Download Using mobile apps to help clients experiencing problematic smartphone use or gambling disorder [PDF].
Introduction: What is PSU and GD?
Problematic smartphone use (PSU) and gambling disorder (GD) can have significant negative impacts on individuals, their families, their friends and other loved ones. PSU is associated with depression, anxiety, social impairments and academic underachievement (Kim et al., 2015; Lemola et al., 2015; Montag & Rumpf, 2021; Sunday et al., 2021). Similarly, individuals with GD often experience mental health impacts in addition to significant negative disruptions to their finances (Langham et al., 2015; Lorains et al., 2011).
Currently, no established definition of PSU exists (Panova & Carbonell, 2018). However, PSU has been thought to include the following components (Billieux, 2012; Chóliz, 2010; Ko et al., 2015; Lee et al., 2014; Lin et al., 2014):
- tolerance (i.e., needing to spend more time on a smartphone to experience the same amount of pleasure)
- compulsion (i.e., the habitual checking of a smartphone)
- functional impairment (i.e., experiencing poor school/work performance and financial problems as a result of impaired smartphone use)
- using a smartphone for self-soothing or to avoid problems
- dangerously using a smartphone (e.g., while driving, in areas where smartphone use is prohibited, etc.).
For the purpose of this evidence-informed practice (EIP) page, “smartphone” is defined as “a mobile phone that performs many of the functions of a computer, typically having a touchscreen interface, Internet access, and an operating system capable of running downloaded apps” (Shimray et al., 2015, p. 369).
Because there is no established definition of PSU, other terms have been used within the literature to describe smartphone usage that leads to negative consequences. These include smartphone addiction, mobile phone addiction, nomophobia and excessive smartphone use. In this EIP, “PSU” is used to include the previously mentioned terms.
In contrast to PSU, GD has an established definition and is currently defined in the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5)―the primary tool used to classify and diagnose mental health disorders in North America―as “persistent and recurrent problematic gambling behavior leading to clinically significant impairment or distress” (American Psychiatric Association, 2013, p. 585).
Furthermore, both PSU and GD share similar assessment and treatment approaches. For example, assessments of PSU and GD often involve self-report measures that have been psychometrically validated or interviews conducted by clinicians (Davey & Davey, 2014; Hodgins et al., 2011; Lin et al., 2014, 2017). Similarly, conventional treatment approaches for PSU and GD have focused on self-reporting and psychological therapies delivered by clinicians (Cowlishaw et al., 2012; Hodgins et al., 2011; Lee et al., 2017).
Mobile applications (mobile apps) are increasingly being used in the assessment of PSU and in the treatment of both PSU and GD. One advantage of mobile apps is that they can capture precise data (e.g., screen time, number of log-ins, etc.) and can provide therapy in real time (Hawker et al., 2021; Hwangbo et al., 2013; Lin et al., 2017).
This EIP provides information for mental health and addictions service providers on how mobile apps can be used to assist clients experiencing either PSU or GD. It contains a section on the current literature available and provides recommendations of features to look for when selecting mobile apps to support clients.
What does the evidence say?
Pairing mobile apps with current screening and diagnosing tools for PSU
An evidence-informed understanding of the utility of mobile apps as screening and diagnostic tools is in its early phases. However, the emerging evidence suggests that such apps can be a useful tool in gaining an objective perspective of an individual’s PSU (Lee et al., 2017; Montag et al., 2015). Presently, standardized screening and diagnostic tools for PSU are administered by clinicians interviewing clients or through self-administered surveys (Khoury et al., 2017; Lee et al., 2017; Lin et al., 2014, 2015; Noë et al., 2019).
While self-reporting is important in getting an individual’s perspective of their PSU, it has also been shown to face problems with recall bias (Lee et al., 2017; Montag et al., 2015; Montag & Rumpf, 2021). Recall bias is a type of research bias when study participants are asked to remember past events or experiences and they do so inaccurately (Althubaiti, 2016). For instance, Lee and colleagues (2017) found that study participants were inaccurate at recalling the apps they used most often and the peak times they used these apps.
