Notifications: Where are we and where are we going?

Andy Madrick
21 min readDec 19, 2020

Abstract

Interaction with smartphone notifications has become a commonplace feature of human life. Notifications are uniquely positioned to exploit the human desire for information. Pairing this with their ubiquity, the efficacy with which notifications encourage engagement with detrimental activities often goes unnoticed. Through review of relevant literature, this paper aims to understand the power of notifications. With respect to smartphone use, I investigate how this power might be leveraged to subvert negative mental health outcomes as well as how notifications can enable a user to better their psychological wellbeing. I categorize the two methodologies of notification design into passive and active and use these categories to make design recommendations. I conclude that a degree regulation must be instituted in order to create notifications that are benign or healthy for the user. Through this regulation, smartphone notifications can shift from entities that exploit human psychology to tools that promote human flourishing.

1. Introduction

“The most ubiquitous feature of the most ubiquitous device on the planet”, the smartphone, is the notification (Fitz et al., 2019). The interaction between smartphone and user, measured in screen-time, is proven to increase levels of stress, anxiety and/or depression in nearly all demographics (Alter, 2018; Elhai et al., 2017; Kelly et al, 2018; O’Keefe et al., 2011; Twenge et al., 2018, Twenge et al., 2020; Wu et al, 2015). This paper aims to uncover the role that notifications play in the smartphone-mental health relationship. Additionally, I will highlight the development of notifications that can avert and, in some cases, reverse the negative mental consequences associated with using a smartphone. I will close by giving concrete design recommendations for the implementation of progressive notifications.

2. Related Works / Background

2.0. What are notifications?

When it comes to access to information, there is no time like the present. With the dawn of the internet, anyone with a connection can access just about any kind of information they desire. Before Apple delivered the world’s first smartphone in 2007, the iPhone (Dolan, 2007), a bulky, often expensive, computer was required to go online. No longer. Globally, 3.5 billion people, or 44% of the world’s population, now have access to the internet through the use of a smartphone (O’Dea, 2020).

Notifications are the most ubiquitous feature of the most ubiquitous device on the planet” and are characterized as “visual cues, auditory signals, and haptic alerts” (Fitz et al., 2019) that deliver information to a user. The kind of information contained within a notification can range from the content of a text message to who won the latest election. Some notifications are means to an end, the aim of these notifications is to encourage a user to open the associated application(s). Others are ends in and of themselves delivering all relevant information, without prompting further engagement with the application.

2.0.1. The smartphone use-cycle as a wicked problem

Throughout this paper, I will be framing the smartphone use cycle — from pick-up to on-screen engagement — as a societal problem. Such problems are built into civilization; thus, many scholars have analyzed this recurrent phenomenon. One of the more compelling investigations of societal problems is Rittel and Webber’s seminal paper, “Dilemmas in a General Theory of Planning” (1973), in which they frame these problems as wicked. What defines a wicked problem? According to Rittel and Webber, there are nine characterizing factors. In what follows, I will focus on the two most relevant: (1) “solutions to wicked problems are not true-or-false, but good-or-bad”, and (2) “every wicked problem can be considered to be a symptom of another problem” (Rittle and Webber, 1973).

2.1. Screen-time and mental health

To understand the role that notifications play within a smartphone use-cycle, we first must understand how mobile device use in general is affecting the users or such devices. Here, one cannot paint a picture of the problem using a broad brush. When reviewing relevant literature, it becomes clear that not all screen-time is created equal: different cohorts are affected by screen-time in different ways. The boundaries that arise most starkly out of the review are the age and sex of users: teenagers and adults, and males and females experience device usage differently.

2.1.2. Screen-time amongst teens

Recently, the interface between screen-time and teenage mental health has become part of the national conversation. The Social Dilemma (Orlowski, 2020), a documentary that centers on the work of the techno-humanist Tristan Harris, is the only documentary to reach Netflix’s number one on their Top 10 list (Bean, 2020). The documentary centers on demonstrating the tangible harm that social networks are doing to the population as a whole, but teenagers specifically.

