使用数字瓶盖评估的用药依从性行为：回顾性队列研究。
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摘要 背景：心肌梗死（MI）患者用药依从性差是心血管疾病管理中的关键障碍。然而，目前缺乏以药物依从性行为的客观测量为重点的研究，尤其是随机对照试验（RCTs）。 目的：本研究旨在（1）利用客观的数字瓶盖数据，描述纳入达格列净治疗心肌梗死患者（DAPA-MI）随机对照试验的MI后患者每日药物依从性行为和剂量一致性，（2）识别与不依从相关的患者及情境因素，以及（3）评估剂量一致性是否与随时间的依从性相关。 方法：DAPA-MI是一项3期、双盲、安慰剂对照随机对照试验，评估在标准治疗基础上，每日一次给予10 mg达格列净与安慰剂相比，对因MI住院的成人患者的影响。本项事后分析使用了来自数字瓶盖的733,490个时间点数据，该瓶盖记录研究药物瓶打开的时间。依从性和剂量一致性采用混合效应回归进行评估。依从性在患者-日层面定义为某日至少打开药瓶一次。剂量一致性根据首次记录打开事件时间的逐日变化得出。数字瓶盖数据与人口统计学、临床、行为和情境变量相关联，以评估与随时间不依从和较低剂量一致性相关的因素。 结果：共有2429名参与者被纳入分析。与较低服药依从性相关的特征包括年龄<40岁（比值比[OR] 0.47，99.9% CI 0.23-0.98）、当前吸烟者（OR 0.67，99.9% CI 0.51-0.87）、非工作日以及严重不良事件。较高的服药依从性与纽约心脏病协会（NYHA）心功能分级III/IV级（OR 1.48，99.9% CI 1.18-1.86）、7天平均时间变异性较低（<15分钟）（OR 1.99，99.9% CI 1.78-2.23）以及早晨服药相关。在40-50岁患者（剂量一致性比率[DCR] 1.23，99.9% CI 1.03-1.47）、当前吸烟者（DCR 1.16，99.9% CI 1.03-1.31）以及周末观察到较低的剂量一致性，而严重心绞痛分级（DCR 0.83，99.9% CI 0.74-0.94）或早晨服药（DCR 0.88，99.9% CI 0.86-0.90）的患者具有较高的剂量一致性。早期具有高剂量一致性的患者倾向于随时间保持更好的依从性，而漏服连续和常规服药中断与一致性恶化相关。 结论：本研究证明，在一项大型随机对照试验（RCT）中，进行客观、连续的数字化服药依从性行为监测具有重要价值。与依赖自我报告或处方数据的研究不同，本研究在大规模范围内量化了依从性和剂量一致性，并揭示了两者之间的密切关联。研究结果表明，服药时间不规律性可能作为识别未来依从性下降高风险患者的实用行为标志物。这些发现可能推动服药依从性行为研究的发展，并为行为改变干预、远程监测策略及预测性依从模型提供参考。 引言 心肌梗死（MI）患者的药物不依从性被认为是管理心血管疾病危险因素的主要障碍，据报道有13%-61%的患者存在不依从现象[-]。这会增加住院、血运重建和死亡的风险[,]，且心脏病发作后一年内死亡风险增加3.8倍[]。此外，在随机对照试验（RCT）环境中，不依从性可能损害结果的完整性和可靠性，并可能使研究结果偏向于高估给药需求[]。 由于不依从对患者、临床研究和医疗保健系统可能产生的重大影响 []，目前正在进行持续努力以寻找有效的干预措施来提高药物依从性 [-] 。然而，依从性研究主要依赖于自我报告的依从性测量 [] ，这些测量可能因社会赞许性或对漏服剂量的记忆不佳而错误估计依从性 [] 。远程监测技术（如数字药瓶和智能瓶盖）的进步可能有助于克服自我报告依从性评估的弱点，通过自动捕获服药行为（如服药时间或剂量时间），而不依赖于个人的记忆 [,] 。因此，除了跟踪依从性本身之外，这些设备也可能有助于评估与依从性相关的因素，包括习惯强度 [] 。 习惯是一种自动化的行为，通过在同一或相似情境下的重复表现而形成[]。在药物依从性方面，这可能涉及在固定时间或作为既定日常程序的一部分服用相同数量的药片。随着时间推移，这些反复出现的环境或情境线索强化了该行为，使其几乎无需有意识努力即可执行，甚至可能独立于刻意意图而自发产生[]。由于习惯的特征是对反复出现的线索作出一致性的执行，因此诸如服药时间变异等指标可作为习惯强度的替代指标，尽管相关线索可能反映的是规律的时间窗或情境，而非精确的钟点时间[]。近期研究表明，自我报告和客观测量的习惯强度均与药物依从性独立相关[,]。 在本研究中，我们考察了在大型心血管临床试验中，以服药时间变异（剂量一致性）测量的习惯强度与药物依从性之间的关系。值得注意的是，剂量一致性是一种方便观察的规律性[]，仅代表服药习惯的一个方面，但习惯总体而言并不总是转化为服药时间的规律性[]。此外，打开和关闭数字药瓶的动作只是依从性的一个替代指标，因为我们无法独立验证剂量是否实际服用[]。最后，在临床试验背景下知晓自身依从性行为正受到监测的参与者，其行为可能不易转化到临床研究之外的其他情境中，因为观察者效应往往会提高基线依从性[]。 本分析的主要目的是描述在一项纵向临床试验中，每日用药依从性与每日剂量一致性之间的关系。次要目的是识别与不依从相关的患者及情境因素。近期关于习惯强度客观测量与用药依从性之间关系的研究，往往涉及患者数量较少[,]，或使用由不同方案或数据收集标准不一致的多项研究汇编而成的大型患者数据库[,]，或将分析局限于评估不同习惯强度测量与依从性之间的相关性[,]。尽管这些研究在确立这两个变量之间的联系方面具有宝贵价值，但均未能在涉及大量患者、且在规范的回归分析中纳入大量已测量混杂因素的长期临床试验背景下证明这种联系。在本研究中，我们使用了固定在研究药物瓶上的数字瓶盖所收集的733,490个时间点数据，这些数据来自达格列净用于心肌梗死患者（DAPA-MI）随机对照试验中随机分组的2429名患者，以了解用药依从行为[]。根据欧洲患者依从性、顺应性与持久性协会（ESPACOMP）用药依从性报告指南（EMERGE）[]，本研究聚焦于依从性的实施阶段，该阶段定义为患者给药与处方方案的匹配程度。 方法 DAPA-MI研究设计 DAPA-MI是一项基于注册登记的3期、多中心、双盲、安慰剂对照随机对照试验，在瑞典和英国的103个研究中心开展。DAPA-MI旨在评估在标准治疗基础上，每日一次给予10 mg达格列净与安慰剂相比，对因心肌梗死住院且存在左心室收缩功能受损、但无已知糖尿病或已确诊心力衰竭（HF）的患者的疗效。纳入的患者年龄≥18岁，因急性心肌梗死而在瑞典或英国住院，有区域性或整体性左心室收缩功能受损的证据，按照心肌梗死管理指南接受标准治疗，并已纳入SWEDEHEART（瑞典）或心肌缺血国家 audit 项目（MINAP，英国）注册登记。完整的 methodology 和 DAPA-MI的主要结局已在此前发表[,]。该研究于2020年9月25日在ClinicalTrials.gov（NCT04564742）前瞻性注册，首例参与者于2020年12月22日入组。 本项事后分析的目的是描述DAPA-MI纳入患者的每日用药依从性行为和剂量一致性。本分析的设计和执行不涉及患者或公众的参与。 研究药物依从性追踪 本分析使用了固定在研究药物瓶上的数字瓶盖（CleverCap Lite，Compliance Meds Technologies）所收集的数据，该瓶盖发放给所有在DAPA-MI中入组并在现场随机化访视期间就诊的患者。数字瓶盖可检测并记录药瓶何时被打开和关闭，并通过3G无线网络自动将此信息上传至中央门户网站，研究团队和研究中心团队可在此访问该信息。与EMERGE []一致，本研究聚焦于依从性的实施阶段，定义为患者的服药与规定给药方案相符的程度。实施阶段的依从性以与规定给药方案一致的记录瓶开次数比例来操作化衡量。患者被指示每日仅服用一次研究药物，未规定具体服药时间。数字瓶盖和移动应用程序（由一部分患者用于每月EQ-5D-5L提交）均未在漏服剂量时向患者发出通知或提醒。然而，意外的使用模式，包括连续至少四次漏服，会触发通知研究团队，并建议其联系患者。 研究人群 本分析纳入了所有最初入组DAPA-MI的患者，排除标准为：撤回知情同意、从未接受治疗或从未获得数字瓶盖、治疗期间从未使用数字瓶盖、或使用药丸分装盒的患者。 