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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Geriatr . 2026 Mar 6;26:518. doi: 10.1186/s12877-026-07216-0 Search in PMC Search in PubMed View in NLM Catalog Add to search Pillbox and patient-oriented manual interventions to improve medication knowledge, attitudes, and practices among older adults with multimorbidity in Shanghai, China Changjia Fan Changjia Fan 1 School of Public Health, Fudan University, Shanghai, China 2 China Research Center on Disability, Fudan University, Shanghai, China 3 Key Laboratory of Health Technology Assessment, National Health Commission, Fudan University, Shanghai, China Find articles by Changjia Fan 1, 2, 3, # , Qi Tang Qi Tang 1 School of Public Health, Fudan University, Shanghai, China 2 China Research Center on Disability, Fudan University, Shanghai, China 3 Key Laboratory of Health Technology Assessment, National Health Commission, Fudan University, Shanghai, China Find articles by Qi Tang 1, 2, 3, # , Lisha Li Lisha Li 1 School of Public Health, Fudan University, Shanghai, China 2 China Research Center on Disability, Fudan University, Shanghai, China 3 Key Laboratory of Health Technology Assessment, National Health Commission, Fudan University, Shanghai, China Find articles by Lisha Li 1, 2, 3 , Huanyun Wu Huanyun Wu 4 Shanghai Jinshan District Health Service Management Center, Shanghai Jinshan District Municipal Health Commission, Shanghai, China Find articles by Huanyun Wu 4 , Fanlei Kong Fanlei Kong 5 Department of Social Medicine and Health Management, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China 6 Key Laboratory of Health Economics and Policy Research, National Health Commission, Shandong University, Jinan, China 7 Center for Health Management and Policy Research, Shandong University (Shandong Provincial Key New Think Tank), Jinan, China Find articles by Fanlei Kong 5, 6, 7, ✉ , Gang Chen Gang Chen 1 School of Public Health, Fudan University, Shanghai, China 2 China Research Center on Disability, Fudan University, Shanghai, China 3 Key Laboratory of Health Technology Assessment, National Health Commission, Fudan University, Shanghai, China Find articles by Gang Chen 1, 2, 3, ✉ , Jun Lu Jun Lu 1 School of Public Health, Fudan University, Shanghai, China 2 China Research Center on Disability, Fudan University, Shanghai, China 3 Key Laboratory of Health Technology Assessment, National Health Commission, Fudan University, Shanghai, China Find articles by Jun Lu 1, 2, 3, ✉ Author information Article notes Copyright and License information 1 School of Public Health, Fudan University, Shanghai, China 2 China Research Center on Disability, Fudan University, Shanghai, China 3 Key Laboratory of Health Technology Assessment, National Health Commission, Fudan University, Shanghai, China 4 Shanghai Jinshan District Health Service Management Center, Shanghai Jinshan District Municipal Health Commission, Shanghai, China 5 Department of Social Medicine and Health Management, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China 6 Key Laboratory of Health Economics and Policy Research, National Health Commission, Shandong University, Jinan, China 7 Center for Health Management and Policy Research, Shandong University (Shandong Provincial Key New Think Tank), Jinan, China ✉ Corresponding author. # Contributed equally. Received 2025 Dec 1; Accepted 2026 Feb 18; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13077909 PMID: 41787303 Abstract Background Multimorbidity and polypharmacy are major challenges for older adults with multimorbidity (OAM) in China, yet demand-side interventions remain limited. This study aimed to assess the effectiveness of pillbox (PB) and patient-oriented manual (POM) interventions in improving medication knowledge (MK), attitudes (MA), and practices (MP) in Shanghai, China. Methods A quasi-experimental pre–post study with a control group was conducted in 11 community health service centers in Jinshan District, Shanghai, between August 2023 and February 2024. Eligible older adults were recruited by family doctor teams, which implemented the intervention primarily using two tools: the PB and the POM. Outcomes included MK, MA, and MP. Intervention effects and heterogeneity across participant characteristics were examined using propensity score matched difference-in-differences (PSM-DID) models. Results A total of 1,406 OAM participated. After the intervention, the proportion rated as “good” increased from 29.6% to 57.1% for MK, 24.5% to 48.8% for MA, and 47.1% to 73.9% for MP. PSM-DID analyses showed significant intervention effects for MK (OR = 3.14), MA (OR = 2.38), and MP (OR = 3.01). Heterogeneity analyses showed stronger effects on MK among participants covered by urban and rural resident insurance ( P = 0.043). For MP, effects weakened with