Mobile apps avoid this problem by tracking behaviour and specific metrics related to smartphone usage in real time, providing an objective picture of an individual’s actual smartphone usage (Lin et al., 2015; Noë et al., 2019; van Velthoven et al., 2018). For instance, frequency of smartphone use, which is suggested as a characteristic of PSU, is difficult for a client to recall through an interview or self-reported questionnaire (Lin et al., 2015; Montag et al., 2015). However, this pattern of behaviour can be recorded in real time and reported with accuracy by a mobile tracking app (Loid et al., 2020; Montag et al., 2015). Using objective measures to track smartphone usage is especially important, as it has been shown that individuals with high levels of PSU are more likely to underestimate their smartphone usage (Lee et al., 2017).
Indicators that are measured for PSU
Mobile apps have been used to measure different indicators of PSU (Lee et al., 2017; Lin et al., 2015; Shin & Lee, 2017). Within the literature, the following indicators have been identified as potentially being associated with PSU (Lee et al., 2017; Lin et al., 2015, 2017; Shin & Lee, 2017):
- duration of smartphone use
- frequency of smartphone use
- types of applications (e.g., social media) used on a smartphone
- physical interactions with a smartphone device/interface.
Multiple studies have looked at the duration of smartphone use and associated this indicator with the tolerance component of PSU (Lin et al., 2015, 2017). Some researchers suggest that tolerance in PSU is demonstrated when an individual increases the amount of time they spend on their smartphone in order to get previous levels of satisfaction with use (Chóliz, 2012; Lin et al., 2015).
While the duration of use is considered an important indicator to measure, multiple studies have also suggested that using mobile apps to measure the duration alone may be inadequate in identifying whether an individual has PSU (Lin et al., 2015, 2017; Shin & Lee, 2017). Other authors suggest that because smartphones have increasing amounts of capabilities, an increase in the time spent on them may reflect an individual using a smartphone instead of other devices (e.g., using a smartphone to watch a show instead of watching it on their television; Noë et al., 2019). The increase in time using a smartphone may not reflect problematic usage. This is why the duration of smartphone use needs to be considered in context, along with the other indicators listed above, when determining if smartphone use is problematic (Noë et al., 2019).
Alternatively, the frequency of use of a smartphone has been found to be more associated with PSU and a stronger predictor of PSU than the duration of use (Lin et al., 2015, 2017). The frequency of use has been studied as the number of times an individual opens and closes their smartphone. Some researchers suggest the frequent checking of a smartphone may reflect the compulsive behaviour that is thought to be present in PSU (Lin et al., 2015, 2017). These frequent interactions with smartphones may impact relationship formation and work, causing impairments in quality of life (Lin et al., 2015, 2017; Montag & Rumpf, 2021; Shin & Lee, 2017).
Other indicators of PSU have also been discussed in the literature. Some authors point to certain applications as having a greater tendency toward PSU. For instance, Noë et al. (2019) found that the use of social media apps such as Snapchat were significantly correlated with PSU. In their study, Lee et al. (2017) also found that individuals with PSU had a greater preference for using social media applications. The way these social media apps are used may point to the presence of PSU (Shin & Lee, 2017).
Physical interactions with a smartphone (e.g., the number of times the screen is touched, the number of log-ins) have also been suggested as a way to gain more information about the compulsive behaviours related to PSU (Shin & Lee, 2017). This indicator has also been suggested as being better at identifying PSU compared to only using duration data because it demonstrates how someone is using their smartphone compulsively (Noë et al., 2019; Shin & Lee, 2017).
PSU presents as a complex phenomenon that has many components and requires the use of multiple measures to gain a clear picture of what type of smartphone use may be harmful or safe (Kent et al., 2021).
