Skip to main content
Download PDF

The effect of exercise on resilience, its mediators and moderators, in a general population during the UK COVID-19 pandemic in 2020: a cross-sectional online study

BMC Public Health volume 22, Article number: 827 (2022) Cite this article

Abstract

Background

Resilience is central to positive mental health and well-being especially when faced with adverse events. Factors such as exercise, location, sleep, mental health, and personality are moderators and mediators of resilience. However, the impact of these factors on resilience during severe adverse events are unknown. The present study examined how the COVID-19 pandemic affected resilience and its moderators and mediators by investigating whether there was a difference in resilience and quality of life between people with varying levels of exercise, including those who changed their exercise levels pre and during a COVID-19-related lockdown, and whether location affected the relationship between levels of exercise and resilience and quality of life.

Methods

Following ethical approval, a cross-sectional online survey capturing data on self-reported key moderators and mediators of resilience before and during the COVID-19 lockdown imposed on the 23rd March 2020 in the UK was distributed via social media and completed over a three week time period during July 2020 via a self-selecting sample of the general population (N = 85). The key moderators and mediators of resilience the survey assessed were exercise, location, life-orientation, mental health, and sleep quality. All data were self-reported.

Results

Participants’ exercise intensity level increased as resilience increased (F(2,82) = 4.22, p = .003: Wilks’ lambda = .82, partial n2 = 0.09). The relationship between exercise, and resilience and quality of life was independent of sleep and mental health status pre-lockdown (p = .013, p = .027 respectively). In the face of the COVID-19 pandemic, this relationship was dependent on mental health but not sleep quality (p = <.001 for resilience p = .010 for quality of life). There were no statistically significant differences between participants living in urban or rural locations.

Conclusion

Exercise is strongly correlated to resilience and during a pandemic such as COVID-19 it becomes a mechanism in which to moderate resilience. The relationship between exercise and resilience is supported by this study. The influence that a pandemic had on mental health is mediated by its effect on quality of life.

Peer Review reports

Background

The roots of resilience theory come from the study of adversity, otherwise known as a pathogenic focus [1] concerning how individuals achieve positive health and wellbeing outcomes. The original concept of resilience was developed as a response to large-scale external change [2], such as the United Kingdom lockdown imposed on the 23rd March 2020 in response to COVID-19. Resilience is complex in nature [3], and is characterised as the process of effectively negotiating, adapting to, or managing significant sources of stress or trauma [4,5,6,7]. Positive adaptation is the ability to maintain or regain mental health, despite experiencing adversity [6]. Resilience provides people with the strength to overcome adversity and decrease the negative effects that adversity, such as a pandemic like COVID-19 can cause [8].

Research into resilience and healthcare outcomes have shown resilience and perceived quality of life (QOL) [9] are correlated with optimism [10, 11] and lower psychological stress [12, 13]. Individuals demonstrating high resilience are less likely to report adverse mental health experiences [7, 14]. The positive impact of exercise on resilience [15,16,17,18], QOL [19] and mental health has been demonstrated in clinical and general populations [12, 20, 21].

Exercise increases resilience at the core biological level; promoting secretion of neurotransmitters and endorphins to induce a state of euphoria ([22, 23]; Highes et al., 2013). Euphoria reduces dysfunctional ideation time, acting as a distraction from stressful events ([22, 23]; Lines et al., 2018; Peluso & de Andrade, 2005).

In a study of 775 adolescents, high self-esteem correlated significantly with good mental health prognoses, with a Pearson’s product-moment correlation demonstrating a significant relationship, with resilience explaining 60% of the variance [18]. However, as the positive mental effects of exercise are enhanced by the social interactions workout sessions provide ([15, 22]; Ka et al., 2015; Peluso & de Andrade, 2005), most research to date fails to control for the effect of these interactions on the outcomes of interest.. Therefore, it cannot be concluded that exercising alone produces positive mental health effects. However, as pandemic restrictions prohibited socialising, this provided a unique research opportunity to investigate the effects of exercise without any social interactions during workout sessions.

Resilience is increased by euphoria as it increases self-esteem by representing our self-rating of self-worth (Peluso & de Andrade, 2005): the biological and psychological benefits of exercise working in unison to increase resilience. Empirical evidence commonly reports exercising at preferred intensity (one’s chosen exercise level) has increased mental health benefits compared with prescribed intensity (imposed exercise level) ([24, 25]; Carter, Bastounis, Guo, Morrell, & Carter, 2019 [26];; Turner, Carter, Sach, Guo, & Callaghan, 2017). As mental health mediates resilience, this suggests the simple act of moving has an impact on one’s resilience.

