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Assessment of Frailty in Community-Dwelling Older Adults Using Smartphone-Based Digital Lifelogging: A Multi-Center, Prospective Observational Study
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kim, Janghyeon | - |
| dc.contributor.author | Hong, Namki | - |
| dc.contributor.author | Jung, Hee-Won | - |
| dc.contributor.author | Baek, Seungjin | - |
| dc.contributor.author | Cho, Sang Wouk | - |
| dc.contributor.author | Kim, Jungheui | - |
| dc.contributor.author | Lee, Changseok | - |
| dc.contributor.author | Lee, Subeom | - |
| dc.contributor.author | Youn, Bo-Young | - |
| dc.date.accessioned | 2026-01-23T05:37:17Z | - |
| dc.date.available | 2026-01-23T05:37:17Z | - |
| dc.date.created | 2026-01-21 | - |
| dc.date.issued | 2025-12 | - |
| dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/210231 | - |
| dc.description.abstract | Highlights What are the main findings? Smartphone-based digital lifelogs, specifically usual gait speed, daily step count, and subjective health, are significantly associated with frailty and explain substantially more variance than traditional clinical indicators alone. Continuous, real-world mobility metrics collected via embedded smartphone sensors provide meaningful insights into functional decline that are not captured by conventional clinic-based frailty assessments. What are the implications of the main findings? Smartphone-based monitoring offers a scalable, low-burden approach to community-based frailty assessment and monitoring, with potential to support future longitudinal risk prediction after validation. Integrating digital lifelog data into geriatric care pathways can enable proactive intervention, personalized management, and more accurate frailty risk stratification in everyday living environments.Highlights What are the main findings? Smartphone-based digital lifelogs, specifically usual gait speed, daily step count, and subjective health, are significantly associated with frailty and explain substantially more variance than traditional clinical indicators alone. Continuous, real-world mobility metrics collected via embedded smartphone sensors provide meaningful insights into functional decline that are not captured by conventional clinic-based frailty assessments. What are the implications of the main findings? Smartphone-based monitoring offers a scalable, low-burden approach to community-based frailty assessment and monitoring, with potential to support future longitudinal risk prediction after validation. Integrating digital lifelog data into geriatric care pathways can enable proactive intervention, personalized management, and more accurate frailty risk stratification in everyday living environments.Abstract Frailty in older adults is a multidimensional syndrome characterized by reduced physiological resilience and heightened vulnerability to adverse outcomes, yet conventional assessments remain largely clinic-based. This study evaluated the feasibility and explanatory utility of smartphone-based digital lifelogs for assessing frailty in community-dwelling older adults. In a prospective observational study, 300 participants (mean age 73.30, SD 5.37 years) from three sites in Seoul, South Korea, used a custom mobile application for two weeks that passively collected sensor-derived gait speed, 30 s sit-to-stand counts, and daily and hourly step counts, alongside self-reported ratings of perceived exertion and subjective health. Frailty Index (FI) scores were computed, and Pearson correlations, hierarchical linear regression, and independent linear regression were applied to examine associations and model explanatory performance. Significant correlations were observed between FI and gait speed, sit-to-stand performance, daily step counts, perceived exertion, and subjective health. Incorporating digital lifelogs significantly improved explained variance in frailty beyond clinical indicators (Delta R2 = 0.183), with gait speed and daily step counts emerging as key predictors. A model including only digital lifelogs also significantly associated with frailty (R2 = 0.288). These findings suggest that smartphone-based lifelogging offers a feasible, practical, and informative method for two-week monitoring and cross-sectional assessment in community settings. | - |
