Download Self care support devices and technologies

Supporting Self Care – A Practical Option
Diagnostic, Monitoring and Assistive
Tools, Devices, Technologies and
Equipment to Support Self Care
Part III
Part III of the report contains Appendices 1 to 3:
Appendix 1 – Search methodology in detail
96
Appendix 2 – List of companies and other resources
97
Appendix 3 – Information in greater detail on a selection of
devices and technologies
98
Acknowledgement and Copyright
123
Appendix 1
Search methodology
Sources
Sources used for this literature review include:
(i) online medical and technology databases using agreed search terms
(see below) including PubMed, MedLine, ACM and IEEE
(ii) popular press articles and online e-Magazines (both health and
technology based)
(iii) references from existing publications
(iv) approaching companies directly
(v) contacting individuals that are working in the field e.g. academics
(vi) google searches based on search terms and devices other
recommendations e.g. Department of Health.
Search terms
These terms were used individually and in combination to ensure a good
search of each database. Terms that had been used in previous Department of
Health reports were used in addition to new terms relevant to this field.
Alternative communication
Fall monitor
Self care
Alzheimer’s
Health
Ambulatory monitoring
Healthcare
Self care information
systems
Assistive technology (AT)
Healthwear
Asthma
Heart disease
Augmentative
communication
Home care
Children
Compliance
Home monitoring
Lifestyle
Dementia
Medicine reminder
systems
Device
Mobile devices
Diabetes
Mobile health monitoring
Diagnostic devices
Monitoring physiological
activity
Digital and technological
enablers
Disease management
Disease monitoring
Electronic assistive
technology (EAT)
Environment controls
Monitoring physiological
parameters
Monitoring
Patient education
Self care support
Self diagnostic tools
Self help
Self testing
Self-help devices
Self-management
systems
Self-monitoring
Self-treatment devices
Smart clothing
Smart house
Smart objects
Technological enablers
Technology
Predictive tests
Treatment reminder
systems
Remote monitoring
Wearables
96
Appendix 2
List of companies and other resources
Information sources:
Health Services/Technology Assessment Text, National Library of Medicine,
AHRQ Evidence Reports, Numbers 1-60: Telemedicine for the Medicare
Population:
http://www.ncbi.nlm.nih.gov/books/bv.fcgi?rid=hstat1.biblist.33600 (Accessed
July 25 2005)
Home Health Care Technology Report: 6 issues/12 months – subscription
required
http://www.pohly.com/mags/homehealthcare.html (accessed July 25 2005)
Guy Dewsbury (Smart Thinking) has collated an extensive set of ‘smart home’
related links
http://www2.rgu.ac.uk/subj/search/research/sustainablehousing/custodian/lin
ks.html
Example companies:
A list of the companies/providers and case studies presented at the American
Telemedicine Association (ATA) Annual Meeting in Denver, April 2005:
http://www.atmeda.org/conf/2005/exhibitssuccessstories.listing.htm
Another company/provider list from an ATA meeting:
http://www.atmeda.org/Forum2004/forum.technology.center.htm
Attendo Systems: www.attendo.co.uk
Example products: Pendants, flood detectors, PIR, fall detectors, gas detectors,
smoke detectors, care sensor, visual call beacon, pill dispenser, extreme
temperature detectors, bogus caller button, carephone and warden call
devices.
Example projects: Sandwell MBC, Midlands; Columba Project, Brockhurst Care
Home, Pilot, Surrey
Avienda: www.avienda.co.uk
Telecare and assistive technologies
Case study: Blaenau Gwent Assist Project
Home Telehealth Ltd: http://www.hometelehealthltd.co.uk/home.htm
CareCompantion Home Monitoring System; Safe Call Personal Alarm
Monitoring System; Web phone; Video phone; Medicine dispenser
Tunstall: www.tunstallgroup.com
Case study: Columba project
97
Appendix 3
Further information on selected devices and technologies to
support self care
Appendix 3 provides a more detailed discussion on some of the studies
briefly described in Part II of the report. A template is used to structure
the discussion and several points are covered in the following order:
-
Title of the Device
Technology
Stage of Development
Description
Demographics, i.e. who will use the device
Health condition for which device may be used
Self care spectrum, i.e. how or by whom device may be used
Training and ease of use
Research trial description and outcomes
Who’s involved
Cost
Keywords
Contact Details
Reference(s)
The following devices are discussed in detail:
•
Body Media SenseWear Armband
•
CGMS
•
E-San t+diabetes
•
Glucowatch
•
Health Buddy
•
Pedometers
•
Viterion Telehealth Monitor
Note: Any research evidence on health or other outcomes is
given in bold text.
98
Description
Title
BodyMedia Body Monitoring System (the SenseWear Armband)
Technology
The Armband comprises a unit that is attached to the back of the upper
arm using an elastic Velcro strap. The unit contains several sensors
including accelerometer, heat flux, galvanic skin response and both
skin and near-body temperature sensors.
Commercially available.
Stage of
Development
Description
The BodyMedia Body Monitoring System is a continuous monitoring
system for clinicians and researchers. The system comprises the
SenseWear Armband that can collect up to 14 days of continuous body
data in any one session. The data can be transmitted wirelessly to a
computer using the SenseWear Wireless Communicator. BodyMedia
software can be used to process and present this data. It enables
continuous monitoring of multiple parameters. Sensors capture
physiological data such as movement, heat flux, skin temperature,
near-body temperature, and galvanic skin response. Sensors can be
used individually or collectively to determine lifestyle patterns e.g.
energy expenditure, step counting, sleep and awake states, etc. The
unit can also act as a receiver for standard heart rate monitors and
communicate wirelessly with other health care systems and lifestyle
technologies. Manual time stamping allows users to mark events that
the monitor cannot distinguish automatically. Future developments
include the addition of new sensors and data models to extract new
physiological features and contextual information. These include finegrained sleep detail, personal duress, fatigue, alertness, drowsiness,
hydration, perfusion, homeostasis, mental stress, anxiety, calories
consumed, glucose level, biometric identification, heart information,
and core body temperature prediction.
Demographics
All
Health condition Marketed for lifestyle data capture and research analysis, weight
problem, fitness, paediatrics, sleep disorder, assisted living, wellness.
Self care spectrm Self-monitoring, shared care
Training and
ease of use
Research trials
Study 1 (Cole, Lemura et al. 2004). Objective: To compare differences
and outcomes
in energy expenditure using the SenseWear Armband and indirect
calorimetry in cardiac rehabilitation patients. Study Design and
Methods. 24 patients completed steady state exercises for 8 minutes
on 4 modes of exercise (arm ergometry, treadmill walking, recumbent
stepping and rowing ergometry). Readings were recorded
simultaneously by the Armband and by indirect calorimetry.
Results: The correlation between indirect calorimetry and the Armband
for arm and rowing ergometry, the treadmill and recumbent stepper
were r=0.90, r=0.67, r=0.80 and r=0.74, respectively. There were no
between method differences during arm ergometry and the recumbent
stepper. Bland and Altman plots revealed the greatest spread of scores
for the rower and the treadmill. Between method differences were
minimal when using the preliminary cardiac software. The correlations
for arm and rowing ergometry, the treadmill and recumbent stepper
99
were r=0.90, r=0.84, r=0.78 and r=0.82 respectively. Conclusions: The
accuracy of the Armband seems to be exercise dependent in those
with heart disease and data should be interpreted cautiously. Further
work with more cardiac-focussed software is necessary.
Study 2 (King, Torres et al. 2004). Objective: To evaluate five physical
activity monitors available for research: the CSA, the TriTrac-R3D, the
RT3, the SenseWear Armband and the BioTrainer-Pro. Study Design
and Methods. 21 participants performed 10 minutes of treadmill walking
and treadmill running. Simultaneous measurements of body motion and
indirect calorimetry were continuously recorded during all exercise.
Results: There was no significant difference in the mean energy
expenditure recorded bilaterally by any of the monitors at any treadmill
speed. The SenseWear Armband, the TriTrac-R3D and the RT3 had
significant increases in mean energy expenditure across all walking
and running speeds. All monitors overestimated energy expenditure
when compared with indirect calorimetry, except for the CSA that
underestimated at the lowest and highest speeds. Conclusions: The
SenseWear Armband was the best estimate of total energy expenditure
at most speeds.
Study 3 (Fruin and Walberg Rankin 2004). Objective: To examine the
reliability and validity of the SenseWear Armband during rest and
exercise compared with indirect calorimetry. Design and Methods.
