This study was approved by the institutional review board at Baylor University (approval number 1655082‐11). All participants provided verbal informed consent prior to participation. Some personally identifiable information was collected as part of the study. All identifiable data were removed prior to analysis, and the remaining materials were securely stored on a university-provided cloud platform with restricted access. Only approved research personnel had access to the data through institutional login credentials. No identifying details (such as names, initials, facial features, or other unique identifiers) are included in the manuscript or any submitted materials. All data are presented in a manner that ensures participant anonymity and privacy. No compensation was provided to the participants.

In total, 79 young participants in their 20s with and those without a history of COVID-19 participated in this study virtually: (1) the non–COVID-19 group comprised 39 participants (age: mean 21.58, SD 1.24 years; years of education: mean 16.32, SD 0.97), and (2) the COVID-19 group comprised 40 participants (age: mean 21.38, SD 1.63 years; years of education: mean 15.60, SD 1.25; Table 1).

Participants in the non–COVID-19 group were recruited between November 11, 2020, and December 4, 2020, and self-reported not having a history of COVID-19. Participants in the COVID-19 group were recruited between April 13, 2021, and September 20, 2022, and provided self-reported information regarding time since infection and number of infections. All COVID-19–positive participants were nonhospitalized, and symptom severity ratings were not collected using a formal scale. Vaccination status was not obtained, and information regarding post–COVID-19 condition symptoms was not collected, as the data were obtained during the early stage of the pandemic, before the concept of post–COVID-19 condition had been clearly defined. Table 2 provides a summary of COVID-19–related participant characteristics.

All data collection procedures were conducted virtually via Zoom (Zoom Video Communications). Each session lasted less than 1 hour per participant.

Statistical analyses for this project were conducted using Python (version 3.10), with the statsmodels and scipy packages for ANOVA and regression modeling. The dataset was assessed for normality, and due to skewed distributions, a natural log transformation (ln) was applied to the story retelling variable (information units per minute [IUs/min]). Two outcome variables were analyzed: (1) log-transformed IUs/min derived from the SRP task and (2) the total number of correct responses on the Alphabet Span task as a measure of verbal working memory.

To address the first research question, a 1-way between-subjects ANOVA was conducted to compare story retelling performance between the COVID-19 and non–COVID-19 groups. The dependent variable was the number of information units produced per minute, log-transformed to meet assumptions of normality.

The results revealed a significant between-group difference (F_1,77=3.99, P=.049), with the COVID-19 group producing fewer information units per minute (mean 0.53, SD 0.21) than the non–COVID-19 group (mean 0.63, SD 0.24). For the second research question, a separate 1-way ANOVA was conducted to compare verbal working memory performance between groups, measured by total number of correct items on the Alphabet Span task. The analysis indicated no significant difference between the COVID-19 (mean 16.21, SD 2.94) and non–COVID-19 (mean 15.30, SD 3.29) groups (F_1,77=1.66, P=.20). To address the third research question, a simple linear regression was conducted to evaluate the extent to which verbal working memory performance predicted story retelling performance. The regression model was significant (F_1,77=5.31, P=.02), and accounted for 6.4% of the variance in log-transformed IUs/min (R²=.064).

The regression equation was:

Story retelling performance (log IUs/min)=−0.493+0.014×verbal working memory performance (Alphabet Span Test: the total correct numbers of items).

The unstandardized regression coefficient (B=0.014) indicates that each additional point on the Alphabet Span Test resulted in a 0.014 increase in log-transformed IUs/min. This corresponds to an approximate 1.4% increase on the raw scale:

This back-transformation should be interpreted cautiously. All statistical inferences were based on log-transformed values due to nonnormality in the original distribution. The 95% CI for the slope ranged from 0.002 to 0.026, suggesting a modest but statistically reliable association between verbal working memory and story retelling performance.

Additional exploratory analyses were conducted within the COVID-19 group to examine whether the number of days since infection was associated with performance on cognitive-linguistic tasks. Time since infection was not significantly associated with story retelling performance (r=.10; P=.57; R²=.009) or with verbal working memory scores (r=.04; P=.83; R²=.001). These results suggest that recovery time did not significantly account for performance variability in either task. All additional statistical analyses were conducted using Python (version 3.10) with the pandas, scipy.stats, and seaborn libraries. Exploratory correlation analyses were performed using the Python pearsonr function to assess linear associations between variables of interest, and simple linear regression was conducted using the linregress function.

