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1 r 6 m and activity fragmentation assessed by accelerometry.
2 port but not with objective measures such as accelerometry.
3 fibres, and free-living physical activity by accelerometry.
4 e time lag between psychiatric diagnoses and accelerometry.
5 and daily physical activity as determined by accelerometry.
6 varying in PA levels which were monitored by accelerometry.
7 er day) measured by combined thigh and waist accelerometry.
8 dated interview assessment corroborated with accelerometry.
9 facilitate clinical applications of wearable accelerometry.
10 outs), respectively, measured objectively by accelerometry.
11 sical activity were measured with the use of accelerometry.
12 ovel measure readily attainable from bedside accelerometry.
13                       Tremor was measured by accelerometry.
14 75 persons aged >/=16 years in England using accelerometry.
15    Nine patients' tremors were recorded with accelerometry.
16  Relative gait speed was assessed with trunk accelerometry.
17 res of physical activity were ascertained by accelerometry.
18 ray absorptiometry, and physical activity by accelerometry.
19 ke and total energy expenditure estimated by accelerometry.
20 ] from the UK Biobank cohort with wrist-worn accelerometry.
21 concurrently recorded using smartphone-based accelerometry.
22 d greater duration of physical activity than accelerometry.
23 s, daily physical activity was measured with accelerometry.
24 f 4 ACT24s and 0.68 (95% CI: 0.61, 0.75) for accelerometry.
25 tes, multiple-pass 24-h dietary recalls, 3-d accelerometry, 24-h respiration calorimetry, measurement
26 imation using photoplethysmography (PPG) and accelerometry (Acc) sensors placed on the neck.
27     It is, therefore, important to calibrate accelerometry across different behaviours to understand
28  activity (MVPA) were derived from ActiGraph accelerometry (ActiGraph LLC, Pensacola, Florida), and r
29  MVPA and sedentary time were measured using accelerometry after reanalyzing raw data.
30  objectively measured physical activity with accelerometry and adiposity with body-mass index and bio
31             Using high-resolution three-axis accelerometry and depth logging, we present the first di
32 uantified tremor during postural tasks using accelerometry and dysmetria with fast, reverse-at-target
33                  PA was measured with 5 d of accelerometry and expressed in min/d of moderate or vigo
34 ng bouts were ascertained through integrated accelerometry and global positioning system data and fro
35 location of daily steps were quantified with accelerometry and Global Positioning System devices.
36                               Using triaxial accelerometry and GPS to track the sleep patterns of a g
37                             We used combined accelerometry and heart rate with branched equation mode
38 40 children aged 3.0 to 4.9 years with valid accelerometry and parent-/caregiver-reported screen time
39 omatic segmentation algorithm for swallowing accelerometry and sounds that works directly on the raw
40 and postural hand tremor were recorded using accelerometry and surface electromyography (EMG) from 10
41 were simultaneously measured with the use of accelerometry and the doubly labeled water method, respe
42 wn because methods for measuring PA, such as accelerometry and the doubly labeled water technique, we
43                    Linking these patterns to accelerometry and the time-depth profiles of 334 free-ra
44                  Using high-resolution depth-accelerometry and video data for little penguins (Eudypt
45 al activity (self-reported and measured with accelerometry) and diet were examined as potential corre
46 al activity (monitored continuously by using accelerometry) and resting energy expenditure (REE).
47  food intake, activity patterns (measured by accelerometry), and body composition (measured by dual-e
48 ometry, blood pressure, physical activity by accelerometry, and body composition by deuterium were me
49                     PA was measured by using accelerometry, and body composition was measured by usin
50                           PA was measured by accelerometry, and indicators of body fatness were the s
51 ysical activity was measured using 6 days of accelerometry, and percentage of body fat was calculated
52 orroborated by plasma alpha-carotene levels, accelerometry, and physical performance assessments, res
53 l X-ray absorptiometry, physical activity by accelerometry, and pubertal timing by age at peak high v
54 f diet by 24-h recalls, physical activity by accelerometry, and risk factors for metabolic diseases b
55 moderate-to-vigorous physical activity using accelerometry, and screen time by average daily hours of
56 rrelations with true activity using DLW PAL, accelerometry, and the PAQ or ACT24 as alternative compa
57 alcium intake, physical activity measured by accelerometry, and time spent viewing television and pla
58           This work illustrates the value of accelerometry as a potential biomarker for subtypes of d
59 urements had lower correlations with DLW and accelerometry as comparison methods.