increasing age ( P = 0.044) but strengthened with increasing medication burden, with the strongest effects observed at moderate levels of polypharmacy ( P = 0.012). Conclusion PB and POM interventions significantly improved MK, MA, and MP among OAM. Variation in intervention effects across participant characteristics underscored the need for tailored, patient-centered strategies integrated into routine family doctor services to promote rational medication use. Supplementary Information The online version contains supplementary material available at 10.1186/s12877-026-07216-0. Keywords: Medication knowledge , Medication attitudes, Medication practices, Older adults with multimorbidity, Rational use of medicines Introduction With global population aging accelerating, the burden of multimorbidity rose substantially and became a major global public health concern [ 1 ]. A meta-analysis across 54 countries reported a global prevalence of 37.2%, with more than half of adults aged ≥ 60 years experiencing multimorbidity [ 2 ]. China exhibited a similar trend, with 310.31 million people aged ≥ 60 years by 2024 [ 3 ], placing older adults with multimorbidity (OAM) at particularly high risk [ 4 , 5 ]. Multimorbidity further complicated clinical management, while fragmented health services, poor information exchange, limited medication literacy, and widespread self-medication practices contributed to inappropriate medication use [ 6 ]. Consequently, polypharmacy, typically defined as the concurrent use of five or more medications [ 7 ], increased rapidly, and OAM in China took an average of 9.1 medications [ 8 ], imposing substantial economic burdens on both patients and the healthcare system [ 9 , 10 ]. The World Health Organization defined the rational use of medicines (RUM) as the practice of patients receiving appropriate medications suited to their clinical needs, in the correct dosages, for adequate durations, and at the lowest possible cost to both patients and society [ 11 ]. In response, China introduced a series of medication management policies. The 2017 “13th Five-Year Plan” on ageing and elder care emphasized RUM [ 12 ], followed by 2018 guidelines from the central government recommending the integration of medication management into family doctor services [ 13 ]. One national policy in the year of 2022 further underscored the importance of medication safety [ 14 ]. Despite these policy initiatives, gaps remain in translating them into practice at the community level, and their impact on OAM is still not well understood. To mitigate polypharmacy risks, supply-side interventions mainly focused on optimizing prescribing practices through prescription reviews, clinical decision support systems, and structured deprescribing programs [ 15 – 17 ], and these strategies yielded measurable improvements [ 18 – 23 ]. In China, hospitals enhanced medication oversight by involving family doctor teams through order review platforms [ 24 ], audit systems [ 25 ], and clinical pathways [ 26 ]. Inappropriate medication use nevertheless remained prevalent, with a prevalence of 30.4% reported in Chinese communities [ 27 ]. This reflected persistent fragmentation in provider management and inadequate patient knowledge, awareness, and safe self-medication practices that were inconsistent with RUM principles [ 28 , 29 ], underscoring the importance of demand-side interventions. Previous studies showed the effectiveness of demand-side interventions on RUM. Nursing-led home education programs improved awareness and adherence to RUM, particularly when brochures and pill boxes were combined to support daily medication management [ 30 ]. Culturally tailored manuals provided structured guidance for specific populations [ 31 ], including electronic pill boxes, reminder apps, and monitoring systems, demonstrated feasibility in enhancing adherence during care transitions [ 32 , 33 ]. Narrative reviews identified pill boxes as low-cost, practical tools that help patients organize multiple medicines [ 34 ], and empirical studies confirmed their role in reducing errors such as missed, duplicated, or mistimed doses [ 35 , 36 ]. Collectively, pill boxes and patient-oriented manuals represented practical demand-side tools that complement institutional measures to mitigate polypharmacy [ 37 ]. However, evidence from China remained limited, even though polypharmacy and inappropriate self-medication among OAM presented urgent public health challenges. Evaluating quasi-experimental interventions in real-world settings posed methodological challenges, particularly due to selection bias and confounding. Propensity score matching (PSM) enhanced comparability between intervention and control groups, while difference-in-differences (DID) controlled for unobserved time-invariant confounders [ 38 ]. Combining these