A measurement burst design was used to track day-to-day and longitudinal changes in sleep, depressive symptoms, and cognition in participants. Participants completed data collection at the following measurement bursts: baseline, 4 months, 8 months, and 12 months. Each burst was comprised of 7 consecutive days during which sleep, mood, and cognitive tasks were monitored, referred to as a study week (Figure 1 for study timeline). Youth and parents completed psychosocial questionnaires on mood, behavior, and sleep. Sleep and physical activity were monitored remotely using a Garmin VivoSmart5 watch and MuseS headband worn by the participant.

Participants from southern California and the western United States were eligible for the study if they met the following inclusion criteria: (1) aged 9 to 13 years, (2) English as the primary language of the participant and parent, (3) typical cognitive development and normal to corrected vision, and (4) could have presence or history of depressive symptoms. We recruited participants whose parents reported a range of depressive symptom severity, from minimal to moderate, on the screener. Participants with anxiety as the sole psychiatric diagnosis were excluded. Additional exclusion criteria included (1) current or prior medical condition that could interfere with the collection or interpretation of data; (2) history of stroke, epilepsy, brain scarring, or head injury causing unconsciousness; (3) presence or history of anxiety symptoms were allowed, but no other history or current presence of psychiatric disorders (eg, eating disorder, attention deficit hyperactivity disorder, and sleep disorder); (4) no evidence of habitual napping; (5) no current or past use of psychiatric medication; (6) no history of premature birth or lack of meeting developmental milestones; (7) pregnancy; and (8) unwillingness to refrain from caffeine and alcohol during quarterly study evaluations. These inclusion and exclusion criteria were used to maintain a relatively homogenous sample in this age range.

Participants were recruited using multiple methods, including web-based social media postings, listserv emails, and website posts, as well as flyers posted at hospitals, psychology and psychiatry offices, schools, and community centers. While we primarily recruited from Southern California and the Western States, participants were not excluded based on geographic location if they were within the United States.

This study was approved by the Institutional Review Board (IRB) at the University of California, Irvine (IRB# 20216879). In accordance with STROBE (Strengthening the Reporting of Observational Studies in Epidemiology), a checklist is included in Checklist 1. Youth participants and their parents received monetary compensation for participation. Youth participants were compensated a total of US $400 for completing all assessments at all visits, which included bonuses for timely completion of sleep diaries, ecological momentary assessments (EMAs), and cognitive testing. For each assessment wave, participants were guaranteed to receive US $30 for attendance, with additional task completion garnering additional daily compensation. In total, participants could receive up to US $75 for participating in the entire measurement burst. To enhance completion of daily testing, if participants completed more than 5 out of 7 days of sleep diaries, EMAs, and cognitive tasks, they could earn an additional US $15 per day. Bonus compensation was based on completing at least the morning sleep diary and 1 cognitive task on days 4 through 7. At study completion, participants received a US $100 bonus for completing the entire study. Given that daily participation took approximately 1 hour per day, this incentive structure was not considered undue influence on youth participants and appropriately accounted for time required to participate.

Parents were compensated a total of US $120 throughout the study, as they were responsible for completing parent-specific study questionnaires regarding youth’s sleep, psychological and social relationships, monitoring wearable device use and safety, and supporting participants in completing daily tasks. Parents received US $10 automatically for each study week for assisting the youth in using the wearables, plus an additional US $20 bonus for completing all parent-specific questionnaires. Compensation for participants and parents was provided at the end of each study week. For participants who withdrew, compensation was provided for completed study activities.

Participants enrolled in the study were remotely monitored over 1 year, with quarterly measurement bursts (Figure 1 outlines the study timeline). During each burst, participants were monitored for 7 consecutive days using our personalized mHealth app, HowRU, and wearable technology. On the HowRU app, participants completed daily sleep diaries, cognitive tasks, responded to thrice-daily EMAs, and completed psychosocial questionnaires. Across each assessment burst, participants wore a Garmin VivoSmart5 watch all day and night to objectively measure their sleep and wake and physical activity patterns. Participants also wore a MuseS electroencephalogram (EEG) headband to monitor sleep oscillations at night (InterAxon Inc). The Minecraft Memory and Navigation (MMN) task was administered on the first and last day of the measurement burst study week. Measurement burst start days were counterbalanced across weekdays and weekends. Bursts began randomly across school years and summer holidays. Scheduling was coordinated to accommodate holidays and vacations.