Preferred intensity maybe indirectly related to improving self-esteem due to the self-controlled nature of exercise, with observed body change results being dependent on the individual choosing the intensity, thus a goal being obtained and intrinsic motivation being stimulated ([27]; Pekrun, Hall, Goetz, & Perry, 2014). Research into preferred intensity is limited; being intervention-based six to twelve-week studies. Therefore, the effect of preferred intensity upon mental health over a randomised self-motivated population is not known. As many of the resilient and continued protective effects from exercise are correlated with the continuation of exercise throughout the lifespan [19, 21], a short intervention study cannot conclude confidently about the continued effect of preferred intensity exercise and resilience. The current study targeted this limitation as all those who exercise had done so autonomously, not knowing it would be investigated for research purposes. The comparison between those who were already exercising before lockdown and those who began once lockdown was imposed gave an insight into the long-term effects of exercise on resilience and how quickly exercise can promote resilience. This is in-line with self-determination theory [27], with exercising during lockdown being intrinsically motivated and causally related to preferred intensity, which as the cited literature has suggested, maximises the effect on resilience, thus providing clearer insights into how the exercise of the population as a whole affects resilience.

To date, studies have researched resilience and its moderators in either clinical or general populations, leaving the general population under-researched, hence the current study. Despite the reviewed literature suggesting a study amongst a self-selecting sample of the population into resilience should not be impacted by poor mental health, it indicates a need to control for mental health as a co-variant. Controlling for mental health as a co-variant would strengthen the statement that exercise increases resilience in the generic population.

The lockdown imposed on the UK on the 23rd March 2020 due to COVID-19 has provided a unique opportunity for this study to investigate areas in which previous research has been limited. The adversity and life-style changes imposed [28] allows comparisons to be made against the broad population within the UK who had to adhere to rules that go against the natural biological and psychological nature of Homo sapiens; pack animals who require social interaction to form a social identity which mediates resilience [15, 29,30,31]. Adversity has included isolation, separation, financial strain, grief, and educational deficits (BBC, 2020 [32];), research shows this negatively impacts resilience [33, 34]. These studies were not conducted in a pandemic, thus, whether these constraints affect resilience similarly in a pandemic is unknown and explored in the current study. Based on data from past recessions; such as the 2008 economic crisis, where suicide rates in Europe increased by 6.5% the prognosis for mental health and wellbeing as a result of lockdown is not predicted to be positive ([35, 36]; Radio4 (BBC), 2020 [37];). As research is scarce, we do not yet know how the COVID-19 lockdown has impacted resilience or its empirical moderators.

The aim of the current study was to examine how the COVID-19 pandemic affected resilience and its moderators by investigating if there was a difference in resilience and quality of life between people with varying levels of exercise, including those who changed their exercise levels pre and during the COVID-19 pandemic, and whether location played a role in this relationship. The authors anticipated:

  1. 1)

    People reporting higher exercise levels would have better resilience and QOL than those reporting low and moderate exercise levels pre-COVID-19 lockdown.

  2. 2)

    People who improve their exercise levels during COVID-19 lockdown would have better resilience and QOL than people whose exercise levels reduced or remained the same.

  3. 3)

    Mental health and sleep quality would moderate the relationship between exercise levels and resilience.

  4. 4)

    Exercise levels and resilience would differ between people living in rural and urban environment during COVID-19.

  5. 5)

    Life-orientation and resilience would differ between people living in rural and urban environments.

  6. 6)

    The relationship between exercise levels, resilience and QOL in people living in rural and urban environments would moderated by mental health and sleep quality during COVID-19.

Methods

Design

The study used a cross-sectional online survey developed on Qualtrics XM (version 26).

Participants

Following ethics approval from the University’s Research Ethics Committee, data was collected from 126 Participants over a three-week period in June 2020. Forty-one were removed due to an incomplete data set, leaving 85 participants. The participants consisted of 31 males and 54 females with the mean age of 47.04 (SD = 18.98) who accessed the survey advertised on social media. Forty percent of participants (n = 34) and 60% (n = 51) described their location as rural and urban respectively. Before the initial lockdown period 52.9, 38.8 and 8.2% of participants were very, moderately, or not active respectively and during the initial lockdown 60, 31.8 and 8.2% of participants were active, moderately active, or not active respectively.

Questionnaires

The survey comprised measures of the following variables:

  • Demographic information: age, gender, urban or rural location.

  • The Connor-Davidson Resilience Scale (CD-RISC) [5]: a 25-item self-report five-point Likert scale, ranging from 0 (not true at all) to 4 (true nearly all the time) to items such as “I am able to adapt when changes occur”, designed to assess level of resilience with higher scores indicating higher resilience. The CD-RISC has a high level of internal consistency (Cronbach’s alpha = .89) and a high test-retest reliability (52.7–52.8).

  • Symptom Checklist − 5 (SCL-5) [38]: a 5-item shortened version of the Hopkins Symptom Checklist, measuring anxiety, depression and their resulting adversity. The response options were measured on a four-point Likert scale from 1 (not at all) to 4 (very much) to statements such as, “In the last 14 days have you been bothered by feeling fearful?”, with the cut-off of 2 recommended as a valid predictor of mental distress. The SCL-5 has been shown to correlate well with the SCL-25 (r = 0.92). It is designed to screen for global psychiatric morbidity, namely anxiety and depression. The SCL-5 has good internal consistency (Cronbach’s alpha = .80).

