Sage Journals: Discover world-class research

Abstract

Background:

Previous systematic reviews have aimed to clarify the effect of telemedicine on diabetes. However, such reviews often have a narrow focus, which calls for a more comprehensive systematic review within the field. Hence, the objective of the present systematic review, meta-analysis, and meta-regression is to evaluate the effectiveness of telemedicine solutions versus any comparator without the use of telemedicine on diabetes-related outcomes among adult patients with type 2 diabetes (T2D).

Methods:

This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We considered telemedicine randomized controlled trials (RCT) including adults (≥18 years) diagnosed with T2D. Change in glycated hemoglobin (HbA1c, %) was the primary outcome. PubMed, EMBASE, and the Cochrane Library Central Register of Controlled Trials (CENTRAL) were searched on October 14, 2020. An overall treatment effect was estimated using a meta-analysis performed on the pool of included studies based on the mean difference (MD). The revised Cochrane risk-of-bias tool was applied and the certainty of evidence was graded using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach.

Results:

The final sample of papers included a total of 246, of which 168 had sufficient information to calculate the effect of HbA1c%. The results favored telemedicine, with an MD of −0.415% (95% confidence interval [CI] = −0.482% to −0.348%). The heterogeneity was great (I² = 93.05%). A monitoring component gave rise to the higher effects of telemedicine.

Conclusions:

In conclusion, telemedicine may serve as a valuable supplement to usual care for patients with T2D. The inclusion of a telemonitoring component seems to increase the effect of telemedicine.

Keywords

telemedicine diabetes mellitus type 2 systematic review meta-analysis meta-regression Denmark

Introduction

Diabetes is one of the most common chronic diseases and a major health care problem worldwide.^1,2 In 2017, an estimated 8.4% of the adult global population had diabetes, which is expected to increase to approximately 9.9% (700 million) in 2045, primarily due to an increase in unhealthy dietary habits, obesity, and sedentary lifestyle.^1,2 The global prevalence is predicted to increase mostly in low- and middle-income countries.³ The economic impact of diabetes is considerable, spans health care services, and affects individuals, families, and national productivity.^1,4

Type 2 diabetes (T2D) constitutes approximately 90% to 95% of diabetes cases.^1,5 The T2D is a progressive disease associated with significant premature mortality, morbidity, and several complications, such as cardiovascular disease, nephropathy, neuropathy, and retinopathy.^6,7 Diabetes patient care is a multifaceted and complex process, mainly aimed at attaining optimal glycemic control to prevent and control diabetes-related complications.⁸ However, sustaining optimal glycemic control for people with diabetes is both demanding and challenging because it requires numerous daily self-management decisions and care activities.⁹ These challenges include estimating the appropriate diabetes medication dosage to avoid hypoglycemic and hyperglycemic events and adherence to obstacles in terms of following the recommended guidelines.^8,9 Adherence to the complex T2D treatment regimen is difficult to maintain¹⁰ and medial adherence rates have been reported to range from 36% to 93%.¹¹

Self-management strategies are considered an essential part of diabetes treatment and are associated with improvements in health-related outcomes.⁹ A potential solution to support ongoing diabetes self-management support is the use of telemedicine,¹² which has been suggested as a promising but unproven approach to support people with diabetes in the management of their disease.¹³ Telemedicine can be defined as the delivery of health care services over a distance using information and communication technologies.¹⁴ However, no definitive definition exists.¹⁴ Telemedicine solutions may include a variety of different technologies and various delivery forms, including monitoring, education, consultative services, coaching, and counseling tasks.^13,15-17 Telemedicine interventions constitute different constellations, such as simple reminders via text messaging, video consultation, and transmission of patient data (eg, blood glucose, blood pressure, dietary and medication intake, and physical activity) with feedback from health care professionals via web portals or via telephone.^13,15,16 As diabetes predominantly needs to be managed outside health care facilities and to a large extent requires self-management, telemedicine holds the potential to provide sufficient self-management support to people with T2D.^18,19

Previous systematic reviews have aimed to clarify the effect of telemedicine on diabetes.^15-17,20-28 However, these previous reviews have often focused on a specific type of telemedicine, a specific outcome, and/or a specific comparator, which calls for a more comprehensive and inclusive systematic review seeking to compare and synthesize findings for treatment outcomes while adjusting for different study characteristics. In addition, the field of telemedicine is developing rapidly; thus, a large number of studies likely have been published recently, calling for an updated review.²⁹ Hence, the objective of the present systematic review, meta-analysis, and meta-regression was to evaluate the effectiveness of telemedicine solutions versus any comparator without the use of telemedicine on diabetes-related outcomes among adult patients with T2D.

Methods

Study Design

This systematic review, meta-analysis, and meta-regression was conducted and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.³⁰ A comprehensive search protocol was published elsewhere³¹ and PROSPERO-registered with identification CRD42020123565 on April 2020. The search was part of a comprehensive search that included T2D as well as type 1 diabetes (T1D) and gestational diabetes³¹, which will be reported separately.

Eligibility Criteria

Studies were considered if they included adults (≥18 years) diagnosed with T2D. Studies that included mixed populations (eg, T1D and T2D) were only included if the data for the T2D population were reported separately. Studies were excluded if only participants at risk of diabetes or participants with prediabetes were included. Furthermore, the studies must have included telemedicine interventions that were substituted for usual practice or served as an alternative to usual practice. The telemedicine interventions had to include remote feedback/communication between a patient and health care professional(s). Alternatively, feedback/communication could occur between the patient and a trained peer. Telemedicine interventions that were wholly automatic were also considered.

Studies that reported on any relevant patient diabetes-related outcome were included. The primary outcome was change in glycated hemoglobin (HbA1c, %).

We only considered randomized controlled trials (RCTs)—both parallel and crossover designs. Studies published in English, Danish, Norwegian, and Swedish as peer-reviewed full-text papers were included. All studies published before October 14, 2020, were considered.

Information Sources

The search was performed in PubMed, EMBASE, the Cochrane Library Central Register of Controlled Trials (CENTRAL), and CINAHL. Two authors performed the database searches (S.H.L. and S.H.). A research librarian assisted. Additional citation searches were performed in the Web of Science, SCOPUS, and Google Scholar.

Search Strategy

Initially, an unstructured search was performed in PubMed, CINAHL, and Google Scholar to identify relevant search terms and thus qualify the systematic search. The systematic search followed the initial search. The search strategy was adapted for each database. The search terms included various synonyms, near-synonyms, acronyms, and spellings for all keywords and index terms. A variety of search functions were applied, including thesaurus, Boolean operators, abstract/title/keywords, phrase, truncation, free text, and advanced search. Citation searches were applied to identify additional studies.

Selection Process

First, all of the identified papers were uploaded into RefWorks (ProQuest RefWorks 2.0, 2010). Second, duplicates were removed using the functions Exact duplicates and Close duplicates. Third, titles and abstracts were screened by two authors with respect to the eligibility criteria of the review (S.H. and S.H.L.). Fourth, the remaining studies underwent full review by three authors with respect to the eligibility criteria of the review (S.H., S.H.L., and J.D.A.). Disagreement between the authors was resolved through discussion by the three authors alone or by inclusion of other authors. During the full review, the reasons for exclusion of studies were recorded, and afterward, a final sample of papers was identified.

Data Extraction

Data were extracted by three independent authors (S.H., S.H.L., and J.D.A.) using a standardized sheet in Microsoft Excel (2016). Extracted data included trial characteristics (author, publication year, country, sample size, and study duration), patient characteristics (age, sex, and body mass index [BMI]), and HbA1c outcomes. In addition, the characteristics of the telemedicine interventions were extracted, including the frequency of contact, implementation setting (primary care, hospital, specialized outpatient clinic, university, community or cross-sectional), peripherals (eg, glucometers, pedometers, blood pressure monitors, and scales), and the general purpose of intervention components (monitoring, consultation, counseling, coaching, education, mentoring, and reminding). Disagreements between the authors were resolved through discussion. Additional authors were included in the discussions when necessary.

Risk of Bias Assessment

The revised Cochrane risk-of-bias tool was applied.³² Four reviewers (J.D.A., S.H., T.K., and F.W.U.) assessed the included studies independently and resolved potential disagreements by discussion.

Data Synthesis

All statistical analyses were performed in Stata 16 (Stata Statistical Software: Release 16, StataCorp 2019.; StataCorp LLC, College Station, Texas). Reported medians, interquartile ranges, ranges, and confidence intervals were transformed to means and standard deviations by traditional methods^33,34 and scaled to HbA1c% when relevant (eg, if outcome was reported as mmol/mol). An overall treatment effect was estimated with a meta-analysis of the pool of included studies based on the mean difference (MD). Heterogeneity was assessed statistically using I² tests. The results were combined with a random-effects model (due to heterogeneity, ie, an I² statistic > 50%). Univariate a priori subgroup analyses based on meta-regression of the telemedicine characteristics were conducted and combined with post hoc analyses of the association of study and patient characteristics with the treatment effect of telemedicine. Publication bias was evaluated using visual inspection of the funnel plot and Egger test.

Certainty Assessment

The Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach was applied. A summary of findings (SoF) table was created using GRADEPro GDT 2015 (McMaster University, Ontario, Canada),^35,36 which presents the absolute risks for the groups (treatment and control), estimates of relative risk, and ranking of the quality of the evidence, which is based on the risk of bias,³⁷ indirectness,³⁸ imprecision,³⁹ inconsistency,⁴⁰ and risk of publication bias.⁴¹

Results

The flowchart in Figure 1 describes our selection of studies. In the literature search, 16 309 studies were found and 1125 eligible studies were assessed by full-text reading after screening titles and abstracts. After full-text assessment, 246 articles met our inclusion criteria.

Figure 1.

Flowchart.

The characteristics of the individual studies are presented in Table 1. One study was multinational, 88 articles were conducted in North America (36%), 84 in Asia (35%), 44 in Europe (18%), 16 in Australia/New Zealand (7%), five in Africa (2%), and five in South America (5%). Four studies were published before 2000 (2%), 50 in the 2000s (21%), 165 in the 2010s (68%), and 24 (10%) in 2020. Sample sizes ranged from 17 to 4078, with an average of 251 participants per study. The study duration ranged from one to 96 months, with a study average of 8.5 months. Across studies, the mean proportion of men was 50.4% (range = 15%-100%), the average age at baseline was 57 (range = 37-73) years, and the baseline mean BMI was 30 (range = 22-40). The average baseline HbA1c% levels ranged from 5.70% to 11.05%, with an average of 8.33%.