Mobile apps as interventions for reducing PSU
Currently, only a few studies exist that look at the use of mobile apps as interventions for PSU. For instance, Kent et al. (2021) created and evaluated an app for 10 undergraduate students to help them with PSU. In the app, they had participants create goals for their smartphone use. The app also provided participants with suggestions for mindfulness and behavioural strategies to follow to achieve their goal. The participants in the study found the intervention effective, and initial data showed improvements in problematic use, well-being and mindfulness (Kent et al., 2021).
Esmaeili Rad and Ahmadi (2018) also created an app to address PSU, specifically focused on social network addictions in undergraduate students. Similar to Kent and colleagues’ (2021) study, the app that Esmaeili Rad and Ahmadi (2018) created provided alternative activities to engage in instead of using social networking applications. Their app also featured the ability to block social media apps to varying degrees (e.g., completely, only on certain days, etc.) and included a section on teaching about social network addictions. Esmaeili Rad and Ahmadi (2018) found that their app was effective in reducing social network usage and other psychological conditions such as depression and anxiety.
Alternatively, while the above-mentioned apps were self-motivated interventions, Ko and colleagues (2015) conducted a preliminary study on an app that was group based. The participants used the app to gain support from their peers and also compete against them in order to see who used their smartphone the least. Ko et al. (2015) found that those in the intervention group experienced significant decreases in their smartphone usage, and their belief that they were able to control distractions increased.
Other interventions have also been suggested, such as “digital detox” apps, which block the use of certain apps for a period of time (Schmuck, 2020). These types of apps have been found to be the most popular type of intervention apps available (Ko et al., 2015). Schmuck (2020) found that digital detox apps may help to reduce problematic social network use on smartphones potentially through impacting compulsive behaviours.
While intervention apps present as a promising avenue to address PSU, Alrobai and colleagues (2019) suggest that the way these apps are designed may result in negative unintended consequences. For instance, social competition in group intervention apps may lead to lower self-esteem and self-efficacy if a participant is not at the level of their peers (Alrobai et al., 2019). Goals may be simplified to just becoming the leader on a leaderboard instead of learning skills to manage smartphone use. Users may also set lower goals so that they are able to achieve them easily to keep up with their peers (Alrobai et al., 2019).
Mobile apps and gambling
Currently, to the best of our knowledge, no research is present on mobile apps to help diagnose, screen or assess GD. This is a topic area that GGTU will continue to update as new information becomes available.
Mobile apps as interventions for GD
Similar to intervention apps for PSU, mobile apps for GD are varied, few in number and lack research (Brownlow, 2021; Giroux et al., 2017). One Australian study found that most of the GD apps available on Google Play and Apple iTunes in Australia had few functions backed by research (Brownlow, 2021). However, a few acceptability and feasibility studies on apps for GD show some promise.
For example, in a pilot study looking at an app that delivers cognitive-behavioural therapy (CBT) homework for people with GD, Pfund et al. (2020) found that the app was a feasible and acceptable way to deliver the CBT homework. They suggest this app may increase compliance with CBT homework, which is considered a factor in the efficacy of CBT for gambling.
Another app focused on using CBT-based interventions to reduce gambling-related cravings in people who gamble (Hawker et al., 2021). The app works by providing random assessments of the individual’s urge throughout the day. If an individual acknowledges that they are experiencing an urge on the assessment, the app recommends CBT-based techniques to reduce their urge. The individual can also request a CBT-based technique at any time regardless of whether they have completed an assessment (Hawker et al., 2021). The participants in the study found the urge-curbing activities helpful and they rated other components of the app as effective. They also experienced a reduction in gambling episodes and incidents of cravings (Hawker et al., 2021).
Other features have also been incorporated into mobile apps geared toward people who gamble. For instance, Humphrey et al. (2019) reported on an app for people who gamble that uses components of CBT and includes a geolocation feature. The app regularly provides CBT-related messages via notifications. It also has a feature where if an individual were to enter a gambling venue, it would provide them with a notification to distract or deter them from gambling (Humphrey et al., 2019). This study experienced significant issues with recruitment and the app had technical problems, which impacted the findings (Humphrey et al., 2019).