伦理考量 DAPA-MI研究获得了瑞典伦理审查机构（Dnr 2020-03087、2021-03037、2022-00101-02和2023-01452-02）以及英国健康研究局（参考号#20/NW/0312）的批准。所有患者均通过数字瓶盖收到有关数据收集的信息，并提供书面知情同意书参与该试验。未同意进行二次分析的患者被排除在本分析之外。本分析中使用的所有数据均已去标识化：患者数据在源头进行假名化处理，患者身份从未与研究人员共享；研究机构名称和位置进一步模糊至国家层面；年龄按入组时最接近的年份提供；GPS位置信息从未共享。参与者因参与研究而获得的经济补偿仅限于合理开支的报销，无其他经济报酬。手稿或附录的任何图像中均无法识别任何个体参与者；因此，无需额外同意。 依从性与剂量一致性的定义 本分析聚焦于两个定量结局（因变量）：依从性和剂量一致性（这是衡量习惯强度的便捷可观察替代指标）。依从性是一个二分类变量，如果患者在给定日期至少打开一次数字瓶盖，则编码为1，否则编码为0。在单个24小时周期内的多次瓶盖打开被标记为“计划外使用”。 第 n 天的服药时间一致性定义为第 tn 天首次开盖事件的时间与前一天首次开盖事件的时间之差的绝对值 |tn − tn–1|。如果第 n–1 天未开盖，则使用有事件记录的最接近日期计算该差值（示例计算见，例如，表 S13）。服药时间一致性仅能在开盖当天计算，且该差值必须小于 24 小时。该客观习惯指标与 Pironet 等人 [] 使用的“逐日服药时刻的标准差”最为接近，但本研究采用平均绝对偏差而非标准差，因为标准差可能会放大离群值的影响。 变量与数据处理 大多数患者变量是在基线访视时收集的。纽约心脏协会（NYHA）功能分级和加拿大心血管学会（CCS）心绞痛分级在每次访视时收集。还要求患者每月通过移动应用程序（Unify []）完成EQ-5D-5L问卷，或在现场访视时（仅瑞典）完成纸质版EQ-5D-3L问卷。对于这些结局，分析中使用了收集到的最新可用值。所包含变量的完整列表见附录中的表S1。由于模型使用了分类变量，缺失值比例低于10%的协变量使用众数（即最常见水平）进行填补。缺失值比例较高的变量被排除。我们使用Jamshidian和Jalal的非参数检验来评估这些值是否完全随机缺失（MCAR）[]，因为大多数变量不符合正态分布。 为进一步生成“依从性数据集”，删除了DAPA-MI治疗期（即首剂日期至末剂日期）之外的来自并行设备及多余数字瓶盖事件的数据点。在患者同一天多次打开数字瓶盖的情况下，数据集中保留首次事件，并将其他事件标记为“计划外使用”，该标记作为解释变量纳入分析。“剂量一致性”数据集仅包含无缺失事件的数据点（即可以计算剂量一致性 |tn – tn–1| 的数据点）。 统计分析 分析中纳入与排除的患者人群之间的单变量比较，分类变量采用卡方检验，近似正态分布的连续变量采用双样本t检验，非正态分布的连续变量采用Kruskal-Wallis检验。 为识别影响用药依从性和剂量一致性的因素，采用混合效应建模方法，以考虑按研究中心分组的患者内数据点的重复测量设计。使用了两个水平的随机截距，即患者和研究中心，由于不允许患者同时参与多项研究，因此将其编码为显式嵌套。依从性模型为逻辑混合效应回归模型，以每个患者日的二元依从性指标为因变量，固定效应列于附录表S1中。 由于剂量一致性是以非正态分布的时间间隔量化的，为使其正态化使用了两种不同的变换：（1）对数变换，yn = ln(|tn – tn–1| + 1秒），这意味着模型系数具有直接的乘法解释；（2）Box–Cox变换，它能够满足正态性和方差齐性的建模假设。然后，将两个变换后的变量分别用于线性混合效应回归模型，固定效应列于附录表S1中。 除两个自变量外，依从性模型与剂量一致性模型中的所有自变量均重叠。“过去7天的平均时间变异”被用作依从性模型中的自变量，以检验平均剂量一致性与依从性之间的关系。出于同样原因，剂量一致性模型纳入了“错过一次剂量后”这一变量，该变量量化了与短期或长期不依从连续期之间的接近程度。在某些情况下，医生会暂时中止通过数字瓶盖进行的数据收集，通常是因为患者休假；这些时段被定义为“数据缺口”。 分析使用Python 3.9.5版 []、R 4.1.0版（R统计计算基金会）[] 以及R中的glmmTMB 1.1.9库 [] 进行。公共假日使用workalendar 16.4.0库计算，统计模型使用R中的glmmTMB 1.1.9库拟合。我们使用了R中mice 3.19.0包 [] 所包含的MCAR检验实现。 附加分析（如 中所述）包括根据EQ-5D-3L/EQ-5D-5L焦虑应答的依从性和剂量一致性、早期建立高度一致性的患者与早期一致性较低的患者之间的比较，以及使用Box–Cox变换数据对剂量一致性模型进行的敏感性分析。 结果 患者特征 在DAPA-MI随机分配的4017名患者中，有2429名（60.5%）被纳入本分析（）。数据集精炼后，依从性数据集包含733，490个患者-日，剂量一致性数据集包含682，731个开盖事件（每天一个事件）（中的图S1）。患者特征总结于。Anderson-Darling秩检验统计量为4.3，Jamshidian和Jalal非参数MCAR检验的中位P=0.15 []，表明在0.05显著性水平下没有足够证据拒绝MCAR假设。单变量比较发现，纳入与排除患者在国籍、种族、身高、末次评估时NYHA心功能分级和CCS心绞痛分级以及缺失开盖事件次数（例如，由于使用药丸整理器）方面存在差异，但除此之外，两组具有可比性。 流程图，显示从DAPA-MI试验中选择分析人群以及用于依从性和剂量一致性分析的数字瓶盖数据集的生成过程。在4017名随机化患者中，排除撤回知情同意、从未接受治疗、治疗期间未获发或未使用数字瓶盖、使用药片整理盒或未同意二次分析的患者后，共纳入2429名患者。经数据清理（包括删除治疗期外记录、来自同时使用设备的数据及多余瓶盖事件）后，依从性数据集包含733,490个患者日，剂量一致性数据集包含682,731次开盖事件。剂量一致性（tdiff）基于连续两天开盖事件时间之间的绝对差异。DAPA-MI：心肌梗死患者应用达格列净；tdiff：连续两天开盖事件之间的时间差异。 总体研究人群的特征以及纳入与排除分析患者之间的比较。a a除非另有说明，所有特征均在基线时记录。被排除分析的患者中，使用药片整理盒的比例较高（n=1267，32.3%；详见）。 bP值来自纳入组与排除组之间的单变量比较。 c分类变量采用卡方检验。 d不适用。 e近似正态分布的连续变量采用双样本 t 检验。 fNYHA：纽约心脏协会。 研究期间多次测量NYHA心功能分级和CCS心绞痛分级；分析采用最近一次收集的值。 g鉴于III/IV级疾病患者数量较少，将这两类合并。 hCCS：加拿大心血管学会。 i对非正态分布的连续变量采用Kruskal-Wallis检验。 总体依从性和给药一致性 在2429例患者中，总体依从性（每位纳入患者的平均依从性值的中位数[IQR]）为96.7%（91.4%-98.9%）。这些结果代表了根据EMERGE框架[]定义的实施阶段依从性结局，通过记录的瓶盖开启次数与规定给药方案进行比较来衡量。连续两天依从性的中位时间差（给药一致性）为34.7（IQR 11.8-83.3）分钟（见附录中的表S2）。 患者在依从性和服药时间方面表现出多种行为依从模式。 示例图显示了DAPA-MI试验中选定的患者个体数字瓶盖记录的随时间变化的服药模式。选取参与试验400-500天（以使轨迹在视觉上更具可比性）的患者，按平均给药一致性排序，分为十分位数，从每个十分位数中选取一名患者进行展示。蓝点表示瓶盖开启事件，连续事件由蓝线连接；横轴上的黄点表示当日无记录瓶盖开启。每个面板右下角显示的百分比表示
全文（开放获取） · 英文原文
