UNASSIGNED: To analyse the perspectives of healthcare professionals (HCPs) regarding the acceptance of digital health solutions for growth hormone (GH) deficiency care. This study identified factors impacting HCPs\' intent to use and recommend digital solutions supporting recombinant-human growth hormone (r-hGH) therapy in Italy and Korea with a use case of connected drug delivery system (Aluetta® with Smartdot™) integrated in a platform for GH treatment support (the Growzen™ digital health ecosystem).
UNASSIGNED: Participatory workshops were conducted in Rome, Italy, and Seoul, Korea, to collect the perspectives of 22 HCPs on various predefined topics. HCPs were divided into two teams, each moderated by a facilitator. The workshops progressed in five phases: introduction of the project and experts, capturing views on the current context of digitalisation, perceived usefulness and ease of use of Aluetta® with Smartdot™, exploration of the perception of health technology evolution, and combined team recommendations. Data shared by HCPs on technology acceptance were independently analysed using thematic analysis, and relevant findings were shared and validated with experts.
UNASSIGNED: HCPs from both Italy and Korea perceived Aluetta® with Smartdot™ and the Growzen™ based digital health ecosystem as user-friendly, intuitive, and easy-to-use solutions. These solutions can result in increased adherence, a cost-effective healthcare system, and medication self-management. Although technology adoption and readiness may vary across countries, it was agreed that using digital solutions tailored to the needs of users may help in data-driven clinical decisions and strengthen HCP-patient relationships.
UNASSIGNED: HCPs\' perspectives on the digitalisation in paediatric GH therapies suggested that digital solutions enable automatic, real-time injection data transmission to support adherence monitoring and evidence-based therapy, strengthen HCP-patient relationships, and empower patients throughout the GH treatment process.

UNASSIGNED: The current six months regimen for drug-susceptible tuberculosis (TB) is long, complex, and requires adherence monitoring. TB hair drug level assay is one innovative approach to monitor TB treatment adherence however, its acceptability in the context of African multi-cultural settings is not known.
UNASSIGNED: To determine the acceptability of hair harvest and testing as a TB therapeutic drug monitoring method.
UNASSIGNED: The study explored perceptions, and lived experiences among TB patients with regard to using hair harvest and testing as a method of tuberculosis therapeutic drug monitoring in the context of their cultural beliefs, and faith. We used a descriptive phenomenological approach.
UNASSIGNED: Four main themes emerged namely: participants\' perceptions about the cultural meaning of their body parts; perceptions about hair having any medical value or meaning; perceptions about hospitals starting to use hair harvest and testing for routine hospital TB treatment adherence monitoring; and perceived advantages and disadvantages of using hair for treatment adherence monitoring. Overall, we found that using hair to monitor adherence was acceptable to TB patients provided the hair was harvested and tested by a medical worker.
UNASSIGNED: Hair harvest for medical testing is acceptable to TB patients on the condition that it is conducted by a medical worker.

Standard nebulizers are intended for general purpose use and typically are continuously operated jet or ultrasonic nebulizers. Evolutionary developments such as breath-enhanced and breath-triggered devices have improved delivery efficiency and ease of use, yet are still suitable for delivery of nebulized medications approved in this category. However, recent developments of vibrating membrane or mesh nebulizers have given rise to a significant increase in delivery efficiency requiring reformulation of former drug products or development of new formulations to match the enhanced delivery characteristics of these new devices. In addition, the electronic nature of the new devices enables tailoring to specific applications and patient groups, such as guiding or facilitating optimal breathing and improving adherence to the therapeutic regimen. Addressing these patient needs leads to new nebulization technologies being embedded in devices with fundamentally distinct functionality, modes of operation and patient interfaces. Therefore, new generation nebulizers can no longer be regarded as one category with fairly similar performance characteristics but must be clinically tested and approved as drug/device combinations together with the specific drug formulation, similar to the approval of pressurized metered-dose inhalers and dry powder inhalers. From a regulatory viewpoint, it is required that drug and device are associated with each other as combinations by clear, mutually conforming labels or, even more desirably, by distinct container-closure systems (closed system nebulizer).

Oral endocrine therapies (OET) for breast cancer treatment need to be taken over a long period of time and are associated with considerable side effects. Therefore, adherence to OET is an important issue and of high clinical significance for breast cancer patients\' caregivers. We hypothesized that a new bioanalytical strategy based on liquid chromatography and high-resolution mass spectrometry might be suitable for unbiased adherence monitoring (AM) of OET. Four different biomatrices (plasma, urine, finger prick blood by volumetric absorptive microsampling (VAMS), oral fluid (OF)) were evaluated regarding their suitability for AM of the OET abemaciclib, anastrozole, exemestane, letrozole, palbociclib, ribociclib, tamoxifen, and endoxifen. An analytical method was developed and validated according to international recommendations. The analytical procedures were successfully validated in all sample matrices for most analytes, even meeting requirements for therapeutic drug monitoring. Chromatographic separation of analytes was achieved in less than 10 min and limits of quantification ranged from 1 to 1000 ng/mL. The analysis of 25 matching patient samples showed that AM of OET is possible using all four matrices with the exception of, e.g., letrozole and exemestane in OF. We were able to show that unbiased bioanalytical AM of OET was possible using different biomatrices with distinct restrictions. Sample collection of VAMS was difficult in most cases due to circulatory restraints and peripheral neuropathy in fingers and OF sampling was hampered by dry mouth syndrome in some cases. Although parent compounds could be detected in most of the urine samples, metabolites should be included when analyzing urine or OF. Plasma is currently the most suitable matrix due to available reference concentrations.

