mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study
mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study
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DOI：
10.1136/bmjopen-2019-034723
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发表时间：
2020-01-01
期刊：
影响因子：
2.9
通讯作者：
Lyles, Courtney R.
中科院分区：
文献类型：
--
作者：
Aguilera, Adrian;Figueroa, Caroline A.;Lyles, Courtney R.
抑郁症和糖尿病是高度致残性疾病，患病率和合并症发生率高，特别是在低收入的少数民族患者中。虽然合并症增加了不良结局和死亡率的风险，但大多数临床干预措施分别针对这些疾病。增加体力活动可能有效地同时降低抑郁症状和改善血糖控制。自我管理应用程序是一种具有成本效益，可扩展且易于访问的治疗方法，可以增加身体活动。然而，尖端技术的应用往往无法惠及弱势群体，也不适合个人的行为和特点。使用机器学习方法定制干预措施可能会提高干预措施的有效性。方法和分析在一项三臂随机对照试验中，我们将检查短信智能手机应用程序对鼓励患有糖尿病和抑郁症共病的低收入少数族裔患者进行体育活动的影响。自适应干预组接收通过强化学习算法从不同消息库选择的消息。均匀随机干预组接收相同的消息，但以相等的概率从消息库中选择。控制组每周接收一次情绪信息。我们的目标是从初级保健诊所招募276名年龄在18-75岁的成年人，他们被诊断患有当前糖尿病并表现出抑郁症状升高（患者健康问卷抑郁量表-8（PHQ-8）>5）。我们将比较被动收集的每日步数、自我报告的PHQ-8和来自基线和6个月随访时干预完成时医疗记录的最新血红蛋白A1 c。加州大学弗朗西斯科分校的机构审查委员会批准了本研究（IRB：17-22608）。我们计划提交描述我们的用户设计方法和自适应学习算法测试的手稿，并将提交试验结果，以便在同行评审期刊上发表，并在（国际）国家科学会议上发表。
Introduction Depression and diabetes are highly disabling diseases with a high prevalence and high rate of comorbidity, particularly in low-income ethnic minority patients. Though comorbidity increases the risk of adverse outcomes and mortality, most clinical interventions target these diseases separately. Increasing physical activity might be effective to simultaneously lower depressive symptoms and improve glycaemic control. Self-management apps are a cost-effective, scalable and easy access treatment to increase physical activity. However, cutting-edge technological applications often do not reach vulnerable populations and are not tailored to an individual's behaviour and characteristics. Tailoring of interventions using machine learning methods likely increases the effectiveness of the intervention. Methods and analysis In a three-arm randomised controlled trial, we will examine the effect of a text-messaging smartphone application to encourage physical activity in low-income ethnic minority patients with comorbid diabetes and depression. The adaptive intervention group receives messages chosen from different messaging banks by a reinforcement learning algorithm. The uniform random intervention group receives the same messages, but chosen from the messaging banks with equal probabilities. The control group receives a weekly mood message. We aim to recruit 276 adults from primary care clinics aged 18-75 years who have been diagnosed with current diabetes and show elevated depressive symptoms (Patient Health Questionnaire depression scale-8 (PHQ-8) >5). We will compare passively collected daily step counts, self-report PHQ-8 and most recent haemoglobin A1c from medical records at baseline and at intervention completion at 6-month follow-up. Ethics and dissemination The Institutional Review Board at the University of California San Francisco approved this study (IRB: 17-22608). We plan to submit manuscripts describing our user-designed methods and testing of the adaptive learning algorithm and will submit the results of the trial for publication in peer-reviewed journals and presentations at (inter)-national scientific meetings.