The Social Dilemma’s premise, that social media, and thus screen-time, is bad for teenagers, is widely supported by recent research/literature or something. In their paper, “Underestimating Digital Media Harm” (2020), Twenge and Haidt demonstrate that social media consumption is nearly as much an indication for self-harm and depression among teenage girls as heroin use is among teenage boys (Figure 1). Electronic device usage more generally, although not as harmful as social media use in particular, is still high on the list, sitting directly above heroin use amongst teenage girls (Twenge et al., 2020).

“The Impact of Social Media on Children, Adolescents, and Families” (O’Keefe, 2020), explores why social media use is so problematic for teens. The authors lay out three factors that lead to negative health impacts on teenagers as a result of social media use: (1) cyberbullying/ harassment, (2) sexting and (3) “Facebook depression” (O’Keefe, 2020). The consequences of the three factors help explain why social media use is akin to heroin use: all three phenomena “tee-up” feelings of intense anxiety and/or depression both directly and indirectly.

2.1.3. Screen-time amongst adults

Screen-time is not only harmful for teens. A study amongst adults shows a direct correlation between an individual’s level of addiction to Facebook usage, and the amount of volume in the brain (He et al., 2017). An fMRI scan of users’ brains showed a “significant reduction in gray matter in the amygdala correlated with their level of addiction to Facebook. This pruning away of brain matter is similar to the type of cell death seen in cocaine addicts” (He et al., 2017).

What is interesting for adults and screen-time is that the negative effects are not an exclusive product of social media engagement. In the study, “Non-social features of smartphone use are most related to depression, anxiety and problematic smartphone use” (2017), Elhai et al. show these ill effects can also be a product of what the authors call process smartphone use: “non-social feature engagement (e.g., news consumption, entertainment, relaxation)” with a mobile device (Elhai et al, 2017).

It may seem that adults, although not as at risk as teens when it comes to problematic screen time, stand to gain only negative effects when engaging with a device. Elhai et al. argue that this is not necessarily the case (2017). They found that social media consumption is, in fact, linked to lower rates of anxiety and depression amongst adults. In fact, they recommend that individuals who are subject to anxiety and depression can quell some of these negative emotions by responsibly tapping into the social features facilitated by mobile devices (Elhai et at, 2017).

2.2. Notifications and Screen-Time

Up to now, I have spent this section of my paper explaining the negative effects of screen-time on mental health. But this is a paper about notifications and how one might leverage their power to improve the mental health of a user. Before we can leverage the power of notifications, we must first understand how they work, namely, how they keep the user engaged.

“Notifications exploit our natural bias for novel, variable rewards” (Fitz et al., 2019). When a notification is delivered, the user is encouraged to pick up their smartphone, and, at minimum, engage with the presented content. Sometimes this content is pleasant, sometimes it is heartbreaking — none the less, it is always information. As humans, we are “information-seeking creatures” (Gazzely and Rosen, 2017), the anticipation of novel content, no matter the quality, is a biologically rewarding experience (Wittman et al., 2007). As Adam Alter states in his 2017 book Irresistible: The Rise of Addictive Technology and the Business of Keeping Us Hooked: “as long as a behavior is rewarding, […] the brain will treat it the same way it treats a drug.”

As discussed above, some notifications are ends in and of themselves, while others are means to an end: more involved engagement. The rewarding quality of notifications is often used to get users “hooked” into using their phone. Although Alter states that this process could be classified as addiction, we should be careful with that term. Clinically, addiction is defined and diagnosed by a criterion found in the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, or DSM V (2017). Panova and Carbonel argue that, according to the DSM-V, smartphone use is more of a problematic or maladaptive behavior than an addition (2018). Although the engagement may not technically be addictive, notifications still tap into human biology to encourage engagement in a problematic activity.

One of the fundamental background assumptions in this paper is the fact that notifications are not solely responsible for the problematic aspects of smartphone usage. This does not, however, absolve them or render them behaviorally impotent. On the contrary, I have shown how notifications discretely prey on human biology to encourage engagement, be that with good, bad and neutral applications.

3. Method

In the following sections, I will show how notifications can be leveraged to (1) avoid imparting negative mental health consequences on a user and (2) increase the quality of an individual’s mental health. These findings were arrived at through a critical review of relevant literature. From academic journal articles to more approachable popular science publications, I have synthesized relevant, scientific oriented, and peer-reviewed literature.