As this was a remote study, participants completed all experimental activities at home. To accomplish this remote design, home addresses were collected from parents at consent to mail equipment, and return address labels were provided. For each measurement burst, all materials for participating were provided, including a Samsung TabA9 preloaded with the HowRU, Garmin, LabFront, and Interaxon Muse apps required for participating, the MuseS headband, the Garmin VivoSmart5 watch, and paper-based questionnaires that were not licensed for mHealth platform administration (see Remote mHealth Platform section). Materials were securely mailed to parents and returned by mail directly to the research lab. For all research activities, daily tasks were broken down into manageable time blocks and took approximately 1 hour in total to complete. Daily tasks were distributed across the day as follows: in the morning, participants completed the sleep diary and EMA in an integrated diary (~5 minutes), followed by the cognitive tasks (~20 minutes). In the afternoon, participants completed the EMA (~3 minutes) jittered within a 2-hour time window. In the evening, participants completed cognitive tasks (~30 minutes), followed by the sleep diary and EMA (~5 minutes).

We developed the HowRU app, a mHealth platform built on the University of California, Irvine Zotcare platform [50], which we used in the current study. Our app included all task components, including sleep diaries, cognitive tasks, EMAs, and psychosocial questionnaires. App reminders were built into the platform to support task completion, sleep diary entry, and questionnaire completion. App reminders were personalized and sent to participants based on their personal sleep and wake patterns. Notification reminders were sent 1 hour before the typical bedtime and 30 minutes after the typical wake time. App reminders repeated every 30 minutes for up to 2 hours until tasks, diaries, or mood checks were completed. This meant that, on the home screen, a message would appear reminding the participant to complete the specific task. While reminders could be ignored, there were no options to opt out or snooze them. All data collected in the app were deidentified, encrypted, and stored on lab-based, password-protected servers. The app ran on a custom platform with no third-party access. Research staff monitored completion of the HowRU task, diary, and questionnaire daily and sent reminder emails to parents and participants to complete tasks as needed.

HowRU is hosted on the Zotcare platform, which serves as the central, deidentified data orchestrator for this research (Figure 2) [50]. Its architecture is specifically designed to decouple personal identity from cognitive and behavioral metrics, ensuring high-integrity research while maintaining strict compliance with US federal and state regulations for minors. This is a custom mobile app with encryption standards including in-transit Transport Layer Security (TLS) 1.2/1.3 Advanced Encryption Standard (AES)-256 and at-rest AES-256 bit encryption, and operated within a secure, virtual private cloud (VPC)-isolated cloud environment for data storage. On Zotcare, platform account ownership is researcher-based, with pregenerated accounts, and participants were assigned to the platform using unique subject-identifying codes. We use RESTful API with token-based authentication and exchange only deidentified JSON payloads. Zotcare uses a zero-protected health information policy (zero-PHI), meaning that no names, birthdays, or contact information were stored. The unique study identification number linking the participant number and identity was stored securely on an external, institutional database that was password-protected and inaccessible to the Zotcare system. Access was restricted via role-based access, such that the research team had a dashboard that could download granular game logs mapped only to specific participant identification numbers. System administrators had blind access for infrastructure maintenance but could not reidentify participants. Lastly, data were retained per IRB-approved protocol, but could have been programmatically purged from production databases upon participant request. Prior to participant recruitment, all technical safeguards (encryption, deidentification, and storage) were reviewed by the IRB as a part of a formal risk assessment. For ethical alignment with pediatric participants, access was granted only after parents and participants provided verbal and written consent. Per regulatory alignment for US minors, while Zotcare stores deidentified data (falling under “Safe Harbor” exclusions), it maintains technical safeguards, such as audit logs and AES-256 encryption, which are consistent with Health Insurance Portability and Accountability Act (HIPAA) Security Rule standards. Zotcare also supports all “Right to Know” and “Right to Delete” requests. Deletion could be executed via study participant number, ensuring the platform never required the participant’s identity to fulfill a request.

After participants and parents completed the consent and assent, participants were administered the Wechsler Abbreviated Scale of Intelligence (WASI) [51] vocabulary subtest and digit span for baseline functioning, the Questionnaire of Unpredictability in Childhood (QUIC) [52], and the depression and anxiety subsections of the Kiddie Schedule for Affective Disorders and Schizophrenia for School Aged Children – Present and Lifetime Version (K-SADS-PL) [53].

