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A B S T R A C T S
S99
nosed hematologic malignancies and its association to
functional activity.
Methods:
The study included 108 patients aged
65 during
the period 2013-2015 year with newly diagnosed hematologic
malignancies.Patientswere classified aswithoutmalnutrition,
malnourished or at risk for malnutrition according to MNA
score (0-7, 8-11, 12-14, respectively). Functional activity was
assessed using scores for activities of daily living (ADL), and
instrumental activities of daily living (IADL).
Results:
There were 50 (46%) patients diagnosed with
chronic lymphoproliferative disease, 14 (12%) with chronic
myeloproliferative disease, 16 (14.8%) with multiple myeloma,
16 (14.8%) with myelodysplastic syndrome and 8 (7.4%) with
acute leucaemia. Normal nutritional status was present in 61
(56.5%), at the risk of malnutrition were 33 (30.6%) patients
and malnourished were 14 (13%) patients. MNE correlated to
ECOG (ro 4.44, p<0.001),ADL (ro 4.38, p<0,001) and IADL (ro 6.05,
p<0.001). IADL shown predictive for malnutrition (p=0.003).
Conclusion:
Nutritional status is poorer in patients with
worse general condition. Functional dependence estimated
with IADL is predictive for nutrition status using short-form
MNA in older patients with newly diagnosed hematologic
malignancy.
Disclosure of interest:
None declared
Keywords:
Elderly, hematology malignacies, malnutrition
P107
COMPARING TRADITIONAL PERFORMANCE STATUS
ASSESSMENT WITH MOBILE HEALTH ACTIVITY DATA AS A
MEASURE OF FUNCTION
J. Shen
1,
*, K. Vander Wall
1
, A. Petruse
1
, J. Trent
1
, R. Ramezani
1
,
M. Sarrafzadeh
1
, A. Naeim
1
1
University of California, Los Angeles, Los Angeles, USA
Introduction:
Traditional performance status (PS) assess-
ments such as those delineated by the Eastern Cooperative
Oncology Group (ECOG) are commonly used in oncology
practices to decide on candidacy for treatment as well
as to monitor tolerance to therapy. Previous research has
demonstrated that ECOG PS can be augmented by additional
assessment of performance in older individuals. Mobile
health technology is rapidly evolving and the use of wearable
sensors is an innovative approach to assessing functional and
physical performance.
Objectives:
This initial cohort study aims to compare
traditional ECOG PS assessments by oncology physicians
to patient activity data collected remotely using a wearable
mobile health device.
Methods:
Patients seen in our comprehensive oncology
center are offered to participate in a one week study to collect
remote activity data. Target enrollment is 100 individuals,
who are age 60 or older with an oncologic diagnosis.
Physician-assessed ECOG PS was extracted from pre-
treatment consultation. Patients were asked to wear a mobile
health device for a continuous 7-day period. Commercial
smartwatches were used with a specially designed
application using data analytics modeled specifically for
older individuals. The smartwatches monitored cumulative
steps taken in addition to positional data. Step count data
was used to stratify patient activity into minimally active
(less than 10000 steps), very active (greater than 35000 steps),
and intermediate groups. Positional data was grouped into
sitting/laying or standing/walking as a percentage of time
that the individual wore the smartwatch. Patients were also
asked to complete a post-watch survey to determine level of
adherence, barriers to use, and overall impression of remote
health monitoring.
Results:
Preliminary interim analysis was performed on 31
patients with an oncologic diagnosis who provided informed
consent for this study. Mean age of participants was 71.9
(range 60-91). Mean steps taken by subjects over the 7-day
study period was 35804 (range 2401-111040). Total step count
was found to be closely representative of physician-assessed
ECOG PS by chi-square analysis (p = 0.016). Positional data
showed a trend towards significance when correlated to
physician-assessed ECOG PS by chi-square analysis (p = 0.068).
Post-watch survey responses revealed that most participants
felt the smartwatch was easy to use. 83% of respondents
indicated they would be willing to wear a mobile health
monitoring device again. The most commonly cited barrier to
adherence was limitations on device battery life.
Conclusion:
Activity data from wearable sensors correlated
with ECOG PS, but also showed variance amongst each ECOG
PS level. This suggests the potential that remote activity
monitoring can be used to better augment and enhance
geriatric assessment screening models. The limitation of this
approach is the variability between individuals and the extent
each individual wears their smartwatch. Patient perspectives
reflected the ease of using a smartwatch and overall positive
experience. Future studies will aim to further correlate mobile
health datawith alternate pre-treatment baseline assessments,
interactive smartwatch questionnaires, and patient outcomes.
Disclosure of interest:
J. Shen: None declared, K. Vander
Wall: None declared, A. Petruse: None declared, J. Trent:
None declared, R. Ramezani: None declared, M. Sarrafzadeh
Shareholder of:WANDA Inc, A. Naeim Shareholder of: INVISTA
Health Corp
Keywords:
Assessment, geriatric, mobile, oncology,
technology
P108
QUALITY OF LIFE AND EARLY MORTALITY IN OLDER
CANCER PATIENTS
J. T. D. O. L. Sales
1,
*, M. J. G. Mello
1
, L. C. S. Thuler
1
,
A. Bergmann
1
, M. Rebelllo
1
, C. R. Ximenes
1
1
Oncology, IMIP, recife, Brazil
Introduction:
Aging is a major risk factor for cancer. Living
longer is a valuable achievement, but the magnitude of this
achievement depends largely on how we live this period.
Health-related Quality of life is an important aspect to be
studied in the elderly population with cancer, not as only the