Table 1.

Study and Participant Characteristics.

Study	Publication year	Country	Sample size	Duration (months)	Mean age years	Male %	Baseline BMI	Baseline HbA1c
Abaza and Marschollek⁴²	2017	Egypt	90	3	52	44	NA	9.66
Agarwal et al⁴³	2019	Canada	50	4	64	55	NA	7.44
Agarwal et al⁴⁴	2019	Canada	240	3	52	52	NA	8.96
Aguiar et al⁴⁵	2018	Brazil	80	12	62	67	NA	8.95
Akinci et al⁴⁶	2018	Turkey	66	2	52	36	32	8.34
Al Omar et al⁴⁷	2020	United Arab Emirates	218	6	42	42	NA	8.45
Alanzi et al⁴⁸	2018	Saudi Arabia	20	6	NA	75	NA	8.53
Albikawi et al⁴⁹	2016	Jordan	168	3	51	46	NA	NA
Alghafri et al⁵⁰	2018	Oman	232	3	44	43	33	7.96
Ali et al⁵¹	2016	India & Pakistan	1146	30	54	46	27	9.90
Ali et al⁵²	2020	India	404	24	53	41	27	9.15
Aliha et al⁵³	2013	Iran	61	3	53	50	28	9.70
Alonso-Domínguez et al⁵⁴	2019	Spain	204	3	61	54	30	6.85
Alotaibi et al⁵⁵	2016	Saudi Arabia	20	6	45	NA	NA	8.55
Anderson et al⁵⁶	2009	USA	310	24	56	41	34	7.48
Anderson et al⁵⁷	2010	USA	295	12	NA	42	35	8.00
Anderson-Loftin et al⁵⁸	2005	USA	97	6	57	23	35	7.90
Andreae et al⁵⁹	2020	USA	230	3	59	20	NA	8.29
Anzaldo-Campos et al⁶⁰	2016	Mexico	301	10	52	38	31	11.05
Arora et al⁶¹	2014	USA	128	6	51	36	NA	10.10
Asante et al⁶²	2020	Ghana	60	3	56	22	28	8.95
Avdal et al⁶³	2011	Turkey	122	6	52	49	NA	8.07
Azizi et al⁶⁴	2016	Iran	72	4	55	50	NA	NA
Benson et al⁶⁵	2018	USA	120	12	60	55	37	8.20
Blackberry et al⁶⁶	2013	Australia	473	18	63	57	NA	8.06
Bluml et al⁶⁷	2019	USA	446	3	54	41	37	10.25
Boels et al¹²	2019	Netherlands	230	6	59	60	32	8.20
Bogner et al⁶⁸	2012	USA	182	3	57	32	34	7.10
Bohingamu Mudiyanselage et al⁶⁹	2018	Australia	177	12	70	53	NA	5.70
Lashkari et al⁷⁰	2013	Iran	50	3	NA	NA	29	9.68
Browning et al⁷¹	2016	China	711	12	64	52	26	10.45
Bujnowska-Fedak et al⁷²	2011	Poland	100	6	55	54	25	7.65
Buysse et al⁷³	2019	Belgium	153	24	37	50	NA	8.30
Capozza et al⁷⁴	2015	USA	156	6	53	37	NA	9.11
Carter et al⁷⁵	2011	USA	47	9	51	36	36	8.91
Castelnuovo et al⁷⁶	2011	Italy	72	12	52	52	NA	NA
Chao et al⁷⁷	2019	Taiwan	121	3	NA	NA	25	8.70
Chen et al⁷⁸	2008	Taiwan	78	6	59	44	26	9.61
Chen et al⁷⁹	2018	China	233	NA	59	51	NA	NA
Chiu et al⁸⁰	2016	Taiwan	182	8	65	52	26	7.65
Cho et al⁸¹	2006	Korea	80	30	53	54	23	7.60
Cho et al⁸²	2017	Korea	484	6	53	64	26	7.84
Choe et al⁸³	2005	USA	80	14	52	47	NA	10.15
Choudhry et al⁸⁴	2018	USA	4078	12	60	55	NA	9.65
Clark et al⁸⁵	2004	UK	100	12	60	58	31	8.42
Crowley et al⁸⁶	2013	USA	369	12	61	28	NA	8.00
Crowley et al⁸⁷	2016	USA	50	6	60	96	NA	10.45
Dale et al⁸⁸	2009	UK	231	6	NA	60	NA	8.56
Dario et al⁸⁹	2017	Italy	299	12	73	56	NA	7.94
Davis et al⁹⁰	2010	USA	165	12	60	25	37	8.96
Del Prato et al⁹¹	2012	Italy	291	5.5	58	52	30	8.86
Delahanty et al⁹²	2019	USA	211	12	62	45	35	7.70
Doupis et al⁷	2019	Greece	457	8	63	51	31	7.85
Dugas et al⁹³	2018	USA	27	3	NA	89	NA	9.23
Duruturk and Özköslü⁹⁴	2019	Turkey	50	1.5	53	NA	31	7.36
Döbler et al⁹⁵	2018	Germany	249	12	52	70	36	7.70
Eakin et al⁹⁶	2013	Australia	302	6	58	56	33	7.45
Eakin et al⁹⁷	2014	Australia	302	24	58	56	33	7.45
Egede et al⁹⁸	2017	USA	113	6	54	19	36	10.10
Egede et al⁹⁹	2018	USA	90	12	63	98	NA	7.11
Estey et al¹⁰⁰	1990	Canada	60	4	NA	46	NA	6.21
Faridi et al¹⁰¹	2008	USA	30	3	56	37	36	6.45
Farsaei et al¹⁰²	2011	Iran	172	3	53	34	NA	9.10
Fernandes et al¹⁰³	2016	Brazil	219	6	63	31	29	8.07
Fortmann et al¹⁰⁴	2017	USA	126	6	48	25	32	9.55
Fottrell et al¹⁰⁵	2019	Bangladesh	2470	18	NA	47	22	NA
Fountoulakis et al¹⁰⁶	2015	Greece	80	12	62	69	30	9.80
Franc et al¹⁰⁷	2020	France	665	12	39	48	26	9.10
Franciosi et al¹⁰⁸	2011	Italy	62	6	49	74	31	7.90
Frosch et al¹⁰⁹	2011	USA	201	6	55	52	33	9.60
García et al¹¹⁰	2015	USA	72	6	50	33	36	8.60
Garg et al¹¹¹	2017	USA	184	12	64	60	35	9.05
Gagliardino et al¹¹²	2013	Argentina	198	12	61	49	33	7.21
Wasif Gillani¹¹³	2016	Malaysia	150	6	53	55	28	9.89
Gimbel et al¹¹⁴	2020	USA	240	12	63	62	33	7.55
Glasgow et al¹¹⁵	2006	USA	327	2	62	50	32	7.45
Glasgow et al¹¹⁶	2006	USA	327	2	61	55	33	7.25
Glasgow and Toobert¹¹⁷	2000	USA	320	6	59	39	NA	7.60
Glasgow et al¹¹⁸	2002	USA	320	12	59	39	NA	7.59
Glasgow et al¹¹⁹	2005	USA	886	12	63	49	NA	7.32
Goodarzi et al¹²⁰	2012	Iran	100	3	54	22	28	7.87
Goodarzi et al¹²¹	2018	Iran	76	3	57	100	NA	NA
Graziano and Gross¹²²	2009	USA	120	3	62	55	NA	8.65
Griffin et al¹²³	2014	UK	478	12	60	62	33	7.12
Gupta et al¹²⁴	2020	India	81	4	50	52	28	8.45
Haider et al¹²⁵	2019	Australia	229	6	59	83	31	NA
Hansen et al¹²⁶	2017	Denmark	165	8	58	64	34	9.30
Hare et al¹²⁷	2011	Australia	223	33	55	56	32	7.55
Hee-Sung¹²⁸	2007	South Korea	60	3	50	42	24	6.82
Heisler et al¹²⁹	2019	USA	290	6	63	98	NA	9.10
Hidrus et al¹³⁰	2020	Malaysia	100	3	NA	50	NA	NA
Hokanson et al¹³¹	2006	USA	114	6	54	57	33	8.60
Holbrook et al¹³²	2009	Canada	511	6	61	51	32	7.05
Holmen et al¹³³	2014	Norway	151	12	57	55	31	8.25
Hordern et al¹³⁴	2009	Australia	223	12	56	55	32	7.55
Huang et al¹³⁵	2019	Singapore	50	3	52	49	29	8.65
Huizinga et al¹³⁶	2010	USA	165	24	56	60	34	6.64
Hunt et al¹³⁷	2014	USA	17	3	NA	41	NA	6.59
Iljaž et al¹³⁸	2017	Slovenia	120	12	55	30	32	6.95
Islam et al¹³⁹	2019	Bangladesh	239	6	48	46	NA	NA
Jahangard-Rafsanjani et al¹⁴⁰	2015	Iran	101	5	57	50	29	7.56
Jain et al¹⁴¹	2018	India	299	6	57	57	24	8.16
Jarab et al¹⁴²	2012	Jordan	171	6	64	57	33	8.45
Jennings et al¹⁴³	2014	Australia	436	8	58	52	33	NA
Jeong et al¹⁴⁴	2018	Korea	338	6	53	67	25	8.30
Jiwani et al¹⁴⁵	2020	USA	26	6	58	30	39	9.30
Kardas et al¹⁴⁶	2016	Poland	62	1.5	59	60	31	6.81
Kassavou et al¹⁴⁷	2020	UK	135	3	NA	54	NA	NA
Kempf et al¹⁴⁸	2017	Germany	202	12	59	54	36	8.30
Keogh et al¹⁴⁹	2011	Ireland	121	6	59	63	32	9.18
Kim and Utz¹⁵⁰	2019	South Korea	155	3	51	48	NA	9.14
Kim¹⁵¹	2007	Korea	60	3	47	43	24	7.84
Kim and Jeong¹⁵²	2007	Korea	60	6	47	43	24	7.84
Kim and Song¹⁵³	2008	Korea	40	12	47	47	25	7.85
Kim and Kim¹⁵⁴	2008	Korea	40	6	47	47	25	7.85
Kim and Oh¹⁵⁵	2003	Korea	50	3	60	30	24	8.50
Kim et al¹⁵⁶	2005	Korea	35	3	61	36	24	8.60
Kim and Kang¹⁵⁷	2006	Korea	73	3	55	53	NA	7.94
Kim et al¹⁵⁸	2009	USA	83	6.5	56	56	26	9.25
Kim et al¹⁵⁹	2010	Korea	100	3	48	50	24	9.80
Kim et al¹⁶⁰	2015	Korea	70	6	66	50	25	8.55
