Background: Multiple chronic conditions (multimorbidity) are becoming more prevalent among aging populations. Digital health technologies have the potential to assist in the self-management of multimorbidity, improving the awareness and monitoring of health and well-being, supporting a better understanding of the disease, and encouraging behavior change. Objective: The aim of this study was to analyze how 60 older adults (mean age 74, SD 6.4; range 65-92 years) with multimorbidity engaged with digital symptom and well-being monitoring when using a digital health platform over a period of approximately 12 months. Methods: Principal component analysis and clustering analysis were used to group participants based on their levels of engagement, and the data analysis focused on characteristics (eg, age, sex, and chronic health conditions), engagement outcomes, and symptom outcomes of the different clusters that were discovered. Results: Three clusters were identified: the typical user group, the least engaged user group, and the highly engaged user group. Our findings show that age, sex, and the types of chronic health conditions do not influence engagement. The 3 primary factors influencing engagement were whether the same device was used to submit different health and well-being parameters, the number of manual operations required to take a reading, and the daily routine of the participants. The findings also indicate that higher levels of engagement may improve the participants’ outcomes (eg, reduce symptom exacerbation and increase physical activity). Conclusions: The findings indicate potential factors that influence older adult engagement with digital health technologies for home-based multimorbidity self-management. The least engaged user groups showed decreased health and well-being outcomes related to multimorbidity self-management. Addressing the factors highlighted in this study in the design and implementation of home-based digital health technologies may improve symptom management and physical activity outcomes for older adults self-managing multimorbidity.

中文翻译：

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使用定性数据增强 K 均值聚类，以发现患有多种疾病的老年人在使用数字健康技术时的参与模式：概念验证试验

背景：多种慢性病（多重发病）在老龄化人群中变得越来越普遍。数字健康技术有潜力协助多种疾病的自我管理，提高对健康和福祉的认识和监测，支持更好地了解疾病，并鼓励行为改变。目的：本研究的目的是分析 60 名患有多种疾病的老年人（平均年龄 74，SD 6.4；范围 65-92 岁）在大约一段时间内使用数字健康平台时如何进行数字症状和健康监测12个月。方法：使用主成分分析和聚类分析根据参与者的参与程度对参与者进行分组，数据分析侧重于不同集群的特征（例如年龄、性别和慢性健康状况）、参与结果和症状结果那些被发现的。结果：确定了三个集群：典型用户组、参与度最低的用户组和参与度高的用户组。我们的研究结果表明，年龄、性别和慢性健康状况的类型不会影响参与度。影响参与度的 3 个主要因素是是否使用同一设备提交不同的健康和福祉参数、读取读数所需的手动操作次数以及参与者的日常生活。研究结果还表明，更高水平的参与可能会改善参与者的结果（例如，减少症状恶化并增加体力活动）。结论：研究结果表明影响老年人参与数字健康技术进行家庭多病自我管理的潜在因素。参与度最低的用户群体表现出与多病自我管理相关的健康和福祉结果下降。在家庭数字健康技术的设计和实施中解决本研究中强调的因素可能会改善老年人自我管理多种疾病的症状管理和身体活动结果。