【Abstract】 BACKGROUND: Medication nonadherence in patients with myocardial infarction (MI) is a key obstacle to managing cardiovascular disease. However, there is a lack of studies, particularly randomized controlled trials (RCTs), focusing on objective measures of medication adherence behavior. OBJECTIVE: This study aimed to (1) characterize daily medication adherence behavior and dose consistency using objective digital bottle cap data in patients post-MI enrolled in the Dapagliflozin in Patients with Myocardial Infarction (DAPA-MI) RCT, (2) identify patient and contextual factors associated with nonadherence, and (3) assess whether dose consistency is associated with adherence over time. METHODS: DAPA-MI was a phase 3, double-blind, placebo-controlled RCT evaluating the effect of 10 mg of dapagliflozin versus a placebo given once daily, in addition to standard-of-care therapy, on adults hospitalized for MI. This post hoc analysis used 733,490 timepoints from a digital bottle cap that recorded when study drug bottles were opened. Adherence and dose consistency were evaluated using mixed-effects regression. Adherence was defined at the patient-day level as at least one bottle opening on a given day. Dose consistency was derived from the day-to-day variation in timing of the first recorded opening event. Digital cap data were linked with demographic, clinical, behavioral, and contextual variables to assess factors associated with nonadherence and lower dose consistency over time. RESULTS: In total, 2429 participants were included in the analysis. Features associated with lower adherence included age <40 years (odds ratio [OR] 0.47, 99.9% CI 0.23-0.98), current smokers (OR 0.67, 99.9% CI 0.51-0.87), nonworking days, and serious adverse events. Higher adherence was associated with the New York Heart Association (NYHA) functional class III/IV (OR 1.48, 99.9% CI 1.18-1.86), low (<15 min) 7-day average time variance (OR 1.99, 99.9% CI 1.78-2.23), and morning medication use. Lower dose consistency was observed in patients aged 40-50 years (dose consistency ratio [DCR] 1.23, 99.9% CI 1.03-1.47), in current smokers (DCR 1.16, 99.9% CI 1.03-1.31), and on weekends, while patients with severe angina grading (DCR 0.83, 99.9% CI 0.74-0.94) or morning medication use (DCR 0.88, 99.9% CI 0.86-0.90) had higher dose consistency. Patients with early high dose consistency tended to maintain better adherence over time, whereas missed-dose streaks and routine disruptions were associated with worsening consistency. CONCLUSIONS: This study demonstrated the value of objective, continuous digital medication adherence behavior monitoring within a large RCT. Unlike studies relying on self-reported or prescription data, it quantified both adherence and dose consistency at scale, revealing a strong association between them. The findings indicate that temporal irregularity of medication taking may serve as a practical behavioral marker for identifying patients at increased risk of future nonadherence. These findings may advance medication adherence behavior research and inform behavior change interventions, remote monitoring strategies, and predictive adherence models. 