Literature DB >> 33142665

Co-Calibrating Physical and Psychological Outcomes and Consumer Wearable Activity Outcomes in Older Adults: An Evaluation of the coQoL Method.

Vlad Manea1, Katarzyna Wac1,2.   

Abstract

Inactivity, lack of sleep, and poor nutrition predispose individuals to health risks. Patient-Reported Outcomes (PROs) assess physical behaviours and psychological states but are subject of self-reporting biases. Conversely, wearables are an increasingly accurate source of behavioural Technology-Reported Outcomes (TechROs). However, the extent to which PROs and TechROs provide convergent information is unknown. We propose the coQoL PRO-TechRO co-calibration method and report its feasibility, reliability, and human factors influencing data quality. Thirty-nine seniors provided 7.4 ± 4.4 PROs for physical activity (IPAQ), social support (MSPSS), anxiety/depression (GADS), nutrition (PREDIMED, SelfMNA), memory (MFE), sleep (PSQI), Quality of Life (EQ-5D-3L), and 295 ± 238 days of TechROs (Fitbit Charge 2) along two years. We co-calibrated PROs and TechROs by Spearman rank and reported human factors guiding coQoL use. We report high PRO-TechRO correlations (rS≥ 0.8) for physical activity (moderate domestic activity-light+fair active duration), social support (family help-fair activity), anxiety/depression (numeric score-sleep duration), or sleep (duration to sleep-sleep duration) at various durations (7-120 days). coQoL feasibly co-calibrates constructs within physical behaviours and psychological states in seniors. Our results can inform designs of longitudinal observations and, whenever appropriate, personalized behavioural interventions.

Entities:  

Keywords:  ambulatory assessment; anxiety; depression; health-related quality of life; memory; nutrition; physical activity; sleep; social support; wearable

Year:  2020        PMID: 33142665      PMCID: PMC7759248          DOI: 10.3390/jpm10040203

Source DB:  PubMed          Journal:  J Pers Med        ISSN: 2075-4426


  56 in total

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3.  Psychometric evaluation of the Pittsburgh Sleep Quality Index.

Authors:  J S Carpenter; M A Andrykowski
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7.  The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: a cross-sectional study.

Authors:  Ty Ferguson; Alex V Rowlands; Tim Olds; Carol Maher
Journal:  Int J Behav Nutr Phys Act       Date:  2015-03-27       Impact factor: 6.457

8.  Validation of the long international physical activity questionnaire: Influence of age and language region.

Authors:  Miriam Wanner; Nicole Probst-Hensch; Susi Kriemler; Flurina Meier; Christine Autenrieth; Brian W Martin
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9.  Social capital predicts accelerometry-measured physical activity among older adults in the U.S.: a cross-sectional study in the National Social Life, Health, and Aging Project.

Authors:  Erin C Ho; Louise Hawkley; William Dale; Linda Waite; Megan Huisingh-Scheetz
Journal:  BMC Public Health       Date:  2018-06-27       Impact factor: 3.295

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  1 in total

1.  Using Consumer-Wearable Activity Trackers for Risk Prediction of Life-Threatening Heart Arrhythmia in Patients with an Implantable Cardioverter-Defibrillator: An Exploratory Observational Study.

Authors:  Diana My Frodi; Vlad Manea; Søren Zöga Diederichsen; Jesper Hastrup Svendsen; Katarzyna Wac; Tariq Osman Andersen
Journal:  J Pers Med       Date:  2022-06-08
  1 in total

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