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The COVID-19 pandemic has become a global public health event, which has raised concerns regarding individuals’ health. Individuals need to cope with the COVID-19 pandemic with guidelines on symptom recognition, home isolation, and maintain mental health. Besides routine use of mobile health (mHealth) such as accessing information to keep healthy, individuals can use mHealth services in situations requiring urgent medical care, which is defined as mHealth emergency use. It is not known whether individuals have increased their daily mHealth services emergency use as a result of disruptions caused by the COVID-19 pandemic.
The purpose of this diary analysis study is to assess the influences of daily disruptions related to the COVID-19 pandemic on individuals’ mHealth emergency use. The secondary purpose of this study is to explore the mediating role of COVID-19–induced strain and the moderating role of promotion regulatory focus in the relationship between daily disruptions of COVID-19 and mHealth emergency use. Drawing from the cognitive activation theory of stress, we investigated the underlying mechanism and boundary condition of the influence of COVID-19–related disruptions on daily mHealth emergency use.
To test the proposed model, this study adopts the experience sampling method to collect daily data. The experience sampling method helps researchers to capture participants’ fluctuations in emotions, mental engagement in an activity, and experienced stress. This study collected 550 cases nested in 110 samples in mainland China to test the conceptual model. In addition, we employed hierarchical linear modeling analysis to test the effect of COVID-19–related disruptions on mHealth emergency use.
We found that COVID-19–related disruptions increased COVID-19–induced strain (γ=0.24,
Event disruption of the COVID-19 pandemic induced mHealth emergency use intention through increased psychological strain. Furthermore, individuals’ promotion regulatory focus amplified this indirect relationship. Our findings extend our understanding of the factors underlying mHealth emergency use intention and illustrate the potential contingent role of promotion regulatory focus in the cognitive activation theory of stress. This study also opens avenues for future research on mHealth emergency use intention in other countries and cultural settings.
The COVID-19 pandemic has become a worldwide public health event. This has resulted in greater concerns regarding one’s health and well-being [
Mobile health (mHealth) service is defined as health care practice supported by mobile devices. Given that our research focuses on the mHealth service in the COVID-19 pandemic, the mHealth service in this study includes apps that health care professionals use to treat clinical disease, reinforce treatment adherence, provide consultation to the users, and educate users on self-monitoring of the disease COVID-19 [
The cognitive activation theory of stress (CATS) [
Whether an individual decides to use mHealth to cope with COVID-19–induced strain is contingent upon their preferred method to deal with problems [
To address our three research questions, we used the experience sampling method to test the conceptual model (see
Conceptual model. H: hypothesis; mHealth: mobile health.
CATS proposes that stress occurs with a discrepancy between desired outcomes and reality [
CATS offers us a framework to elaborate on the influences of the COVID-19 pandemic on individuals’ mHealth emergency use. The unpredictable and detrimental characteristics of the COVID-19 pandemic change individuals’ life and work. Confronted with such changes, the strain will arise in individuals and further shape their attitudes and coping behavior. mHealth is an effective instrument to realize disease prevention and health promotion [
When events are urgent, unpredictable, unexpected, and threatening, they are regarded as stressful and may result in negative psychological, physical, and physiological outcomes [
This corresponds with the event disruptions of the COVID-19 pandemic, which reflect change and discontinuity of the external situation [
Hypothesis (H)1: Event disruption is positively associated with COVID-19–induced strain on a daily basis.
CATS links stressful events with coping behavior [
Regarding the context of stress, an individual’s coping response depends on their appraisal of the demands and resources available to handle the stressful event [
In terms of expectations, the choice of coping behavior is determined by an individual’s anticipated outcome [
H2: COVID-19–induced strain is positively correlated with mHealth emergency use intention on a daily basis.
The event disruption of the COVID-19 pandemic makes the external situations unpredictable and uncertain. The changes in life induce stress experience in individuals. To cope with event disruption and COVID-19–induced strain on a daily basis, individuals are more likely to use mHealth in urgent situations to promote health status and prevent a specific disease. In this vein, we further hypothesize that:
H3: COVID-19–induced strain mediates the relationship between event disruption and daily mHealth emergency use intention.
CATS suggests that the choice of coping behavior is determined by the interaction of personal and contextual variables [
In the context of COVID-19–induced strain, individuals with high promotion regulatory focus may regulate their actions and attitudes to achieve favorable outcomes [
H4: Promotion regulatory focus will moderate the relationship between daily COVID-19–induced strain and mHealth emergency use intention, such that the relationship is stronger in the condition of high promotion regulatory focus than in the condition of low promotion regulatory focus.
