Effectiveness of health coaching on diabetic patients:A systematic review and meta-analysis

REVIEW
TMR | November 2019 | vol. 4 | no. 6 | 314
Submit a manuscript: https://www.tmrjournals.com/tmr
doi: 10.12032/TMR20191024143
Nashwa Mohamed Radwan1, 2*
, Hisham Al Khashan3
, Fahad Alamri3
, Ahmed Tofek El Olemy4
1
Department of Public Health and Community Medicine, Tanta University, Tanta, Egypt. 2
Consultant in Clinical
Education Department, Ministry of Health, Riyadh, Kingdom of Saudi Arabia. 3
Family Medicine Consultant,
Ministry of Health, Riyadh, Kingdom of Saudi Arabia. 4
National Center for Complementary and Alternative
Medicine, Ministry of Health, Riyadh, Kingdom of Saudi Arabia.
*Corresponding to: Nashwa Mohamed Radwan, Department of Chronic Diseases Prevention, Ministry of Health,
Zarga Alyamamah St Riyadh 12628, Riyadh 11176, Kingdom of Saudi Arabia. Email: radwan.n.m@gmail.com.
Highlights
Health coaching intervention has a significant effect on hemoglobin A1c and high-density lipoprotein
cholesterol of patients with diabetes mellitus.
Traditionality
The first use of the term "coach" arose around 1830 in Oxford University as a slang in relation with an
instructor or trainer or tutor who "carried" a student through an exam. The term “coaching” thus refers to
the process of transporting people from where they are to where they want to be. In 1915, the National
Board of Medical Examiners was founded. In 2002, Wellcoaches partnered with the American College of
Sports Medicine. In 2010, the National Consortium for Credentialing Health and Wellness Coaches was
founded. In 2017, the International Consortium for Health and Wellness Coaching was established.
Coaching as a method to improve healthy lifestyle behaviors has received special attention in recent years.

REVIEW
TMR | November 2019 | vol. 4 | no. 6 | 315
Submit a manuscript: https://www.tmrjournals.com/tmr
doi: 10.12032/TMR20191024143
Abstract
Background: Using health coaching to improve the quality of life and health outcomes of the patients with
diabetes mellitus, has emerged as a possible intervention. However, the few published randomized controlled trials
using health coaching for patients with diabetes mellitus have reported mixed results. The present meta-analysis
aimed to determine the effectiveness of health coaching on modifying health status and quality of life among
diabetic patients and to clarify the characteristics of coaching delivery that make it most effective. Methods: This
study searched for articles on randomized controlled trials of health coaching interventions targeting type 2 diabetic
patients that were published in the English language from January 2005 through December 2018 in the Cochrane,
Medline, PubMed, Trip, and Embase databases. Patients in the control group received usual diabetes mellitus care,
and those in the experimental group received health coaching based on usual diabetes mellitus care. The primary
outcomes included Hemoglobin A1c (HbA1c) and cardiovascular disease risk factors, including systolic blood
pressure, diastolic blood pressure, triglyceride, high-density lipoprotein cholesterol (HDL-C), low-density
lipoprotein cholesterol, total cholesterol, and body weight. The secondary outcomes included quality of life,
self-efficacy, self-care skills, and psychological outcomes. Results: Health coaching intervention has a significant
effect on HbA1c [mean difference (MD) = -0.35, confidence interval (CI) = -0.47, -0.22, I2
= 83%, P < 0.001] and
HDL-C (MD = -0.50, CI = -0.93, -0.07, I2
= 10%, P = 0.02). The most effective strategy for health coaching
delivery associated with improvement of HbA1c was decreasing the number of sessions and increasing the duration
of each session. However, no significant difference was found for weight, SBP, diastolic blood pressure,
triglyceride, low-density lipoprotein cholesterol, or total cholesterol. Mixed results were reported for the effect of
health coaching on quality of life, self-efficacy, self-care skills, and depressive symptoms outcome. Conclusion:
Health coaching intervention has a significant effect on HbA1c and HDL-C, and the most effective strategy is
decreasing the number of sessions while increasing session duration. However, these results should be interpreted
with caution as the evidence comes from studies at some risk of bias with considerable heterogeneity and
imprecision.