Energy expenditure was measured with the Armband and indirect
calorimetry in 13 participants during two resting and one cycle
ergometry sessions. In a second experiment 20 participants walked on
a treadmill for 30 minutes at three intensities whilst energy expenditure
was measured by the Armband and indirect calorimetry. Results: At
rest no significant differences were found between measurements from
the Armband and indirect calorimetry, and the two methods were highly
correlated. For the ergometer protocol no significant differences were
found between methods, although measurements were poorly
correlated. The Armband significantly overestimated energy
expenditure on flat walking and significantly underestimated energy
expenditure on inclined walking. Conclusions: The Armband provided
reliable estimates of energy expenditure at rest and generated similar
mean estimates of energy expenditure as indirect calorimetry on the
ergometer, although individual error was large.
Study 4 (Jakicic, Marcus et al. 2004). Objective: To asses the accuracy
of the SenseWear Armband to estimate energy expenditure during
exercise. Design and Methods. 40 participants performed four
exercises each lasting 20 to 30 minutes. Estimated energy expenditure
from the Armband was compared to energy expenditure as determined
from indirect calorimetry. Results: When a generalised algorithm was
applied to data the Armband significantly underestimated energy
expenditure during walking, cycling and stepping. It overestimated
energy expenditure during arm ergometer exercise. Altered, exercise
specific algorithms resulted in non-significant differences between the
results from the Armband and those of indirect calorimetry.
Conclusions: Exercise specific algorithms enhance the accuracy of
estimating energy expenditure using the Armband.
100
Who’s involved
Self, remote healthcare support
Cost
BodyMedia SenseWear Armband US$399-449, supporting
softwareUS$3,999. Various package deais are provided.
Keywords
BodyMedia SenseWear Armband, wearable, motion sensors,
multiparameters, continuous monitoring, mobile, lifestyle, physical
activity, activity monitoring, weight control.
http://www.bodymedia.com/index.jsp
Cole, P., L. Lemura, et al. (2004). "Measuring energy expenditure in
cardiac patients using the BodyMedia Armband versus indirect
calorimetry." The Journal of Sports Medicine and Physical Fitness
44(3): 262-71.
Contact Details
References
Fruin, M. and J. Walberg Rankin (2004). "Validity of a multi-sensor
armband in estimating rest and exercise energy expenditure." Medicine
and Science in Sports and Exercise 36(6): 1063-69.
Jakicic, J., M. Marcus, et al. (2004). "Evaluation of the SenseWear Pro
Armband to assess energy expenditure during exercise." Medicine and
Science in Sports and Exercise 36(5): 897-904.
King, G., N. Torres, et al. (2004). "Comparison of activity monitors to
estimate energy cost of treadmill exercise." Medicine and Science in
Sports and Exercise 36(7): 1244-51.
Further
information
Company-provided papers, abstract, articles and whitepapers available
from http://www.bodymedia.com/research/whitepapers.jsp. Company
literature/Stanford preliminary study suggests that SenseWear could be
comparable to a Holter monitor for ambulatory ECG montioring
(Al-Ahmad et al, 2004).
http://www.bodymedia.com/pdf/stanford_heartrate.pdf.
SenseWear armband was also included in a study of comfort of
wearable devices (Bodine 2003)
http://www.bodymedia.com/pdf/bodine-iswc03.pdf.
101
Description
Title
Medtronic MiniMed Continuous Glucose Monitoring System
(CGMS)
Technology
A small sensor that is inserted under the skin, a small monitor unit,
supporting software
Commercially available.
Stage of
Development
Description
Demographics
Health condition
Self care
spectrum
Training and
ease of use
Research trials
and outcomes
The CGMS® System is a continuous glucose monitor that works by a
sensor that is placed into the patient’s abdomen (by a clinician), the
sensor sitting just beneath the surface of the skin and secured by tape.
A wire connects this sensor to a small monitor unit that is worn by the
patient on their belt or in their pocket. Fingerstick blood glucose
readings are entered into the monitor for calibration. The sensor then
automatically records blood glucose levels in average every five
minutes throughout the day for up to 72 hours in any one session (up
to 864 readings in 72 hours). (Note: the current version does not
provide any real-time feedback to patients of their levels. The patient
can also mark key events in the day e.g. time of meals, medicines and
exercise. After three days the patient visits their healthcare provider
who can download the information from the monitor for analysis.
CGMS Solutions Software can be used to organise the data into easyto-read charts, graphs and tables.
All
Diabetes
Physician supported self-monitoring
Study 1 (Jeha, Karaviti et al. 2004). This study looked at whether using
the CGMS twice-daily in insulin injection therapy achieved adequate
control with preschool children with Type 1 diabetes and whether the
CGMS was more informative than self-monitoring of blood glucose
(SMBG). Ten children <6 years of age with Type 1 diabetes were
monitored twice daily using the CGMS. The distribution of glucose
values was analysed, particularly the frequency, duration and
distribution of hypoglycemia. The study analysed the accuracy of the
CGMS and the relevance of the difference between CGMS and SMBG
values. Findings showed that the CGMS correlated well with SMBG
without significant discrepancy. The CGMS’s sensitivity to detect
hypoglycemia was 70% with a specificity of 99%; however the CGMS
detected twice as many total episodes as SMBG (82 vs. 40). The
CGMS is well tolerated by patients and reveals daily glucose trends
that are missed by SMBG.
Study 2 (Ludvigsson and Hanas 2003). This study assessed the
effectiveness of the CGMS for self care among people with diabetes.
A controlled crossover study was carried out using 27 people with
diabetes. All patients were treated with intensive insulin therapy,
14 with multiple injections, and 13 with pumps. The patients were
randomised into an open or blind study arm. Both arms wore the
102
CGMS sensor for 3 days every 2 weeks. CGMS profiles were used in
the open study arm to adjust insulin therapy at follow-up visits every
6 weeks. Both the patients and the diabetes team were masked to the
CGMS profiles in the blinded arm. At 3 months the 2 study arms were
crossed over. Despite some initial problems (e.g. the diabetes’ teams
lack of experience with CGMS; some unwillingness by patients to use
the device because it required new pricks, a subcutaneous catheter
and an instrument to be connected to the body; and some design faults
with the device itself), hemoglobin decreased significantly in the open
arm but not in the blind arm. CGMS provided valuable information that
had not been previously available, facilitating better treatment. CGMS
also provided a useful tool for education and patient motivation.
Ludvigsson et al recommended CGMS for patients with elevated
HbA(1)C, patients that have elevated HbA(1)C with known or
suspected hypoglycemia during the night or a tendency to severe
hypoglycemia, patients who lack motivation for self-monitoring or do
not understand how to interpret blood glucose profiles and adjust their
treatment accordingly and patients who want to learn more about the
effects of food or physical exercise on their condition.
Study 3 (Fleishman, Sayoc Nocon et al. 2005). In this case study the
New England Healthcare Institute (NEHI) analysed the potential impact
of continuous glucose monitoring (CGM). This paper reviews the
technology (including the MiniMed CGMS) and assesses the costs,
benefits and value of adoption. The NEHI make a number of
recommendations to improve CGM adoption. The study consisted of
over 100 expert interviews, literature surveys and quantitative
modelling to predict the potential value of this kind of technology. NEHI
also engaged a panel of experts to oversee the research and findings.
Authors identified a number of barriers to CGM adoption.
Recommendations included: (i) a re-evaluation of glucose monitoring
accuracy standards. CGM devices are less accurate on a point-in-time
basis than episodic glucose monitors but they have the ability to
provide more ‘predictive accuracy’; (ii) clarify the appropriate trial
designs and end points earlier in the production process to encourage
manufacturers to complete necessary evaluations; (iii) Improve
reimbursement for diabetes staff. In order for CGM devices to be
effective patients need to be educated on how to interpret the data.
Self care of diabetes, i.e. education, glucose data interpretation and
regular patient physician follow-up is currently poorly reimbursed;
(iv) enable practitioner adoption. Identified a number of incentives to
encourage the adoption of new technology into everyday clinical
practice e.g. ease of use, financial incentives, etc; (v) plan education
programmes. A significant education infrastructure must be developed
for patients and the public to successfully adopt new technology;
(vi) ensure patient/public acceptance. The technology must be
designed with the end user in mind. A more user-centred iterative
design process is encouraged. Overall, findings suggest that CGM has
the potential to be a highly valuable and cost-effective tool in taking
care of diabetes, particularly as a long-term, daily self care device. But
certain barriers need to be overcome to ensure future adoption.
