Smartphones have become integral parts of students' everyday life. Research has shown that students excessively use their smartphones during semester times, in lectures, and while studying and that their smartphone use seldomly serves educational purposes. Unsurprisingly, smartphone interferences within such academically relevant situations can impair students' performance. For instance, it has been shown that students are more distracted, experience less study-related flow, evaluate their own performance more negatively, and achieve lower grades when engaging with their smartphones in academic contexts. Besides these performance-related downsides, research also suggests that smartphone use can impair students' well-being. Excessive use of smartphones and social media applications has been linked to various well-being-related issues such as negative affect, stress, and anxiety. As students have been identified as a high-risk group prone to smartphone overuse and smartphone addiction, they should be particularly susceptible to such well-being-related consequences.
The overall goal of all institutions of higher education must be the promotion of students' academic success as well as students' well-being as these two interrelated factors act as important predictors for both individual and public health and functioning. Consequently, while it is valuable to examine the negative effects of smartphone use on performance and well-being in academic contexts and understand their underlying processes, it is just as important to explore possible interventions to mitigate such negative outcomes. Here, it is necessary to answer questions regarding the effectiveness of such interventions (e.g., smartphone abstinence) on a variety of outcome variables and incorporate possible mediating or moderating influences relevant to the effects of such interventions on students' performance and well-being. Unfortunately, intervention studies in this regard are scarce. Yet, existing research indicates inconsistent findings. In fact, there is some evidence that short separation phases from smartphones result in higher anxiety levels. Moreover, phases of smartphone and social media abstinence appear to go along with smartphone cravings and potential overuse after the intervention is over. However, some studies found promising effects of separation phases on well-being, life satisfaction, procrastination, perceived stress or depression. A first study that investigated separation phases from smartphones among students revealed positive effects on individual well-being and performance by enhancing personal lifestyle, health, and academic management and reducing smartphone overuse. Yet, such intervention studies are extremely limited and need to be studied more rigorously. Especially moderating or mediating variables need to be taken into account to explain the effectiveness of smartphone abstinence interventions. In this light, smartphone addiction and fear of missing out (FOMO) seem to play an important role concerning the detrimental effect of smartphone abstinence on well-being. Finally, existing studies have mainly focused on the effects of smartphone separation phases lasting several hours or even days. As these are rather unrealistic settings, future interventions should be designed in ways that integrate pauses from smartphone use into people's everyday life.
Consequently, the present study aims at investigating the effectiveness of an intervention in which students are to develop action plans (BCT 1.4; ) as well as coping plans (BCT 1.2) allowing them to study without smartphone interferences. Planning is a very simple strategy with impressive effects, as indicated by medium to large effect sizes on behavior observed across various populations and behaviors. During a planning intervention, an individual is linking a situational cue (when/where) to an intended behavioral response (how) by mental simulation of anticipated situations. Thus, the goal is to link a specific cue to an intended action in order to translate goal intentions into behavior. In addition, planning is often complemented by coping planning (anticipation of barriers and the formation of plans on how to overcome them). In this study, individuals are to complete a planning sheet that contains both action and coping plans to restrict their own smartphone use during learning periods.
The measured outcomes include a variety of performance- (i.e., ability to concentrate, perceived learning achievement, exam grade, exam-related stress) and well-being-related variables (e.g., positive and negative affect, anxiety, subjective well-being). Furthermore, in this study, the mediating role of variables sought to be promoted through the intervention (i.e. decreased daily smartphone use, decreased daily use of social media applications, increased exam preparation-related flow) and possible moderators (i.e. smartphone addiction, FoMO) are also investigated.
The aims of the present study are threefold. First, the effectiveness of planning a separation from the smartphone during an exam phase is compared against a control group on a device-based assessment of smartphone use. Besides this first main aim, it is also aimed at specifically comparing the effectiveness of the planning intervention to a control group on academic performance and well-being among students. Third, this study examines the assumed underlying mechanisms as well as possible moderators of the planning intervention.
Research questions and hypotheses
Research question 1: Is planning an effective strategy to reduce smartphone use among students during an exam period?
Hypotheses 1.a - 1.b: Students in the planning intervention group will display a) shorter overall smartphone use, b) decreased use of social media applications than students in the control group.
Research question 2: What are the underlying mechanisms of the planning intervention in students regarding smartphone use reduction?
Hypotheses 2.a - 2.b: The effect of planning on smartphone use reduction is mediated by a) individual action planning, and b) individual coping planning.
Research question 3: Does the planning intervention result in higher academic performance?
Hypotheses 3.a - 3.d.: Students in the planning intervention group will a) display greater ability to concentrate, b) experience lower study-related stress, c) evaluate their perceived learning achievement more positively, and d) achieve better exam grades than students in the control group.
Research question 4: What are possible moderators of the relationship between the planning intervention and academic performance?
Hypotheses 4.a - 4.b.: The effect of the intervention on students' academic performance will be moderated by their levels of a) fear of missing out, and b) smartphone addiction.
Research question 5: What are the underlying mechanisms of the planning intervention regarding academic performance in students?
Hypotheses 5.a - 5.d.: The effect of the planning intervention on students' academic performance will be mediated by a) shorter overall smartphone use, b) decreased use of social media applications, and c) enhanced exam preparation-related flow.
Research question 6: Does the planning intervention result in more well-being in students?
Hypotheses 6.a - 6.d.: Students in the planning intervention group will a) display higher levels of positive affect, b) lower levels of negative affect, c) less anxiety, and d) higher subjective well-being than students in the control group.
Research question 7: What are possible moderators of the relationship between the planning intervention and well-being in students?
Hypotheses 7.a - 7.b.: The effect of the intervention on students' well-being will be moderated by their levels of a) fear of missing out and b) smartphone addiction.
