Challenging the Concept of Smartphone Addiction: An Empirical Pilot Study of Smartphone Usage Patterns and Psychological Well-Being

Abstract

Smartphone use is ubiquitous, however, scholarly debate regarding the addictive nature of smartphones abounds. In this context, it is integral to distinguish between the content that users experience and the medium that facilitates access to the former, as users may experience addictive-like responses to the specific activities they engage in through the context experienced rather than the device that facilitates access to these activities. The present study aimed to explore conceptualizations of smartphone addiction by (a) investigating user preferences for specific smartphone functionalities, (b) examining behavioral changes associated with limited access to preferred functionalities, and (c) exploring links between aspects of smartphone use and self-reported psychological well-being. A total of 471 participants completed an online survey, providing data on sociodemographics, actual and hypothetical smartphone usage, and psychological well-being (depression, anxiety, and stress symptoms). The results showed that communication functionalities were most frequently cited as being preferred among smartphone users. Notably, participants reported that they would check their smartphones significantly fewer times if their top-three functionalities were inaccessible. This suggests that smartphone users are likely to become addicted to the functionalities they access on their smartphones (content) and not the smartphones themselves (medium), rendering unviable the notion of smartphone addiction as a construct. Further analyses suggested negligible to small correlations between aspects of smartphone use and psychological well-being variables. The findings imply that rather than focusing on frequency of smartphone use, it is recommended that future research examines the type and quality of specific smartphone usages and their effects on user well-being.

Introduction

The pervasiveness of smartphone adoption and use has risen exponentially over the course of the last decade. In the United States of America (USA), ∼85% of the population now owns a smartphone,¹ with users reportedly checking their devices 52 times per day on average.¹ Similarly, 87% of the population in the United Kingdom (UK) own or have access to smartphones. Smartphone penetration has grown significantly across all age groups in the United Kingdom, with users between 55 and 75 years of age witnessing the highest increase from 40% in 2013 to 77% in 2018.²

Notwithstanding the extensive practical benefits associated with smartphone use, the rapid increase in use and exposure has led to concerns regarding the potential detrimental psychosocial effects stemming from excessive use. Recently, scholarly debates emerged regarding whether or not smartphone use should be banned at schools to facilitate academic achievement and improved health.^3,4 In Australia, ∼39% of smartphone users believe they use their devices too much.⁵ Moreover, 34% of Australian smartphone users report being unsuccessful in controlling their usage despite multiple attempts,⁵ illustrating the potentially compulsive nature of smartphone use.

Arguably, peoples' perceptions regarding negative outcomes associated with smartphone usage, coupled with the emerging body of research suggesting that excessive use hampers psychological health,^6,7 has contributed to the emergence of several labels describing addictive-related symptoms precipitated by excessive use, such as: “smartphone use disorder”,⁸ “problematic smartphone use”,⁶ “problematic mobile phone use”,⁹ “compulsive smartphone use”,¹⁰ and even “smartphone addiction”.¹¹

Recently published scholarly debates caution researchers and practitioners not to overpathologize everyday behaviors by creating additional labels for new behavioral addictions arguably lacking robust empirical evidence.¹² However, the phenomenon of “smartphone addiction” appears to receive a relatively large amount of support from researchers despite the overt conceptual weaknesses that render this concept theoretically inadequate. It is well established that in the context of behavioral addictions, a distinction must be made between the “content” being experienced by the user that is likely to elicit addictive-like responses, and the “medium” that facilitates the user's access to the “content” that is capable of inducing such addictive responses.¹³

The validity of certain behavioral addictions in relation to technology use has been deemed theoretically unfeasible due to the distinction between the “content” and the “medium.” More recently, “generalized Internet addiction” has been highlighted as a conceptually inadequate construct at the theoretical level. Research^14,15 found that Internet users tend to exhibit preference toward specific online activities and when prevented hypothetically from using their preferred activity, they would not change to another activity and their Internet usage would reduce exponentially. A substantial proportion of users reported they would not use the Internet at all in the absence of their preferred online activities. Consequently, these studies support the notion that online behavior (particularly through smartphone use) may not be “generalized,” as users tend to exhibit preferences for specific functionalities when using their smartphones, likely reflecting natural individual differences among users concerning smartphone adoption, usage, and general configuration.¹⁶

Given the lack of research examining the way in which smartphone addiction has been traditionally conceptualized within the broad spectrum of behavioral addictions, the present study aimed to investigate (a) users' preferences for specific smartphone functionalities, (b) expected behavioral changes associated to limited accessibility of smartphone functionalities; and (c) the link between smartphone use and self-reported psychological well-being.

