PDF(Pubmed)
Abstract：
BACKGROUND: Hypoglycemia threatens cognitive function and driving safety. Previous research investigated in-vehicle voice assistants as hypoglycemia warnings. However, they could startle drivers. To address this, we combine voice warnings with ambient LEDs.
OBJECTIVE: The study assesses the effect of in-vehicle multimodal warning on emotional reaction and technology acceptance among drivers with type 1 diabetes.
METHODS: Two studies were conducted, one in simulated driving and the other in real-world driving. A quasi-experimental design included 2 independent variables (blood glucose phase and warning modality) and 1 main dependent variable (emotional reaction). Blood glucose was manipulated via intravenous catheters, and warning modality was manipulated by combining a tablet voice warning app and LEDs. Emotional reaction was measured physiologically via skin conductance response and subjectively with the Affective Slider and tested with a mixed-effect linear model. Secondary outcomes included self-reported technology acceptance. Participants were recruited from Bern University Hospital, Switzerland.
RESULTS: The simulated and real-world driving studies involved 9 and 10 participants with type 1 diabetes, respectively. Both studies showed significant results in self-reported emotional reactions (P<.001). In simulated driving, neither warning modality nor blood glucose phase significantly affected self-reported arousal, but in real-world driving, both did (F2,68=4.3; P<.05 and F2,76=4.1; P=.03). Warning modality affected self-reported valence in simulated driving (F2,68=3.9; P<.05), while blood glucose phase affected it in real-world driving (F2,76=9.3; P<.001). Skin conductance response did not yield significant results neither in the simulated driving study (modality: F2,68=2.46; P=.09, blood glucose phase: F2,68=0.3; P=.74), nor in the real-world driving study (modality: F2,76=0.8; P=.47, blood glucose phase: F2,76=0.7; P=.5). In both simulated and real-world driving studies, the voice+LED warning modality was the most effective (simulated: mean 3.38, SD 1.06 and real-world: mean 3.5, SD 0.71) and urgent (simulated: mean 3.12, SD 0.64 and real-world: mean 3.6, SD 0.52). Annoyance varied across settings. The standard warning modality was the least effective (simulated: mean 2.25, SD 1.16 and real-world: mean 3.3, SD 1.06) and urgent (simulated: mean 1.88, SD 1.55 and real-world: mean 2.6, SD 1.26) and the most annoying (simulated: mean 2.25, SD 1.16 and real-world: mean 1.7, SD 0.95). In terms of preference, the voice warning modality outperformed the standard warning modality. In simulated driving, the voice+LED warning modality (mean rank 1.5, SD rank 0.82) was preferred over the voice (mean rank 2.2, SD rank 0.6) and standard (mean rank 2.4, SD rank 0.81) warning modalities, while in real-world driving, the voice+LED and voice warning modalities were equally preferred (mean rank 1.8, SD rank 0.79) to the standard warning modality (mean rank 2.4, SD rank 0.84).
CONCLUSIONS: Despite the mixed results, this paper highlights the potential of implementing voice assistant-based health warnings in cars and advocates for multimodal alerts to enhance hypoglycemia management while driving.
BACKGROUND: ClinicalTrials.gov NCT05183191; https://classic.clinicaltrials.gov/ct2/show/NCT05183191, ClinicalTrials.gov NCT05308095; https://classic.clinicaltrials.gov/ct2/show/NCT05308095.
OBJECTIVE: The study assesses the effect of in-vehicle multimodal warning on emotional reaction and technology acceptance among drivers with type 1 diabetes.
METHODS: Two studies were conducted, one in simulated driving and the other in real-world driving. A quasi-experimental design included 2 independent variables (blood glucose phase and warning modality) and 1 main dependent variable (emotional reaction). Blood glucose was manipulated via intravenous catheters, and warning modality was manipulated by combining a tablet voice warning app and LEDs. Emotional reaction was measured physiologically via skin conductance response and subjectively with the Affective Slider and tested with a mixed-effect linear model. Secondary outcomes included self-reported technology acceptance. Participants were recruited from Bern University Hospital, Switzerland.