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Other research
Study 4 (Tanenberg, Bode et al. 2004). This study assessed the
effectiveness of the CGMS in self care of diabetes . Over a 9 month
period 128 patients aged between 19 and 76 years old with insulin
treated diabetes were assigned to insulin therapy adjustments based
on either CGMS or SMBG values. At the end of the study patients in
both groups were given the CGMS for 3 days; these values were used
to calculate measures of hypoglycemia. Repeated-measures analysis
of variance with post-hoc comparisons was used to test differences in
hemoglobin A1c levels and hypoglycemia between the two study
groups. Findings showed no significant differences in demographics or
baseline characteristics between the two groups. There were no
significant differences in hemoglobin A1c levels between the CGMS
group and the SMBG group at baseline, and both groups showed
significant and similar improvement in hemoglobin A1c levels after
12 weeks of study. However, the CGMS group had a significantly
shorter duration of hypoglycemia at week 12 of the study. The use of
the CGMS to guide therapy adjustments in patients with insulin treated
diabetes reduces the duration of hypoglycemia compared with therapy
adjustments guided by SMBG values alone.
Bode et al (Bode, Gross et al. 2004) assessed the accuracy of the
CGMS, finding the device to be ‘reasonably accurate’. Jamali et al
(Jamali, Bachrach-Lindstrom et al. 2005) assessed the feasibility of
subcutaneous glucose measurements in the elderly using the CGMS.
They found it to be a convenient way of detecting hyperglycemia in this
age group. Sachedina et al (Sachedina and Pickup 2003) assessed the
accuracy, reliability and measurement of glycaemic control using the
CGMS in comparison with self-monitoring. This study found the CGMS
to have ‘acceptable clinical accuracy’ and promising potential as a tool
for self care of diabetes. Gross et al (Gross, Bode et al. 2000)
assessed the performance of the CGMS in patients during home use.
Results: Results showed the agreement of the CGMS to blood glucose
meter values and again demonstrated promising potential as a tool for
self care of diabetes. However a study by ‘The Diabetes Research in
Children Network (DirecNet) Study Group’ (The Diabetes Research in
Children Network (DirecNet) Study Group 2004) found that both the
GlucoWatch and the MiniMed CGMS might be more useful in reducing
HbA1c levels than in detecting hypoglycemia. In another paper (The
Diabetes Research in Children Network (DirecNet) Study Group 2003)
they note that this inaccuracy in the CGMS may have been rectified by
‘recent modifications to the sensor (that) have resulted in substantially
better accuracy and reliability’. Buhling et al (Buhling, Kurzidum et al.
2004) carried out a study looking at the effectiveness of the CGMS with
non-pregnant women and pregnant women with impaired glucose
tolerance and gestational diabetes. Findings showed that the CGMS
detected more frequent and longer durations of hyperglycemia in
gestational diabetes compared to pregnant women who did not have
diabetes, than SMBG. Kerssen et al (Kerssen, de Valk et al. 2004) also
assessed the accuracy of the CGMS during pregnancy finding it to be
‘an accurate tool for additional glucose monitoring’. Chico et al (Chico,
Vidal Rios et al. 2003) evaluated whether the CGMS was useful for
investigating the incidence of unrecognised hypoglycemias in people
with Type 1 and Type 2 diabetes and for improving metabolic control in
people with Type 1 diabetes. Findings found it to be a useful tool for
104
Who’s involved
Cost
Keywords
Contact Details
References
detecting unrecognised hypoglycemias in people with Type 1 & Type 2
diabetes but not better than standard capillary glucose measurements
for improving metabolic control of Type 1 diabetes. A paper by Tavris
and Shoaibi (Tavris and Shoaibi 2004) provides an overview of a
number of studies. They suggest that findings show that the CGMS
could ‘result in a substantial reduction of morbidity and mortality
associated with diabetes’. However they point to the paucity of
controlled studies over long durations in assessing the ability of the
device to result in improved control of diabetes.
Self, healthcare provider
Unavailable
Medtronic MiniMed Continuous Glucose Monitoring System, CGMS,
diabetes, wearable, self-monitoring, mobile, education, education,
invasive glucose monitoring, sensors, alarm
http://www.minimed.com/index.html
Bode, B., K. Gross, et al. (2004). "Alarms based on real-time sensor
glucose values alert patients to hypo and hyperglycemia: The
Guardian Continuous Monitoring System." Diabetes Technology
and Therapeutics 6(2): 105-13.
Buhling, K., B. Kurzidum, et al. (2004). "Introductory experience with
the continuous glucose monitoring system (CGMS; Medtronic
Minimed) in detecting hyperglycemia by comparing the selfmonitoring of blood glucose (SMBG) in non-pregnant women
and in pregnant women with impaired glucose tolerance and
gestational diabetes." Experimental and clinical endocrinology
and diabetes 112(10): 556-60.
Chico, A., P. Vidal Rios, et al. (2003). "The continuous glucose
monitoring system is useful for detecting unrecognised
hypoglycemias in patients with Type 1 and Type 2 diabetes but
is not better than frequent capillary glucose measurements for
improving metabolic control." Diabetes Care 26(4): 1153-7.
Fleishman, V., R. Sayoc Nocon, et al. (2005). "Continuous glucose
monitoring: Planning for innovation." Diabetes Technology and
Therapeutics 7(3): 563-69.
Gross, T., B. Bode, et al. (2000). "Performance evaluation of the
MiniMed Continuous Glucose Monitoring System during patient
home use." Diabetes Technology and Therapeutics 2(1): 49-56.
The Diabetes Research in Children Network (DirecNet) Study Group
(2003). "The accuracy of the CGMS in children with Type 1
Diabetes: Results of the Diabetes Research in Children
Network (DirecNet) Accuracy Study." Diabetes Technology and
Therapeutics 5(5): 781-789.
The Diabetes Research in Children Network (DirecNet) Study Group
(2004). "Accuracy of the GlucoWatch G2 biographer and the
Continuous Glucose Monitoring System during hypoglycemia."
Diabetes care 27(3): 722-26.
Jamali, R., M. Bachrach-Lindstrom, et al. (2005). "Continuous glucose
monitoring system signals the occurrence of marked postrandial
hyperglycemia in the elderly." Diabetes Technology and
Therapeutics 7(3): 509-15.
Jeha, G. S., L. P. Karaviti, et al. (2004). "Continuous glucose
monitoring and the reality of metabolic control in preschool
children with Type 1 diabetes." Diabetes care 27(12): 2881-6.
105
Kerssen, A., H. de Valk, et al. (2004). "The Continuous Glucose
Monitoring System during pregnancy of women with Type 1
diabetes mellitus: accuracy assessment." Diabetes Technology
and Therapeutics 6(5): 645-51.
Ludvigsson, J. and R. Hanas (2003). "Continuous subcutaneous
glucose monitoring improved metabolic control in pediatric
patients with Type 1 diabetes: a controlled crossover study."
Pediatrics 111(5): 933-38.
Sachedina, N. and J. Pickup (2003). "Performance assessment of the
Medtronic-MiniMed Continuous Glucose Monitoring System and
its use for measurement of glycaemic control in Type 1 diabetic
subjects." Diabetic Medicine 20(12): 1012-15.
Tanenberg, M., B. Bode, et al. (2004). "Use of the Continuous Glucose
Monitoring System to guide therapy in patients with insulintreated diabetes: a randomised controlled trial." Mayo Clinic
Proceedings 79(12): 1521-1526.
Tavris, D. and A. Shoaibi (2004). "The public health impact of the
MiniMed Continuous Glucose Monitoring System (CGMS) An assessment of the literature." Diabetes Technology and
Therapeutics 6(4): 518-22.
106
Description
Title
Technology
Stage of
Development
Description
Demographics
Health condition
Self care
spectrum
Training and
Ease of Use
Research trials
e-San’sThink Positive Diabetes System (t+diabetes) and Think
Positive Asthma System (t+asthma)
Bluetooth enabled mobile phone, used in conjunction with an ‘ordinary’
blood glucose meter (diabetes) or peak flow device (asthma)
e-San’s Think Positive Diabetes System (t+diabetes) is commercially
available, but the Think Positive Asthma System (t+asthma) System is
still in development
The t+diabetes system is a self care system in which blood glucose
readings and entries in an electronic patient diary are transmitted via a
GPRS-enabled mobile phone to the e-San server, where a real-time
analysis of the data by the server and personalised feedback to the
patient using the mobile phone is carried out. Departures from expected
behaviour in the readings are brought to the attention of the patient’s
healthcare professional. The GP or healthcare professional can review
the patient’s data online, where they are also provided with information
on their patient’s compliance and medicine usage. Also includes a
prescription ordering service. Winner of e-Health 2005 Innovation Award.