approaches (PSM-DID), a widely used method for assessing intervention effects, strengthened internal validity and provided more robust estimates [ 39 – 41 ]. To date, few studies have ever evaluated a patient-centered intervention integrated into routine family doctor team services to promote RUM among OAM, not to mention among the Chinese older adults. Accordingly, the study aimed to assess the effectiveness of a combined pill box (PB) and patient-oriented manual (POM) intervention on medication knowledge (MK), attitudes (MA), and practices (MP) using a PSM–DID approach, and to examine whether intervention effects varied across participant characteristics. Methods Study design and setting A quasi-experimental pre–post study with a control group was conducted in Jinshan District, Shanghai, China. The intervention was implemented between August 2023 and February 2024. Cluster sampling at the institutional level was combined with purposive sampling at the individual level. All 11 community health service centers in Jinshan District were included, with six assigned to the intervention group and five to the control group. Community health service centers were allocated according to geographic location and township-level medical resource categories [ 42 ]. All participating community health service centers routinely provided primary care services, including medication management and education on RUM. Each family doctor team, typically consisting of family doctors, pharmacists, nurses and public health physicians, delivered comprehensive team-based care. To ensure consistency across centers, the research team developed standardized recruitment guidelines and conducted centralized training for participating family doctor teams before study initiation. Training covered eligibility assessment, study introduction, and informed consent procedures. Regular communication was maintained throughout the recruitment period to support adherence to the study protocol. Participants Of 1,440 eligible older adults invited, 1,406 were included in the final analysis (response rate: 97.6%), of whom 697 were in the intervention group and 709 in the control group (Figure. 1 ). Fig. 1. Open in a new tab Flowchart of participant recruitment and data collection Participants were recruited from participating community health service centers through family doctor teams. Each family doctor screened approximately 10 contracted residents for eligibility according to predefined criteria and invited eligible individuals during routine outpatient visits. Recruitment followed standardized procedures established by the research team, including eligibility assessment, study introduction, and informed consent. Written informed consent was obtained from all participants before enrollment. Family doctor contract coverage among adults aged ≥ 65 years in Jinshan District exceeded 80%, facilitating community-based recruitment. Inclusion criteria OAM were eligible if they: (1) were aged 65 years or older; (2) had two or more chronic diseases and were concurrently prescribed at least two medications; (3) had an established contract with a family doctor for at least three months; (4) possessed basic reading and writing abilities; and (5) voluntarily agreed to participate and provided written informed consent. Exclusion criteria Exclusion criteria included: (1) unstable or acute health conditions; (2) lack of reading ability or communication impairments; (3) concurrent enrollment in other research studies; and (4) plans to leave the study area within six months. Intervention The intervention was delivered by family doctor teams within community health service centers, incorporating two tools: a pill box (PB) and a patient-oriented manual (POM). All components were implemented as part of routine community education and medication management, without involving clinical trials or experimental procedures. Pill box (PB) training The PB served as a practical aid for organizing medications. Designed to hold a one-week supply with four daily compartments (morning, noon, evening, bedtime), it helped participants structure their MP. Family doctor teams provided initial hands-on training, demonstrating correct allocation by daytime and nighttime, safe handling, packaging, and storage. Patient-oriented manual (POM) The POM, entitled Health Care Handbook of Medication , was developed to facilitate bidirectional communication between patients and healthcare providers (a brief description was provided in Supplementary Material 1). Its design was informed by a literature review, typical case analysis, key informant interviews, and expert consultation to ensure evidence-based content and clinical applicability. The POM included an integrated education and guidance section aimed at improving medication knowledge among OAM. It outlined RUM