Kim et al¹⁶¹	2016	China	220	6	54	48	26	7.95
King et al¹⁶²	2006	USA	335	2	61	49	32	NA
Kirkman et al¹⁶³	1994	USA	275	12	64	99	NA	10.70
Kleinman et al¹⁶⁴	2017	India	91	6	48	70	29	9.25
Krein et al¹⁶⁵	2004	USA	246	18	61	97	NA	9.25
Ku et al¹⁶⁶	2020	Australia	40	3	50	35	28	8.95
Kusnanto et al¹⁶⁷	2019	Indonesia	30	3	NA	43	NA	8.46
Kwon et al¹⁶⁸	2004	Korea	110	3	54	61	24	7.39
Lauffenburger et al¹⁶⁹	2019	USA	1400	12	55	63	NA	9.35
Lazo-Porras et al¹⁷⁰	2020	Peru	172	18	61	37	28	8.55
Lee et al¹⁷¹	2017	Malaysia	85	3	53	51	30	8.74
Lee et al¹⁷²	2020	South Korea	72	6	NA	NA	26	7.44
Lee et al¹⁷³	2019	Malaysia	240	12	56	54	NA	9.00
Li et al¹⁷⁴	2016	China	53	6	62	53	24	7.73
Liebreich et al¹⁷⁵	2009	Canada	49	3	54	41	34	NA
Lim et al¹⁷⁶	2011	Korea	154	6	68	21	25	7.85
Lim et al¹⁷⁷	2016	Korea	100	6	65	75	26	8.00
Lorig et al¹⁷⁸	2010	USA	761	6	54	29	NA	6.41
Lujan et al¹⁷⁹	2007	USA	150	6	58	22	NA	7.96
Luley et al¹⁸⁰	2011	Germany	70	6	58	49	35	7.55
Lutes et al¹⁸¹	2017	USA	200	12	53	NA	38	9.09
Lynch et al¹⁸²	2014	USA	61	6	54	33	36	7.65
MacPhail et al¹⁸³	2014	Australia	87	4	68	NA	31	NA
Marios et al¹⁸⁴	2012	Australia	39	6	63	53	33	7.73
Maslakpak et al¹⁸⁵	2017	Iran	90	3	50	60	29	8.00
Mayberry et al¹⁸⁶	2020	USA	379	6	57	46	NA	8.13
McEwen et al¹⁸⁷	2017	USA	157	9	54	35	33	9.93
McKay et al¹⁸⁸	2001	USA	78	2	52	47	NA	NA
McKay et al¹⁸⁹	2002	USA	160	3	59	45	NA	7.48
McKee et al¹⁹⁰	2011	USA	55	6	60	33	33	8.22
McMahon et al¹⁹¹	2012	USA	152	12	61	93	34	9.85
Mease¹⁹²	2000	USA	28	3	63	39	NA	9.50
Mons et al¹⁹³	2013	Germany	204	18	68	61	NA	8.10
Moriyama et al¹⁹⁴	2009	Japan	75	12	66	46	NA	7.47
Mwangi et al¹⁹⁵	2020	Kenya	104	3	62	32	25	NA
Namjoo Nasab et al¹⁹⁶	2017	Iran	64	3	52	48	27	NA
Nesari et al¹⁹⁷	2010	Iran	61	3	51	28	28	9.26
Nicolucci et al¹⁹⁸	2015	Italy	302	12	58	65	29	7.95
Niswender et al¹⁹⁹	2014	Several	611	6	57	51	34	7.95
O’Neil et al²⁰⁰	2016	USA	563	12	NA	29	NA	8.32
Odegard and Christensen²⁰¹	2012	USA	165	12	63	48	NA	NA
Odnoletkova et al²⁰²	2016	Belgium	574	18	63	62	30	7.00
Oh et al²⁰³	2003	Korea	50	3	61	36	25	8.55
Orsama et al²⁰⁴	2013	Finland	56	10	62	54	32	6.98
Owolabi et al²⁰⁵	2019	South Africa	216	6	NA	16	32	NA
Parsons et al²⁰⁶	2019	UK	446	12	62	57	33	8.60
Patja et al²⁰⁷	2012	Finland	1535	12	65	57	32	7.57
Peasah et al²⁰⁸	2019	USA	78	3	62	53	35	8.20
Peimani et al²⁰⁹	2016	Iran	150	3	52	53	28	7.41
Piette et al²¹⁰	2011	USA	339	12	56	49	38	7.60
Plotnikoff et al²¹¹	2013	Canada	287	12	62	51	30	7.16
Presley et al²¹²	2020	USA	120	6	55	28	35	9.98
Quinn et al²¹³	2017	USA	142	12	52	51	35	9.59
Quinn et al²¹⁴	2011	USA	213	12	53	51	35	9.24
Raj and Mathews²¹⁵	2020	India	50	6	69	54	NA	10.26
Ralston et al²¹⁶	2009	USA	83	12	57	51	NA	8.05
Ramadas et al²¹⁷	2018	Malaysia	132	12	51	69	NA	9.00
Rasmussen et al²¹⁸	2016	Denmark	40	6	63	68	32	8.55
Rodríguez-Idígoras et al²¹⁹	2009	Spain	328	12	64	52	NA	7.51
Ruggiero et al²²⁰	2014	USA	270	12	53	32	33	8.63
Sacco et al²²¹	2009	USA	62	6	52	42	36	8.50
Sacco et al²²²	2012	USA	62	6	52	42	36	8.50
Samuel-Hodge et al²²³	2009	USA	201	12	59	36	35	7.78
Sarayani et al²²⁴	2018	Iran	100	9	55	58	30	7.95
Saslow et al²²⁵	2020	USA	64	12	NA	NA	NA	NA
Sazlina et al²²⁶	2015	Malaysia	69	8	64	57	27	8.20
Schillinger et al²²⁷	2009	USA	339	12	56	39	31	9.35
Shahid et al²²⁸	2015	Pakistan	440	4	49	61	27	9.97
Shahsavari and Bakhshandeh Bavarsad²²⁹	2020	Iran	60	3	NA	15	29	8.93
Shetty et al²³⁰	2011	India	215	12	50	NA	27	9.00
Shreck et al²³¹	2014	USA	526	12	56	33	NA	8.65
Sigurdardottir et al²³²	2009	Iceland	58	6	61	68	32	7.99
Skelly et al²³³	2009	USA	180	9	67	NA	NA	8.37
Sone et al²³⁴	2002	Japan	2205	36	59	55	23	7.74
Sone et al²³⁵	2010	Japan	2033	96	59	46	23	7.85
Song and Kim²³⁶	2009	Korea	49	3	50	43	25	9.20
Spencer et al²³⁷	2018	USA	222	6	49	39	33	7.93
Steventon et al²³⁸	2014	England	513	12	65	58	31	8.42
Sun et al²³⁹	2019	China	91	6	68	46	23	7.86
Sunil Kumar et al²⁴⁰	2020	India	300	6	65	60	NA	7.60
Takenga et al²⁴¹	2014	Democratic Republic of Congo	40	2	NA	73	NA	8.63
Tamban et al²⁴²	2013	Philippines	104	6	50	27	28	7.84
Tan et al²⁴³	2018	Singapore	142	2	62	NA	NA	9.72
Tang et al²⁴⁴	2013	USA	415	12	54	60	NA	9.26
Teston et al²⁴⁵	2017	Brazil	134	5	NA	32	NA	NA
Thom et al²⁴⁶	2013	USA	299	6	55	48	34	9.99
Torbjørnsen et al²⁴⁷	2014	Norway	164	4	58	59	32	7.85
Tu et al²⁴⁸	1993	USA	31	3	65	33	NA	NA
Van Dyck et al²⁴⁹	2013	Belgium	92	12	62	69	30	7.30
Van Dyck et al²⁵⁰	2011	Belgium	92	12	62	NA	30	NA
Van Vugt et al²⁵¹	2016	The Netherlands	132	6	68	53	30	6.59
Varney et al²⁵²	2014	Australia	94	12	62	68	32	8.35
Vaughan et al²⁵³	2020	USA	89	6	55	28	34	8.86
Vervloet et al²⁵⁴	2014	The Netherlands	604	24	55	55	NA	NA
Vinithia et al²⁵⁵	2019	India	248	24	43	68	27	9.50
Von Storch et al²⁵⁶	2019	Germany	2441	3	59	81	31	6.99
Wakefield et al²⁵⁷	2011	USA	302	12	68	94	33	7.15
Wakefield et al²⁵⁸	2012	USA	302	12	68	94	33	7.15
Wakefield et al²⁵⁹	2014	USA	108	6	60	44	NA	7.30
Waki et al²⁶⁰	2014	Japan	54	3	57	76	27	7.05
Walker et al²⁶¹	2011	USA	527	12	56	33	31	8.65
Wang et al²⁶²	2020	Mongolia	171	12	55	48	26	9.15
Wang et al²⁶³	2019	China	120	6	45	32	NA	8.65
Wang et al²⁶⁴	2017	China	212	6	54	55	25	7.95
Warren et al²⁶⁵	2018	Australia	157	6	61	55	34	8.27
Weinberger et al²⁶⁶	1995	USA	275	12	64	99	NA	10.70
Whittemore et al²⁶⁷	2004	USA	53	6	58	NA	35	7.65
Wichit et al²⁶⁸	2017	Thailand	140	3	58	27	27	6.65
Wild et al²⁶⁹	2016	UK	321	9	61	67	33	8.85
Williams et al²⁷⁰	2012	Australia	120	6	57	65	33	8.55
Williams et al²⁷¹	2017	New Zealand	138	6	55	38	40	8.15
Wolever et al²⁷²	2010	USA	56	6	53	23	NA	7.93
Wolf et al²⁷³	2004	USA	147	12	53	40	38	7.70
Wu et al²⁷⁴	2017	Australia & Taiwan	181	1	66	61	NA	NA
Yang et al²⁷⁵	2020	South Korea	401	3	56	51	26	7.96
Yasmin et al²⁷⁶	2020	Bangladesh	320	12	52	23	NA	NA
Yoo et al²⁷⁷	2009	Korea	123	3	58	59	26	7.50
Yoon and Kim²⁷⁸	2008	South Korea	60	12	47	43	24	7.83
Young et al²⁷⁹	2005	UK	591	12	67	58	30	7.93
Yu et al²⁸⁰	2019	China	185	6	52	28	26	8.60
Zamanzadeh et al²⁸¹	2017	Iran	66	3	49	41	NA	NA
Zhou et al²⁸²	2014	China	114	3	NA	NA	24	8.33

Abbreviations: BMI, body mass index; HbA1c, glycated hemoglobin; NA, not available.

