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The year 2020 has been challenging for many, particularly for young adults who have been adversely affected by the COVID-19 pandemic. Emerging adulthood is a developmental phase with significant changes in the patterns of daily living; it is a risky phase for the onset of major mental illness. College students during the pandemic face significant risk, potentially losing several protective factors (eg, housing, routine, social support, job, and financial security) that are stabilizing for mental health and physical well-being. Individualized multiple assessments of mental health, referred to as
To assess the feasibility and provide an in-depth examination of the impact of the COVID-19 pandemic on college students through multimodal personal chronicles, we present a case study of an individual monitored using a longitudinal subjective and objective assessment approach over a 9-month period throughout 2020, spanning the prepandemic period of January through September.
The individual, referred to as Lee, completed psychological assessments measuring depression, anxiety, and loneliness across 4 time points in January, April, June, and September. We used the data emerging from the multimodal personal chronicles (ie, heart rate, sleep, physical activity, affect, behaviors) in relation to psychological assessments to understand patterns that help to explicate changes in the individual’s psychological well-being across the pandemic.
Over the course of the pandemic, Lee’s depression severity was highest in April, shortly after shelter-in-place orders were mandated. His depression severity remained mildly severe throughout the rest of the months. Associations in positive and negative affect, physiology, sleep, and physical activity patterns varied across time periods. Lee’s positive affect and negative affect were positively correlated in April (r=0.53,
We observed more pandemic concerns in April and noticed other contextual events relating to this individual’s well-being, reflecting how college students continue to experience life events during the pandemic. The rich monitoring data alongside contextual data may be beneficial for clinicians to understand client experiences and offer personalized treatment plans. We discuss benefits as well as future directions of this system, and the conclusions we can draw regarding the links between the COVID-19 pandemic and college student mental health.
Young adults between the ages of 18-25 years report greater feelings of loneliness than other age groups [
College students are a diverse cross-section of the population [
The year 2020 has largely been defined by the COVID-19 pandemic, which has disrupted the daily lives of people across the world. All of the aforementioned health-related concerns for college student mental health have been exacerbated by the COVID-19 pandemic [
Examining student wellness in the context of their experiences may help to inform programming and intervention-based efforts to maintain student mental health, a key priority of campus wellness. From a biopsychosocial framework, wellness not only embodies internal functioning but also the social and external environment [
Precision medicine and idiographic approaches to health have gained traction to address the heterogeneity of symptomatology [
Given the novelty of the COVID-19 pandemic, we reasoned that the use of a case study was an important step in the scientific investigation of this phenomenon, one that would highlight the potential of the multimodal personal chronicles
Our recent work [
How does a multimodal (passive and active) sensing approach via multimodal personal chronicles capture individualized and contextual experiences of well-being?
What is the experience of daily living for a college student in the context of the COVID-19 pandemic?
We discuss the procedures for the larger study in which our case study is derived from. In January 2020, we began an Institutional Review Board (IRB)–approved (#2019-5153) investigation to pilot and examine the utility of this multimodal personal chronicles system in understanding changes in mental health symptoms (eg, depression, anxiety) and general psychological distress (eg, loneliness, negative emotion) over time among emerging adults in college. Participants completed a comprehensive psychological battery at study intake (eg, depression, anxiety, loneliness) and used devices within the multimodal system (ie, Oura ring, Samsung Gear Sport, ecological momentary assessments, and Personicle [
The battery of psychological and well-being–related questionnaires were administered at baseline in person and during subsequent follow-up assessments (see
Lee’s depression, anxiety, and loneliness scores at 4 different time points in the study. Note: the sum of anxiety scores is reported rather than the means to show score distribution over time.
Participants completed the Beck Depression Index II (BDI-II) [
The Brief Symptom Inventory (BSI) [
Participants were administered the UCLA (University of California, Los Angeles) 3-item Loneliness Scale, a frequently used measure of perceived loneliness (eg, “How often do you feel you are left out?”; α=.72) [
As part of the intensive active and passive data sensing, over the course of the study, we tracked participants’ emotional states, physiological patterns, and behavioral habits through WIoT devices and ecological momentary assessments.