Keywords: Health coaching, Type 2 diabetes mellitus, Randomized controlled trials, Hemoglobin A1c, Weight,
High-density lipoprotein cholesterol.
Abbreviations:
HbA1c, Hemoglobin A1c; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; HDL-C,
High-density lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol; GRADE, Grading of
recommendations assessment, development and evaluation; MD: Mean difference; CI: Confidence interval;
RCT, Randomized controlled trials.
Competing interests:
The authors declare that there is no conflict of interest. The authors alone are responsible for the content of
the paper.
Citation:
Nashwa Mohamed Radwan, Hisham Al Khashan, Fahad Alamri, et al. Effectiveness of health coaching on
diabetic patients: A systematic review and meta-analysis. Traditional Medicine Research 2019, 4 (6):
314-325.
Executive Editor: Nuo-Xi Pi.
Submitted: 20 August 2019, Accepted: 21 October 2019, Online: 5 November 2019.

REVIEW
Submit a manuscript: https://www.tmrjournals.com/tmr TMR | November 2019 | vol. 4 | no. 6 | 316
doi: 10.12032/TMR20191024143
Background
Chronic diseases represent a growing public health
problem around the world. Approximately half the
adults in the United States have at least 1 chronic
disease, and 26% have multiple chronic diseases [1].
Diabetes mellitus is an emerging global epidemic
disease, and more than 346 million people suffer
from type 2 diabetes mellitus in the world [2]. By the
year 2030, diabetes mellitus is predicted to become
the seventh leading cause of death in the world [3].
The prevalence of diabetes mellitus has been
increasing. Seven million people are diagnosed with
this disease each year and every 10 seconds, a person
dies because of diabetes mellitus-related causes [4].
Diabetes mellitus is a chronic disease which needs
the lifelong medical and nursing intervention and
lifestyle adjustment [5]. The Diabetes Mellitus
Control and Complications Trial (1998) showed that
for every 1% reduction in hemoglobin A1c (HbA1c)
levels, there was a 40-50% reduction in risk for
microvascular and neuropathic complications [6, 7].
However, patient’s non-compliance with treatment
often exceeds 50% and have even been reported as
high as 93% [8, 9], emphasizing the clear need for
interventions focused on the lasting behavior change
and accountability. To change patients’ health
behaviors, education-based initiatives needs patient’s
self-management support and cooperation [10, 11].
Tools that provide information about options and
consequences may increase patient’s knowledge and
improve their attitudes to self-care, but the impact on
behavior change is little [12]. Approaches of
improving self-efficacy increase the possibility of
behavior change and contribute to better health
outcomes and more appropriate healthcare utilization
[11].
The first use of the term “coach” arose around
1830 in Oxford University as a slang in relation with
an instructor or trainer or tutor who “carried” a
student through an exam. The term coaching thus
refers to the process of transporting people from
where they are to where they want to be. In 1915, the
National Board of Medical Examiners was founded.