How do I put this evidence into practice?
Currently, to the best of our knowledge, no guidelines exist regarding the use of mobile apps for identifying and treating PSU and GD. However, with the current body of research available, some preliminary suggestions can be made.
Mobile apps that track smartphone usage may complement validated questionnaires and clinical interviews to identify and assess PSU by providing an objective measure of smartphone usage (Lin et al., 2015; Montag et al., 2015). Based on what is currently known about promising indicators of PSU, apps that measure the following metrics may be suggested to clients when recommending that they track their smartphone usage patterns:
- duration of smartphone use with the addition of another tracking metric below
- frequency of smartphone use
- types of applications (e.g., social media) used on a smartphone
- physical interactions with a smartphone device/interface.
Presently, many popular smartphone operating systems (e.g., Android, iOS) provide the ability to track several of these indicators without the use of an additional app.
While it is currently recommended that mobile app interventions do not replace conventional therapies in the treatment of PSU, preliminary evidence suggests that these app innovations may hold promise as additional or complementary support tools, particularly apps that use mindfulness and behavioural strategies as well as blocking features (Alrobai et al., 2019; Esmaeili Rad & Ahmadi, 2018; Kent et al., 2021).
For mobile apps for treating GD, clinicians can suggest intervention apps with components of CBT, which have shown some initial promise in being effective for GD intervention apps (Hawker et al., 2021; Humphrey et al., 2019; Pfund et al., 2020). Additionally, CBT delivered online through self-directed programs has been shown to be effective (Carlbring et al., 2012). However, further research is needed to determine the effectiveness of CBT delivered through a mobile app for people with GD.
Prior to having discussions around different types of mobile apps that clients can download, it is also important to discuss certain practical concerns that come with downloading and using mobile apps. For instance, clinicians should consider discussing concerns related to privacy, data security and bandwidth requirements with their clients. This will ensure that their clients are fully informed before downloading and using any mobile app. In addition, clinicians should adhere to regulations pertaining to technology use practices outlined by their respective regulatory colleges.
The lack of standardized terminology/definitions and diagnostic criteria in relation to PSU introduces significant challenges in synthesizing the literature on this topic.
Additionally, there is an acknowledged lack of research available regarding mobile apps for PSU and GD (Humphrey et al., 2019; Giroux et al., 2017; Lam & Lam, 2016). Available studies have reported limited sample sizes and lack diversity in the populations they studied. Many of these studies are also pilot studies, so their findings should be treated as preliminary.
Finally, another limitation that comes from a lack of research in this area is that there are currently no health equity considerations specific to mobile apps for PSU and GD that we can recommend at this time.
At GGTU, we are always reviewing the most current research and will update these sections of our EIPs with any limitations or considerations you may need to know when putting this evidence into practice.
- A mobile app created by the Centre for Addiction and Mental Health (CAMH) researchers for treatment planning and relapse prevention for problem gambling (available via Google Play or Apple App Store):
- This website evaluates mental health apps based on their credibility, user experience and transparency. Although the website does not have any reviews on mobile apps specific to PSU and GD, it may serve as a useful resource for clinicians: https://onemindpsyberguide.org
- This resource from the American Psychiatric Association provides a tool to evaluate mental health apps and takes into consideration different aspects of mobile apps as well as privacy and safety concerns: https://www.psychiatry.org/psychiatrists/practice/mental-health-apps
- A mobile app for Android devices that tracks multiple metrics including duration, how often the smartphone is checked and how often an app is checked: https://play.google.com/store/apps/details?id=com.a0soft.gphone.uninstaller (This is an example of an app that may be suggested to clients to track their smartphone usage. Please note that apps are constantly being updated, so the features of this app may change.)