RESULTS: The simulated and real-world driving studies involved 9 and 10 participants with type 1 diabetes, respectively. Both studies showed significant results in self-reported emotional reactions (P<.001). In simulated driving, neither warning modality nor blood glucose phase significantly affected self-reported arousal, but in real-world driving, both did (F2,68=4.3; P<.05 and F2,76=4.1; P=.03). Warning modality affected self-reported valence in simulated driving (F2,68=3.9; P<.05), while blood glucose phase affected it in real-world driving (F2,76=9.3; P<.001). Skin conductance response did not yield significant results neither in the simulated driving study (modality: F2,68=2.46; P=.09, blood glucose phase: F2,68=0.3; P=.74), nor in the real-world driving study (modality: F2,76=0.8; P=.47, blood glucose phase: F2,76=0.7; P=.5). In both simulated and real-world driving studies, the voice+LED warning modality was the most effective (simulated: mean 3.38, SD 1.06 and real-world: mean 3.5, SD 0.71) and urgent (simulated: mean 3.12, SD 0.64 and real-world: mean 3.6, SD 0.52). Annoyance varied across settings. The standard warning modality was the least effective (simulated: mean 2.25, SD 1.16 and real-world: mean 3.3, SD 1.06) and urgent (simulated: mean 1.88, SD 1.55 and real-world: mean 2.6, SD 1.26) and the most annoying (simulated: mean 2.25, SD 1.16 and real-world: mean 1.7, SD 0.95). In terms of preference, the voice warning modality outperformed the standard warning modality. In simulated driving, the voice+LED warning modality (mean rank 1.5, SD rank 0.82) was preferred over the voice (mean rank 2.2, SD rank 0.6) and standard (mean rank 2.4, SD rank 0.81) warning modalities, while in real-world driving, the voice+LED and voice warning modalities were equally preferred (mean rank 1.8, SD rank 0.79) to the standard warning modality (mean rank 2.4, SD rank 0.84).
CONCLUSIONS: Despite the mixed results, this paper highlights the potential of implementing voice assistant-based health warnings in cars and advocates for multimodal alerts to enhance hypoglycemia management while driving.
BACKGROUND: ClinicalTrials.gov NCT05183191; https://classic.clinicaltrials.gov/ct2/show/NCT05183191, ClinicalTrials.gov NCT05308095; https://classic.clinicaltrials.gov/ct2/show/NCT05308095.
摘要：
背景：低血糖威胁认知功能和驾驶安全。先前的研究调查了车载语音助手作为低血糖警告。然而,他们可能会吓到司机。为了解决这个问题，我们将语音警告与环境LED相结合。
目的：该研究评估了车载多模式警告对1型糖尿病驾驶员的情绪反应和技术接受的影响。
方法：进行了两项研究，一个是模拟驾驶，另一个是真实世界驾驶。准实验设计包括2个独立变量（血糖阶段和警告模式）和1个主要因变量（情绪反应）。通过静脉导管控制血糖，通过结合平板电脑语音警告应用程序和LED来操纵警告方式。通过皮肤电导反应和情感滑块主观测量情绪反应，并用混合效应线性模型进行测试。次要结果包括自我报告的技术接受度。参与者从伯尔尼大学医院招募，瑞士。
结果：模拟和真实世界驾驶研究涉及9和10名1型糖尿病患者，分别。两项研究都显示了自我报告的情绪反应的显着结果（P<.001）。在模拟驾驶中,警告方式和血糖阶段都不会显着影响自我报告的唤醒，但在现实世界的驾驶中,两者都有（F2,68=4.3；P<.05和F2,76=4.1；P=.03）。警告模态影响模拟驾驶中的自我报告效价(F2,68=3.9；P<.05)，而血糖阶段在现实世界驾驶中影响它(F2,76=9.3;P<.001)。在模拟驾驶研究中，皮肤电导反应均未产生显着结果（模态：F2,68=2.46；P=.09，血糖阶段：F2,68=0.3；P=.74），在现实世界驾驶研究中也没有（模态：F2,76=0.8；P=.47，血糖阶段：F2,76=0.7；P=.5）。在模拟和真实世界的驾驶研究中，语音+LED警告模式是最有效的(模拟:平均3.38,SD1.06和真实世界:平均3.5,SD0.71)和紧急(模拟:平均3.12,SD0.64和真实世界:平均3.6,SD0.52)。烦恼因设置而异。标准警告方式效果最低（模拟：平均2.25，SD1.16和现实世界：平均3.3，SD1.06）和紧急（模拟：平均1.88，SD1.55和现实世界：平均2.6，SD1.26）和最烦人（模拟：平均2.25，SD1.16和现实世界：平均1.7，SD0.95）。在偏好方面,语音警告模式优于标准警告模式。在模拟驾驶中,语音+LED警告模式（平均等级1.5，SD等级0.82）优于语音（平均等级2.2，SD等级0.6）和标准（平均等级2.4，SD等级0.81）警告模式，在现实世界中驾驶，语音+LED和语音警告模式(平均等级为1.8,SD等级为0.79)优于标准警告模式(平均等级为2.4,SD等级为0.84).