As previously mentioned, event disruptions of the COVID-19 pandemic cause unpredictable and unfavorable changes in personal and work life, which elevates stress experience. This induced strain may drive individuals to use mHealth in urgent situations, especially those with high promotion regulatory focus, as this will allow them to promote good health and prevent disease. We hypothesize that:
H5: Promotion regulatory focus will moderate the indirect relationship between event disruption and daily mHealth emergency use intention through COVID-19–induced strain, such that the indirect relationship is stronger in the condition of high promotion regulatory focus than in the low promotion regulatory focus.
Based on the research of Du et al [
The data collection contained two stages. On February 23, 2020, participants were asked to complete a baseline questionnaire regarding demographic information (gender, age, education) and promotion regulatory focus. From February 24 to 28, 2020, participants were sent a website link at 11 AM that assessed event disruptions and at 5 PM that assessed COVID-19–induced strain and mHealth emergency use intention on each day. Participants were asked to complete the questionnaires within 2 hours. Of the 150 individuals invited, we collected 550 matched responses from a total of 110 participants, yielding an effective response rate of 73.3%. The 110 participants received a ¥25 (about US $3.53) inconvenience allowance.
All of the measures of the constructs were developed based on previous research. We adapted each item to fit the daily gathering of data. For instance, one item of the original work strain scale is “I often feel too tense due to my work.” We adapted it as “Due to COVID-19 Pandemic, I lived and worked under a great deal of tension today” to fit the COVID-19 pandemic and the daily research context. Specifically, in accordance with suggestions from Donald et al [
Measures for event disruptions were adapted from four items developed by Morgeson et al [
The COVID-19–induced strain was measured by three items adapted from the scale developed by House and Rizzo [
mHealth emergency use intention was measured by three items developed by Liu et al [
The regulatory focus has been regarded as a personality trait, which is stable and not probable to change in a short time. Thus, this study put promotion regulatory focus at the baseline measurement [
We also collected demographic data including gender, education, age, and chronic disease, as they may influence mHealth use intention [
The data was nested, as the data were collected using the experience sampling method. The data had a two-level hierarchical structure, where daily level or within-person level data was positioned at level one and individual level or between-person level data was positioned at level two [
The analysis contained two stages. First, we investigated the within-person level variance in the daily variables. The results showed about a 71%-85% variance for the within-person level for event disruption, COVID-19–induced strain, and mHealth emergency use intention, justifying the use of HLM. Second, we performed HLM (version 6.08) using a restricted maximum likelihood estimation for the parameter analyses. We conducted a moderated mediation model analysis with a random slope and used robust estimators in level one to indicate the daily or within-person effect. The daily variables (event disruption, COVID-19–induced strain, and mHealth emergency use intention) were group-centered.
Participants’ demographic data (N=110).
Characteristic | Participants, n (%) | |
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Male | 60 (54.5) |
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Female | 50 (45.5) |
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No | 97 (88.4) |
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Yes | 13 (11.6) |
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Primary school | 1 (0.9) |
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Senior school | 2 (1.8) |
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High school | 30 (27.3) |
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College | 26 (23.6) |
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Bachelor’s and above | 51 (46.4) |
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<26 | 2 (1.8) |
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26-35 | 50 (45.5) |
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36-45 | 36 (32.7) |
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≥46 | 22 (20.0) |
Given that our daily data were nested, we adopted multilevel confirmatory factor analysis rather than exploratory factor analysis to test the validity of the measurements and the common method variance [
Results of multilevel confirmatory factor analysis.
Models | Chi-square ( |
△ chi-square | RMSEAa | CFIb | TLIc | SRMRd (within) | |
EUe, EDf, LSg, PFh | 151.22 (57) | N/Ai | N/A | 0.06 | 0.96 | 0.95 | 0.03 |
EU+ED, LS, PF | 451.15 (59) | 299.93 | <.001 | 0.11 | 0.84 | 0.78 | 0.19 |
EU+LS, ED, PF | 420.60 (59) | 269.38 | <.001 | 0.11 | 0.85 | 0.80 | 0.12 |
EU, LS+ED, PF | 440.35 (59) | 289.13 | <.001 | 0.11 | 0.85 | 0.79 | 0.16 |
EU+LS+ED, PF | 658.05 (60) | 506.83 | <.001 | 0.14 | 0.76 | 0.67 | 0.21 |
aRMSEA: root mean square error of approximation.
bCFI: comparative fit index.
cTLI: Tucker–Lewis index.
dSRMR: standardized root mean square residual.
eEU: mobile health emergency use intention.
fED: event disruption.
gLS: COVID-19–induced strain.
hPF: promotion regulatory focus.
iN/A: not applicable.
Within-person level (N=550) means, SDs, and correlations.
