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Problem Solving in Diabetes Self-management and ControlA Systematic Review of the Literature
Felicia Hill-Briggs, PhD and
Leigh Gemmell, PhD
From the Department of Medicine, Johns Hopkins School of Medicine,
Baltimore, Maryland (Dr Hill-Briggs); the Department of Health, Behavior, and
Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
(Dr Hill-Briggs); and VA Pittsburgh Healthcare System (Ms Gemmell).
Correspondence to Felicia Hill-Briggs, PhD, Division of General Internal
Medicine, 2024 East Monument Street, Suite 2-600, Baltimore, MD 21205
(fbriggs3{at}jhmi.edu).
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Abstract
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Purpose
The purpose of this systematic review is to assess the published literature
on problem solving and its associations with diabetes self-management and
control, as the state of evidence exists.
Data Sources
PubMed, PsychINFO, and ERIC electronic databases were searched for the
years 1990 to the present and for English-language articles, and reference
lists from included studies were reviewed to capture additional studies.
Study Selection
Quantitative and qualitative studies that addressed problem solving as a
process or strategy for diabetes self-management were included. Fifty-two
studies met the criteria for inclusion.
Data Extraction
Study design, sample characteristics, measures, and results were
reviewed.
Data Synthesis
Thirty-six studies were quantitative; 16 were conceptual or qualitative.
Studies were classified as addressing the problem-solving
definition/framework, assessment, intervention, or health care professional
issues.
Conclusions
Problem solving is a multidimensional construct encompassing verbal
reasoning/rational problem solving, quantitative problem solving, and coping.
Aspects of problem solving can be assessed using newly developed
diabetes-specific problem-solving measures for children/adolescents and
adults. Cross-sectional studies in adults, but not children/adolescents,
provide consistent evidence of associations between problem solving and A1C
level. Only 25% of problem-solving intervention studies with
children/adolescents and 50% of interventions with adults reported improvement
in A1C. Most intervention studies reported an improvement in behaviors, most
commonly global adherence in children/adolescents and dietary behavior in
adults. Methodological limitations (noninclusion of problem-solving measures,
inadequate descriptions of problem-solving interventions, homogenous samples)
need to be addressed in future research to clarify the effect of problem
solving on diabetes outcomes, identify characteristics of effective
interventions, and determine the utility across age and racial/ethnic
groups.
Problem solving has long been recognized as a core component of effective
diabetes
self-management.1-3
Within the American Association of Diabetes Educators (AADE) core outcomes
framework, problem solving is defined as "a learned behavior that
includes generating a set of potential strategies for problem resolution,
selecting the most appropriate strategy, applying the strategy, and evaluating
the effectiveness of the
strategy."4
Importantly, not only is problem solving included among the AADE 7TM
self-management
behaviors,4,5
but it is a strategy that has been used in diabetes self-management education
(DSME) to facilitate patients' attainment of each of the remaining
self-management behaviors (healthy eating, being active, taking medications,
monitoring, healthy coping, reducing risks). For systematic integration of
problem solving into DSME and outcomes measurement, a compilation and review
of the evidence for the role of problem solving in diabetes self-management
and control are needed. The purpose of this systematic review of the
literature is to compile and examine published evidence for problem solving as
(1) an outcome that can be assessed, (2) a behavior associated with
self-management behaviors and physiological outcomes, (3) an effective
intervention for improving self-management and/or disease control, and (4) a
tool used by health care professionals.
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Methods
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Search
PubMed (National Library of Medicine and National Institutes of Health),
PsychINFO (a database of psychological literature), and ERIC (Educational
Resource Information Center) databases were searched. The following medical
subject heading (MeSH) terms were included in the searches conducted in each
database: diabetes mellitus/diabetes, problem solving, problem focused,
decision making, self-management, and self-care. Searches were
limited to the years 1990 to the present, English-language articles, and human
subjects.
