Author(s):

  • Päivi Heikkilä
  • Anita Honka
  • Eija Kaasinen
  • Kaisa Väänänen

Abstract:

The work on the factory floor is gradually changing to resemble knowledge work due to highly automated manufacturing machines. In the increasingly automated work environment, the machine operator’s task is to keep the production running and to solve possible problems quickly. This work is expected to become more autonomous, which raises the importance of supporting the workers’ well-being. An important aspect of that is giving concrete feedback of success at work as well as feedback on physical and mental load. We implemented a smartphone optimized web application, Worker Feedback Dashboard that offers feedback to machine operators about their well-being at work and personally relevant production data as well as their connections to each other. The feedback is personal and based on objective, near real-time measurements. We present the results of a field study, in which ten machine operators used the application for 2–3 months. We studied the operators’ user experience, usage activity, perceived benefits and concerns for the application with questionnaires, interviews and application log data. The operators found the feedback interesting and beneficial, and used the application actively. The perceived benefits indicate impacts on well-being as well as on work performance. Based on the results, we highlight three design implications for quantified worker applications: presenting meaningful overviews, providing guidance to act based on the feedback and refraining from too pervasive quantification not to narrow down the meaningful aspects in one’s work.

Documentation:

https://doi.org/10.1007/s10111-021-00671-2

References:

Ackerman MS (2000) The intellectual challenge of CSCW: the gap between social requirements and technical feasibility. Human-Comput Interact 15(2–3):179–203

Article  Google Scholar 

Asimakopoulos S, Asimakopoulos G, Spillers F (2017) Motivation and user engagement in fitness tracking: heuristics for mobile healthcare wearables. Informatics 4(1):5

Article  Google Scholar 

Choe EK, Lee NB, Lee B, Pratt W, Kientz JA (2014) Understanding quantified-selfers’ practices in collecting and exploring personal data. In: Proceedings of the 32nd annual ACM conference on Human factors in computing systems, pp 1143–1152

Chung C-F, Gorm N, Shklovski IA, Munson S (2017) Finding the right fit: understanding health tracking in workplace wellness programs. In: Proceedings of the 2017 CHI conference on human factors in computing systems, ACM, pp 4875–4886

De Zambotti M, Cellini N, Goldstone A, Colrain IM, Baker FC (2019) Wearable sleep technology in clinical and research settings. Med Sci Sports Exerc 51(7):1538

Article  Google Scholar 

Fuller D, Colwell E, Low J, Orychock K, Tobin MA, Simango Bo, Buote R et al (2020) Reliability and validity of commercially available wearable devices for measuring steps, energy expenditure, and heart rate: systematic review. JMIR mHealth uHealth 8(9):e18694

Article  Google Scholar 

Gagné M, Deci EL (2005) Self-determination theory and work motivation. J Organ Behav 26(4):331–362

Article  Google Scholar 

Gavriloff D, Sheaves B, Juss A, Espie CA, Miller CB, Kyle SD (2018) Sham sleep feedback delivered via actigraphy biases daytime symptom reports in people with insomnia: implications for insomnia disorder and wearable devices. J Sleep Res 27(6):e12726

Article  Google Scholar 

Gorecky D, Schmitt M, Loskyll M, Zühlke D (2014) Human-machine-interaction in the industry 4.0 era. In: Industrial Informatics (INDIN), 2014 12th IEEE International Conference on Industrial Informatics (INDIN), IEEE, pp 289–294

Gouveia R, Karapanos E, Hassenzahl M (2018) Activity tracking in vivo 2018. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, ACM, p 362

Gurrin C, Smeaton AF, Doherty AR (2014) Lifelogging: personal big data. Found Trends Inform Retr 8(1):1–125

Article  Google Scholar 

Hackman RJ, Oldham GR (1975) Development of the job diagnostic survey. J Appl Psychol 60(2):159

Article  Google Scholar 

Heikkilä P, Honka A, Kaasinen E (2018a) Quantified factory worker: designing a worker feedback dashboard. In: Proceedings of the 10th Nordic Conference on Human–Computer Interaction, pp 515–523. ACM, 2018

Heikkilä P, Honka A, Mach S, Schmalfuß F, Kaasinen E, Väänänen K (2018b) Quantified factory worker – expert evaluation and ethical considerations of wearable self-tracking devices. In: Proceedings of the 22nd International Academic Mindtrek Conference, ACM, pp 202–211

ISO 9241–210 (2010) Ergonomics of human system interaction-Part 210: human-centred design for interactive systems. International Standardization Organization (ISO). Switzerland