- Trainings and webinars delivered by GGTU
Althubaiti, A. (2016). Information bias in health research: Definition, pitfalls, and adjustment methods. Journal of Multidisciplinary Healthcare, 9, 211–217. Available: https://doi.org/10.2147/JMDH.S104807. Accessed March 31, 2022.
Alrobai, A., McAlaney, J., Phalp, K. & Ali, R. (2019). Exploring the risk factors of interactive e-health interventions for digital addiction. In Information Resources Management Association (Ed.), Substance Abuse and Addiction: Breakthroughs in Research and Practice (pp. 375–390). Hershey, PA: IGI Global. https://doi.org/10.4018/978-1-5225-7909-0.ch021.
American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders (5th ed.). https://doi.org/10.1176/appi.books.9780890425596.
Billieux, J. (2012). Problematic use of the mobile phone: A literature review and a pathways model. Current Psychiatry Reviews, 8 (4), 299–307. http://dx.doi.org/10.2174/157340012803520522.
Brownlow, L. (2021). A review of mHealth gambling apps in Australia. Journal of Gambling Issues, 47. 1–19. Available: https://doi.org/10.4309/jgi.2021.47.1. Accessed March 22, 2022.
Carlbring, P., Degerman, N., Jonsson, J. & Andersson, G. (2012). Internet-based treatment of pathological gambling with a three-year follow-up. Cognitive Behaviour Therapy, 41 (4), 321–334. Available: https://doi.org/10.1080/16506073.2012.689323. Accessed March 22, 2022.
Chóliz, M. (2010). Mobile phone addiction: A point of issue. Addiction, 105 (2), 373–375. Available: https://doi.org/10.1111/j.1360-0443.2009.02854.x. Accessed March 22, 2022.
Chóliz, M. (2012). Mobile-phone addiction in adolescence: The test of mobile phone dependence (TMD). Progress in Health Sciences, 2 (1), 33–44.
Cowlishaw, S., Merkouris, S., Dowling, N., Anderson, C., Jackson, A. & Thomas, S. (2012). Psychological therapies for pathological and problem gambling. Cochrane Database of Systematic Reviews, (11). https://doi.org/10.1002/14651858.CD008937.pub2.
Davey, S. & Davey, A. (2014). Assessment of smartphone addiction in Indian adolescents: A mixed method study by systematic-review and meta-analysis approach. International Journal of Preventive Medicine, 5 (12), 1500–1511. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4336980. Accessed March 22, 2022.
Esmaeili Rad, M. & Ahmadi, F. (2018). A new method to measure and decrease the online social networking addiction. Asia‐Pacific Psychiatry, 10 (4), Article e12330. https://doi.org/10.1111/appy.12330.
Hawker, C.O., Merkouris, S.S., Youssef, G.J. & Dowling, N.A. (2021). A smartphone-delivered ecological momentary intervention for problem gambling (GamblingLess: Curb your urge): Single-arm acceptability and feasibility trial. Journal of Medical Internet Research, 23 (3), Article e25786. Available: https://doi.org/10.2196/25786. Accessed March 22, 2022.
Hodgins, D.C., Stea, J.N. & Grant, J.E. (2011). Gambling disorders. The Lancet, 378 (9806), 1874–1884. https://doi.org/10.1016/S0140-6736(10)62185-X.
Humphrey, G., Newcombe, D., Whittaker, R., Parag, V. & Bullen, C. (2019). SPGeTTI: A smartphone-based problem gambling evaluation and technology testing initiative final report. The Ministry of Health. Available: https://www.health.govt.nz/system/files/documents/publications/20190424-spgetti-354913-00-final-report.pdf. Accessed March 22, 2022.
Hwangbo, H., Yoon, S.H., Jin, B.S., Han, Y.S. & Ji, Y.G. (2013). A study of pointing performance of elderly users on smartphones. International Journal of Human-Computer Interaction, 29 (9), 604–618. https://doi.org/10.1080/10447318.2012.729996.