Table 2 illustrates the telemedicine technologies implemented across studies. Seventy-one studies (29%) were conducted in a hospital setting, 58 studies (24%) in the primary care sector, 37 in communities (15%), 34 in specialized outpatient clinics (14%), 14 in a university setting (6%), and four in cross-sectorial implementations (2%). The frequency of contact with patients across studies was daily (30 studies, 12%), weekly (83 studies, 34%), every two weeks (26 studies, 11%), monthly (40 studies, 16%), and more rarely (12 studies, 5%). Twenty-four (10%) of the studies reported a “tailored” number of contacts with participants (10%). Across studies, the explicitly reported peripherals were scales (13 studies, 5%), glucometers (45 studies, 19%), blood pressure monitors (19 studies, 8%), and pedometers (16 studies, 7%).

Table 2.

Telemedicine Intervention Characteristics.

Study	Publication year	Setting	Frequency of contact	Included peripherals				Intervention components
Study	Publication year	Setting	Frequency of contact	Glucometer	Pedometer	BP monitor	Scale	Monitoring	Consultation	Counseling	Coaching	Education	Mentoring	Reminding
Abaza and Marschollek⁴²	2017	Hospital	Daily	x			x					x		x
Agarwal et al⁴³	2019	Community	Weekly									x
Agarwal et al⁴⁴	2019	Hospital	Daily									x		x
Aguiar et al⁴⁵	2018	Hospital	Once						x
Akinci et al⁴⁶	2018	University												x
Al Omar et al⁴⁷	2020	Primary	Daily									x
Alanzi et al⁴⁸	2018	Community		x				x
Albikawi et al⁴⁹	2016	Specialized	Once						x
Alghafri et al⁵⁰	2018	Primary	Monthly									x
Ali et al⁵¹	2016	Specialized	Monthly						x
Ali et al⁵²	2020	Hospital	Weekly					x
Aliha et al⁵³	2013	Specialized	Weekly						x
Alonso-Domínguez et al⁵⁴	2019	Primary	Daily					x
Alotaibi et al⁵⁵	2016	Hospital	Weekly	x				x				x
Anderson et al⁵⁶	2009	Community	Monthly								x
Anderson et al⁵⁷	2010	Community	Tailored						x		x
Anderson-Loftin et al⁵⁸	2005	Primary	Weekly								x	x
Andreae et al⁵⁹	2020	Community	Weekly									x
Anzaldo-Campos et al⁶⁰	2016	Primary		x				x				x
Arora et al⁶¹	2014	Hospital	Daily									x		x
Asante et al⁶²	2020	Specialized	Weekly									x
Avdal et al⁶³	2011	University		x				x
Azizi et al⁶⁴	2016	Specialized						x						x
Benson et al⁶⁵	2018	Primary	Monthly								x	x
Blackberry et al⁶⁶	2013	Primary	Monthly								x
Bluml et al⁶⁷	2019	Primary	Weekly						x			x
Boels et al¹²	2019	Hospital	Daily									x
Bogner et al⁶⁸	2012	Primary	Twice					x				x
Bohingamu Mudiyanselage et al⁶⁹	2018	Community	Daily					x
Lashkari et al⁷⁰	2013		Weekly							x
Browning et al⁷¹	2016	Community	Monthly								x
Bujnowska-Fedak et al⁷²	2011	Primary	Weekly	x				x
Buysse et al⁷³	2019	Hospital	Monthly					x
Capozza et al⁷⁴	2015	Primary	Daily									x		x
Carter et al⁷⁵	2011	Primary	Weekly	x		x	x	x	x
Castelnuovo et al⁷⁶	2011	Hospital	Fortnight		x			x		x
Chao et al⁷⁷	2019	Hospital										x
Chen et al⁷⁸	2008	Hospital	Weekly											x
Chen et al⁷⁹	2018	Hospital	Weekly									x
Chiu et al⁸⁰	2016	Community	Weekly								x
Cho et al⁸¹	2006	Hospital	Weekly					x
Cho et al⁸²	2017	Specialized	Weekly	x		x		x
Choe et al⁸³	2005	University	Monthly									x
Choudhry et al⁸⁴	2018	Primary							x
Clark et al⁸⁵	2004	Specialized	Fortnight								x
Crowley et al⁸⁶	2013	Primary	Monthly									x
Crowley et al⁸⁷	2016	Hospital	Fortnight					x	x
Dale et al⁸⁸	2009	Primary	Tailored						x				x
Dario et al⁸⁹	2017		Tailored	x				x
Davis et al⁹⁰	2010	Community	Monthly		x							x
Del Prato et al⁹¹	2012			x				x
Delahanty et al⁹²	2019	Community	Weekly									x
Doupis et al⁷	2019	Specialized	Weekly									x
Dugas et al⁹³	2018	Specialized	Daily		x			x					x
Duruturk and Özköslü⁹⁴	2019	Hospital	Weekly					x
Döbler et al⁹⁵	2018	Specialized	Monthly							x
Eakin et al⁹⁶	2013	Primary	Fortnight		x		x			x
Eakin et al⁹⁷	2014	Primary	Fortnight		x		x			x
Egede et al⁹⁸	2017	Community	Weekly	x		x		x
Egede et al⁹⁹	2018	Community
Estey et al¹⁰⁰	1990	University	Fortnight							x		x
Faridi et al¹⁰¹	2008	Community	Daily					x						x
Farsaei et al¹⁰²	2011	Specialized	Weekly									x
Fernandes et al¹⁰³	2016	Primary	Monthly								x
Fortmann et al¹⁰⁴	2017	Community	Daily					x				x		x
Fottrell et al¹⁰⁵	2019	Community	Weekly									x
Fountoulakis et al¹⁰⁶	2015	Hospital	Tailored	x				x
Franc et al¹⁰⁷	2020	Primary	Daily					x
Franciosi et al¹⁰⁸	2011	Specialized	Monthly	x								x
Frosch et al¹⁰⁹	2011	Primary	Tailored								x
García et al¹¹⁰	2015	Community	Fortnight	x							x
Garg et al¹¹¹	2017	Hospital	Weekly	x						x
Gagliardino et al¹¹²	2013	Specialized	Tailored										x
Wasif Gillani¹¹³	2016	Hospital	Twice	x				x
Gimbel et al¹¹⁴	2020	Primary	Daily			x		x
Glasgow et al¹¹⁵	2006	Primary care	Twice								x
Glasgow et al¹¹⁶	2006		Twice								x
Glasgow and Toobert¹¹⁷	2000	Specialized	Fortnight							x
Glasgow et al¹¹⁸	2002	Specialized	Fortnight						x
Glasgow et al¹¹⁹	2005	Primary care								x
Goodarzi et al¹²⁰	2012		Tailored									x
Goodarzi et al¹²¹	2018	University	Weekly									x
Graziano and Gross¹²²	2009	Cross-sectional	Daily					x		x
Griffin et al¹²³	2014	Primary	Monthly								x
Gupta et al¹²⁴	2020	Hospital	Weekly								x	x
Haider et al¹²⁵	2019	Hospital	Weekly									x
Hansen et al¹²⁶	2017	Hospital	Monthly					x	x
Hare et al¹²⁷	2011	Hospital	Tailored							x
Hee-Sung¹²⁸	2007	Hospital	Monthly					x				x		x
Heisler et al¹²⁹	2019	Hospital	Monthly									x
Hidrus et al¹³⁰	2020	Hospital	Daily								x
Hokanson et al¹³¹	2006	Specialized	Tailored							x
Holbrook et al¹³²	2009	Primary												x
Holmen et al¹³³	2014	Primary	Monthly					x		x
Hordern et al¹³⁴	2009	Hospital	Tailored					x		x
Huang et al¹³⁵	2019	Specialized	Daily									x
Huizinga et al¹³⁶	2010	University	Monthly							x
Hunt et al¹³⁷	2014	Specialized	Weekly					x
Iljaž et al¹³⁸	2017	Primary	Tailored					x		x				x
Islam et al¹³⁹	2019	Hospital	Daily								x
Jahangard-Rafsanjani et al¹⁴⁰	2015	Community	Monthly							x
Jain et al¹⁴¹	2018	Community	Weekly								x	x
Jarab et al¹⁴²	2012	Specialized	Weekly							x
Jennings et al¹⁴³	2014									x
Jeong et al¹⁴⁴	2018	Hospital	Twice	x				x	x
Jiwani et al¹⁴⁵	2020	Community	Weekly									x
Kardas et al¹⁴⁶	2016	Primary		x	x	x	x	x
Kassavou et al¹⁴⁷	2020	Primary	Weekly									x
Kempf et al¹⁴⁸	2017	Specialized	Weekly								x
Keogh et al¹⁴⁹	2011	Specialized	Once								x
Kim and Utz¹⁵⁰	2019	Hospital	Weekly								x	x
Kim¹⁵¹	2007	Hospital	Weekly					x						x
Kim and Jeong¹⁵²	2007	Hospital	Weekly					x						x
Kim and Song¹⁵³	2008	Hospital	Weekly					x						x
Kim and Kim¹⁵⁴	2008	Hospital	Weekly					x						x
Kim and Oh¹⁵⁵	2003	Hospital	Weekly							x		x
Kim et al¹⁵⁶	2005	Hospital	Weekly							x		x
Kim and Kang¹⁵⁷	2006	Hospital	Tailored							x
Kim et al¹⁵⁸	2009	Community	Monthly	x		x		x		x
Kim et al¹⁵⁹	2010	Hospital	Daily	x				x						x
Kim et al¹⁶⁰	2015	Hospital						x
Kim et al¹⁶¹	2016	Hospital	Tailored					x
King et al¹⁶²	2006	Primary	Twice								x
Kirkman et al¹⁶³	1994	Primary	Monthly							x
Kleinman et al¹⁶⁴	2017	Specialized						x						x
Krein et al¹⁶⁵	2004	Specialized								x
Ku et al¹⁶⁶	2020	Hospital	Weekly	x				x			x
Kusnanto et al¹⁶⁷	2019	Primary	Daily								x
Kwon et al¹⁶⁸	2004	Hospital	Tailored					x						x
Lauffenburger et al¹⁶⁹	2019		Weekly								x	x
Lazo-Porras et al¹⁷⁰	2020	Hospital	Weekly					x			x
