 6.992 | 0.008 |
The 9-month cumulative relapse rate was significantly lower in the intervention group than in the control group (9.62% vs 23.08%; hazard ratio = 0.39, 95%CI: 0.14-0.99, P = 0.048, log-rank test). Cumulative relapse rates at 6 months also showed a significant between-group difference (P < 0.05) (Table 3). Kaplan-Meier survival curves showed a significantly lower risk of relapse in the intervention group over the 9-month follow-up period (Figure 2A).
The intervention group had significantly lower all-cause hospitalization rates, drug-related re-visit rates, per capita emergency visits, and total hospitalization days at 6 months and 9 months than the control group (all P < 0.05) (Table 4). Kaplan-Meier survival analysis showed a significantly lower cumulative risk of hospitalization and drug-related re-visit in the intervention group (all P < 0.05) (Figure 2B and C).
| Indicator | Time | Intervention group (n = 52) | Control group (n = 52) | t/χ2 value | P value |
| Hospitalization rate | 3 months | 1 (1.92) | 3 (5.77) | 1.026 | 0.311 |
| 6 months | 3 (5.77) | 11 (21.15) | 5.836 | 0.016 | |
| 9 months | 6 (11.54) | 18 (34.62) | 9.126 | 0.003 | |
| Drug-related re-visit rate | 3 months | 1 (1.92) | 5 (9.62) | 2.836 | 0.092 |
| 6 months | 3 (5.77) | 10 (19.23) | 4.563 | 0.033 | |
| 9 months | 4 (7.69) | 13 (25.00) | 6.325 | 0.012 | |
| Per capita emergency visits, times/month | 3 months | 0.15 ± 0.20 | 0.32 ± 0.40 | 2.463 | 0.015 |
| 6 months | 0.28 ± 0.30 | 0.60 ± 0.50 | 3.625 | < 0.001 | |
| 9 months | 0.41 ± 0.38 | 0.87 ± 0.65 | 4.263 | < 0.001 | |
| Per capita hospitalization days | 3 months | 0.15 ± 0.45 | 0.60 ± 0.85 | 2.836 | 0.005 |
| 6 months | 0.60 ± 1.40 | 2.80 ± 3.50 | 3.248 | 0.002 | |
| 9 months | 1.20 ± 2.10 | 4.50 ± 5.80 | 3.852 | < 0.001 | |
| Per capita medical cost, 10000 CNY | 3 months | 0.65 ± 0.25 | 1.10 ± 0.45 | 5.248 | < 0.001 |
| 6 months | 1.25 ± 0.45 | 2.10 ± 0.85 | 5.836 | < 0.001 | |
| 9 months | 1.82 ± 0.75 | 2.94 ± 1.12 | 5.836 | < 0.001 |
Total per capita direct medical costs at 9 months were 38.1% lower in the intervention group than in the control group (1.82 ± 0.75 ten thousand CNY vs 2.94 ± 1.12 ten thousand CNY, P < 0.001) (Table 4). The incremental cost-effectiveness ratio was 36400 CNY per additional patient with good adherence, and -24200 CNY per additional patient in clinical remission, indicating that the intervention is dominant (more effective and less costly) compared to conventional care. Indirect costs (e.g., lost productivity) and societal costs were not included in this analysis because of data limitations.
The average monthly frequency of remote pharmacist consultations was significantly higher in the intervention group than in the control group at 3 months, 6 months, and 9 months (all P < 0.001) (Table 5, Figure 3).
| Time point | Intervention group (n = 52) | Control group (n = 52) | t value | P value |
| 3 months | 1.30 ± 0.60 | 0.08 ± 0.25 | 11.246 | < 0.001 |
| 6 months | 1.50 ± 0.70 | 0.10 ± 0.30 | 12.875 | < 0.001 |
| 9 months | 1.60 ± 0.75 | 0.12 ± 0.35 | 13.428 | < 0.001 |
There were no significant between-group differences in baseline SF-36 Physical Health, SF-36 Mental Health, and WHOQOL-BREF Social Function scores (all P > 0.05) (Table 6). At all follow-up time points, the intervention group had significantly higher scores in all three quality-of-life dimensions than the control group (all P < 0.001) (Table 6).