Participants were instructed to wear 2 devices (Oura ring and Samsung Gear Sport smartwatch) as well as download the corresponding Oura and Samsung Android mobile apps to measure and store their physiology, sleep, and behavioral patterns. The Oura ring [
Lastly, the Personicle app [
Participants completed daily assessments of positive and negative affect using the Positive and Negative Affect Schedule (PANAS), a widely used scale that has high internal consistency, validity, and reliability (for positive affect, α=.85; for negative affect, α=.91) [
We followed participants from January through September, in which each individual completed a total of 4 battery assessments of psychological well-being (across roughly 3-month time frames—January, April, June, and September) in addition to intensive longitudinal assessments of heart rate, heart rate variability measures, respiration rate, skin temperature, sleep quality, step count, and affect throughout the study period. We focused on heart rate (BPM), heart rate variability (RMSSD), sleep quality, step count, and affect for this case study.
For our data analytic plan, we will use a descriptive approach to illustrate the data over the course of 9 months, examining any changes in participants’ depression, anxiety, and loneliness ratings at each of the 4 assessments as well as their BPM, RMSSD, sleep quality, step count, and positive and negative affect aggregated across the 2-week period after completion of the psychological assessment. Then, we will run correlations to examine associations between the aggregated physiological, sleep, behavioral, and affect measures at each time point. Doing so offers a broader perspective of how these multimodal assessments relate to one another and how they may differ over the course of the pandemic.
We will then narrow down to explore 2-week periods of data to better understand contextual factors potentially relevant to the fluctuations in their emotional and physiological patterns over the 2-week period (ie, data points for each day for 14 days). Focusing on the occurrences at 4 different, 2-week time points may further help us observe relationships across multimodal measures at the daily level rather than aggregates across months. We note that the adjustments made to the study in response to the pandemic were approved by the IRB in April, thus limiting the available self-reported contextual data we can examine. Therefore, while we present the 2-week data for each of the 4 time points, we only describe in detail the contextual data for the periods of June and September. Finally, we will offer clinical recommendations based on the data derived from the multimodal personal chronicles as potential suggestions for the single subject this case study focuses on.
For the purposes of this case study, we focused on the results of one individual. Like many other participants in our study, this individual exhibited a decline in mental health as the pandemic-enforced period of social isolation extended. In order to preserve the confidentiality of this participant, we have modified details regarding this participant’s case.
“Lee,” as we shall refer to this individual, is an Asian-American male student in the middle of his sophomore year of college. We report Lee’s pandemic narrative in chronological order since the start of his participation in the study in January through to September. Lee began the study with mental health symptoms that were low and not within the clinically significant range. For instance, his depression score at intake was 8, below the cut-off for mild depression (see
Lee’s multimodal assessments at the 4 different assessment time points in the study. Note: we did not collect data on step count through the mobile phone app until May, thus limiting our interpretation and examination of step count during the January and April periods. Neg: negative, pos: positive, BPM: beats per minute, RMSSD: root mean square of successive differences between normal heartbeats.
By the second time point in April, compared to his baseline assessment in January, his depression score had increased to 24—well within the moderate depression range. His anxiety and perceived feelings of loneliness also slightly increased (
Since his assessment in April, Lee’s depression remained in the mildly severe range in June and September. His reported anxiety had lowered since, but we noted that his loneliness was consistent between the periods of April and September. From his wearable sensor and ecological momentary assessment data, we noted that there were fairly minimal fluctuations in his physiology and sleep. Although his BPM was highest in April, it was not largely different from his BPM at other time points. His sleep was lower in the January and September periods, which reflect the time of the start of the winter and fall quarter, possibly relating to these differences than the April (spring quarter) and June (close to summer) periods. We report correlations of the measures at each time point in
To further illustrate the potential of a personalized approach in helping to inform our understanding of college student mental health, we focused on the June and September 2-week time periods.
Lee’s weekly “feel-in” responses to weekly highs and lows across the June and September time frames.
