Covid-19 and PA

Several viral epidemics have occurred in the past two decades, such as severe acute respiratory syndrome in 2003 [10], influenza A virus subtype H1N1 in 2009 [11], and Ebola virus in 2014 [12]. Covid-19 is unique in the sense that it spread quickly around the world and infected more people outside than inside China (i.e., the outbreak country). The USA is the leading country with regard to the number of infected people and Covid-19-attributed deaths [6].

Covid-19 forced governments around the world to limit the spread of the disease by implementing restrictions (e.g., closures of shops, schools, and manufactures; closure of borders to limit traveling; and implementation of social distancing rules). These restrictions may have led people to break their PA routines and become less physically active [5, 7]. Importantly, sustained low levels of PA and high levels of sedentariness are associated with poor physical and mental health and hold the potential to increase disease-specific and all-cause mortality risks [13].

Industry actors that analyzed people’s mobility via wearables or smartphones found a significant decline in average step count during the Covid-19 pandemic (e.g., Fitbit Inc. [8] and Apple Inc. [14]). Additionally, cross-sectional studies showed reduced PA and increased sedentariness during Covid-19-caused lockdown among people of all ages in China [15], Italy [16], Canada [17], and Australia [18]. Those studies provide descriptive information on patterns of PA and the associated negative health effects. However, to the best of our knowledge, there are no longitudinal studies (with one exception, which recruited 70 out of 631 participants for a follow-up [15]) and none of the studies examined PA app use-related determinants that might have been helpful to residents to remain physically active during Covid-19-caused lockdown. In what follows, we briefly review the existing literature on PA apps and their potential to buffer the decline in PA during Covid-19-caused lockdown.

Smartphone apps and their potential to buffer the decline in PA

With the rapid development of technology, mobile health apps (e.g., smartphone PA apps) present cost-effective means to promote PA and prevent sedentariness [9]. Studies have emphasized the importance of such apps for remaining physically active during the critical period of Covid-19 [7, 19]. For instance, PA apps can be appealing to users, can be tailored to many people, and can be used in small spaces during lockdown. However, adherence to PA apps tends to be rather poor [9] and is influenced by as many as 89 factors [20]; among these, the perceived playfulness of PA apps might be particularly important [20].

To date, it remains largely unknown which app features help people remain physically active. Conroy et al. have cluster-analyzed PA apps and found two broad features: motivational and educational features [21]. Motivational app features emphasize social and self-regulation of PA (e.g., feedback, social support, and goal setting); educational app features focus on PA tutoring (e.g., instructions, coaching, and learning) [21]. Furthermore, gamification-related features are increasingly being used in PA apps to help individuals improve their health and fitness [22]. Gamification describes the use of game design elements, such as points, levels, and badges, to make the experience more playful and enjoyable [23, 24]. In the present study, we consider gamification-related features besides motivational and educational features of PA apps as factors to describe three relevant clusters of app features that might help predict the maintenance of PA.

Procedures

The first-wave (T0) data collection was conducted between March 12 and March 17, 2020, a time when no restricting regulations (e.g., stay-at-home order) were imposed at the U.S. state level. At T0, 867 respondents participated in the survey, and 839 were eligible after a quality check (e.g., after having eliminated incomplete surveys).

The second wave of the survey (T1) took place after the U.S. government and the states responded to the Covid-19 pandemic with restricting regulations to slow its progression (e.g., California first imposed a stay-at-home order on March 19 [28]; South Carolina did so on April 7; see Supplementary Table 1) and after these restrictions had been in place for at least 4 weeks. For example, T1 started on April 16 in California and on May 5 in South Carolina, both exactly 28 days after the lockdown. The average duration between T0 and T1 was 43.7 days (standard deviation = 4.7). Four hundred and fifty-nine participants filled in the survey at T1 and 431 were eligible after a quality check, which yields an attrition rate of 49%. We expected that those stringent regulations would reduce individuals’ PA levels [29]. We further expected that the use of PA apps (and their features) would help prevent the potential decline in PA (see Research questions of the present study).

Measures

Sample size consideration

We conducted an a priori power analysis using G*Power Version 3.1 [34] (F-tests, multiple regression with six predictors, and R² deviation from 0). The analysis revealed a sample size of at least N = 146 to determine a medium effect size of f² = .15 (alpha = .05; power = .95; noncentrality parameter = 21.90, critical F = 2.16). Allowing for an attrition rate of 50% at T1, a sample size of 292 is needed. The final sample size was N = 431 (i.e., 48% bigger than the recommended minimum sample size [to be able to include control variables in the analyses]).

