VTT Technical Research Centre of Finland Ltd, Oulu, Finland.
Center for Machine Vision and Signal Analysis (CMVS), Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland.
J Med Internet Res. 2024 Oct 31;26:e50461. doi: 10.2196/50461.
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.
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.
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.
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.
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.
普及技术被用于研究实验室环境之外的各种现象，为研究真实世界中的人类行为以及人与环境的相互作用提供了有价值的见解。然而，由于招募成功率低以及参与者负担过重或无线传感器在不断变化的环境中数据质量等问题导致的高辍学率，在自然环境中进行纵向现场试验仍然具有挑战性。
本研究从 3 项评估人类行为的真实纵向现场研究中收集了见解和经验教训，并得出了影响其研究成功的因素。我们旨在对挑战进行分类，观察它们是如何被管理的，并为涉及自然环境中人类参与者和普及技术的研究设计和开展提供建议。
我们开发了一个定性编码框架，对与有影响力的因素识别、利益相关者管理、数据采集和管理以及分析和解释相关的真实研究中遇到的独特挑战进行分类和处理。我们应用归纳推理在 2018 年至 2022 年期间进行的 3 项独立现场研究中识别问题和相关缓解措施。这 3 项现场研究依赖于采集带注释的传感器数据。研究主题涉及办公室和学校的压力和环境评估、从 27 名参与者处采集 3.5 至 7 个月的自我报告和腕部设备及环境传感器数据、建筑工地的工作活动识别、从 15 名参与者处采集 3 个月的观察和可穿戴传感器数据以及位置独立知识工作中的压力识别、从 57 名参与者处采集 2 至 5 个月的自我报告和计算机使用数据。我们的编码框架的主要扩展是使用利益相关者识别方法来识别所涉及的利益相关者群体的类型和角色，评估他们参与的性质和程度以及他们对现场试验成功的影响。
我们的分析确定了 17 条与规划、实施和管理基于传感器的人类行为纵向现场研究相关的关键经验教训。研究结果强调了识别不同利益相关者群体的重要性，包括那些虽然不直接参与但因研究而影响其责任领域并因此有能力影响研究的利益相关者群体。一般来说，定制沟通策略以满足利益相关者的需求并解决他们的关切和期望至关重要，而规划辍学、为参与者提供激励、进行现场测试以识别问题以及使用质量保证工具对于取得成功的结果也是相关的。
我们的研究结果表明，现场试验的实施应该包括额外的努力来澄清利益相关者的期望，并在整个过程中与他们进行沟通。我们的框架提供了一种结构化的方法，可以被该领域的其他研究人员采用，从而在不同的背景下进行稳健且可比的研究。不断管理可能出现的挑战将有助于纵向现场试验取得更好的成果，并为未来基于技术的解决方案的发展奠定基础。
