Exercise and Sport Sciences Reviews,
Journal Year:
2019,
Volume and Issue:
47(4), P. 206 - 214
Published: July 8, 2019
Body-worn
devices
that
estimate
physical
behavior
have
tremendous
potential
to
address
key
research
gaps.
However,
there
is
no
consensus
on
how
and
processing
methods
should
be
developed
evaluated,
resulting
in
large
differences
summary
estimates
confusion
for
end
users.
We
propose
a
phase-based
framework
developing
evaluating
emphasizes
robust
validation
studies
naturalistic
conditions.
Journal for the Measurement of Physical Behaviour,
Journal Year:
2019,
Volume and Issue:
2(3), P. 188 - 196
Published: Sept. 1, 2019
Recent
technological
advances
have
transformed
the
research
on
physical
activity
initially
based
questionnaire
data
to
most
recent
objective
from
accelerometers.
The
shift
availability
of
raw
accelerations
has
increased
measurement
accuracy,
transparency,
and
potential
for
harmonization.
However,
it
also
shifted
need
considerable
processing
expertise
researcher.
Many
users
do
not
this
expertise.
R
package
GGIR
been
made
available
all
as
a
tool
convermulti-day
high
resolution
accelerometer
wearable
movement
sensors
into
meaningful
evidence-based
outcomes
insightful
reports
study
human
daily
sleep.
This
paper
aims
provide
one-stop
overview
package,
papers
underpinning
theory
GGIR,
how
contributes
continued
growth
package.
includes
range
literature-supported
methods
clean
day-by-day,
well
full
recording,
weekly,
weekend,
weekday
estimates
sleep
parameters.
In
addition,
comes
with
shell
function
that
enables
user
process
set
input
files
produce
csv
summary
single
call,
ideal
less
proficient
in
R.
used
over
90
peer-reviewed
scientific
publications
date.
evolution
time
widespread
use
across
areas
highlights
importance
open
source
software
development
community
advancing
behavior
research.
BMJ,
Journal Year:
2020,
Volume and Issue:
unknown, P. l6927 - l6927
Published: March 20, 2020
Machine
learning,
artificial
intelligence,
and
other
modern
statistical
methods
are
providing
new
opportunities
to
operationalise
previously
untapped
rapidly
growing
sources
of
data
for
patient
benefit.
Despite
much
promising
research
currently
being
undertaken,
particularly
in
imaging,
the
literature
as
a
whole
lacks
transparency,
clear
reporting
facilitate
replicability,
exploration
potential
ethical
concerns,
demonstrations
effectiveness.
Among
many
reasons
why
these
problems
exist,
one
most
important
(for
which
we
provide
preliminary
solution
here)
is
current
lack
best
practice
guidance
specific
machine
learning
intelligence.
However,
believe
that
interdisciplinary
groups
pursuing
impact
projects
involving
intelligence
health
would
benefit
from
explicitly
addressing
series
questions
concerning
reproducibility,
ethics,
effectiveness
(TREE).
The
20
critical
proposed
here
framework
inform
design,
conduct,
reporting;
editors
peer
reviewers
evaluate
contributions
literature;
patients,
clinicians
policy
makers
critically
appraise
where
findings
may
deliver
World Psychiatry,
Journal Year:
2021,
Volume and Issue:
20(2), P. 154 - 170
Published: May 18, 2021
For
many
years,
psychiatrists
have
tried
to
understand
factors
involved
in
response
medications
or
psychotherapies,
order
personalize
their
treatment
choices.
There
is
now
a
broad
and
growing
interest
the
idea
that
we
can
develop
models
decisions
using
new
statistical
approaches
from
field
of
machine
learning
applying
them
larger
volumes
data.
In
this
pursuit,
there
has
been
paradigm
shift
away
experimental
studies
confirm
refute
specific
hypotheses
towards
focus
on
overall
explanatory
power
predictive
model
when
tested
new,
unseen
datasets.
paper,
review
key
predict
outcomes
psychiatry,
ranging
psychotherapies
digital
interventions
neurobiological
treatments.
Next,
some
sources
data
are
being
used
for
development
based
learning,
such
as
electronic
health
records,
smartphone
social
media
data,
potential
utility
genetics,
electrophysiology,
neuroimaging
cognitive
testing.
Finally,
discuss
how
far
come
implementing
prediction
tools
real-world
clinical
practice.
Relatively
few
retrospective
to-date
include
appropriate
external
validation
procedures,
even
fewer
prospective
testing
feasibility
effectiveness
models.
Applications
psychiatry
face
same
ethical
challenges
posed
by
these
techniques
other
areas
medicine
computer
science,
which
here.
short,
nascent
but
important
approach
improve
mental
care,
several
suggest
it
may
be
working
already.
Nature Communications,
Journal Year:
2018,
Volume and Issue:
9(1)
Published: Dec. 4, 2018
Physical
activity
and
sleep
duration
are
established
risk
factors
for
many
diseases,
but
their
aetiology
is
poorly
understood,
partly
due
to
relying
on
self-reported
evidence.
Here
we
report
a
genome-wide
association
study
(GWAS)
of
device-measured
physical
in
91,105
UK
Biobank
participants,
finding
14
significant
loci
(7
novel).
These
account
0.06%
0.39%
variation.
