Loneliness and well-being in Finnish immigrants: A multimodal dataset from wearables and passive data collection

This dataset was collected from first-generation immigrants between 2022 and 2023. Over a 28-day period, 39 participants aged 18 to 65, fluent in English and experiencing loneliness (UCLA Loneliness Scale score ≥ 28) contributed to the study. Data collection utilized Samsung Watch Active 2, Oura Ring, AWARE, and Centralive smartphone application. This dataset contains raw data from photoplethysmogram (PPG), inertial measurement unit (IMU) readings, air pressure, and processed data on heart rate, heart rate variability, sleep metrics (bedtime, stages, quality), physical activity (steps, active calories, activity types), and smartphone usage patterns (screen time, notifications, call and message logs). Participants also completed ecological momentary assessments (EMA) and weekly surveys, including instruments like the Beck Depression Inventory (BDI), Patient Health Questionnaire-9 (PHQ-9), Perceived Stress Scale, Sense of Coherence Scale, Social Connectedness Scale, Twente Engagement with..., Design and set up
This study was designed to create a longitudinal dataset capturing physiological, behavioral, and psychological data from first-generation immigrants living in Finland. The dataset aims to support research on the relationship between mental health and daily lifestyle factors, providing a foundation for further detection algorithm development.
To achieve this, the study collected multimodal data over a 28-day period from every participant. Objective data were gathered from wearable devices, which recorded sleep patterns, physical activity, and cardiovascular health metrics and raw PPG signals. Passive smartphone data, such as screen usage, notifications, calls, and messages, were also collected to capture digital behavior patterns.
Subjective data were collected through EMAs delivered via push notifications and weekly self-report surveys. These assessments measured daily emotional states—loneliness, stress, depression, and social connectedness. By integrating multiple d..., , # Loneliness and well-being in Finnish immigrants: A multimodal dataset from wearables and passive data collection

## Overview

The dataset consists of longitudinal physiological, behavioral, and self-reported data collected from first-generation immigrants in Finland during 2022 and 2023. The study included 39 participants aged 18–65, all fluent in English and experiencing loneliness (UCLA Loneliness Scale score ≥28). Data were collected over a 28-day period using multimodal sources, including the Samsung Watch Active 2, Oura Ring, and the AWARE smartphone application.

The dataset includes raw and processed data on cardiovascular health, sleep patterns, physical activity, smartphone usage, and mental health assessments. Daily and weekly ecological momentary assessments (EMA) captured momentary emotional states, while structured surveys administered through Centralive provided insights into participants' mental health and well-being.

## Data and File Structure

At the root of the dat..., All participants provided written informed consent to share their de-identified data for public research purposes at the time of enrollment.

To protect participant privacy and minimize the risk of re-identification, we applied the following de-identification procedures:

1. All direct identifiers (e.g., names, contact information, device IDs) were removed.
2. Each participant was assigned a pseudonymous identifier in the format Participant_#.
3. Timestamp fields were randomly shifted to obscure precise timing while preserving temporal patterns essential for analysis.
4. App identifiers were generalized into broader categories (e.g., “social media app”).
5. GPS location data were excluded.