This document summarizes methodological issues in objectively measuring physical activity using actigraphy monitors. It discusses differences in devices, data collection procedures, and analytical methods that make comparing results across studies difficult. The document presents results from a community-based study that compared two actigraphy devices, found differences in activity levels based on wear location, day of week, age, and time of day. It concludes standardization is needed and recommends future studies use raw tri-axial data from multiple devices, include important covariates, and allow calibration across monitors.

1. Methodologic issues in objective assessment of activity in community samples
Brian Smith1, Diane Lameira1, Lihong Cui1, Haochang Shou2, Vadim Zipunnikov3, Ciprian Crainiceanu3, Kathleen Merikangas1
1Genetic Epidemiology Research Branch, Intramural Research Program, NIMH, Bethesda, MD 20892
2Biostatistics and Epidemiology Department, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
3Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205
Address: 35 Convent Drive, Building 35A, Room 2E480 (MSC 3720), Bethesda, MD 20814 Phone: 301-402-6395 Email: brian.smith4@nih.gov
Background
Objectives
• Physical activity has been linked to general health,
and to mental health in particular.
• Mobile technologies, including a wide range of
actigraphy monitors (Table 1) can assist in objectively
measuring physical activity.
• Differences in procedural and analytical
methodologies in studies measuring physical activity
(Table 2) make it difficult to review the aggregate
evidence.
• To summarize specifications of available devices for
recording of actigraphy and health.
• To review procedural and analytical methods in
existing studies of mood disorders.
• To illustrate data collection methods in a large
community based family study of comorbidity of
mood disorders and medical disorders.
Sample
0
100
200
300
400
500
600
700
800
900
Activity
Time
Actiwatch vs. GENEActiv
Average of Actiwatch Activity Counts Average of GENEActiv Sum of Vector Magnitudes
0
20
40
60
80
100
120
AverageSumofVectorMagnitudes(SVMgs)
Date
Dominant vs. Non-Dominant
Average of Non-Dominant Wrist Average of Dominant Wrist
0
50
100
150
200
250
Activity
Date
Actiwatch vs. GENEActiv
Average of Actiwatch Activity Counts Average of GENEActiv Sum of Vector Magnitudes
• Community-based family study in the greater
Washington, DC metropolitan area.
• 339 participants
- Sex: 137 male, 202 female
- Age: mean= 41.9, range=10-84
- BMI: mean= 27.2, SD=7.1
Methods
• The Actiwatch® Score Minimitter was worn on the
non-dominant wrist for two weeks to acquire data
from at least one weekend.
• To examine measurement accuracy and continuity of
different devices, we compared data obtained from
the Actiwatch simultaneously with another actimetry
monitor, GENEActiv® (ActivInsights) in a small subset
of study participants.
Results
• There were discrepancies in measured activity
between the Actiwatch and GENEActiv devices (Figure
2A-B).
• Differences in activity levels exist when comparing
dominant vs. non-dominant wear (Figure 2C).
• Activity results vary for weekend vs. weekday (Figure
2D), age (Figure 2E), and time of day (Figure 2D-E).
Conclusions
• Standardization of procedural and statistical
methodology is critical for the emerging field of
actimetry research.
• Data collection spanning one weekend is necessary to
acquire representative information on activity
patterns.
• Wrist-worn activity monitors should be placed on non-
dominant wrist for more conservative estimate of
physical activity.