Methods

Participants and procedure

English-speaking smartphone users (n = 471) were recruited using convenience sampling from social media platforms, including Facebook, Twitter, and LinkedIn. A call for participants was disseminated indirectly to potential participants through timeline posting and directly through personalized messages within these social media platforms. Data were collected from April to October 2016. Ethics approval was obtained by the School of Social Sciences Research Ethics Committee (SREC) at Nottingham Trent University and all participants provided informed consent before partaking in the study.

Data collection occurred through an anonymous online survey hosted on Qualtrics. Data were inspected for cases with missing values and cleaned accordingly, resulting in the removal of 125 incomplete cases and a final sample of 346 participants (M_age = 27 years, SD = 10.59 years), of which 78.6% were female (Table 1).

Table 1.

Sociodemographics, Smartphone Use, and Perceived Quality of Life Without Smartphone Access (n = 346)

Variable
Gender, n (%)
Male	74 (21.4)
Female	272 (78.6)
Age (years), mean (SD)	27.92 (10.59)
Relationship status, n (%)
In a relationship	218 (63)
Not in a relationship	128 (37)
Occupational status, n (%)
Currently working	111 (32.1)
Currently studying	144 (41.6)
Currently working and studying	66 (19.1)
Currently unemployed	25 (7.2)
History of smartphone use (years), mean (SD)	5.17 (3.13)
Smartphone checking,^a mean (SD)	35.40 (40.08)
Smartphone checking without key functionalities,^b mean (SD)	11.45 (20.11)
Smartphone use without key functionalities,^c n (%)
Yes	231 (66.8)
No	115 (33.2)
Perceived quality of life without smartphone access, n (%)
Much worse	11 (3.2)
Worse	122 (35.3)
Same	146 (42.2)
Better	60 (17.3)
Much better	7 (2.0)

This variable relates to the following question: “How many times do you check your smartphone on a normal day?.”

This variable relates to the following question: “How many times would you check your smartphone in a day if the three most used functionalities could not be used anymore?.”

This variable relates to the following question: “If, for any technical reason, the three most used functionalities of your smartphone could not be used anymore, would you still use your smartphone?.”

Measures

Sociodemographics and smartphone use

The survey gathered data regarding participants' gender, age, and relationship status, as well as their smartphone usage patterns (e.g., frequency) and preferences for particular functionalities. Further questions addressed hypothetical self-reported usage patterns (e.g., willingness to use the smartphone without the most used functionalities).

Psychological well-being variables

To assess perceived psychological well-being states, participants were asked, “If you could not use your smartphone anymore the quality of your life would be” (responded to from 1 = much worse, to 5 = much better). Additionally, the Depression Anxiety and Stress Scales–21 (DASS-21),¹⁷ was included in the survey. The DASS-21 measures depression, anxiety, and stress symptoms along a 4-point scale (from 0 = did not apply to me at all to 3 = applied to me very much, or most of the time). Cronbach's alpha for the DASS-21 in the sample was 0.897 (depression), 0.818 (anxiety), and 0.881 (stress).

Results

In-depth demographic characteristics of the sample and smartphone engagement are specified in Tables 1 and 2. Frequencies were calculated for participants' top three smartphone functionalities and forced choice preference. Correlational analyses were then used to examine associations between aspects of well-being and smartphone use.

Table 2.

Specific Aspects of Smartphone Engagement

Variable
I consider myself a smartphone user, n (%)
Strongly disagree	6 (1.73)
Disagree	3 (0.87)
Neither disagree or agree	6 (1.73)
Agree	92 (26.59)
Strongly agree	239 (69.08)
I could not live without my smartphone, n (%)
Strongly disagree	28 (8.09)
Disagree	88 (25.43)
Neither disagree or agree	69 (19.94)
Agree	115 (33.24)
Strongly agree	46 (13.29)
I usually use my smartphone to access the Internet, n (%)
Strongly disagree	6 (1.73)
Disagree	7 (2.02)
Neither disagree or agree	7 (2.02)
Agree	121 (34.97)
Strongly agree	205 (59.25)
I usually use my smartphone with the same frequency even when I do not have mobile Internet access, n (%)
Strongly disagree	30 (8.67)
Disagree	160 (46.24)
Neither disagree or agree	49 (14.16)
Agree	80 (23.12)
Strongly agree	27 (7.80)

Some of the percentages were automatically rounded by the software used in the estimation process.