The t+asthma system is still in development but combines a peak flow
device with the above technology. People will be able to choose to
monitor their symptoms or their peak flows (or both). A personalised care
plan is agreed between the individual and the GP and stored on the
phone. This will remind people which action they should take if their
symptoms worsen or their peak flows decrease significantly with respect
to their personal best values.
All
Diabetes; Asthma; has also been used in early diagnosis of events for
Cystic Fibrosis
Self-monitoring, shared care
A demonstration of how to use the t+diabetes system can be seen at
http://www.thinkdiabetes.com/tpdiabetes/demo.php; and http://www.esan.co.uk/presdemo.html
Study 1 (Farmer, Gibson et al. 2005; Farmer, Gibson et al. 2005).
Objective: To investigate the effectiveness of the automated transfer of
blood glucose readings, real-time analysis and immediate feedback,
using the development version of the t+diabetes system. Study Design
and Methods. A random controlled trial was carried out, the intervention
group was given graphical feedback on the phone screen and diabetes
specialist nurse-initiated support using the web based analysis of the
readings. Patients aged 18-30 were recruited from the Young Adult Clinic
in Oxford; all those with HbA1c levels of 8%-12% were eligible for
inclusion. 93 patients (55 men) with mean diabetes duration of 12.1 years
[SD 6.7] were recruited. Intention-to-treat analysis demonstrated a
significant reduction in HbA1c in the intervention group after 9 months
from 9.2% to 8.5% (difference 0.7%, 95% CI 0.3-1.0, P = 0.001). The
median BG level for the intervention group over the 9-month trial was 8.9
mmol/l (IQR 5.4 to 13.5) versus 10.3 (IQR 6.5 to 14.4) for the control
group (P < 0.001). There was one recorded grade 3 hypoglycaemic
107
Other Research
Who’s involved
Cost
Keywords
Contact Details
References
episode in the control group. Conclusions: The number of participants in
the intervention group who were well-controlled quadrupled during the 9
months of the trial from 10.6% to 46.8%, whereas it was virtually
unchanged in the control group from 19.6% to 23.9%. These outcomes in
the intervention group were achieved by high usage compliance
throughout the trial averaging 19.8 readings a week.
Initial findings from other small-scale trials with t+diabetes (findings to be
published), have evaluated the t+diabetes system for people with Type 2
diabetes. These in-pharmacy trials involved three community pharmacies
and their local surgeries in Oxfordshire. The initial findings from these
trials show that the commonly-asserted view that people in their 60’s and
70’s would not make use of a mobile-phone based product was
unfounded. 90% of the people were found to be using t+diabetes
regularly, sending on average one blood glucose reading a day.
An early version of t+ asthma was evaluated during a 9-month
observational study (Ryan, Cobern et al. 2005). People between 12 and
55 years of age, requiring treatment with regular inhaled steroids and
bronchodilators, were recruited from nine General Practices. People were
included if their asthma was considered stable with no history of
exacerbation in the previous 3 months. No therapeutic intervention was
proposed. The primary outcome measure was usage compliance. 69% of
the 46 participants who filled in the post-study questionnaire were
“satisfied” or “very satisfied” by the study, citing the ease of use and the
increased autonomy and understanding of asthma as the main
advantages. 74% indicated that the system had helped their ability to
take care of their symptoms, with no one indicating a negative impact.
Self, Remote monitoring by health professionals, Remote monitoring by
computer.
With a ‘typical’ airtime contract the phone is available free of charge. The
e-San healthcare bundle provides the individual’s personal self care plan
as outlined above for the cost of £7 a month + the cost of the call.
E-san, compliance, mobile phone, education, diabetes, blood glucose
self-monitoring, point-monitoring, asthma, usage compliance
e-San Ltd http://www.e-san.co.uk/
t+diabetes http://www.thinkdiabetes.com/tpdiabetes/ - UK enquiries 0800
389 3205
Farmer, A. J., O. Gibson, et al. (2005). "Impact of real-time telemedicine
support on glucose self-monitoring and blood glucose control in young
adults with Type 1 diabetes: a randomised controlled trial." Submitted to:
Annual meeting of the European Association for the Study of Diabetes.
Farmer, A. J., O. Gibson, et al. (2005). "A randomised controlled trial of
the effect of real-time telemedicine support on glycemic control in young
adults with Type 1 diabetes." Diabetic Medicine 22(2): 82-3.
Further
information
Ryan, D., W. Cobern, et al. (2005). "Mobile phone technology in the
management of asthma." Journal of Telemedicine and Telecare, in press.
Company reports of research trials available via http://www.e-san.co.uk/
Press release examples also discussing trials:
http://www.e-san.co.uk/resources/030303pr.pdf ;
http://www.e-san.co.uk/resources/Clinical_Trial_Release.pdf
108
Description
Title
Technology
Stage of
Development
Description
Demographics
Health
Condition
Self care
spectrum
Training and
Ease of use
Research trials
and outcomes
Cygnus GlucoWatch Biographer. http://www.glucowatch.com/
Recently Cygnus was sold to Animas Corp
(http://www.animascorp.com)
A wrist worn device that uses a low level electrical current to extract
glucose painlessly into an ‘AutoSensor’. This sensor adheres to the skin
and is attached to the back of the GlucoWatch monitor.
Commercially available.
GlucoWatch G2 approved by US FDA.
The GlucoWatch was piloted by the West Suffolk Diabetes Service in
2003. (see
http://www.diabetesuffolk.com/Managing%20Diabetes/Glucowatch.asp)
The GlucoWatch is a watch-like device that is worn on the forearm,
measuring glucose values and displaying readings to the patient in real
time. It provides frequent (every 20 minutes), automatic and non-invasive
glucose measurements. The monitor also has a built in alarm that can be
programmed to alert the person at pre-defined levels. Data can be
downloaded via a downloading dock and Analyser software can be used to
assess trends and patterns. Data can then be emailed to a care provider.
All
Diabetes
Self-monitoring, shared care
Hathout et al (2005) suggest that it is usable and safe in children older
than 7 in a home setting. However, another study with teenagers found
that, even though it was well tolerated and people reported finding it
helpful, 50% of patients were unable to use the device properly under reallife conditions and reported difficulties with calibration and premature
switching off (Bozetti et al, 2003).
Most studies suggest the device is well tolerated overall. Some people
experience skin irritations (4 out of 35 people in Bozetti et al, 2003; 2 out
of 66 people, plus some mild in Eastman, Chase et al, 2002). Sweating
can cause skipped readings (5 out of 32 people in Bozetti et al, 2003; 20%
of readings in Gandrud et al 2004). Overnight use can cause sleep
disruption (32% of nights in Gandrud et al 2004).
Study 1 (Pitzer, Desai et al. 2001). Objective: This study evaluated the
hypoglycemia alert performance in a large and demographically diverse
patient population in both controlled and home environments. Design and
Methods. The analysis was based on 1,091 Biographer users from four
research trials, which generated 14,487 paired (Biographer and blood
glucose) readings. Results: The results show that as the low-glucose alert
level of the Biographer is increased, the number of true positive alerts and
false positive alerts increased. When analysed as a function of varying
low-glucose alert levels, the results show receiver operator characteristic
curves consistent with a highly useful diagnostic tool. When the same
blood glucose data is analysed for typical monitoring practices, the results
show that fewer hypoglycaemic events are detected than those detected
with the Biographer. Conclusions: The frequent and automatic nature of
the Biographer readings allows more effective detection of hypoglycemia
than that achieved with current health care practice.
109
Study 2 (Gandrud, Paguntalan et al. 2004). Objective: This study
conducted an open-label trial of the Biographer to detect nocturnal
hypoglycemia in a diabetes camp, a non-clinical environment with multiple
activities. Design and Methods. 45 campers aged 7-17 wore a Biographer.
Overnight glucose monitoring occurred per usual camp protocol.
Counsellors were asked to check and record blood glucose values if the
Biographer alarmed. Results: 45 campers wore Biographers for 154
nights. After a 3-hour warm-up period, 67% of Biographers were
calibrated, of which 28% were worn the entire night (12 hours). 34% of
readings were skipped because of data errors (65%), sweat (20%) and
temperature change (16%). Reported Biographer values correlated with
meter glucose values measured 11 to 20 minutes later. Of 20 low-glucose
alarms with corresponding meter values measured within 20 minutes,
there were 10 true-positive alarms, 10 false-positive alarms, and no falsenegative alarms. Campers reported sleep disruption 32% of the nights, and
74% found the Biographer helpful. Campers reported that they would wear
the Biographer 4 to 5 nights each week. Conclusions: Half of the
Biographer low-glucose alarms that had corresponding blood meter values
were true-positive alarms, and the remaining were false-positive alarms.