Lee et al¹⁷¹	2017	Community		x				x		x				x
Lee et al¹⁷²	2020	Hospital	Weekly					x				x
Lee et al¹⁷³	2019	Primary	Weekly	x				x
Li et al¹⁷⁴	2016	Hospital	Monthly								x	x
Liebreich et al¹⁷⁵	2009		Weekly							x		x
Lim et al¹⁷⁶	2011	Hospital		x				x
Lim et al¹⁷⁷	2016	Hospital		x	x			x
Lorig et al¹⁷⁸	2010		Weekly										x
Lujan et al¹⁷⁹	2007	Community	Fortnight							x		x
Luley et al¹⁸⁰	2011	Hospital	Weekly		x		x	x
Lutes et al¹⁸¹	2017	Primary	Monthly	x	x		x			x
Lynch et al¹⁸²	2014	Community	Weekly										x
MacPhail et al¹⁸³	2014	Primary	Twice								x
Marios et al¹⁸⁴	2012	Primary	Weekly					x		x
Maslakpak et al¹⁸⁵	2017		Weekly									x
Mayberry et al¹⁸⁶	2020	Primary	Monthly								x
McEwen et al¹⁸⁷	2017	Community	Tailored								x
McKay et al¹⁸⁸	2001		Weekly					x			x
McKay et al¹⁸⁹	2002	Primary	Weekly								x
McKee et al¹⁹⁰	2011	Community		x		x		x			x
McMahon et al¹⁹¹	2012		Tailored					x		x				x
Mease¹⁹²	2000	Primary	Weekly			x		x
Mons et al¹⁹³	2013	Primary	Monthly							x
Moriyama et al¹⁹⁴	2009	Hospital	Fortnight								x	x
Mwangi et al¹⁹⁵	2020	Hospital	Monthly									x		x
Namjoo Nasab et al¹⁹⁶	2017	Community	Weekly							x
Nesari et al¹⁹⁷	2010	Specialized	Weekly							x		x
Nicolucci et al¹⁹⁸	2015	Primary	Monthly	x		x	x	x						x
Niswender et al¹⁹⁹	2014	Cross-sectional	Fortnight						x
O’Neil et al²⁰⁰	2016	University	Weekly							x		x
Odegard and Christensen²⁰¹	2012	Community	Monthly							x		x
Odnoletkova et al²⁰²	2016		Fortnight								x
Oh et al²⁰³	2003	Hospital	Weekly							x		x
Orsama et al²⁰⁴	2013	Community	Tailored	x	x	x	x	x
Owolabi et al²⁰⁵	2019	Primary	Daily								x	x		x
Parsons et al²⁰⁶	2019	Primary	Weekly						x			x
Patja et al²⁰⁷	2012	Primary	Monthly								x
Peasah et al²⁰⁸	2019	Primary	Weekly								x			x
Peimani et al²⁰⁹	2016	Hospital	Weekly									x
Piette et al²¹⁰	2011	Community	Weekly							x
Plotnikoff et al²¹¹	2013	Community	Tailored								x
Presley et al²¹²	2020	Community	Weekly					x			x
Quinn et al²¹³	2017	Community	Tailored					x				x
Quinn et al²¹⁴	2011	Community	Tailored					x				x
Raj and Mathews²¹⁵	2020	Hospital	Weekly								x			x
Ralston et al²¹⁶	2009	Hospital	Weekly					x
Ramadas et al²¹⁷	2018	Hospital								x				x
Rasmussen et al²¹⁸	2016	Specialized							x
Rodríguez-Idígoras et al²¹⁹	2009	Community	Tailored	x				x
Ruggiero et al²²⁰	2014	Primary	Monthly								x
Sacco et al²²¹	2009	University	Weekly								x
Sacco et al²²²	2012	University	Weekly								x
Samuel-Hodge et al²²³	2009	Community	Monthly							x
Sarayani et al²²⁴	2018		Weekly									x
Saslow et al²²⁵	2020	University	Daily									x
Sazlina et al²²⁶	2015	Primary			x								x
Schillinger et al²²⁷	2009		Weekly									x
Shahid et al²²⁸	2015	Specialized	Weekly	x				x		x
Shahsavari and Bakhshandeh Bavarsad²²⁹	2020	Specialized	Weekly								x
Shetty et al²³⁰	2011	Specialized	Weekly											x
Shreck et al²³¹	2014		Fortnight							x
Sigurdardottir et al²³²	2009	Specialized	Weekly								x
Skelly et al²³³	2009	Cross-sectional	Fortnight							x		x
Sone et al²³⁴	2002	Specialized	Fortnight		x					x
Sone et al²³⁵	2010	Specialized	Fortnight		x					x		x
Song and Kim²³⁶	2009	Specialized	Weekly							x		x
Spencer et al²³⁷	2018	Community	Fortnight								x		x
Steventon et al²³⁸	2014	Cross-sectional	Daily	x		x		x				x
Sun et al²³⁹	2019	Hospital	Daily	x				x	x					x
Sunil Kumar et al²⁴⁰	2020	Hospital	Daily					x				x		x
Takenga et al²⁴¹	2014	Hospital						x
Tamban et al²⁴²	2013		Weekly											x
Tan et al²⁴³	2018	Primary	Fortnight									x
Tang et al²⁴⁴	2013			x				x				x
Teston et al²⁴⁵	2017		Fortnight								x
Thom et al²⁴⁶	2013	Community	Fortnight								x		x
Torbjørnsen et al²⁴⁷	2014		Monthly	x						x
Tu et al²⁴⁸	1993	Hospital	Weekly							x		x
Van Dyck et al²⁴⁹	2013	Hospital	Fortnight		x					x	x
Van Dyck et al²⁵⁰	2011	Hospital	Fortnight		x					x	x
Van Vugt et al²⁵¹	2016	Primary									x			x
Varney et al²⁵²	2014	Hospital	Monthly								x
Vaughan et al²⁵³	2020	Primary	Weekly						x		x
Vervloet et al²⁵⁴	2014													x
Vinithia et al²⁵⁵	2019	Hospital	Weekly								x	x
Von Storch et al²⁵⁶	2019		Weekly	x				x			x
Wakefield et al²⁵⁷	2011	Primary	Daily	x		x		x				x
Wakefield et al²⁵⁸	2012	Primary	Daily	x		x		x				x
Wakefield et al²⁵⁹	2014	University	Tailored	x		x		x
Waki et al²⁶⁰	2014	University	Tailored	x	x	x	x	x
Walker et al²⁶¹	2011	Specialized	Monthly								x
Wang et al²⁶²	2020	Hospital	Weekly									x
Wang et al²⁶³	2019	Hospital	Weekly					x	x
Wang et al²⁶⁴	2017	Hospital	Fortnight	x				x						x
Warren et al²⁶⁵	2018	Primary	Daily	x		x		x	x
Weinberger et al²⁶⁶	1995	Primary	Monthly							x		x
Whittemore et al²⁶⁷	2004	Specialized	Monthly								x
Wichit et al²⁶⁸	2017	Hospital	Once								x	x
Wild et al²⁶⁹	2016	Primary		x		x	x	x
Williams et al²⁷⁰	2012	Hospital	Weekly					x
Williams et al²⁷¹	2017	Primary	Monthly							x
Wolever et al²⁷²	2010		Weekly								x
Wolf et al²⁷³	2004	Primary	Monthly								x	x
Wu et al²⁷⁴	2017	University	Once								x			x
Yang et al²⁷⁵	2020	Primary	Daily					x	x
Yasmin et al²⁷⁶	2020	Hospital	Fortnight								x	x		x
Yoo et al²⁷⁷	2009	University	Tailored	x		x	x	x				x
Yoon and Kim²⁷⁸	2008	Hospital	Weekly					x						x
Young et al²⁷⁹	2005	Primary	Monthly							x		x
Yu et al²⁸⁰	2019	Hospital	Daily						x			x
Zamanzadeh et al²⁸¹	2017		Daily									x
Zhou et al²⁸²	2014	Hospital	Fortnight	x		x	x	x						x

Abbreviation: BP, blood pressure.

The final sample included 86 studies (35%) with a monitoring component in the telemedicine intervention, 22 studies with a consultation opportunity (9%), 53 studies with a counseling purpose (22%), 63 studies with an opportunity for patients to receive coaching (26%), 81 studies with a patient education component (33%), and eight studies with the possibility of mentoring (3%). Furthermore, 38 studies were able to send reminders (16%).

The evaluation of risk of bias is described for each study in Online Appendix 1 and across studies in Figure 2. Overall, there was a high risk of bias in 45% of the included studies, a moderate risk in 38% and a low risk in 17%. This result was largely attributed to two factors. First were missing outcomes at follow-up, where a high proportion of studies (43%) only reported results of a complete case or per-protocol analysis without appropriate consideration of the relationship with covariates and missingness (ie, tests for missing completely at random or an assumption of missing at random with imputation and/or tests for association with baseline variables and missingness and/or adjusted analyses). Second, there were some concerns regarding the risk of selecting published results, especially due to a lack of registered or published research protocols in the majority of studies (72%).

Figure 2.

Summary of risk of bias assessment.

Effect on HbA1c%

Of the 243 studies, 168 had sufficient information to calculate an MD with standard errors for the effect on HbA1c% and reported treatment effects of telemedicine from one to 96 months. Figure 3 and Table 3 present the results from the individual studies and the meta-analysis. Overall, the results favored telemedicine, with an MD of −0.415%, which was statistically significant (95% confidence interval [CI] = −0.482% to −0.348%). The heterogeneity was great (I² = 93.05%).