Baseline questionnaires were provided to parents and youth via REDCap links emailed after the consent session. Parents completed the following: a general demographics questionnaire; Pediatric Sleep Questionnaire (PSQ and SRBD), which measured sleep-related breathing disorder (SRBD) symptoms [54]; modified video game experience (VGE), which measured parent-reported perceptions of child VGE [55]; the Alabama Parenting Questionnaire (APQ)-Parent Report, which measured parent-reported use of positive and negative parenting behaviors [56]; the Parenting Self Agency Measure (PSAM), which measured parent beliefs related to their parenting abilities [57]; the COVID-19 Family Stressors Scale (FSS), which measured family stressors as a result of the COVID-19 pandemic [58]; and the COVID-19 Fear of Illness and Virus Evaluation (FIVE), which measured COVID-19–related fears in parents and youth [59].

Youth completed the following: APQ-Child Report for ages 6 to 13 years, which measured youths’ “perception of parents” use of positive and negative parenting behaviors [56]; Inventory of Parent and Peer Attachments-Revised (IPPA-R) for ages 9 to 15 years, which measured the quality of youth relationships with parents and peers [60]; Child and Adolescent Social Support Scale (CASSS), which measured youth and adolescent perceived social support from people in their life [61]; the Internet Addiction Test-Short Form (IAT-SF), which measured youth self-reported social media use and habits [62]; Difficulties in Emotional Regulation (DERS-SF), which measured emotional regulation in youth [63]; Morningness-Eveningness Questionnaire (MEQ), which measured morning and evening preferences in youth [64]; and Pittsburgh Sleep Quality Index (PSQI), which measured sleep behavior, quality, and disturbances in youth over the past month [65].

During each measurement burst (0, 4, 8, and 12 months), youth completed the following questionnaires once within the 7-day burst: (1) a paper-based Children’s Depression Inventory (CDI), which measured depressive symptoms in youth ages 7 to 17 years [66] and was mailed with the equipment at each burst and completed at home, and (2) using the HowRU app, youth completed the Center for Epidemiological Studies Depression Scale for Children (CES-DC), which measured levels of depressive symptoms in youth and adolescents ages 6 to 17 years [67]; the Adolescent Sleep-Awake Scale (ASWS), which assessed sleep quality [68]; the General Anxiety Disorder-7 (GAD-7), which measured symptoms of anxiety [69]; the Inclusion of Self and Others (ISO), which measured how close youths felt to their parents [70]; and the IAT-SF, which measured internet use [62]. Youth were requested to complete the Perceived Stress Scale (PSS), which measured youth’s perceived stress on the last day of the burst and reported their perceived stress during the course of that week [71]. Together, these measures provided the information necessary to assess youth’s report of their sleep behaviors or disturbances, depressive symptoms, and perceptions of their parents’ parenting behaviors, which could be linked to their control of sleep activities. Youth also reported pubertal onset and progression at each burst with the Pubertal Development Scale (PDS) [72]. The PDS is a validated measure for sex-specific physical development changes (eg, hair growth, breast development, and voice changes) associated with puberty. Based on self-report, youth were categorized into 1 of the 5 specific pubertal stages, ranging from prepubertal to postpubertal. Menarche and menstrual cycle frequency were also reported by female participants. Puberty status will be used as a time-varying covariate in analyses.

Parents completed the following: a paper-based Child Behavior Checklist (CBCL), which measured parents’ identification of emotional, behavioral, and/or social competence or adaptation problems in youth aged 6 to 18 years [73], and a paper-based Children’s Depression Inventory Parent (CDI-Parent), which measured parent perception of depressive symptoms in youth aged 7 to 17 years [66]. The paper-based CBCL and CDI were included in the mailed equipment box at each burst, and returned with the equipment at the end of each burst. Parents also completed the following questionnaires on HowRU (or via RedCap [Vanderbilt University] if preferred): the Children’s Sleep Habit Questionnaire (CSHQ), which measured sleep patterns and behaviors for 7- to 13-year-olds [74]; the Inclusion of Self and Child (ISC); and an adapted version of the VGE, which measured parent-reported perceptions of child VGE [55]. Together, these measures provided the information to assess parents’ identification of youths’ sleep behaviors and disturbances, internal and external symptoms, and their own parenting behaviors, which could be linked to their youth’s control of sleep activities.