  • Mental Health Inventory (MHI) [39] consisting of 34 items designed to measure psychological well-being and distress on a 5-point Likert scale that ranges from 1 (all the time) to 5 (none of the time) to statements such as, “Did you feel depressed?”, quantified people’s mental health state during adversity. A higher score indicates better mental health. The MHI has a high level of internal consistency (Cronbach’s alpha = .93).

  • Revised Life-Orientation Test – Revised (LOT-R) [40],is a 10 item life orientation test to assess people’s outlook on life measured on a 5-point Likert scale of 0 (strongly disagree) to 4 (strongly agree) to items such as, “I enjoy my friends a lot.”, with higher scores indicating a more pessimistic attitude. The correlation between the original and revised scale is .95. The LOT-R has an acceptable level of internal consistency (Cronbach’s alpha = .72)

  • Physical activity was measured using the International Physical Activity Questionnaire short form (IPAQ-SF) [41,42,43], assessing frequency, intensity and duration of physical activity in days and minutes; a higher self-reported score indicates a higher level of physical activity. The IPAQ has an acceptable level of internal consistency (Cronbach’s alpha = .73) and has been deemed suitable for national population-based prevalence studies of participation in physical activity.

  • The Insomnia Severity Index (ISI) [44]. A 7-item measure in which participants respond to statements such as, ‘How satisfied/dissatisfied are you with your current sleep pattern?’. There are a variety of different scales of response, each raw score for the seven items is added to form a total score of sleep quality, the higher the total score, the higher the level of insomnia or lower the sleep quality. The ISI has an appropriate level of internal consistency (Cronbach’s alpha = .84) and a high test-retest reliability (0.84–1) and a strong positive correlation with the Pittsburgh sleep quality index.

  • QOL was measured using the WHOQOL-BREF [45], a 26- question short version of the original WHOQOL-100 designed to assess QOL. The response options for each item are rated on a 5-point Likert scale from 1 to 5 to statements such as, “How satisfied are you with yourself?”. The questionnaire splits into four domains of QOL: physical health, psychological health, social relationships and environment, with two questions to reflect overall QOL and general health. Higher scores indicate higher QOL. The WHOQOL-BREF has a high level of internal consistency (Cronbach’s alpha = .89)

Procedure

The survey was distributed using the link generated by Qualtrics via the social media channels ‘Facebook’ and ‘WhatsApp’, and email. Participants voluntarily opted into the study. Data were coded and analysed using the Statistic Package for the Social Sciences version 25 (SPSS).

Data analysis

The data was inputted into SPSS from Qualtrics and cleansed, removing any participants who did not complete the survey and checking the survey was transferred appropriately without mistakes. The remaining data were coded and scored and a total score for each participant generated. The normality of distribution and variance were checked via SPSS and we used Pearson’s correlation coefficient, to check the data met the assumptions of each test. Descriptive statistics were produced describing age, exercise categorial level, resilience, QOL, mental health, sleep, and life-orientation. Based upon their responses to the IPAQ during lockdown, participants who increased, decreased, or kept their exercise level the same were classified as progressors, regressors and maintainers respectfully.

A MANOVA was used to compare resilience and QOL life scores, before lockdown and used to compare resilience and QOL during lockdown at three different levels of exercise: low, moderate, and high. Independent sample t-tests compared differences in QOL and resilience scores between people changing exercise levels on each dependant variable. The independent t-tests allowed the authors to report the exercise level change that increased QOL and resilience. ANCOVA was used to test whether mental health and sleep quality moderated the level of exercise on resilience. A t-test compared differences in location on self-reported exercise levels and resilience. Finally, MANCOVA compared the relationship between exercise level and resilience and QOL whilst controlling for sleep quality and mental health.

Results

Variable descriptive statistics

Descriptive statistics of participants’ responses are shown Table 1:

Table 1 Mean, standard deviation of participants’ scores on each variable
Full size table

A Pearson’s correlation coefficient to measure the relationship strength between resilience and QOL was carried out to check the data met the assumptions of each test. A Pearson’s correlation coefficient between resilience and QOL was statistically significant p < .001. Based on a critical skewness value of 1.96 [46], data was normally distributed on all measures except sleep quality (see Table 2).

Table 2 Skewness and Kurtosis scores for participants’ responses to each measure
Full size table

MANOVA showed a statistically significant difference between the groups on the combined dependant variables before lockdown, F (2,82) = 4.22, p = .003: Wilks’ lambda = .82, partial n2 = 0.09. Analysis of each dependant variable, using a Bonferroni adjusted alpha level of 0.17 showed a statistically significant contribution of resilience F (2,82) = 6.65, p = .002, partial n2 = 0.14 and QOL F (2,82) = 6.62, p = .002, partial n2 = 0.14. As exercise level (low, moderate, vigorous) before lockdown increased, so did QOL and resilience.