Figure 3.

Forest plot of the meta-analysis (magenta line is effect size difference of 0).

Table 3.

Meta-Analysis Summary.

Study	MD	Lower 95% CI	Higher 95% CI	Weight
Abaza and Marschollek⁴²	−0.11	−0.72	0.50	0.47
Aguiar et al⁴⁵	−0.63	−1.08	−0.18	0.57
Akinci et al⁴⁶	−0.57	−1.21	0.07	0.45
Alanzi et al⁴⁸	−1.25	−2.14	−0.36	0.33
Ali et al⁵¹	−0.40	−0.59	−0.22	0.73
Aliha et al⁵³	−1.30	−1.88	−0.72	0.49
Alotaibi et al⁵⁵	−0.74	−1.56	0.08	0.36
Anderson et al⁵⁶	−0.29	−0.60	0.02	0.66
Anderson-Loftin et al⁵⁸	−1.00	−1.56	−0.44	0.50
Anzaldo-Campos et al⁶⁰	−1.37	−1.81	−0.94	0.58
Arora et al⁶¹	−0.13	−0.56	0.31	0.58
Avdal et al⁶³	−0.69	−1.04	−0.35	0.64
Azizi et al⁶⁴	−0.40	−0.55	−0.25	0.74
Blackberry et al⁶⁶	−0.06	−0.27	0.15	0.72
Bogner et al⁶⁸	−1.20	−1.52	−0.88	0.65
Lashkari et al⁷⁰	−0.99	−1.62	−0.36	0.46
Browning et al⁷¹	0.03	−0.16	0.22	0.73
Bujnowska-Fedak et al⁷²	−0.06	−0.71	0.59	0.45
Chen et al⁷⁸	−0.76	−1.33	−0.19	0.49
Cho et al⁸¹	−0.70	−1.16	−0.24	0.56
Cho et al⁸²	−0.15	−0.32	0.02	0.74
Choe et al⁸³	−1.30	−1.88	−0.72	0.49
Crowley et al⁸⁶	−0.10	−0.17	−0.04	0.76
Crowley et al⁸⁷	−1.00	−1.35	−0.65	0.64
Dale et al⁸⁸	0.10	−0.23	0.43	0.65
Dario et al⁸⁹	0.01	−0.23	0.25	0.70
Davis et al⁹⁰	−0.70	−0.90	−0.50	0.72
Del Prato et al⁹¹	0.00	−0.06	0.06	0. 77
Dugas et al⁹³	0.18	−0.87	1.23	0.27
Döbler et al⁹⁵	−0.80	−1.11	−0.49	0.66
Eakin et al⁹⁶	0.00	−0.29	0.29	0.67
Estey et al¹⁰⁰	−0.20	−0.77	0.37	0.49
Faridi et al¹⁰¹	−0.40	−0.98	0.18	0.49
Farsaei et al¹⁰²	−1.50	−1.85	−1.15	0.64
Fortmann et al¹⁰⁴	−0.90	−1.34	−0.46	0.58
Fountoulakis et al¹⁰⁶	−0.70	−1.16	−0.24	0.56
Franciosi et al¹⁰⁸	−0.50	−0.74	−0.26	0.70
Frosch et al¹⁰⁹	−0.30	−0.42	−0.18	0.75
García et al¹¹⁰	−1.20	−1.45	−0.95	0.69
Garg et al¹¹¹	−0.30	−0.65	0.05	0.64
Gagliardino et al¹¹²	−0.20	−0.51	0.11	0.66
Glasgow and Toobert¹¹⁷	0.10	−0.27	0.47	0.62
Glasgow et al¹¹⁸	−0.20	−0.55	0.15	0.64
Glasgow et al¹¹⁹	0.01	−0.13	0.15	0.74
Glasgow et al¹¹⁵	−0.20	−0.48	0.08	0.68
Glasgow et al¹¹⁶	0.00	−0.27	0.27	0.68
Goodarzi et al¹²⁰	−0.46	−0.88	−0.04	0.59
Graziano and Gross¹²²	−0.07	−0.45	0.31	0.62
Griffin et al¹²³	−0.01	−0.18	0.16	0.73
Hare et al¹²⁷	−0.10	−0.41	0.21	0.66
Hee-Sung¹²⁸	−0.43	−0.81	−0.05	0.62
Holbrook et al¹³²	−0.50	−0.71	−0.30	0.72
Holmen et al¹³³	−0.20	−0.68	0.28	0.55
Hordern et al¹³⁴	−0.70	−0.99	−0.41	0.67
Iljaž et al¹³⁸	−0.30	−0.69	0.09	0.61
Jahangard-Rafsanjani et al¹⁴⁰	−0.40	−0.89	0.09	0.54
Jarab et al¹⁴²	−0.90	−1.46	−0.34	0.50
Jeong et al¹⁴⁴	−0.12	−0.40	0.16	0.68
Kardas et al¹⁴⁶	−0.03	−0.51	0.45	0.55
Kempf et al¹⁴⁸	−0.60	−0.91	−0.29	0.66
Keogh et al¹⁴⁹	−0.39	−0.78	0.00	0.61
Kim and Oh¹⁵⁵	−1.20	−1.74	−0.66	0.51
Kim et al¹⁵⁶	−1.00	−1.63	−0.38	0.46
Kim and Kang¹⁵⁷	−0.90	−1.58	−0.22	0.43
Kim¹⁵¹	−0.72	−1.22	−0.22	0.54
Kim and Jeong¹⁵²	−0.66	−1.20	−0.12	0.51
Kim and Song¹⁵³	−1.52	−2.02	−1.02	0.54
Kim and Kim¹⁵⁴	−0.59	−1.21	0.03	0.46
Kim et al¹⁵⁸	−0.90	−1.40	−0.40	0.54
Kim et al¹⁵⁹	−0.40	−0.74	−0.06	0.64
Kim et al¹⁶⁰	−0.70	−1.13	−0.27	0.58
Kim et al¹⁶¹	−0.70	−0.96	−0.44	0.69
Kleinman et al¹⁶⁴	−0.30	−0.77	0.17	0.56
Krein et al¹⁶⁵	0.10	−0.28	0.48	0.62
Kwon et al¹⁶⁸	−0.68	−0.82	−0.55	0.75
Lee et al¹⁷¹	−0.93	−1.49	−0.37	0.50
Li et al¹⁷⁴	−0.35	−0.95	0.25	0.48
Lim et al¹⁷⁶	−0.40	−0.79	−0.01	0.61
Lim et al¹⁷⁷	−0.60	−1.00	−0.20	0.60
Lorig et al¹⁷⁸	−0.11	−0.26	0.05	0.74
Lujan et al¹⁷⁹	−0.25	−0.68	0.18	0.58
Luley et al¹⁸⁰	−1.00	−1.33	−0.67	0.65
Lutes et al¹⁸¹	−0.26	−0.66	0.14	0.60
Marios et al¹⁸⁴	0.49	−0.25	1.23	0.40
Maslakpak et al¹⁸⁵	−0.50	−1.09	0.09	0.48
McEwen et al¹⁸⁷	−0.01	−0.46	0.44	0.57
McKay et al¹⁸⁹	0.36	−0.17	0.89	0.52
McKee et al¹⁹⁰	−0.60	−1.39	0.19	0.37
McMahon et al¹⁹¹	−0.10	−0.56	0.36	0.56
Nesari et al¹⁹⁷	−1.56	−2.18	−0.94	0.46
Nicolucci et al¹⁹⁸	−0.34	−0.57	−0.11	0.71
Niswender et al¹⁹⁹	−0.13	−0.17	−0.10	0.77
Odnoletkova et al²⁰²	−0.10	−0.27	0.07	0.73
Oh et al²⁰³	−1.30	−1.88	−0.72	0.49
O’Neil et al²⁰⁰	−0.39	−0.59	−0.19	0.72
Orsama et al²⁰⁴	−0.44	−0.88	0.01	0.58
Peimani et al²⁰⁹	−0.49	−0.95	−0.03	0.56
Piette et al²¹⁰	0.00	−0.28	0.28	0.68
Plotnikoff et al²¹¹	0.21	0.13	0.29	0.76
Quinn et al²¹⁴	−0.80	−1.26	−0.34	0.56
Ramadas et al²¹⁷	0.10	−0.39	0.59	0.55
Rodríguez-Idígoras et al²¹⁹	0.05	−0.18	0.28	0.70
Sacco et al²²¹	−0.40	−0.95	0.15	0.51
Samuel-Hodge et al²²³	−0.10	−0.19	−0.01	0.76
Sarayani et al²²⁴	−0.30	−0.80	0.20	0.54
Schillinger et al²²⁷	−0.30	−0.66	0.06	0.63
Shahid et al²²⁸	−0.73	−0.94	−0.52	0.72
Sigurdardottir et al²³²	0.25	−0.26	0.76	0.53
Sone et al²³⁴	−0.17	−0.26	−0.08	0.76
Sone et al²³⁵	0.10	0.01	0.20	0.76
Song and Kim²³⁶	−1.50	−2.13	−0.87	0.46
Tamban et al²⁴²	−0.35	−0.71	0.01	0.63
Tan et al²⁴³	−0.38	−0.77	0.01	0.61
Tang et al²⁴⁴	−0.23	−0.48	0.02	0.69
Thom et al²⁴⁶	−0.57	−0.90	−0.24	0.65
Torbjørnsen et al²⁴⁷	0.00	−0.41	0.41	0.60
Tu et al²⁴⁸	0.43	−0.65	1.51	0.26
Van Dyck et al²⁴⁹	−0.30	−0.79	0.19	0.55
Varney et al²⁵²	−0.20	−0.65	0.25	0.57
Wakefield et al²⁵⁹	−0.10	−0.26	0.06	0.74
Waki et al²⁶⁰	−0.40	−0.91	0.11	0.53
Walker et al²⁶¹	0.10	0.04	0.16	0.77
Wang et al²⁶⁴	−0.60	−0.87	−0.33	0.68
Warren et al²⁶⁵	−0.57	−0.89	−0.24	0.65
Weinberger et al²⁶⁶	−0.60	−0.74	−0.46	0.74
Whittemore et al²⁶⁷	0.00	−0.56	0.56	0.50
Wichit et al²⁶⁸	−0.30	−0.68	0.08	0.62
Wild et al²⁶⁹	−0.50	−0.75	−0.25	0.69
Williams et al²⁷⁰	−0.80	−1.22	−0.38	0.59
Wolever et al²⁷²	−0.50	−1.22	0.22	0.41
Yoo et al²⁷⁷	−0.50	−0.84	−0.17	0.65
Yoon and Kim²⁷⁸	−1.63	−2.11	−1.15	0.55
Zhou et al²⁸²	−0.76	−1.20	−0.32	0.58
Agarwal et al⁴³	0.80	0.13	1.47	0.44
Alghafri et al⁵⁰	0.30	−0.01	0.61	0.66
Al Omar et al⁴⁷	−0.70	−1.05	−0.35	0.63
Andreae et al⁵⁹	−0.10	−0.46	0.26	0.63
Asante et al⁶²	−1.30	−2.02	−0.58	0.41
Bluml et al⁶⁷	0.00	−0.25	0.25	0.69
Boels et al¹²	−0.20	−0.52	0.12	0.66
Bohingamu Mudiyanselage et al⁶⁹	−0.21	−0.41	−0.01	0.72
Buysse et al⁷³	−0.10	−0.67	0.47	0.49
Chao et al⁷⁷	−0.90	−1.35	−0.45	0.57
Choudhry et al⁸⁴	0.10	0.02	0.19	0.76
Delahanty et al⁹²	0.00	−0.37	0.37	0.62
Doupis et al⁷	0.10	−0.04	0.24	0.74
Duruturk and Özköslü⁹⁴	−1.99	−2.80	−1.18	0.36
Gupta et al¹²⁴	−0.52	−0.98	−0.06	0.56
Haider et al¹²⁵	−0.40	−0.75	−0.05	0.64
Huang et al¹³⁵	−0.40	−1.18	0.38	0.37
Jain et al¹⁴¹	0.26	−0.08	0.60	0.64
Jiwani et al¹⁴⁵	−2.20	−3.26	−1.14	0.26
Kim and Utz¹⁵⁰	0.24	−0.18	0.66	0.59
Ku et al¹⁶⁶	−1.20	−1.91	−0.49	0.41
Kusnanto et al¹⁶⁷	−0.27	−1.02	0.48	0.39
Lauffenburger et al¹⁶⁹	−0.06	−0.21	0.09	0.74
Lee et al¹⁷²	−0.50	−0.94	−0.06	0.58
Parsons et al²⁰⁶	−0.97	−1.20	−0.74	0.71
Peasah et al²⁰⁸	0.00	−0.52	0.52	0.53
Presley et al²¹²	0.50	0.00	1.00	0.54
Shahsavari and Bakhshandeh Bavarsad²²⁹	−1.38	−1.89	−0.87	0.53
Sun et al²³⁹	−0.38	−0.75	−0.01	0.62
Sunil Kumar et al²⁴⁰	−0.87	−1.11	−0.63	0.70
Vaughan et al²⁵³	−0.67	−1.24	−0.10	0.49
Vinithia et al²⁵⁵	−0.40	−0.71	−0.10	0.66
Von Storch et al²⁵⁶	−0.37	−0.45	−0.29	0.76
Wang et al²⁶³	−0.80	−1.32	−0.28	0.53
Yu et al²⁸⁰	−0.40	−0.92	0.12	0.53
Overall	−0.42	−0.48	−0.35	100.00