principles, correct dosing and timing, potential interactions, and practical advice for managing common chronic conditions. In practice, the POM functioned as both an educational resource and a communication tool. Family doctor teams used it during structured education sessions, while participants and caregivers used it as a take-home reference to reinforce MK and support safe MP and sustainable self-management. Frequency of delivery At baseline, participants in the intervention group received a single, standardized, face-to-face PB training session delivered by family doctor teams during routine community health visits. The training followed a uniform protocol and focused on correct PB use and the establishment of daily medication routines. The session lasted approximately 10–15 min. Following the baseline training, the intervention was implemented continuously throughout the intervention period through monthly follow-up contacts with family doctor teams. During these monthly contacts, family doctors reinforced key medication-related messages, addressed participants’ medication-related questions, and supported sustained use of the PB. In parallel, the POM was consistently applied within the same monthly follow-up consultations, serving as an ongoing educational and communication tool to ensure continuity and sustainability of the intervention. Measures All participants completed assessments at baseline and at final assessment using a structured self-administered electronic online questionnaire. Trained family doctors provided standardized assistance to support questionnaire completion without influencing participants’ answers. The full questionnaire, including full items assessing MK, MA, and MP were provided in Supplementary Material 2. Demographic and socioeconomic characteristics Collected data included gender, age, education, occupation, marital status, living arrangements, family per capita monthly income, insurance, mobile phone usage, number of diseases, and number of medications. Medication Knowledge (MK) MK was assessed using the scale developed by Ma et al. [ 43 ]. In their study, the scale demonstrated acceptable reliability (Cronbach’s α = 0.860) and satisfactory construct validity (Kaiser Meyer Olkin (KMO) = 0.772; Bartlett’s test of sphericity, P < 0.001). The scale comprises 12 items, each rated on a 5-point scale from 0 (“not at all”) to 4 (“Know completely”). Total MK scores were categorized as poor (< 30), medium (30–39), or good (≥ 40). Medication Attitudes (MA) MA was measured using a scale adapted from Ma et al. [ 43 ]. Pilot testing in the current study indicated good reliability (Cronbach’s α = 0.772). Construct validity was further supported, with a KMO value above 0.7 and a significant Bartlett’s test of sphericity ( P < 0.001) [ 44 , 45 ]. The scale contains seven items, and the total MA scores were classified as poor (≤ 3), medium (4–5), or good (≥ 6). Responses were coded as “No” (= 1), “Yes” (= 0), and “Not sure” (= 0), except for item six, which was reverse-scored (“Yes” = 1; “No” = 0; “Not sure” = 0). Medication Practices (MP) MP was assessed using the scale developed by Chen et al. [ 46 ]. In their study, the scale demonstrated satisfactory reliability and validity (Cronbach’s α, KMO > 0.7, and Bartlett’s test of sphericity, P < 0.001). The scale includes 11 items, with responses coded as “No” (= 1) and “Yes” (= 0). Total MP scores were categorized as poor (≤ 6), medium (7–9), or good (≥ 10). Statistical analysis Descriptive analysis Descriptive analyses were conducted to compare baseline characteristics between the intervention and control groups. Categorical variables were summarized as frequencies and percentages and compared using chi-square tests. Continuous variables were summarized as means with standard deviations and compared using the Mann–Whitney U test. Difference-in-differences analysis To estimate the intervention effects on rational medication outcomes, difference-in-differences (DID) models were first applied to the full sample using the following specification [ 47 , 48 ]: where denotes the outcome of interest for the i th participant at time t. In separate models, was specified as MK, MA, or MP. equals 1 if the i th participant was assigned to the treatment group and 0 for the control group; t indicates the survey wave (baseline in August 2023 and final assessments in February 2024); and the interaction term captures the average intervention effect, with a coefficient representing the change attributable to the intervention. denotes a vector of covariates used as controls. All DID models adjusted for potential confounders, including gender, age, education, occupation, marital status, living arrangements, family per capita monthly income, insurance, mobile phone usage, number of chronic diseases, and number of medications. Moreover, propensity