There was a statistically significant difference between the groups on the combined dependant variables before lockdown, F (2,82) = 7.31, p < .001: Wilks’ lambda = .72, partial n2 = 0.15. Analysis of each dependant variable, using a Bonferroni adjusted alpha level of 0.17 showed a statistically significant contribution of resilience F (2,82) = 11.46, p < .001, partial n2 = 0.22 and QOL F (2,82) = 8.88, p < .001, partial n2 = 0.18. As exercise level during lockdown increased, so did QOL and resilience.

A higher QOL was reported by people who progressed an exercise category from low to moderate or moderate to vigorous (mean = 355.47, SD = 52.61) (mean = 335.66, SD = 60.56). A higher resilience score was reported by people who progressed an exercise category (mean = 72.60, SD = 14.45) (mean = 69.69, SD = 17.89). There was no statistically significant difference in QOL (t (83) = 1.17, p = .244) and resilience scores (t (83) = .59, p = .56) between progressors and maintainers.

ANCOVA showed there was a statistically significant effect of exercise level before lockdown on resilience, F (1, 85) = 4.59, p = .013 even when controlling for sleep quality and mental health scores. During lockdown, ANCOVA showed there was a statistically significant effect of exercise level on resilience, F (1, 85) = 6.53, p = .002 even when controlling for sleep quality and mental health scores.

There were no statistically significant differences in exercise levels (t (83) = 1.81, p = .07) and resilience scores (t (83) = 1.28, p = .21) between those in an urban location and those living in a rural location before lockdown.

There was no statistically significant difference in exercise levels and resilience and life orientation before lockdown, F (2,82) = 0.86, p = .43: Wilks’ lambda = .98, partial n2 = 0.02. Analysis of each dependant variable, using a Bonferroni adjusted alpha level of 0.17 showed no statistically significant contribution of resilience F (2,82) = 1.63, p = .206, partial n2 = 0.09 and life-orientation F (2,82) = 1.01, p = .318, partial n2 = 0.01. A one way between-subjects MANCOVA showed there was a statistically significant difference between the exercise levels and resilience and QOL scores even when controlling for sleep quality and mental health scores before lockdown, F (2,82) = 2.89, p = .024: Wilks’ lambda = .87, partial n2 = 0.07. Analysis of each dependant variable, using a Bonferroni adjusted alpha level of 0.17 showed a statistically significant contribution of resilience to exercise levels F (2,82) = 4.59, p = .013, partial n2 = 0.10, and QOL F (2,82) = 3.79, p = .027, partial n2 = 0.09. As exercise level before lockdown increased, so did QOL and resilience independent of sleep quality and mental health.

During lockdown, a between-subjects one-way MANCOVA showed there was a statistically significant difference between exercise levels and resilience even when controlling for sleep quality and mental health scores, F (2,82) = 3.42, p = .010: Wilks’ lambda = .85, partial n2 = 0.08. Analysis of each dependant variable, using a Bonferroni adjusted alpha level of 0.17 showed a statistically significant contribution of resilience F (2,82) = 6.53, p = .002, partial n2 = 0.14, but not QOL F (2,82) = 1.83, p = .168, partial n2 = 0.04. As exercise level before lockdown increased, so did resilience independent of sleep quality and mental health, whereas QOL was affected by sleep quality and mental health. When the MANCOVA was repeated using the results during lockdown, with just the co-variate of sleep, analysis of each dependant variable, using a Bonferroni adjusted alpha level of 0.17 showed a statistically significant contribution of resilience to exercise level F (2,82) = 10.11, p < .001, partial n2 = 0.20, and QOL F (2,82) = 4.92, p = .010, partial n2 = 0.12. As exercise level before lockdown increased, resilience and QOL increased independent of sleep quality but not mental health.

Discussion

The study investigated how the COVID-19 pandemic affected the mediators and moderators of resilience with respect to exercising. The current study found that as exercise level increases so does resilience. The relationship between exercise and resilience is independent of sleep and mental health under normal conditions. During a pandemic, this relationship is independent of sleep quality, but not mental health. Location does not play a statistically significant role in resilience.

Resilience and its mediators and moderators

As no exercise intensity was imposed on participants, all exercise was likely to be at the participants’ preferred intensity. This strengthens earlier findings that to increase resilience and QOL the exercise preferred intensity exercise is sufficient [24,25,26, 47]. A previous study by Carter et al., [26] reported an increase in some QOL domains [26]. The limitation suggested by using a clinical sample, exercise level being lower than the normal distribution [48], was counterbalanced in this research by hypothesis 3 and 6 which controlled for mental health. We found that the relationship between resilience and perceived QOL is independent of mental health under normal conditions but not during a pandemic. The strong p value in this study suggests exercising at a higher level has a stronger effect [22, 49,50,51], perhaps explaining the difference in QOL findings between Carter et al. [26] and this study. Higher intensity exercise being associated with higher self-efficacy [24, 50] could add further insight into explaining this difference, as confidence in body image leads to increased optimism [52, 53], which is correlated with increased resilience. Carter and colleagues’ [26] short intervention time may limit the effects seen [23]. Sustained exercise at a high level being required to exert positive effects is further suggested by our (non-significant findings), replicating the result Dilornzo et al., found, although the non-significant suggests caution in drawing this conclusion. However, this result is more likely to do with a lower than expected and an underpowered study. Our study takes the knowledge of the relationship between exercise, resilience and QOL a step further, suggesting that exercising continuously at a higher preferred intensity increases perceived QOL and resilience, but further research is needed to test this hypothesis.

