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Record W2902874026 · doi:10.2196/12199

Feasibility and Acceptability of Technology-Based Exercise and Posture Training in Older Adults With Age-Related Hyperkyphosis: Pre-Post Study

2018· article· en· W2902874026 on OpenAlexvenueno aff
Wendy B. Katzman, Amy Gladin, Nancy E. Lane, Shirley Wong, Felix Liu, Chengshi Jin, Yoshimi Fukuoka

Bibliographic record

VenueJMIR Aging · 2018
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsnot available
FundersNational Institute of Nursing ResearchNational Institute of Arthritis and Musculoskeletal and Skin DiseasesUniversity of California, San FranciscoNational Institute on AgingNational Institutes of Health
KeywordsPhysical medicine and rehabilitationPhysical therapyTraining (meteorology)PsychologyMedicineGerontology

Abstract

fetched live from OpenAlex

Background: Hyperkyphosis is common among older adults, and is associated with multiple adverse health outcomes. A kyphosis-specific exercise and posture training program improves hyperkyphosis, but in-person programs are expensive to implement and maintain over longer-periods of time. It is unknown if a technology-based posture training program disseminated through a smartphone is a feasible or acceptable alternative to in-person training among older adults with hyperkyphosis. Objective: The primary purpose of this study was to assess the feasibility of subject recruitment, short-term retention and adherence, and the acceptability of a technology-based exercise and postural training program disseminated as video clip links and text messaging prompts via a smartphone. The secondary purpose was to explore the potential efficacy of this program on kyphosis, physical function and health-related quality of life in older adults with hyperkyphosis. Methods: This was a 6-week pre-post design pilot trial. We recruited community-dwelling adults ≥65 years with hyperkyphosis ≥40 (±5) degrees and access to a smartphone. The intervention had two parts: 1) exercise and posture training via video clips sent to participants daily via text messaging which included 6 weekly video clip links to be viewed on the participant's smartphone and 2) text messaging prompts to practice good posture. We determined subject recruitment, adherence, retention and acceptability of the intervention. Outcomes included change in kyphometer-measured kyphosis, occiput to wall (OTW), Short Physical Performance Battery (SPPB), Scoliosis Research Society SRS-30, Center for Epidemiological Studies Depression (CESD) and Physical Activity Scale for the Elderly (PASE). Results: 64 potential participants were recruited, 17 participants were enrolled and 12 completed post-intervention testing at 6-weeks. Average age was 71.6 (SD=4.9) years and 50% were female. Median adherence to daily video viewing was 100%, (range 14 to 100) and to practicing good posture 3 times or more per day was 71%, (range 0 to 100). Qualitative evaluation of acceptability of the intervention revealed the smartphone screen was too small for participants to view the videos well and daily prompts to practice posture were too frequent. Kyphosis, OTW and physical activity significantly improved after the 6-week intervention. Kyphosis decreased by 8 (95% CI: 12, 5) degrees (p<0.001), OTW decreased 1.9 (95% CI: 3.3, 0.7) cm (p=0.007), and physical activity measured by PASE increased 29 (95% CI: 3, 54) points (p=0.03). The health-related quality of life SRS-30 score increased 0.11 (SD=0.19) points, but it was not statistically significant, p=0.09. Conclusions: Technology-based exercise and posture training using video clip viewing and text messaging reminders is feasible and acceptable in a small cohort of older adults with hyperkyphosis. Technology-based exercise and posture training warrants further study as a potential self-management program for age-related hyperkyphosis that may be more easily disseminated than in-person training.

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 score0.033
Threshold uncertainty score0.544

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 studies0.0000.000
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 head0.016
GPT teacher head0.301
Teacher spread0.285 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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