Is life brighter when your phone is not? The efficacy of a grayscale smartphone intervention addressing digital well-being: a mixed-method study

by Cynthia Dekker

Thesis supervisor: dr. Susanne Baumgartner

The ubiquity of digital devices like smartphones, taking them everywhere and using them constantly, has resulted in a status quo of being permanently online (Vorderer & Kohring, 2013). As mobile connectivity became ubiquitous, a challenge has arisen for users: balancing positive and negative experiences of using digital devices (Vanden Abeele, 2021). This quest for well-being “both thanks to and despite the constant use of digital media” (Büchi, 2021, p. 1) has been termed digital well-being. Central to digital well-being is the user’s sense of self-control over their device use (Lyngs, 2019) in coping with an environment defined by “digital communication overabundance” in virtually all life domains (Gui et al., 2017). For instance, both excessive and mindless smartphone use have been associated with lower productivity (e.g., Duke & Montag, 2017) and sleep quality (Exelmans, 2019), and higher stress levels (e.g., Reinecke et al., 2017).


In response to the growing public concern about digital well-being, many tools have been developed for individuals to monitor smartphone use and improve digital well-being. A relatively understudied but promising self-nudge intervention is turning off smartphone screen colors (i.e., grayscaling; black and white display). Previous research has shown that having a smartphone in grayscale results in significantly less screen time (e.g., Holte & Ferraro, 2020). Although these findings are promising, it is yet unclear whether a fulltime grayscale intervention does also affect day-to-day indicators of mental and digital well-being, such as stress, sleep quality, and productivity.

The potential effectiveness of grayscaling might be explained by the design friction it brings to the smartphone, while all basic functionality is preserved (Zimmerman & Sobolev, 2020). Smartphones and apps are designed to be easy to use with little friction, which contributes to the product appeal but also habitual use (Anderson and Wood, 2020). By creating a design friction (i.e., decreasing the aesthetic appeal of sensory stimuli), habitual, mindless smartphone use can be disrupted, thereby creating more mindful and intentional interactions (Cox et al., 2016). A grayscaled smartphone is less enjoyable and gratifying (Holte & Ferraro, 2020), since grayscales are less stimulating and pleasurable than bright and saturated colors (e.g., Wolfe & Horowitz, 2004). The grayscale mode thus serves as a sensory stimulus reduction that nudges the user to self-regulate (Almourad et al., 2021). Thus, a grayscale intervention may enhance (digital) well-being by reducing excessive and habitual smartphone use and by increasing users’ sense of self-control.

This study assessed whether grayscaling could improve digital well-being, productivity, sleep quality, and stress levels. In addition, this study aimed not only to replicate previous findings regarding reductions in objective screen time, but also to test the logical assumption that screen time would be more strongly reduced for apps focused on visual content than apps with a less visual focus (e.g., textual). In sum, the main aim of the current study was to assess the efficacy of a grayscale intervention by addressing effects on both objective smartphone behavior as well as broader, subjective indicators of (digital) well-being. In doing so, this study is the first to use a smartphone logging app and assess subjective outcomes both daily and weekly.


After completing an intake survey via Qualtrics (T1), participants were asked to install the research app Murmuras on their phone (N = 49). From the next day onwards, Murmuras tracked participants’ smartphone behavior for 13 days and sent out short daily surveys in the evening at 20:00 PM for participants to complete before going to bed, which assessed daily experiences of digital well-being, stress, sleep quality, and productivity.

After completing the daily survey of day 7, participants were redirected to another Qualtrics survey (T2), after which they were instructed to set their smartphone to grayscale and keep it on grayscale for the rest of the study (i.e., one week). Thus, the first week served as a baseline measure (tracking-only), followed by the second week in which the grayscale intervention took place. At the end of the study, on day 14, participants filled in an exit survey via Qualtrics (T3), which also asked them about their experiences with the intervention. Self-reported adherence to the intervention was good (M = 92.31%, SD = 12.77).


To assess whether the outcome variables were significantly affected by the grayscale intervention, repeated measures analyses with a linear mixed models approach were performed, using a Compound Symmetry covariance structure and Maximum Likelihood estimation.

For the weekly subjective assessments (T2 vs. T3), grayscaling resulted in higher digital well-being, productivity, and sleep quality, and lower stress levels. However, for the daily subjective assessments (intervention vs. baseline week means) only digital well-being was positively affected by grayscaling. Regarding objective smartphone use, general daily screen time, as well as screen time of apps that focus on visual content, was marginally significantly reduced by the intervention. The number of daily phone unlocks was not affected, which seems to suggest deep-rooted checking habits.

Importantly, both intervention evaluations and implementation intentions varied widely among participants. Thus, the current study found that the grayscale intervention is potentially successful for and appreciated by some individuals, which underlines the importance to tailor interventions to specific persons. Our findings demonstrate that, although screen time was only marginally significantly reduced, and checking frequency did not change, people felt more in control over their own smartphone behavior, thus indicating enhanced digital well-being. Since a grayscale setting is available on most phones, it offers an easy solution for individuals to retain or regain control over using the device that has become essential to life.


Almourad, M. B., Alrobai, A., Skinner, T., Hussain, M., & Ali, R. (2021). Digital wellbeing tools through users lens. Technology in Society, 67, 101778.

Anderson, I. A., & Wood, W. (2021). Habits and the electronic herd: The psychology behind social media’s successes and failures. Consumer Psychology Review, 4(1), 83–99.

Büchi, M. (2021). Digital well-being theory and research. New Media & Society, 1–18.

Cox, A. L., Gould, S. J., Cecchinato, M. E., Iacovides, I., & Renfree, I. (2016). Design frictions for mindful interactions. Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems.

Duke, É., & Montag, C. (2017). Smartphone addiction, daily interruptions and self-reported productivity. Addictive Behaviors Reports, 6, 90–95.

Exelmans, L. (2019). Electronic media use and sleep: A self-control perspective. Current Sleep Medicine Reports, 5(3), 135–140.

Gui, M., Fasoli, M., & Carradore, R. (2017). “Digital well-being”. Developing a new theoretical tool for media literacy research. Italian Journal of Sociology of Education, 9(1), 155-173.

Holte, A. J., & Ferraro, F. R. (2020). True colors: Grayscale setting reduces screen time in college students. The Social Science Journal, 1–17.

Lyngs, U. (2019). Putting self-control at the centre of digital wellbeing. 2019 ACM CHI Conference on Human Factors in Computing Systems.

Reinecke, L., Aufenanger, S., Beutel, M. E., Dreier, M., Quiring, O., Stark, B., Wölfling, K., & Müller, K. W. (2017). Digital stress over the life span: The effects of communication load and internet multitasking on perceived stress and psychological health impairments in a German probability sample. Media Psychology, 20(1), 90–115.

Vanden Abeele, M. M. P. (2021). Digital wellbeing as a dynamic construct. Communication Theory, 31(4), 932–955.

Vorderer, P., & Kohring, M. (2013). Permanently online: A challenge for media and communication research. International Journal of Communication, 7(1), 188-196.

Wolfe, J. M., & Horowitz, T. S. (2004). What attributes guide the deployment of visual attention and how do they do it? Nature Reviews Neuroscience, 5(6), 495–501.
Zimmermann, L., & Sobolev, M. (2020). Digital nudges for screen time reduction: A randomized control trial with performance and well-being outcomes. PsyArXiv.