Kent, S., Masterson, C., Ali, R., Parsons, C.E. & Bewick, B.M. (2021). Digital intervention for problematic smartphone use. International Journal of Environmental Research and Public Health, 18 (24), 13165. Available: https://doi.org/10.3390/ijerph182413165. Accessed March 22, 2022.
Khoury, J.M., de Freitas, A.A.C., Roque, M.A.V., Albuquerque, M.R., das Neves, M.dC.L. & Garcia, F.D. (2017). Assessment of the accuracy of a new tool for the screening of smartphone addiction. PloS One, 12 (5), Article e0176924. Available: https://doi.org/10.1371/journal.pone.0176924. Accessed March 22, 2022.
Kim, S.E., Kim, J.W. & Jee, Y.S. (2015). Relationship between smartphone addiction and physical activity in Chinese international students in Korea. Journal of Behavioral Addictions, 4 (3), 200–205. Available: https://doi.org/10.1556/2006.4.2015.028. Accessed March 22, 2022.
Ko, M., Yang, S., Lee, J., Heizmann, C., Jeong, J., Lee, U. et al. (2015). NUGU: A group-based intervention app for improving self-regulation of limiting smartphone use. Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, 1235–1245. https://dl.acm.org/doi/10.1145/2675133.2675244.
Lam, L.T. & Lam, M.K. (2016). eHealth intervention for problematic internet use (PIU). Current Psychiatry Reports, 18 (12), Article 107. Available: https://doi.org/10.1007/s11920-016-0747-5. Accessed March 22, 2022.
Langham, E., Thorne, H., Browne, M., Donaldson, P., Rose, J. & Rockloff, M. (2015). Understanding gambling related harm: A proposed definition, conceptual framework, and taxonomy of harms. BMC Public Health, 16, Article 80. Available: https://doi.org/10.1186/s12889-016-2747-0. Accessed March 22, 2022.
Lee, H., Ahn, H., Choi, S. & Choi, W. (2014). The SAMS: Smartphone addiction management system and verification. Journal of Medical Systems, 38 (1), Article 1. Available: https://doi.org/10.1007/s10916-013-0001-1. Accessed March 22, 2022.
Lee, H., Ahn, H., Nguyen, T.G., Choi, S.W. & Kim, D.J. (2017). Comparing the self-report and measured smartphone usage of college students: A pilot study. Psychiatry Investigation, 14 (2), 198–204. Available: https://doi.org/10.4306/pi.2017.14.2.198. Accessed March 22, 2022.
Lemola, S., Perkinson-Gloor, N., Brand, S., Dewald-Kaufmann, J.F. & Grob, A. (2015). Adolescents’ electronic media use at night, sleep disturbance, and depressive symptoms in the smartphone age. Journal of Youth and Adolescence, 44 (2), 405–418. Available: https://doi.org/10.1007/s10964-014-0176-x. Accessed March 22, 2022.
Lin, Y.H., Chang, L.R., Lee, Y.H., Tseng, H.W., Kuo, T.B. & Chen, S.H. (2014). Development and validation of the smartphone addiction inventory (SPAI). PloS One, 9 (6), Article e98312. Available: https://doi.org/10.1371/journal.pone.0098312. Accessed March 22, 2022.
Lin, Y.H., Lin, P.H., Chiang, C.L., Lee, Y.H., Yang, C.C., Kuo, T.B. et al. (2017). Incorporation of mobile application (app) measures into the diagnosis of smartphone addiction. The Journal of Clinical Psychiatry, 78 (7), 866–872. https://doi.org/10.4088/JCP.15m10310.
Lin, Y.H., Lin, Y.C., Lee, Y.H., Lin, P.H., Lin, S.H., Chang, L.R. et al. (2015). Time distortion associated with smartphone addiction: Identifying smartphone addiction via a mobile application (app). Journal of Psychiatric Research, 65, 139–145. Available: https://doi.org/10.1016/j.jpsychires.2015.04.003. Accessed March 22, 2022.