All sleep diaries were administered via HowRU. Each morning, participants completed a sleep diary reporting bedtime, wake time, sleep-onset latency, arousals, and sleep quality. Participants also reported if they remembered dreams and, if so, the valence of the dream. At night, participants completed a sleep diary in which they reported if they napped (if so, for how long), the intentionality of the nap, dream occurrence, and dream valence. In the night diary, participants also reported the time spent on social media and associated mood.

All EMAs were administered via HowRU thrice daily: after waking up in the morning, between 3 and 5 PM in the afternoon, and prior to bedtime. EMA reminders were set at each measurement burst according to the participants’ schedule; for example, if an after-school activity ended at 4 PM, the notification window was moved from 4 to 6 PM to accommodate the change. These time points were chosen for feasibility and participants’ mobile technology access and were adequate for statistical modeling of affect variability within a day. App reminders were sent until completion and set with respect to the participant’s time zone (Pacific Standard Time, Mountain Time, Central Time, and Eastern Standard Time). All app reminders were flexibly set, that is, we aimed to send the morning reminder approximately 30 minutes after a participant’s typical wake-up time and 1 hour prior to typical bedtime. On the EMA, participants reported their current location, any individuals present at the time of the EMA completion, and their real-time emotions using the Positive and Negative Affect Scale ratings of excited, mad, sad, focused, guilty, happy, confident, and scared on a scale from 1 (not at all) to 100 (a lot) [75]. Participants also reported their highest level of stress in the last hour on the same 1-to-100 Likert scale and briefly responded using free text to explain what was on their mind at the time.

As this was a remote research study involving youth who could experience depressive symptoms, the protocol included specific procedures to ensure participant safety. First, all participants completed oral and written informed consent and assent. During the consent process and prior to signing, research staff clearly explained the limitations of confidentiality, noting that confidentiality was maintained except unless information was disclosed by the youth or parent indicating risk of harm to self or others (including suicidality, homicidality, child abuse, or neglect). Participants and parents were also informed that parents would be notified if assessments indicated moderate or severe levels of depression. If safety concerns arose through self-disclosure, parent disclosure, or questionnaire response, a licensed psychologist conducted a risk assessment (including evaluation of suicidal or homicidal ideation, plan, or intent), engaged in safety planning, and provided appropriate behavioral health referrals. Had an imminent safety risk been present, emergency services would have been contacted for further assessment and support. If child abuse or neglect was disclosed, a report would have been filed with California Child Protection Services, consistent with mandated reporting requirements. Documentation of all risk assessments was maintained in confidential, password-protected files only accessible by the licensed psychologist. Daily mood and sleep diaries did not assess self-harm or suicidal ideation. These topics were instead evaluated during the initial administration of the K-SADS-PL and subsequently, during assessment waves in the Child Depression Inventory. As the latter questionnaire was mailed to and returned by participants, it was reviewed immediately upon receiving the equipment box. Finally, if participants reported symptoms consistent with moderate to severe depressive symptoms on the questionnaires, parents were contacted by phone and email to discuss the results and were provided with mental health resources.

The primary outcomes of this study are to (1) characterize sleep and depressive symptom trajectories across the year and (2) determine the relationship between sleep and depressive symptoms on sleep-dependent cognitive performance across the year.

To characterize sleep and depression trajectories, we will use multilevel models to characterize the changes in sleep and depressive symptoms across measurement bursts. The primary contrasts of interest are the between-person differences in sleep and depressive symptom change over time. Our planned contrasts are to (1) evaluate the mean slope of sleep duration and depressive symptoms across measurement bursts, (2) determine the presence of nonlinear change (acceleration or deceleration) with age and pubertal status, including the use of quadratic terms to determine the rate of change, and (3) to analyze the covariance between the intercepts and slopes of sleep duration, sleep variability, and depressive symptoms. Exploratory analyses will examine correlations between the intercepts and slopes over time, allowing evaluation of how correlated the longitudinal trajectories are.

