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Record W4407707131 · doi:10.2196/70855

Mainstream Smart Home Technology–Based Intervention to Enhance Functional Independence in Individuals With Complex Physical Disabilities: Single-Group Pre-Post Feasibility Study

2025· article· en· W4407707131 on OpenAlexvenueno aff
Dan Ding, Lindsey Morris, Gina Novario, Andrea D. Fairman, Kacey Roehrich, Palma Foschi Walko, Jessica Boateng

Bibliographic record

VenueJMIR Rehabilitation and Assistive Technologies · 2025
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintIndependence (probability theory)Intervention (counseling)Assistive technologyPsychologyGerontologyPhysical therapyMedicineComputer scienceHuman–computer interactionWorld Wide WebMathematicsStatisticsPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Mainstream smart home technologies (MSHTs), such as home automation devices and smart speakers, are becoming more powerful, affordable, and integrated into daily life. While not designed for individuals with disabilities, MSHT has the potential to serve as assistive technology to enhance their independence and participation. OBJECTIVE: The study aims to describe a comprehensive MSHT-based intervention named ASSIST (Autonomy, Safety, and Social Integration via Smart Technologies) and evaluate its feasibility in enhancing the functional independence of individuals with complex physical disabilities. METHODS: ASSIST is a time-limited intervention with a design based on the human activity assistive technology model, emphasizing client-centered goals and prioritizing individual needs. The intervention follows a structured assistive technology service delivery process that includes 2 assessment sessions to determine technology recommendations, installation and setup of the recommended technology, and up to 8 training sessions. An occupational therapist led the intervention, supported by a contractor and a technologist. Feasibility was evaluated through several measures: (1) the ASSIST Functional Performance Index, which quantifies the number of tasks transitioned from requiring assistance to independent completion and from higher levels of assistance or effort to lower levels; (2) pre- and postintervention measures of perceived task performance and satisfaction using a 10-point scale; (3) the number and types of tasks successfully addressed, along with the costs of devices and installation services; and (4) training effectiveness using the Goal Attainment Scale (GAS). RESULTS: In total, 17 powered wheelchair users with complex physical disabilities completed the study with 100% session attendance. Across participants, 127 tasks were addressed, with 2 to 10 tasks at an average cost of US $3308 (SD US $1192) per participant. Of these tasks, 95 (74.8%) transitioned from requiring partial or complete assistance to independent completion, while 24 (18.9%) either improved from requiring complete to partial assistance or, if originally performed independently, required reduced effort. Only 8 (6.3%) tasks showed no changes. All training goals, except for 2, were achieved at or above the expected level, with a baseline average GAS score of 22.6 (SD 3.5) and a posttraining average GAS score of 77.2 (SD 4.5). Perceived task performance and satisfaction showed significant improvement, with performance score increasing from a baseline mean of 2.6 (SD 1.2) to 8.8 (SD 1.0; P<.001) and satisfaction score rising from an average of 2.9 (SD 1.3) to 9.0 (SD 0.9; P<.001). CONCLUSIONS: The ASSIST intervention demonstrated the immediate benefits of enhancing functional independence and satisfaction with MSHT among individuals with complex physical disabilities. While MSHT shows promise in addressing daily living needs at lower costs, barriers such as digital literacy, device setup, and caregiver involvement remain. Future work should focus on scalable models, caregiver engagement, and sustainable solutions for real-world implementation.

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)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.046
GPT teacher head0.420
Teacher spread0.373 · 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.

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

Citations7
Published2025
Admission routes1
Has abstractyes

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