This study aimed to investigate the impact of COVID-19 on spoken story retelling and verbal working memory and to examine the predictive relationship between the story retelling and the verbal working memory in young adults. The results found that the story retelling performance of the COVID-19 group was significantly lower than that of the non–COVID-19 group. However, despite this performance difference in story retelling, no significant difference was observed between the groups in verbal working memory. This highlights an interesting contrast, suggesting that while the COVID-19 group showed reduced story retelling performance, their verbal working memory capacity did not differ significantly from that of the non–COVID-19 group, indicating that other factors beyond memory might contribute to the observed differences in story retelling performance. In the results of regression analysis, the spoken story retelling performance, measured by %IUs/min, was significantly predicted by the verbal working memory span, even though the prediction was weak, indicating that the relationship between these 2 variables is complex and may be influenced by other factors not captured in this analysis. These results indicate several critical updates of the impact of the COVID-19 on spoken language processing and verbal working memory.

Our findings reveal a surprising impact of COVID-19 on the spoken language of young adults, challenging the prevailing assumption that only infants and children in the developmental stage are susceptible to such effects. Although prior research has predominantly linked COVID-19 with developmental language disorders or delays, particularly in infants and children [36-39], this study underscores the unique vulnerability of healthy young adults to disruptions in story retelling as a result of COVID-19 infection. While children in the developmental stage are generally considered more vulnerable to language impairments, young adults typically exhibit stable story retelling abilities. However, this study suggests that COVID-19 may be associated with disruptions in these abilities. The duration and trajectory of such effects remain unclear, particularly in individuals experiencing post–COVID-19 condition. Future longitudinal research is needed to clarify whether and how story retelling performance may change over time. Additionally, this study suggests that story retelling may be especially sensitive in detecting the cognitive impact of COVID-19. This heightened sensitivity may be attributed to the complexity of the language system, wherein discourse-level processing involves multiple cognitive and linguistic components. In this context, the SRP, which heavily relies on auditory comprehension and various memory functions [16,29,40-42], emerges as a particularly sensitive tool. Both auditory comprehension and memory have been identified as vulnerable indicators of disruptions in language processing abilities [43,44]. Given the complexity of language processing assessed by the SRP, it holds promise as an invaluable tool for detecting the nuanced effects of COVID-19 on story retelling in young adults.

Conversely, we observed no significant impact of COVID-19 on verbal working memory. These findings contrast with those reported in previous literature, which has documented negative effects of COVID-19 on working memory [45]. One possible explanation for this discrepancy is the age group of the participants in this study. According to recent research [45], the detrimental effects of COVID-19 on working memory function are primarily observed in adults aged 25 years and older. In contrast, the average age of the participants in the COVID-19 group in this study was 21.37 years, with the non–COVID-19 group also consisting of individuals in a similar age range. This age-related difference may account for the absence of significant working memory decline in the younger cohort in this study. Another potential factor is that none of the participants had been hospitalized for COVID-19, and most reported only mild symptoms. It is also possible that some were asymptomatic. This limited severity may help explain the absence of significant group differences in working memory performance. Moreover, this study did not collect data on whether the COVID-19 group experienced brain fog or post–COVID-19 condition, as data collection took place shortly after the acute phase of COVID-19 infection. It is possible that participants did not exhibit symptoms of post–COVID-19 condition related to cognitive decline or other persistent neuropsychological impairments. Given these factors, further research is needed to examine verbal working memory in individuals experiencing symptoms of post–COVID-19 condition, as this cohort may exhibit different cognitive outcomes.

Regression modeling revealed that the story retelling performance was predicted by the verbal working memory test, specifically the Alphabet Span task. This association between SRP and verbal working memory was previously identified in a study involving stroke survivors with aphasia [16]. Revisiting these results with a narrowed focus on individuals exhibiting symptoms of post–COVID-19 condition is crucial to further clarify this predictive relationship further.

As discussed earlier, a notable limitation of this study is the absence of information regarding whether participants with a history of COVID-19 had experienced post–COVID-19 condition. This gap in data collection stems from the timing of the study, which occurred shortly after the onset of COVID-19. In addition, this study was conducted in a virtual testing environment, which may have introduced variability in participants’ attention, environmental distractions, or audio quality, potentially affecting performance. Another limitation is the possibility of selection bias, as participants volunteered to complete the study remotely and may not fully represent the broader young adult population. Furthermore, the regression analyses yielded relatively low predictive power, suggesting that while certain predictors showed statistical associations, they may not account for a substantial proportion of variance in story retelling performance.

This study identified differences in spoken story retelling performance between individuals with and those without a history of COVID-19. Additionally, story retelling performance was predicted by the Alphabet Span Test, a measure of verbal working memory. These findings suggest that story retelling may be sensitive to subtle post–COVID-19 cognitive-linguistic changes in young adults. This has potential clinical implications for early cognitive-linguistic screening and targeted support interventions, particularly in educational and clinical settings.