To evaluate the longitudinal associations between sleep, depressive symptoms, and cognition, we will use multilevel models. The primary contrasts of interest are the within-person differences from each individual’s personal mean in sleep and cognitive performance. Planned contrasts are to test whether sleep duration predicts next-day sleep-dependent cognitive performance and the association with youth’s depressive symptom severity. We frame our hypotheses sequentially. First, we hypothesize that, relative to a participant’s average sleep metrics, a night of short sleep duration will predict worse next-day cognitive performance compared to that participant’s typical performance. If this effect is established, we will then test whether these effects are stronger among youth with higher depressive symptoms. Our main model also includes a mediation analysis component to test if insufficient sleep duration predicts worse sleep-dependent cognitive performance indirectly via elevated depressive symptoms over time. In parallel to the main model, we will consider 2 alternative specifications if the main model does not fit the data adequately: alternative model 1, in which depressive symptoms predict sleep-dependent cognitive performance deficits indirectly via insufficient sleep; and alternative model 2, which examines only the direct associations between insufficient sleep, depressive symptoms, and sleep-dependent cognitive performance. These competing models will be evaluated and compared using fit statistics. Distinguishing the most distal (early) predictor is key for informing future intervention efforts.

Secondary outcomes include (1) the impact of sleep and depressive symptoms on the additional 2 cognitive tasks, the rule switch and motor sequence learning task, and (2) the parent and youth report of parenting factors on sleep patterns. Additional secondary outcomes will be explored, including changes in daily mood, heart rate variability, physical activity, and psychosocial questionnaires (eg, social media use, parenting behaviors, peer support, and parent-reported child behavioral and emotional patterns). Secondary and exploratory outcomes will be interpreted with caution as smaller effects may be underpowered.

Funding was obtained from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (Grant: K08 HD107161) with a budget period of August 1, 2022, to July 31, 2027. Participant enrollment occurred from May 2023 to October 2024. Data collection began in July 2023 and was completed in October 2025.

Figure 3 displays participant adherence and retention rates across the study year from initial study screener contact through the final assessment wave. Fifty-six participants were enrolled in the study (female n=27). The average age was 11.01 (SD 1.39) years. From consent through the end of the last measurement burst, participant completion was 75% (42/56). Among those who successfully completed the first measurement burst, 86% (42/49) completed all 4 assessment weeks. Attrition was the highest between the first and second measurement bursts at 10.2% (5/49) and decreased between the second and third measurement bursts at 4.5% (2/44); attrition between the final third and fourth measurement bursts was 0% (42/42). These findings indicated strong participant retention over the year-long study, with greater confidence in retention among participants if they completed the first measurement burst.

Participants demonstrated high rates of adherence to cognitive tasks, sleep diaries, and EMAs. We have included results in Table 1 across the measurement bursts. Adherence was lowest for the EMA assessments, likely brought down by the afternoon EMA. In the MMN task, all missing data were primarily due to the lack of access to a computer that could host Minecraft (Mojang Studios) or due to a technical error that prevented Minecraft from working.

The neural mechanisms linking depression-related cognitive impairments and sleep remain poorly understood. Given sleep’s dual role in supporting both mood regulation and cognition, sleep disturbances may represent a critical risk factor for long-term cognitive outcomes in youth with depression. However, precision medicine approaches to early-onset depression are still in their infancy, and few studies track candidate biomarkers longitudinally in real-world environments. To optimize early intervention time windows and targets, there is an urgent need to track real-world biomarkers that are dually linked to early-onset depression and cognitive impairments. Remote and accessible mHealth testing platforms are the future for longitudinal integrated investigations of clinical and cognitive health in adult as well as pediatric populations [93-95]. Technology access is reported in 67% to 78% of 9- to 11-year-olds [96]; thus, using a technological platform to investigate mechanistic questions of cognitive development has a strong potential for success.

Longitudinal, measurement burst designs are unusual in sleep and cognition research, where most investigators rely on short-term experiments of 1 day or a few weeks [47]. In contrast, the current study’s longitudinal design allows for the disentanglement of long-term trajectories from the relative contributions of sleep and depressive symptoms across different time scales throughout development. mHealth assessment increases access and participation feasibility and simplifies intensive repeated-testing procedures in youths [93-95]. Furthermore, wearables that track sleep patterns, physiology, and physical activity in naturalistic settings (eg, home and school) offer a robust platform for investigating the effects of interventions for depression in large-scale longitudinal studies [48,49]. Findings from this study will support the early detection of depression risk through sleep-related biosignals captured by accessible, cost-effective consumer wearable devices. The results may also identify critical time windows during which sleep-based interventions could help prevent the onset of mental health symptoms. Clinically, integrating consumer wearable devices offers a practical and scalable approach to monitoring physiological biomarkers associated with mental health, enabling practitioners or schools to implement effective, first-line screening and prevention strategies. Lastly, tracking cognitive performance across multiple timescales and its associations with sleep and mental health may provide novel insights into how cognitive skills can be more effectively targeted in therapy to alleviate depressive symptoms.