【Introduction】 Medication nonadherence in patients with myocardial infarction (MI) is considered a major barrier to managing cardiovascular disease risk factors, with 13%-61% of patients reported as nonadherent [-]. This can increase the risk of hospitalization, revascularization, and death [,], with a 3.8-fold increased risk of death in the year after a heart attack []. Moreover, in a randomized controlled trial (RCT) setting, nonadherence can compromise the integrity and dependability of results and may skew findings toward overestimation of dosing requirements []. Because of the significant impact that nonadherence can have on patients, clinical research, and health care systems [], there are ongoing efforts to find effective interventions to improve medication adherence [-]. However, adherence research has primarily relied on self-reported measures of adherence [] that can misestimate adherence due to social desirability or poor recall of missed doses []. Advances in remote monitoring technologies (eg, digital pill bottles and smart caps) may help overcome the weaknesses of self-reported assessments of adherence by automatically capturing medication-taking behavior, such as pill or dose timing, without depending on a person’s memory [,]. As a result, beyond tracking adherence itself, these devices may also be useful for assessing adherence-related factors, including habit strength []. A habit is an automatic behavior that develops through repeated performance in the same or similar circumstances []. In the case of medication adherence, this might involve taking the same number of pills at a regular time or as part of an established routine. Over time, these recurring environmental or situational cues strengthen the behavior so that it is enacted with little conscious effort and can even occur independently of deliberate intentions []. Because habits are characterized by consistent enactment in response to recurring cues, measures such as variation in pill-taking time may serve as a proxy for habit strength, although the relevant cues may reflect a routine time window or context rather than an exact clock time []. Recent research has shown that both self-reported and objective measures of habit strength are independently correlated with medication adherence [,]. In this study, we scrutinized the relationship between habit strength, measured as pill-timing variance (dose consistency), and medication adherence in a large cardiovascular clinical trial. Noteworthily, dose consistency is a convenient observable regularity [] that represents only one aspect of the medication-taking habit, but habit in general will not always translate into consistency in timing []. Furthermore, the action of opening and closing the digital medication bottle is only a proxy for adherence, because we cannot verify independently whether the dose was actually taken []. Finally, the behavior of participants who know that their adherence patterns are being monitored in the context of a clinical trial may not easily translate into other contexts outside of clinical research owing to the observer effect, which tends to increase the baseline adherence []. The main objective of this analysis was to characterize the relationship between daily medication adherence and daily dose consistency across a longitudinal clinical trial. The secondary aim was to identify patient and contextual factors associated with nonadherence. Recent studies investigating the relationship between objective measures of habit strength and medication adherence have tended to involve few patients [,], use large patient databases compiled from various studies with inconsistent protocols or data collection standards [,], or restrict the analysis to evaluation of correlations between different measures of habit strength and adherence [,]. Although invaluable in establishing a link between these two variables, these studies have been unable to demonstrate a link