In Brief

The decisions most affecting the health and well-being of patients with diabetes are made by the patients themselves. Thus, technologies that target patient empowerment and behavior change are making a large impact on diabetes-related health outcomes. This article highlights a variety of technologies that encourage insulin dosing changes, provide motivation for checking blood glucose, organize blood glucose meter data, and motivate patients to lose weight. It includes discussion of several theories of patient engagement and health behavior change involving consumer-facing patient-centered technologies.

Diabetes is a unique chronic disease in which the decisions most affecting the health and well-being of patients are made by the patients themselves. This is the crucial reason that technologies that target patient/consumer engagement and behavior change are making a large impact on diabetes-related health outcomes.1 

Numerous studies have confirmed the difficulty patients have with adherence to diabetes regimens.2,3  Thankfully, new technologies have been created to improve a variety of the specific difficulties that have been noted in relation to nonadherence to diabetes regimens. These technologies are often called “consumer-facing technologies,” in that they are hardware or software products that a businesses' customers deal with directly.

This article highlights a variety of consumer-facing technologies that encourage glucose pattern management, motivate patients to perform self-monitoring of blood glucose (SMBG), organize SMBG data, and motivate patients to lose weight—all behaviors that have been shown to have significant effects on health outcomes for people with type 2 diabetes. Theories of patient engagement and health behavior change involving consumer-facing technologies will also be discussed in terms of their effects on type 2 diabetes–related health outcomes.

Daily SMBG (four to five times per day) is especially important for patients with insulin-requiring diabetes to monitor for and prevent asymptomatic hypo- and hyperglycemia.4  The data provided by SMBG is the fuel needed for pattern management, or insulin dosing adjustment, that profoundly affects health outcomes in diabetes.

Pattern management is a systematic approach to help patients identify patterns in their blood glucose readings to determine whether changes are needed to optimize their glucose control.2  Evaluation of patients who were involved in the Diabetes Control and Complications Trial and followed after its completion showed that one of the five identified factors associated with lower A1C—a key health outcome quality indicator for patients with diabetes—was not glucose record-keeping alone, but rather whether individuals actually reviewed their SMBG records and made regimen adjustments based on principles including pattern management.4  Unfortunately, pattern management principles can be difficult for patients to fully comprehend despite extensive education.5 

With the increasing shortages of primary care and specialist physicians, as well as reduced access to health care, some patients may never receive pattern management education.6  In addition, a majority of people with diabetes do not receive any structured diabetes education at all.7,8  Many patients, and indeed their providers, are not aware that these services are available.

Nonetheless, physician-extender technologies have been recently developed to address this need. A recent study9  showed how related health outcomes in adults with insulin-dependent type 2 diabetes can be affected by the growing use of glucose monitoring within a system of communication technologies (e.g., Internet, telephone, mobile phone, and Bluetooth) that track and transmit blood glucose results for assistance with pattern management. This randomized, controlled trial showed a 1.9% decrease in A1C in the intervention group who used a mobile phone–based remote monitoring and insulin-dosing coaching system called Diabetes Manager (WellDoc, Wilmington, Del.).

The Diabetes Manager system is an example of successful telemonitoring. Telemonitoring systems have been developed with the goals of improving outcomes and reducing the high costs associated with chronic diseases such as heart disease and chronic obstructive pulmonary disease. However, the results of previous trials were inconsistent, largely because of the diverse interventions under investigation and varying study designs.1012  There remains a lack of insight into the features required for successful telemonitoring solutions. Yet, a systematic review of 17 studies of telemonitoring interventions in adults with diabetes showed that telemonitoring was consistently found to have a positive effect on clinical outcomes and a positive impact on patient and clinician behavior.13 

In a study of insulin-using patients with type 2 diabetes, SMBG alone (without using its results to adjust therapy) was shown to lead to better health outcomes.14  However, sustained engagement is difficult.

Fogg's Behavior Model (FBM),15  used frequently by consumer technology designers to sustain engagement with their online or mobile platforms, is now being applied to health technologies to sustain engagement with positive health behaviors such as performing SMBG. Within this model, three elements (motivation, ability, and trigger) must converge at the same moment when patients use a technology for a health behavior or outcome to occur. The model includes three core health behavior motivators (pleasure, hope, and acceptance); six health behavior ability factors that make the behavior simple to execute (time, money, physical effort, brain cycles, social deviance, and non-routine); and three health behavior triggers that remind users to perform the health behavior (spark, facilitator, and signal).