In 2002, Wellcoaches partnered with the American
College of Sports Medicine. In 2010, the National
Consortium for Credentialing Health and Wellness
Coaches was founded. In 2017, the International
Consortium for Health and Wellness Coaching was
established [13]. Coaching as a method to improve
healthy lifestyle behaviors has received special
attention in recent years [14]. A healthy lifestyle is
important in patients care and help to prevent many
lifestyle diseases that are dramatically increasing
during recent years [15, 16]. Health coaching has
become as a widely adopted intervention to help
individuals with chronic conditions adopt
health-supportive behaviors that improve both the
health outcomes and quality of life [17]. A
comprehensive conceptual definition of health
coaching was provided by Wolever et al. (2013). The
authors defined coaching as “a patient-centered
approach wherein patients at least partially determine
their goals, use self-discovery or active learning
processes together with content education to work
toward their goals, and self-monitor behaviors to
increase accountability, all within the context of an
interpersonal relationship with a coach” [18]. The
few published randomized controlled trials (RCT) on
the use of health coaching for patients with diabetes
mellitus have reported mixed results. There is a need
for evidence synthesis to evaluate the effectiveness
of health coaching, particularly to examine the
components that are necessary for its effectiveness
and settings in which it is most applicable.
This systematic review and meta-analysis aimed to
determine the effectiveness of health coaching on
modifying health status and lifestyle among diabetic
patients and to clarify the characteristics of coaching
delivery that makes it most effective.
Materials and methods
Inclusion criteria
Types of studies. We included RCT on health
coaching interventions reported in articles that were
published in English during the past 15 years (from
January 2005 through December 2018).
Types of participants. Patients with type 2 diabetes
mellitus with no mental or physical limitations that
would preclude participation were included in the
study.
Types of interventions. Details of the interventions
provided in the article descriptions of the
characteristics of the included studies were as
follows: The interventions were primarily health
coaching interventions that focused on diabetes
mellitus education, diabetes mellitus
self-management skills, providing social and
emotional support, providing assistance for lifestyle
modification (advice on diet, physical activity,
weight control, and tobacco cessation), and
facilitating medication adherence. The comparator
used was usual diabetes mellitus care. All services
available to patients in normal situation care are
included in the usual care, including access to a
nutritionist and diabetes mellitus educator through
referral from a primary care clinician.
Types of outcome measures. Primary outcomes
included hemoglobin A1c (HbA1c) and
cardiovascular disease risk factors, including systolic
blood pressure (SBP), diastolic blood pressure (DBP),
triglyceride, high-density lipoprotein cholesterol
(HDL-C), low-density lipoprotein cholesterol

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doi: 10.12032/TMR20191024143
(LDL-C), total cholesterol, and body weight.
Secondary outcomes included quality of life,
self-efficacy, self-care skills and psychological
outcome.
Exclusion criteria
We excluded non-RCT, patients with mental or
physical limitations, patients with HbA1c < 7%, SBP
< 140 mmHg, cholesterol level < 100 mg/dL, and
non-English material.
Search methods for identification of studies
We identified studies through systematic searches of
the Cochrane, Medline, PubMed, Trip, and Embase
databases. We adapted the preliminary search
strategy for Medline (Ovid) for use in the other
databases. The search was conducted using the
following terms: diabetes, OR diabetes mellitus, OR
type 2 diabetes AND diabetic coaching, OR health
coaching for diabetes, OR telephone coaching, OR
behavior support intervention, OR health coaching
intervention, OR diabetic patient engagement, OR
patient engagement and coaching, OR peer coaching,
OR nurse health coaching, OR medical assistant for
diabetes AND RCT. We checked the reference lists of
all primary studies and review articles for additional
references.
Data collection and analysis
Selection of studies. The titles and abstracts of
potential articles were read independently by (NMR
and ATE). An article was rejected only if both review
authors determined from the title or abstract that the
article was not a randomized controlled trial. After
reviewing the full articles, the studies that were not
relevant to the review were excluded. Remaining
records were independently checked by the same
review authors. All papers that were thought to be of
relevance were obtained and read by (NMR and ATE)
independently.
Data extraction and management. We used a data
collection form for study characteristics and outcome
data. One author (NMR) extracted the study
characteristics from the included studies. We
extracted the following characteristics
(Supplementary annex 1): Methods: study design,
total duration of study, and study setting; Participants:
number, mean age, diagnostic criteria, and inclusion
and exclusion criteria; Interventions: intervention and
comparison, including method, duration, frequency
of coaching, and coaching qualification; Outcomes:
primary and secondary outcomes specified and
collected; Risk of bias: 2 authors (NMR and ATE)
independently extracted outcome data from the
included studies. Any disagreements were resolved
by discussion. One author (NMR) transferred the
data into the Review Manager (RevMan) 5.3
software [19].