Contrary to previous literature [54], we found that location does not influence resilience and QOL. The research on how location effects exercise and resilience is in its infancy, with studies having focused on the attachment between the self and environment, drawing on Bowlby and Ainsworth attachment theory [55]. Therefore, research has mostly focussed on infants, and it is unknown whether this can be applied to adult’s attachment with the environment. In-line with our hypothesis on the relationship between location exercise, QoL and resilience, and previous literature [54, 56, 57], this was reversed during lockdown, where more people began to exercise in rural locations and demonstrated higher resilience levels. However, this was not statistically significant. Therefore, the effect of location on exercise and resilience remains unproven. The current study’s non-significant findings in conjunction with the unexpected findings that exercise levels were higher in urban populations before lockdown, demonstrates the need for further investigations into exercise and environment.

The effect of a pandemic on resilience

Descriptive statistics showed that 17.6% of participants increased their exercise levels during lockdown. The analysis of the findings reported in this study suggests that exercise seemed to become a coping mechanism to moderate resilience. This is demonstrated by resilience increasing with exercise: the pandemic only affecting QOL during lockdown as mental health became a mediator. Exercise becoming a moderator of resilience during a pandemic is further supported by the F value almost doubling in hypothesis 1 during lockdown, showing the results of the pandemic are more than one would expect to see by chance. To the authors’ best knowledge, this provides the first piece of evidence into the effects of exercise on resilience in a pandemic. This is further strengthened by the decreased p value demonstrating that the relationship between exercise, resilience and QOL is even more significant during a pandemic with exercise contributing more strongly to the model. The significant p value when just the co-variant of sleep was controlled for, suggests that although the pandemic has had a positive effect on resilience it is dependent on mental health, further suggesting the use of exercise to moderate resilience. This is in line with the current knowledge that exercise is often used as a coping mechanism for stress [58,59,60,61], shown across student, aging and clinical populations.

Strengths and limitations

The study is the first of its kind to investigate how a pandemic affects resilience and its moderators and mediators in an under-represented non-clinical population. Despite an opportunistic sample, and a relatively small sample size, data were acceptably normal. As to limitations, the study required participants to remember past states and conditions so a bias may have been introduced in which positive and negative attitudes would be enhanced [62, 63] due to the episodic encoding and retrieval process attaching emotion to each event stored [64,65,66]. Exercise levels could not be verified, therefore, we relied on people to correctly self-report. The relatively low sample size, not surprising considering the pandemic, does not rule out a type two error for non-significant results. The authors would like to clarify, the limitation of time hindered the opportunity to leave data collection open for longer.

Although exercise as a coping mechanism increases resilience and reduces stress, it has been linked to personality type [61], with those who are extroverted and less neurotic being more likely to exercise. In accordance with social determination theory, extroverts demonstrate more internal motivation [27, 67], being more likely to exercise. The personality type of each participant in this study was not measured, however, distribution of the survey on social media, coupled with extroverts’ increased use of these platforms [68] suggests extroverts are more likely to have completed it.

The authors recommend further studies capturing larger samples, a measure of actual exercise levels, and more longitudinal studies capturing the longer-term impact of the pandemic on the relationships reported in the current study. Future perspectives to support resilience strategies can be carried out once rules on social distancing are relaxed enough to take a measure of actual exercise levels.

Conclusion

Exercise is strongly correlated to resilience and during a pandemic such as COVID-19 it becomes a mechanism in which to moderate resilience. The relationship between exercise and resilience has been supported by this study. However, the influence that a pandemic had on mental health is mediated by its effect on quality of life.

Availability of data and materials

All date generated or analysed during this study are included in this published article.

References

  1. Van Breda A. A critical review of resilience theory and its relevance for social work. Soc Work. 2018;54(1):1–19.

    Google Scholar 

  2. Jessop E. Resilience in rare disease networks. Ann 1st Super Sanita. 2019;55(3):292–5.

    Google Scholar 

  3. Southwick SM, Bonanno GA, Masten AS, Panter-Brick C, Yehuda R. Resilience definitions, theory, and challenges: interdisciplinary perspectives. Eur J Psychotraumatol. 2014;5:1–14.

    Article  Google Scholar 

  4. Bonanno GA. Loss, trauma, and human resilience: have we underestimated the human capacity to thrive after extremely aversive events? Am Psychol. 2004;59(1):20–8.

    Article  PubMed  Google Scholar 

  5. Connor KM, Davidson JRT. Development of a new resilience scale: the Connor-Davidson resilience scale (CD-RISC). Depress Anxiety. 2003;18:76–82.

