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Sensor-derived physical activity parameters can predict future falls in people with dementia
Schwenk M, Hauer K, Zieschang T, Englert S, Mohler J, Najafi B.. Gerontology, 2014-08-28
Topic: Risk, prevention and care in population studies
Background There is a need for simple clinical tools that can objectively assess the fall risk in people with dementia. Wearable sensors seem to have the potential for fall prediction; however, there has been limited work performed in this important area. Objective To explore the validity of sensor-derived physical activity (PA) parameters for predicting future falls in people with dementia. To compare sensor-based fall risk assessment with conventional fall risk measures. Methods This was a cohort study of people with confirmed dementia discharged from a geriatric rehabilitation ward. PA was quantified using 24-hour motion-sensor monitoring at the beginning of the study. PA parameters (percentage of walking, standing, sitting, and lying; duration of single walking, standing, and sitting bouts) were extracted using specific algorithms. Conventional assessment included performance-based tests (Timed Up and Go Test, Performance-Oriented Mobility Assessment, 5-chair stand) and questionnaires (cognition, ADL status, fear of falling, depression, previous faller). Outcome measures were fallers (at least one fall in the 3-month follow-up period) versus non-fallers. Results 77 people were included in the study (age 81.8 ± 6.3; community-dwelling 88%, institutionalized 12%). Surprisingly, fallers and non-fallers did not differ on any conventional assessment (p = 0.069-0.991), except for 'previous faller' (p = 0.006). Interestingly, several PA parameters discriminated between the groups. The 'walking bout average duration', 'longest walking bout duration' and 'walking bout duration variability' were lower in fallers, compared to non-fallers (p = 0.008-0.027). The 'standing bout average duration' was higher in fallers (p = 0.050). Two variables, 'walking bout average duration' [odds ratio (OR) 0.79, p = 0.012] and 'previous faller' (OR 4.44, p = 0.007) were identified as independent predictors for falls. The OR for a 'walking bout average duration' Discussion RESULTS demonstrate that sensor-derived PA parameters are independent predictors of the fall risk and may have higher diagnostic accuracy in persons with dementia compared to conventional fall risk measures. Our findings highlight the potential of telemonitoring technology for estimating the fall risk. RESULTS should be confirmed in a larger study and by measuring PA over a longer period of time.
Key findings
- Outcome measures were fallers (at least one fall in the 3-month follow-up period) versus non-fallers.
- Results 77 people were included in the study (age 81.8 ± 6.3; community-dwelling 88%, institutionalized 12%).
- The 'walking bout average duration', 'longest walking bout duration' and 'walking bout duration variability' were lower in fallers, compared to non-fallers (p = 0.008-0.027).
- The OR for a 'walking bout average duration' Discussion RESULTS demonstrate that sensor-derived PA parameters are independent predictors of the fall risk and may have higher diagnostic accuracy in persons with dementia compared to conventional fall risk measures.
- Our findings highlight the potential of telemonitoring technology for estimating the fall risk.
Why it matters
In 77 people with dementia, certain sensor-derived walking parameters differed between those who fell and those who did not over three months, while conventional assessments mostly did not differ. This is about predicting falls, not about treating, curing, or preventing Alzheimer's disease.
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Study details
- Study designcohort study
- Participants77
- Effect sizeOR 4.44
- Evidence strengthModerate
How this study scored 59/100
Published in a peer-reviewed journal, and it is a cohort study following people over time. Scored zero for randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Gerontology) |
| Journal's citation record | 2 / 4 | Gerontology: journal h-index 151 (OpenAlex, 2026-10-29) |
| Study design | 7 / 12 | A cohort study following people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2014 — within 20 year(s) of the newest study here |
| Sample size | 1 / 4 | 77 participants |
| Effect reported | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 94 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 6.7 citations a year over 14 year(s) since 2014 |
| Impact for its field and year | 5 / 6 | Cited 22.1× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 3 other studies in this library |
| Still being cited | 2 / 4 | Cited 16 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Bijan Najafi: h-index 68 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Heidelberg University: institution h-index 1,344 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 25171300) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 59 / 100 | — |
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Read the original
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Schwenk, M., Hauer, K., Zieschang, T., Englert, S., Mohler, J., & Najafi, B. (2014). Sensor-derived physical activity parameters can predict future falls in people with dementia. Gerontology. https://doi.org/10.1159/000363136
Vancouver
Schwenk M, Hauer K, Zieschang T, Englert S, Mohler J, Najafi B. Sensor-derived physical activity parameters can predict future falls in people with dementia. Gerontology. 2014. doi:10.1159/000363136. PMID: 25171300.
BibTeX
@article{schwenk2014-25171300,
author = {Schwenk, M. and Hauer, K. and Zieschang, T. and Englert, S. and Mohler, J. and Najafi, B.},
title = {{Sensor-derived physical activity parameters can predict future falls in people with dementia}},
journal = {Gerontology},
year = {2014},
doi = {10.1159/000363136},
pmid = {25171300},
url = {https://doi.org/10.1159/000363136}
}
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