Assessment of risk of bias in included studies. Two
authors (NMR and ATE) independently assessed risk
of bias for each study using the criteria outlined in
the Cochrane Handbook for Systematic Reviews of
Interventions [20]. We resolved any disagreements
by discussion. We graded each potential source of
bias as high, low, or unclear, and provided a quote
from the study report together with a justification for
our judgement in the risk of bias table. We assessed
risk of bias according to the following domains:
random sequence generation (selection bias),
allocation concealment (selection bias), blinding of
outcome assessment (performance bias), incomplete
outcome data (attrition bias), and selective outcome
reporting (reporting bias).
Assessment of quality of evidence. We assessed the
quality of evidence of the primary outcomes using
the Grading of Recommendations Assessment,
Development and Evaluation (GRADE) approach
[21]. The results are presented in Table 1. The
GRADE system considers quality to be a judgement
of the extent to which we can be confident that the
estimates of effect are correct. The level of “quality”
is judged on a 4-point scale: High quality: further
research is very unlikely to change our confidence in
the estimate of effect; Moderate quality: further
research is likely to have an important impact on our
confidence in the estimate of effect and may change
the estimate; Low quality: further research is very
likely to have an important impact on our confidence
in the estimate of effect and is likely to change the
estimate; Very low quality: any estimate of effect is
very uncertain.
We initially graded the evidence from RCT as high,
and downgraded them later by either 1 or 2 levels
after full consideration of the 4 recommended
domains affecting study limitation: risk of bias in the
included studies, directness of the evidence,
consistency across studies, and precision of the
pooled estimate or the individual study estimates.
Measures of treatment effect. We used RevMan 5.3
to manage the data and to conduct the analyses [19].
We calculated mean difference (MD) with 95%
confidence interval (CI) when the studies use the
same scale.
Dealing with missing data. We contacted
investigators to obtain missing numerical outcome
data where possible.
Dealing with heterogeneity. We used the I² statistic
to measure heterogeneity among the studies in each
analysis [22].
Significant difference analysis We summarized and
analyzed all eligible studies in RevMan 5.3. Two
authors (NMR and ATE) extracted the data; the first
author entered all the data and the second author
checked all the entries. Disagreements were resolved
by discussion. We undertook meta-analyses only

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Table 1 Summary of the main outcomes of the included studies
Outcome MD CI Number of
participants
Heterogeneity Quality of evidence
HbA1c - 0.35 -0.47, -0.22 3,645 I2
= 83%, P < 0.001 Moderate
SBP - 0.82 -2.83, 1.20 2,949 I2
= 94%, P < 0.001 Moderate
DBP 0.13 -1.52, 1.78 2,556 I2
= 98%, P < 0.001 Moderate
Weight -5.09 -10.12, -0.07 2,099 I2
= 92%, P < 0.001 Low
Total cholesterol -1.88 -9.06, 5.29 2,816 I2
= 96%, P < 0.001 Low
HDL-C -0.50 -0.93, -0.07 1,405 I2
= 10%, P = 0.35 Low
LDL-C -1.97 -9.37, 5.43 1,841 I2
= 96%, P < 0.001 Low
Triglyceride -3.11 -7.54, 1.33 1,665 I2
= 72%, P = 0.01 Low
HbA1c, Hemoglobin A1c; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; HDL-C, High-density
lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol; MD, Mean difference; CI, Confidence
interval.
where this was meaningful. We combined the data
using a random effects model.
Results
Search results
Our search found 1,679 potential articles. A total of
533 remained after removal of duplicates. Abstracts
were reviewed based on inclusion and exclusion
criteria by 2 authors (NMR and ATE) independently.