score matching (PSM) was applied to further strengthen the robustness of the estimated intervention effects [ 49 ]. Propensity scores were estimated using a logistic regression model including the same covariates listed above. Participants in the intervention and control groups were matched at a 1:1 ratio, and the DID analysis was repeated in the matched sample. Heterogeneity analysis Heterogeneity in intervention effects was examined using PSM–DID models within the matched sample. All covariates from the primary analyses were sequentially specified as potential effect modifiers by introducing corresponding Group × Time × Covariate interaction terms into the models [ 50 ]. When a covariate was tested as an effect modifier, it was not additionally included as a main-effect covariate, while all other covariates were retained for adjustment [ 51 ]. Ordered logistic regression models were applied, given the ordinal outcomes. Statistical heterogeneity was assessed based on the interaction terms, with results reported as odds ratios (ORs) and 95% confidence intervals (CIs). All statistical analyses were performed using Stata version 13.0 (Stata Corp, College Station, TX, USA), and two-sided P values < 0.05 were considered statistically significant. Ethical considerations This study was reviewed and approved by the Medical Research Ethics Committee of the School of Public Health, Fudan University (International Registration Number: IRB00002408 & FWA00002399). Written informed consent was obtained from all participants before enrollment. As the study evaluated routine community-based service interventions and did not involve drugs, medical devices, or invasive procedures, it was classified as a public health service study rather than a clinical trial; therefore, trial registration was not required (Clinical trial number: not applicable). All procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and the 1964 Helsinki Declaration and its later amendments. Results Participants characteristics Table 1 presents the demographic characteristics. A total of 1,406 OAM participated in the study, including 708 females (50.4%) and 1,247 married individuals (88.7%). Of the participants, 984 (70.0%) lived with their spouses, 700 (49.8%) had a family per capita monthly income below ¥3,000 (approximately $422.9), 920 (65.4%) had basic medical insurance for urban and rural residents, and 965 (68.6%) used alternative mobile phones or did not use smartphones at all. Table 1. Characteristics of participants at the baseline ( N = 1406) Total Treatment group Control group P * n % n % n % 1406 100.0 697 49.6 709 50.4 Gender 0.069 Male 698 49.6 329 47.2 369 52.0 Female 708 50.4 368 52.8 340 48.0 Age (Mean, SD) 73.5 5.8 73.6 5.8 73.4 5.7 0.572 # Education < 0.001 Primary school and below 926 65.9 486 69.7 440 62.1 Middle school 332 23.6 161 23.1 171 24.1 High school and above 148 10.5 50 7.2 98 13.8 Occupation 0.585 Personnel of enterprises and institutions 212 15.1 96 13.8 116 16.4 Farmers 884 62.9 446 64.0 438 61.8 Self-employed 250 17.8 126 18.1 124 17.5 Others 60 4.3 29 4.2 31 4.4 Marital status 0.635 Married 1247 88.7 621 89.1 626 88.3 Others 159 11.3 76 10.9 83 11.7 Living arrangements 0.208 Living alone 81 5.8 38 5.5 43 6.1 Living with a spouse 984 70.0 473 67.9 511 72.1 Living with children 152 10.8 84 12.1 68 9.6 Living with a spouse and children 189 13.4 102 14.6 87 12.3 Family per capita monthly income 0.327 <¥3000 ($422.9) 700 49.8 355 50.9 345 48.7 ¥3000 ($422.9) ~ ¥4999 ($704.7) 465 33.1 233 33.4 232 32.7 ≥¥5000 ($704.9) 241 17.1 109 15.6 132 18.6 Insurance 0.214 Basic medical insurance for urban workers 486 34.6 252 36.2 234 33.0 Basic medical insurance for urban and rural residents 920 65.4 445 63.8 475 67.0 Mobile Phone Usage 0.322 Smartphone 441 31.4 210 30.1 231 32.6 Other types or did not use 965 68.6 487 69.9 478 67.4 Number of diseases 0.084 2 742 52.8 364 52.2 378 53.3 3 527 37.5 253 36.3 274 38.6 4 105 7.5 58 8.3 47 6.6 ≥ 5 32 2.3 22 3.2 10 1.4 Number of medications 0.182 2 102 7.3 41 5.9 61 8.6 3 594 42.2 306 43.9 288 40.6 4 359 25.5 173 24.8 186 26.2 ≥ 5 351 25.0 177 25.4 174 24.5 Open in a new tab SD Standard Deviation * Chi-Square Tests # Mann-Whitney U test Univariate analysis showed no significant differences between the intervention and control groups for most variables at the baseline (except for education, P < 0.001). Changes in MK, MA, and MP Scores Table 2 summarized the baseline MK, MA, and MP scores. For MK, 31.6% of participants were classified as poor, 39.0% as medium, and 29.4% as good; for MA, 51.4% were poor, 24.6% medium, and 24.0% good; and for MP, 23.8% were poor, 29.5% medium, and 46.7% good. No statistically significant differences were observed between the two groups across MK, MA, and MP ( P = 0.897, 0.786, and 0.948, respectively). Table 2. Baseline scores of MK, MA, and MP ( N = 1406) Total Treatment group Control group P * n % n % n % 1406 100.0 697 49.6 709 50.4 MK 0.897 poor (< 30) 444 31.6 223 32.0 221 31.2 medium (30 ~ 39) 549 39.0 268 38.5 281 39.6 good (40 ~ 48) 413 29.4 206 29.6 207 29.2 MA 0.786 poor (< 4) 723 51.4 352 50.5 371 52.3 medium (4 ~ 5) 346 24.6 174 25.0 172 24.3 good (6 ~ 7) 337 24.0 171 24.5 166 23.4 MP 0.948 poor (< 7) 334 23.8 166 23.8 168 23.7 medium (7 ~ 9) 415 29.5 203 29.1 212 29.9 good (10 ~ 11) 657 46.7 328 47.1 329 46.4 Open in a new tab SD Standard deviation, MK Medication knowledge, MA Medication attitudes, MP Medication practices *Chi-Square Tests As shown in Figure. 