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Differentiating dementia disease subtypes with gait analysis: feasibility of wearable sensors?
Mc Ardle R, Del Din S, Galna B, Thomas A, Rochester L.. Gait & posture, 2019-12-28
Topic: Risk, prevention and care in population studies
Background There are unique signatures of gait impairments in different dementia disease subtypes, such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Parkinson's disease (PDD). This suggests gait analysis is a useful differential marker for dementia disease subtypes, but this has yet to be assessed using inexpensive wearable technology. Research question This study aimed to assess whether a single accelerometer-based wearable could differentiate dementia disease subtypes through gait analysis. Methods 80 people with mild cognitive impairment or dementia due to AD, DLB or PD performed six ten-metre walks. An accelerometer-based wearable (Axivity) assessed gait. Data was processed using algorithms validated in other neurological disorders and older adults. Fourteen spatiotemporal characteristic were computed, that broadly represent pace, variability, rhythm, asymmetry and postural control features of gait. One way analysis of variance and Kruskall Wallis tests identified significant between-group differences, and post-hoc independent t-tests and Mann Whitney U's established where differences lay. Receiver Operating Characteristics and Area Under the Curve (AUC) demonstrated overall accuracy for single gait characteristics. Results The wearable was able to differentiate dementia disease subtypes (p ≤ .05) and demonstrated significant differences between the groups in 7 gait characteristics with modest accuracy. For reference the instrumented walkway showed 2 between-group differences in gait characteristics. Significance This study found that a wearable device can be used to differentiate dementia disease subtypes. This provides a foundation for future research to investigate the application of wearable technology as a clinical tool to aid diagnostic accuracy, allowing the correct treatment and care to be applied. Wearable technology may be particularly useful as its use is less restricted to context, making it easier to implement.
Key findings
- One way analysis of variance and Kruskall Wallis tests identified significant between-group differences, and post-hoc independent t-tests and Mann Whitney U's established where differences lay.
- Receiver Operating Characteristics and Area Under the Curve (AUC) demonstrated overall accuracy for single gait characteristics.
- Results The wearable was able to differentiate dementia disease subtypes (p ≤ .05) and demonstrated significant differences between the groups in 7 gait characteristics with modest accuracy.
Why it matters
A single accelerometer-based wearable differentiated dementia disease subtypes, including Alzheimer's disease, dementia with Lewy bodies, and Parkinson's disease dementia, through gait analysis. This is a diagnostic tool and does not treat, cure, or prevent Alzheimer's disease.
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Study details
- Study designpost-hoc analysis
- Participants80
- Evidence strengthLow
How this study scored 51/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Gait & posture) |
| Journal's citation record | 2 / 4 | Gait & Posture: journal h-index 218 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | Design recorded as “post-hoc analysis”, which our rubric does not rank (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 1 / 4 | 80 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| 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 | 5 / 7 | Cited 113 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 14.1 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 5 / 6 | Cited 27.6× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 7 other studies in this library |
| Still being cited | 2 / 4 | Cited 43 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Lynn Rochester: h-index 111 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Newcastle University: institution h-index 932 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31901765) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 51 / 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
Mc Ardle, R., Del Din, S., Galna, B., Thomas, A., & Rochester, L. (2020). Differentiating dementia disease subtypes with gait analysis: feasibility of wearable sensors? Gait & posture. https://doi.org/10.1016/j.gaitpost.2019.12.028
Vancouver
Mc Ardle R, Del Din S, Galna B, Thomas A, Rochester L. Differentiating dementia disease subtypes with gait analysis: feasibility of wearable sensors? Gait & posture. 2020. doi:10.1016/j.gaitpost.2019.12.028. PMID: 31901765.
BibTeX
@article{mcardle2020-31901765,
author = {Mc Ardle, R. and Del Din, S. and Galna, B. and Thomas, A. and Rochester, L.},
title = {{Differentiating dementia disease subtypes with gait analysis: feasibility of wearable sensors?}},
journal = {Gait \& posture},
year = {2020},
doi = {10.1016/j.gaitpost.2019.12.028},
pmid = {31901765},
url = {https://doi.org/10.1016/j.gaitpost.2019.12.028}
}
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