Genome-wide
estimates
~
15%
phenotypic
variation
indicate
high
polygenicity.
Heritability
higher
women
than
men
overall
(23
vs.
20%,
p
=
1.5
×
10-4)
sedentary
behaviours
(18
15%,
9.7
10-4).
partitioning,
enrichment
pathway
analyses
the
central
nervous
system
plays
role
behaviours.
Two-sample
Mendelian
randomisation
suggests
that
increased
might
causally
lower
diastolic
blood
pressure
(beta
mmHg/SD:
-0.91,
SE
0.18,
8.2
10-7),
odds
hypertension
(Odds
ratio/SD:
0.84,
0.03,
4.9
10-8).
Our
results
advocate
value
reducing
pressure.
npj Digital Medicine,
Journal Year:
2020,
Volume and Issue:
3(1)
Published: March 23, 2020
In
recent
years,
there
has
been
a
significant
expansion
in
the
development
and
use
of
multi-modal
sensors
technologies
to
monitor
physical
activity,
sleep
circadian
rhythms.
These
developments
make
accurate
monitoring
at
scale
possibility
for
first
time.
Vast
amounts
multi-sensor
data
are
being
generated
with
potential
applications
ranging
from
large-scale
epidemiological
research
linking
patterns
disease,
wellness
applications,
including
coaching
individuals
chronic
conditions.
However,
order
realise
full
these
individuals,
medicine
research,
several
challenges
must
be
overcome.
There
important
outstanding
questions
regarding
performance
evaluation,
as
well
storage,
curation,
processing,
integration,
modelling
interpretation.
Here,
we
leverage
expertise
across
neuroscience,
clinical
medicine,
bioengineering,
electrical
engineering,
epidemiology,
computer
science,
mHealth
human-computer
interaction
discuss
digitisation
inter-disciplinary
perspective.
We
introduce
state-of-the-art
sleep-monitoring
technologies,
opportunities
acquisition
eventual
application
insights
consumer
settings.
Further,
explore
strengths
limitations
current
emerging
sensing
methods
particular
focus
on
novel
data-driven
such
Artificial
Intelligence.
Nature Medicine,
Journal Year:
2022,
Volume and Issue:
28(12), P. 2521 - 2529
Published: Dec. 1, 2022
Abstract
Wearable
devices
can
capture
unexplored
movement
patterns
such
as
brief
bursts
of
vigorous
intermittent
lifestyle
physical
activity
(VILPA)
that
is
embedded
into
everyday
life,
rather
than
being
done
leisure
time
exercise.
Here,
we
examined
the
association
VILPA
with
all-cause,
cardiovascular
disease
(CVD)
and
cancer
mortality
in
25,241
nonexercisers
(mean
age
61.8
years,
14,178
women/11,063
men)
UK
Biobank.
Over
an
average
follow-up
6.9
during
which
852
deaths
occurred,
was
inversely
associated
all
three
these
outcomes
a
near-linear
fashion.
Compared
participants
who
engaged
no
VILPA,
at
sample
median
frequency
3
length-standardized
bouts
per
day
(lasting
1
or
2
min
each)
showed
38%–40%
reduction
all-cause
risk
48%–49%
CVD
risk.
Moreover,
duration
4.4
26%–30%
32%–34%
We
obtained
similar
results
when
repeating
above
analyses
for
(VPA)
62,344
Biobank
exercised
(1,552
deaths,
35,290
women/27,054
men).
These
indicate
small
amounts
nonexercise
are
substantially
lower
mortality.
appears
to
elicit
effects
VPA
exercisers,
suggesting
may
be
suitable
target,
especially
people
not
able
willing
British Journal of Sports Medicine,
Journal Year:
2021,
Volume and Issue:
56(18), P. 1008 - 1017
Published: Sept. 6, 2021
To
improve
classification
of
movement
behaviours
in
free-living
accelerometer
data
using
machine-learning
methods,
and
to
investigate
the
association
between
machine-learned
risk
incident
cardiovascular
disease
(CVD)
adults.
British Journal of Cancer,
Journal Year:
2022,
Volume and Issue:
128(4), P. 519 - 527
Published: Nov. 19, 2022
UK
Biobank
is
a
large-scale
prospective
study
with
deep
phenotyping
and
genomic
data.
Its
open-access
policy
allows
researchers
worldwide,
from
academia
or
industry,
to
perform
health
research
in
the
public
interest.
Between
2006
2010,
recruited
502,000
adults
aged
40-69
years
general
population
of
United
Kingdom.
At
enrolment,
participants
provided
information
on
wide
range
factors,
physical
measurements
were
taken,
biological
samples
(blood,
urine
saliva)
collected
for
long-term
storage.
Participants
have
now
been
followed
up
over
decade
more
than
52,000
incident
cancer
cases
recorded.
The
continues
be
enhanced
repeat
assessments,
web-based
questionnaires,
multi-modal
imaging,
conversion
stored
other
'-omic'
has
already
demonstrated
its
value
enabling
into
determinants
cancer,
future
planned
enhancements
will
make
resource
even
valuable
researchers.
Over
26,000
worldwide
are
currently
using
data,
performing
research.
uniquely
placed
transform
our
understanding
causes
development
progression,
drive
improvements
treatment
prevention
coming
decades.