Selection
Results from the searches of the 3 databases were compared to identify
unique results and eliminate duplicates. Abstracts from each unique result
were reviewed (by L.G. and F.H.B.) for relevance to the topic. Studies were
excluded that (1) were not investigations of problem solving (eg, were
unrelated to the topic or were reports only of problems/barriers without
investigation of problem solving), (2) investigated clinical problem solving
or clinical decision making (eg, medical diagnostics) by professionals, (3)
did not report on persons with diabetes (eg, mixed disease samples that
excluded or included very few persons diagnosed with diabetes), or (4) were
dissertation abstracts. Qualitative as well as quantitative study designs were
included throughout the selection process. Selected studies were then reviewed
in full, and their reference lists were scanned for additional studies not
captured in the search.
Validity Assessment
Selected studies were reviewed (by F.H.B.) for validity assessment, which
included determination of whether study methodology and findings were reported
in sufficient detail to describe and evaluate in the current review. The
American Diabetes Association (ADA) Evidence Grading System for Clinical
Practice Recommendations was considered as well. So as not to eliminate a
large percentage of the studies conducted to date, well-conducted studies with
research designs not specifically included in the ADA grading system (eg,
qualitative studies, wait list control, preintervention/postintervention
designs) were maintained for review. Three studies were assessed as not
providing a sufficient description of study methodology and/or findings for
evaluation and were therefore excluded.
Data Extraction
Data abstraction was performed by one investigator independently (L.G.),
and the abstractions were independently reviewed by another investigator
(F.H.B.). Any discrepancies were resolved through discussion and consensus.
During the data abstraction phase, additional studies were identified that did
not meet the quality grading criteria; these studies were excluded.
Study Characteristics and Data Synthesis
Study characteristics examined included the following:
- Sample characteristics: sample size, type 1 or type 2, age, gender,
race/ethnicity.
- Study design: qualitative or quantitative; cross-sectional, prospective,
randomized controlled trial; quasi-experimental design.
- Methods: measurement tools, procedures.
- Results: problem solving, self-management behaviors, and physiological,
psychosocial, and process outcomes.
Missing data were designated as such in the reporting of results and are
noted in the tables as not reported. In a few cases, when primary review data
were missing, the authors were contacted for information.
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Results
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Trial Flow
The number of studies identified and excluded at each stage of the search,
selection, and validity assessment are presented in
Figure 1. Fifty-two studies
were included in the review.
Study Characteristics
Thirty-six (69%) of the studies were quantitative, 11 (21%) were
conceptual, and 5 (10%) were qualitative. The quantitative studies employed
the following research designs: cross-sectional (n = 15), prospective (n = 1),
randomized controlled trial (n = 13), wait-list control or crossover design (n
= 4), and preintervention/postintervention design (n = 3).
Of the quantitative and qualitative studies with patients, 16 (43%)
examined children and/or adolescents and 21 (57%) examined adults. One study
examined women only, while all others examined both genders. With regard to
the race/ethnicity of patient samples, 13 (36%) studies did not report the
ethnicity of participants, 9 (25%) samples were Caucasian, 8 (22%) samples
included multiple ethnicities (generally Caucasian, African American, and
Latino), 4 (11%) samples were African American, and 2 (8%) samples were
international (Japanese, Italian).
Data Synthesis
Studies were divided into the following categories for reporting of results
(some studies were reported in more than 1 category): the construct of problem
solving in diabetes self-management, including definitions and conceptual
frameworks (n = 10); development of diabetes-specific problem-solving measures
(n = 6); associations of problem solving with health outcomes in
cross-sectional, qualitative, and prospective studies (n = 20);
problem-solving intervention studies (n = 21, 15 unique studies; others were
reports of follow-up results for the same participants); and studies of health
professionals' use of problem solving (n = 7).
The Construct of Problem Solving in Diabetes Self-management
Definition and frameworks. Within the AADE 7 core outcomes
framework, problem solving is recognized as a behavior that is malleable and
one that goes beyond acquisition of diabetes information or
skills.4,6
The definition comprises a process of sequential steps that are commonly
associated with teaching an effective problem-solving approach (eg,
identifying the problem, generating alternative solutions, selecting a
solution/decision making, implementing a solution, and evaluating the
outcome).7,8
A comprehensive model of problem solving in diabetes self-management was
described in a review of classical theories of problem solving (from cognitive
psychology, social problem solving, and
education/learning)9
and applied in a qualitative
study.10 In this
model, an understanding of patient problem solving includes the steps
characterizing not only effective problem solving but also ineffective problem
solving, a patient's emotional and cognitive orientation toward solving
diabetes-related problems, a patient's ability to learn from past experiences
and to use this learning to prevent or resolve new problems, and the
environmental context of the problem situation, including aspects of the
problem itself (eg, novelty, difficulty) and social context (eg, interpersonal
relationships and societal factors) that can influence decision making.