One hundred and thirty-six full-text articles were
independently assessed for eligibility. Of these, 19
met the inclusion criteria. Details of the flow of
studies through the review are presented in the paper
flow chart (Figure 1).
Included studies
Details of the methods, participants, interventions,
comparison groups, and outcome measures for each
of the included studies in the review are provided in
a table depicting the characteristics of included
studies (Table 1 in the supplementary material).
Study participants
The participants of this review (3,573) were diabetic
patients (type 2) aged 18 and over (in all the studies),
with no mental or physical limitations that would
preclude participation in the study. The inclusion
criteria were as follows: HbA1c > 7.5% [23-30]; at
high risk for diabetic complications e.g.,
cardiovascular risk factors: body mass index > 27
kg/m2 [23, 30], SBP > 140 mm Hg [28], LDL-C
level > 100 mg/dL [30].
Risk of bias in included studies
We present details of risk of bias for each of the
included studies in the risk of bias table (Figure 2).
Overall, the studies included in this review were at
some risk of bias, except the study of Kempf et al.
[23]. All studies had at least 1 domain with unclear
risk of bias, except the study of Kempf et al. [23],
and 2 studies were unclear for attrition bias [29, 31].
One study was at high risk for reporting bias [32].
Random sequence generation (selection bias). The
random sequence generation was adequate in 8
studies [23, 25, 30-35] and unclear in 10 studies [24,
26-29, 36-40].
Allocation concealment (selection bias). Allocation
concealment was unclear in 6 studies [24-25, 35, 38,
39, 41] and adequate in the remaining 13 studies.
Blinding (performance bias). Three studies masked
outcome assessors to the treatment allocation (low
risk) [23, 29, 32], and 5 studies did not (high risk)
[28, 35-36, 38, 41]. The risk was unclear in 11
studies [24-27, 30-31, 33-34, 37, 39, 40].
Incomplete outcome data (attrition bias). Attrition
bias was unclear in 2 studies [29, 31] and low in the
remaining 17 studies [23-28, 30, 32-35, 36-41].
Selective reporting (reporting bias). There was low
risk of selective reporting bias in 15 studies [23-29,
30, 31, 33, 35, 38-41], unclear risk in 3 studies [29,
36-37], and high risk in 1 study [32].
Other potential source of bias. There was
insufficient information to judge whether there were
other risks of bias.
Characteristics of health coaching interventions
With regard to the characteristics of the health
coaching interventions among the included studies, it
was observed that there were differences in the
strategy of health coaching delivery, including
number of participants, method, duration and
frequency of coaching, and coaching qualification.
All these factors affected the outcome in the
intervention groups. Number of participants in the
intervention groups ranged from 21 [34] to 385 [33].
Most of the coaching sessions (in 14 studies) were
conducted through telephone calls, either alone or
with other coaching methods. Other studies used
either educational classes, discussion, in-person visits,
person contact, video, or electronic action plan with
different levels of outcome. Duration of health
coaching varied: 3 months in 1 study, 6 months in 8
studies, 9 months in 3 studies, 12 months in 4 studies,

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Figure 1 The paper flow chart
13 months in 1 study, 15 months in 1 study, and 18
months in 1 study. Frequency of sessions included 2
separate days, weekly, biweekly, monthly, every 6
weeks, every 2.5 months, and every 3 months. Also,
qualifications differed among the studies. The
coaches were nurses in 8 studies; specialists in 3
studies; social workers or psychologists in 4 studies;
dietitians, trained medical assistants, or community
health workers in 2 studies; and trained diabetes
coaches, peer coaches, behavior counselling
specialists, health coaches or professional caregivers
in 1 study. The considerable variation in the
characteristics of health coaching indicates that there
is no standardization in health coaching with regard
to number of participants, method, duration, or
frequency of coaching in addition to coaching
qualification, and hence variations in outcome.