The EndoGoal application (or “app”), developed by the author, is one example of a consumer-facing mobile app (Figure 1). Its goal is to create sustained positive health behaviors for people with type 1 and insulin-requiring type 2 diabetes by incorporating the three elements of the FBM. The main behavior focus of the EndoGoal app is self-entry glucose journaling, which can lead to financial rewards upon achievement of the designated goal of performing SMBG four times daily.

Figure 1.

The EndoGoal app, a consumer-facing mobile app developed by the author that aims to create sustained positive health behaviors for people with type 1 or insulin-requiring type 2 diabetes by incorporating the three elements in FBM.

Figure 1.

The EndoGoal app, a consumer-facing mobile app developed by the author that aims to create sustained positive health behaviors for people with type 1 or insulin-requiring type 2 diabetes by incorporating the three elements in FBM.

Close modal

A motivating gaming feature that is a vital part of the EndoGoal app is a virtual pet dog named Cooper who is fed each time users check their glucose but remains hungry when they do not. This feature is similar to the well-known Japanese Tamagotchi digital pet toy that needed to be fed every 3 hours and involved user engagement based on appointment dynamic game mechanics theory. The ability feature of the EndoGoal app, as well as an additional motivating feature now under development, is the rewards points program that drives daily SMBG behavior and engagement and translates directly into prepaid Visa cards and purchasing power with several leading retailers. The trigger features in the app include individualized alarm reminders set by users, as well as barking reminders from the virtual dog, who will be hungry if an SMBG result has not been entered.

Enrollment in a clinical trial with 250 users, funded through crowd-funding, is ongoing. The study hypothesizes that engagement with the app will directly correlate with improved health outcomes for people with type 1 or insulin-requiring type 2 diabetes.16 

Bant is another diabetes-related mobile app for iPhones that similarly adheres to the FBM and uses an iTunes rewards system to which teens have been receptive. In a pilot study involving adolescents with type 1 diabetes, Bant use resulted in a 50% improvement in daily average frequency of blood glucose engagement. However, A1C did not change significantly.17  Future studies are planned for adults with type 2 diabetes.

The benefits of SMBG for patients with insulin-requiring type 2 diabetes are clear.18  However, there is no consensus regarding the optimal frequency and timing of SMBG for patients with type 2 diabetes who do not take insulin. A meta-analysis of daily SMBG testing in noninsulin-treated patients with type 2 diabetes concluded that some SMBG regimens were associated with an A1C reduction of 0.4%.19  However, many of the studies in this analysis were confounded by interventions that also included patient education with diet and exercise counseling and, in some cases, pharmacological intervention, thereby making it difficult to assess the specific contribution of SMBG to improved control. The cost-effectiveness and clinical utility of daily blood glucose testing for patients with non-insulin regimens has also been questioned in several randomized trials.2022  Therefore, it is unclear whether the above-mentioned technologies would affect health outcomes in patients with type 2 diabetes who are not on insulin therapy.

Polonsky et al.23  showed that people who use organized glucose data, or “structured data,” gained more confidence in their diabetes care, used fewer test strips, and achieved lower A1C levels. With structured blood glucose monitoring, research subjects were taught to write down events that could affect their blood glucose control, such as exercise and changes in diet, along with blood glucose results from seven significant points—before and 2 hours after eating and at bed-time. The subjects and their health care providers were then taught how to interpret the SMBG data and identify patterns to best address any issues with their diabetes control.

The concept of structured monitoring is closely related to pattern management. Structured monitoring puts patients in charge of their diabetes, whereas mindless monitoring is just another job they must perform on top of all the other work diabetes requires of them. Following are a few examples of Food and Drug Administration–approved technologies that provide platforms for structured organization of blood glucose testing results to be presented to health care providers.

  • iBGStar (Sanofi-Aventis, Bridgewater, N.J.) is an iPhone-specific glucose meter that provides structured data by connecting the meter to the iPhone to better organize the data through the accompanying iBGStar app.

  • Glooko (Glooko, Palo Alto, Calif.) is a cable cord product that provides structured data by connecting a variety of different glucose meters directly to smartphones with an accompanying Glooko app.

