A Mixed-Methods, Multimedia Pilot Study to Investigate Sleep Irregularity Determinants Among Undergraduate Students

PURPOSE

To pilot a novel approach investigating the interplay of social and institutional determinants influencing university undergraduate student sleep patterns.

DESIGN

A two-part, three-phase mixed-methods approach.

SETTING

A mid-size US university conducted in spring and fall 2020.

PARTICIPANTS

191 undergraduate students (69 first-years, 43 second-years, 48 third-years, 31 fourth-years).

METHOD

For Part A, participants texted their activities and emotions in real time, producing a data-rich, weeklong diary of comprehensive activity logs, emoticons, multimedia submissions, and juxtapositions of ideal vs real schedules. Semi-structured contextual interviews were also conducted. For Part B, a one-time survey examined Part A insights across all class years. These diverse datasets were triangulated using thematic, comparative, and content analyses through MAXQDA software and visual mapping methods.

RESULTS

Three preliminary themes were identified as encouraging an irregular sleep schedule: a prevailing academic ethos emphasizing busyness, time management challenges, and the rhythm of institutional schedules and deadlines. An overarching theme suggests that perceptions of peer sleep habits and academic prioritization above all else could be influential across factors.

CONCLUSION

This pilot study indicates that sleep regularity among undergraduates is potentially shaped by individual choices combined with broader institutional paradigms. While it is limited by its exploratory nature, timing, and small sample size, the results highlight the promise of this methodology for more extensive studies and suggest that future interventions should emphasize systemic changes that prioritize sleep.

Identifying Resilience Factors of Distress and Paranoia During the COVID-19 Outbreak in Five Countries

The ongoing COVID-19 pandemic outbreak has affected all countries with more than 100 million confirmed cases and over 2.1 million casualties by the end of January 2021 worldwide. A prolonged pandemic can harm global levels of optimism, regularity, and sense of meaning and belonging, yielding adverse effects on individuals' mental health as represented by worry, paranoia, and distress. Here we studied resilience, a successful adaptation despite risk and adversity, in five countries: Brazil, Colombia, Germany, Israel, and Norway. In April 2020, over 2,500 participants were recruited for an observational study measuring protective and obstructive factors for distress and paranoia. More than 800 of these participants also completed a follow-up study in July. We found that thriving, keeping a regular schedule, engaging in physical exercise and less procrastination served as factors protecting against distress and paranoia. Risk factors were financial worries and a negative mindset, e.g., feeling a lack of control. Longitudinally, we found no increase in distress or paranoia despite an increase in expectation of how long the outbreak and the restrictions will last, suggesting respondents engaged in healthy coping and adapting their lives to the new circumstances. Altogether, our data suggest that humans adapt even to prolonged stressful events. Our data further highlight several protective factors that policymakers should leverage when considering stress-reducing policies.

Nocturnal heart rate variability moderates the association between sleep-wake regularity and mood in young adults

STUDY OBJECTIVES

Sleep-wake regularity (SWR) is often disrupted in college students and mood disorders are rife at this age. Disrupted SWR can cause repetitive and long-term misalignment between environmental and behavioral cycles and the circadian system which may then have psychological and physical health consequences. We tested whether SWR was independently associated with mood and autonomic function in a healthy adult cohort.

METHODS

We studied 42 college students over a 3 week period using daily sleep-wake diaries and continuous electrocardiogram recordings. Weekly SWR was quantified by the interdaily stability of sleep-wake times (ISSW) and mood was assessed weekly using the Beck Depression Inventory-II. To assess autonomic function, we quantified the high-frequency (HF) power of heart rate variability (HRV). Linear mixed effects models were used to assess the relationship between repeated weekly measures of mood, SWR, and HF.

RESULTS

Low weekly ISSW predicted subsequent poor mood and worsening mood independently of age, sex, race, sleep duration, and physical activity. Although no association was found between ISSW and HF, the ISSW-mood association was significantly moderated by nocturnal HF, i.e. reported mood was lowest after a week with low ISSW and high HF. Prior week mood scores did not significantly predict the subsequent week's ISSW.

CONCLUSIONS

Irregular sleep-wake timing appears to precede poor mood in young adults. Further work is needed to understand the implications of high nocturnal HRV in those with low mood and irregular sleep-wake cycles.

Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study

Background

Wearable and mobile devices that capture multimodal data have the potential to identify risk factors for high stress and poor mental health and to provide information to improve health and well-being.

Objective

We developed new tools that provide objective physiological and behavioral measures using wearable sensors and mobile phones, together with methods that improve their data integrity. The aim of this study was to examine, using machine learning, how accurately these measures could identify conditions of self-reported high stress and poor mental health and which of the underlying modalities and measures were most accurate in identifying those conditions.

Methods

We designed and conducted the 1-month SNAPSHOT study that investigated how daily behaviors and social networks influence self-reported stress, mood, and other health or well-being-related factors. We collected over 145,000 hours of data from 201 college students (age: 18-25 years, male:female=1.8:1) at one university, all recruited within self-identified social groups. Each student filled out standardized pre- and postquestionnaires on stress and mental health; during the month, each student completed twice-daily electronic diaries (e-diaries), wore two wrist-based sensors that recorded continuous physical activity and autonomic physiology, and installed an app on their mobile phone that recorded phone usage and geolocation patterns. We developed tools to make data collection more efficient, including data-check systems for sensor and mobile phone data and an e-diary administrative module for study investigators to locate possible errors in the e-diaries and communicate with participants to correct their entries promptly, which reduced the time taken to clean e-diary data by 69%. We constructed features and applied machine learning to the multimodal data to identify factors associated with self-reported poststudy stress and mental health, including behaviors that can be possibly modified by the individual to improve these measures.

Results

We identified the physiological sensor, phone, mobility, and modifiable behavior features that were best predictors for stress and mental health classification. In general, wearable sensor features showed better classification performance than mobile phone or modifiable behavior features. Wearable sensor features, including skin conductance and temperature, reached 78.3% (148/189) accuracy for classifying students into high or low stress groups and 87% (41/47) accuracy for classifying high or low mental health groups. Modifiable behavior features, including number of naps, studying duration, calls, mobility patterns, and phone-screen-on time, reached 73.5% (139/189) accuracy for stress classification and 79% (37/47) accuracy for mental health classification.

Conclusions

New semiautomated tools improved the efficiency of long-term ambulatory data collection from wearable and mobile devices. Applying machine learning to the resulting data revealed a set of both objective features and modifiable behavioral features that could classify self-reported high or low stress and mental health groups in a college student population better than previous studies and showed new insights into digital phenotyping.