However, it was observed that the most effective
delivery strategy for health coaching was few
coaching sessions with increased duration of each
session (Figure 3).
Primary outcomes
HbA1c.
HbA1c was assessed in 15 studies with 3,645
participants (Figure 4). A statistically significant
reduction in the mean HbA1c is reported among
intervention groups after health coaching
interventions compared with the control groups (MD
= -0.35, CI = -0.47, -0.22). A statistically signiﬁcant
high heterogeneity between studies is observed (I²=
83%, P < 0.01), indicating considerable
inconsistency among the included studies in the
estimate, which may be due to differences in study
participants, strategy, method, duration, or frequency
of health coaching and coaching qualification. The
test for funnel plot asymmetry is presented in Figure
5.
Cardiovascular disease risk factors
SBP. SBP was recorded in 9 studies among 2,949
participants (Figure 1 in the supplementary material).
The pooled effect shows no statistically significant
difference in mean SBP in the intervention groups
after the health coaching interventions (MD = -0.82,
CI = -2.83, 1.20, P = 0.43) with evidence of
statistically signiﬁcant considerable between-study

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heterogeneity (I²= 94%, P < 0.01). The considerable
heterogeneity may be explained by the variations in
baseline level of SBP among the included studies
along with the variations in management method and
coaching strategy. Test for funnel plot asymmetry
was not applied because the number of included
studies in the meta-analysis was less than 10 [23].
DBP. DBP was assessed among 2,556 participants in
7 studies (Figure 2 in the supplementary material).
No statistically significant difference is found in the
mean DBP after health coaching interventions (MD =
0.13, CI = -1.52, 1.78, P = 0.88) with considerable
heterogeneity between studies (I2
= 98%, P < 0.01,
which can be attributed to the same reasons as SBP).
Weight. Weight was analyzed among 2,099
participants in 5 studies (Figure 3 in the
supplementary material). No statistically significant
reduction in the mean weight is observed among
intervention groups after health coaching
interventions (MD = -5.09, CI = -10.12, -0.07， P =
0.05). However, the analysis showed considerable
imprecision with wide CIs, as the analysis included
only 5 studies and considerable heterogeneity (I2
=
92%, P < 0.01), which could be attributed to
variations in the coaching strategy (e.g., follow-up
period).
LDL-C. LDL-C was compared among 1,841
participants in 6 studies (Figure 4 in the
supplementary material). Clinical improvement is
evident with reduction in the mean LDL-C in all
included studies after health coaching intervention;
however, the pooled effect is not statistically
significant with low precision (wide CIs) (MD =
-1.97, CI = -9.37, 5.43, P = 0.60) and considerable
between-study heterogeneity (I2
= 96%, P < 0.01).
HDL-C. HDL-C was studied among 1,405
participants in 5 studies (Figure 6) with statistically
significant improvement in the MD of HDL-C after
health coaching among studied participants (MD =
-0.50, CI = -0.93, -0.07, I2
= 10%, P = 0.02).
Triglyceride. The mean triglyceride was evaluated in
subgroup analysis among 1,665 participants in 4
studies (Figure 5 in the supplementary material). No
statistically significant improvement in mean
triglyceride is observed among the intervention
groups after health coaching interventions with low
precision (wide CI) (MD = -3.11, CI = -7.54, 1.33, P
= 0.17) and moderate heterogeneity between studies
(I2
= 72%, P = 0.17).
Total cholesterol. The mean total cholesterol was
examined in 8 studies among 2,816 participants
(Figure 6 in the supplementary material). The pooled
effect of total cholesterol between intervention and
control groups is not statistically significant (MD =
-1.88, CI = -9.06, 5.29, P = 0.61) and considerable
between-study heterogeneity (I2
= 96%, P < 0.01).