    Article  PubMed  Google Scholar 

  6. Herrman H, Stewart DE, Diaz-Granados N, Berger EL, Jackson B, Yuen T. What is resilience? Can J Psychiatry. 2011;56(5):258–65.

    Article  PubMed  Google Scholar 

  7. Rosenberg AR, Syrjala KL, Martin PJ, Flowers ME, Carpenter PA, Salit RB, et al. Resilience, health, and quality of life among long-term survivors of hematopoietic cell transplantation. Cancer. 2015;206:4250–7.

    Article  Google Scholar 

  8. Çuhadar D, Tanriverdi D, Pehilvan M, Kumaz G, Alkan S. Determination of the psychiatric symptoms and psychological resilience levels of hematopoietic stem cell transplant patients and their relatives. Eur J Cancer Care. 2016;25:112–21.

    Article  Google Scholar 

  9. Gotay CC, Isaacs P, Pagano I. Quality of life in patients who survive a dire prognosis compared to control Cancer survivors. Psycho Oncol. 2004;13:882–92.

    Article  Google Scholar 

  10. Eicher M, Matzka M, Dubey C, White K. Resilience in adult Cancer care: an integrative literature review. Oncl Nurs Forum. 2015;42(1):3–16.

    Article  Google Scholar 

  11. Schwartz CE, Michael W, Rapkin BD. Resilience to health challenges is related to different ways of thinking: mediators of physical and emotional quality of life in a heterogeneous rare-disease cohort. Qual Life Res. 2017;26:3075–88.

    Article  PubMed  Google Scholar 

  12. Matzka M, Mayer H, Köck-Hódi S, Moses-Passini C, Dubey C, Jahn P, et al. Relationship between resilience, psychological distress and physical activity in Cancer patients: a cross-sectional observation study. PLoS One. 2016;11(4):1–13.

    Article  CAS  Google Scholar 

  13. Min J, Yoon S, Lee C, Chae J, Lee C, Song K, et al. Psychological resilience contributes to low emotional distress in cancer patients. Support Care Cancer. 2013;21:2469–76.

    Article  PubMed  Google Scholar 

  14. Schumacher A, Sauerland C, Silling G, Berdel WE, Stelljes M. Resilience in patients after allogeneic stem cell transplantation. Support Care Cancer. 2014;22:487–93.

    Article  PubMed  Google Scholar 

  15. Avila M, Jimilly C, Lucchetti A, Lucchetti G. The role of physical activity in the association between resilience and mental health in older adults. J Aging Phys Act. 2018;26:248–53.

    Article  Google Scholar 

  16. Childs E, De Wit H. Regular exercise is associated with emotional resilience to acute stress in healthy adults. Front Psychol. 2014;5(May):1–7.

    Google Scholar 

  17. Fox KR. The influence of physical activity on mental well-being. Public Health Nur. 1999;2(3a):411–8.

    CAS  Google Scholar 

  18. Ho FKW, Louie LH, Chow CB, Wong WHS, Ip P. Physical activity improves mental health through resilience in Hong Kong Chinese adolescents. BMC Pediatr. 2015;15(48):1–9.

    CAS  Google Scholar 

  19. Svantesson U, Jones J, Wolbert K, Alricsson M. Impact of physical activity on the self-perceived quality of life in non-frail older adults. J Clin Med Res. 2015;7(8):585–93.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Hosseini S, Besharat M. Relation of resilience with sport achievement and mental health in a sample of athletes. Procedia Soc Behav Sci. 2010;5:633–8.

    Article  Google Scholar 

  21. Sahin M, Bademli K, Lok N, Uzun G, Sari A, Lok S. Relationship between physical activity levels and well-being of individuals. Sci Movement Health. 2018;18(2):337–43.

    Google Scholar 

  22. Callaghan P. Exercise: a neglected intervention in mental health care? J Psychiatr Ment Health Nurs. 2004;11(4):476–83.

    Article  CAS  PubMed  Google Scholar 

  23. Dilorenzo TM, Bargman EP, Stucky-ropp R, Brassington GS, Frensch PA, Lafontaine T. Long-term effects of aerobic exercise on psychological outcomes. Prev Med. 1999;28:75–85.

    Article  CAS  PubMed  Google Scholar 

  24. Callaghan P, Khalil E, Morres I, Carter T. Pragmatic randomised controlled trial of preferred intensity exercise in women living with depression. BMC Public Health. 2011;11(465):1–8.

    Google Scholar 

  25. Carter M, I., Repper, J., & Callaghan, P. Exercise for adolescents with depression: valued aspects and perceived change. J Psychiatr Ment Health Nurs. 2016;23(1):37–44.

    Article  CAS  PubMed  Google Scholar 

  26. Carter, Guo B, Turner D, Morres I, Khalil E, Brighton E, et al. Preferred intensity exercise for adolescents receiving treatment for depression: a pragmatic randomised controlled trial. BMC Psychiatr. 2015;247(15):1–12.

