Background: Medication adherence is poor among individuals with serious mental illness (SMI). Few studies have demonstrated the effectiveness of remote medication dispensing and adherence monitoring interventions among individuals with SMI. Objective: This study aimed to understand medication dispensing rates for users of a remote medication dispensing and adherence monitoring device and to identify associated demographic and clinical characteristics. Methods: In this cohort study, individuals’ characteristics were measured at baseline, and dispensing records were followed from their enrollment and subsequent device installation as early as January 2019 until June 2023. Individuals were eligible to participate if they had an SMI diagnosis, were aged 18 to 64 years, were currently being prescribed psychiatric medications, and were receiving #MentalHealth services from a participating community #MentalHealth agency. Participants were recruited through a combination of self-selection and referrals from agency staff. Our intervention involved using a remote medication dispensing and adherence monitoring device to measure participants’ daily medication dispensing. Results: The final sample consisted of 99 participants. The mean age of the participants was 49 (SD 12.08) years; 64% (n=63) of the participants identified as men and 41% (n=41) as Black or African American. The overall dispensing rate was 92.9%, with 90 (91%) individuals having dispensing rates >80%. The results of the hierarchical Bayesian logistic regression model showed that participants adhered better to evening doses than morning doses (incidence rate ratio [IRR] 1.11, 95% credible interval [CrI] 1.06-1.16). Dispensing adherence was poorer on weekends than on weekdays (IRR 0.87, 95% CrI 0.83-0.91). For every additional year of using the device, the rate of adherence increased by 1% (IRR 1.01, 95% CrI 1.00-1.01). The rate of dispensing dropped by 22% after the onset of the COVID-19 pandemic (IRR 0.78, 95% CrI 0.71-0.86), and African American participants had a 29% lower rate of dispensing than White participants (IRR 0.71, 95% CrI 0.55-0.90). The rate of dispensing did not differ by age; sex; educational attainment; or the level of sadness, emotional and behavioral dyscontrol, cognitive function, or psychotic symptoms at baseline. Conclusions: The high adherence rate observed, regardless of baseline psychopathology levels, highlights the potential of remote medication dispensing and adherence monitoring devices to address adherence challenges in people with SMI. Observed variation in dispensing behavior by dose timing and contextual factors suggests opportunities for intervention, including aligning dosing schedules with patient routines, providing additional support during periods of disruption (eg, weekends or major life events), and tailoring strategies to address disparities across patient groups. These findings highlight the role of targeted, context-aware #Approaches to improve adherence in community-based SMI care. These findings support the integration of #Digital adherence monitoring within #MentalHealth services, especially in settings where traditional adherence support may be challenging. Clinical Trial: ClinicalTrials.gov NCT03775044; https://clinicaltrials.gov/study/NCT03775044

London, Ontario--(Newsfile Corp. - August 6, 2026) - Jones Healthcare Group has acquired an operating thermoforming facility and related business assets in Brampton, Ont., adding in-house production of custom-formed packaging components to its integrated offering.The acquisition includes the facility, equipment and an experienced team of colleagues who will join Jones Healthcare. The addition complements the company's existing expertise in printed packaging components, contract packaging...

New analysis: Most patients who stop GLP-1 drugs like Ozempic eventually return to treatment. Gastrointestinal side effects and co… #GLP1receptoragonist #Medicationadherence #Ozempic #Treatmentpersistence #Type2diabetes

Up to 40% of participants in a study of more than 600,000 patients had potentially inappropriate prescriptions corrected after receiving a structured medication review (SMR). Publishing their…

Background: Alzheimer disease (AD) affects cognition, treatment adherence, family connections, and health care resource allocation. Most patients with AD have low adherence to medication therapy due to the limitations associated with cognitive impairment. Therefore, increasing the involvement of patients and their family members in medication management is important to improve treatment outcomes and reduce the burden of care. Objective: This study explores the potential application of artificial intelligence (#AI) (AI) in medication management for Chinese patients with early- to mid-stage AD focusing on enhancing medication adherence. The study first predicts and evaluates key factors through an online Delphi study, which provides a basis for their subsequent incorporation into the AI model as input variables to enable prediction of medication-taking behaviors. Since AI research in medication management for this population is still undeveloped, this paper further explores the multiple potentials of AI from a theoretical view, including drug dosage optimization, multidrug interaction detection, and family education support. It will provide a preliminary direction and theoretical basis for the development of an intelligent medication management system in the future. Methods: The exploratory online Delphi study with no modification predicted the key factors influencing medication adherence. Based on the results, the study confirmed the potential of AI to improve adherence. Participation by 12 experts in 3 rounds systematically assessed the core elements influencing patients’ adherence to their medication. Results: Family care, social support, environmental factors, emotional support, and patient behaviors were identified as the primary factors influencing medication adherence among Chinese patients with AD. These factors were validated and ranked through iterative Delphi rounds, with family care and social support receiving the highest importance scores. The Wilcoxon signed-rank test indicated no significant difference between rounds (=.06), supporting the stability of the consensus. These findings establish a foundational set of variables for AI systems that predict and enhance medication adherence. Conclusions: This study highlights the critical factors affecting medication adherence by Chinese patients with AD. It was designed as an exploratory online Delphi study to identify and prioritize key influencing factors, rather than to validate a specific AI-based system, and the findings provide a theoretical foundation for future AI-informed interventions. The results also indicate theoretical potential roles for AI in supporting medication management, such as optimizing drug dosage, detecting multidrug interactions, and enhancing family education.