Quantitative problem solving. Much of the classical
research in problem solving and cognition has relied on quantitative problem
solving (eg, mathematical problem solving), also called numeracy, to
study human problem-solving behavior. Attention to numeracy in diabetes
research has expanded the diabetes problem-solving construct to incorporate
quantitative problem-solving tasks in
self-management.11
Such tasks include interpretation of and decision making from self-monitoring
data and the ability to make accurate insulin dose adjustments based on
self-monitoring.12
In addition, portion size/ingredient calculations, carbohydrate-to-insulin
calculations, and the ability to understand numerical presentations of risks
and benefits comprise quantitative problem
solving.13
Coping. Another aspect of problem solving pertains to its
relationship with the construct of coping and coping style (eg,
problem-focused coping style). Associations between problem solving and
depression are evidenced in the long-standing efficacy of problem solving as a
treatment for depression in patients without
diabetes14 and in
more recent trials demonstrating evidence of such efficacy in depressed adults
with
diabetes.15,16
The inclusion of problem solving in coping skills training interventions,
which combine multiple cognitive and behavioral treatments (eg, social skills
training, communication skills, self-efficacy, stress management) for
children, adolescents, and adults with
diabetes,3,17
illustrates the use of problem solving to facilitate effective emotional and
instrumental coping in diabetes.
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Development of Diabetes-Specific Problem-Solving Measures
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Six studies reported the development of new measures of diabetes-specific
problem solving: 3 for
children/adolescents18-20
and 3 for
adults.13,21,22
These studies and the problem-solving measure characteristics are presented in
Table 1. Each instrument was a
measure of verbal analytical/verbal reasoning aspects of problem solving, with
1 exception. The Diabetes Numeracy Assessment Tool is a measure of diabetes
quantitative problem-solving ability and the ability to understand numerical
presentations of risks and
benefits.11,13
The most commonly used assessment format was vignettes of hypothetical problem
situations to which participants were asked to
respond.19-21
One vignette format also elicited from respondents representative problem
situations that have occurred in their own
lives.21
Studies varied in the types of reliability and validity data reported, but
each study presented acceptable psychometric performance in the development
samples. Associations reported in more than 1 study were between higher scores
on the problem-solving measure and better global adherence in
children/adolescents,18,19
dietary behavior in adolescents and
adults,18,21
and A1C in adolescents and
adults.20,22
Scales studied most extensively to date appear to be the Diabetes Problem
Solving
Inventory,21,23
which was developed in large samples of adult, type 2 patients over several
years, and the Situational Obstacles to Dietary
Adherence.18 The
Test of Diabetes Knowledge and
Problem-Solving,1
which was developed and validated for type 1 children, adolescents, and their
parents prior to the period of this review (scale not shown), has demonstrated
utility in previous studies and was used in 1 study conducted during the
inclusion period for this
review.24 Two of
the studies describing scales developed for children and adolescents reported
either difficulty for younger children (eg, younger than 12 years) to complete
the measure18 or
significantly lower problem-solving scores in younger children as compared
with adolescents,19
likely reflecting issues related to cognitive development of problem-solving
abilities.9
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Associations of Problem Solving With Health Outcomes
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Table 2 presents
cross-sectional, qualitative, and prospective studies examining the
associations of problem solving with self-management behaviors, disease
control, and process and psychosocial outcomes. Problem solving was assessed
using a variety of methods including informal ratings, diabetes-specific
instruments reported in Table
1, generic problem-solving scales (Test of Diabetes Knowledge and
Problem Solving,1
Coping Scale,25
Social Problem-Solving
Inventory–Revised,26
Melbourne Decision-Making
Questionnaire27),
and 1 general health-related problem-solving scale (Health Problem-Solving
Scale28). A1C was
the most frequently reported outcome, followed by process and psychosocial
outcomes. Self-management behaviors were the outcomes examined least
frequently.