Study limitations were primarily due to the nature of our remote study design. First, participants completed the study remotely from their homes, meaning that research staff could not control the environment for noise or interruptions to the same degree as in-lab studies. To reduce this potential problem, we provided instructions to parents and youth to plan accordingly and ensure they were alone and focused, without distractions from phones, family, or friends, during participation. Further, we temporarily provided a study tablet to each participant to reduce measurement error caused by different devices or screen sizes. While these steps were taken to mitigate measurement error and vulnerability to noise, real-world data collection remains vulnerable to at-home conditions. A second limitation was that participant adherence to daily cognitive tasks, sleep diaries, and EMAs was lower than in lab participation. To combat data loss, participants’ app-based engagement was monitored daily by research staff. To reduce the loss of app-based data, personalized notifications for youth were timed to daily tasks and personal schedules. Reminders repeated every 30 minutes for 2 hours or until task completion. Research staff also monitored data collection daily, and if tasks were not completed, research staff contacted parents and participants via email with reminders to complete the daily tasks in the required time windows. Overall, our adherence to daily tasks was very high but was lowest for the EMA. We believe this is due to the afternoon EMA completion. Future studies can better target windows that participants are more likely to be near devices (ie, after school) or possibly enhance the incentive to complete the afternoon EMA. A third limitation in our study was the loss of wearable data due to equipment failure, incorrect device use, or no device wear. In this study, participants were asked to wear the Garmin VivoSmart 5 watch all day and night, and the MuseS headband each night, for the duration of the burst. Participants, with assistance from parents, were requested to sync the data daily. To increase the likelihood of strong data collection, at the start of each measurement burst, participants and parents met with research staff via Zoom and learned how to place and wear the devices, sync the data, and practiced wearing the devices in front of the staff to confirm the fit was correct. Further, during the week, research staff monitored daily uploads of wearable data, including the total daily and nightly wear time of the devices and any data loss due to poor wear. Emails were sent to parents to remind the participants to wear the devices, and additional Zoom-based meetings were conducted as needed to assess fit and improve adherence. This increased our data collection adherence and the accuracy of wearable biosignal data. Lastly, we administered the MEQ and the IAT-SF, which were both validated in young adults and have limited validity in children, limiting our ability to accurately determine participant chronotype and internet use. Future directions in longitudinal remote research can expand on the sample population to include youth with common comorbid mental health and sleep disorders. As we excluded youth participants with other neurobehavioral and sleep disorders, the generalizability and external validity of our findings will be somewhat limited to those with depressive symptoms. However, this trade-off will allow us to examine sleep, mood, and cognitive performance trajectories within a more homogenous sample, reducing confounds from co-occurring symptoms. Future studies could broaden the age range to include older adolescents and incorporate longer longitudinal follow-up periods.

Depressive symptoms that emerge in early adolescence can lead to long-term psychological, physical, and cognitive consequences. Given that poor sleep is predictive of depression onset and symptom severity, and a primary symptom of depression itself, understanding how sleep behavior and neurophysiology predict early depressive symptoms may be key in understanding adolescent depression. Further, depressive symptoms and insufficient sleep can lead to cognitive performance deficits. Anticipated contributions from this protocol include a roadmap for longitudinally tracking sleep, depressive symptoms, and cognitive trajectories, with the aim of providing critical insights and identifying essential time periods for proactive intervention. Additionally, results from this study will also help determine which sleep behavior or neurophysiological metric offers the most promising target for altering mental health and cognitive trajectories. By tracking changes in sleep alongside mental health over time, we anticipate revealing how their dynamic interplay influences cognitive development. Our protocol is a robust and reproducible framework for tracking young adolescents over time using mobile health platforms and wearable technology, sleep monitoring, mental health assessments, and cognitive processes. This study will help fill a critical gap in understanding adolescent day-to-day variability in the real world.