Well-Being of Adolescents During the COVID-19 Pandemic
Ambulatory Assessment of Physical and Sport Activity, Social Contacts, and Screen Time
Abstract
Abstract: During the COVID-19 pandemic, implemented social distancing measures led to behavioral changes and decreased well-being in adolescents. The aim of this study was to examine the relation between daily behaviors (physical and sport activity, social contacts, screen time) and adolescent well-being. For this, we conducted a 28-day ambulatory assessment study. Daily data of 125 German adolescents (11–20 years) were collected every evening through self-report and analyzed with multilevel models. Between and within individuals, physical activity was positively related to well-being and screen time was negatively related to well-being. Social contacts were positively related to well-being within individuals. Explorative analyses revealed differences between sport activity contexts (sports club, leisure time, school), and between in-person and digital social contacts. Our findings suggest that physical activity and in-person social contacts are positively related to adolescent well-being and should, thus, be enabled during the pandemic. Furthermore, the role of screen time should be considered in health promotion.
People’s daily lives and behaviors were affected by social distancing rules, lockdowns, and school closures implemented during the COVID-19 pandemic. Warnings about negative effects of distancing rules were raised – especially for youth as they were affected in a particularly crucial phase of development (Fegert et al., 2020). In this context, Thiel et al. (2021) speak of a COVID-19 paradox of infection prevention: While social distancing protects against infections, the people to be protected are socially isolated, put under enormous psychological strain, and might suffer a decline of physical health.
Accordingly, a growing body of research reported decreased well-being (Meherali et al., 2021) and increased mental health problems (Racine et al., 2021) in adolescents during the pandemic. Most studies reported cross-sectional data, but an increasing number of longitudinal studies have been published. Those examining well-being before and after the pandemic onset in healthy German adolescents reported decreases (Ravens-Sieberer et al., 2021; Vogel et al., 2021). However, longitudinal data considering possible impacting factors in youth are still scarce.
First findings suggest that decreases in adolescent mental health were related to the perceived pandemic-related impact on lifestyle (De France et al., 2022). Thus, it is important to examine the effect of lifestyle changes on adolescent well-being and to differentiate which daily behaviors might pose as risk or resilience factors.
Physical and Sport Activity
Among other daily behaviors, physical activity (PA) and sport activity (SA) have been discussed (e.g., Cosma et al., 2021). While PA denotes any bodily activity resulting in increased energy expenditure, SA constitutes a subtype of PA characterized by clear aims (e.g., performance, fitness, health) and a structured setting. During the pandemic, PA and SA levels decreased globally (Kharel et al., 2022).
Scientific evidence suggests that these declined PA or SA levels were negatively related to adolescent well-being (Cosma et al., 2021; Marckhoff et al., 2022). First findings comparing PA levels during different pandemic waves in Germany suggest that PA levels did not increase from the pandemic onset to the second lockdown (January 2021; Poulain et al., 2022). Hence, examining the role of PA and SA for adolescent well-being in different pandemic phases is important to better understand the relation and draw conclusions for future phases.
Moreover, it is important to consider the context of PA and SA. Sports club participation during the pandemic was positively related to well-being (Basterfield et al., 2022). School-based fitness interventions are believed to aid adolescent well-being and diminish negative outcomes of pandemic-related declined PA levels (Cataldi et al., 2021). Yet, no studies have jointly examined the effects of PA and SA in different contexts on adolescent well-being during the pandemic.
Social Contacts
Another aspect of daily life highly affected by the pandemic were social contacts. In adolescence, peer social contacts play an important role for well-being (Brown & Larson, 2009) and a decreased number of social contacts poses a risk factor for loneliness (Victor & Yang, 2012). Studies before the pandemic showed that adolescents’ isolation from peers and loneliness was negatively related to well-being (Loades et al., 2020). After the pandemic onset, the number of social contacts in youth decreased (Vogel et al., 2021) and loneliness was related to more mental health problems (Cooper et al., 2021). Thus, it is important to further observe the effect of limited social contacts on adolescent well-being.
With social isolation, the use of social media and online platforms has become increasingly important to stay in contact (Pandya & Lodha, 2021). Consequences for adolescent well-being have often been discussed as research prior to the pandemic suggests a negative association between social media use and well-being (Orben, 2020). However, in the pandemic context, this association is not yet fully understood (Pandya & Lodha, 2021).