    Google Scholar 

  27. Deci EL, Ryan RM. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000;55(1):68–78.

    Article  PubMed  Google Scholar 

  28. Brown J. Coronavirus: the lockdown laws; 2020.

    Google Scholar 

  29. Armstrong EA, Hamilton LT, Armstrong EM, Seeley JL. ‘“ Good girls ”’: gender, social class, and slut discourse on campus. Soc Psychol Q. 2014;77(2):100–22.

    Article  Google Scholar 

  30. Pavuluri M, May A. I feel, therefore, I am: the insula and its role in human emotion, cognition and the sensory-motor system. AIMS Neurosci. 2015;2(1):18–27.

    Article  Google Scholar 

  31. Villicana AJ, Rivera LM, Garcia DM. When one’s group is beneficial: the effect of group-affirmation and subjective group identification on prejudice. Group Processes Intergr Relat. 2018;21(6):962–76.

    Article  Google Scholar 

  32. Campbell, D. UK lockdown causing “serious mental illness in first-time patients.” Guardian. 2020. https://www.theguardian.com/society/2020/may/16/uk-lockdown-causing-serious-mental-illness-in-first-time-patients.

  33. Bacikova-sleskova M, Benka J, Orosova O. Parental employment status and adolescents’ health: the role of financial situation, parent-adolescent relationship and adolescents’ resilience. Psychol Health. 2015;30(4):400–22.

    Article  PubMed  Google Scholar 

  34. Collins AL, Smyer MA. The resilience of self-esteem in late adulthood. J Aging Health. 2005;17(4):471–89.

    Article  PubMed  Google Scholar 

  35. Barr B, Mrc DT, Scott-samuel A, McKee M, Stuckler D. Suicides associated with the 2008-10 economic recession in England : time trend analysis. BMJ. 2012;345(5142):1–7.

    Google Scholar 

  36. Chang S-S, Stuckler D, Yip P, Gunnell D. Impact of 2008 global economic crisis on suicide: time trend study in 54 countries. BMJ. 2013;347(5239):1–15.

    Google Scholar 

  37. Wahlbeck K, McDaid D. Actions to alleviate the mental health impact of the economic crisis. World Psychiatry. 2012;11:139–45.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Boon L, Nicklason F, Burvill P. Screening for depression: clinical validation of geriatricians’ diagnosis, the brief assessment schedule depression cards and the 5-item version of the symptom check list among non-demented geriatric inpatients. Int J Geriatr Psychiatr. 1996;11:461–5.

    Article  Google Scholar 

  39. Ritvo, P., Fischer, J., Miller, D., Andrews, H., Paty, D., & LaRocca, N. Multiple sclerosis quality of life inventory: a User’s manual. 1997. http://walkcoc.nationalmssociety.org/docs/HOM/MSQLI_Manual_and_Forms.pdf.

  40. Scheier M, Carver C, Bridges M. Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): a re-evaluation of the life orientation test. J Pers Soc Psychol. 1994;67:1063–78.

    Article  CAS  PubMed  Google Scholar 

  41. Booth M. Assessment of physical activity: an international perspective. Res Q Exerc Sport. 2000;71(2):114–20.

    Article  PubMed  Google Scholar 

  42. Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95.

    Article  PubMed  Google Scholar 

  43. Lee PH, Macfarlane DJ, Lam TH, Stewart SM. Validity of the international physical activity questionnaire short form (IPAQ-SF): a systematic review. Int J Behav Nutr Phys Act. 2011;8:115.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Morin, C. M. (1993). Insomnia severity index. in insomnia, psychological assessment and management (p. 28).

  45. Harper, A. (1996). WHOQOL-BREF introduction, Administration, Scoring and Generic Version of the Assessment.

  46. Kim H. Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restorat Dentist Endodontics. 2013;7658(2):52–4.

    Article  Google Scholar 

  47. Turner D, Carter T, Sach T, Guo B, Callaghan P. Cost-effectiveness of a preferred intensity exercise programme for young people with depression compared with treatment as usual: an economic evaluation alongside a clinical trial in the UK. BMJ Open. 2017a;7(11):1–9.

    Article  Google Scholar 

  48. Craft LL, Perna FM. The benefits of exercise for the clinically depressed. J Clin Psychiatry. 2004;6(3):104–11.

    Google Scholar 

  49. Goswami U. In the Beginning Was the Rhyme? A Reflection on Hulme, Hatcher, Nation, Brown, Adams, and Stuart. J Exp Child Psychol. 2002;82:47–57.

    Article  PubMed  Google Scholar 

  50. Plante TG, Oppezzo MA, Diaz LA, Pistoresi S, Santos M, Fahey JE, et al. The influence of exercise environment and gender on mood and exertion. Int Exerc Sci. 2014;7(3):220–7.

    Google Scholar 

  51. Schmitz N, Kruse J, Kugler J. The association between physical exercises and health-related quality of life in subjects with mental disorders: results from a cross-sectional survey. Prev Med. 2004;39:1200–7.