Variables | Mean (SD) | 1 | 2 | 3 | |
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4.84 (0.99) |
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1 |
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N/Ac |
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3.81 (0.69) |
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0.20 | 1 |
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<.001 | N/A |
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4.20 (0.77) |
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0.29 | 0.27 | 1 |
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<.001 | <.001 | N/A |
amHealth: mobile health.
bCronbach alpha=.92.
cN/A: Not applicable.
dCronbach alpha=.93.
eCronbach alpha=.76.
Between-person level (N=110) means, SDs, and correlations.
Variables | Mean (SD) | 1 | 2 | 3 | 4 | 5 | |||||||
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1.45 (0.50) |
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1 |
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N/Aa |
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4.13 (0.94) |
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0.68 | 1 |
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<.001 | N/A |
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1.12 (0.32) |
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0.06 | 0.07 | 1 |
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.19 | .12 | N/A |
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2.71 (0.80) |
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–0.49 | –0.24 | –0.16 | 1 |
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<.001 | <.001 | <.001 | N/A |
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4.08 (0.35) |
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0.03 | –0.15 | –0.02 | –0.15 | 1 | ||||||
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.69 | <.001 | .57 | <.001 | N/A |
aN/A: Not applicable.
bCronbach alpha=.93.
Daily event disruption had a significant positive relationship with both COVID-19–induced strain (Model 1:
To further explore the mediating role of COVID-19–induced strain on the temporal relationship between event disruption and mHealth emergency use intention, a Monte Carlo bootstrapping test was performed using R (version 3.5.3; R Foundation for Statistical Computing). Both the direct relationship (effect=0.18, 95% CI 0.06-0.30) and indirect relationship (effect=0.09, 95% CI 0.05-0.14) were significant. The results are summarized in
The results for promotion regulatory focus are shown in model 4. The interaction of promotion regulatory focus with COVID-19–induced strain was positively associated with mHealth emergency use intention (
Finally, we used a Monte Carlo bootstrapping test to examine the moderated mediation model. The results showed that the indirect relationship between daily event disruption and mHealth emergency use intention through COVID-19–induced strain was significantly stronger when promotion regulatory focus was high (effect=0.12, 95% CI 0.06-0.19) than when it was low (effect=0.06, 95% CI 0.02-0.11). The difference between the two effects was significant (effect=0.06, 95% CI 0.001-0.12), supporting H5.
Results of hierarchical linear model analysis.
Variables | COVID-19–induced strain | mHealtha emergency use intention | |||||||||||
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Model 1b | Model 2c | Model 3d | Model 4e | |||||||||
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SE |
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SE |
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SE |
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SE | |||||
Intercepts | 3.43 | 0.21 | <.001 | 4.23 | 0.30 | <.001 | 4.22 | 0.31 | <.001 | 4.20 | 0.31 | <.001 | |
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Gender | –0.14 | 0.09 | .115 | –0.13 | 0.20 | .51 | –0.11 | 0.19 | .55 | –0.12 | 0.19 | .55 |
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Education | 0.09 | 0.04 | <.001 | 0.08 | 0.09 | .38 | 0.08 | 0.09 | .38 | 0.08 | 0.09 | .36 |
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Chronic disease | 0.13 | 0.10 | .20 | 0.31 | 0.14 | .03 | 0.29 | 0.15 | .06 | 0.29 | 0.15 | .06 |
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Age | 0.02 | 0.05 | .59 | 0.04 | 0.08 | .61 | 0.05 | 0.08 | .52 | 0.05 | 0.08 | .50 |
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Promotion regulatory focus | 0.30 | 0.09 | <.001 | 0.37 | 0.16 | .02 | 0.43 | 0.16 | .009 | 0.39 | 0.16 | .02 |
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Event disruption | 0.24 | 0.05 | <.001 | 0.28 | 0.06 | <.001 | 0.19 | 0.06 | <.001 | 0.18 | 0.06 | .004 |
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COVID-19–induced strain | N/Af | N/A | N/A | N/A | N/A | N/A | 0.36 | 0.06 | <.001 | 0.37 | 0.06 | <.001 |
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COVID-19–induced strain × promotion regulatory focus | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 0.35 | 0.15 | .02 |
amHealth: mobile health.
bPseudo
cPseudo
dPseudo
ePseudo
fN/A: not applicable.
Results of the Monte Carlo bootstrapping test.
Effect | Estimator | SE | 95% CIa | |
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Low (Mb – SD) | 0.25 | 0.08 | 0.09-0.40 |
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High (M + SD) | 0.49 | 0.08 | 0.34-0.65 |
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Difference | 0.24 | 0.11 | 0.04-0.45 |
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Direct effect | 0.18 | 0.06 | 0.06-0.30 |
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Indirect effect | 0.09 | 0.02 | 0.05-0.14 |
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Low (M – SD) | 0.06 | 0.02 | 0.02-0.11 |
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High (M + SD) | 0.12 | 0.03 | 0.06-0.19 |
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Difference | 0.06 | 0.03 | 0.01-0.12 |
aBootstrapping=20,000.
bM: mean.