On the one hand, digital social contacts can positively impact well-being through support and connection (Popat & Tarrant, 2022). Espinoza and Hernandez (2022) observed that only in adolescents with limited digital social contacts, perceived negative pandemic-related changes were related to more loneliness. On the other hand, a negative impact of digital social contacts on youth mental health has been reported (Cooper et al., 2021). One study found that in-person contacts could not be replaced digitally (Jusienė et al., 2022). Hence, differentiating between in-person and digital social contacts is important when examining their role for adolescent well-being.
Screen Time
Another currently discussed daily behavior is screen time (e.g., Nagata et al., 2022). It has often been reported as a risk factor for well-being in youth. Prepandemic evidence consistently suggested a negative association between leisure time screen use and mental health (Hoare et al., 2016). During the pandemic, studies repeatedly observed increases in youth screen time (Kharel et al., 2022).
Negative associations between this pandemic-related increase in screen time and well-being have globally been reported in adolescent samples (Cosma et al., 2021; Nagata et al., 2022). Longitudinal studies found more screen time to be related to worse mental health (Rosen et al., 2021). This indicates that screen time poses a risk factor for adolescent well-being, but the relation in the pandemic context should be further investigated (Pandya & Lodha, 2021).
Since mental health was found to be related to perceived pandemic-related lifestyle change (De France et al., 2022), examining the effects of changed daily behaviors for adolescents’ well-being is of particular importance. For instance, Pandya and Lodha (2021) argue that negative effects of screen time on well-being might be reduced during the pandemic as it presented the best possibility for social interactions. So far, most research examining the relation between daily behaviors and adolescent well-being has used cross-sectional study designs and relied on comparisons between individuals. To study changes within individuals, ambulatory assessment studies are the method of choice.
Ambulatory Assessment During the COVID-19 Pandemic
Ambulatory assessment studies repeatedly measure behavior in real-life situations, ensuring high ecological validity. They allow separating effects found between individuals (between-person) and effects found within individuals over time (within-person). So far, only a limited number of ambulatory assessment studies have examined PA, social contacts or screen time, and their relation to well-being in adolescents during the pandemic. Regarding screen time, one study collected monthly data in adolescents between autumn 2020 and spring 2021 (Camerini et al., 2022). Between individuals, higher screen time was related to more mental health problems, while within individuals, no relation was observed. This highlights the importance to differentiate between-person from within-person associations. To our knowledge, only one ambulatory assessment study examined all three daily behaviors of interest in adolescents from November 2020 to April 2021 (Munasinghe et al., 2020): After the implementation of pandemic-related restrictions, PA levels decreased and adolescents spent more time alone, while screen time increased. Furthermore, the authors observed a decline in adolescents’ happiness.
Purpose of the Study
The aim of our study was to investigate the relation between daily PA, SA, social contacts, screen time, and well-being in adolescents over four weeks in the year after the COVID-19 pandemic onset in a small city in Southwestern Germany. In the first German lockdown (March–May 2020), amateur SA in groups and sports clubs was prohibited, sport facilities and schools were closed, and social gatherings were only allowed in small groups. Restrictions in all domains were loosened between June and December 2020. After this, in Southwestern Germany, amateur SA was only allowed for individuals or two people and strict social distancing rules applied again. Starting in March 2021, SA without physical contact was allowed but depended on the regional number of infections, and SA in schools was only allowed for exam preparation. In June 2021, the restrictions were loosened allowing SA in schools outdoors, SA in bigger groups, and SA with physical contact. Yet, rules for SA in sports clubs and leisure time depended on the regional number of infections.
Thus, at the time point of our study, adolescents were again more flexible in their daily routines, as SA was allowed in sports clubs, leisure time, and school, and social distancing rules were loosened. Different pandemic-related effects on youth’s well-being have been reported depending on the pandemic phase (Widnall et al., 2022), and our study enhances the scientific understanding of the relation of daily behaviors and adolescent well-being over the multiannual pandemic trajectory. We aimed to examine possible beneficial effects of daily behaviors enabled again at that time point in Germany (e.g., SA in sports clubs) and chose an ambulatory assessment study design to differentiate between-person from within-person relations.