Time frame and time stamp | Response | LIWCa analysis | |
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Negative emotion word frequency=2.63; positive emotion word frequency=5.26 | ||
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Sun, Jun 7, 2020; 09:56:00 | “Some high points this week are leading a talk on the Black Lives Matter movement with my [group of people] and [group of people] which was really successful. Another high point is completing three of my courses. A low point is having to listen to racist comments and people that don’t support the Black Lives Matter movement. Also, I still have to study for two more finals and a term paper” |
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Sun, Jun 14, 2020; 11:53:57 | “High points include being done with [a year of school]. I went running with friends and had so much fun. I got to see the night sky and great views in [location redacted]. Low points include listening to my friends’ problems and trying to help them both through it. Another low point is hanging out with a friend and another person joined us who I don't like, so I didn’t really enjoy myself anymore.” |
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Sun, Jun 21, 2020; 12:45:24 | “High points in my week are hiking and playing [game] with friends. I did not have any low points. This has been my best week in months” |
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Sun, Jun 28, 2020; 14:55:40 | “Some high points include starting a [program] that will help me work on a project for the future as well as going to the beach with friends. Some low points include having to help friends through a difficult time, the large amount of work I have to take care of, and beginning to delay responsibilities and need to fix my schedule.” |
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Negative emotion word frequency=2.84; positive emotion word frequency=5.88 | ||
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Sun, Aug 30, 2020; 12:44:13 | “The biggest high point of my week was being able to spend some more time with the [person] I like. We were able to eat dinner, talk, and walk on the boardwalk. It felt like nothing else really mattered and I let go of my responsibilities and things that I had to do. I also got to lead a group meant to hear [opinions] about career resources for the project that I am involved in. It felt good to hear from [group of people] and I want to be able to help them with their concerns. Low points this week was having to revise my [project] again because it didn’t meet certain expectations. It's irritating having to change it multiple times even though I already address the revisions that my [supervisor] requires.” |
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Sun, Sep 6, 2020; 14:16:27 | “The high points in my week are my hikes with people. It feels good to hike and being active again since I cannot play games. I also was able to complete a project for a [group of people] that I hope gets approved by the [group of people]. These past few days have really sucked because I wasn’t able to work on my [project] and being stuck. It's been frustrating and I have a difficult time when I’m not able to do work and I’ve been having [sic] feelings of inadequacy. It's been hard but one of my best friends has been really helpful because they always reassure me and give me words of affirmation.” |
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Sun, Sep 13, 2020; 11:55:19 | “This past week has had such ups and downs with so many emotions. I was sad to discover that my cousin, who was assaulted last year at [name of a different college campus], has been going through a difficult time and [they’ve] been experiencing triggers. I got angry with myself for not being able to physically support [them] last year when she needed it most. I was able to talk on the phone and text them now which was helpful but I wanted to be there in person to show that I'm there for [them]. Another low point is studying for an upcoming [a test]. The grade isn’t that important to me but I want to do good. A couple high points is having a virtual get together. It was great to hear their voices and see their smiles. I also got to spend a day with my friend and had a lot of fun. I also finished another draft of my [project] and that was giving me headaches all week.” |
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Sun, Sep 20, 2020; 16:10:00 | “This week had it's [sic] highs and lows. A high is finishing the [test] which felt great. Another high is being accepted into [a program] but the low part of that is finalizing edits which has been really hard to get a start on. Another low is finding out my friends [relative] passed away in [country and helping [them] through it. It's been hard for [them], especially with [their] other [relative] passing away recently.” |
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aLIWC: Linguistic Inquiry Word Count System.
Raw numbers of all measures during the 2-week period on June 10-24 and September 7-21. Note: negative (neg) total, positive (pos) total, and Oura sleep score range between 0 and 100. Mood total: blue line signifies negative emotion and yellow line signifies positive emotion; resting beats per minute (BPM): sampled 15 minutes every 2 hours, including sleep; resting RMSSD (root mean square of successive differences between normal heartbeats): index of heart rate variability, sampled 15 minutes every 2 hours, including sleep; day (resting) BPM: sampled 15 minutes every 2 hours, not including sleep; day RMSSD: sampled 15 minutes every 2 hours, not including sleep; Oura sleep score: summary score of overall sleep quality, a weighted sum of sleep contributors, which combines sleep latency, onset, restfulness, rapid eye movement, deep sleep, timing, and efficiency; step counts: generated from the watch, total number of steps taken per day.
In contrast, during September, Lee exhibited more erratic mood states, which we interpret to potentially be an indication of low well-being. Lee’s negative and positive affect show greater variations from day to day. Compared to when Lee’s positive affect was more distinct from his negative affect in June, his negative affect was nearly as high as his positive affect in September. Further, in September, he experienced days in which his negative affect was higher than his positive affect. Between the two time periods, there were relatively similar fluctuations in his physical activity, heart rate levels, heart rate variability (as indexed by the RMSSD score), sleep, and step count (
The use of a case study enabled us to closely examine the experience and daily life of Lee across distant time points within the past year. Lee’s depressive symptoms increased sharply from January to April, a time coinciding with the onset of the COVID-19 pandemic and supporting prior evidence for increased depressive symptoms and distress among young adults during this period [
We are able to understand Lee’s unique pandemic experiences from this longitudinal assessment. Although his depression lowered from severe to mildly severe in the following months from April, Lee’s loneliness score remained consistent between the months of April through September, possibly relating to the continued social distancing and having to rely on alternative methods of keeping in contact with friends (eg, online chatting and hangouts). We also observed relatively higher levels of positive affect and lower levels of negative affect throughout Lee’s participation in the study. His negative affect was lowest in June, which is reflected in the 2-week period we examined more closely. While he may have experienced external events that affected his depressive symptoms (eg, his mother’s situation), there were potentially supportive factors (eg, social networks, meaningful work) that sustained his positive affect while maintaining lower negative affect during the pandemic. Thus, the meaning and significance of the multimodal data becomes most clear when aligned with the contextual data.