60   These correlations were similar when using accelerometry as the comparison method.
61 m disorders from any time before the date of accelerometry, as well as polygenic risk scores for majo
62            The primary outcome was change in accelerometry assessed average daily step-counts between
63 between environmental factors and walking or accelerometry-assessed steps taken in trips.
64  measure was daily sitting time, assessed by accelerometry, at 12 month follow-up.
65 reports did not replicate those observed for accelerometry-based measures; in some cases, these assoc
66 cted to characterize balance, the lower limb accelerometry-based metrics proved to be most informativ
67              This relationship suggests that accelerometry-based technologies such as mobile phones c
68               Sedentary time was measured by accelerometry between 2009 and 2013.
69                        Our results show that accelerometry can be used to reliably estimate energy ex
70 nce (ESS) and actual daytime sleep activity (accelerometry) correlated strongly with fatigue severity
71                       MAIN OUTCOME MEASURES: Accelerometry counts of activity per day.
72                                              Accelerometry counts were highest in the affluent younge
73        Machine learning models trained using accelerometry data achieved better test performance in d
74 rt leisure-time exercise) who had wrist-worn accelerometry data available.
75  across Europe and Australia with thigh-worn accelerometry data collected between 2011 and 2021.
76                              Cross-sectional accelerometry data from 1,111 adults with radiographic k
77                       This is an analysis of accelerometry data from 3 sensors incorporated into righ
78 m was to apply functional linear modeling to accelerometry data from osteoarthritic dogs participatin
79 However, analytic techniques for time-series accelerometry data have advanced with the development of
80  the recording of local field potentials and accelerometry data in real time, a flexible 32-electrode
81 4.1 (IQR: 22.8-25.1) hours of high-frequency accelerometry data per patient from a prospective cohort
82 hysical activity was assessed by reanalyzing accelerometry data using a harmonized data-processing pr
83   Fasting blood samples, dietary intake, and accelerometry data were collected.
84        Correlations between self-reports and accelerometry data were low to modest for momentary and
85                                              Accelerometry data were processed and analyzed by resear
86  = 0.01), but this was not replicated in the accelerometry data.
87 ients (115 in each trial arm) provided valid accelerometry data.
88 -18 years) from the International Children's Accelerometry Database.
89             We then present the longest orca accelerometry dataset from the ingested MiniPAT tag, wit
90  replicated across ancestries and sexes, and accelerometry-derived measures were concordant with self
91                                    Daily PA [accelerometry-derived moderate and vigorous physical act
92                                      Cranial accelerometry detected micromovements of the head follow
93                    Tremor was assessed using accelerometry, digital spiral analysis, and a standard c
94 sensing applications, besides optomechanical accelerometry discussed in this paper.
95 , a threshold-free approach using the entire accelerometry distribution, showed an association betwee
96 nguished between trait and state profiles of accelerometry domains in major depressive disorder (MDD)
97 ision grip task; tremor was quantified using accelerometry during index finger extension.
98           Patients wore wristbands recording accelerometry, electrodermal activity, blood volume puls
99                                        Using accelerometry, electrophysiology, and metabolomics, we s
100                              JIVE reduced 28 accelerometry features to 3 joint and 6 individual compo
101                        Features derived from accelerometry for 14 days; current and remitted MDD.
102 ges in sleep were objectively assessed using accelerometry for 2 weeks before the intervention and du
103 h, including physical activity (PA; based on accelerometry for 2 weeks), muscular strength (measured
104 ut known HF, who completed hip-worn triaxial accelerometry for 7 consecutive days.
105 current MDD, and 968 with remitted MDD) with accelerometry for at least 7 days.