Descriptive statistics and difference testing between waves

While self-reports of PA might be subject to overreporting [35] (this might also be true for the present study), the within-participant design allowed us to assess differences between the two waves. The changes in PA and sedentariness between the two waves are shown in Table 2. From T0 to T1, there was a significant decrease in moderate PA (−10.4 ± 51.5 min/day, p < .01) and vigorous PA (−8.5 ± 46.0 min/day, p < .001). There was no significant difference in walking (−4.5 ± 51.5 min/day, p = .067) and sedentary time (1.6 ± 170.1 min/day, p = .85). Both active PA (−23.4 ± 93.3 min/day, p = .003) and PA MET (−605.1 ± 2,453.5 MET min/week, p < .001, indicating a decline by 18.2%) decreased significantly. These results, thus, provide an answer to Research Question 1: PA decreased significantly during lockdown.

Table 2

Changes in physical activity and sedentariness between Waves 1 and 2

	Wave 1		Wave 2
Variables	Mean	SD	Mean	SD	t (430)	p-value
PA MET (MET min/week)	3,323	2,451	2,718	2,205	5.12	<.001
Moderate PA (min/day)	57.15	42.67	46.77	41.37	2.15	<.01
Vigorous PA (min/day)	47.94	41.91	39.47	40.00	3.82	<.001
Active PA (min/day)	157.80	92.73	134.45	90.89	2.97	.003
Walking (min/day)	52.71	47.70	48.21	44.41	1.83	.067
SED (min/day)	367.99	167.01	369.55	152.85	−0.19	.85

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p-value refers to t-tests between Waves 1 and 2.

Active PA sum of walking, moderate PA, and vigorous PA; PA physical activity; PA MET PA calculated as metabolic equivalent of task minutes per week; SD standard deviation; SED sedentary time.

Theoretical contribution

Given that physical inactivity has been considered as a pandemic itself, one could argue that the world is currently facing two pandemics at the same time [5]. Mobile health technology, such as smartphone PA apps, might help tackle the inactivity pandemic [9, 36], particularly in the light of restrictions in people’s access to PA-enhancing sites (e.g., fitness clubs, parks) during Covid-19-caused lockdown. The study provides evidence on whether PA levels have changed during Covid-19-caused lockdown, whether PA app use helps prevent declines in PA, and which app features are particularly helpful in this context.

First, to fill the void of research into how PA is affected by Covid-19-related restrictions [37], we designed a longitudinal study that was timed so that each individual had experienced the lockdown for at least 28 days before they participated in the second-wave survey. The first-wave survey took place before the lockdown. The results of the study showed that moderate PA and vigorous PA decreased (but sedentary times did not increase) during lockdown—despite the fact that several recommendations were issued that aim to encourage people to stay physically active during Covid-19 [7, 29, 38]. While previous studies have reported similar decreases in PA during Covid-19 [15–18], these studies were largely cross-sectional in nature. The studies cannot rule out that within-participant differences drive the change over time. Thus, it remains unclear what contribution the Covid-19 lockdown made to the change in PA. Our study addresses these limitations, using a longitudinal design, and it revealed a decrease of 18.2% when PA is measured in MET. In the present study, there was no increase in sedentariness, despite the fact that home environments may have made it more convenient for people to be sedentary during Covid-19-caused lockdown [5]. The results could be explained by a potential increase in tasks that require nonsedentary behaviors at home.

Second, while the determinants for PA maintenance have been extensively explored [39, 40], studies have rarely considered the role of PA app use, controlling for intentions to be physically active and baseline PA. Controlling for these variables is important because a lack of intent to be active might explain low PA levels (in particular, when there are few opportunities to be active, such as during lockdown [5]) and because the baseline level of PA might influence how individuals respond to changes in the environment [41]. Our study revealed a positive effect of the usage of PA apps. Although previous meta-analyses indicated poor adherence and modest effects by using PA apps to increase PA in the long run [9, 42], any increase of PA, regardless of the intensity, was shown to be associated with reduced health risks [43].

Lastly, while there is research on the general effectiveness of PA apps [9, 42, 44] there are few answers to the question regarding which app features are effective to maintain PA. The present study revealed that gamification-related app features particularly helped individuals remain active during Covid-19-caused lockdown. The findings thus help deepen our understanding of the role of app features for helping people maintain their PA during pandemics.

Practical contribution

The study provides implications for individuals and health professionals, as well as policymakers. With regard to individuals, they can be recommended to use PA apps, and particularly those with gamification features, to maintain their PA levels during a pandemic; this reduces health risks and increases well-being [45]. With regard to health professionals, they can be recommended to use apps to engage with their customers. Gamification-related features might need constant updates to arouse individuals, so health professionals might look for gamification elements that help people to both use the app and remain active. Lastly, regarding policymakers in public health, they can be recommended to fight decreases in PA during lockdown to establish a healthy environment during Covid-19 [7] by collaborating with stakeholders from the digital world, such as smartphone and PA app providers. These collaborations might be directed at increasing the pleasure of using technology and being physically active when access to PA-enhancing external resources is limited.

The authors would like to thank participants in the current study.