4. Findings

4.0. Designing healthy interactions with notifications.

A review of relevant literature shows that in order to design “healthier” notifications, it is helpful to group different design approaches into two categories: passive and active.

4.1. Passive

Passive notifications are classified as such because they capture the delivery method of notifications. Rather than inviting certain kinds of interactions through a notification, an application or interface can control the method, the timing and the quantity of delivered notifications. Remember, the model of notification delivery at present plays on the human “appetite” for information, so much so, that the process reflects certain aspects of drug use (He et al., 2017). In what follows, I will discuss two methods of notification delivery that offer an alternative to this manipulative standard model: delivering notifications at the opportune moment (Park et al., 2017) and “batching” notifications (Fitz et al., 2019).

4.1.1. Opportune Moment

In the paper “Don’t Bother Me. I’m Socializing!”, Park et al. study how notifications might interrupt and/or distract a smartphone user while socializing (2017). In this study, the subjects were placed in a restaurant setting, sharing a meal with one another. In order to study the optimal timing of notification delivery, the authors prototyped a smartphone application, called SCAN, that monitors the activity of both the user as well as the surrounding environment. Rather than deliver notifications in real-time, the SCAN application serves notifications at four opportune moments, or “breakpoints” throughout the meal: “(1) a long silence, (2) a user leaving the table, (3) others using smartphones, (4) and a user left alone” (Park et al., 2017). Critically, the subjects still received the notifications they would have otherwise, however, these notifications were delivered at appropriate windows in time.

Coming back to how notifications entice and entangle humans with their smartphone, we can see how “breakpoint” delivery might offer an alternative to the gamified model that currently exists. When a user is actively socializing and their smartphone is not delivering notifications, the exploitative entrapments subside, theoretically freeing the user.

4.1.2. Batching

When it comes to implementing this kind of specialized notification-delivery, however, some smartphone users may balk at installing a third-party application which monitors and remembers user activity. Here, it’s important to explore an alternative notification serving methodology, one that requires no monitoring and minimal programing: batching. Batching notifications is outlined by Fitz et al. as delivering notifications at a given interval, rather than real-time delivery or no delivery at all (2019).

To understand the efficacy of batching notification, Fitz et al. performed an experiment in which they gave subjects four different “schedules” of notifications: real-time (continuous delivery), batched hourly, batched every three hours, and no notifications (2019).

Compared to those in the control [real time delivery] group, participants whose notifications were batched 3x/day unlocked their phones fewer times […] and felt more control over their phone. […] Accordingly, participants whose notifications were batched three times a day also experienced lower inattention […] and higher concentration […] as compared to controls (Fitz et al., 2019).

Delivering zero notifications was shown to “backfire” (Fitz et al., 2019). According to Fitz et al., “[p]articipants without notifications did, however, experience higher levels of phone-related fear of missing out […] and feelings of missing out on important notifications […] than controls. They also experienced significantly more anxiety than controls” (2019).

This theme emerges in study after study: some notification is better than no notification (Fiitz et al., 2019; Piellot and Rello, 2017). With zero notifications, users report a fear of missing out (FOMO) or anxiety about missing an important notification and failing to respond in an appropriate time window. Both the idea of batching notification as well as the negative outcomes that accompany a wholesale elimination of notifications will be revisited in multiple sections to follow (Piellot and Rello, 2017).

4.2. Passive Example: Apple iOS

So, what would it look like if notifications were delivered at the appropriate time and in the appropriate volume? Does something like this already exist? Kind of. Here, I will focus on a few of Apple’s iOS, Do Not Disturb Mode and Notifications.

The Apple iPhone is currently used by nearly 45% of all American smartphone users (O’Dea, 2020b). Thus, the Apple iOS, the software found on an iPhone, is readily accessible to a large portion of the population. Therefore, these aforementioned principles of recommended passive notification delivery are conveniently available to much of the smartphone owning world.

4.2.0. Do Not Disturb

The iOS feature, Do Not Disturb (DND), allows users to effectively “silence” undesired notifications. DND, however, is not a wholesale removal of notifications — the user is allowed a high degree of control over when and even where to toggle alerts on and off (Apple, 2020a). When a user enables DND, they are given multiple options for how long the feature is activated.