| Indicator | Time point | Intervention group (n = 52) | Control group (n = 52) | t value | P value |
| SF-36 Physical Health score | Baseline | 45.32 ± 8.25 | 44.87 ± 7.93 | 0.294 | 0.769 |
| 3 months | 52.45 ± 8.62 | 46.32 ± 8.15 | 3.625 | < 0.001 | |
| 6 months | 62.14 ± 9.37 | 52.45 ± 8.62 | 5.321 | < 0.001 | |
| 9 months | 68.75 ± 10.21 | 54.38 ± 9.15 | 7.248 | < 0.001 | |
| SF-36 Mental Health score | Baseline | 38.24 ± 7.63 | 37.85 ± 7.28 | 0.258 | 0.797 |
| 3 months | 52.45 ± 8.34 | 42.38 ± 7.95 | 6.246 | < 0.001 | |
| 6 months | 65.83 ± 8.92 | 49.76 ± 8.34 | 9.263 | < 0.001 | |
| 9 months | 72.41 ± 9.56 | 51.62 ± 8.73 | 11.037 | < 0.001 | |
| WHOQOL-BREF Social Function score | Baseline | 42.56 ± 6.87 | 41.93 ± 6.54 | 0.432 | 0.666 |
| 3 months | 48.67 ± 7.83 | 43.25 ± 7.15 | 3.625 | < 0.001 | |
| 6 months | 60.34 ± 8.25 | 48.67 ± 7.83 | 6.735 | < 0.001 | |
| 9 months | 66.89 ± 9.12 | 50.24 ± 8.45 | 8.924 | < 0.001 |
The overall incidence of adverse drug reactions was significantly lower in the intervention group than in the control group at 3 months, 6 months, and 9 months (all P < 0.05) (Table 7, Figure 4). The incidence of individual adverse reactions (including dry mouth, constipation, sexual dysfunction, and sleep disturbance) was also lower in the intervention group, with statistically significant differences at 9 months for all items (all P < 0.05) (Table 7, Figure 4).
| Adverse reaction type | Time point | Intervention group (n = 52) | Control group (n = 52) | χ2 value | P value |
| Nausea/vomiting | 3 months | 3 (5.77) | 8 (15.38) | 2.385 | 0.123 |
| 6 months | 6 (11.54) | 12 (23.08) | 3.385 | 0.066 | |
| 9 months | 8 (15.38) | 15 (28.85) | 3.924 | 0.048 | |
| Dry mouth | 3 months | 7 (13.46) | 14 (26.92) | 3.038 | 0.081 |
| 6 months | 11 (21.15) | 19 (36.54) | 3.836 | 0.050 | |
| 9 months | 13 (25.00) | 22 (42.31) | 4.217 | 0.040 | |
| Constipation | 3 months | 3 (5.77) | 7 (13.46) | 1.785 | 0.181 |
| 6 months | 6 (11.54) | 12 (23.08) | 3.385 | 0.066 | |
| 9 months | 7 (13.46) | 14 (26.92) | 4.563 | 0.033 | |
| Sexual dysfunction | 3 months | 2 (3.85) | 6 (11.54) | 2.385 | 0.123 |
| 6 months | 6 (11.54) | 14 (26.92) | 4.217 | 0.040 | |
| 9 months | 10 (19.23) | 18 (34.62) | 5.038 | 0.025 | |
| Sleep disturbance | 3 months | 6 (11.54) | 12 (23.08) | 3.385 | 0.066 |
| 6 months | 10 (19.23) | 18 (34.62) | 4.217 | 0.040 | |
| 9 months | 12 (23.08) | 20 (38.46) | 4.872 | 0.027 | |
| Overall adverse reaction incidence | 3 months | 12 (23.08) | 22 (42.31) | 4.217 | 0.040 |
| 6 months | 20 (38.46) | 34 (65.38) | 7.836 | 0.005 | |
| 9 months | 27 (51.92) | 40 (76.92) | 8.326 | 0.004 |
This single-center prospective RCT evaluated the efficacy of an MTM-based “in-hospital assessment - home-based intervention” pharmaceutical care model for adults with depression. The core findings of this study were that the MTM-based “in-hospital assessment - home-based intervention” model was associated with significant improvements in medication adherence, relief of depressive symptoms, lower relapse risk, reduced healthcare resource utilization, and better quality of life than conventional depression care. These findings provide evidence-based support for the app
Our findings align with the established evidence base for MTM in chronic disease management. In a systematic review and meta-analysis, Deng et al[5] confirmed that MTM services can significantly improve clinical, economic, and humanistic outcomes in chronic disease management, which aligns with the multidimensional benefits observed in our study. Jones et al[6], Marupuru et al[7] and Liu et al[8] showed that standardized MTM optimizes medication regimens, reduces adverse events, and improves adherence in chronic diseases such as hypertension, diabetes, and chronic obstructive pulmonary disease. The present study extends this evidence to mental health, specifically depression.