There was close correlation between the Biographer and meter glucose
values. The majority of campers found that the Biographer was helpful.
Study 3 (Eastman, Chase et al. 2002). Objective: To evaluate the
accuracy and safety of measuring glucose with the Biographer in children
and adolescents with diabetes. Design and Methods. 66 participants each
wore three Biographers at sites including the upper arm, the leg and torso.
Accuracy was assessed by comparing the Biographer’s glucose
measurements with hourly blood glucose measurements using the
HemoCue Photometer for up to 12 hours of monitoring. Safety was
evaluated by examining the biographer application sites upon removal of
the devices and then at regular intervals there after. Results: For the
forearm Biographers, the mean absolute relative difference between
Biographer readings and blood glucose was 21%. 95% of Biographer
readings fell into the A or B regions of the Clark error grid, and 97.3% into
the A or B regions of the consensus error grid. Data from Biographers
worn at the alternative sites were similar to data from the forearm
Biographers. Although two strong reactions to the adhesive pad of the
Biographer were observed, most skin reactions were mild. Conclusions:
The study shows that the Biographer can measure glucose quite
accurately and safely. The device was well tolerated. Frequent and
automatic glucose monitoring could provide patients and providers with
data that might significantly enhance self care of diabetes.
Study 4 (Bozzetti, Viscardi et al. 2003). Objective: To carry out an
independent testing of the Biographer under ‘real-life’ conditions. Design
and Methods. This study tested 74 young people, children and adolescents
randomly selected from an outpatient diabetes clinic. The Biographer was
initially worn by children attending the outpatient clinic and then used at
home. Patients were asked to assess their capillary glucose level at least
four times a day using the Accu-chek Glucose Monitor every time they
used the Biographer. Patients were also asked to complete questionnaires.
Results: Only 37 out of 72 patients (51%) managed to obtain data from the
110
Biographer, while the remaining patients experienced an early shutting off
(13/35), suffered skin irritations (4/35), error in the battery management at
home and consequent inability to obtain glucose data (10/35), excessive
sweating (5/35), or difficulty in problem-solving of the device (3/35). The
total number of paired glucose levels obtained was 343, and these were
corrected for the time delay of the Biographer. The median of the capillary
glucose levels was 134 mg/dL (range 40-461) vs. 112 mg/dL (range 37437) from the Biographer; the Biographer gave a lower value for the
glucose concentration in 73% of cases. The difference between the two
methods expressed as median end range was -20 mg/dL (-299-194). On
average the Biographer underestimated the capillary glucose level value
by 7.6%. The correlation between the Biographer and the Accu-chek
values was r=0.74. Data from the questionnaires showed that the
Biographer was well tolerated by patients. 50% of patients were unable to
use the device properly under real-life conditions and reported difficulties
with calibration and premature switching off. Conclusions: The Biographer
can measure glucose levels accurately and without safety concerns in
children and adolescents under real-life conditions. The device was well
tolerated and most patients reported that they would have used it
continuously. But the device was unable to obtain glucose levels in 50% of
the patients and therefore further studies are required.
Other research
Other studies have favourably assessed the accuracy and precision of the
GlucoWatch in both clinical and home environments. These studies
include (Garg, Potts et al. 1999; Tamada, Garg et al. 1999; Tierney,
Tamada et al. 2000, Hathout, Patel et al. 2005). However a study by ‘The
Diabetes Research in Children Network (DirecNet) Study Group’ found that
both the GlucoWatch and the MiniMed CGMS may be more useful in
reducing HbA1c levels than in detecting hypoglycemia (The Diabetes
Research in Children Network (DirecNet) Study Group. 2004). In a
separate study (The Diabetes Research in Children Network (DirecNet)
Study Group. 2003) the same organisation found that the GlucoWatch was
more accurate during episodes of hyperglycemia than hypoglycemia and
that the accuracy of the sensor did not approach the accuracy of current
home glucose meters. They concluded however that this device might be
sufficient for detecting trends and modifying self care of diabetes.
Weinzimer et al (2004) provides a detailed review of the current state of
research with GlucoWatch.
Who’s involved
Cost / business
models
Keywords
Contact Details
Self, local carer
At 2004, cost of GlucoWatch2 US$698 plus US$96 for a box of 16
sensors.
An economic modelling simulation of 10,000 cohorts (based on the 40
cohorts and treatment results of a RCT with GlucoWatch (Chase, Roberts
et al 2003)) suggests that use of GlucoWatch can be cost-effective but that
definitive analysis will require larger longer trials with diverse populations
(Eastman, Leptien et al 2003).
GlucoWatch Biographer, diabetes, wearable, self-monitoring, mobile, wristworn, watch, education, noninvasive glucose monitoring
http://www.glucowatch.com/
111
References
Bozzetti, V., M. Viscardi, et al. (2003). "Results of continuous glucose
monitoring by GlucoWatch Biographer in a cohort of diabetic
children and adolescents under real-life conditions." Pediatric
Diabetes 4(1): 57-8.
Eastman, R., H. Chase, et al. (2002). "Use of the GlucoWatch Biographer
in children and adolescents with diabetes." Pediatric Diabetes 3(3):
127-34.
Gandrud, L., H. Paguntalan, et al. (2004). "Use of the Cygnus GlucoWatch
Biographer at a diabetes camp." Pediatrics 113: 108-11.
Garg, S., R. Potts, et al. (1999). "Correlation of fingerstick blood glucose
measurements with GlucoWatch Biographer glucose readings in
young participants with Type 1 diabetes." Diabetes care 22:
1708-1714.
The Diabetes Research in Children Network (DirecNet) Study Group
(2004). "Accuracy of the GlucoWatch G2 biographer and the
Continuous Glucose Monitoring System during hypoglycemia."
Diabetes care 27(3): 722-26.
Hathout, E., N. Patel, et al. (2005). "Home use of the GlucoWatch G2
Biographer in children with diabetes." Pediatrics 115(3): 662-6.
Pitzer, K., S. Desai, et al. (2001). "Detection of hypoglycemia with the
GlucoWatch Biographer." Diabetes care 24(5): 881-5.
The Diabetes Research in Children Network (DirecNet) Study Group
(2003). "The accuracy of the GlucoWatch G2 Biographer in children
with Type 1 Diabetes: Results of the Diabetes Research in Children
Network (DirecNet) Accuracy Study." Diabetes Technology and
Therapeutics 5(5): 791-800.
Tamada, J., S. Garg, et al. (1999). "Noninvasive glucose monitoring:
comprehensive clinical results." Journal of the American Medical
Association 282: 1839-1844.
Tierney, M., J. Tamada, et al. (2000). "The GlucoWatch Biographer: a
frequent, automatic and non-invasive glucose monitor." Annals of
Medicine 32: 632-641.
Further
information
This website is by a freelance health writer specialising in diabetes. It
provides a fairly comprehensive and regularly updated listing of currently
available glucose meters (http://www.mendosa.com/meters.htm)
112
Description
Title
Technology
Stage of
Development
Description
Demographics
Health condition
Self care
spectrum
Research trials
and outcomes
Pedometer (Various brands)
Pedometer (small unit that is clipped to your belt)
Commercially available
The pedometer is a small, wearable unit with a digital display that can tell
you how many steps you have taken. Positioned on the waist, forward
and downward movement causes a hammer to hit a sensor, which in turn
activates the counter. Further functionality is available.
All
Cardiac rehabilitation, health maintenance, weight loss, lifestyle
monitoring
Self-monitoring
Study 1 (Eakin, Brown et al. 2004).Objective: Evaluate the efforts to
increase physician counselling on physical activity during routine
practice. This paper reports on the results of a primary care-based
physical activity-counselling project, the 10,000 Steps Rockhampton
Project in Australia. Study Design and Methods. Practices were provided
with promotional resources and pedometers. Evaluation included GP
surveys, collection of process data, and a random, community-based
telephone survey. Results: 91% of practices approached agreed to
participate. GP survey response rates were 67% at baseline and 71%
after 14 months. At follow-up 62% had displayed the programme poster,
81% were using the brochures and 70% had loaned pedometers to
patients. No change was seen in GP self-report of the percentage of
patients counselled on physical activity. Data from the telephone surveys
showed a 31% in the likelihood of recalling GP advice on physical activity
in Rockhampton compared to 16% decrease in the comparison
community. Conclusions: This study showed high rates of GP uptake,
reasonable levels of implementation, and a significant increase in the
number of local residents counselled on physical activity. These findings
suggest that such a programme can be translated into routine practice.