At present (December 2020), when I go to toggle DND, I am offered a suite of options (Figure 2). Again, it is the dynamism of this feature that aligns it with the spirit (not necessarily the exact tenets) of optimal passive notification delivery. Although I am not yet able to specify breakpoints such as “when I leave the table”, the core idea discussed in Park et al.’s study of delivering notifications at the opportune moment is there. With DND, I can choose to have notifications off until I leave this current location (which, at present, is my desk, where I am actively writing — it would be optimal to not worry about notations until I move somewhere new). Optimizing this feature could look like offloading the decision and having my smartphone turn off notifications until it would be appropriate to switch them on once again.

4.2.1. Notification Settings

Remember, receiving zero notifications throughout the day has shown to do more harm than good, what is critical is delivering the right kind of notifications at the right time. Although the feature does not currently exist, iOS allows a user to personalize their notification settings to enable a pseudo-batching delivery system.

This “half-batched” approach can be achieved through toggling certain aspects of the notification settings (Figure 3). Rather than being delivered a notification, which typically entails “visual cues, auditory signals, and haptic alerts” (Fitz et al., 2019), accompanied by information displayed on the lock screen, the user can choose exactly how the notification may appear. According to Apple, iOS offers two different alternatives to this real-time, attention-centered method: “[1] ‘Deliver Quietly’: These notifications appear in the Notification Center, but don’t show up on the Lock screen, play sounds, or show a banner or badge icon, and [2] ‘Turn Off’: This turns off all notifications for a specific app” (Apple, 2020b).

Obviously, this is not an explicit implementation of batching. However, it is close and is much better than the baseline of at times, invasive, real-time, delivery. It is easy to imagine how Apple could implement the tenets of batched notifications and apply them to their notification delivery methodology.

4.3. Active

Passive notification delivery focuses on the ways in which a notification delivery system might head off the adverse effects of notifications that play the “human as information seeking animal” motif. Active notifications are the other side of this coin, these are alerts that invite engagement with a practice that imparts objectively beneficial effects on the psyche of the user.

Now, “objectively beneficial” is a large, cumbersome and possibly pointless term, so we need to understand exactly what we are discussing here. For the purpose of this argument, I will lean on those who have done extensive research in the field and who have enumerated activities proven to benefit an individual’s mental state. In this case, I will pull from the expertise of the social psychologist, Jonathan Haidt. In his book, The Happiness Hypothesis, Haidt collects and analyzes historic methods of making people happier. In Chapter Two, “Changing Your Mind”, Haidt makes the case that the most effective forms of “mental hygiene” are “meditation, cognitive behavior therapy (CBT), and Prozac” (Haidt, 2006). Haidt argues that these three practices are, presently, the only objectively proven methods by which an individual can radically “change their mind”, i.e., experience lasting recovery from depression or anxiety (2006).

When it comes to how these three practices might interface with a smartphone — which, remember, is used by 44% of the human population (O’Dea, 2020a) — both mindfulness and CBT stand out as opportunities to more effectively leverage the power of notifications. This is not to discount the power of medications like Prozac, it is simply that the design opportunities that surround smartphones and medication are more limited in nature. The practice of taking a medication is, fundamentally, the taking of a pill (which is not always easy for the afflicted individual, who often has to overcome stigma and taboo to admit they need medication in the first place). Of course, a good psychiatrist will recommend that the medication be accompanied by exercise, meditation or even CBT, but the heavy lifting, in this case, is done by the chemical process spurred by the medication.

A mobile device is positioned as an outstanding tool to employ the practices of mindfulness and CBT precisely because of its built-in ability to deliver notifications at the right time and in the right place. Through consistent and regular reminding, notifications can enable an individual to adopt otherwise challenging routines into everyday life.

4.4. Active Example: Unwinding Anxiety

It just so happens that there exists an application that has been clinically proven, through the implementation of mindfulness and CBT based practices, to actively improve the mental health of the user. Unwinding Anxiety (UA) is a mobile application, developed by Dr. Judson Brewer and his team, which aims to help individuals experiencing anxiety. UA centers on engaging users with activities: a user might be asked to watch a video lesson about anxiety, to engage in a CBT exercise like journaling or identifying “cognitive distortions” or engage in a mindfulness exercise, like focusing on the breath. UA employs the above techniques to (1) “uncover what triggers your anxiety, (2) identify your ‘anxiety habits’, (3) break the cycle of worry & panic [and] (4) learn specific anti-anxiety tools” (Unwinding Anxiety, 2020). Through these four objectives, they may find relief from anxiety. In a study performed with a beta-version of UA, it was found that using such an application could effectively lessen self-reported anxiety by 57% in three months of use (Roy et al., 2020).