in the context of a long clinical trial involving a large number of patients and a large number of measured confounders included in a principled regression analysis. In this work, we used 733,490 timepoints from digital bottle caps fixed to study drug bottles used by 2429 patients randomized in the Dapagliflozin in Patients with Myocardial Infarction (DAPA-MI) RCT to understand medication adherence behavior []. In line with European Society for Patient Adherence, Compliance, and Persistence (ESPACOMP) Medication Adherence Reporting Guideline (EMERGE) [], this study focused on the implementation phase of adherence, defined as the extent to which patients’ dosing matched the prescribed regimen. 【Methods】 【DAPA-MI Study Design】 DAPA-MI was a phase 3 registry-based, multicenter, double-blind, placebo-controlled RCT conducted at 103 sites in Sweden and the United Kingdom. DAPA-MI aimed to evaluate the effect of 10 mg of dapagliflozin versus a placebo given once daily, in addition to standard-of-care therapy, on patients hospitalized for MI and with impaired left ventricular systolic function without known diabetes or established heart failure (HF). Included patients were aged ≥18 years, hospitalized for acute MI in Sweden or the United Kingdom, had evidence of regional or global impairment of left ventricular systolic function, were treated with standard therapies according to guidelines for MI management, and were enrolled in the SWEDEHEART (Sweden) or Myocardial Ischaemia National Audit Project (MINAP, UK) registries. The full methodology and the primary outcome of DAPA-MI have been published previously [,]. The study was registered prospectively on September 25, 2020, with ClinicalTrials.gov (NCT04564742), with the first participant enrolled on December 22, 2020. The aim of this post hoc analysis was to characterize the daily medication adherence behavior and dose consistency of patients enrolled in DAPA-MI. The design and execution of this analysis involved no patient or public contribution. 【Study Medication Adherence Tracking】 This analysis used data collected from a digital cap fixed to study drug bottles (CleverCap Lite, Compliance Meds Technologies) issued to all patients enrolled in DAPA-MI during their in-person randomization visit. The digital cap detected and recorded when the bottle was opened and closed, and it automatically uploaded this information to a central portal using a 3G wireless network, where it could be accessed by study and site teams. In line with EMERGE [], this study focused on the implementation phase of adherence, defined as the extent to which patients’ dosing matched the prescribed regimen. Adherence during the implementation phase was operationalized as the proportion of recorded bottle openings consistent with the prescribed dosing schedule. Patients were instructed only to take the study drug once daily, without specification of the time of day. Neither the digital caps nor the mobile app (used by a subset of patients for monthly EQ-5D-5L submissions) issued notifications or reminders to patients if doses were missed. However, unexpected user patterns, including at least four consecutive missed doses, triggered a notification to the study team, who were advised to contact the patient. 【Study Population】 This analysis included all patients originally enrolled into DAPA-MI, with exclusions for patients who withdrew consent, were never treated or never issued a digital cap, never used the digital cap during treatment, or used a pill organizer. 