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Enregistrement W2341391590 · doi:10.1111/jgs.14051

Wrist Accelerometry in the Health, Functional, and Social Assessment of Older Adults

2016· letter· en· W2341391590 sur OpenAlexaboutno aff
Megan Huisingh‐Scheetz, Masha Kocherginsky, Lara R. Dugas, Carolyn Payne, William Dale, David E. Conroy, Linda J. Waite

Notice bibliographique

RevueJournal of the American Geriatrics Society · 2016
Typeletter
Langueen
DomaineMedicine
ThématiquePhysical Activity and Health
Établissements canadiensnon disponible
Organismes subventionnairesNational Center for Advancing Translational SciencesNational Institute on Aging
Mots-clésMedicineWristAccelerometerActigraphyGerontologyPhysical therapyPhysical medicine and rehabilitationActivities of daily livingCircadian rhythmInternal medicineSurgery

Résumé

récupéré en direct d'OpenAlex

To the Editor: The importance of accelerometry as an indicator of older adult health is increasingly recognized. Low physical activity as assessed using hip accelerometry is associated with disability, cardiovascular risk, and poor health outcomes. Hip accelerometry is considered to be a more-precise measure of activity and sedentary behavior than wrist accelerometry,1 but wrist accelerometers have become ubiquitous commercially and are being used increasingly in research as a result of new activity monitor protocols (e.g., National Health and Nutrition Examination Study). Data relating wrist accelerometry to older adult health is lacking. The objective of the current analysis was to associate wrist accelerometry with an extensive set of health outcomes essential to older adult well-being in a nationally representative community sample. Data from a wrist accelerometry substudy (n = 738) in Wave 2 of the National Social Life, Health, and Aging Project (NSHAP) were analyzed.2 An accelerometer (ActiWatch Spectrum, Philips Respironics, Aurora, IL) worn on the nondominant wrist continuously measured activity and sleep over 72 hours (not removed for water activities).3 Activity counts were recorded every 15 seconds (epoch). A galvanic sensor excluded nonwear time. Wake time was determined using software protocols and investigator adjustment to align event markers, ambient light, and activity data.4 Only days with 10 hours or more of wake time were included. Average daily activity was calculated as the sum of wake time counts divided by total number of epochs. Valid hours worn, number of weekend days worn (categorical, reference: no weekend days), and month of wear (categorical, reference: January) to approximate season were calculated. Self-rated physical and mental health (excellent, very good, good, fair, poor) were assessed. Systolic and diastolic blood pressure, nonfasting glycosylated hemoglobin (%), C-reactive protein (CRP, mg/L), and body mass index (BMI, kg/m2) were measured.2 Obesity was identified as BMI of 30 kg/m2 or greater. Participants reported any diagnoses of heart problems; diabetes mellitus; stroke; cancer (other than skin); or asthma, chronic obstructive pulmonary disease, or emphysema5 and performed a 3-m timed walk twice and five timed serial chair stands.3 The fastest walk and chair stands times were recorded. Difficulty performing any activity of daily living (ADL) or instrumental activity of daily living (IADL) was self-reported.3 Frequency of 11 depressive symptoms and seven anxiety symptoms during the past week,6 frequency of attending meetings of organized groups and socializing with friends or family at least once per month, and current alcohol or tobacco use were self-reported. Age at the time of survey, sex, education, race, Hispanic ethnicity, household assets, and current working status were self-reported. Cognitive function was determined using the survey-adapted Montreal Cognitive Assessment.7 Association between average daily activity and each outcome was assessed using survey-adjusted regression models controlling for covariates, wear time, weekend days, and wear month. Effects per 10 activity counts are reported. Analyses were conducted using Stata version 14 (Stata Corp., College Station, TX). Of 738 participants, 631 had complete accelerometer and covariate data. Age ranged from 71.2 to 72.4, 52.8% were female, and 83.6% were white. Average daytime activity count was 54.2 (95% confidence interval (CI) = 52.1–56.4), mean number of valid wake hours was 36.4 (95% CI = 35.4–37.3), and 59.6% of participants did not wear the accelerometer on a weekend day (95% CI = 55.6–63.4%). Multivariate regression models demonstrated that all relationships between accelerometry and health outcome were in the expected direction (Tables 1 and 2). More physical activity was significantly associated with better reported physical (P < .001) and mental (P = .009) health, lower diastolic blood pressure (P = .01), lower BMI (P < .001), lower CRP (P = .02), less obesity (P < .001), lower HbA1c (P = .01), fewer reported heart problems (P = .01), less diabetes mellitus (P < .001), faster 3-m walk (P < 0.001), faster chair stands (P = .002), and less reported ADL (P = .002) and IADL (P = .006) difficulty. For example, a 10-point increase in mean activity count was associated with a 0.98-second (=e−0.02) faster walk and 20% lower risk of reporting an ADL disability. Similar to hip devices, wrist accelerometry-measured average daily activity was significantly associated with many physical and functional older adult health outcomes but with few mental health and social engagement outcomes. To the knowledge of the authors, this is the first report showing significant associations between wrist accelerometry and a wide range of health outcomes in a nationally representative sample of older adults.8 Accelerometry was assessed over 3 days, and the findings of strong associations with this brief wear duration suggest that accelerometry may be feasible in clinic settings. Longer durations (e.g., ≥5 days9) may more fully represent habitual activity, increase reliability, and therefore strengthen the accelerometry associations found with health outcomes. With newer wrist devices and algorithms for interpreting output, ease of wear, and low battery requirements, wrist accelerometers may become clinically useful moving forward. Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper. This work was supported by National Institute on Aging Grants 1R01AG033903–01, R01 AG030481–01A1, and R37 AG030481. Author Contributions: Huisingh-Scheetz: concept, design, analysis, interpretation, drafting, revising, writing, approval of final draft. Kocherginsky: concept, design, analysis, revising, approval of final draft. Dugas: interpretation, revising, approval of final draft. Payne: acquisition of data, revising, approval of final draft. Dale, Conroy: concept, revising, approval of final draft. Waite: concept, design, interpretation of data, revising, approval of final draft. Sponsor's Role: None.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,015
score de la tête « metaresearch » (Gemma)0,059
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,080

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0150,059
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,002
Communication savante0,0030,002
Science ouverte0,0030,001
Intégrité de la recherche0,0060,007
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,033
Tête enseignante GPT0,340
Écart entre enseignants0,308 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations11
Publié2016
Routes d'admission1
Résumé présentoui

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