Wearable-Based Study of Depression and Sleep in Older Adults

By 2025, Taiwan will become a super-aged society, with a rapidly growing number of older adults living with depression and related sleep problems. Late-life depression is often underdiagnosed and undertreated, and current assessments rely heavily on self-reported or clinician-rated questionnaires, which only provide snapshots of symptoms and may not accurately reflect daily variations in mood and sleep. These challenges highlight the urgent need for more objective, continuous, and accessible tools to monitor and manage depression in older adults.

Wearable devices, such as actigraphy and photobiomodulation (PBM), offer new opportunities for both assessment and intervention. Actigraphy can provide objective data on sleep-wake cycles, activity levels, and circadian rhythms, while PBM uses near-infrared light to modulate brain activity and improve mood and sleep quality. Building on our previous collaboration with Harvard University and recent findings from wearable PBM trials, this project seeks to establish an integrated, technology-assisted model for managing late-life depression in Taiwan.

This four-year project will begin by validating the feasibility and acceptability of a research-grade actigraphy device (Geneactiv) among older adults diagnosed with major depressive disorder (MDD). Objective activity and sleep data will be compared with questionnaire-based assessments to evaluate reliability and consistency, forming the foundation for Taiwan's first longitudinal wearable-based geriatric depression database.

In the second and third years, the study will conduct a double-blind, randomized, sham-controlled clinical trial to evaluate the safety and effectiveness of a wearable PBM headband device in improving depressive symptoms and sleep quality. Participants will continue their regular treatments while using the PBM device at home for daily sessions.

In the final year, longitudinal data from actigraphy, PBM intervention, clinical evaluations, and biological markers will be analyzed to identify digital phenotypes and predictive biomarkers associated with depression severity, treatment response, and functional outcomes. The long-term goal is to establish a comprehensive digital mental health platform that integrates biological, behavioral, and clinical dimensions, providing a scalable model for improving geriatric mental health in Taiwan's aging population.

Inclusion criteria

• Subjects must meet the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for Major Depressive Disorder (MDD), either single or recurrent episode.
• Have a Hamilton Depression Rating Scale (HAM-D, 17-item) total score of 14 or higher at baseline.
• Participants who are currently taking medications that may affect mood, sleep, or circadian rhythms (e.g., antidepressants, sedative antipsychotics, or sleep aids) may be included, provided that the dosage has remained stable for at least two weeks prior to enrollment.
• Prior to inclusion, informed consent must be obtained. If cognitive impairment prevents subjects from signing, consent may be obtained from their primary caregiver (who may be a legal or professional representative).

Exclusion criteria

• Diagnosis of dementia of any severity, bipolar disorder, psychotic disorder, psychotic episode, or substance use disorder.
• Active suicidal ideation or suicide attempt within the past three months.
• Any other conditions deemed unsuitable for participation by the clinical physician, such as:

• Currently experiencing acute delirium.
• Suffering from respiratory infections, including COVID-19.
• Participants with significant skin conditions on the head or conditions that could affect the efficacy of light therapy will be excluded. These include cases with:

• Hemangiomas.
• Scleroderma.
• Psoriasis.
• Rashes.
• Open wounds.
• Tattoos on the head. Additionally, individuals with head implants or those who have undergone photosensitizing drug treatments within two weeks prior to trial participation will also be excluded.