Based on previous findings, we hypothesized the following associations for both levels (between-, within-person): (1) We expected total PA (including SA) to be positively related to adolescent well-being. (2) We expected the number of overall social contacts (in-person and digital) to be positively related to well-being. (3) We hypothesized screen time to be negatively related to well-being. We further aimed to exploratively examine (A) if there are inter-relations between these daily behaviors, (B) if SA in different contexts (sports club, leisure time, school) is differently related to well-being, and (C) if in-person social contacts are more positively related to well-being than digital social contacts.
Methods
Sample and Procedure
Adolescents were recruited at secondary schools in Tübingen (Southwestern Germany) and in regional sports clubs. Between June and August 2021, a total of 131 adolescents agreed to participate in the study. Only data of participants who completed at least one daily questionnaire were considered for this study. Thus, the final sample size consisted of 125 participants (61% female) aged between 11 and 20 years (M = 14.79, SD = 1.58). Study participation started individually as soon as postal informed consent by parents and adolescents reached the study personnel. All data were collected through online questionnaires using the software SoSci Survey (Leiner, 2019).
First, participants filled out a baseline questionnaire (15–20 min) assessing among others sociodemographic data. Starting on the next day, participants were asked to fill out a short daily questionnaire (5 min) every evening for 28 consecutive days (see the Material section). Participants received the link to the daily questionnaire every evening at 8 p.m. optionally via text message or e-mail, and it could be filled out until 1 a.m. of the next day. After the 28 days, participants were asked to fill out a follow-up questionnaire (10 min). Study participation was voluntary and could be ended at any time without disadvantages for participants. Every adolescent filling out at least half of the questionnaires participated in a lottery of prizes (e.g., visit of a famous German TV show, book vouchers). The study was approved by the ethics committee of the University of Tübingen (May 19, 2021) and the regional board of Tübingen.
Material
The complete questionnaires (baseline, daily, follow-up) and information and interpretation of reliabilities of the relevant scales are provided in the Electronic Supplementary Material 1 (ESM 1).
Daily Measures
To assess daily psychological well-being, we adapted four items of the Short Form-36 Health Survey (Bullinger et al., 1995) and two items from the WHO-5 questionnaire of well-being (Brähler et al., 2007) for the daily context. Participants answered on a 6-point Likert scale (1 – never to 6 – always; e.g., “How often did you have a lot of energy today?”). The mean score was calculated, with higher values indicating better well-being.
Daily PA and SA were assessed through the German Motoric Activity Module PA-Questionnaire (Motorik-Modul Aktivitätsfragebogen; Schmidt et al., 2016). Duration of daily PA was assessed in minutes with two adapted items (e.g., “How many minutes did you walk by foot today?”). Duration of SA in minutes was assessed in three different contexts with each one adapted item: sports club, leisure time, and school. PA and SA were transformed to 10-min units. Total PA was operationalized as the sum of time spent in PA and SA across all contexts.
We assessed the number of daily social contacts outside of school with two self-developed items, one for in-person (e.g., “Today, how many friends did you meet personally outside of school and spend time with them?”) and one for digital social contacts (e.g., through social media). The number was a standard deviation on the between-person SD.
We assessed daily screen time with four items of the Health Behavior in School-aged Children questionnaire (Ottova et al., 2012; e.g., "How many hours today did you spend looking up for information on the internet, browsing the internet?"). Participants answered on a scale from 1 – not at all to 8 – 5 h or more. Following previous analyses (Iannotti et al., 2009), we converted the time indicated by participants to numerical values (in hrs) and computed a sum score.
To assess days spent in quarantine, we adapted one item from the COVID-19 in German Competitive Sports prospective multicenter cohort study (Niess et al., 2022) for the daily context (i.e., “Did you spend today in quarantine?”).
Baseline Measures
We assessed sociodemographic data at baseline with self-developed items. For this article, only sex and age will be considered.
Data Analysis
All analyses for the current work (including explorative analyses) were conducted with the statistical software R version 4.1.3 and pre-registered (https://aspredicted.org/blind.php?x=2KN_K1B, submission: March 16, 2022). All predictors were centered around the grand mean (between-person) and the personal mean (within-person). In all models, the dependent variable was daily well-being, and we controlled for continuous autocorrelation of level 1 residuals as well as sex, age, and days spent in quarantine. In addition to the pre-registered variables, we controlled for study day.