  • Diabetes Pal (Telcare, Bethesda, Md.) is a glucose meter that provides structured data via wireless transmission to the Diabetes Pal smartphone app, which provides space for recording food, medication, or other comments to be used for pattern management.

Traditional standard-of-care counseling methods for type 2 diabetes prevention and weight loss are primarily based on providing health knowledge only, without a theoretical base and in a fairly non-engaging manner. However, engaging interactive media technologies are now being used to complement the delivery of health care and education. These technologies have been shown to be beneficial in permanently changing over-nutrition–related behavior, which is strongly correlated with type 2 diabetes development, compared to delivery of health knowledge only through standard-of-care nutrition education programs in an obesity clinic.24 

One example is the ongoing Project Not Me study, which is assessing 300 adults with prediabetes in Pennsylvania and Tennessee. Project Not Me is a public-private collaboration among UnitedHealth Group, YMCA, Comcast, and the Centers for Disease Control and Prevention–led National Diabetes Prevention Program (NDPP). Its aim is to show that sustained health behavior change can occur through an intervention that engages patients by providing ongoing access to health information and weekly coaching via a reality-style TV show. Its hypothesis is that participants' similar experiences will help them to achieve outcome goals similar to those demonstrated in the Diabetes Prevention Program: a nearly 60% reduction in risk for type 2 diabetes brought about by participants' loss of 5–7% of their body weight and achievement of 150 minutes per week of moderate physical activity.25 

For 16 weeks, weekly episodes of the reality TV show can be viewed through Comcast TV's On Demand service. The show follows six people who are at high risk for developing type 2 diabetes and who are following a diabetes prevention intervention designed by the NDPP. The show documents the subjects' emotional ups and downs, physician visits, family tensions, and efforts to lose weight.

In addition to access to the TV show, Project Not Me provides online tools that correspond to the show to help viewers follow the same healthy steps undertaken by the six people followed in the show. Participation in the 12-month study is free and includes 16 weeks of TV show episodes, as well as weekly evaluations and coaching by the research team for the first 5 months. Participants also receive at the time of enrollment an electronic scale and information about mobile tracking or journaling apps, as well as mobile-integrated pedometers to track their physical activity. Participants are reassessed after 12 months. However, the TV show is available to anyone who wants to view it through Comcast On Demand.

Project Not Me hopes to demonstrate that people who are more engaged in daily decisions that affect their health and wellness can work to achieve a better health outcome and live a healthier life. More information about the study is available online at projectnotmedp.com.

When compared to face-to-face interaction and knowledge-based methods alone, interactive media technologies that incorporate a specific theoretical base in their design are a more comprehensive and influential method of influencing behavior.26  Project Not Me, for example, applies both social cognitive theory and technology to lifestyle-based diabetes prevention.

Social cognitive theory defines human behavior as a triadic, dynamic, and reciprocal interaction of personal factors, behavior, and the environment. According to this theory, individuals' overall behavior is uniquely determined by the inter-play among these three factors and is largely regulated through cognitive processes. Social cognitive theory also proposes that individuals' knowledge acquisition, and thus behavior regulation, can be directly related to observing others within the context of relatable social interactions, experiences, and outside media influences. Thus, this theory can be easily applied in informative, interactive media platforms to influence overall health behavior.

Consumer-facing technologies that encourage individualized patient engagement specific to type 2 diabetes therapies and prevention are clearly in keeping with the recent American Diabetes Association/European Association for the Study of Diabetes position statement recommending a patient-centered approach to care for adults with type 2 diabetes.27  Additional research is needed to confirm the effectiveness of such approaches in improving health outcomes goals for all patients with type 2 diabetes. However, diabetes health care professionals should consider learning more about these tools and recommending them as appropriate to patients with type 2 diabetes who may not be achieving their personal health goals because of a lack of engagement. A quote that has circulated widely in recent months on Twitter may summarize it best: “Patient engagement is the blockbuster drug of this century.”