    Article  PubMed  Google Scholar 

  52. Cash TF, Jakatdar TA, Fleming E. The body image quality of life inventory: further validation with college men and women. Body Image. 2004;1:279–87.

    Article  PubMed  Google Scholar 

  53. Dalley SE, Vidal J. Optimism and positive body image in women: the mediating role of the feared fat self. Personal Individ Differ. 2013;55(5):465–8.

    Article  Google Scholar 

  54. Ower C, Kemmler G, Vill T, Martini C, Schmitt A, Sperner-Unterweger B, et al. The effect of physical activity in an alpine environment on quality of life is mediated by resilience in patients with psychosomatic disorders and healthy controls. Eur Arch Psychiatry Clin Neurosci. 2019;269(5):543–53.

    Article  PubMed  Google Scholar 

  55. Bretherton I. The origins of attachment theory: John Bowlby and Mary Ainsworth. Dev Psychol. 1992;28:759–75.

    Article  Google Scholar 

  56. Ellaway A, Macintyre S, Bonnefoy X. Graffiti, greenery, and obesity in adults: secondary analysis of European cross sectional survey. BMJ. 2005;331:611–2.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Peen J, Schoevers R, Beeekman A, Dekker J. The current status of urban-rural differences in psychiatric disorders. Acta Psychiatr Scand. 2010;121(2):84–93.

    Article  CAS  PubMed  Google Scholar 

  58. Blumenthal JA, Sherwood A, Babyak MA, Watkins LL, Waugh R, Georgiades A, et al. Effects of exercise and stress management training on markers of cardiovascular risk in patients with ischemic heart disease: a randomized controlled trial. JAMA. 2005;293(13):1626–34.

    Article  CAS  PubMed  Google Scholar 

  59. Garber MC. Exercise as a stress coping mechanism in a pharmacy student population. Am J Pharm Educ. 2017;81(3):1–6.

    Article  Google Scholar 

  60. Jacobsen PB, Le-Rademacher J, Jim H, Syrjala K, Wingard JR, Logan B, et al. Exercise and stress management training prior to hematopoietic cell transplantation: blood and marrow transplant clinical trials network ( BMT CTN ) 0902. Biol Blood Marrow Transplant. 2014;20(10):1530–6.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Saklofske DH, Austin EJ, Rohr BA, Andrews JJ. Personality, emotional intelligence and exercise. J Health Psychol. 2007;12(6):937–48.

    Article  PubMed  Google Scholar 

  62. Christianson S-A, Loftus E. Remembering emotional events: the fate of detailed information. Cognit Emot. 1991;5(2):81–108.

    Article  Google Scholar 

  63. Kensinger EA. Remembering the details: effects of emotion. Emot Rev. 2009;1(2):99–113.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Eichenbaum H. The hippocampus and declarative memory : cognitive mechanisms and neural codes. Behav Brain Res. 2001;127:199–207.

    Article  CAS  PubMed  Google Scholar 

  65. Squire LR. Memory systems of the brain : a brief history and current perspective. Neurobiol Learn Mem. 2004;82(3):171–7.

    Article  PubMed  Google Scholar 

  66. Tulving E, Kapur S, Craik FIM, Moscovitch M, Houle S. Hemispheric encoding / retrieval asymmetry in episodic memory : positron emission tomography findings. Proc Natl Acad Sci U S A. 1994;91:2016–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Clark MH, Schroth CA. Examining relationships between academic motivation and personality among college students. Learn Individ Differ. 2010;20(1):19–24.

    Article  Google Scholar 

  68. Harbaugh ER. The effect of personality styles (level of introversion- extroversion) on social media use. Elon J Undergrad Res Commun. 2010;1(2):70–86.

    Google Scholar 

Download references

Acknowledgements

The lead author would like to acknowledge Ms. Lisa Helen Wason BA for her contribution to the writing of this article.

Funding

N/A

Author information

Authors and Affiliations

  1. Conducted research at London Southbank University, 103 Borough Road, London, SE1 0AA, UK

    Molly Rose Lancaster

  2. Professor of Mental Health Science and Associate Pro Vice-Chancellor Research, London Southbank University, 103 Borough Road, London, SE1 0AA, UK

    Patrick Callaghan

Authors
  1. Molly Rose Lancaster
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Patrick Callaghan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

ML and PC designed the study, analysed the data, and wrote the manuscript. ML collected the data, wrote the main manuscript text, and prepared all figures and tables. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Molly Rose Lancaster.

Ethics declarations

Ethics approval and consent to participate

Ethical approval was obtained from ‘London South Bank University’s Research Ethics Committee’. Experiment protocol for involving humans was in accordance with guidelines of the institution. Informed consent was obtained from all subjects.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lancaster, M.R., Callaghan, P. The effect of exercise on resilience, its mediators and moderators, in a general population during the UK COVID-19 pandemic in 2020: a cross-sectional online study. BMC Public Health 22, 827 (2022). https://doi.org/10.1186/s12889-022-13070-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-022-13070-7

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us