106                       To evaluate the use of accelerometry for measuring physical activity (PA) in pe
107 r, against physical activity, measured using accelerometry, for a large (n = 332) sample of adults li
108                     Two 7-day assessments of accelerometry from 2005 to 2007 were collected 6 months
109 t Study participated in MVPA assessments via accelerometry from September 16, 1998, to December 9, 20
110  dietary records), and PA (measured with 7-d accelerometry) from 87 Latina and African American girls
111 s multimodal physiological signals including accelerometry, gyroscope, photoplethysmography (PPG), an
112                                              Accelerometry has been increasingly used as an objective
113                                              Accelerometry has proven a powerful tool to estimate ene
114  quantify sleep in osteoarthritic dogs using accelerometry have not demonstrated a beneficial effect
115 and brain MRI, serial vascular tonometry and accelerometry) have been performed repeatedly.
116                  Physical activity measures (accelerometry, health-related quality of life, and frail
117 cal activity was additionally assessed using accelerometry in 50 of the participants.
118 ank, we investigated the predictive value of accelerometry in identifying prodromal Parkinson's disea
119 d confirm the great potential for the use of accelerometry in studies of animal energetics.
120                       We collected tri-axial accelerometry in young patients with IBD in clinical rem
121                                              Accelerometry is a potentially important, low-cost scree
122 ported, sedentary time was assessed by using accelerometry (<100 counts/min), and abdominal adiposity
123            The primary exposure was baseline accelerometry-measured moderate to vigorous PA, insuffic
124 articipants were patients in the substudy of accelerometry-measured PA from 8 locations in the United
125                         We aimed to describe accelerometry-measured physical activity levels in paedi
126                             Penetrometry and accelerometry measurements during the probe impact event
127                                              Accelerometry measures of adolescent moderate to vigorou
128 - 8 years, 56.4% women) undergoing 1 week of accelerometry, median sedentary time was 9.4 h/d (Q1-Q3:
129 essed objectively by combined heart rate and accelerometry monitor (Actiheart).
130 rticipating in the Osteoarthritis Initiative accelerometry monitoring ancillary study were assessed f
131 ere particularly inactive based on objective accelerometry monitoring.
132  and physical activity (PA) were measured by accelerometry over 6 consecutive days; total energy expe
133 jective sleep assessment was performed using accelerometry over 7 days.
134  activity was assessed using tri-axial wrist accelerometry over seven days and quantified using the a
135                                              Accelerometry provides a scalable way to objectively mea
136 f-weighing, objective physical activity (via accelerometry), psychological variables, and cost-effect
137  points 24 hr apart, averaged over 7 days of accelerometry (range 0-100, with 100 being perfectly reg
138 nsisted of age and season at the time of the accelerometry recording, sex, Townsend deprivation index
139 e and sleep duration-were derived from 7-day accelerometry recordings across 89,205 participants (age
140 rly light physical activity as assessed with accelerometry seems to play an important role.
141                  Cortical coherence with the accelerometry signal was also calculated in the absence
142 tural (P2) upper limb tremor was detected by accelerometry/spectral analysis above which tremor was a
143 s are supported by exercise intervention and accelerometry studies reporting positive correlations be
144        The proposed method is applied to the accelerometry study of mild Alzheimer's disease (AD).
145 ted nonexercising adults from the UK Biobank accelerometry subsample.
146 nexercisers from the 2013 to 2015 UK Biobank accelerometry substudy (56.2% women) with a mean+/-SD ag
147 omentary reports were closer in magnitude to accelerometry than were 1-week recall reports.
148 % CI: 0.39, 0.58) for MVPA, and for averaged accelerometry, these estimated correlations were 0.72 (9
149 ration, and radio tracking) collar that used accelerometry to continuously monitor energetics, moveme
150  of comparison methods, we found the PAQ and accelerometry to have moderate validity for assessing ph
151  with 851 participants having complete 7-day accelerometry to measure MVPA at both time points.
152  the wide usage of smartphones with built-in accelerometry to measure physical activity at the global
153                                   Thigh-worn accelerometry was feasibly deployed and should be consid
154                                       Tremor accelerometry was shown to be coherent with the cortical
155 ctivity and MVPA measured by PAQ, ACT24, and accelerometry were all significantly correlated with bod
156 ge, 62 [7.8] years; 56% women) who underwent accelerometry were included.
157 ment and daily physical activity measured by accelerometry) were measured at 12, 24, and 36 weeks.
158 t-form health survey] and daily step counts [accelerometry]) were compared to norm-based or healthy c
159 acterized by acceleration of hand movements (accelerometry), wrist flexor and extensor muscle activat

 
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