2 , participants in the treatment group demonstrated marked improvements after the intervention. The proportion classified as poor decreased substantially (MK: 32.0% to 12.9%; MA: 50.5% to 31.7%; MP: 23.8% to 9.5%), while those rated as good increased significantly (MK: 29.6% to 57.1%; MA: 24.5% to 48.8%; MP: 47.1% to 73.9%). Fig. 2. Open in a new tab Changes in MK, MA, and MP in the treatment group ( N = 697). Note: MK= medication knowledge; MA= medication attitudes; MP= medication practices In contrast, Figure. 3 illustrated that distributions in the control group remained largely stable over the same period, with only minor shifts observed across categories (MK good: 29.2% to 30.9%; MA good: 23.4% to 24.4%; MP good: 46.4% to 46.8%). Fig. 3. Open in a new tab Changes in MK, MA, and MP in the control group ( N = 709). Note: MK= medication knowledge; MA= medication attitudes; MP= medication practices Intervention Effects Overall intervention effects As shown in Table 3 , the interaction term (Treatment*Time) showed consistent positive effects: MK (DID OR = 2.991; PSM-DID OR = 3.143), MA (DID OR = 2.301; PSM-DID OR = 2.381), and MP (DID OR = 3.024; PSM-DID OR = 3.012). Table 3. Overall intervention effects on MK, MA, and MP Dependent variables MK MA MP DID PSM-DID DID PSM-DID DID PSM-DID OR (95% CI) OR (95% CI) OR (95% CI) Treatment 1.042 1.017 1.083 1.009 1.031 1.052 (0.86,1.27) (0.81,1.27) (0.89,1.32) (0.80,1.27) (0.85,1.26) (0.84,1.32) Time 1.061 1.006 1.146 1.120 1.072 1.077 (0.88,1.29) (0.79,1.28) (0.94,1.39) (0.87,1.43) (0.88,1.30) (0.84,1.38) Treatment*Time 2.991 *** 3.143 *** 2.301 *** 2.381 *** 3.024 *** 3.012 *** (2.26,3.96) (2.29,4.32) (1.74,3.05) (1.73,3.28) (2.25,4.06) (2.16,4.19) Open in a new tab All models were adjusted for gender, age, education, occupation, marital status, living arrangement, family per capita monthly income, insurance, mobile phone usage, number of diseases, and number of medications MK Medication knowledge, MA Medication attitudes, MP Medication practices, DID Difference-in-differences, PSM Propensity score matching, Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using ordered logistic regression models with a difference-in-differences specification; PSM-DID models were weighted using propensity score matching * P < 0.10, ** P < 0.05, *** P < 0.01 Heterogeneity of intervention effects Statistically significant effect modification was observed for three covariates (Table 4 ). Table 4. Heterogeneity analysis of intervention effects on MK, MA, and MP Outcome Effect modifier Subgroup P for interaction OR (95% CI) MK Insurance BMI-UW 0.043 ** Ref. BMI-URR 2.74 (1.80, 4.17) MP Age (continuous) - 0.044 ** 0.94 (0.88, 1.00) Number of medications 2 0.012 ** Ref. 3 1.81 (1.00, 3.28) 4 8.53 (3.90, 18.66) ≥ 5 5.67 (2.54, 12.65) Open in a new tab BMI-UW Basic medical insurance for urban workers, BMI-URR Basic medical insurance for urban and rural residents, MK Medication knowledge, MA Medication attitudes, MP Medication practices; Heterogeneity analyses were conducted using ordered logistic regression models with Group × Time × Covariate interaction terms within the PSM-DID framework; Odds ratios (OR) represent relative differences in intervention effects compared with the reference category *P < 0.10, **P < 0.05, ***P < 001 For MK, insurance significantly modified the intervention effect ( P for interaction = 0.043). Relative to participants covered by basic medical insurance for urban workers, those insured under the basic medical insurance for urban and rural residents exhibited a stronger intervention effect on MK, with a 2.74-fold greater improvement (OR = 2.74). For MP, significant effect modification was observed for both age ( P for interaction = 0.044) and number of medications ( P for interaction = 0.012). Increasing age was associated with a weaker effect (OR = 0.94), indicating that each additional year of age was linked to a modest reduction in the magnitude of improvement. In contrast, compared with participants taking two medications, those taking a higher number of medications showed progressively stronger intervention effects ( P for interaction = 0.012), with the