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Table 2 Associations of Problem Solving With Self-management Behaviors, Disease
Control, and Process and Psychosocial Outcomes (Cross-Sectional, Qualitative,
and Prospective Studies)
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Disease control. Disease control variables (A1C,
hyperglycemia, hypoglycemia, emergency department visits) were consistently
associated with problem solving across studies conducted in adults, with
Caucasian, African American, and multiethnic
samples.10,28-32
The 1 exception was a study conducted in a Japanese sample, which used a
coping styles questionnaire and found that higher problem-oriented style was
not associated with A1C in women and was associated with a worse A1C in
men.33 Glasgow et
al32 examined an
additional disease control marker, non–high-density lipoprotein (HDL)
cholesterol, and found that higher exercise-related problem-solving ability
(but not diet-related problem solving) was associated with lower non-HDL
cholesterol level.
Of the 2 studies conducted with children, one found no association between
children's A1C and either their or their parents' diabetes problem-solving
scores,24 while the
other found that better family problem-solving ability was associated with a
lower A1C level in the diabetic
children.34
Decision-making competence was not associated with A1C level in the 1 study of
adolescents.35
Psychosocial outcomes. With regard to psychosocial
outcomes, 2 studies reported associations of ineffective problem-solving
styles with depressive
symptoms.31,36
Investigated process variables included how patients learn problem solving
with diabetes
experience,7,37
family communication styles and problem
solving,34,35
and characterization of 4 different types of problem solvers based on
cognitive-affective problem-solving orientation and problem-solving
ability.36
Self-management behaviors. In adults, problem solving was
associated with selected self-management behaviors (diet, exercise) when
diabetes-specific problem-solving assessments were
used,32,38
while a generic problem-solving measure was not sensitive to diabetes
self-management
behaviors.31 In the
only child or adolescent study to report on self-management, Miller et
al35 found no
association between adolescents' decision-making competence and the number of
glucose tests performed per day, but higher decision-making competence was
associated with higher parental (but not physician) report of adolescents'
adherence to self-care.
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Intervention Studies of the Effect of Problem Solving on Diabetes Outcomes
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Intervention study characteristics are presented in
Table 3. Eight studies reported
interventions with children and/or adolescents
(child/adolescent),39-49
and 8 reported interventions with
adults.15,16,50-57
Although problem solving was a component of each of these interventions, the
degree to which it was emphasized and how it was used varied greatly from
study to study. For example, although most interventions focused specifically
on problem solving as a primary treatment
approach,41,48
other studies, such as those conducted by Grey and
associates42-44
and Wysocki and
associates,46,47
included problem solving as 1 component of a multifaceted coping skills
training or behavioral intervention. In addition, 2 of the studies that
focused on problem solving as the treatment approach specifically targeted
depression as an intermediate outcome for improving diabetes
outcomes.15,16
Four sets of outcomes were reviewed for each intervention study: problem
solving, self-management behaviors, physiological outcomes, and psychosocial
outcomes.
Problem solving. Problem solving was reported in 5 (62%) of
the 8 studies with children and adolescents and in 2 (25%) of the 8 studies
with adults. Each assessed problem solving using a different measure. Of the
studies that reported problem solving, 2 (40%) child/adolescent
studies45,48
found a positive intervention effect on problem-solving ability. Both adult
studies found significant improvements in problem solving following
intervention and saw maintenance of increased problem solving at 6-month
follow-up50 and at
a 5-year intervention
point.57
Self-management behaviors. Six (75%) child/adolescent
studies reported effects of the intervention on self-management behaviors. In
about half of these child/adolescent studies, improvements were reported in
behaviors, including dietary
intake,39
self-monitoring of blood
glucose,40 and
general treatment adherence, as compared with control groups, for up to 12
months of
follow-up.47
Six (75%) of the adult studies reported self-management behavior outcomes,
with dietary behaviors as the most consistent behavior studied. Four of these
studies found improvements in the dietary behaviors of the problem-solving
intervention
participants.15,50-54,56
Other self-management behaviors that were found to show improvements at 6 or
12 months were self-monitoring of blood
glucose,50,56
