Developing Action Plans Based on Machine Learning Analysis to Prevent Sick Leave in a Manufacturing Plant

Kurisu, Ken; Song, You Hwi; Yoshiuchi, Kazuhiro

doi:10.1097/jom.0000000000002700

Cited by 2 publications

(1 citation statement)

References 41 publications

(90 reference statements)

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“…One reason for this is that many studies on machine learning, etc., have only been published in the last three years [ 88 ], so the latest developments in the field of artificial intelligence (AI), machine learning, etc., have not yet been able to establish themselves in health promotion in general [ 89 ], and thus not in the occupational context either. However, initial studies and reviews, e.g., on the primary prevention of work-related musculoskeletal disorders [ 88 ] or sick leave [ 90 ], deal with machine learning approaches. Due to the fact that machine learning, AI, etc., show several advantages (e.g., increased availability of online data, low developmental costs) once they become established [ 89 , 91 ], it can be assumed that such new data analysis approaches will be widely used in the upcoming years.…”

Section: Discussionmentioning

confidence: 99%

Precision prevention in worksite health–A scoping review on research trends and gaps

Mess,

Blaschke,

Schick

et al. 2024

PLoS ONE

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Objectives To map the current state of precision prevention research in the workplace setting, specifically to study contexts and characteristics, and to analyze the precision prevention approach in the stages of risk assessment/data monitoring, data analytics, and the health promotion interventions implemented. Methods Six international databases were searched for studies published between January 2010 and May 2023, using the term “precision prevention” or its synonyms in the context of worksite health promotion. Results After screening 3,249 articles, 129 studies were reviewed. Around three-quarters of the studies addressed an intervention (95/129, 74%). Only 14% (18/129) of the articles primarily focused on risk assessment and data monitoring, and 12% of the articles (16/129) mainly included data analytics studies. Most of the studies focused on behavioral outcomes (61/160, 38%), followed by psychological (37/160, 23%) and physiological (31/160, 19%) outcomes of health (multiple answers were possible). In terms of study designs, randomized controlled trials were used in more than a third of all studies (39%), followed by cross-sectional studies (18%), while newer designs (e.g., just-in-time-adaptive-interventions) are currently rarely used. The main data analyses of all studies were regression analyses (44% with analyses of variance or linear mixed models), whereas machine learning methods (e.g., Algorithms, Markov Models) were conducted only in 8% of the articles. Discussion Although there is a growing number of precision prevention studies in the workplace, there are still research gaps in applying new data analysis methods (e.g., machine learning) and implementing innovative study designs. In the future, it is desirable to take a holistic approach to precision prevention in the workplace that encompasses all the stages of precision prevention (risk assessment/data monitoring, data analytics and interventions) and links them together as a cycle.

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Section: Discussionmentioning

confidence: 99%

Precision prevention in worksite health–A scoping review on research trends and gaps

Mess,

Blaschke,

Schick

et al. 2024

PLoS ONE

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Precision prevention in occupational health: a conceptual analysis and development of a unified understanding and an integrative framework

Mess,

Blaschke,

Gebhard

et al. 2024

Front. Public Health

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IntroductionPrecision prevention implements highly precise, tailored health interventions for individuals by directly addressing personal and environmental determinants of health. However, precision prevention does not yet appear to be fully established in occupational health. There are numerous understandings and conceptual approaches, but these have not yet been systematically presented or synthesized. Therefore, this conceptual analysis aims to propose a unified understanding and develop an integrative conceptual framework for precision prevention in occupational health.MethodsFirstly, to systematically present definitions and frameworks of precision prevention in occupational health, six international databases were searched for studies published between January 2010 and January 2024 that used the term precision prevention or its synonyms in the context of occupational health. Secondly, a qualitative content analysis was conducted to analyze the existing definitions and propose a unified understanding. Thirdly, based on the identified frameworks, a multi-stage exploratory development process was applied to develop and propose an integrative conceptual framework for precision prevention in occupational health.ResultsAfter screening 3,681 articles, 154 publications were reviewed, wherein 29 definitions of precision prevention and 64 different frameworks were found, which can be summarized in eight higher-order categories. The qualitative content analysis revealed seven themes and illustrated many different wordings. The proposed unified understanding of precision prevention in occupational health takes up the identified themes. It includes, among other things, a contrast to a “one-size-fits-all approach” with a risk- and resource-oriented data collection and innovative data analytics with profiling to provide and improve tailored interventions. The developed and proposed integrative conceptual framework comprises three overarching stages: (1) data generation, (2) data management lifecycle and (3) interventions (development, implementation and adaptation).DiscussionAlthough there are already numerous studies on precision prevention in occupational health, this conceptual analysis offers, for the first time, a proposal for a unified understanding and an integrative conceptual framework. However, the proposed unified understanding and the developed integrative conceptual framework should only be seen as an initial proposal that should be critically discussed and further developed to expand and strengthen both research on precision prevention in occupational health and its practical application in the workplace.

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Developing Action Plans Based on Machine Learning Analysis to Prevent Sick Leave in a Manufacturing Plant

Cited by 2 publications

References 41 publications

Precision prevention in worksite health–A scoping review on research trends and gaps

Precision prevention in worksite health–A scoping review on research trends and gaps

Precision prevention in occupational health: a conceptual analysis and development of a unified understanding and an integrative framework

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