Trajectories of Depression: Investigating the Correlation between Human Mobility Patterns and Mental Health Problems by means of Smartphones

抑郁症的轨迹：通过智能手机研究人类移动模式与心理健康问题之间的相关性

• 批准号: EP/L006340/1
• 负责人: Mirco Musolesi
• 金额: $ 12.08万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FL006340%2F1
• 关键词: Trajectories; Depression; Investigating; Correlation; between

Depression does not only affect the personal life of individuals and their families and social circles but it has also a strongly negative economic impact as shown in several reports. According to a recent study, workers in the United Kingdom suffer high levels of depression than those anywhere else in Europe. The survey found that 1 in 10 employees had taken time off at some point in their working lives because of depression problems. Novel strategies for tackling the problem of depression and preventing suicides are needed. We believe that new emerging technologies, in particular mobile ones, together with the possibility of mining large amount of data in real-time can help to tackle this problem in new and more effective ways. Existing interview-based studies have shown that depression is significantly associated with a marked decline of physical activity. The goal of this project is to investigate how mobile phones can be used to collect and analyse mobility patterns of individuals in order to understand how mental health problems affect their daily routines and behaviour and how potential changes can be automatically detected. In particular, mobility patterns and levels of activity can be quantitatively measured by means of mobile phones, exploiting the GPS receiver and the accelerometers embedded in the devices. The data can be extremely helpful to understand the behaviour of a depressed person, and in particular, to detect potential changes in his or her behaviour, which might be linked to a worsening depressive state. By monitoring this information in real-time, health officers and charity workers might intervene by means of digital behaviour intervention delivered through mobile phones or by means of traditional methods such as by inviting the person for a meeting or by calling him or her by phone.In order to support these novel applications, it is necessary to build mathematical tools for analysing the mobility traces in real-time for the detection of gradual or sudden changes related to the emotional states of the individual. More specifically, we plan to devise analytical techniques for studying the relationships between human mobility patterns and emotional states. We plan to use existing datasets of human mobility and to collect data by means of a smartphone application distributed to people affected by depression. This can be considered as a sort of pilot study for a wider deployment of these technologies and it will provide a sound theoretical basis for further studies in this area. Finally, a key aspect of the proposed research work is the implementation of mechanisms for preserving the privacy of the individuals involved in the study.

正如多份报告所示，抑郁症不仅影响个人及其家人和社交圈的个人生活，还会对经济产生强烈的负面影响。根据最近的一项研究，英国的工人比欧洲其他任何地方的工人都患抑郁症。调查发现，十分之一的员工在工作生涯中的某个时候因为抑郁问题而休假。需要解决抑郁症问题和预防自杀的新策略。我们相信，新兴技术，特别是移动的技术，以及实时挖掘大量数据的可能性，可以帮助以新的和更有效的方式解决这一问题。现有的基于访谈的研究表明，抑郁症与身体活动的显着下降显着相关。该项目的目标是研究如何使用移动的电话收集和分析个人的移动模式，以了解心理健康问题如何影响他们的日常生活和行为，以及如何自动检测潜在的变化。特别是，移动模式和活动水平可以通过移动的电话，利用GPS接收器和嵌入在设备中的加速度计进行定量测量。这些数据对于了解抑郁症患者的行为非常有帮助，特别是检测他或她的行为的潜在变化，这可能与抑郁状态恶化有关。通过实时监测这些信息，卫生官员和慈善工作者可以通过移动的电话提供数字行为干预或通过传统方法进行干预，例如邀请该人参加会议或通过电话给他或她打电话。有必要建立用于实时分析移动轨迹的数学工具，以检测与个人情绪状态相关的逐渐或突然变化。更具体地说，我们计划设计分析技术来研究人类流动模式和情绪状态之间的关系。我们计划使用现有的人类移动数据集，并通过分发给受抑郁症影响的人的智能手机应用程序收集数据。这可以被视为更广泛地部署这些技术的一种试点研究，它将为这一领域的进一步研究提供良好的理论基础。最后，拟议研究工作的一个关键方面是实施保护参与研究的个人隐私的机制。

项目成果

Graph input representations for machine learning applications in urban network analysis

城市网络分析中机器学习应用的图形输入表示

• DOI: 10.1177/2399808319892599
• 发表时间: 2019
• 期刊: Urban Analytics and City Science
• 作者: Pagani A

EPB892599 Supplemental Material - Supplemental material for Graph input representations for machine learning applications in urban network analysis

EPB892599 补充材料 - 城市网络分析中机器学习应用的图形输入表示的补充材料

• DOI: 10.25384/sage.11948520
• 发表时间: 2020
• 作者: Pagani A

Evaluating Machine Learning Algorithms for Prediction of the Adverse Valence Index Based on the Photographic Affect Meter

基于摄影影响计评估用于预测不良效价指数的机器学习算法

• DOI: 10.1145/3325426.3329948
• 发表时间: 2019
• 作者: Mikelsons G