Critically, the heavy lifting in UA is being performed by a user’s engagement with the various exercises noted above. But this is a paper about notifications and how they might be used to avert and quell negative mental outcomes such as depression and anxiety. Here, we will look at how UA engages the user through notifications,

Figure 4 shows an example of a notification a user might receive from Unwinding Anxiety. Notice how the notification is not an inert, ineffectual alert. Rather, it poses a question and, even if the user is prone to ignore engaging with it any further, it forces them to at least hold the question in mind. This, I argue, is a small way of increasing a user’s sense of mindfulness, thus the notification becomes an end, in and of itself.

Because some of the alerts are regularly scheduled while others are more spontaneous, they exploit, in a healthy way, the human appetite for information. I argue, that without such a notification, it would be far less likely that an individual would be prompted to engage in an often-difficult practice of self-reflection or mindfulness.

5. Discussion

5.0 Design Recommendations

Up to now, we have seen the role that notifications play in inducing engagement with a smartphone and, consequently, just how harmful this engagement might be. We have seen alternative methods of notification delivery and how these are implemented in existing applications and interfaces. Now, I will synthesize this information and offer some concrete methodologies for using notifications to avoid harming the mental health of the user, and in some cases, improve that same user’s psychic wellbeing.

5.1. Where do design solutions need to be offered?

In today’s digital world, we are constantly bombarded by notifications. As we have continually touched on, most of these alerts are designed to tap into the human lust for information, making them incredibly effective at promoting engagement with a particular application. This brings us to an interesting, and crucial point: in the digital realm, engagement is synonymous with monetization. In the advertising economy, attention is synonymous with profit (Johnston, 2020).

Here, we find another opportunity to invoke Rittel and Webber’s characterization of the wicked problem: if notifications are an inevitable feature of a digital product’s business model, where does the problem actually lie? Should we fault the corporation responsible for an application or product that actively harms the mental health of millions, or is it incumbent upon the gatekeepers, or platforms (i.e., Google and Android) to make the necessary changes?

The reality of the digital economy is it is a capitalistic enterprise. Products that attract the most eyeballs will accrue the most capital. One approach to mend the smartphone-mental-health harm cycle is to try to convince these profitable corporations to make healthier, but less lucrative notifications. Is this reasonable? Maybe, but it is quite the task to convince massively successful tech giants such as Facebook or Robinhood to take monetary losses. If we investigate further, we realize that this is not even solving the issue with as sweeping a solution as possible. Let us refer back to Rittel and Webber: “one should not try to cure the symptoms [of a wicked problem], […] one should try to settle the problem on as high a level as possible” (1973).

Manipulative notifications are a symptom of the present, smartphone centered world. How do we correct for their role in the smartphone-mental-health harm cycle on as high a level as possible? By working with the decision makers and designers who create the vehicles for notifications themselves: Apple and Google.

Presently, Apple and Google represent an effective duopoly in the global smartphone market (O’Dea, 2020b). As discussed above, Apple already offers users control over notifications and Google offers very similar personalization (Google, n.d.). So, how might the designers at these corporations “turn the dials” even further in the direction of stopping screen-time related mental health issues?

5.2. Understanding and Curating for the User

I argue that the mental health of users must shift from a responsibility of the user to the responsibility of the developer. In almost every developed society in the world, the government regulates activities that are proven to be objectively dangerous — why should screen time be exempted? In the same way that we do not allow teenagers to consume alcohol and prohibit adults from driving while under the influence of alcohol, we should reconsider an average user’s ability to curate their own notifications.

Many individuals, including myself, could and should recoil at the thought of governmental regulation when it comes to our personal device use. But we need to find a middle ground between the Wild West of screen time, the moment in which we currently find ourselves, and its opposite: the Orwellian, big brother scenario. How can we find this gray area?