largest effect observed among participants taking four medications. Discussion Overall effectiveness of the intervention This study demonstrated improvements in MK, MA, and MP following the PB and POM intervention, as shown by the PSM–DID analysis. These results aligned with previous studies, which showed MK and MP may be improved by the PB and POM. For instance, nursing-led interventions in Turkey that incorporated education materials and PB significantly improved MK among OAM [ 30 ]; nurse-led self-management initiatives on PB and POM strengthened older adults’ MK and MP for daily medication management [ 52 ]; and pharmacy-based eHealth programs in Europe improved patients’ correct use of medicines through tailored guidance and follow-up support [ 53 ]. Likewise, multidisciplinary interventions in Thailand and Spain that integrated PB, education modules, and provider collaboration demonstrated favorable changes in MP [ 54 , 55 ]. Our findings extended the empirical evidence to the Chinese community health context, demonstrating that PB and POM were feasible and effective tools for improving MK and fostering safe MP among OAM. Notably, the PB served not only as an organizational aid but also as a behavioral training device, helping older adults internalize correct routines of dosing, timing, and storage, thereby reinforcing RUM in daily life. A distinctive strength of our intervention lay in its dual focus: it strengthened patients’ MK and MP on the demand side, while also promoting supply–demand interaction. Family doctors, pharmacists, nurses, and public health physicians jointly engaged in the education and follow-up, using the POM as a communication bridge and the PB as a practice tool. This team-based and bidirectional approach created a supportive loop between patients and providers, reinforced behavior change, and enhanced continuity and sustainability in community settings. Crucially, the simplicity, affordability, and adaptability of these tools were in accordance with the existing studies, supporting their use in resource-limited settings [ 56 ]. Interpretation of heterogeneous intervention effects Intervention effects varied across participant characteristics, indicating meaningful heterogeneity in responsiveness to the intervention. Improvements in MK were greater among participants covered by the basic medical insurance for urban and rural residents than among those covered by basic medical insurance for urban workers. Previous studies in China have consistently reported insurance-related disparities in medication literacy and health knowledge, with urban and rural resident insurance beneficiaries exhibiting lower baseline levels compared with urban workers’ insurance holders [ 57 – 59 ]. Under such circumstances, structured educational inputs may generate larger marginal gains in MK among urban and rural resident insurance beneficiaries. For MP, the weaker intervention effects were observed with increasing age. Age-related declines in cognitive flexibility, physical function, and self-management capacity have been widely documented and may limit the extent to which older adults can implement and maintain medication-related behavioral adjustments [ 60 , 61 ]. In contrast, Participants with higher medication counts showed greater improvements in MP, with the strongest effects observed among those taking four medications. Notably, the intervention effects weakened among participants taking five or more medications. While moderate medication complexity may enhance engagement with management strategies, very high medication burden may overwhelm older adults’ self-management capacity, limiting sustained behavioral improvement [ 62 ]. This pattern suggested a threshold effect, whereby medication complexity facilitates responsiveness to behavior-focused interventions up to a certain level, beyond which additional barriers emerge [ 63 , 64 ]. Implication These findings underscored the importance of differentiated intervention strategies in primary care. Targeted educational interventions may yield greater benefits among disadvantaged insurance groups and patients with moderate polypharmacy, whereas older adults of advanced age or with a high medication burden may require additional clinical support beyond standard educational approaches. At the same time, these subgroup patterns should be interpreted as exploratory and used to inform future intervention targeting rather than definitive subgroup-specific recommendations. Limitations Several limitations should be noted. First, potential selection and measurement biases remain a concern. Participants were recruited by family doctors from their contracted residents, which may have introduced selection bias despite the use of standardized recruitment procedures. Although standardized assistance was provided to facilitate