exercise,15,50,56
and medication
adherence.16
Another study found no improvements in medication adherence or foot
inspections.15
Physiological outcomes. With 2
exceptions,41,45
each intervention study assessed A1C or an A1C equivalent (eg, GHb). Of the
child/adolescent studies, 2 (25%) found significant decreases in A1C level for
the intervention group at
postintervention39
and for more than 1 year of
follow-up.44 In
contrast, 3 studies found no effect on glycemic
control,16,40,48
and 1 study reported a higher A1C level in both the intervention and control
groups following intervention, which the investigators attributed to the age
of the
participants.49
Another study reported no improvements in blood glucose
levels.45
Of the adult studies, 4 (50%) found decreases in A1C following the
intervention,50,55,56
with 1 study reporting maintenance of improved A1C in the intervention versus
control patients over 2 years and 5 years of
follow-up.57,58
One study found improved blood glucose levels immediately following the
intervention but without maintenance at 3-month
follow-up.54 Other
physiological outcomes reported in individual adult studies were decreased
weight in 2
studies50,57
and no improvement in weight in
another.56 Glasgow
and associates53
reported decreased total cholesterol for up to 6 months of follow-up. Trento
and associates also reported cardiovascular disease (CVD) markers (total
cholesterol, HDL cholesterol, and triglycerides) and found improvement in HDL
at 2 years but no intervention effect on any of these CVD variables at 5
years.57,58
Psychosocial outcomes. In 3 studies of problem-solving
interventions in children/adolescents, improvements in self-efficacy were
reported45 for up
to 12 months of
follow-up.44,49
Other outcomes reported were improvements in
adjustment,47 some
aspects of parent-adolescent relationships and diabetes-related
conflict,47 ability
to use sick-day self-management
guidelines,41 and
quality of
life.44
Of the adult studies, 1 reported improved self-efficacy
postintervention,56
while another did not find differences between the intervention and control
participants in diet or exercise
self-efficacy.50
Three studies found improvements in depressive symptoms for intervention
participants at 6
months55 and up to
12
months.15,16
Two studies found no differences between the intervention and control groups
in quality of
life,50-53
while 1 study found improved quality of life sustained over 5 years in the
intervention versus control
group.57
Problem Solving for Hyperglycemia, Hypoglycemia, and Sick-Day Management
The AADE 7 outcomes framework describes problem solving as particularly
important for managing hyperglycemia/hypoglycemia and sick
days.4,5
Three of the identified studies specifically addressed problem solving for
these acute
conditions.7,41,59
Based on observational records from a type 1 sample, Kovatchev et
al59 developed a
mathematical model to describe symptom-based decision making for hypoglycemia,
including blood glucose level estimation, detection, and decision making for
treatment. Paterson and
Thorne7 conducted a
qualitative study with a small, selected group of experienced adults with type
1 diabetes to identify processes they used to make decisions regarding
unanticipated blood glucose levels (either hyperglycemia or hypoglycemia).
From their observational data, 2 decision models were developed to
differentiate identified thinking patterns/action choices for familiar versus
unfamiliar situations. In an intervention study, Pichert et
al41 tested a
problem-solving anchored instruction to improve adolescents' cognitive grasp
of sick-day management guidelines. Although no intervention effect was seen on
knowledge, recall, or understanding of guidelines, the problem-solving
instruction method improved adolescents' ability to link the guidelines to
hypothetical situations they might encounter, an important step in potential
guideline use and generalizability.
Health Care Professionals and Problem Solving
Although relatively few studies addressed health professionals and problem
solving, each was deemed informative in addressing different aspects of the
experiences or needs of health professionals with regard to problem solving. A
survey of diabetes educators identified problem solving as the most difficult
of skills to teach
patients.60 In an
observational study of interactions between health care professionals and
diabetic patients, Zoffman and
Kirkevold61
examined how 3 different approaches to problem solving can either disempower
patients (eg, failure-expecting or compliance-expecting approaches in which
the provider views the patient as a problem or the provider does the problem
solving) or empower patients (eg, mutuality-expecting approach in which
patients are the problem solvers). Similarly, the educator philosophy proposed
for patient empowerment programs by Arnold and
colleagues62
describes encouragement of patients to solve their own problems and respecting
the rights of patients to make their own choices as keys to promoting
follow-through and maintenance.
