Effects of interventions to reduce the negative consequences of interruptions on task performance: A systematic review, meta-analysis, and narrative synthesis of laboratory studies

Guo, Jingya; Chen, Tianrong; Xie, Zhenzhen; Or, Calvin

doi:10.1016/j.apergo.2021.103506

Cited by 6 publications

(5 citation statements)

References 44 publications

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“…Similar to the implementation of other healthcare technologies, their usability, acceptance, and effects on knee health are worth further investigation among the end users. 33 – 39…”

Section: Discussionmentioning

confidence: 99%

Automated machine learning-based prediction of the progression of knee pain, functional decline, and incidence of knee osteoarthritis in individuals at high risk of knee osteoarthritis: Data from the osteoarthritis initiative study

Chen,

2023

DIGITAL HEALTH

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Objective This study aimed to examine the performance of machine learning models in predicting the progression of knee pain, functional decline, and incidence of knee osteoarthritis (OA) in high-risk individuals, with automated machine learning (AutoML) being used to automate the prediction process. Design There were four stages in the process of our AutoML-integrated prediction. Stage 1—Data preparation: The data of 3200 eligible individuals in the Osteoarthritis Initiative (OAI) study who were considered at high risk of knee OA at the baseline visit were extracted and used. Specifically, 1094 variables from the OAI study were used to predict the changes in knee pain, physical function, and incidence of knee OA (i.e. the first occurrence of frequent knee symptoms and definite tibial osteophytes (Kellgren and Lawrence grade ≥2)) over a 9-year period. Stage 2—Model training: The AutoML approach was used to automatically train nine widely used machine learning (ML) models. Stage 3—Model testing: The AutoML approach was used to automatically test the performance of the ML models. Stage 4—Selection of important input variables: The AutoML approach automated the process of computing the importance scores of all input variables and identifying the most important ones, using the technique of permutation feature importance. Results Using the AutoML approach, the weighted ensemble model and the CatBoost model showed the best performance among all nine ML models. For the prediction of each outcome in each year, the five most important input variables were identified, most of which were obtained from self-reported questionnaire surveys and radiographic imaging reports. Conclusion The AutoML approach has shown potential in automating the process of using ML models to predict long-term changes in knee OA-related outcomes. Its use could support the deployment of ML solutions, facilitating the provision of personalized interventions to prevent the deterioration of knee health and incident knee OA.

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“…Similar to the implementation of other healthcare technologies, their usability, acceptance, and effects on knee health are worth further investigation among the end users. 33 – 39…”

Section: Discussionmentioning

confidence: 99%

Automated machine learning-based prediction of the progression of knee pain, functional decline, and incidence of knee osteoarthritis in individuals at high risk of knee osteoarthritis: Data from the osteoarthritis initiative study

Chen,

2023

DIGITAL HEALTH

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“…In contrast, the other two interventions (i.e., providing reminder cues and training) do not involve the cognitive processes related to WM. Moreover, as Guo et al (2021) pointed out, there are inadequate data to determine the positive effects of interruption lag management. Therefore, we focus on the intervention of managing interruption lag.…”

Section: Interventions For Mitigating the Negative Effects Of Interru...mentioning

confidence: 99%

“…The second limitation of our study is that the current study only examined a computer-based procedural primary task and the "management of interruption lag" as an intervention. As reported by Guo et al (2021), the effectiveness of interventions requires further investigation, because it depends on the type of interrupted primary task (e.g., decision-making, or problem-solving tasks). Furthermore, although we examined whether interruption onset management mitigates the detrimental effects of interruptions, we did not specifically distinguish the role of interruption alerts and long interruption lags in this context.…”

Section: Limitationsmentioning

confidence: 99%

“…Especially in safety‐critical work situations, such as aviation (Wilson et al, 2018) and medicine (Westbrook et al, 2018; Williams & Drew, 2017), errors can lead to serious damage, which threatens the safety and security of social systems. For this reason, researchers develop different types of interventions to reduce the negative consequences of interruptions (Guo et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…However, since it is difficult to directly manipulate WM, we consider alternative approaches to mitigate the negative effects of interruptions. According to Guo et al (2021), effective interventions can mitigate the negative effects of interruptions by reducing the cognitive load on WMC and supporting the cognitive processes that underlie the resumption of interrupted tasks. Although they reported that three types of interventions (i.e., managing interruption lag, providing reminder cues, and training) were particularly effective in reducing the disruptive effects of interruptions, only interruption lag management can experimentally manipulate the disruptiveness of interruptions and reduce the disruptive effects of interruptions (Altmann & Trafton, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Interventions to reduce the negative consequences of interruptions on task performance and individual differences in working memory capacity

Zhang,

Kawashima,

Shinohara

2023

Applied Cognitive Psychology

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The current study aimed to investigate whether individual differences in working memory capacity (WMC) are associated with differences in the subsequent task performance and whether intervention (interruption onset management) can reduce the negative effects of interruption. Experiment 1 compared task performances before and after interruptions and examined their relationship with WMC. The findings suggested that individuals with high levels of WMC were able to recover more quickly to the same level of performance as prior to the interruption. In Experiment 2, we examined whether manipulating the intervention could mitigate the detrimental effects of the interruption. The results demonstrated that individuals with high levels of WMC made fewer errors after the interruption, which were reduced by interventions for both low‐ and high‐WMC groups. These results confirm that the impact of interruptions is proportional to differences in WMC, and interventions can reduce the negative impact of interruptions irrespective of WMC.

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The moderation effects of task attributes and mental fatigue on post-interruption task performance in a concurrent multitasking environment

et al. 2022

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Effects of interventions to reduce the negative consequences of interruptions on task performance: A systematic review, meta-analysis, and narrative synthesis of laboratory studies

Cited by 6 publications

References 44 publications

Automated machine learning-based prediction of the progression of knee pain, functional decline, and incidence of knee osteoarthritis in individuals at high risk of knee osteoarthritis: Data from the osteoarthritis initiative study

Automated machine learning-based prediction of the progression of knee pain, functional decline, and incidence of knee osteoarthritis in individuals at high risk of knee osteoarthritis: Data from the osteoarthritis initiative study

Interventions to reduce the negative consequences of interruptions on task performance and individual differences in working memory capacity

The moderation effects of task attributes and mental fatigue on post-interruption task performance in a concurrent multitasking environment

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