Back

Comparisons Between GPS-based and Self-reported Life-space Mobility in Older Adults

Bai, C.; Zapata, R.; Karnati, Y.; Smail, E. J.; Hajduk, A. M.; Gill, T. M.; Ranka, S.; Manini, T. M.; Mardini, M. T.

2022-09-23 health informatics
10.1101/2022.09.19.22280116 medRxiv
Show abstract

Assessments of Life-space Mobility (LSM) evaluate the locations of movement and their frequency over a period of time to understand mobility patterns. Advancements in and miniaturization of GPS sensors in mobile devices like smartwatches could facilitate objective and high-resolution assessment of life-space mobility. The purpose of this study was to compare self-reported measures to GPS-based LSM extracted from 27 participants (44.4% female, aged 65+ years) who wore a smartwatch for 1-2 weeks at two different site locations (Connecticut and Florida). GPS features (e.g., excursion size/span) were compared to self-reported LSM with and without an indicator for needing assistance. Although correlations between self-reported measures and GPS-based LSM were positive, none were statistically significant. The correlations improved slightly when needing assistance was included, but statistical significance was achieved only for excursion size (r=0.40, P=0.04). The poor correlations between GPS-based and self-reported indicators suggest that they capture different dimensions of life-space mobility.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 8%
19.3%
Similar papers in this journal
2
Scientific Reports
○ 3612 papers in training set
Top 2%
12.4%
Similar papers in this journal
3
BMJ Open
● 601 papers in training set
Top 2%
9.3%
Similar papers in this journal
4
Journal of Medical Internet Research
◐ 87 papers in training set
Top 0.6%
4.2%
Similar papers in this journal
5
DIGITAL HEALTH
○ 17 papers in training set
Top 0.1%
4.2%
Similar papers in this journal
6
International Journal of Environmental Research and Public Health
○ 128 papers in training set
Top 1%
3.4%
50% of probability mass above
7
Age and Ageing
◐ 28 papers in training set
Top 0.2%
2.2%
8
JMIR Public Health and Surveillance
◐ 45 papers in training set
Top 0.4%
2.2%
9
JMIR mHealth and uHealth
◐ 11 papers in training set
Top 0.1%
2.2%
10
Sensors
○ 43 papers in training set
Top 0.5%
2.0%
11
Advanced Biology
○ 29 papers in training set
Top 0.2%
1.8%
12
Frontiers in Digital Health
○ 24 papers in training set
Top 0.5%
1.8%
13
BMC Public Health
○ 158 papers in training set
Top 3%
1.8%
14
Biogerontology
○ 10 papers in training set
Top 0.1%
1.4%
15
International Journal of Medical Informatics
○ 26 papers in training set
Top 0.9%
1.2%
16
Frontiers in Public Health
○ 148 papers in training set
Top 4%
1.2%
17
Frontiers in Aging Neuroscience
○ 74 papers in training set
Top 1%
1.2%
18
Preventive Medicine Reports
○ 15 papers in training set
Top 0.3%
1.2%
19
PLOS Digital Health
● 106 papers in training set
Top 4%
1.1%
20
IEEE Transactions on Neural Systems and Rehabilitation Engineering
● 49 papers in training set
Top 0.8%
1.1%
21
Journal of Clinical and Translational Science
◐ 14 papers in training set
Top 0.4%
1.0%
22
Medicine & Science in Sports & Exercise
○ 16 papers in training set
Top 0.4%
1.0%
23
JAMIA Open
◐ 42 papers in training set
Top 1%
0.9%
24
Neurorehabilitation and Neural Repair
○ 21 papers in training set
Top 0.4%
0.9%
25
Sleep Advances
◐ 11 papers in training set
Top 0.2%
0.9%
26
Journal of the American Medical Informatics Association
◐ 71 papers in training set
Top 2%
0.9%
27
Biology Methods and Protocols
◐ 61 papers in training set
Top 2%
0.6%
28
BMC Geriatrics
○ 18 papers in training set
Top 0.5%
0.6%
29
BMC Medical Informatics and Decision Making
○ 43 papers in training set
Top 2%
0.6%
30
Life
○ 29 papers in training set
Top 1%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.