Background: Older adults frequently experience cognitive, physical, sensory, motivational, and environmental barriers that affect medication management. Medication adherence technologies (MATs) can support adherence, but their usability varies widely depending on individual abilities and device features. Prior research has largely focused on overall adherence or user experience, providing limited insight into feature-level usability challenges. Objective: The aim of the study is to develop and internally validate a personalized predictive model to predict the success of MAT subtasks for older adults with diverse cognitive, physical, sensory, motivational, and environmental capabilities. Methods: A mixed methods approach was used, incorporating the assessment of impairments using various standardized questionnaires, measurement of usability metrics through cognitive walkthroughs, and one-on-one semistructured interviews. For this study, we used “subtasks” as the representative of features of the devices. A subtask is a discrete, individual action that forms part of a larger task, specifically designed to achieve a step in the overall process. Participants tested between 1 and 7 devices from a selection of 13 devices. The proportion of subtask success was taken as the outcome measure. Predictors included demographic, clinical, cognitive, physical, sensory, motivational, and environmental characteristics. Personalized predictive modeling using cosine similarity and generalized linear models were compared with nonpersonalized and naive models. Model performance was evaluated using mean square error (MSE) through cross-validation and held-out validation. Results: A total of 117 participants (mean age 74.6, SD 7.9 years) were recruited, including 96 participants for usability testing and 21 for the validation, all varying in cognitive, physical, sensory, motivational, and environmental abilities. Both personalized (=0.25) and nonpersonalized models (=1.0) outperformed naive predictions (=1.21), demonstrating that subtask-level success can be predicted using routinely measurable demographic and functional characteristics. During cross-validation, personalized models achieved optimal performance at a matching proportion of =0.25, with MSEs lower than those observed at higher matching levels, although differences compared with nonpersonalized models were not statistically significant (Self-Medication Assessment Tool [SMAT]: =.50; Daily Living Tasks Dependent on Vision [DLTV]: =.43). In the held-out validation cohort, personalized models achieved MSEs of 0.89 (SMAT-based) and 1.16 (DLTV-based) at m=0.20, whereas nonpersonalized models demonstrated better performance with MSEs of 0.726 (SMAT-based) and 0.815 (DLTV-based). Models incorporating performance-based vision measures (SMAT-based) consistently outperformed those using self-reported vision scores (DLTV-based) across both personalized and nonpersonalized settings. Conclusions: This study demonstrates the feasibility of predicting subtask success of MATs in older adults. While personalization showed limited added benefit in this dataset, the subtask-focused model provides clinically meaningful insights to support evidence-informed selection of medication technologies, reduce usability-related medication errors, and improve adherence outcomes.

Background: Achieving optimal adherence to anticoagulation therapy is a major challenge in the management of venous thromboembolism (VTE). Mobile health (mHealth) technologies may offer a scalable approach to supporting medication adherence and self-management. Objective: This pilot study aimed to assess the #feasibility and preliminary impact of a smart technology–assisted, patient-centered care mHealth app for managing VTE (mVTEA) on short-term anticoagulation adherence among patients with VTE or at moderate-to-high risk of VTE. Methods: Baseline medication adherence and beliefs were assessed using the Chinese versions of the 8-item Morisky Medication Adherence Scale and the Beliefs about Medicines Questionnaire–Specific to characterize baseline status only. The primary outcome was perfect adherence at 1 month, assessed through structured telephone interviews, outpatient visits, and the mVTEA physician-patient communication module. During follow-up, researchers verified current medication regimens, recorded missed doses, assessed therapy continuation, and whenever possible, confirmed adherence through pharmacy refill records or remaining medication packaging. Secondary outcomes included the mVTEA check-in rate and clinical safety events (VTE recurrence, major bleeding per International Society on Thrombosis and Haemostasis criteria, VTE-related hospitalizations, VTE-related rehospitalizations, all-cause mortality). Results: In total, 45 participants completed the study (mean age 60.80, SD 15.20 years; n=16, 36% female). Baseline 8-item Morisky Medication Adherence Scale scores indicated suboptimal adherence (mean 6.24, SD 1.80), with 29% (13/45) classified as good adherence and 71% (32/45) as moderate or poor adherence. The primary contributors to nonadherence were forgetting to take medication. Baseline Beliefs about Medicines Questionnaire–Specific scores showed stronger beliefs in medication necessity than concerns (17.58, SD 2.52 vs 14.56, SD 3.34;