The answer might lie in the practice of more closely linking the developer with the user. This could look different for different individuals, but in all cases, choices about notification delivery should shift from the responsibility of the user to the responsibility of the developer. An application should be able to synthesize and put into practice relevant data about its user. Remember that different demographics respond differently to different kinds of screen time. At the minimum, age and gender will be all that such a device will need in order to curate appropriate notification delivery. At the maximum, a quantitative portrait of a user could be mobilized to curate highly appropriate and even beneficial notifications.

5.3. Highlight active notifications when appropriate

The above recommendation works within the framework of passive notifications. However, it’s important to remember that there is evidence that appropriately delivering the right kind of notifications can lead to an increased sense of wellbeing. We can synthesize the above evidence on optimal notification delivery with the evidence about activenotifications to create alerts that deliver the right kind of information at the right time.

Here, I want to offer my take on the optimal active notification. In principle, these notifications will look quite similar to those found in Dr. Brewer’s Unwinding Anxiety. They will be simple and offer engagement with activities that are proven to be objectively beneficial. However, I argue that they should take on the sensing capacities used in Park et al.’s SCAN application in order to be delivered at the opportune moment. These notifications should also be batched and delivered at regular intervals, optimally, every three hours. In a perfect world, this kind of feature could be a stock, in-the-box, feature of all Apple and Google phones.

If we can tap into the objective evidence that exists for creating notifications that offer opportunities to not only circumvent possible problematic use practices, but also notifications that actively boost the mental health of the user, we will be in a much better spot than the present.

6. Conclusion

In this paper, I have defined and demonstrated the role that notifications play in inducing engagement with a smartphone. I have also, importantly shown and how harmful this engagement might be. I have illustrated alternative methods of notification delivery, both passive and active, and how these are implemented in existing applications and interfaces. This article concludes with recommendations about how a developer, or operating system might better design notifications and exemplified why this is important.

References

Alter, A. (2018). Irresistible: The rise of addictive technology and the business of keeping us hooked. New York, NY: Penguin Press.

Apple. (2020, September 30). Use Do Not Disturb on your iPhone, iPad, and iPod touch. Retrieved December 12, 2020, from https://support.apple.com/en-us/HT204321

Apple. (2020, October 01). Use notifications on your iPhone, iPad, and iPod touch. Retrieved December 13, 2020, from https://support.apple.com/en-us/HT201925

Bean, T. (2020, September 24). ‘The Social Dilemma’ Is About To Become The First Documentary On Netflix To Achieve This Incredible Milestone. Retrieved December 12, 2020, from https://www.forbes.com/sites/travisbean/2020/09/24/the-social-dilemma-is-about-to-become-the-first-documentary-on-netflix-to-achieve-this-incredible-milestone/?sh=4480b28e22f2

DeRubeis RJ, Hollon SD, Amsterdam JD, et al. Cognitive Therapy vs Medications in the Treatment of Moderate to Severe Depression. Arch Gen Psychiatry. 2005;62(4):409–416. doi:10.1001/archpsyc.62.4.409

Diagnostic and statistical manual of mental disorders: DSM-5. (2017). Arlington, VA: American Psychiatric Association.

Dolan, Brian. “Timeline of Apple “iPhone” Rumors (1999–Present)”. Archived from the original on April 15, 2008. Retrieved December 11, 2020.

Elhai, J. D., Levine, J. C., Dvorak, R. D., & Hall, B. J. (2017). Non-social features of smartphone use are most related to depression, anxiety and problematic smartphone use. Computers in Human Behavior, 69, 75–82. doi:10.1016/j.chb.2016.12.023

Fitz, N., Kushlev, K., Jagannathan, R., Lewis, T., Paliwal, D., & Ariely, D. (2019). Batching mobile device notifications can improve well-being. Computers in Human Behavior, 101, 84–94. doi:10.1016/j.chb.2019.07.016

Gazzely, A., & Rosen, L. D. (2017). The Distracted Mind: Ancient brains in a high-tech world. Cambridge, MA: MIT Press.

Google. (n.d.). Control notifications on Android — Android Help. Retrieved December 13, 2020, from https://support.google.com/android/answer/9079661?hl=en

Haidt, J. (2006). The Happiness Hypothesis: Putting ancient wisdom and philosophy to the test of modern science. London: Random House Business Books.