Specific smartphone functionalities

Most frequently used functionalities

Two-thirds of the sample indicated that text-messaging online (e.g., using Facebook Messenger/WhatsApp) was among their top three most used smartphone functionalities (66.8%, n = 231), followed by online social networking (50.9%, n = 176), and text messaging offline (i.e., short message service; 37.6%, n = 130). The least used functionality was meeting new friends and sexual partners (0.6%, n = 2) (Fig. 1).

FIG. 1.

Participants' three most used smartphone functionalities (n = 346). This figure depicts the percentage of participants who listed a smartphone function in the top three “most used” when asked: “Please select the three functionalities of your smartphone that you use the most.”

Forced choice preference

Participants were asked to further narrow their choice to a single functionality as a means of gauging preference and utility; “If the use of your smartphone was limited to only one functionality, which one would you choose?” The fundamental smartphone uses related to communication were most highly represented in these choices, where text messaging (online) was the top-rated choice (33.2%, n = 115), followed by voice/video calls (26.9%, n = 93), and text messaging (offline) (18.2%, n = 63) (Fig. 2).

FIG. 2.

Participants' preferred functionality if they were limited to choosing only one (n = 346). This figure depicts the percentage of participants who selected a function as their preferred option, when asked: “If the use of your smartphone was limited to only one functionality, which one would you choose?.”

Associated changes in predicted smartphone behavior

Participants were also asked to indicate how many times they would check their smartphone per day if the three most used functionalities could not be used anymore (Table 1). A paired samples t-test demonstrated that participants would check their smartphones significantly fewer times in the absence of their top functionalities, t(345) = 14.02, p < 0.001, 95% confidence interval (20.59–27.30), d = 0.656. One-third of participants (33.2%) indicated that without their top three functionalities, they would not use their smartphones at all.

Smartphone use and well-being

Although the majority of the sample reported their perceived quality of life would be the same without smartphone access, 38.5% (n = 133) of participants perceived that their quality of life would become “worse” (35.3%, n = 122) or “much worse” (3.2%, n = 11). Almost a fifth of participants (19.3%, n = 67) perceived that their quality of life would improve without smartphone access and nearly half of the sample (46.5%, n = 161) “agreed” or “strongly agreed” that they could not live without their smartphone.

Bivariate correlations showed significant small-to-moderate, positive associations between participants' agreement with the statement “I could not live without my smartphone” and depression (r = 0.166, p = 0.002), anxiety (r = 0.267, p < 0.001), and stress symptoms (r = 0.304, p < 0.001) (Table 3). Daily smartphone checking also showed significant small, positive associations with depression (r = 0.110, p = 0.041) and anxiety (r = 0.117, p = 0.029) symptoms. Finally, negligible correlations emerged between history of smartphone use and symptoms of depression (r = −.020, p = 0.705), anxiety (r = 0.095, p = 0.079), and stress (r = 0.105, p = 0.052).

Table 3.

Bivariate Correlations for Life Without a Smartphone, Depression, Anxiety, and Stress (n = 346)

	LWS	SC	H	D	A	S	α	M	SD
Life w/o smartphone^a	—	0.16^**	0.14^*	0.17^**	0.27^***	0.30^***	—	3.18	1.19
Smartphone checking^b		—	0.27^***	0.11^*	0.12^*	0.09	—	35.40	40.08
History of smartphone use			—	−0.02	0.09	0.10	—	5.17	3.13
Depression				—	0.53^***	0.65^***	0.897	3.50	4.16
Anxiety					—	0.66^***	0.818	3.16	3.62
Stress						—	0.881	5.54	4.55

This variable relates to the following item: “I could not live without my smartphone.”

This variable relates to the following question: “How many times do you check your smartphone on a normal day?”

p < 0.05; ^**p < 0.01; ^***p < 0.001.

A, anxiety; D, depression; H, history of smartphone use; LWS, life w/o smartphone; M, mean; S, stress; SC, smartphone checking; SD, standard deviation.

Discussion

The implications of conceptualizing smartphone addiction at the empirical level by examining users' smartphone-related preferences, behavioral changes, and psychological well-being were investigated. Smartphone addiction is not currently considered a bona fide behavioral addiction, so it is necessary to clarify the psychosocial underpinnings of excessive use, which could potentially lead to harmful behavioral patterns. Moreover, the potential psychological effects of smartphone use are still debated in the literature,^3,4 therefore it remains unclear if, and to what extent, decreased psychological well-being may be related to increased smartphone usage patterns. Although it has been argued that people experiencing stressful life events can resort to excessive usage to cope with their negative emotions,¹⁸ little research has ascertained such a relationship at the empirical level.