1.
Yu
CH
,
Bahniwal
R
,
Laupacis
A
,
Leung
E
,
Orr
M
,
Straus
SE
:
Systematic review and evaluation of web-accessible tools for management of diabetes and related cardiovascular risk factors by patients and health care providers
.
J Am Med Inform Assoc
19
:
514
522
,
2012
2.
Bergenstal
R
,
Pearson
J
,
Pearson
T
:
Pattern Control: A Guide for Adjusting Your Insulin Dose
.
Minneapolis, Minn.
,
International Diabetes Center
,
1997
3.
Ziegler
R
,
Heidtmann
B
,
Hilgard
D
,
Hofer
S
,
Rosenbauer
J
,
Holl
R
the DPV-Wiss-Initiative
:
Frequency of SMBG correlates with HbA1c and acute complications in children and adolescents with type 1 diabetes
.
Pediatr Diabetes
12
:
11
17
,
2011
4.
Bergenstal
R
,
Callahan
T
,
Johnson
M
,
Upham
P
,
Hollander
P
,
Spencer
M
,
Castle
G
,
Nelson
J
,
Roth
L
,
Etzwiler
D
:
Management principles that most influence glycemic control: a follow-up study of former DCCT participants
.
Diabetes
45
(
Suppl. 2
):
124A
,
1996
5.
Sprague
M
,
Shulz
J
,
Branen
L
:
Understanding patient experiences with goal setting for diabetes self-management after diabetes education
.
Fam Community Health
29
:
245
255
,
2006
6.
Salsberg
E
,
Grover
A
:
Physician workforce shortages: implications and issues for academic health centers and policymakers
.
Acad Med
81
:
782
787
,
2006
7.
Siminerio
LM
,
Piatt
GA
,
Emerson
S
,
Ruppert
K
,
Saul
M
,
Solano
F
,
Stewart
A
,
Zgibor
JC
:
Deploying the chronic care model to implement and sustain diabetes self-management training programs
.
Diabetes Educ
32
:
253
260
,
2006
8.
Siminerio
LM
,
Piatt
G
,
Zgibor
JC
:
Implementing the chronic care model for improvement in diabetes care and education in a rural primary care practice
.
Diabetes Educ
31
:
225
234
,
2005
9.
Quinn
CC
,
Shardell
MD
,
Terrin
ML
,
Barr
EA
,
Ballew
SH
,
Gruber-Baldini
AL
:
Cluster-randomized trial of a mobile phone personalized behavioral intervention for blood glucose control
.
Diabetes Care
34
:
1934
1942
,
2011
10.
Chaudhry
SI
,
Mattera
JA
,
Curtis
JP
,
Spertus
JA
,
Herrin
J
,
Lin
Z
,
Phillips
CO
,
Hodshon
BV
,
Cooper
LS
,
Krumholz
HM
:
Telemonitoring in patients with heart failure
.
N Engl J Med
363
:
2301
2309
,
2010
11.
Koehler
F
,
Winkler
S
,
Schieber
M
,
Sechtem
U
,
Stangl
K
,
Böhm
M
,
Boll
H
,
Baumann
G
,
Honold
M
,
Koehler
K
,
Gelbrich
G
,
Kirwan
BA
,
Anker
SD
Telemedical Interventional Monitoring in Heart Failure Investigators
:
Impact of remote telemedical management on mortality and hospitalizations in ambulatory patients with chronic heart failure: the Telemedical Interventional Monitoring in Heart Failure study
.
Circulation
123
:
1873
1880
,
2011
12.
Klersy
C
,
De Silvestri
A
,
Gabutti
G
,
Regoli
F
,
Auricchio
A
:
A meta-analysis of remote monitoring of heart failure patients
.
J Am Coll Cardiol
54
:
1683
1694
,
2009
13.
Jaana
M
,
Pare
G
:
Home telemonitoring of patients with diabetes: a systematic assessment of observed effects
.
J Eval Clin Pract
13
:
242
253
,
2007
14.
Murata
GH
,
Shah
JH
,
Hoffman
RM
,
Wendel
CS
,
Adam
KD
,
Solvas
PA
,
Bokhari
SU
,
Duckworth
WC
:
Intensified blood glucose monitoring improves glycemic control in stable, insulin-treated veterans with type 2 diabetes: the Diabetes Outcomes in Veterans Study (DOVES)
.
Diabetes Care
26
:
1759
1763
,
2003
15.
Fogg