UNASSIGNED: This study aimed to design a persuasive game, using objective adherence data, to motivate people with asthma to adhere to their medication regimen.
UNASSIGNED: A participatory user-centered design approach was employed, involving end-users and other stakeholders throughout the study. The approach consisted of four phases. Semi-structured interviews and a survey were conducted to understand user needs and reasons for poor adherence (Phase 1: define). Key themes were identified, leading to the formulation of behavior change strategies and design and game requirements. Several design directions were ideated, resulting in a concept for a serious game (Phase 2: ideate). Two rounds of user-tests were performed to evaluate a prototype of the serious game in terms of usability, perceived impact on medication adherence and motivation (Phase 3: prototype and Phase 4: evaluate).
UNASSIGNED: Findings from semi-structured interviews (n = 6) and the online survey (n = 20) revealed that people\'s non-adherence was often attributed to the perception of asthma as an episodic condition, the delayed experienced effect of maintenance inhalers, and lack of knowledge regarding difference of effect between maintenance and reliever inhalers. The study used behavior change strategies to translate these insights into design requirements for the development of the narrative-based persuasive game Ademgenoot. This six-week challenge-based game combines various behavior change strategies, including personal goal setting and continuous visual feedback, as well as persuasive game design elements, such as a narrative and rewards, with the aim of enhancing motivation to adhere to their medication regimen. User-testing (n = 8; round 1 and 2) showed that Ademgenoot is feasible in clinical practice and has the potential to support people with mild asthma in adherence to their maintenance medication.
UNASSIGNED: Future efforts should be directed towards a larger evaluation to assess the impact on motivation and inhaler use behaviour.

BACKGROUND: This is the first scoping review to focus broadly on the topics of machine learning and medication adherence.
OBJECTIVE: This review aims to categorize, summarize, and analyze literature focused on using machine learning for actions related to medication adherence.
METHODS: PubMed, Scopus, ACM Digital Library, IEEE, and Web of Science were searched to find works that meet the inclusion criteria. After full-text review, 43 works were included in the final analysis. Information of interest was systematically charted before inclusion in the final draft. Studies were placed into natural categories for additional analysis dependent upon the combination of actions related to medication adherence. The protocol for this scoping review was created using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines.
RESULTS: Publications focused on predicting medication adherence have uncovered 20 strong predictors that were significant in two or more studies. A total of 13 studies that predicted medication adherence used either self-reported questionnaires or pharmacy claims data to determine medication adherence status. In addition, 13 studies that predicted medication adherence did so using either logistic regression, artificial neural networks, random forest, or support vector machines. Of the 15 studies that predicted medication adherence, 6 reported predictor accuracy, the lowest of which was 77.6%. Of 13 monitoring systems, 12 determined medication administration using medication container sensors or sensors in consumer electronics, like smartwatches or smartphones. A total of 11 monitoring systems used logistic regression, artificial neural networks, support vector machines, or random forest algorithms to determine medication administration. The 4 systems that monitored inhaler administration reported a classification accuracy of 93.75% or higher. The 2 systems that monitored medication status in patients with Parkinson disease reported a classification accuracy of 78% or higher. A total of 3 studies monitored medication administration using only smartwatch sensors and reported a classification accuracy of 78.6% or higher. Two systems that provided context-aware medication reminders helped patients to achieve an adherence level of 92% or higher. Two conversational artificial intelligence reminder systems significantly improved adherence rates when compared against traditional reminder systems.
CONCLUSIONS: Creation of systems that accurately predict medication adherence across multiple data sets may be possible due to predictors remaining strong across multiple studies. Higher quality measures of adherence should be adopted when possible so that prediction algorithms are based on accurate information. Currently, medication adherence can be predicted with a good level of accuracy, potentially allowing for the development of interventions aimed at preventing nonadherence. Monitoring systems that track inhaler use currently classify inhaler-related actions with an excellent level of accuracy, allowing for tracking of adherence and potentially proper inhaler technique. Systems that monitor medication states in patients with Parkinson disease can currently achieve a good level of classification accuracy and have the potential to inform medication therapy changes in the future. Medication administration monitoring systems that only use motion sensors in smartwatches can currently achieve a good level of classification accuracy but only when differentiating between a small number of possible activities. Context-aware reminder systems can help patients achieve high levels of medication adherence but are also intrusive, which may not be acceptable to users. Conversational artificial intelligence reminder systems can significantly improve adherence.