Background: The Michigan Oncology Quality Consortium developed a rapid patient-reported outcome measure (RapidPRO) focused on oral anti#Cancer agents (OAAs). We piloted this measure in 6 oncology practices to determine its usefulness in representing the symptom experience and medication adherence among individuals taking OAAs. It is common in oncology for #Cancer-specific approaches to be used. We sought to use 1 instrument for all OAAs as a means to simplify future implementation in practice. Objective: This study aimed to describe the use of RapidPRO in practice and quantify clinical metrics in RapidPRO for symptom burden, confidence to manage symptoms, confidence to know when to seek care, and OAA medication adherence. Methods: This observational study was conducted across 6 practices from July 2016 to December 2018. RapidPRO assesses symptoms, patient confidence, and medication adherence with respect to OAAs. Results: There were 2252 RapidPROs completed by 695 patients. Among individuals completing at least 2 RapidPROs, the median number of days between them was 28 (IQR 14-42). Of the 2252 completed RapidPROs, 1213 (53.9%) reported at least one moderate or severe symptom, and 28% (485/1705) reported medication nonadherence. Most bothersome symptoms (MBSs; n=1045) were reported in 35.1% (790/2252) of the RapidPROs, and 46.5% (323/695) of all patients reported an MBS. In exploratory analyses, RapidPROs that reported a moderate or severe symptom or lower confidence to manage symptoms were more likely to be nonadherent to OAA therapy. The most common reason for medication nonadherence was “experienced side effects.” Conclusions: These results show that most RapidPROs reported at least one moderate or severe symptom and 28% (485/1705) reported medication nonadherence. As well, RapidPRO was able to capture most patients’ MBSs. By implementing RapidPRO, practices can identify patients who experience symptoms, as well as those who report medication nonadherence.

Background: Allogeneic hematopoietic stem cell transplant (HCT) is a complex but essential treatment for malignant and nonmalignant conditions, requiring strict posttransplant adherence to immunosuppressant medications to prevent complications such as graft-versus-host disease. Adolescents and young adults undergoing HCT face unique challenges, including balancing growing independence with ongoing reliance on care partners, often parents. Medication adherence in this group is often suboptimal, and few interventions address adolescent and young adult–care partner dyads. To address this gap, we aim to develop a mobile health (mHealth) app that engages both the patients and care partner to improve adherence. Objective: As formative research for early-stage intervention development, this study aimed to (1) explore current HCT medication adherence strategies and challenges; (2) understand attitudes toward digital technology, including dyadic perspectives on app use to support adherence; and (3) assess adolescent and young adult–care partner relationships, including views on care partner involvement. This process was intended to inform the design of a relevant, user-centered mHealth app. Methods: Eligible participants included adolescents and young adult patients aged 12-39 years and primary care partners, such as parents, involved in medication management. Participants were recruited from a large academic medical center through direct outreach and electronic health records. Data collection involved 2 focus groups (6 dyads and 2 additional adolescents and young adults), 4 individual interviews (2 patients and 2 care partners), and 6 dyadic interviews. Semistructured sessions (in person or virtual) gathered feedback on medication adherence practices and app design preferences. All sessions were audio recorded with consent and professionally transcribed. Qualitative data were analyzed systematically: transcripts were deidentified, coded using both inductive and deductive strategies, and themes were refined through team consensus. Patterns were organized into major themes, and representative quotations were selected to illustrate findings. Data management was facilitated by NVivo (version 13; Lumivero) software. Results: We included 28 participants (15 adolescents and young adults and 13 care partners). The median age of adolescents and young adults was 18 (range 13-39) years and 53% (8/15) were female. Adolescents and young adults were 47% (7/15) White, 40% (6/15) Black, and 13% (2/15) mixed race. Care partners’ median age was 48 (range 36-72) years, with 92% (12/13) female and 77% (10/13) White. Three principal themes emerged: (1) existing reminders and organizational tools are often insufficient for consistent adherence; (2) adherence barriers are multifaceted, often involving autonomy vs care partner support; and (3) both adolescents and young adults and care partners showed strong interest in a dyadic digital health intervention to foster collaboration and support shared adherence goals. Conclusions: This formative study highlights the complex dynamics of medication adherence in adolescent and young adult–care partner dyads and supports the need for a dyadic mHealth app to enhance adherence, collaboration, and relationship quality.

HB 1214 would reduce Virginia's insulin cap from $50 to $35 per 30‑day supply and extend a $35 aggregate cap to diabetes supplies. The subcommittee reported the bill after patient advocates and clinicians testified that reduced cost shares improve adherence and outcomes.