BJ
:
BJ Fogg's behavior model [article online]
. Available from http://www.behavior-model.org.
Accessed 1 March 2013
16.
Hughes
V
:
Strapped for funding, medical researchers pitch to the crowd
.
Nature Med
18
:
1307
,
2012
(
doi:10.1038/nm0912-1307
)
17.
Cafazzo
JA
,
Casselman
M
,
Hamming
N
,
Katzman
DK
,
Palmert
MR
:
Design of an mHealth app for the self-management of adolescent type 1 diabetes: a pilot study
.
J Med Internet Res
14
:
e70
,
2012
18.
American Diabetes Association
:
Standards of Medical Care in Diabetes—2013
.
Diabetes Care
36
(
Suppl. 1
):
S11
S66
,
2013
19.
Welschen
LM
,
Bloemendal
E
,
Nijpels
G
,
Dekker
JM
,
Heine
RJ
,
Stalman
WA
,
Bouter
LM
:
Self-monitoring of blood glucose in patients with type 2 diabetes who are not using insulin: a systematic review
.
Diabetes Care
28
:
1510
1517
,
2005
20.
Farmer
A
,
Wade
A
,
Goyder
E
,
Yudkin
P
,
French
D
,
Craven
A
,
Holman
R
,
Kinmonth
AL
,
Neil
A
:
Impact of self-monitoring of blood glucose in the management of patients with non-insulin treated diabetes: open parallel group randomised trial
.
BMJ
335
:
132
135
,
2007
21.
O'Kane
MJ
,
Bunting
B
,
Copeland
M
,
Coa tes
VE
:
Efficacy of self-monitoring of blood glucose in patients with newly diagnosed type 2 diabetes (ESMON study): randomised controlled trial
.
BMJ
336
:
1174
1177
,
2008
22.
Simon
J
,
Gray
A
,
Clarke
P
,
Wade
A
,
Neil
A
,
Farmer
A
:
Cost-effectiveness of self-monitoring of blood glucose in patients with non-insulin treated type 2 diabetes: economic evaluation of data from the DiGEM trial
.
BMJ
336
:
1177
1180
,
2008
23.
Polonsky
WH
,
Fisher
L
,
Schikman
CH
,
Hinnen
DA
,
Parkin
CG
,
Jelsovsky
Z
,
Axel-Schweitzer
M
,
Petersen
B
,
Wagner
RS
:
A structured self-monitoring of blood glucose approach in type 2 diabetes encourages more frequent, intensive, and effective physician interventions: results from the STeP study
.
Diabetes Technol Ther
13
:
797
802
,
2011
24.
Svetkey
LP
,
Stevens
VJ
,
Brantley
PJ
,
Appel
LJ
,
Hollis
JF
,
Loria
CM
,
Vollmer
WM
,
Gullion
CM
,
Funk
K
,
Smith
P
,
Samuel-Hodge
C
,
Myers
V
,
Lien
LF
,
Laferriere
D
,
Kennedy
B
,
Jerome
GJ
,
Heinith
F
,
Harsha
DW
,
Evans
P
,
Erlinger
TP
,
Dalcin
AT
,
Coughlin
J
,
Charleston
J
,
Champagne
CM
,
Bauck
A
,
Ard
JD
,
Aicher
K
Weight Loss Maintenance Collaboration Research Group
:
Comparison of strategies for sustaining weight loss: the weight loss maintenance randomized controlled trial
.
JAMA
299
:
1139
1148
,
2008
25.
Knowler
WC
,
Barrett-Connor
E
,
Fowler
SE
,
Hamman
RF
,
Lachin
JM
,
Walker
EA
,
Nathan
DM
Diabetes Prevention Program Research Group
:
Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin
.
N Engl J Med
346
:
393
403
,
2002
26.
Painter
JE
,
Borba
CP
,
Hynes
M
,
Mays
D
,
Glanz
K
:
The use of theory in health behavior research from 2000 to 2005: a systematic review
.
Ann Behav Med
35
:
358
362
,
2008
27.
Inzucchi
SE
,
Bergenstal
RM
,
Buse
JB
,
Diamant
M
,
Ferrannini
E
,
Nauck
M
,
Peters
AL
,
Tsapas
A
,
Wender
R
,
Matthews
DR
American Diabetes Association (ADA)
European Association for the Study of Diabetes (EASD)
:
Management of hyperglycemia in type 2 diabetes: a patient-centered approach: position statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)
.
Diabetes Care
35
:
1364
1379
,
2012