The results obtained showed that communication functionalities were rated among the most used, even if only one smartphone functionality could be retained. In terms of behavioral changes, one-third of participants reported they would not use their smartphones at all if their preferred functionalities were unavailable. This echoes findings for generalized Internet addiction whereby individuals prevented from using their preferred online activities decreased their use significantly,^14,15 further rendering the concept of generalized Internet addiction unfeasible.

Theoretically, the notion of smartphone addiction as a construct is similarly unfeasible as the findings obtained suggest that people do not become addicted to their smartphones (i.e., medium) but to the functionalities they use (i.e., contents). To add perspective, supporting the notion of smartphone addiction would equate to support the notion of “bottle addiction” in alcohol use disorders, whereby the bottle is represented as the medium and the alcohol represents the content experienced by the individual.^19,20 This study extends previous theoretical works suggesting the unviability of smartphone addiction as a construct, as there is currently no conclusive support from the literature confirming the existence of smartphone addiction.^21,22

This study also aimed to clarify at the empirical level whether smartphone use was related to decreased psychological well-being given the existence of conflicting evidence regarding the nature of this relationship. Recent longitudinal research did not find direct effects of excessive use of smartphones and decreased psychological well-being (i.e., stress and loneliness),²³ despite cross-sectional research reporting several negative outcomes related to excessive smartphone use, including addictive-like symptoms,²⁴ sleep disturbance,²⁵ risky driving behaviors,²⁶ and aggressive behaviors.²⁷

Additionally, a recent cross-cultural study found that smartphone addiction was associated with anxiety but not depression.²⁸ Although the present results indicated small statistically significant associations between frequency of smartphone checking and depression and anxiety, smartphone checking behaviors accounted for only 1.21% of the total variance on depression and 1.44% of the total variance on anxiety symptoms. Moreover, no association was found between frequency of smartphone checking behaviors and stress, and history of smartphone use was unrelated to any of the psychological well-being variables assessed. These findings suggest that frequency of smartphone use itself may not be inherently harmful at the psychological level even in the context of prolonged use.

Although smartphone use is often conceptualized holistically, the present findings highlight the need to further investigate the type and quality of peoples' specific smartphone usages and associated outcomes for psychological well-being, as the device is merely the medium providing accessibility to different content experienced by the user. In light of recent research,²⁹ the present findings pave the way to future smartphone research aimed at distinguishing between mobile versus nonmobile Internet use when investigating Internet use disorders.

Although the present findings illustrate the nature of smartphone use and challenge the theoretical feasibility of smartphone addiction as a construct, potential limitations should be taken into account when interpreting the results. Specifically, the conclusions of this study were derived from self-report as opposed to behavioral data, and for this reason actual smartphone usage patterns (e.g., history and frequency) may have not been accurately reported. It is also important to consider the sample recruited, as the results may not generalize to all smartphone users. Potential biases emerging from the use of self-report measures (e.g., memory recall bias, social desirability) may have influenced the results obtained. In future, the assessment of psychological well-being could also be expanded to include more diverse and robust psychometric tools measuring different facets of well-being (e.g., eudaimonic/hedonic); enabling a deeper understanding of the interplay between smartphone use and psychological well-being. In conclusion, the present study informs future research investigating the nature of specific types of smartphone use and associated psychological well-being effects.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

Funding Information

No funding was received for this study.

References

Deloitte. Global mobile consumer survey, US edition. https://www2.deloitte.com/content/dam/Deloitte/us/Documents/technology-media-telecommunications/us-tmt-global-mobile-consumer-survey-extended-deck-2018.pdf (accessed Mar. 27, 2020).

Deloitte. Global mobile consumer survey 2018: the UK cut. https://www.deloitte.co.uk/mobileuk2018/ (accessed Mar. 27, 2020).

Wiederhold

. Should smartphone use be banned for children?. Cyberpsychology, Behavior, and Social Networking, 2019; 22:235–236.

Ellis

, Davidson

, Kaye

. Should smartphones be banned for children or does cyberpsychology have a bigger problem?. Cyberpsychology, Behavior, and Social Networking, 2019; 22:508–509.

Deloitte. Global mobile consumer survey 2018: the Australian cut. http://images.content.deloitte.com.au/Web/DELOITTEAUSTRALIA/%7B51598b8c-1c72-4ceb-8272-3a052f322992%7D_20181103-tel-inbound-mobile-consumer-survey-2018-report-2.pdf (accessed Mar. 28, 2020).

Dey

, Studer

, Schaub

, et al. Problematic smartphone use in young Swiss men: its association with problematic substance use and risk factors derived from the pathway model. Journal of Behavioral Addictions, 2019; 8:326–334.