| dc.language | English | - |
| dc.publisher | MDPI | - |
| dc.relation.isPartOf | SENSORS | - |
| dc.relation.isPartOf | SENSORS | - |
| dc.subject.MESH | Aged | - |
| dc.subject.MESH | Aged, 80 and over | - |
| dc.subject.MESH | Female | - |
| dc.subject.MESH | Frail Elderly | - |
| dc.subject.MESH | Frailty* / diagnosis | - |
| dc.subject.MESH | Frailty* / physiopathology | - |
| dc.subject.MESH | Gait / physiology | - |
| dc.subject.MESH | Geriatric Assessment* / methods | - |
| dc.subject.MESH | Humans | - |
| dc.subject.MESH | Independent Living | - |
| dc.subject.MESH | Male | - |
| dc.subject.MESH | Mobile Applications | - |
| dc.subject.MESH | Prospective Studies | - |
| dc.subject.MESH | Republic of Korea | - |
| dc.subject.MESH | Smartphone* | - |
| dc.subject.MESH | Walking Speed / physiology | - |
| dc.title | Assessment of Frailty in Community-Dwelling Older Adults Using Smartphone-Based Digital Lifelogging: A Multi-Center, Prospective Observational Study | - |
| dc.type | Article | - |
| dc.contributor.googleauthor | Kim, Janghyeon | - |
| dc.contributor.googleauthor | Hong, Namki | - |
| dc.contributor.googleauthor | Jung, Hee-Won | - |
| dc.contributor.googleauthor | Baek, Seungjin | - |
| dc.contributor.googleauthor | Cho, Sang Wouk | - |
| dc.contributor.googleauthor | Kim, Jungheui | - |
| dc.contributor.googleauthor | Lee, Changseok | - |
| dc.contributor.googleauthor | Lee, Subeom | - |
| dc.contributor.googleauthor | Youn, Bo-Young | - |
| dc.identifier.doi | 10.3390/s26010215 | - |
| dc.relation.journalcode | J03219 | - |
| dc.identifier.eissn | 1424-8220 | - |
| dc.identifier.pmid | 41516649 | - |
| dc.subject.keyword | frailty | - |
| dc.subject.keyword | older adults | - |
| dc.subject.keyword | mobile health | - |
| dc.subject.keyword | digital health | - |
| dc.subject.keyword | digital lifelogs | - |
| dc.contributor.affiliatedAuthor | Hong, Namki | - |
| dc.contributor.affiliatedAuthor | Baek, Seungjin | - |
| dc.contributor.affiliatedAuthor | Cho, Sang Wouk | - |
| dc.contributor.affiliatedAuthor | Kim, Jungheui | - |
| dc.identifier.scopusid | 2-s2.0-105027059670 | - |
| dc.identifier.wosid | 001657620100001 | - |
| dc.citation.volume | 26 | - |
| dc.citation.number | 1 | - |
| dc.identifier.bibliographicCitation | SENSORS, Vol.26(1), 2025-12 | - |
| dc.identifier.rimsid | 91141 | - |
| dc.type.rims | ART | - |
| dc.description.journalClass | 1 | - |
| dc.description.journalClass | 1 | - |
| dc.subject.keywordAuthor | frailty | - |
| dc.subject.keywordAuthor | older adults | - |
| dc.subject.keywordAuthor | mobile health | - |
| dc.subject.keywordAuthor | digital health | - |
| dc.subject.keywordAuthor | digital lifelogs | - |
| dc.subject.keywordPlus | GERIATRIC ASSESSMENT | - |
| dc.subject.keywordPlus | GAIT SPEED | - |
| dc.subject.keywordPlus | HEALTH | - |
| dc.subject.keywordPlus | PERFORMANCE | - |
| dc.subject.keywordPlus | DISABILITY | - |
| dc.subject.keywordPlus | MORTALITY | - |
| dc.subject.keywordPlus | SCALE | - |
| dc.subject.keywordPlus | INDEX | - |
| dc.type.docType | Article | - |
| dc.description.isOpenAccess | Y | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
| dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
| dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
| dc.relation.journalResearchArea | Chemistry | - |
| dc.relation.journalResearchArea | Engineering | - |
| dc.relation.journalResearchArea | Instruments & Instrumentation | - |
| dc.identifier.articleno | 215 | - |
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