【Ethical Considerations】 DAPA-MI was approved by the Swedish Ethical Review Authority (Dnr 2020-03087, 2021-03037, 2022-00101-02, and 2023-01452-02) and the UK Health Research Authority (reference #20/NW/0312). All patients received information about data collection through the digital cap and provided written informed consent to participate in the trial. Patients who did not consent to secondary analyses were excluded from this analysis. All data used in this analysis were de-identified: patient data were pseudonymized at source, and patient identities were never shared with the researchers; site names and locations were further obscured to the level of country; age was provided to the nearest year at enrollment; and GPS locations were never shared. Participants received no financial compensation for their involvement in the study other than reimbursement of reasonable expenses. No individual participant can be identified in any images of the manuscript or appendix; therefore, no additional consent was required. 【Definition of Adherence and Dose Consistency】 The analysis focused on two quantitative outcome (dependent) variables: adherence and dose consistency (which is a conveniently observable proxy for habit strength). Adherence was a binary variable coded as 1 if a patient opened the digital cap at least once on a given day and 0 if not. Multiple cap openings in a single 24-hour period were labeled as “off-schedule use.” Dose consistency on day n was defined as the absolute value of the difference in the time, tdiff, of the first cap-opening event on day tn and on the previous day |tn − tn–1|. If the cap was not opened on day n–1, then the difference was calculated using the closest available day when an event was recorded (see Table S13 in for example calculations). The dose consistency could be calculated only on the days when the cap was opened and had to be less than 24 hours. This objective habit metric is most closely related to “standard deviation of the day-to-day hour of intake” used by Pironet et al. [], but it uses a mean absolute deviation rather than the standard deviation because the latter can emphasize the influence of outliers. 【Variables and Data Processing】 Most patient variables were collected at the baseline visit. The NYHA functional class and the Canadian Cardiovascular Society (CCS) angina grade were collected at each visit. Patients were also asked to complete the EQ-5D-5L questionnaire every month via a mobile app (Unify []) or the paper-based EQ-5D-3L questionnaire at in-person visits (Sweden only). For these outcomes, the most recent available value collected was used in the analyses. A full list of included variables is shown in Table S1 in . Because the models used categorical variables, covariates with less than 10% of missing values were imputed using the mode (ie, most common level). Variables with higher proportions of missing values were excluded. We used Jamshidian and Jalal’s nonparametric test to evaluate whether the values were missing completely at random (MCAR) [], because most of the variables were not normally distributed. Further refinements to generate the “adherence dataset” included removal of data points outside the DAPA-MI treatment period (ie, date of first dose to date of last dose) from concurrent devices and from superfluous digital cap events. On days when a patient opened the digital cap multiple times, the first event was kept in the dataset and additional events labelled as “off-schedule use,” which was included as an explanatory variable. The “dose consistency” dataset included only data points for which there were no missing events (ie, for which dose consistency, |tn – tn–1|, could be calculated). 