He, Q., Turel, O. & Bechara, A. (2017). Brain anatomy alterations associated with Social Networking Site (SNS) addiction. Sci Rep 7, 45064. https://doi.org/10.1038/srep45064

Johnston, M. (2020, December 09). How Facebook Makes Money. Retrieved December 13, 2020, from https://www.investopedia.com/ask/answers/120114/how-does-facebook-fb-make-money.asp

Kelly, Y., Zilanawala, A., Booker, C., & Sacker, A. (2018). Social Media Use and Adolescent Mental Health: Findings From the UK Millennium Cohort Study. EClinicalMedicine, 6, 59–68. doi:10.1016/j.eclinm.2018.12.005 (Kelly et al, 2018)

Kushlev, K., Proulx, J., & Dunn, E. W. (2016). “Silence Your Phones”. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. doi:10.1145/2858036.2858359

O’Dea, P. (2020, August 20). Smartphone users 2020. Retrieved December 06, 2020, from https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/

O’Dea, P. (2020, November 25). Smartphone OS U.S. market share 2020. Retrieved December 13, 2020, from https://www.statista.com/statistics/266572/market-share-held-by-smartphone-platforms-in-the-united-states/

O’keeffe, G. S., & Clarke-Pearson, K. (2011). The Impact of Social Media on Children, Adolescents, and Families. Pediatrics, 127(4), 800–804. doi:10.1542/peds.2011–0054

Orlowski, Jeff (2020–09–09), The Social Dilemma (Documentary, Drama), Tristan Harris, Jeff Seibert, Bailey Richardson, Joe Toscano, Exposure Labs, Argent Pictures, The Space Program, retrieved 2020–10–28

Panova, T., & Carbonell, X. (2018, June 01). Is smartphone addiction really an addiction? Retrieved December 06, 2020, from https://pubmed.ncbi.nlm.nih.gov/29895183/

Park, C., Lim, J., Kim, J., Lee, S., & Lee, D. (2017). Don’t Bother Me. I’m Socializing! Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. doi:10.1145/2998181.2998189

Pielot, M., & Rello, L. (2017). Productive, anxious, lonely. Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services. doi:10.1145/3098279.3098526

Rittel, H. W. & Webber, M. M. (2018). Dilemmas in a General Theory of Planning. Classic Readings in Urban Planning, 52–63. doi:10.4324/9781351179522–6

Roy, A., Druker, S., Hoge, E. A., & Brewer, J. A. (2020). Physician Anxiety and Burnout: Symptom Correlates and a Prospective Pilot Study of App-Delivered Mindfulness Training. JMIR MHealth and UHealth, 8(4). doi:10.2196/15608

Shamsi T. Iqbal and Eric Horvitz. 2010. Notifications and awareness: A field study of alert usage and preferences. In Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work (CSCW ’10): 27–30. http://doi.org/10.1145/1718918.1718926

Twenge, J. M., Haidt, J., Joiner, T. E., & Campbell, W. K. (2020). Underestimating digital media harm. Nature Human Behaviour, 4(4), 346–348. doi:10.1038/s41562–020–0839–4

Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in Depressive Symptoms, Suicide-Related Outcomes, and Suicide Rates Among U.S. Adolescents After 2010 and Links to Increased New Media Screen Time. Clinical Psychological Science, 6(1), 3–17.

Unwinding Anxiety. (2020, May 13). Unwinding Anxiety® is a step-by-step program developed by psychiatrist and neuroscientist Judson Brewer MD PhD and delivered on your smartphone or tablet. Retrieved December 13, 2020, from https://www.unwindinganxiety.com/

Wittmann, B. C., Bunzeck, N., Dolan, R. J., & Düzel, E. (2007). Anticipation of novelty recruits reward system and hippocampus while promoting recollection. NeuroImage, 38(1), 194–202. doi:10.1016/j.neuroimage.2007.06.038

Wu X, Tao S, Zhang Y, Zhang S, Tao F (2015) Low Physical Activity and High Screen Time Can Increase the Risks of Mental Health Problems and Poor Sleep Quality among Chinese College Students. PLoS ONE 10(3): e0119607. https://doi.org/10.1371/journal.pone.0119607

--

--

Andy Madrick

I’m a designer in grad school at the University of Washington.