Abbreviations: CI, confidence interval; MD, mean difference.

A series of univariate meta-regressions are illustrated in Table 4 and were assessed with a 5% significance level. Compared with North American studies (the reference), Asian studies reported larger effects of telemedicine (difference in MD = −0.287, P = .000), as did studies with higher baseline HbA1c% levels (difference in MD = −0.086 per %, P = .008). Compared with primary care settings (reference), hospital settings were also associated with an increase in the effect of telemedicine (difference in MD = −0.290, P = .004). Furthermore, a monitoring component gave rise to greater effects of telemedicine (difference in MD = −0.195, P = .004). Treatment effects of telemedicine were lesser for studies with longer duration (difference in MD = 0.008 per month, P = .015) and for studies with higher proportions of men (difference in MD = 0.005 per %, P = .035) and higher age (difference in MD = 0.022 per year, P = .000). A coaching component led to a lower effect (difference in MD = 0.215, P = .007). There were no statistically significant associations between the effect of telemedicine on HbA1c% levels and publication date, baseline BMI, contact frequency, the included peripherals, or risk of bias.

Table 4.

Association Between Study Covariates and Effect of Telemedicine on HbA1c% (Meta-Regression).

Covariate	N	Difference in MD (SE)	P value	I² (%)
Study characteristics
Publication decade
Before 2020	4	Reference		92.86
2020s	50	−0.175 (0.287)	.542
2010s	165	−0.082 (0.281)	.769
2020	24	−0.418 (0.312)	.180
Continent
North America	88	Reference		91.15
Europe	44	−0.037 (0.095)	.696
South America	5	−0.104 (0.303)	.731
Africa	5	−0.366 (0.362)	.312
Asia	84	−0.287 (0.078)	.000*
Australia/New Zealand	16	−0.040 (0.140)	.773
Study duration (range = 1-96 months)	168	0.008 per month (0.003)	.015*	92.60
Proportion of men (range = 15%-100%)	159	0.005 per % (0.002)	.035*	93.05
Age (range = 37-73 years)	157	0.022 per year (0.006)	.000*	92.64
Baseline BMI (range = 22-40)	123	0.018 per score (0.010)	.066	92.51
Baseline HbA1c% (range = 5.70%-11.05%)	165	−0.093 per % (0.035)	.007*	92.80
Telemedicine characteristics
Setting
Primary care	58	Reference		90.25
Community	37	−0.019 (0.116)	.868
Hospital	71	−0.290 (0.100)	.004*
Specialized outpatient clinic	34	−0.148 (0.114)	.194
University	14	−0.180 (0.169)	.287
Cross-sectorial	4	0.203 (0.301)	.500
Contact frequency
Daily	30	Reference		91.42
Weekly	83	−0.150 (0.129)	.246
Every two weeks	26	0.045 (0.157)	.776
Monthly	40	0.101 (0.144)	.481
More seldom	12	−0.006 (0.199)	.977
Tailored	24	0.076 (0.148)	.611
Included peripherals
Glucometer	45	−0.052 (0.081)	.523	92.81
Pedometer	16	0.102 (0.132)	.440	92.96
BP monitor	19	−0.008 (0.132)	.953	93.09
Scale	13	0.014 (0.137)	.919	93.12
Intervention components
Monitoring	86	−0.195 (0.068)	.004*	92.30
Consultation	22	−0.015 (0.114)	.895	92.25
Counseling	53	−0.030 (0.083)	.720	92.95
Coaching	63	0.215 (0.080)	.007*	92.44
Education	81	−0.125 (0.072)	.085	92.76
Mentoring	8	0.265 (0.201)	.188	93.03
Reminding	38	−0.151 (0.092)	.100	92.87
Risk of bias
Low	33	Reference		92.82
Some concerns	76	−0.101 (0.100)	.310
High	88	−0.067 (0.100)	.492

Abbreviations: HbA1c%, glycated hemoglobin; MD, mean difference; BMI, body mass index; BP, blood pressure.

Statistically significant at a 5% level.

Certainty of the Evidence

Table 5 summarizes the findings. Overall, the certainty of evidence of the calculated effect on HbA1c% was judged as low due to serious problems with the risk of bias and inconsistency.

Table 5.

Summary of Findings Table.

Certainty assessment							No of patients		Effect		Certainty	Importance
No of studies	Study design	Risk of bias	Inconsistency	Indirectness	Imprecision	Other considerations	Telemedicine	Usual practice	Relative (95% CI)	Absolute (95% CI)	Certainty	Importance
HbA1c%
243	Randomized trials	Serious^a	Serious^b	Not serious	Not serious	None	29 671	27 465	−	MD 0.415 lower (0.482 lower to 0.348 lower)	⨁⨁◯◯ LOW	IMPORTANT

Abbreviations: CI, confidence interval; HbA1c%, glycated hemoglobin; MD, mean difference.

The size of the review implies that no single study contributes dominant weights in the meta-analysis. Indeed, study weights range from 0.26 to 0.77. The correlation between effect size, that is, the MD, and risk of bias across studies was low and insignificant (Spearman ρ = −0.06, P = .3961). However, only 17% of the studies were evaluated to have a low risk of bias. Consequently, the risk of bias was downgraded to one level and assessed as serious.

Effect size point estimates vary widely between studies, with significant effects favoring both alternatives. I² was high, both with and without controlling for statistically significant study covariates simultaneously in the meta-regression (I² = 87.8% and I² = 93%).

Discussion

This review aimed to evaluate the effectiveness of telemedicine solutions among adult patients with T2D. Overall, the results favored telemedicine. Asian studies, studies with higher baseline HbA1c% levels, and studies in hospital settings reported larger effects of telemedicine. Moreover, inclusion of a monitoring component in the telemedicine solution gave rise to a higher effect of telemedicine, whereas inclusion of a coaching component led to a lower effect. The results reflect the findings from previous systematic reviews, which have found significant reductions in HbA1c favoring telemedicine.^{13,22,283,284} In line with our findings, Faruque et al and Wu et al found a larger effect among studies with a higher baseline HbA1c.^13,283 In contrast to our findings, Faruque et al found a larger effect in studies that used web portals or text messaging.¹³ In terms of telemonitoring, Hanlon et al found improved glycemic control in studies that included telemonitoring with feedback¹⁷ and Jaana et al found significantly reduced HbA1 through telemonitoring.²² Hence, the ideal telemedicine setup remains to be determined, although the inclusion of a telemonitoring component seems advisable.