【Statistical Analysis】 Univariate comparisons of included and excluded patient populations in the analysis were performed using the chi-squared test for categorical variables, the two-sample t test for continuous variables that had approximately normal distributions, and the Kruskal-Wallis test for continuous variables with nonnormal distributions. To identify factors driving medication adherence and dose consistency, a mixed-effects modeling approach accounted for the repeated measures design of within-patient data points grouped within site. Two levels of random intercept were used, patient and site, which were coded to be explicitly nested because patients were not permitted to participate in multiple studies at the same time. The adherence model was a logistic mixed-effects regression model with a binary adherence indicator for each patient-day and with the fixed-effects listed in Table S1 in . Because dose consistency was quantified as a time interval with a nonnormal distribution, two separate transformations were used to normalize it: (1) a logarithmic transformation, yn = ln(|tn – tn–1| + 1 second), which implies a straightforward multiplicative interpretation of the model coefficients, and (2) a Box–Cox transformation, which enables fulfilment of the modeling assumptions of normality and homoscedasticity. Both transformed variables were then used in linear mixed-effects regression models, with the fixed effects listed in Table S1 in . All except two independent variables overlapped between the adherence and dose consistency models. “Average time variance in the past 7 days” was used as an independent variable in the adherence model to test the relationship between average dose consistency and adherence. For the same reason, the dose consistency model included the “after a missed dose” variable that quantified proximity to short or long nonadherence streaks. In some cases, data collection via the digital cap was temporarily suspended by the physician, usually owing to a patient holiday; these periods were defined as “data gaps.” Analyses were performed using Python version 3.9.5 [], R version 4.1.0 (R Foundation for Statistical Computing) [], and the glmmTMB 1.1.9 library in R []. Public holidays were calculated using the workalendar 16.4.0 library, and statistical models were fitted using the glmmTMB 1.1.9 library in R. We used the MCAR test implementation found in the mice 3.19.0 package in R []. Additional analyses (as reported in ) included adherence and dose consistency according to EQ-5D-3L/EQ-5D-5L anxiety responses, a comparison of patients who established early high consistency versus those with early lower consistency, and a sensitivity analysis for the dose consistency model using Box–Cox-transformed data. 