Our intervention is built on the five core elements of the MTM service model (version 2.0) defined by the American Pharmacists Association and the National Association of Chain Drug Stores Foundation[9]. As Funk et al[10] note, the value of MTM and its extended comprehensive medication management model lies in its integration into primary care and patient-centered continuity—precisely the logic underlying our MTM-based “in-hospital assessment - home-based intervention” model. Isetts[11] showed that integrating community pharmacist-led MTM into clinical care improves continuity; the same integration applies to the home-based management of depression.
Most MTM research in depression has concentrated on in-hospital medication review and short-term post-discharge follow-up, with little attention to long-term home-based intervention. Reidt et al[12] first demonstrated the feasibility of integrating home-based MTM into outpatient care, showing that home visits can directly assess environmental factors affecting medication use—consistent with the design of our home-based module. Villanueva-Bueno et al[13] showed that telepharmacy-based home care improves adherence in chronic disease; we extend this approach to depression, with a 30.77% absolute increase in 9-month good adherence in the intervention group, consistent with effect sizes reported in earlier telepharmacy MTM studies.
For the core clinical outcome of relapse prevention, our finding that the intervention model reduced the 9-month relapse rate by 13.46% is supported by existing evidence. Rubio-Valera et al[14] found that pharmacist-led intervention reduces relapse risk in patients with depression in primary care, and our results suggest that continuous, closed-loop home-based intervention strengthens this effect. Kiosses and Alexopoulos[15] pointed out that early intervention for subsyndromal depressive symptoms is the key to preventing relapse, and the monthly proactive follow-up in our model achieved timely intervention for subsyndromal symptoms, which may be an important mechanism for reducing relapse risk.
Our finding that the model reduced per capita medical costs by 38.1% at 9 months aligns with prior MTM economic studies. Hui et al[16] found that the Medicare MTM program significantly reduced hospitalization risk and medical costs in patients with chronic disease, and Bird et al[17] and Dajczman et al[18] both confirmed that integrated continuous care models can reduce healthcare resource use in chronic disease. The MAPDep study protocol proposed by Del Pino-Sedeño et al[19] hypothesized that multi-component pharmaceutical intervention for depression can improve adherence and reduce medical costs—a hypothesis our trial now supports with prospective RCT evidence.
For the implementation of remote MTM services, Ward and Xu[20] confirmed that telephonic MTM can achieve good intervention effects, which is consistent with our telephone follow-up-based intervention model. Li et al[21] found that MTM is well suited to Chinese primary care, and our study adds a low-cost, practical model for primary hospitals in China. Although digital tools can enhance MTM, as noted by Dou et al[22], patient acceptance of digital health technology is affected by multiple factors. Our model, which is based on telephone follow-up and paper medication records, avoids the barrier of low digital technology acceptance in some patient groups and has better generalizability in resource-limited primary care settings. Roosan et al[23] discussed the potential of artificial intelligence in MTM; by contrast, our stan
To protect patient safety and privacy, we established a rigorous anonymization and data management system fo
Finally, our model fits the broader trajectory of MTM services. Isetts[29] traced the evolution of MTM from pharmaceutical care to standardized service models and argued that its sustainable growth depends on practicality and clinical value in primary care. With a defined two-stage structure and standardized operating procedures, our model offers a practical route to scaling up MTM in Chinese psychiatric care. The behavioral health care model evaluated by Nordberg et al[30] likewise found that structured, continuous intervention models are effective and clinically beneficial in mental health care, reinforcing the case for wider adoption of our model.
