Lessons From 3 Longitudinal Sensor-Based Human Behavior Assessment Field Studies and an Approach to Support Stakeholder Management: Content Analysis.

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Johanna Kallio, Atte Kinnula, Satu-Marja Mäkelä, Sari Järvinen, Pauli Räsänen, Simo Hosio, Miguel Bordallo López
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引用次数: 0

Abstract

Background: Pervasive technologies are used to investigate various phenomena outside the laboratory setting, providing valuable insights into real-world human behavior and interaction with the environment. However, conducting longitudinal field trials in natural settings remains challenging due to factors such as low recruitment success and high dropout rates due to participation burden or data quality issues with wireless sensing in changing environments.

Objective: This study gathers insights and lessons from 3 real-world longitudinal field studies assessing human behavior and derives factors that impacted their research success. We aim to categorize challenges, observe how they were managed, and offer recommendations for designing and conducting studies involving human participants and pervasive technology in natural settings.

Methods: We developed a qualitative coding framework to categorize and address the unique challenges encountered in real-life studies related to influential factor identification, stakeholder management, data harvesting and management, and analysis and interpretation. We applied inductive reasoning to identify issues and related mitigation actions in 3 separate field studies carried out between 2018 and 2022. These 3 field studies relied on gathering annotated sensor data. The topics involved stress and environmental assessment in an office and a school, collecting self-reports and wrist device and environmental sensor data from 27 participants for 3.5 to 7 months; work activity recognition at a construction site, collecting observations and wearable sensor data from 15 participants for 3 months; and stress recognition in location-independent knowledge work, collecting self-reports and computer use data from 57 participants for 2 to 5 months. Our key extension for the coding framework used a stakeholder identification method to identify the type and role of the involved stakeholder groups, evaluating the nature and degree of their involvement and influence on the field trial success.

Results: Our analysis identifies 17 key lessons related to planning, implementing, and managing a longitudinal, sensor-based field study on human behavior. The findings highlight the importance of recognizing different stakeholder groups, including those not directly involved but whose areas of responsibility are impacted by the study and therefore have the power to influence it. In general, customizing communication strategies to engage stakeholders on their terms and addressing their concerns and expectations is essential, while planning for dropouts, offering incentives for participants, conducting field tests to identify problems, and using tools for quality assurance are relevant for successful outcomes.

Conclusions: Our findings suggest that field trial implementation should include additional effort to clarify the expectations of stakeholders and to communicate with them throughout the process. Our framework provides a structured approach that can be adopted by other researchers in the field, facilitating robust and comparable studies across different contexts. Constantly managing the possible challenges will lead to better success in longitudinal field trials and developing future technology-based solutions.

从 3 项基于传感器的纵向人类行为评估实地研究中汲取的经验教训以及支持利益相关者管理的方法:内容分析。
背景:普适技术被用于研究实验室环境之外的各种现象,为真实世界中的人类行为以及与环境的互动提供了宝贵的见解。然而,在自然环境中开展纵向实地试验仍具有挑战性,原因包括招募成功率低、参与负担导致的辍学率高,以及在不断变化的环境中无线传感的数据质量问题等:本研究从 3 项评估人类行为的真实世界纵向实地研究中收集见解和经验,并得出影响研究成功的因素。我们旨在对挑战进行分类,观察如何应对这些挑战,并为在自然环境中设计和开展涉及人类参与者和普适技术的研究提供建议:我们开发了一个定性编码框架,用于对现实研究中遇到的与影响因素识别、利益相关者管理、数据采集和管理以及分析和解释相关的独特挑战进行分类和处理。我们在 2018 年至 2022 年期间开展的 3 项独立实地研究中,运用归纳推理法确定问题和相关缓解行动。这 3 项实地研究依赖于收集带注释的传感器数据。研究主题包括:在办公室和学校进行压力和环境评估,收集 27 名参与者为期 3.5 至 7 个月的自我报告以及腕部设备和环境传感器数据;在建筑工地进行工作活动识别,收集 15 名参与者为期 3 个月的观察结果和可穿戴传感器数据;以及在不受地点限制的知识工作中进行压力识别,收集 57 名参与者为期 2 至 5 个月的自我报告和计算机使用数据。我们对编码框架的关键扩展采用了利益相关者识别方法,以确定所涉及的利益相关者群体的类型和作用,评估其参与的性质和程度以及对实地试验成功的影响:我们的分析确定了与规划、实施和管理基于传感器的人类行为纵向实地研究相关的 17 项关键经验。研究结果强调了认识不同利益相关者群体的重要性,其中包括那些没有直接参与但其职责范围受到研究影响、因而有能力影响研究的利益相关者群体。一般来说,根据利益相关者的条件定制沟通策略,让他们参与进来,并解决他们所关心的问题和期望,这一点至关重要,而为辍学制定计划、为参与者提供激励措施、进行实地测试以发现问题,以及使用质量保证工具,这些都与取得成功结果息息相关:我们的研究结果表明,实地试验的实施应包括更多努力,以明确利益相关者的期望,并在整个过程中与他们沟通。我们的框架提供了一种结构化方法,可供该领域的其他研究人员采用,有助于在不同情况下开展稳健、可比的研究。不断应对可能出现的挑战将有助于纵向实地试验取得更大成功,并有助于开发未来的技术解决方案。
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来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
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