Loid, K., Täht, K. & Rozgonjuk, D. (2020). Do pop-up notifications regarding smartphone use decrease screen time, phone checking behavior, and self-reported problematic smartphone use? Evidence from a two-month experimental study. Computers in Human Behavior, 102, 22–30. https://doi.org/10.1016/j.chb.2019.08.007.
Lorains, F.K., Cowlishaw, S. & Thomas, S.A. (2011). Prevalence of comorbid disorders in problem and pathological gambling: Systematic review and meta‐analysis of population surveys. Addiction, 106 (3), 490–498. Available: https://doi.org/10.1111/j.1360-0443.2010.03300.x. Accessed March 22, 2022.
Montag, C., Błaszkiewicz, K., Lachmann, B., Sariyska, R., Andone, I., Trendafilov, B. et al. (2015). Recorded behavior as a valuable resource for diagnostics in mobile phone addiction: Evidence from psychoinformatics. Behavioral Sciences, 5 (4), 434–442. Available: https://doi.org/10.3390/bs5040434. Accessed March 22, 2022.
Montag, C. & Rumpf, H.J. (2021). The potential of digital phenotyping and mobile sensing for psycho-diagnostics of internet use disorders. Current Addiction Reports, 8 (3), 422–430. Available: https://doi.org/10.1007/s40429-021-00376-6. Accessed March 22, 2022.
Noë, B., Turner, L.D., Linden, D.E.J., Allen, S.M., Winkens, B. & Whitaker, R.M. (2019). Identifying indicators of smartphone addiction through user-app interaction. Computers in Human Behavior, 99, 56–65. Available: https://doi.org/10.1016/j.chb.2019.04.023. Accessed March 22, 2022.
Panova, T. & Carbonell, X. (2018). Is smartphone addiction really an addiction? Journal of Behavioral Addictions, 7 (2), 252–259. Available: https://doi.org/10.1556/2006.7.2018.49. Accessed March 22, 2022.
Pfund, R.A., Whelan, J.P., Meyers, A.W., Peter, S.C., Ward, K.D. & Horn, T.L. (2020). The use of a smartphone application to complete therapeutic homework in cognitive-behavioral therapy for gambling disorder: A pilot study of acceptability and feasibility. Journal of Technology in Behavioral Science, 5 (2), 156–163. Available: https://doi.org/10.1007/s41347-019-00123-9. Accessed March 22, 2022.
Schmuck, D. (2020). Does digital detox work? Exploring the role of digital detox applications for problematic smartphone use and well-being of young adults using multigroup analysis. Cyberpsychology, Behavior, and Social Networking, 23 (8), 526–532. https://doi.org/10.1089/cyber.2019.0578.
Shimray, S.R., Keerti, C. & Ramaiah, C.K. (2015). An overview of mobile reading habits. DESIDOC Journal of Library & Information Technology, 35 (5), 364–375. Available: https://doi.org/10.14429/djlit.35.5.8901. Accessed March 30, 2022.
Shin, M. & Lee, K. (2017). Measuring smartphone usage time is not sufficient to predict smartphone addiction. Journal of Theoretical and Applied Information Technology, 95 (19), 5296–5303.
Sunday, O.J., Adesope, O.O. & Maarhuis, P.L. (2021). The effects of smartphone addiction on learning: A meta-analysis. Computers in Human Behavior Reports, 4, Article 100114. Available: https://doi.org/10.1016/j.chbr.2021.100114. Accessed March 22, 2022.
van Velthoven, M.H., Powell, J. & Powell, G. (2018). Problematic smartphone use: Digital approaches to an emerging public health problem. Digital Health, 4, 1–9. Available: https://doi.org/10.1177/2055207618759167. Accessed March 22, 2022.