All directional hypotheses (1, 2, 3) were tested in one multilevel model including following variables: On level 2, each adolescent i’s mean time of total PA (γ01), mean number of social contacts (γ02), and mean screen time (γ03) were included to account for between-person effects. On level 1, total PA (γ10), social contacts (γ20), and screen time (γ30) on day t were added to consider within-person effects. We controlled for study day (γ40; range = 0–27), school day (γ50; dummy-coded: 0 = weekend/holiday, 1 = school day), and days spent in quarantine (γ60; dummy-coded: 0 = no, 1 = yes) on level 1 and for sex (γ04; dummy-coded: 0 = male, 1 = female) and age (γ05; centered on grand mean) on level 2. Random effects were estimated for intercept (υoi) and within-person effects of total PA (υ1i), social contacts (υ2i), and screen time (υ3i). Correlations between random effects were estimated. The following equation 1 describes the full model:
For the explorative analysis A investigating inter-relations between the daily behaviors, we expanded the presented model with between- and within-person interactions between the daily behaviors. The explorative Hypotheses B and C were tested in separate sequentially constructed models that we compared using the likelihood ratio to specify the best-fitting model. The models for the explorative analyses are described in ESM 2. All models were calculated using maximum likelihood estimation and α = .05.
Results
Descriptive Results
With 125 participants, a total of 3,500 daily observations would be possible. Participants completed 2,464 daily questionnaires, resulting in a compliance rate of 70% (SD = 25; range = 4–100). In our study, the likelihood of missing values decreased by 5% with each year of age (OR = 0.95, 95% CI [0.93, 0.97]) and increased by 4% with each study day (OR = 1.04, 95% CI [1.03, 1.04]). Missing values were 30% less likely in girls compared with boys (OR = 0.70, 95% CI [0.65, 0.75]).
At baseline, only 10% of participants (n = 12) reported spending at least 60 min in moderate-to-vigorous PA every day of the week prior to study participation, thereby fulfilling the World Health Organization (WHO) guideline on PA for youth. Approximately a quarter of participants (24%) were not members of a sports club, while half were members of one sports club and about a quarter (26%) were members of more than one sports club. On average, participants reported medium to high daily well-being (M = 4.29, SD = 0.89). The descriptive statistics of the constructs employed in the models are provided in Table 1. The intraclass correlation coefficient (ICC) indicates the proportion of total variance that reflects the between-person variance; thus, how strongly values from one individual resemble each other. In our sample, 62% of the variance in well-being could be explained by between-person differences, so 38% of the variance consisted of within-person fluctuations and measurement error. We observed a significant but small correlation between screen time and digital social contacts on the between-person level (r = .257, p < .001) and very small on the within-person level (r = .039, p < .001).
Hypothesis Testing
The results of the multilevel model testing all directional hypotheses (1, 2, 3) are provided in Table 2.
Regarding total PA, we found a significant positive relation to adolescent well-being between (γ01 = 0.03, 95% CI[0.01, 0.04], p = .005) and within individuals (γ10 = 0.01, 95% CI[0.00, 0.01], p < .001). This suggests that across assessments, adolescents with higher levels of total PA reported better well-being. Furthermore, adolescents reported higher well-being on days with more PA than usual. The random effect of total PA was significant, indicating substantial differences in its within-person relation to well-being (SD(υ1i) = 0.01).
Regarding overall social contacts, we found no significant between-person effect (γ02 = 0.05, 95% CI[−0.06, 0.16], p = .396). Individuals with overall more social contacts did not generally report higher well-being. However, we found a significant positive within-person association (γ20 = 0.01, 95% CI[0.01, 0.02], p < .001). Hence, on days with more social contacts than usual, adolescents reported better well-being. The random effect of social contacts was significant, indicating substantial differences in its within-person relation to well-being (SD(υ2i) = 0.02). Furthermore, the random effects of intercept and social contacts were negatively correlated (r(υoi, υ3i) = −0.51), indicating that participants with higher initial well-being showed a smaller association between number of social contacts and well-being.