Secondary outcomes
The secondary outcomes included quality of life,
self-efficacy, self-care skills, and depressive
symptoms. Quality of life was assessed in 6 studies,
and significant improvement was found in 3 studies
[23, 34, 41]. No significant improvement was found
in the other 3 studies (1,471 participants) [32, 33, 36].
Self-efficacy was assessed in 6 studies, and
significant improvement was found in 5 (1,220
participants) [26, 34, 37-38, 41], and only the study
of Blackberry et al., with 56 participants found no
significant improvement [36]. Diabetes self-care was
assessed in 3 studies, and significant improvement
was found in Raanawongsa et al. among 252
participants [39], whereas the studies of Frosh et al.,
and Chapman et al., found no significant
improvement in self-care activities among 882
participants [29, 33]. Psychological distress and
depressive symptoms were assessed in 6 studies with
significant improvement in 4 among 934 participants
[25-26, 31, 34, 40]. While the study of Blackberry et
al., recorded no significant improvement [36].
Quality of evidence
We assessed quality of evidence in this review using
the GRADE approach [21], and considered the 4
domains that are recommend for evaluation of study
limitation: risk of bias in the included studies,
directness of the evidence, consistency across studies,
and precision of the pooled estimate or the individual
study estimates. The studies included in this review
were RCT with considerable risk of bias, as shown in
Figure 2. Also, directness was not found to be
lacking in this review, as all the included studies
reported health coaching interventions aimed at
improving health outcome and quality of life.
Regarding the pooled estimate of HbA1c, SBP, and
DBP, we judged the quality of evidence to be
moderate, indicating moderate confidence that the
evidence reflects the true effect, and further research
is likely to change the estimate. We downgraded the
evidence by 1 level because of considerable
heterogeneity (I2
= 83%, 94%, and 98%,
respectively), indicating very high inconsistency
among included studies in the estimate. Concerning
the quality of evidence for weight, triglycerides, total
cholesterol, HDL-C, and LDL-C, we judged the
quality of evidence to be low, indicating low
confidence that the evidence reflects the true effect,
and further research is very likely to change the
estimate. We downgraded the evidence by 2 levels
because of considerable heterogeneity and
imprecision (small sample size, as few studies were
included in the analysis, resulting in wide CIs). We
detected statistically significant heterogeneity in
most of the meta-analyses, suggesting that the
percentage of the variability in effect estimate is due
to heterogeneity rather than to sampling error
(chance). The heterogeneity may be due to

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Figure 2 Risk of bias percentages of included studies
Figure 3 Characteristics of the health coaching delivery
Figure 4 Forest plot of the mean difference of HbA1c after health coaching
Figure 5 Funnel plot of the mean difference for HbA1c

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Figure 6 Forest plot of high-density lipoprotein cholesterol after coaching intervention
differences in study participants, geographical location,
strategy, method, duration, or frequency of health
coaching and coaching qualification.
Discussion
This review examined the effectiveness of health
coaching interventions on diabetic patients. It included
3,573 participants from 19 RCT published during the
past 15 years in different countries. We found that the
pooled effect of health coaching interventions was a
statistically significant reduction in HbA1c and
HDL-C. The most effective delivery strategy for health
coaching associated with significant improvement of
HbA1c was decreasing the frequency of coaching
sessions while increasing the duration of each session.
Also, no significant difference was found for SBP,
DBP, weight, triglyceride, LDL-C, or total cholesterol.
Mix results were found for the effect of health
coaching on quality of life, self-efficacy, diabetic
self-care, and depressive symptoms. It is important to
note that these results should be interpreted with
caution because of high heterogeneity due to many
factors, including variations among the interventions
(methods, duration, frequency of health coaching
interventions, and coaching qualification), variations
among the participants, and a small number of
included studies in the analysis of some measuring
outcomes.