UNASSIGNED: The aim of this study is to estimate the proportion of virologically suppressed People living with HIV on second-line ART and to identify factors associated with virologic suppression. With an increasing population of patients on complex second-line anti retroviral therapy (ART), understanding the factors associated with viral suppression and adherence is critical for ensured longevity of ART.
UNASSIGNED: A retrospective study was conducted of patients on second-line ART in 17 facilities supported by University of Maryland, Baltimore, in Nairobi, Kenya, covering the period beginning October 2016 up to August 2019. Viral suppression was defined as viral load <1000 copies/mL in a test conducted in the last 12 months. Adherence was assessed through self-reports and classified as optimal (good) or suboptimal (inadequate/poor). Associations were presented as adjusted risk ratios with 95% confidence intervals. Statistical significance was considered when p value ⩽0.05.
UNASSIGNED: Of 1100 study participants with viral load data, 974 (88.5%) reported optimal adherence while on first-line ART and 1029 (93.5%) reported optimal adherence to second-line ART. Overall, viral load suppression on second-line ART was 90%. Optimal adherence ((adjusted risk ratio) 1.26; 95% confidence interval 1.09-1.46)) and age 35-44 versus 15-24 years ((adjusted risk ratio) 1.06; 95% confidence interval 1.01-1.13)) were associated with viral suppression . Adherence to first-line ART ((adjusted risk ratio) 1.19; 95% confidence interval 1.02-1.40)) was associated with adherence to second-line ART.
UNASSIGNED: Viral suppression remains high and adherence was strongly associated with viral suppression, underscoring the need to adequately address the barriers to adherence before switching regimens.

Sufficient drug concentrations are required for efficacy of antiretroviral drugs used in HIV care and prevention. Measurement of nucleotide analogs, included in most HIV medication regimens, enables monitoring of short- and long-term adherence and the risk of treatment failure. The REverSe TRanscrIptase Chain Termination (RESTRICT) assay rapidly infers the concentration of intracellular nucleotide analogs based on the inhibition of DNA synthesis by HIV reverse transcriptase enzyme. Here, we introduce a probabilistic model for RESTRICT and demonstrate selective measurement of multiple nucleotide analogs using DNA templates designed according to the chemical structure of each drug. We measure clinically relevant concentrations of tenofovir diphosphate, emtricitabine triphosphate, lamivudine triphosphate, and azidothymidine triphosphate with agreement between experiment and theory. RESTRICT represents a new class of activity-based assays for therapeutic drug monitoring in HIV care and could be extended to other diseases treated with nucleotide analogs.

Volumetric absorptive microsampling (VAMS), an emerging microsampling technique, is a promising tool for adherence monitoring. This study focused on development of an analytical methodology to improve VAMS-based strategies for adherence assessment by analyzing angiotensin-converting-enzyme (ACE) inhibitors, loop diuretics, a potassium-sparing diuretic, and a thiazide diuretic. Development included sample preparation, chromatographic conditions, mass spectrometry settings, validation, and demonstrating proof of concept. Quantification of analytes, by name furosemide, hydrochlorothiazide, lisinopril, torasemide, and the active metabolites, canrenone, enalaprilat, and ramiprilat in finger prick blood (FPB), was validated based on international guidelines. Selectivity, carryover, and within/between-run accuracy and precision were in accordance with the recommendations. The matrix effect was evaluated at three different hematocrit levels (HT: 20%, 40%, 60%) and the coefficients of variation did not exceed 15%. Dilution integrity (1:10 and 1:20) was given for all analytes except lisinopril, yet for lisinopril, the therapeutic range was already covered by the calibration range. Long-term stability in VAMS tips was tested for 2 weeks at 24 °C in the dark and revealed no degradation of analytes. The proof of concept was performed by analyzing 35 intakes of ACE-inhibitors and diuretics in 18 VAMS and matched plasma samples. Hereby, determined concentration in FPB and plasma cannot be used interchangeably, and thus specific reference ranges for whole blood must be established. Nevertheless, the VAMS-based strategy was shown to be suitable for assessing adherence of all classes of antihypertensive drugs used in the guidelines to manage hypertension.

Telemedicine uptake in allergy/immunology was slow before the coronavirus disease 2019 pandemic, but has accelerated since. This review examines where telemedicine has been in allergy/immunology and where it is headed in the future. Focus is placed on patient, physician, and health care professional satisfaction with telemedicine, capacity to expand access to allergy/immunology care, cost considerations, the regulatory environment, and future applications of telemedicine including adherence monitoring, wearable biosensors, artificial intelligence, and machine learning addressed.