With regard to the pandemic, the approach allowed us to understand fluctuations in health and well-being that are linked to the unique experiences an individual may encounter during the pandemic. For example, the situation with Lee’s mother was a significant stressor for him, and this was only possible to document with the multiple assessments of depression and having this data accessible to a clinical psychologist to follow up with him regarding his well-being. Alternatively, we also recognize that there were some fluctuations that were relatively low (eg, physiology across the 4 time periods as well as in the 2-week period of June and September). This may also point to important coping and resiliency factors that allow individuals like Lee to navigate this stressful pandemic. With any kind of stressful event, Lee may be using his current resources and available coping strategies to self-regulate during the pandemic.
The case study highlights preliminary data from our larger ongoing study, which in turn enables researchers and clinicians to better understand the context and unique experiences of individuals and how these factors relate to well-being. The utility of multimodal personal chronicles for mental health may be clearest when it is used in conjunction with the collaborative care of a clinician. From a health navigation framework [
Access to such rich amounts of personalized data can help clinicians or therapists offer evidence-based treatments or recommendations. If the provider in the loop were to offer recommendations to Lee based on his pattern of behaviors, it would be to increase and maintain a moderate to ideal level of physical and interpersonal activity with his support networks. As noted, based on his weekly open-ended responses, many of his high points included spending time with friends or enjoying time outdoors doing either physical activity or enjoying nature with others. Interpersonal support and interactions were particularly relevant, and thus seeking strategies to ensure consistent social connectedness with others may be helpful for Lee in sustaining well-being. Given that public health recommendations of social distancing preclude in-person social interactions, Lee may wish to savor memories of times when he has enjoyed spending time with other people (eg, relational savoring [
Limitations to our study include the fact that, as with many case studies, it is primarily exploratory in our understanding of a phenomenon in a specific space and time. As we are considering the feasibility of multimodal personal chronicles, we restrain from making causal claims regarding the experiences of Lee. Additionally, self-reported emotion data were only captured once a day toward the evening, which allows for recall bias. Recent studies are now suggesting that the PANAS scale is in need of improvement [
Emerging adults navigate a number of stressors during this developmental period; experiencing all of this while facing a global pandemic may exacerbate psychopathology or detriments to psychological well-being among college students. For example, students may have to juggle negative impacts of the pandemic on their family while also adjusting to online and remote learning. Previous studies examining student well-being during the pandemic have largely conducted single time-point assessments [
The feasibility of multimodal personal chronicles to assess mental health may help to inform what types of interventions may be best for individuals. Future studies may consider assessing the feasibility of the multimodal personal chronicles in other populations (eg, clinical samples, across cultures) and incorporating interventions or guidance in the process of data collection as a way to examine how self-monitoring of health alongside guidance may then have trickle-down effects on improving health. In addition, this approach offers the potential for a system in which individuals can tweak their own health behaviors and see its associations with changes in their health trajectory [
From our ongoing study, we present results from a case study examining 1 participant and how the trajectory of his well-being may be assessed through a holistic multimodal personal chronicles system that captures personalized experiences. Case studies have the benefit of presenting in-depth information regarding a single individual, enabling a fine-grained analysis of the circumstances, characteristics, and experiences for that person at a specific point in time. Future studies may consider the multimodal personal chronicles approach and how it may inform treatment and intervention to mitigate psychopathology and aid in maintaining well-being.
Correlation tables of the 2-week assessments for January, April, June, and September.
Beck Depression Index II
beats per minute
Brief Symptom Inventory
internet of things
Institutional Review Board
Positive and Negative Affect Schedule
root mean square of successive differences between normal heartbeats
University of California, Los Angeles
wearable IoT
Preparation for this case study occurred in collaboration with the School of Nursing, the Department of Computer Sciences, and the Department of Psychological Science at the University of California, Irvine.
None declared.