结论：尽管结果参差不齐，本文重点介绍了在汽车中实施基于语音助手的健康警告的潜力,并倡导多模式警报以加强驾驶时的低血糖管理.
背景：ClinicalTrials.govNCT05183191;https://classic.clinicaltrials.gov/ct2/show/NCT05183191,ClinicalTrials.govNCT05308095;https://classic.clinicaltrials.gov/ct2/show/NCT05308095.
目的：该研究评估了车载多模式警告对1型糖尿病驾驶员的情绪反应和技术接受的影响。
方法：进行了两项研究，一个是模拟驾驶，另一个是真实世界驾驶。准实验设计包括2个独立变量（血糖阶段和警告模式）和1个主要因变量（情绪反应）。通过静脉导管控制血糖，通过结合平板电脑语音警告应用程序和LED来操纵警告方式。通过皮肤电导反应和情感滑块主观测量情绪反应，并用混合效应线性模型进行测试。次要结果包括自我报告的技术接受度。参与者从伯尔尼大学医院招募，瑞士。
结果：模拟和真实世界驾驶研究涉及9和10名1型糖尿病患者，分别。两项研究都显示了自我报告的情绪反应的显着结果（P<.001）。在模拟驾驶中,警告方式和血糖阶段都不会显着影响自我报告的唤醒，但在现实世界的驾驶中,两者都有（F2,68=4.3；P<.05和F2,76=4.1；P=.03）。警告模态影响模拟驾驶中的自我报告效价(F2,68=3.9；P<.05)，而血糖阶段在现实世界驾驶中影响它(F2,76=9.3;P<.001)。在模拟驾驶研究中，皮肤电导反应均未产生显着结果（模态：F2,68=2.46；P=.09，血糖阶段：F2,68=0.3；P=.74），在现实世界驾驶研究中也没有（模态：F2,76=0.8；P=.47，血糖阶段：F2,76=0.7；P=.5）。在模拟和真实世界的驾驶研究中，语音+LED警告模式是最有效的(模拟:平均3.38,SD1.06和真实世界:平均3.5,SD0.71)和紧急(模拟:平均3.12,SD0.64和真实世界:平均3.6,SD0.52)。烦恼因设置而异。标准警告方式效果最低（模拟：平均2.25，SD1.16和现实世界：平均3.3，SD1.06）和紧急（模拟：平均1.88，SD1.55和现实世界：平均2.6，SD1.26）和最烦人（模拟：平均2.25，SD1.16和现实世界：平均1.7，SD0.95）。在偏好方面,语音警告模式优于标准警告模式。在模拟驾驶中,语音+LED警告模式（平均等级1.5，SD等级0.82）优于语音（平均等级2.2，SD等级0.6）和标准（平均等级2.4，SD等级0.81）警告模式，在现实世界中驾驶，语音+LED和语音警告模式(平均等级为1.8,SD等级为0.79)优于标准警告模式(平均等级为2.4,SD等级为0.84).
结论：尽管结果参差不齐，本文重点介绍了在汽车中实施基于语音助手的健康警告的潜力,并倡导多模式警报以加强驾驶时的低血糖管理.
背景：ClinicalTrials.govNCT05183191;https://classic.clinicaltrials.gov/ct2/show/NCT05183191,ClinicalTrials.govNCT05308095;https://classic.clinicaltrials.gov/ct2/show/NCT05308095.
