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Affective Computing for Late-Life Mood and Cognitive Disorders
Erin Smith, Eric A Storch, Ipsit Vahia, Stephen T C Wong, Helen Lavretsky, Jeffrey L Cummings, Harris A Eyre. Frontiers in psychiatry, 2021-12-23
Topic: Brain imaging, connectivity and assessment
Affective computing (also referred to as artificial emotion intelligence or emotion AI) is the study and development of systems and devices that can recognize, interpret, process, and simulate emotion or other affective phenomena. With the rapid growth in the aging population around the world, affective computing has immense potential to benefit the treatment and care of late-life mood and cognitive disorders. For late-life depression, affective computing ranging from vocal biomarkers to facial expressions to social media behavioral analysis can be used to address inadequacies of current screening and diagnostic approaches, mitigate loneliness and isolation, provide more personalized treatment approaches, and detect risk of suicide. Similarly, for Alzheimer's disease, eye movement analysis, vocal biomarkers, and driving and behavior can provide objective biomarkers for early identification and monitoring, allow more comprehensive understanding of daily life and disease fluctuations, and facilitate an understanding of behavioral and psychological symptoms such as agitation. To optimize the utility of affective computing while mitigating potential risks and ensure responsible development, ethical development of affective computing applications for late-life mood and cognitive disorders is needed.
Key findings
- Affective computing (also referred to as artificial emotion intelligence or emotion AI) is the study and development of systems and devices that can recognize, interpret, process, and simulate emotion or other affective phenomena.
- With the rapid growth in the aging population around the world, affective computing has immense potential to benefit the treatment and care of late-life mood and cognitive disorders.
- For late-life depression, affective computing ranging from vocal biomarkers to facial expressions to social media behavioral analysis can be used to address inadequacies of current screening and diagnostic approaches, mitigate loneliness and isolation, provide more personalized treatment approaches, and detect risk of suicide.
- Similarly, for Alzheimer's disease, eye movement analysis, vocal biomarkers, and driving and behavior can provide objective biomarkers for early identification and monitoring, allow more comprehensive understanding of daily life and disease fluctuations, and facilitate an understanding of behavioral and psychological symptoms such as agitation.
- To optimize the utility of affective computing while mitigating potential risks and ensure responsible development, ethical development of affective computing applications for late-life mood and cognitive disorders is needed.
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Study details
- Evidence strengthLow
How this study scored 41/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record, multi-centre collaboration and free to read, so those were left out: it earned 23 of the 56 points we could judge it on, which is 41 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Frontiers in psychiatry) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (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 | 3 / 5 | 2021 — within 6 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 35002802) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 23 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 23 of the 56 points we could judge it on is 41 out of 100. |
| Total | 41 / 100 | — |
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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. The record names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Smith, E., Storch, E. A., Vahia, I., Wong, S. T. C., Lavretsky, H., Cummings, J. L., et al. (2021). Affective Computing for Late-Life Mood and Cognitive Disorders. Frontiers in psychiatry. https://doi.org/10.3389/fpsyt.2021.782183
Vancouver
Smith E, Storch EA, Vahia I, Wong STC, Lavretsky H, Cummings JL, et al. Affective Computing for Late-Life Mood and Cognitive Disorders. Frontiers in psychiatry. 2021. doi:10.3389/fpsyt.2021.782183. PMID: 35002802.
BibTeX
@article{smith2021-35002802,
author = {Smith, Erin and Storch, Eric A and Vahia, Ipsit and Wong, Stephen T C and Lavretsky, Helen and Cummings, Jeffrey L and Eyre, Harris A},
title = {{Affective Computing for Late-Life Mood and Cognitive Disorders}},
journal = {Frontiers in psychiatry},
year = {2021},
doi = {10.3389/fpsyt.2021.782183},
pmid = {35002802},
url = {https://doi.org/10.3389/fpsyt.2021.782183}
}