questionnaire completion among older adults with limited literacy or digital skills, residual comprehension or interviewer-related bias cannot be fully excluded. Cognitive function and health literacy were not directly assessed due to limitations of the baseline database, which may have resulted in residual confounding. Second, the generalizability of the findings may be constrained by the study setting. This study was conducted in a well-resourced urban district with relatively strong primary care infrastructure. Consequently, the findings may not be directly transferable to less-resourced settings or regions with weaker primary care capacity, and caution is warranted when extrapolating the results to other contexts. Third, the intervention duration was relatively short. Although improvements in medication-related practices were observed, the longer-term sustainability of these effects beyond the intervention period remains to be determined. Conclusions This study found that PB and POM interventions significantly improved MK, MA, and MP among older adults with multimorbidity in China. Heterogeneity analyses further showed that intervention effects varied by insurance coverage, age, and medication burden, underscoring the need for more targeted strategies in primary care. Integrating multimodal, team-based approaches that enhance patient capacity and provider engagement into community health services should be prioritized to promote rational medication use, mitigate polypharmacy-related risks, and alleviate pressures on the healthcare system. Supplementary Information Supplementary Material 1. (20.4KB, docx) Supplementary Material 2. (27.5KB, docx) Acknowledgements We want to thank all older adults with multimorbidity for participating in the study. We also thank the external facilitators and internal facilitators who supported and delivered the intervention. In addition, we are grateful to Zhuang Hong, Yunyun Huang, Taiqing Luo, Lanqing Chen, Liang Du and Yuting Liu (School of Public Health, Fudan University) for their comments on an earlier version of this manuscript. Abbreviations OAM Older adults with multimorbidity MK Medication knowledge MA Medication attitudes MP Medication practices DID Difference in differences PSM Propensity score matching RUM Rational use of medicines PB Pill box POM Patient-oriented manual BMI-UW Basic medical insurance for urban workers BMI-URR Basic medical insurance for urban and rural residents Authors’ contributions Changjia Fan and Qi Tang contributed equally to this work and should be considered joint first authors. They were jointly responsible for the conception and design of the study, data acquisition, analysis, interpretation of findings, and drafting of the manuscript. Lisha Li, Huanyun Wu, and Fanlei Kong provided critical revisions and reviewed the manuscript, ensuring the accuracy of the interpretations and conclusions. Fanlei Kong, Gang Chen and Jun Lu supervised the study, provided overall guidance and approved the final version as corresponding authors. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work, including the integrity and accuracy of the data presented. Funding This work was supported by the Major Projects of the National Social Science Fund of China (grant number 17ZDA078), the National Natural Science Foundation of China (grant numbers 72004030 and 71774030) and the One Rice Warm Yang’ Project of Tang Zhongying Foundation in 2023. Data availability All data generated and analyzed during the current study are not publicly available due to ethical and privacy considerations involving human participants but are available from the corresponding author upon reasonable request. Requests for data access can also be directed to the Medical Research Ethics Committee of the School of Public Health, Fudan University (Email: [email protected]). Declarations Consent for publication Not Applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Changjia Fan and Qi Tang are contributed equally to this work. Contributor Information Fanlei Kong, Email: [email protected]. Gang Chen, Email: [email protected]. Jun Lu, Email: [email protected]. References 1. Hu YD, Wang ZX, He HJ, et al. 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[ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplementary Material 1. (20.4KB, docx) Supplementary Material 2. (27.5KB, docx) Data Availability Statement All data generated and analyzed during the current study are not publicly available due to ethical and privacy considerations involving human participants but are available from the corresponding author upon reasonable request. Requests for data access can also be directed to the Medical Research Ethics Committee of the School of Public Health, Fudan University (Email: [email protected]). 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