One study specifically evaluated a program for teaching professionals how
to use problem solving in patient
education.63 This
Effective Patient Teaching and Problem Solving program, which involved 24
hours of training over 3 days, resulted in posttraining improvements in
professionals' assessment, brainstorming, collaboration, and direct
instruction skills, including differentiating when direct instruction versus
problem solving is needed. Impact on subsequent patient education or outcomes
was not included in the reported
evaluation.63
llaDavis and
colleagues64
described solution-focused therapy strategies for patient diabetes education
that use a positive environment, goal setting, and client-centered approaches.
With regard to tools for professionals' use, a videodisc problem-solving
education video to facilitate problem-solving instruction with adolescents was
introduced by Pichert and
colleagues8 and used
in subsequent intervention
studies.45,65
A final study described a tool, the Patient Assessment of Chronic Illness
Care, for evaluating the extent to which health care professionals have
addressed aspects of problem solving in counseling of patients during the
medical
encounter.66 With a
large sample representing non-Latino and Latino patients, this measure
identified a need for improvement in patient receipt of problem
solving/contextual and goal-setting/tailoring assistance during the medical
encounter.66
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Discussion and Conclusions
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From the systematic review, the following can be concluded regarding
evidence for problem solving in diabetes self-management:
- Definition. Problem solving is a learned skill that is most commonly
characterized as involving a sequence of rational steps. Studies have used
problem solving as a multidimensional construct, comprising both effective and
ineffective problem-solving strategies, emotional and cognitive orientation to
problem solving, ability to learn from past experience, and environmental
context. Problem solving is also conceptualized as a construct related to
emotional and instrumental coping with diabetes. Research has focused
primarily on the verbal reasoning and coping aspects of problem solving, with
relatively little attention to quantitative problem solving (numeracy) to
date.
- Assessment. Diabetes-specific assessments are available to measure
self-reported problem solving, and the 6 new assessment instruments identified
during this review period reported adequate internal consistency and aspects
of validity in the development samples. However, for generalizability, each
requires additional studies of reliability and validity in multiple samples
and in patients with differing sociodemographic characteristics (eg, gender,
age, race/ethnicity, language, settings). A second limitation is that few
procedures have been reported for the behavioral assessment of actual problem
solving to validate self-report. Evidence strongly supports the continued use
of the assessment instruments, in research, to identify psychometric
parameters, utility, and revision needs as appropriate for additional
populations. Evidence is premature for clinical use of these assessments to
make treatment decisions for an individual patient. However, evidence supports
using the assessments in an observational manner, along with well-established
education and self-management assessments, and evaluating the performance of
the problem-solving instrument in this context.
- Association with self-management behaviors, disease control, and process
and psychosocial outcomes. There appears to be rather consistent evidence
from cross-sectional studies of adults (including the diabetes-specific
measure development studies) that ineffective/poor problem-solving ability is
associated with poorer glycemic control, both in Caucasian and African
American samples. Evidence regarding associations of problem solving with A1C
level in children and adolescents is inconsistent. Overall, more studies are
needed to draw conclusions regarding associations of problem solving with
diabetes self-management behaviors. Studies to date have reported isolated
areas of associations across behaviors (nutrition, exercise, self-monitoring).
There may be an interaction between how problem solving is assessed and
outcomes, with diabetes-specific problem-solving measures demonstrating more
sensitivity to diabetes behaviors (but not to A1C level) than generic
problem-solving measures. With regard to acute hyperglycemia, hypoglycemia,
and sick-day management, there were fewer problem-solving studies specifically
addressing these behaviors and outcomes. An important addition to the
literature would be studies that examine how to translate models of decision
making and action choices for hyperglycemia and
hypoglycemia7,59
into problem-solving strategies that can be taught effectively. Moreover,
research examining the differential effectiveness of direct instruction
(proscribed steps/actions to take,
guidelines)63
versus problem solving or how both direct instruction and problem solving can
be optimally combined for acute hyperglycemia, hypoglycemia, and sick-day
management is needed.
- Effectiveness of problem-solving interventions. Evidence appears
strongest for effectiveness of the interventions on isolated self-management
behaviors in children, adolescents, and adults and on depression in adults.