Moderating role of promotion regulatory focus. mHealth: mobile health.
We developed a conceptual model to explore how daily event disruptions of the COVID-19 pandemic predict mHealth emergency use intention. This study provides key findings and contributions to both mHealth research and practitioners.
This study presents three significant key findings. First, event disruptions of the COVID-19 pandemic are associated with increased daily mHealth emergency use intentions. Event disruptions of the COVID-19 pandemic represent the discontinuity of daily routines [
Second, this study found that COVID-19–induced strain mediated the relationship between event disruption and mHealth emergency use intention on a daily basis. According to CATS, stressful experiences or strain arises from the lack of resources to effectively deal with the demands of stressful events [
Third, our research findings suggest that promotion regulatory focus amplifies the indirect effect of event disruption on mHealth emergency use intention through daily COVID-19–induced strain. The interaction of personality traits and contextual variables determines the choice of coping behavior [
This study provides several theoretical contributions to mHealth literature. First, this study contributes to the mHealth literature by identifying the temporal influences of event disruption and mHealth emergency use intention. The COVID-19 pandemic was used as an example situation to explore the influence of event disruption caused by an emergent health crisis on the use of mHealth. This study extended this line of research by not only incorporating event disruption as an influencing factor for mHealth emergency use intention but also by examining mHealth emergency use intention on a daily basis. In doing so, this study contributes to mHealth literature by identifying a new type of mHealth use intention and examining its proximal antecedent.
Second, our study uncovered an underlying mechanism by examining the mediating role of COVID-19–induced strain. Previous studies investigating mHealth use intention mainly focused on the influences of technological and psychological factors [
Third, we have also enriched the understanding of CATS by incorporating promotion regulatory focus into our model. Previous research using CATS primarily focused on the role of expectations in shaping an individual’s response to stressful events [
This study has practical implications for mHealth providers during a public health crisis. When the public experiences a health crisis, many people use mHealth services, which helps deal with psychological strain. We recommend that service providers develop specific services to cater to the needs of the public. For instance, remote primary diagnosis and health monitoring for a specific disease can be integrated into mHealth. This would enable individuals to incorporate mHealth into their daily lives and allow effective self-monitoring, even in urgent situations.
In addition, it would be useful for service providers to consider the role of regulatory focus, as individuals with high promotion regulatory focus are more likely to use mHealth when confronted with a health emergency. Service providers may adopt the regulatory focus scale as a primary screening method to select potential users and provide them with specific functions and services. This would offer providers with opportunities to increase user compliance.
This study has several limitations and points out directions for future research. First, we did not establish a causal relationship between event disruption and mHealth emergency use intention. Moreover, although we collected two-wave data on a daily basis, we cannot conclude that daily event disruption predicts psychological strain and mHealth emergency use intention because we did not manipulate the event disruptions of the COVID-19 pandemic. Future research could use a cross-lagged panel design to infer the causal relationship between event disruption and mHealth use intention.
Second, it is also not possible to rule out common method variance. The experience sampling method controls for common method variance to a certain degree, as confirmed by the multilevel confirmatory factor analysis. However, our data were collected through self-report questionnaires, and therefore, our results may still have been impacted by common method variance. Future research could acquire objective data to minimize the potential effects of common method variance. This could be implemented through gathering mHealth app browsing history during a public health emergency.
Another limitation is that our study was conducted in China. Further research is needed to test the generalizability of our findings in other countries. The development of the mHealth industry differs across the world and mHealth use will depend on the stage of development of this technology. Therefore, future research in other countries will need to additionally consider these factors.
mHealth provides individuals with a platform to access health care services. The results showed that event disruption of the COVID-19 pandemic induced mHealth emergency use intention through increased psychological strain. Furthermore, individuals’ promotion regulatory focus amplified this indirect relationship. Our findings extend our understanding of the factors underlying mHealth emergency use intention and illustrate the potential contingent role of promotion regulatory focus in CATS. This study also opens avenues for future research on mHealth emergency use intention in other countries and cultural settings.
Questionnaire: measurement of the major constructs.
cognitive activation theory of stress
hypothesis
hierarchical linear modeling
mobile health
Our research is supported by the National Natural Science Foundation of China (71701083), Yunnan Province Basic Research Planning Project (Grant No. 2019FB084), and the 12th Five-years Plan of Beijing science of education (CAA15006).
None declared.