Our model offers clinical value in three respects. First, it bridges the discontinuity of care between hospital and home that characterizes current depression management, linking structured in-hospital assessment to continuous, closed-loop home-based intervention. Second, it is practical in primary care, requiring no complex equipment or costly infrastructure, and it can be adopted readily by primary hospitals and community health centers in China. Third, it yields benefits across multiple dimensions—improved adherence, symptom relief, relapse prevention, lower costs, and better quality of life—in keeping with the goal of comprehensive, long-term depression management.
Several mechanisms may explain the model’s efficacy: (1) The standardized MTM framework supports systematic identification and resolution of MRPs, optimizing antidepressant regimens and reducing adverse events; (2) Proactive monthly follow-up and a 24-hour consultation channel allow timely intervention for adherence problems and symptom fluctuations, preventing deterioration; (3) Continuous patient education strengthens self-management and patients’ understanding of their illness, underpinning sustained adherence; and (4) Stratified intervention for high-risk patients targets the key factors affecting outcomes, improving the precision of care. These mechanisms are hypothesized from the study findings and were not directly tested here.
We considered several potential confounders. The antidepressant class distribution, psychotherapy participation rate, and baseline anxiety score did not differ significantly between groups, suggesting that these factors are unlikely to explain the observed effects. We could not adjust for family support, socioeconomic status, or health literacy in the primary analysis given the relatively small sample. Larger studies should use multivariable regression to adjust for these confounders and isolate the independent effect of the MTM intervention.
Our use of the STOPP/START criteria for MRP assessment in patients with depression warrants comment. Although developed for older adults, these criteria have been validated in adult psychiatric populations and provide a comprehensive framework for identifying potentially inappropriate medications. Psychiatric-specific tools were considered but not adopted because of their limited availability in Chinese. Future work should develop and validate culturally appropriate, psychiatric-specific medication appropriateness tools for MTM services.
This study has several limitations. First, it was a single-center trial conducted in a traditional Chinese medicine hospital in China, which limits the generalizability of the findings to other healthcare settings or international contexts. Second, the relatively small sample may limit statistical power for some secondary outcomes and multivariable analyses. Third, because of the nature of the behavioral intervention, neither the participants nor the pharmacists delivering the inter
Future studies should pursue several directions: (1) Large-scale, multicenter RCTs to verify the model’s efficacy in more demographically and psychiatrically diverse populations, including patients with treatment-resistant depression, comorbid anxiety disorders, and bipolar depression; (2) Long-term follow-up of ≥ 2 years to evaluate sustained effects on relapse prevention and prognosis; (3) Formal health economic evaluation using incremental cost-utility ratios and quality-adjusted life years to confirm long-term cost-effectiveness from a societal perspective; (4) Integration of digital tools such as smartphone applications and wearable devices to improve the efficiency of continuous monitoring and intervention; (5) Comparative effectiveness studies against digital psychiatric monitoring platforms or integrated collaborative care models to inform scalability and resource allocation; and (6) Exploration of the model’s applicability in other mental disorders such as schizophrenia and substance use disorders.
In adults with depression, the MTM-based “in-hospital assessment - home-based intervention” pharmaceutical care model was associated with improved medication adherence, reduced depressive symptom severity, lower relapse risk, lower healthcare resource use, and better quality of life. It offers a practical, low-cost strategy for the continuous, long-term management of depression in primary care. Larger, multicenter RCTs are needed to confirm its long-term efficacy, cost-effectiveness, and generalizability.
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