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Record W4416916732 · doi:10.1080/10400435.2025.2586605

Improvement on task performance and satisfaction with the use of dynamic arm support MOMO series on five users with upper limb dysfunction: A case series

2025· article· en· W4416916732 on OpenAlexaboutno aff
Yuichi Yokoyama, Koshi Matsuoka, Shintaro SHIMADA, Kaoru Inoue

Bibliographic record

VenueAssistive Technology · 2025
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsTask (project management)UsabilitySeries (stratigraphy)Psychological interventionQuality of life (healthcare)Activities of daily livingOccupational therapy

Abstract

fetched live from OpenAlex

The dynamic arm support MOMO series by Reharo Corporation, Japan, is an upper limb orthosis that primarily supports the daily lives of people with impaired upper limb function. The MOMO series comprises two types: MOMO and MOMO Prime, which support horizontal movement and antigravity control of the upper limb, respectively. This case series describes occupational therapy interventions using the MOMO series and assistive devices tailored to the individual needs of five participants. Outcomes were assessed using the Canadian Occupational Performance Measure (COPM) and the System Usability Scale (SUS). In all cases, COPM performance and satisfaction scores improved, indicating enhanced ability to engage in meaningful activities such as eating, smartphone use, and leisure. On the SUS score, higher satisfaction was observed when participants could apply the device independently and achieve tasks immediately after introduction. In several cases, the use of the MOMO series contributed to functional recovery, allowing tasks to be continued without the device. These findings suggest that the MOMO series can enhance independence, satisfaction, and quality of life when introduced with appropriate occupational therapy support.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.232
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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