Computer Mouse Movements as an Indicator of Work Stress: Longitudinal Observational Field Study

Banholzer, Nicolas; Feuerriegel, Stefan; Fleisch, Elgar; Bauer, Georg F.; Kowatsch, Tobias

doi:10.2196/27121

Cited by 12 publications

(7 citation statements)

References 34 publications

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“…stress based on keystroke features in a laboratory study [12]. Although Banholzer et al [13], for example, followed the participants in real life for seven weeks, most of the computer usage studies on stress have been carried out in laboratories [14,15] or have lasted only a short time [11,16]. We observed location independent knowledge workers for five months to explore whether group-level data on mouse and keyboard usage and daily self-reports of stress could be useful as a basis for a long-term assessment of stress in real-life work.…”

Section: Different Methods To Detect Stress In Knowledge Workers: a P...mentioning

confidence: 97%

Different Methods to Detect Stress in Knowledge Workers: A Pilot Study for Long-term Monitoring

VANTTOLA,

VILDJIOUNAITE,

SALLINEN

et al. 2024

J UOEH

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Stress is a common part of working life, but knowledge is lacking on how to identify it early and with little effort on the part of the employee. We investigated whether simple stress reports and computer usage data could be useful tools for long-term assessment of stress in real life. 38 experts responded to a baseline questionnaire on need for recovery (NFR) and psychological distress (General Health Questionnaire, GHQ12). Their computer usage for work was recorded for 5 months, during which they filled in a 4-month simple diary and a 2-week detailed diary on, for example, stress and productivity. Salivary cortisol and heart rate variability were collected on 3 consecutive days. Generalized estimating equations models were used for the analyses. High NFR and GHQ12 predicted self-reported stress during work, and a decrease in (some) mouse usage features, but not keyboard usage features, over the following months. Some mouse usage features were associated with stress and productivity. The results provide some support for the usefulness of simple stress questions and mouse usage features in assessing long-term stress in real life.

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Section: Different Methods To Detect Stress In Knowledge Workers: a P...mentioning

confidence: 97%

Different Methods to Detect Stress in Knowledge Workers: A Pilot Study for Long-term Monitoring

VANTTOLA,

VILDJIOUNAITE,

SALLINEN

et al. 2024

J UOEH

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“…The emotions of the driver were assessed according to the circumplex model of affect, which is composed of the 2 dimensions arousal and valence [ 33 , 34 ]. The arousal dimension describes the drivers' feeling of being awake, and the valence dimension indicates the corresponding level of happiness.…”

Section: Methodsmentioning

confidence: 99%

Understanding the Interactions Between Driving Behavior and Well-being in Daily Driving: Causal Analysis of a Field Study

Stephan¹,

Wortmann²,

Koch³

2022

J Med Internet Res

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Background Investigating ways to improve well-being in everyday situations as a means of fostering mental health has gained substantial interest in recent years. For many people, the daily commute by car is a particularly straining situation of the day, and thus researchers have already designed various in-vehicle well-being interventions for a better commuting experience. Current research has validated such interventions but is limited to isolating effects in controlled experiments that are generally not representative of real-world driving conditions. Objective The aim of the study is to identify cause–effect relationships between driving behavior and well-being in a real-world setting. This knowledge should contribute to a better understanding of when to trigger interventions. Methods We conducted a field study in which we provided a demographically diverse sample of 10 commuters with a car for daily driving over a period of 4 months. Before and after each trip, the drivers had to fill out a questionnaire about their state of well-being, which was operationalized as arousal and valence. We equipped the cars with sensors that recorded driving behavior, such as sudden braking. We also captured trip-dependent factors, such as the length of the drive, and predetermined factors, such as the weather. We conducted a causal analysis based on a causal directed acyclic graph (DAG) to examine cause–effect relationships from the observational data and to isolate the causal chains between the examined variables. We did so by applying the backdoor criterion to the data-based graphical model. The hereby compiled adjustment set was used in a multiple regression to estimate the causal effects between the variables. Results The causal analysis showed that a higher level of arousal before driving influences driving behavior. Higher arousal reduced the frequency of sudden events (P=.04) as well as the average speed (P=.001), while fostering active steering (P<.001). In turn, more frequent braking (P<.001) increased arousal after the drive, while a longer trip (P<.001) with a higher average speed (P<.001) reduced arousal. The prevalence of sunshine (P<.001) increased arousal and of occupants (P<.001) increased valence (P<.001) before and after driving. Conclusions The examination of cause–effect relationships unveiled significant interactions between well-being and driving. A low level of predriving arousal impairs driving behavior, which manifests itself in more frequent sudden events and less anticipatory driving. Driving has a stronger effect on arousal than on valence. In particular, monotonous driving situations at high speeds with low cognitive demand increase the risk of the driver becoming tired (low arousal), thus impairing driving behavior. By combining the identified causal chains, states of vulnerability can be inferred that may form the basis for timely delivered interventions to improve well-being while driving.

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“…In another recent study, Banholzer et al concluded that computer mouse can be used to infer work stress [24]. This finding was reported based on a dataset collected from 71 persons using their computers for usual daily work.…”

Section: A Mouse Trajectory Analysismentioning

confidence: 95%

SapiAgent: A Bot Based on Deep Learning to Generate Human-Like Mouse Trajectories

Antal¹,

Búza

Fejer

2021

IEEE Access

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The growing interest in bot detection can be attributed to the fact that fraudulent actions performed by bots cause surprisingly high economical damage. State-of-the-art bots aim at mimicking as many as possible aspects of human behavior, ranging from response times and typing dynamics to humanlike phrasing and mouse trajectories. In order to support research on bot detection, in this paper, we propose an approach to generate human-like mouse trajectories, called SapiAgent. To implement SapiAgent, we employ deep autoencoders and a novel training algorithm. We performed experiments on our publicly available SapiMouse dataset which contains human mouse trajectories collected from 120 subjects. The results show that SapiAgent is able to generate more realistic mouse trajectories compared with Bézier curves and conventional autoencoders.

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Computer Mouse Movements as an Indicator of Work Stress: Longitudinal Observational Field Study

Cited by 12 publications

References 34 publications

Different Methods to Detect Stress in Knowledge Workers: A Pilot Study for Long-term Monitoring

Different Methods to Detect Stress in Knowledge Workers: A Pilot Study for Long-term Monitoring

Understanding the Interactions Between Driving Behavior and Well-being in Daily Driving: Causal Analysis of a Field Study

SapiAgent: A Bot Based on Deep Learning to Generate Human-Like Mouse Trajectories

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