Kaasinen E, Liinasuo M, Schmalfuß F, Koskinen H, Aromaa S, Heikkilä P, Honka A et al (2018) A worker-centric design and evaluation framework for operator 4.0 solutions that support work well-being. In: IFIP Working Conference on Human Work Interaction Design. Springer, Cham, pp 263–282

Kaasinen E, Roto V, Hakulinen J, Heimonen T, Jokinen JPP, Karvonen H, Keskinen T et al (2015) Defining user experience goals to guide the design of industrial systems. Behav Inform Technol 34(10):976–991

Article  Google Scholar 

Lavallière M, Burstein AA, Arezes P, Coughlin JF (2016) Tackling the challenges of an aging workforce with the use of wearable technologies and the quantified-self. Dyna 83(197):38–43

Article  Google Scholar 

Li I, Dey A, Forlizzi J (2010) A stage-based model of personal informatics systems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp 557–566

Li X, Dunn J, Salins D, Zhou G, Zhou W, Schüssler-Fiorenza SM, Rose DP et al (2017) Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biol 15(1):1–30

Article  Google Scholar 

Lupton D (2016) The diverse domains of quantified selves: self-tracking modes and dataveillance. Econ Soc 45(1):101–122

Article  Google Scholar 

Kuhn S, Muller MJ (1993) Participatory design. Commun ACM 36(6):24–29

Article  Google Scholar 

Masson CB, Martin D, Colombino T, Grasso A (2016) The device is not well designed for me” on the use of activity trackers in the workplace? In: Proceedings of the 12th International Conference on the Design of Cooperative Systems (COOP 2016), Springer, Cham, pp 39–55

MIT Technology Review: Stephen Wolfram on Personal Analytics. Retrieved November 24, 2020 from https://www.technologyreview.com/s/514356/stephen-wolfram-on-personal-analytics/

Moore VP, Piwek L (2016) Regulating wellbeing in the brave new quantified workplace. Empl Relat 39(3):308–316

Article  Google Scholar 

Pantzar M, Ruckenstein M (2015) The heart of everyday analytics: emotional, material and practical extensions in self-tracking market. Consum Mark Cult 18(1):92–109

Article  Google Scholar 

Pardamean B, Soeparno H, Budiarto A, Mahesworo B, Baurley J (2020) Quantified self-using consumer wearable device: predicting physical and mental health. Healthc Inform Res 26(2):83–92

Article  Google Scholar 

Piwek L, Ellis DA, Andrews S, Joinson A (2016) The rise of consumer health wearables: promises and barriers. PLoS Med 13(2):1–9

Article  Google Scholar 

Rock Health, Digital Health Consumer Adoption Report 2019. Retrieved November 24, 2020 from https://rockhealth.com/reports/digital-health-consumer-adoption-report-2019/

Romero D, Bernus P, Noran O, Stahre J, Fast-Berglund A (2016) The Operator 4.0: human cyber-physical systems & adaptive automation towards human-automation symbiosis work systems. In: IFIP International Conference on Advances in Production Management Systems, Springer, Cham, pp 677–686

Romero D, Mattsson S, Fast-Berglund A, Wuest T, Gorecky D, Stahre J (2018) Digitalizing occupational health, safety and productivity for the operator 4.0. In: IFIP International Conference on Advances in Production Management Systems. Springer, Cham, pp 473–481

Roto V, Kaasinen E, Heimonen T, Karvonen H, Jokinen JPP, Mannonen P, Nousu H et al (2017) Utilizing Experience Goals in Design of Industrial Systems. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, ACM, pp 6993–7004

Schuler D, Namioka A (eds) (1993) Participatory design: Principles and practices. CRC Press, Boca Raton

Google Scholar 

Selke S (ed) (2016) Lifelogging: digital self-tracking and lifelogging-between disruptive technology and cultural transformation. Springer, Berlin

Google Scholar 

Stiglbauer B, Weber S, Batinic B (2019) Does your health really benefit from using a self-tracking device? Evidence from a longitudinal randomized control trial. Comput Hum Behav 94:131–139

Article  Google Scholar 

Swan M (2013) The quantified self: fundamental disruption in big data science and biological discovery. Big Data 1(2):85–99

Article  Google Scholar 

Vanderhaegen F, Wolff M, Mollard R (2020) Non-conscious errors in the control of dynamic events synchronized with heartbeats: a new challenge for human reliability study. Saf Sci 129:104814

Article  Google Scholar 

Wolf G (2010) The data-driven life. The New York Times, New York

Google Scholar 

Zimmerman J, Forlizzi J, Evenson S (2007) Research through design as a method for interaction design research in HCI. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp 4

The SELF Institute