Evidence for intervention effectiveness on physiological outcomes is
inconsistent and weaker; only 37% of the studies reported an improvement in
A1C following intervention (25% of child/adolescent studies and 50% of adult
studies). Of the studies reviewed, the most promising outcomes with regard to
self-management behaviors, physiologic outcomes, and psychosocial outcomes
were reported for Glasgow and associates' group self-care education program
for older adults, focusing on individualized goal-setting and problem
solving,50 and for
Grey and associates' group coping skills training, in which problem solving
serves as 1 treatment
component.42-44
Methodological limitations in the reviewed studies reduce the strength of the
evidence regarding the effectiveness of the interventions as a whole. Very few
interventions assessed problem solving as an outcome to identify the extent to
which the intervention modified patient problem-solving ability or the extent
to which problem solving was associated with outcomes. Among the studies that
assessed problem solving, each used a different measure of problem solving,
and findings were varied. With few exceptions, the problem-solving
intervention was not well-defined in the articles, making it difficult to
identify what common factors exist across interventions. Future studies should
provide more detail regarding how problem solving was used, what
problem-solving materials were used, and how prominent a role problem solving
played in the intervention time and focus. Finally, studies examining the
effect of interventions within racial/ethnic minority groups, including
African Americans, Asians, Latinas(os), and Native Americans, are needed.
- Health care professionals/training issues. Currently, there is
insufficient evidence to evaluate either health care professionals' general
use of problem solving or effectiveness of methods for training professionals
to teach patient problem solving because of the paucity of research to date in
these areas. However, findings from the reviewed studies reinforce
conventional wisdom that problem solving is a difficult skill to teach
patients, that it may be underused as a counseling tool during routine medical
visits, and that it can be empowering if patients are the problem solvers and
can be disempowering and lead to conflict if providers usurp the
problem-solving process. A promising tool is available to evaluate patient
ratings of the extent to which problem solving is included in routine medical
encounters.66
Further research is needed to evaluate tools health care professionals can use
for patient education in problem solving and to evaluate systematic methods
for training health care professionals in the effective use of problem solving
in routine medical encounters and in educational programs. Evaluation of the
effectiveness of training on both health professionals' skill attainment and
subsequent patient education/outcomes is needed.
This systematic review aimed to compile and evaluate problem-solving
research in diabetes self-management and control. The review has yielded a
small to moderate body of recent work addressing definitions of problem
solving, important progress in the development of instruments to assess
problem solving, and limited but compelling evidence that problem solving may
be an effective intervention tool (or component of an intervention) for select
outcomes. However, specific recommendations for systematic integration of
problem-solving training methods into DSME, for different patient groups and
outcomes, are premature based on the current state of evidence.
Future research needs to take a few key directions to increase the
understanding of problem solving and effective and appropriate use of problem
solving in DSME. First, additional research designs are recommended, including
well-conducted prospective cohort studies and meta-analyses. Second, a full
evaluation of problem-solving interventions necessitates that researchers
provide the following details regarding intervention study design and conduct:
(1) problem-solving content of the intervention (eg, intervention protocol or
curriculum, materials used, percentage of time and content devoted to
problem-solving training), (2) delivery of the intervention (eg, session
number, duration, and frequency; face-to-face or remote modality; individual
vs group), (3) information on interventionists (eg, who delivered the
intervention/interventionist disciplines and experience; how interventionists
were trained), (4) information on participants (eg, age, gender,
race/ethnicity; whether outcomes differed by subgroups), (5) measurement of
problem solving, (6) measurement of specific self-management behaviors, (7)
measurement of specific clinical markers of diabetes control (eg, A1C) and
also including CVD markers (eg, total, HDL, and low-density lipoprotein
cholesterol; triglycerides; blood pressure), and (8) information on costs and
cost-effectiveness (eg, costs involved in the conduct and implementation of
the intervention relative to usual care costs and/or savings from therapeutic
benefit). Third, conduct of meta-analyses, with quality ratings for studies
included in the meta-analysis, will facilitate answering the question needed
for optimal use of problem solving in DSME: what problem-solving training
methods are effective for what outcomes in what patients.
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Acknowledgments
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This research was supported by National Institutes of Health grant 1 K01
HL076644.
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The Diabetes Educator, Vol. 33, No. 6,
1032-1050 (2007)
DOI: 10.1177/0145721707308412
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Abstract
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