Regarding screen time, we found significant negative effects between (γ30 = −0.21, 95% CI[−0.28, −0.13], p < .001) and within individuals (γ03 = −0.04, 95% CI[−0.07, −0.02], p = .001). These findings suggest that adolescents with overall higher screen time reported lower well-being across observations. Adolescents also reported better well-being on days with less screen time than usual. The random effect of screen time was significant, indicating substantial differences in its within-person relation to well-being (SD(υ3i) = 0.09).
Concerning the control variables, we found a significant positive relation of well-being and study day (γ40 = 0.01, 95% CI [0.01, 0.01], p < .001) and a significant negative relation with school day (γ50 = −0.09, 95% CI[−0.14, −0.04], p < .001). The increase of well-being over study course is congruent with findings from other ambulatory assessment studies suggesting that repeatedly reflecting on one’s well-being can increase it (De Vries et al., 2021). Furthermore, the random intercept was significant (SD(υoi) = 0.61), indicating substantial differences in individual well-being at study start.
Explorative Analyses
Here, we will only describe the findings relevant to our explorative hypotheses. The models testing the explorative hypotheses (A, B, C) and the results are provided in ESM 2.
In explorative analysis A examining possible inter-relations between daily behaviors, we found no such inter-relations as no two-way interaction was significant (all p > .05).
In explorative analysis B, we examined PA and SA in different contexts (sports club, leisure time, school). Within individuals, we observed positive main effects for PA (γ40 = 0.013, 95% CI[0.008, 0.017], p < .001), SA in leisure time (γ20 = 0.011, 95% CI[0.006, 0.016], p < .001), and SA in sports club (γ10 = 0.006, 95% CI[0.000, 0.011], p = .048), and the interaction between PA and SA in leisure time was slightly negative (γ80 = −0.001, 95% CI[−0.002, −0.000], p < .001). Between individuals, PA was positively related to well-being (γ04 = 0.034, 95% CI[0.001, 0.067], p = .044).
In explorative analysis C, we examined in-person and digital social contacts. We found a significant positive effect of in-person social contacts within (γ20 = 0.02, 95% CI[0.02, 0.03], p < .001), but not between individuals (γ02 = −0.02, 95% CI[−0.15, 0.12], p = .804). For digital social contacts, we found no significant relation to well-being (between: γ01 = 0.05, 95% CI[−0.08, 0.19], p = .422; within: γ10 = 0.01, 95% CI[−0.01, 0.02], p = .456). Only the random effect of digital social contacts (SD(υ1i) = 0.04) was significant.
Discussion
In a 28-day ambulatory assessment study, we examined associations between adolescents’ daily PA, SA, social contacts, screen time, and their well-being one year after the COVID-19 pandemic onset. Consistent with Hypothesis 1, total PA was positively related to well-being between and within individuals. The between-person relation is consistent with previous findings (Marckhoff et al., 2022) and strengthens the understanding that being overall more physically active is related to better well-being in youth. This is important to highlight as only a minority of our sample fulfilled the WHO PA guideline. Additionally, higher levels of daily total PA may aid adolescent well-being: On days with more total PA than usual, participants reported better well-being.
Explorative analysis B investigating PA and SA in different contexts revealed that PA, SA in sports clubs, and SA in leisure time were positively related to well-being within individuals and PA also between individuals. This emphasizes the importance to facilitate participation in SA in sports clubs and leisure time during the pandemic. SA in school, however, was not related to well-being. This might be explained by this type of SA not being voluntary and, thus, related to a higher variability in motivation (Bagøien et al., 2010).
Hypothesis 2 was supported by our data within individuals: Adolescents reported better well-being on days with more social contacts than usual. However, between individuals, Hypothesis 2 was not supported. Substantial differences in the within-person association suggest that the role of social contacts for well-being might be influenced on the between-person level by, for instance, personality traits (e.g., self-esteem; Çivitci & Çivitci, 2009). We could not consider personality traits in our analysis, which might explain the null finding.
Our explorative Hypothesis C was supported: In-person social contacts were positively related to well-being within individuals, while the within-person relation to digital social contacts was not significant and differed substantially between individuals. This supports previous findings (Jusienė et al., 2022) and emphasizes the relevance of in-person social contacts for adolescent well-being.