In addition, Sherifali et al., found that the pooled
effect of diabetes health coaching overall was a
statistically significant reduction of HbA1c levels by
0.32 (95% CI = -0.50, -0.15) [42]. Longer diabetes
health coaching exposure (> 6 months) resulted in a
0.57% reduction in HbA1c levels (95% CI = -0.76,
-0.38), compared with shorter diabetes health coaching
exposure (≤ 6 months) (-0.23%; 95% CI = -0.37,
-0.09). There are numerous definitions of health
coaching, and there is no standardized strategy of
coaching delivery nor is there a clearly defined role of
the health coach (e.g., educator, navigator, facilitator,
or partner). Numerous studies have examined health
coaching, with mixed results. For example, studies on
health coaching for patients with diabetes [43-45],
obesity [32, 46-47], cancer [48], and cardiovascular
disease [49-51] have demonstrated positive effects on
health behaviors or health outcomes. However, other
studies that have examined health coaching found
non-significant benefits for health outcomes [30,
52-54]. Mixed results on the effect of health coaching
on quality of life, self-efficacy, diabetic self-care, and
depressive symptoms were reported in the present
review. Also, Ekong et al. found that motivational
interventions in type 2 diabetic patients showed
promising results for dietary behaviors [55]; however,
only 4 out of 7 studies had a significant improvement
in healthy eating compared with usual care, and the
magnitude of the impact was not determined. Only a
statistically significant difference was recorded.
However, the authors did not report a significant effect
on physical activity (6 studies; 1,884 participants),
alcohol reduction (2 studies; 1,192 participants),
smoking cessation (3 studies; 994 participants), waist
circumference (2 studies; 309 participants), or
cholesterol levels (5 studies; 2,797 participants). Only
1 study out of 7 demonstrated a significant effect on
self-management of diabetes, blood glucose level,
weight loss, body mass index, and blood pressure
compared with usual care. Similarly, some systematic
reviews have suggested that the use of health coaching
through telephone can be effective in improving
quality of life and health outcomes of older people
[56-58]. Others have shown inconclusive results
[59-60]. Also, Veazie et al. examined the evidence,
usability, and features of commercially available
mobile applications for self-management of type 2
diabetes and found that patients experienced clinical
and statistical improvement in HbA1c but no
improvements in quality of life, blood pressure, weight,
or body mass index outcomes [61]. Correspondingly,
Cramer JA reported that health coaching though
electronic monitoring systems was useful in improving
medication adherence of diabetic patients [8]. Sapkota
et al. found that interventions addressing
non-adherence factors demonstrated mixed results,
making it difficult to determine effective coaching
intervention strategies to promote quality of life and
health outcome [62].
Limitations of this review include low or insufﬁcient
strength of evidence for most outcomes across the

REVIEW
doi: 10.12032/TMR20191024143
various included studies. These low grades were driven
by high or unclear risk of bias within individual studies
(mainly due to inability to blind patients in the
intervention group to health coaching), and lack of
consistency and precision among outcomes included in
subgroup analysis with few studies and small number
of participants with wide CIs. Also, there was
considerable heterogeneity between studies due mainly
to differences in study participants, geographical
location, method and duration, frequency of health
coaching, and coaching qualification. Further,
systematic review and meta-analysis with considerable
consistency and precision focusing on different
methods (e.g., educational classes, face-to-face,
telephone, and video), number of participants (e.g.,
single and group therapy), and different coaching
qualifications (e.g., doctor, nurse, social worker, and
peer coach) are needed to confirm the effectiveness of
health coaching on controlling other diabetic risk
factors and to standardize the effective coaching
strategy and settings in which it is most applicable.
Conclusion
Health coaching intervention has significant effect on
HbA1c and HDL-C. The most effective strategy for
coaching delivery found in the current review was
decreasing the frequency of coaching sessions while
increasing the duration of each session. However, these
results should be interpreted with caution, as the
evidence comes from studies at some risk of bias with
considerable heterogeneity and imprecision.
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