Study 2 (Beets, Patton et al. 2005). Objective: To determine the
accuracy of pedometer step counts and time during self-paced walking
and treadmill walking in children aged 5-11 years old. Four pedometers
were assessed in this study (Digiwalker SW-200, Walk4Life 2505,
Digiwalker SW-701, Sun TrekLINQ). Study Design and Methods. 10 boys
and 10 girls completed 3 single-lap self-paced walking trials around an
athletic track. Treadmill walking was performed at 5 different speeds.
Pedometer steps and time and observed steps and time were recorded.
Results: During self-paced walking there was high agreement on
observed steps for two of the pedometers. For treadmill walking there
was low agreement between pedometer steps and observed steps for all
models. Results for pedometer time also had mixed success.
Study 3 (Cyarto, Myers et al. 2004). Objective: To examine the effects of
walking speed and gait disorders on the accuracy of Yamax pedometers
with nursing home residents (26 participants) relative to older adults living
in the community (28 participants). Study Design and Methods.
113
Pedometer accuracy was evaluated against observed steps taken during
a self-paced walking test at three speeds (slow, medium and fast).
Results: The walking speeds of both samples increased across selfdetermined paces. The community-based seniors walked significantly
faster in all trials and had higher gait assessment scores (indicating fewer
gait problems). Pedometers significantly underestimated nursing home
residents’ observed steps by 74%, 55% and 46% respectively. This
underestimation was repeated with the community-based trial by 25%,
13% and 7% respectively. Conclusions: Slow walking speed and gait
disorders affect the utility of pedometers for physical activity
measurement in frail, senior adults. They can however be used
confidently with healthy, senior adults for assessment and motivational
purposes.
Other research
Who’s involved
Cost / business
models
Keywords
Contact Details
Study 4 (Le Masurier, Lee et al. 2004). Objective: Two studies were
carried out to examine the concurrent accuracy of the Yamax SW-200
(YAM), Omran HJ-105 (OM), and Sportline 330(SL) pedometers and the
CSA accelerometer. Study Design and Methods. In the first study motion
sensor performance was evaluated against observed steps taken during
5-minute sessions at five different treadmill speeds. In the second study
pedometer performance was assessed during 24 hours of free-living and
evaluated against the steps detected by the CSA criterion. Results: In the
first study the SL showed significant differences from actual steps at all
treadmill speeds. At the slowest treadmill speed the absolute value of
percent error increased for the YAM and OM. In the second study only
the SL detected less steps than the CSA criterion. Conclusions: Different
brands of motion sensors detect steps differently; therefore caution must
be used when comparing studies. In this study the YAM pedometer was
the most accurate under both controlled and free-living conditions.
Schneider et al (Schneider, Crouter et al. 2003) have reported that given
the variations between different models of pedometer not all pedometers
count steps accurately. Researchers using pedometers to asses physical
activity need to be aware that differences in accuracy and reliability
occur. Crouter et al (Crouter, Schneider et al. 2003) ran a study
examining the effects of walking speed on the accuracy and reliability of
10 pedometers. They found that pedometers were most accurate for
assessing steps, less accurate for assessing distance and even less
accurate for assessing kilocalories.
Self, Remote healthcare support
Various prices. For example the ‘Walking style II Pedometer’ at £24.97
includes an online support programme to help track, record and monitor
your activity. The Reebok Cyber Rider at around £160 uses pedalling
rather than steps. Connecting to a Play Station, you can play a game
whilst exercising, your pedal rate affecting your success in the game.
Basic pedometers can be found as cheaply as £7.99.
Pedometer, wearable, continuous monitoring, mobile, lifestyle, physical
activity, activity monitoring, motion sensors, exercise, health
maintenance, interactive games.
“Walking the way to Health” Initiative (WHI) http://www.whi.org.uk/
10,000 Steps Rockhampton Project, Australia http://10000steps.org.au/
index2.html
114
References
Further
information
Beets, M., M. Patton and S. Edwards (2005). "The accuracy of
pedometer steps and time during walking in children." Medicine
and Science in Sports and Exercise 37(3): 513-20.
Crouter, S., P. Schneider, M. Karabulut and D. Bassett (2003). "Validity
of 10 electronic pedometers for measuring steps, distance and
energy cost." Medicine and Science in Sports and Exercise 35(8):
1455-60.
Cyarto, E., A. Myers and C. Tudor Locke (2004). "Pedometer accuracy in
nursing home and community dwelling older adults." Medicine
and Science in Sports and Exercise 36(2): 205-209.
Eakin, E., W. Brown, A. Marshall, K. Mummery and E. Larsen (2004).
"Physical activity promotion in primary care." American Journal of
Preventive Medicine 27(4): 297-303.
Jarrett, H., D. Peters and P. Robinson. (2004). Walking the way to health
initiative: Evaluating the 2003 'Step-o-meter' loan pack trial. Final
report.
http://www.whi.org.uk/details.asp?key=E14%7C0%7C426634909
63%7Cp%7C66%7C0. Accessed on 5 August 2005. University
College Worcester.
Le Masurier, G., S. Lee and C. Tudor Locke (2004). "Motion sensor
accuracy under controlled and free-living conditions." Medicine
and Science in Sports and Exercise 36(5): 905-10.
Peters, D., H. Jarrett and P. Robinson. (2002). Walking the way to health
initiative: Evaluating the 'Step-o-meter' Campaign. Final report.
http://www.whi.org.uk/uploads/documents/e14/step-ometer%20full%20evaluation%20report.pdf. Accessed on 5 August
2005. University College Worcester.
Schneider, P., S. Crouter, O. Lukajic and D. Bassett (2003). "Accuracy
and reliability of 10 pedometers for measuring steps over a 400m
walk." Medicine and Science in Sports and Exercise 35(10): 177984.
The British Heart Foundation recommends that 10,000 steps a day can
give you a healthy heart and reduce body fat. Pedometers are a cheap
and easy way for people to adopt a healthier lifestyle and keep track of
how far they walk in a day. This article
(http://news.bbc.co.uk/1/hi/magazine/3723704.stm) refers to a pilot
scheme in the UK in which GPs are prescribing pedometers.
115
Description
Title
Technology
Stage of
Development
Description
Demographics
Health condition
Self care
spectrum
Training and
Ease of use
Research trials
and outcomes
Viterion Telehealth Monitor
Data collection unit, public network (phone line), remote monitoring
center or healthcare provider
Commercially available. This device is currently being piloted by Kent
County Council with 275 participants http://extranet6.kent.gov.uk/kcc/home/newsarticle.aspx?id=2307
Home data collection and interaction unit. The unit combines question/
answer interaction,
vital signs measuring, personalised advice messages and scheduling
reminders. The unit is modular allowing the individual to measure a range
of vital signs including blood pressure, blood sugar, blood oxygen,
temperature, weight, ECG and peak flow. Once collected this information
is sent to the health provider. Recent versions of the monitor also include
digital photography capabilities, web access, video conferencing and
graph displays of the results making it easier for the healthcare provider
to interpret the data and identify trends.
All
Cardiac disease, asthma, diabetes, depression, chronic obstructive
pulmonary disease.
Self-monitoring, shared care
People given 1 hour tutorial (Lehmann et al, 2004) – no comments re
adequacy of training or ease of use.
Study 1: 6 month Pilot study (Lehmann et al, 2002; Lehmann et al, 2004)
with congestive heart failure patients – control group (10) report daily
weight via phone; study group (10 patients) reports vital signs (BP,
weight, O2 saturation) via Viterion 500 to a nurse who would follow up on
abnormal results. Study group found to have 50% fewer clinician visits
and hospital re-admissions, are more compliant with reporting vital signs,
and nurses found the ability to monitor vital signs to be more diagnostic
than sporadic single vital sign measurements. Nurses also reported that
they would have liked real-time video sessions.
Who’s involved
Study 2: The aim of this study (Noel, Vogel et al. 2004) was to see
whether home telehealth reduced healthcare costs and improved the
patient’s quality of life. This single blind, single site, randomised trial
involved elderly patients with complex heart failure, chronic lung disease
and/or diabetes. The control group of 57 patients received usual home
healthcare services plus nurse case management. The intervention
group of 47 patients received home telehealth plus nurse case
management; the system used was the Viterion Telehealth Monitor and
associated products. The intervention group showed a significant
decrease in bed days of care, emergency room visits and A1C levels.
There were no significant differences in functional levels and patientrated health between groups. However home telehealth was found to
reduce use of resources, improve the patients cognitive status, improve
medicine compliance and the stability of long-term conditions.