Hypothesis 3 assuming a negative relation of screen time and well-being was confirmed: Consistent with another ambulatory assessment study conducted during the pandemic (Camerini et al., 2022), adolescents with overall higher screen time reported lower well-being. We also observed a negative within-person relation, albeit it was smaller in magnitude. Adolescents reported lower well-being on days with more screen time than usual, contradicting the other study’s null finding within individuals. This inconsistency might be explained methodologically: Camerini and colleagues analyzed monthly data, while we collected daily data.
As we tested all daily behaviors in one model, our findings allow us to conclude that the effects of the single daily behaviors held true when controlling for the others. Our explorative analysis A revealed that the daily behaviors were not inter-related, suggesting that the relations between the single daily behaviors and well-being do not depend on the levels of the other daily behaviors.
Practical Implications
In the pandemic context, the following practical conclusions can be drawn from our study when discussing restrictions to daily life: Opportunities for PA should be facilitated and encouraged to aid adolescent well-being. Since negative pandemic-related mental health outcomes can be expected, the treatment of mental health problems will pose a long-lasting challenge (Fegert et al., 2020). PA and SA are cost-efficient as well as easily and widely applicable and should be considered in health promotion.
While previous work suggests that school-based fitness interventions could aid adolescent well-being (Cataldi et al., 2021), our findings indicate that school might not be the best context for SA to benefit well-being. Instead, our study highlights the importance of enabling SA in sports clubs or in leisure time (e.g., by conducting COVID-19 rapid tests before practice). We also found PA including walking or active transport (e.g., biking) to be beneficial, and thus, active commuting to school should be promoted.
Furthermore, our study highlights the relevance of in-person social contacts compared to digital ones. Albeit social isolation is important to control the spread of infectious diseases, its negative impact on adolescent well-being should be considered (Thiel et al., 2021). Opportunities for social interactions under safety standards (e.g., wearing face masks) should be enabled.
The observed negative relation of screen time and well-being between and within individuals in our study highlights the importance of dependable daily routines to mitigate aversive pandemic-related effects on adolescent well-being (Racine et al., 2021). Our findings suggest that limited and stable screen times are one possibility of reducing adverse effects.
Limitations and Suggestions for Future Research
Despite the numerous strengths of our study examining the relation of daily behaviors and well-being in adolescents, some limitations should be considered: Low socioeconomic status (SES) has been reported as a risk factor for negative pandemic-related effects on well-being (Vogel et al., 2021), but in our study, we could not control for SES. Our sample size was limited, and the sample was not representative for the general population, with many participants attending higher-level education of the German educational system.
Regarding our measures, we observed a significant correlation between screen time and digital social contacts; however, it was only small (between-person) or very small (within-person) in magnitude. Still, this suggests that these constructs cannot be completely separated. In our study, we examined quantity of social contacts because this was strongly affected by pandemic-related restrictions. Future research could examine quality of social contacts and its relation to well-being. Furthermore, all variables were assessed through self-report. More objective measures may be better to assess rather objective data (e.g., accelerometer-based PA-assessment, digital applications for screen time measurement), but participant burden should be considered. It should also be mentioned that we exclusively examined linear relations between the variables and, thus, cannot exclude the possibility of nonlinear associations.
Our results indicate that future research should further investigate possible negative influences of daily behaviors during the pandemic. The observed random effects for all examined daily behaviors imply that possible between-person moderators affecting the within-person relationships should also be investigated (Sudeck et al., 2018). Methodological implications can be drawn from associations observed on the within-person level, but not on the between-person level (i.e., SA in sports club, SA in leisure time, social contacts). To examine the role of these behaviors, comparisons between individuals as conducted in cross-sectional studies are insufficient. Thus, future research in this field should employ more ambulatory assessment studies accounting for differences between and within individuals.
Electronic Supplementary Material
The electronic supplementary material is available with the online version of the article at https://doi.org/10.1027/2151-2604/a000518
The authors wish to thank all adolescents for participating in this study. Additionally, we want to thank Sven Waigel, Alexander Wütz, and Stephan Gutermann for their help and advice in participant recruitment and study planning, as well as Tobias Renner and Ute Dürrwächter for their expert advice on the study design. Furthermore, we thank our student assistants for their help with data collection and preparation.
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