Research question 8: What are the underlying mechanisms of the planning intervention in students regarding well-being?
Hypotheses 8.a - 8.d.: The effect of the planning intervention on students' well-being will be mediated by a) shorter overall smartphone use and b) decreased use of social media applications.
Study Design
The present study utilizes an online longitudinal randomized control trial conducted at nationwide universities in Germany over a course of 3 months per data collection period. Assessments will be conducted in a student sample weekly before the examination phase (t1-t3), after the first exam (t4), and after the exam grades have been announced (t5). Students will be randomly assigned to an intervention and a control group.
First, interested individuals have to fill in a prescreening questionnaire. In case all inclusion criteria are met, students have to fill in the Baseline assessment. At the end of the Baseline measurement, all students will be given general advice on how to organize their study environment and behavior to improve their overall learning performance (e.g., organization of materials for exam preparation, pauses during exam preparation). Participants in the intervention group will also be instructed to develop individual action and coping plans to decrease smartphone interferences when studying. Students in the control group have to fill out questionnaires on general health behavior instead. Students in both groups will also be asked to install a mobile application on their smartphones which objectively measures each participants' daily smartphone use, screen activations, and specific application usage. The application will not inform participants about their smartphone use but log the data in the background. Participants will be instructed to not uninstall the application before the measurement time point t4.
One week after the Baseline measurement and the intervention, participants receive the online questionnaire t2, and two weeks after the Baseline measurement, questionnaire t3 follows. After the first exam which was asked for in the Baseline assessment, participants will receive questionnaire t4. Two month later participants will receive a short questionnaire (t5) that ask for the exam grades. All participants will be debriefed at the end of the study. Through their participation, students can participate in a voucher raffle; this information is provided before study participation and again at the end of each questionnaire.
Additional analyses according to the COVID-19 pandemic:
During data collection of the originally planned study "Study Smart", the global pandemic COVID-19 occurred. Because this gives us data from students across different phases of the pandemic, we plan to conduct additional analyses of the data as described below.
In this additional analyses, we compare the emotional well-being (i.e., positive and negative affect, general well-being, and perceived stress) and academic functioning (i.e., academic well-being encompassing study-related stress and test anxiety, academic self-perception encompassing students' academic self-concept and perceived study-related self-efficacy, academic motivation encompassing students' achievement motivation and study-related flow, and academic self-regulation encompassing students' concentration, frequency of study activities, and procrastination) of three student cohorts. The first student cohort was assessed before the COVID-19 pandemic, and therefore represents a pre-pandemic control group. The second cohort was assessed after the first lockdown in Germany. These students experienced emergency remote learning and eased lockdown measures. The third student cohort was assessed during the second lockdown in Germany, when coronavirus cases dramatically increased. These students experienced both emergency remote learning and social detriments from rigorous lockdown measures.
Students' general emotional well-being
Research indicates that COVID-19-related lockdown measures engender affective detriments in students (e.g., negative emotions, stress, and depression). However, research also shows that students' general emotional well-being appears to rapidly improve to pre-pandemic levels when lockdown measures are eased. In line with this, for students' general emotional well-being (H1), we hypothesize the following:
H1.1: The third student cohort will report (a) less positive and (b) more negative affect, (c) less general well-being, and (d) higher perceived stress than the first cohort.
H1.2: The third student cohort will report (a) less positive and (b) more negative affect, (c) less general well-being, and (d) higher perceived stress than the second cohort.
H1.3: The second student cohort will report similar levels of (a) positive and (b) negative affect, (c) general well-being, and (d) perceived stress to the first cohort.
Students' academic functioning
Research also indicates that COVID-19-related lockdown measures can impair students' academic functioning. Furthermore, the transition to emergency remote learning can result in academic detriments. We therefore assume that the student cohort assessed during the second lockdown in Germany (i.e., students experiencing both emergency remote learning and social detriments from rigorous lockdown measures) will exhibit a severe decline in their academic functioning compared to the pre-pandemic cohort. Yet, the student cohort assessed after the first lockdown (i.e., students experiencing emergency remote learning and eased lockdown measures) should also be affected in their academic functioning due to remaining emergency remote learning albeit not as pronounced as those students assessed during the second lockdown. Therefore, as for students' academic functioning encompassing academic well-being (H2), academic self-perception (H3), academic motivation (H4), and academic self-regulation (H5), we hypothesize the following:
H2.1: The third student cohort will report (a) more study-related stress and (b) more test anxiety than the first cohort.
H2.2: The third student cohort will report (a) more study-related stress and (b) more test anxiety than the second cohort.
H2.3: The second student cohort will report (a) more study-related stress and (b) more test anxiety than the first cohort.
H3.1: The third student cohort will report (a) a lower academic self-concept and (b) less study-related self-efficacy than the first cohort.
H3.2: The third student cohort will report (a) a lower academic self-concept and (b) less study-related self-efficacy than the second cohort.
H3.3: The second student cohort will report (a) a lower academic self-concept and (b) less study-related self-efficacy than the first cohort.
H4.1: The third student cohort will report (a) less study-related motivation and (b) less study-related flow than the first cohort.
H4.2: The third student cohort will report (a) less study-related motivation and (b) less study-related flow than the second cohort.
H4.3: The second student cohort will report (a) less study-related motivation and (b) less study-related flow than the first cohort.
H5.1: The third student cohort will report (a) less concentration and (b) lower frequency of study activities, and (c) more procrastination than the first cohort.
H5.2: The third student cohort will report (a) less concentration and (b) lower frequency of study activities, and (c) more procrastination than the second cohort.
H5.3: The second student cohort will report (a) less concentration and (b) lower frequency of study activities, and (c) more procrastination than the first cohort.