Self, Remote monitoring by health professionals.
Cost
NA
116
Keywords
Contact Details
References
Further
information
Viterion Telehealth Monitor, self care, remote monitoring, tabletop, home,
point monitoring, medicine compliance, education, care co-ordination,
mobility, cardiac disease, asthma, diabetes, depression, chronic
obstructive pulmonary disease.
Louann Page/ [email protected]
www.viterion.com
Noel, H., D. Vogel, et al. (2004). “Home telehealth reduces healthcare
costs” Telemedicine Journal and e-Health 10(2):170-183.
Lehmann, C., Mintz, N. and Doherty, M. (2002) Impact of Technology on
home bound congestive heart failure patients, Journal of
Telemdicine and e-Health 8:253-4.
[Poster available at:
http://www.viterion.com/web_docs/ATA%202004%20Poster.pdf
(accessed July 25 2005)]
Lehmann, C. and Giacini, J.M. (2004) Pilot Study: The impact of
technology on home bound congestive heart failure patients.
Telemedicine Information Exchange.
http://tie.telemed.org/articles/article.asp?path=articles&article=chf
PilotStudy_cljmg_hhct04.xml [Reprinted from the Home Health
Care Technology Report, v1(4):50,59-60, 2004] (accessed July
25 2005).
White paper summarising panelist discussions, "Using Telehealth to
Reduce the High Cost of Chronic Care: Perspectives from
Homecare, Veterans Affairs, and Disease Management",
presented at the 16th Annual National Managed
http://www.viterion.com/web_docs/WhitePaperFinal.pdf (accessed July
25 2005).
Key Components in Reducing Expenditures and Increasing Quality of
Patient Care
The Remington Report, July/August 2004 - An article by Craig
Lehmann, Ph.D., CC (NRCC) FACB and Jean Marie Giancini, BS
http://www.viterion.com/web_docs/C%20Lehmann%20Article%20Reming
ton%20Rpt.pdf (accessed July 25 2005).
BBC News: Home computer monitors patients.
http://news.bbc.co.uk/1/hi/england/kent/4322041.stm (accessed
July 25 2005)
117
Description
Title
Technology
Stage of
Development
Description
Demographics
Health condition
Self care
spectrum
Training and
Ease of Use
Research trials
Health Buddy
The Health Buddy is an independent in-home communication and
monitoring unit that connects to the internet providing a connection
between the individual and the care provider. The unit has a large screen
and four response buttons.
Commercial product - http://www.healthhero.com/
Recently received approval from the US Food and Drug
Administration (FDA) – (see E-Health Insider Primary Care
Newsletter, Issue 12, April 2005
http://www.ehiprimarycare.com/News/Newsletters.cfm?ID=186 )
The Health Hero technology platform provides an integrated, web-based
health care tool. The Health Buddy assists the user in monitoring his or
her health condition using a variety of health care devices including blood
glucose meters, weight scales, peak flow meters and blood pressure
cuffs. Users answer daily, personalised questions that monitor their
disease symptoms, medicine compliance, and disease knowledge as well
as providing education about their condition. The user’s responses are
sent to a remote monitoring centre.
The Health Hero iCare Desktop provides a set of enrolment, scheduling
and monitoring tools to enable the ‘case manager’ to communicate with
their patient, monitor the patient condition’s and prevent critical situations
from arising through preventative intervention.
All age groups
Diabetes, asthma, congestive heart failure, coronary artery disease,
hypertension, chronic obstructive pulmonary disease, mental illness
Self-monitoring, shared care
12 month study with 911 patients - easy to understand (93%) and easy
to use (95%). (Ryan, Kobb et al, 2003); Similar results reported in other
studies (e.g., Huddleston and Kobb 2004)
Study 1 (Cherry, Moffatt et al. 2002).
Objective: The MHC study was carried out at the Mercy Health Centre,
Texas, USA. It aimed to asses the effect of telemedicine technology on
the health of indigent border residents with diabetes. Using the
technology patients were monitored daily at home and nurses were
alerted if patients reported abnormalities. The goals of the programme
were to decrease hospital-based utilisation, improve patient compliance
with treatment plans, improve patient satisfaction with healthcare
services and improve patients’ perceived quality of life. Study Design and
Methods. To be included in the study patients had to be indigent or
economically disadvantaged adults with a diagnosis of diabetes mellitus.
Participants were expected to answer daily questions about diabetes on
their Health Buddy. Data collected included information on signs,
symptoms, health behaviours e.g. changes in feet, blood sugar and
medicine compliance. If the values were irregular case managers
contacted the patient, physician or clinic. A focus of the study was to
measure the effect of technology on patient behaviour. The evaluation
lasted a year and involved 169 participants, 130 females and 39 males
with an average age of 53 for both sexes. Results: Outcomes included
118
inpatient admissions, emergency room visits, outpatient visits, post
discharge care visits and healthcare costs. Subjective outcomes included
quality of life and patient satisfaction based on survey data collected pre,
post and during the trial. Results: Patients in the programme showed the
reduced overall charges of $747 per patient per year. Inpatient
admissions were reduced 32%, emergency room visits were reduced
34%, outpatient visits were reduced 49% and post discharge care visits
were reduced 44%. Quality of life was assessed using the Health
Outcomes Study 12-item Short Form health survey. The mean
improvement in the mental component after 6 months in the programme
was 2.8, from 45.1 pre-programme to 47.9 within the programme
(p<0.0264). The mean improvement in the physical component after 6
months in the programme was 2.1, from 41.7 pre-programme to 43.8
within the programme (p<0.0518). 95% of the patients reported increased
satisfaction regarding communication with their doctors or nurses on an
ongoing basis. All patients reported that the Health Buddy was easy or
very easy to use. 97% of patients had no difficulty in using the Health
Buddy to answer daily questions. 93% of patients reported that they had
a better understanding of their health condition since using the device.
93% of patients felt that they were better able to do self care of their
condition with the device. 99% of patients believed that Health Buddy
helped them to improve their health. Over time the percentage of patients
that reported feeling more connected to their doctor, nurses and hospital
increased from 88% after 3 months to 95% at 1 year. At the beginning of
the study only 34% of the patients reported no problems with missing
medicine doses. 65% of the patients reported missing medicine doses
before starting on the programme. After receiving the Health Buddy 94%
of patients reported that they took their medicines more regularly.
Conclusions: The reductions in utilisation and improvement in quality of
life can likely be attributed to the patient’s enhanced self care and the
nurse’s ability to intervene when necessary. Without technology and daily
monitoring, standard patient care is based on episodic encounters
between patients and their care providers, which does not allow for
prevention, education or early intervention.
Study 2 (Ryan, Kobb et al. 2003).
Objective: The Community Care Coordination Service (CCCS)
programme was implemented by the Veterans Health Administration
(VHA) to improve the coordination of care for complex morbidity patients
and to increase their access to care whilst reducing complications,
hospital admissions and emergency room visits. A telemedicine
programme was initiated in its ‘Sunshine Network’ covering veterans in
South Georgia, Florida, Puerto Rico and the Virgin Islands. In this
programme several technologies were implemented including the Health
Buddy. This paper provides a case study. Study Design and Evaluation.
This case study involves 791 participants with long-term conditions and
120 mental health patients. Range of conditions included hypertension,
diabetes, congestive heart failure, lung disease, post-traumatic stress
disorder and schizophrenia. This study involved an evaluation
methodology using periodic data collected at 6-month intervals. A series
of repeated measure analysis of covariance modelling was used. Survey
data was used to determine patient and provider satisfaction. Results:
Initial medicine compliance upon enrolment was 63% increasing to 93%
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over the programme. There was 88% compliance with answering the
device. Patients had a 94% satisfaction rate with their primary technology
device after 12 months. Patients also reported the technology easy to
understand (93%), easy to use (95%) and generally reliable (87%).
Results for patient satisfaction with the programme were also favourable,
with improved health care, education and communication with healthcare
providers being reported, e.g., 90% of patients reported that the
programme helped educate them about their condition. Conclusions:
Home telehealth technology can build ‘bridges’ between the patient and
the healthcare system. Technology must have a positive business effect
for the healthcare system as well as effect positive health outcomes for
the patient. Technologies also must fit into a well-defined process of care.
Study 3 (Cherry, Dryden et al. 2003).