【Results】 【Patient Characteristics】 Of 4017 patients randomized in DAPA-MI, 2429 (60.5%) were included in this analysis (). After dataset refinement, 733,490 patient-days were included in the adherence dataset and 682,731 patient-days of cap-opening events (one event per day) in the dose consistency dataset (Figure S1 in ). Patient characteristics are summarized in . The Anderson-Darling rank test statistic was 4.3 and median P=.15 for the Jamshidian and Jalal’s nonparametric MCAR test [], showing insufficient evidence to reject MCAR at the .05 significance level. Univariate comparisons identified differences between included and excluded patients in country, race, height, NYHA functional class and CCS angina grade at the final assessment, and number of missing cap-opening events (eg, due to use of pill organizers), but otherwise, the groups were comparable. Flow chart showing selection of the analysis population from the DAPA-MI trial and derivation of the digital cap datasets used for adherence and dose consistency analyses. Of 4017 randomized patients, 2429 were included after excluding those who withdrew consent, were never treated, were not issued or did not use a digital cap during treatment, used a pill organizer, or did not consent to secondary analyses. After data cleaning, including removal of records outside the treatment period, from concurrent devices, and superfluous cap events, the adherence dataset comprised 733,490 patient-days and the dose consistency dataset comprised 682,731 cap-opening events. Dose consistency (tdiff) was based on the absolute difference in timing between cap-opening events on consecutive days. DAPA-MI: Dapagliflozin in Patients with Myocardial Infarction; tdiff: time difference between cap-opening events on consecutive days. Characteristics of the overall study population and comparison of patients included versus excluded from the analysis.a aAll characteristics were recorded at baseline, unless otherwise specified. A high proportion of patients excluded from analysis were users of pill organizers (n=1267, 32.3%; see for details). bP values are from univariate comparisons between included and excluded groups. cChi-squared test for categorical variables. dNot applicable. eTwo-sample t test for approximately normally distributed continuous variables. fNYHA: New York Heart Association. gThe NYHA functional class and CCS angina grade were measured several times during the study; analyses were conducted using the most recent value collected. hGiven the small number of patients with class III/IV disease, these two categories were merged. iCCS: Canadian Cardiovascular Society. jKruskal-Wallis test for nonnormally distributed continuous variables. 【Overall Adherence and Dose Consistency】 Among the 2429 patients, overall adherence (median [IQR] of average adherence values for each included patient) was 96.7% (91.4%-98.9%). These findings represent implementation phase adherence outcomes according to the EMERGE framework [], as measured by recorded bottle openings compared with the prescribed dosing regimen. The median time difference in adherence on consecutive days (dose consistency) was 34.7 (IQR 11.8-83.3) minutes (Table S2 in ). There was a wide variety of behavioral adherence patterns exhibited by patients in terms of adherence and timing (). Illustrative plots showing individual digital cap–recorded medication-taking patterns over time in selected patients from the DAPA-MI trial. Patients with 400-500 days of trial participation (to make the traces more visually comparable) were ranked by average dose consistency, divided into deciles, and one patient was selected from each decile for display. Blue dots indicate cap-opening events, with consecutive events connected by blue lines; yellow dots on the horizontal axis indicate days with no recorded cap opening. The percentage shown in the bottom-right corner of each panel indicates