Name Manufacturer Size Weight Memory
Battery (Battery
Life)
Body
Placement
Maximum
Recording
Time
Cost per
Unit
Waterproof
Data Type
(Raw or
Processed)
GENEActiv
Original
ActivInsights
(UK)
43mm x
40mm x
13mm
16g 0.5 GB
Rechargeable
lithium polymer (45
days)
Wrist, Waist,
Ankle
45 days $247 Yes
Tri-axial
(Raw)
Actiwatch Score
Minimitter
Phillips
Respironics
(Bend, OR)
37mm x
35mm x
12mm
25g 32 KB CR 2025 (90 days)
Wrist, Waist,
Ankle
22.4 days $1,645 No
Uni-axial
(Raw)
wGT3X-BT
ActiGraph
(Pensacola, FL)
46mm x
33mm x
15mm
19g 2 GB
Rechargeable
lithium ion (25
days)
Wrist, Waist,
Ankle, Thigh
120 days $225 Resistant
Tri-axial
(Raw)
Motionlogger
Watch
Ambulatory
Monitoring
(Ardsley, NY)
55mm x
45mm x
18mm
65g 2 MB DL2450 (30 days) Wrist N/A $1,295 Resistant N/A
Fitbit Zip
Fitbit, Inc. (San
Francisco, CA)
35.5mm x
28mm x
9.65mm
8g 256 KB
CR 2025 (4-6
months)
Waist, Chest 7 days $60 No
Tri-axial
(Processed)
Nike Fuelband
Nike, Inc.
(Beaverton, OR)
Varies 27-32g 256 KB
Rechargeable
lithium ion (4 days)
Wrist 4 days $79 Resistant
Tri-axial
(Processed)
Study Sample Population Device Used Data Type Body location Length of wear
Ankers and Jones (2009) Bipolar Actiwatch Uni-axial non-dominant wrist 7 days
Gonzalez et al. (2014) Bipolar Motionlogger Tri-axial non-dominant wrist 7 days
Indic et al. (2011) Bipolar Actiwatch Score Minimitter Uni-axial non-dominant wrist 3-7 days
Harvey et al. (2005) Bipolar Motionlogger Uni-axial non-dominant wrist 8 days
Jones et al. (2005) Bipolar Actiwatch Uni-axial non-dominant wrist 7 days
Janney et al. (2013) Bipolar Actigraph Uni-axial waist 7 days
Krane-Gartiser et al. (2014) Bipolar Actiwatch Spectrum Uni-axial wrist of choice 1 day
St-Amand et al. (2013) Bipolar Actiwatch Score Minimitter Uni-axial non-dominant wrist 14 days
McKenna et al. (2014) Bipolar Actiwatch Spectrum Uni-axial left wrist 7 days
Faurholt-Jepsen (2012) Bipolar and Depression Actiheart Uni-axial chest 3 days
Todder et al. (2009) Depression Actiwatch Uni-axial non-dominant wrist 11 days
Berle et al. (2010) Schizophrenia Actiwatch Uni-axial right wrist 14 days
Winkler et al. (2005)
Seasonal Affective
Disorder
Actiwatch Uni-axial non-dominant wrist 28 days
Naslund et al. (2015)
Schizophrenia, Bipolar,
Depression
Fitbit, Nike Tri-axial wrist of choice 80-133 days
Hauge et al. (2011)
Schizophrenia and
Depression
Actiwatch Uni-axial right wrist 14 days
Table 2: Actigraphy studies on sample populations with mood disorders in the past decade.
Future Directions
• Future studies should utilize raw tri-axial data from
easy-to-use activity monitors.
• Covariates such as age, time of day, and weekend vs.
weekday that influence both activity and health
outcomes should be included in analyses of activity.
• It may be possible to calibrate measures across devices
that provide raw triaxial data.
Figure 2: (A-B) Large differences in measured activity data from one patient wearing both Actiwatch and GENEActiv devices
shows that direct comparison of data obtained from two different brands cannot be done unless a standard measurement is in
place, such as raw tri-axial vector magnitudes; (C) Wearing the GENEActiv device on the dominant wrist results in a higher
magnitude of activity.
A
Figure 1: Actiwatch (left) and GENEActiv (right) devices.
B
C
Weekday vs. Weekend Activity
Age and Time of Day Activity
Time of Day
Time of Day
A
B
Figure 3: Data from 339
participants wearing
Actiwatch monitors
highlight differences in
measured activity on
weekdays versus
weekends (A), as well as
differences across ages
and differences in time
of day (B). Investigations
into activity differences
based on other
covariates such as BMI,
season, and medication
use are ongoing.
Table 1: Comparison of select commercially-available* actigraphy monitors. *As of February, 2015
This research is supported by NIH Grant number:
1 ZIA MH002804-12 GEB