Objective: The Community Care Coordination Service (CCCS)
programme was implemented by the Veterans Health Administration
(VHA) to improve the coordination of care for complex morbidity patients
and to increase their access to care whilst reducing complications,
hospital admissions and emergency room visits. In this programme
several technologies were implemented. This study is an evaluation of
the Health Buddy. Study Design and Methods. Three different sites were
featured in this case study i.e. Ft. Myers, Lake City and Miami. 345
patients identified as high cost and diagnosed with congestive heart
failure, coronary artery disease, diabetes mellitus, hypertension and
COPD were selected from a pool of 8,704 veterans. Results: An analysis
of utilisation and health impact was conducted after 18 months. It found
that inpatient admissions were reduced by 46% at Ft. Myers, 68% at
Lake City and 13% at Miami. ER encounters were reduced by 19% at Ft.
Myers, 70% at Lake City and 15% at Miami. Reductions in bed days were
demonstrated at Ft. Myers (29%) and lake City (71%). In Miami there
was a 13% increase in the number of bed days of care for patients after
1 year in the programme. Before the study 68% of patients reported that
they took their medicines as prescribed. At the end of the first 12 months
on the programme 93% of patients took their medicines as prescribed.
Conclusions: Remote patient monitoring technology such as Health
Buddy reduces utilisation and health-care costs in patients with multiple
long-term conditions. Patient quality of life was also significantly
improved.
Study 4 (Kobb, Hoffman et al. 2003).
Objective: The Rural Home Care Project is one of eight demonstration
pilots in an initiative of the Veterans health Administration (VHA)
Sunshine Network in Florida and Puerto Rico. In this programme home
telehealth devices were used to monitor and educate patients. This paper
outlines the Rural Home Care Project as a case study. Study Design and
Methods. The sample for this study, with 12 months of before and after
enrolment data, was 281. 12% of the total number of those contacted to
participate in the study declined, 90% of those indicated ‘discomfort with
the technology’ as the reason. The evaluation methodology was a quasi
experimental design that used a non-equivalent control group of usual
care veterans. Data was gathered through interviews with both patients
and providers. Statistical analysis was based on a series of repeatedmeasure of covariance modelling. Results: Project patients showed a
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greater improvement in healthcare resource consumption than the usual
care group. Project patients had a medicine compliance rate of 68% at
the start of the study rising to 93% during the study. High compliance was
also reported for usage of the health buddy (90-92%). Conclusions:
Findings show that care coordination through technology reduces
hospital admissions, bed days of care, emergency room visits and
prescriptions as well as providing high patient and provider satisfaction.
Veterans also had an improved perception of their physical health.
Study 5 (LaFramboise, Todero et al. 2003).
Objective: To determine the feasibility of providing shared care of heart
failure using the Health Buddy and to compare the effectiveness of the
Health Buddy with more traditional home care strategies in achieving
selected patient outcomes i.e. self-efficacy, functional status, depression
and health-related quality of life. Study Design and Methods. A
randomised research trial in which the participants were randomised to
four groups. All groups received the same educational content of shared
care of Heart Failure, however the method of delivery differed between
groups. The types of delivery were: telephone, home visits, the Health
Buddy, and home visits and the Health Buddy combined. 90 participants
completed the study over a 2-month period. Results: From the 66
patients originally assigned to the Health Buddy group or Health Buddy
and home visits group, 20 participants or 30% were not able to use the
Health Buddy. For outcomes of the intervention a mixed model ANOVA
compared outcome variables. For self-efficacy a significant group by time
effect (p=0.027) was found. There was a significant difference (p < 0.05)
for the telephone group, showing decreased confidence in their ability to
take care of their heart failure and all other groups showing an increased
confidence. Further ANOVA analyses indicated improvement over time
with no group differences for functional status, depression or healthrelated quality of life. Conclusions: These findings suggest that using a
device like Health Buddy is feasible for doing self care and may be as
effective as more traditional methods.
Study 6 (Huddleston and Kobb 2004).
Objective: The Tech Care Coordination Programme (TCCP) is one of
eight research demonstration pilots in an initiative of the Veterans Health
Administration (VHA) Sunshine Healthcare Network. In this programme
older veterans with long-term conditions were monitored using the Health
Buddy. This paper outlines the TCCP as a case study. Study Design and
Methods. The evaluation methodology was a prospective, quasiexperimental design implementing quarterly data collection. A group of
1,120 ‘like-care’ veterans with similar demographics, diagnoses, health
outcomes, healthcare utilisation and healthcare costs were compared to
the patients in the TCCP. Results: Health Buddy patients showed a
greater improvement in healthcare consumption compared with the likecare group. Patient satisfaction was high. Patients reported that the
Heath Buddy was easy to use (94%), helpful in assisting take care of the
condition (94%), and that the Health Buddy helped them to feel more
secure (96%). Provider satisfaction was also favourable. Baseline
medicines adherence was 64%. Over the first 12 months of the
programme this rose to 90%. There was a 45% decrease in hospital
admissions, a 67% decrease in nursing home admissions, 54% decrease
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in emergency room visits and 38% decrease in pharmacy prescriptions.
Improved medicine compliance was also demonstrated. Conclusions:
The use of technology and care coordination supports frequent patient
contact whilst allowing patients to remain in the home.
Who’s involved
Cost
Keywords
Contact Details
References
Additional
references
Study 7 (Gundelman, Meade et al, 2002)
Objective: to assess the efficacy of an interactive device for self care and
education for children with asthma. Study Design and Methods: 90 day
RCT with 66 children in the intervention group and 68 in the control group
(8-16 years old). The intervention group used Health Buddy to assess
and monitor symptoms and quality of life and transmit these to health
care providers. Control group used a diary. Results: Intervention group
were significantly less likely to have any limitation in activity as a result of
their asthma and significantly less likely report peak flow readings in the
yellow or red zones or to make urgent calls to hospital. Conclusions:
Compared with the diary, use of Health Buddy to monitor asthma
symptoms and functional status increases self care skills and improves
asthma outcomes.
Self, Remote monitoring by health professionals.
Quantified cost savings were reported of US$747/patient/year (Cherry,
Moffatt et al 2002; other cost impacts can be inferred from data such as
reduced hospital admissions, and reduced bed days (eg Cherry, Moffatt
et al 2002; Cherry, Dryden et al, 2003; Huddleston and Kobb 2004)
Health Buddy, self care, remote monitoring, tabletop, home, point
monitoring, diabetes, asthma, medicine compliance, education, mobile,
care co-ordination, congestive heart failure, coronary artery disease,
COPD, mental illness
Health Hero Network http://www.healthhero.com/
Cherry, J., K. Dryden, et al. (2003). "Opening a window of opportunity
through technology and coordination: a multisite case study."
Telemedicine Journal and e-Health 9(3): 265-71.
Cherry, J., T. Moffatt, et al. (2002). "Diabetes disease management
program for an indigent population empowered by telemedicine
technology." Diabetes Technology and Therapeutics 4(6): 783-91.
Huddleston, M. and R. Kobb (2004). "Emerging technology for at-risk
chronically ill veterans." Journal for Healthcare Quality 26(6).
Kobb, R., N. Hoffman, et al. (2003). "Enhancing elder chronic care
through technology and care coordination: report from a pilot."
Telemedicine Journal and e-Health 9(2): 189-95.
LaFramboise, M., C. Todero, et al. (2003). "Comparison of Health Buddy
with traditional approaches to heart failure management." Family and
Community Health 26(4): 275-88.
Ryan, P., R. Kobb, et al. (2003). "Making the right connection: Matching
patients to technology." Telemedicine Journal and e-Health 9(1): 81-88.
Guendelman, S., Meade, K., et al (2002) “Improving asthma outcomes
and self-management behaviours of inner city children“ Archives of
Pediatrics & Adolescent Medicine 156(2): 114-120.
http://www.healthhero.com/papers/form_whitepaper.html for HealthHero
Case Studies
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Acknowledgements
Project and Editorial team:
Dr Geraldine Fitzpatrick, Senior Lecturer, Department of Informatics, University
of Sussex.
Ayesha Dost, Principal Analyst and Policy Adviser, Strategy Directorate,
Department of Health.
Anthony Phillips, Research Assistant, Department of Informatics, University of
Sussex.
Dr Tom Hamilton, Research Fellow, Department of Informatics, University of
Sussex.
Contributors:
We would like to thank the following individuals for helpful discussions:
Prof James Barlow
Dr Dick Curry
Dr Guy Dewsbury
Thanks to all of the example company representatives (included in this report)
who responded to phone calls and emails and provided relevant information.
Copyright:
This report is copyright of the Department of Health and reproduction of any
part of this report is not permitted; any reference to any of the information or
material contained in the report should acknowledge the source as Strategy
Directorate, Department of Health.
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