Wang

, Liu

, Zhao

, et al. How is problematic smartphone use related to adolescent depression? A moderated mediation analysis. Children and Youth Services Review, 2019; 104:104384.

Peterka-Bonetta

, Sindermann

, Elhai

, et al. Personality associations with smartphone and internet use disorder: a comparison study including links to impulsivity and social anxiety. Frontiers in Public Health, 2019; 7:127.

Vally

, El Hichami

. An examination of problematic mobile phone use in the United Arab Emirates: prevalence, correlates, and predictors in a college-aged sample of young adults. Addictive Behaviors Reports, 2019; 9:100185.

10.

Chen

, Zhang

KZK

, Gong

, et al. Understanding compulsive smartphone use: an empirical test of a flow-based model. International Journal of Information Management, 2017; 37:438–454.

11.

Tateno

, Kim

, Teo

, et al. Smartphone addiction in Japanese college students: usefulness of the Japanese version of the Smartphone Addiction Scale as a screening tool for a new form of internet addiction. Psychiatry Investigation, 2019; 16:115–120.

12.

Billieux

, Schimmenti

, Khazaal

, et al. Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research. Journal of Behavioral Addictions, 2015; 4:119–123.

13.

Griffiths

. Internet addiction: internet fuels other addictions. Student British Medical Journal, 1999; 7:428–429.

14.

Pontes

, Szabo

, Griffiths

. The impact of Internet-based specific activities on the perceptions of Internet addiction, quality of life, and excessive usage: a cross-sectional study. Addictive Behaviors Reports, 2015; 1:19–25.

15.

Griffiths

, Szabo

. Is excessive online usage a function of medium or activity? An empirical pilot study. Journal of Behavioral Addictions, 2014; 3:74–77.

16.

Shaw

, Ellis

, Kendrick

L-R

, et al. Predicting smartphone operating system from personality and individual differences. Cyberpsychology, Behavior, and Social Networking, 2016; 19; DOI: 10.1089/cyber.2016.0324.

17.

Lovibond

, Lovibond

. The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behaviour Research and Therapy, 1995; 33:335–343.

18.

Rozgonjuk

, Elhai

, Hall

. (2019) Studying psychopathology in relation to smartphone use. In Baumeister

, Montag

, eds. Digital Phenotyping and Mobile Sensing: New Developments in Psychoinformatics. Cham: Springer International Publishing, pp. 185–199.

19.

Kuss

, Griffiths

. Social networking sites and addiction: ten lessons learned. International Journal of Environmental Research and Public Health, 2017; 14:311.

20.

Kim

, Kim

. Cross-validation of reliability, convergent and discriminant validity for the problematic online game use scale. Computers in Human Behavior, 2010; 26:389–398.

21.

Panova

, Carbonell

. Is smartphone addiction really an addiction?. Journal of Behavioral Addictions, 2018; 7:252–259.

22.

Billieux

, Maurage

, Lopez-Fernandez

, et al. Can disordered mobile phone use be considered a behavioral addiction? An update on current evidence and a comprehensive model for future research. Current Addiction Reports, 2015; DOI: 10.1007/s40429-015-0054-y.

23.

Karsay

, Schmuck

, Matthes

, et al. Longitudinal effects of excessive smartphone use on stress and loneliness: the moderating role of self-disclosure. Cyberpsychology, Behavior, and Social Networking, 2019; 22:706–713.

24.

Lopez-Fernandez

, Kuss

, Romo

, et al. Self-reported dependence on mobile phones in young adults: A European cross-cultural empirical survey. Journal of Behavioral Addictions, 2017; 6:168–177.

25.

Lemola

, Perkinson-Gloor

, Brand

, et al. Adolescents' electronic media use at night, sleep disturbance, and depressive symptoms in the smartphone age. Journal of Youth and Adolescence, 2014; 44:405–418.

26.

White

, Eiser

, Harris

. Risk perceptions of mobile phone use while driving. Risk Analysis, 2004; 24:323–334.

27.

Nicol

, Fleming

. “i h8 u”: the influence of normative beliefs and hostile response selection in predicting adolescents' mobile phone aggression—a pilot study. Journal of School Violence, 2010; 9:212–231.

28.

Panova

, Carbonell

, Chamarro

, et al. Specific smartphone uses and how they relate to anxiety and depression in university students: a cross-cultural perspective. Behaviour & Information Technology, 2019:1–13.

29.

Montag

, Wegmann

, Sariyska

, et al. How to overcome taxonomical problems in the study of Internet use disorders and what to do with “smartphone addiction”?. Journal of Behavioral Addictions, 2019:1–7 [Epub ahead of print]; DOI: 10.1556/2006.8.2019.59.