The heterogeneity of the included studies was large (I² = 93.05%), which is often seen in reviews of telemedicine.^{13,15,284-286} In the before-mentioned systematic review by Faruque et al, the heterogeneity (I²) ranged from 58% to 75% depending on the time point Hba1c was measured.¹³ A systematic review and meta-analysis by Tchero found a heterogeneity (I²) of 99% in studies comparing telemedicine with usual care in T2D.²⁸⁴ Hence, the large heterogeneity found in the present review is not unusually large when compared with similar reviews. The large heterogeneity found in the present review may be explained by differences in the inclusion criteria and context of studies that vary greatly in terms of patient subgroup, provider, technology, organization, communication frequency, outcome, and so on. Moreover, the inclusion of studies from the 1990s and 2000s may have added to the heterogeneity. Telemedicine interventions in diabetes have evolved significantly during the last decades due to technological advances and differences in the interventions are thus expected to have affected heterogeneity. However, the inclusion of studies from the 1990s and 2000s has maintained the broad and inclusive scope that was intended for the present review.

The certainty of the evidence was judged as poor. First, most of the evidence (45%) stems from studies with a high risk of bias and only 17% stems from studies with a low risk of bias. However, the size of the review implies that no single study contributed dominant weights in the meta-analysis, where study weights ranged from 0.26 to 0.77. The correlation between effect size, that is, the MD, and risk of bias across studies was low and nonsignificant (Spearman ρ = −0.06, P = .3961). Taken together, the certainty of evidence was downgraded one level due to risk of bias. Second, imprecision was assessed as not serious, as the effect size was statistically significant, and the MD and lower confidence limit (MD = −0.415, 95% CI = −0.482% to −0.348%) were both clinically relevant, and all were comparable with expected effects for other quality improvement strategies,²⁸⁷ which is also why no upgrading due to large effects was conducted. The total number of patients (N = 57 136) included in the review was also much higher than the optimal information size threshold (the OIS criterion). Third, inconsistency was assessed as serious. Although confidence intervals overlap considerably, effect size point estimates vary widely between studies, with significant effects favoring both alternatives. I² was high both with and without controlling for statistically significant study covariates simultaneously in the meta-regression (I² = 87.8% and I² = 93%). Fourth, indirectness was not serious in this review, as all included studies were head-to-head experimental trials assessing the same outcome, and the inclusion criteria for both the population and type of intervention were broad. Fifth, although publication bias cannot be rejected with high confidence, the risk of publication bias was evaluated as undetected. Less than 100 participants were included in 35% of the studies. The Egger test for small-study effects was statistically significant (P < .000) and the funnel plot in Figure 4 reveals studies reporting both significant and insignificant positive and negative effects. The funnel plot is somewhat asymmetrical, with more studies demonstrating significant effects favoring telemedicine than negative effects. Of the 243 included studies, 32 reported industry sponsorship and 44 studies did not report whether there were sponsorships or other relevant conflicts of interest. However, the relationship between disclosed industry sponsorship and/or undisclosed relationships with both study size (above/below 100 participants) and MD (above/below mean effect across studies) was statistically nonsignificant (Fisher exact test = 0.31 and 0.11, respectively). Finally, no dose-response gradient was detected, as the contact frequency was insignificant in the meta-regression (Table 4).

Figure 4.

Funnel plot.

The present systematic review has some limitations. First, more baseline data, such as diabetes years, blood pressure, cholesterol levels, and medication, could have been extracted. However, due to differences in reporting style, such an extraction would have resulted in a high proportion of missing data in the analysis. Second, we could have contacted the authors of the individual studies to minimize the amount of missing HbA1c data. However, due to the large sample of included papers, such a search for data was deemed too time-consuming. Third, relevant studies may have been overlooked. Although a very broad search was conducted, the search was still limited to English and Scandinavian.

Conclusion

Telemedicine may serve as an effective supplement to usual care for patients with T2D. The inclusion of a telemonitoring component seems to increase the effect of telemedicine. It seems that those with a higher HbA1c are more likely to benefit from telemedicine. Patients with poor glycemic control may benefit more from telemedicine interventions, as a high HbA1c level leaves further room for improvement. However, the ideal glycemic target group for telemedicine in T2D remains to be determined.

Supplemental Material

sj-docx-1-dst-10.1177_19322968211064633 – Supplemental material for The Effectiveness of Telemedicine Solutions for the Management of Type 2 Diabetes: A Systematic Review, Meta-Analysis, and Meta-Regression

Supplemental material, sj-docx-1-dst-10.1177_19322968211064633 for The Effectiveness of Telemedicine Solutions for the Management of Type 2 Diabetes: A Systematic Review, Meta-Analysis, and Meta-Regression by Stine Hangaard, Sisse H. Laursen, Jonas D. Andersen, Thomas Kronborg, Peter Vestergaard, Ole Hejlesen and Flemming W. Udsen in Journal of Diabetes Science and Technology

Footnotes

Acknowledgements

The authors would like to thank the research librarian Connie Skrubbeltrang, who assisted in the literature search.

Abbreviations

GRADE, The Grading of Recommendations, Assessment, Development and Evaluation; HbA1c, glycated hemoglobin A1c; MD, mean difference; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; RCT, randomized controlled trial; T1D, type 1 diabetes; T2D, type 2 diabetes.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This is an independent manuscript commissioned and jointly funded by the Steno Diabetes Center North Denmark and Aalborg University.

ORCID iDs

Stine Hangaard

Jonas D. Andersen

Flemming W. Udsen

Supplemental Material

Supplemental material for this article is available online.

References

Cho

Shaw

Karuranga

, et al. IDF Diabetes Atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018;138:271-281. doi:10.1016/j.diabres.2018.02.023.

. Globalization of diabetes: the role of diet, lifestyle, and genes. Diabetes Care. 2011;34(6):1249-1257.

World Health Organization. Global Status Report on Noncommunicable Diseases 2014. World Health Organization; 2014. http://apps.who.int/iris/bitstream/handle/10665/148114/9789241564854_eng.pdf?sequence=1. Accessed November 26, 2021.

Zheng

Ley

. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14:88-98. doi:10.1038/nrendo.2017.151.

Liu

Sun

, et al. Prevalence of diagnosed type 1 and type 2 diabetes among US adults in 2016 and 2017: population based study. BMJ. 2018;362:k1497.

Bonora

DeFronzo

. Diabetes Complications, Comorbidities and Related Disorders. Springer; 2018. doi:10.1007/978-3-319-44433-8.

Doupis

Alexandrides

Elisaf

, et al. Influence of supervised disease understanding and diabetes self-management on adherence to oral glucose-lowering treatment in patients with type 2 diabetes. Diabetes Ther. 2019;10:1407-1422. doi:10.1007/s13300-019-0648-9.

American Diabetes Association. Standards of medical care in diabetes—2020 abridged for primary care providers. Clin Diabetes. 2020;38:10-38. doi:10.2337/cd20-as01.

Powers

Bardsley

Cypress

, et al. Diabetes self-management education and support in type 2 diabetes: a joint position statement of the American Diabetes Association, the American Association of Diabetes Educators, and the Academy of Nutrition and Dietetics. Diabetes Care. 2015;38:1372-1382. doi:10.2337/dc15-0730.

10.

Edelman

Polonsky

. Type 2 diabetes in the real world: the elusive nature of glycemic control. Diabetes Care. 2017;40:1425-1432. doi:10.2337/dc16-1974.

11.

Cramer

. A systematic review of adherence with medications for diabetes. Diabetes Care. 2004;27:1218-1224.

12.

Boels

Vos

Dijkhorst-Oei

Rutten

GEHM

. Effectiveness of diabetes self-management education and support via a smartphone application in insulin-treated patients with type 2 diabetes: results of a randomized controlled trial (TRIGGER study). BMJ Open Diabetes Res Care. 2019;7:e000981. doi:10.1136/bmjdrc-2019-000981.

13.

Faruque

Wiebe

Ehteshami-Afshar

, et al. Effect of telemedicine on glycated hemoglobin in diabetes: a systematic review and meta-analysis of randomized trials. Cmaj. 2017;189:E341-E364. doi:10.1503/cmaj.150885.

14.

WHO Global Observatory for eHealth. Telemedicine: opportunities and developments in member states. Observatory. 2010;2:96.

15.

Baron

Mcbain

Newman

Psych

Hons

. The impact of mobile monitoring technologies on glycosylated. J Diabetes Sci Technol. 2012;6:1185-1196.

16.

Holtz

Lauckner

. Diabetes management via mobile phones: a systematic review. Telemed J E Health. 2012;18(3):175-184.

17.

Hanlon

Daines

Campbell

McKinstry

Weller

Pinnock

. Telehealth interventions to support self-management of long-term conditions: a systematic metareview of diabetes, heart failure, asthma, chronic obstructive pulmonary disease, and cancer. J Med Internet Res. 2017;19(5):e172.

18.

Crico

Renzi

Graf

, et al. mHealth and telemedicine apps: in search of a common regulation. Ecancermedicalscience. 2018;12:853.

19.

Mahar

Rosencrance

Rasmussen

. Telemedicine: past, present, and future. Cleve Clin J Med. 2018;85(12):938-942.

20.

Greenwood

Young

Quinn

. Telehealth remote monitoring systematic review. J Diabetes Sci Technol. 2014;8(2):378-389.

21.

Jaana

Paré

. Home telemonitoring of patients with diabetes: a systematic assessment of observed effects. J Eval Clin Pract. 2007;13:242-253. doi:10.1111/j.1365-2753.2006.00686.x.

22.

Pare

Janna

Sicotte

. Systematic review of home telemonitoring for chronic diseases: the evidence base home telemonitoring: a definition. J Am Med Informatics Assoc. 2007;14(3):269-277.

23.

Forbes

Griffiths

Milligan

While

. Telephone follow-up to improve glycaemic control in patients with type 2 diabetes: systematic review and meta-analysis of controlled trials. Diabet Med. 2010;27(11):1217-1225.

24.

Yun

Park

Lee

Park

. Comparative effectiveness of telemonitoring versus usual care for heart failure: a systematic review and meta-analysis. J Card Fail. 2018;24(1):19-28.

25.

Cassimatis

Kavanagh

. Effects of type 2 diabetes behavioural telehealth interventions on glycaemic control and adherence: a systematic review. J Telemed Telecare. 2012;18(8):447-450.

26.

Lee

SWH

Ooi

Lai

. Telemedicine for the management of glycemic control and clinical outcomes of type 1 diabetes mellitus: a systematic review and meta-analysis of randomized controlled studies. Front Pharmacol. 2017;8:330.

27.

Kongstad

Valentiner

Ried-Larsen

Walker

Juhl

Langberg

. Effectiveness of remote feedback on physical activity in persons with type 2 diabetes: a systematic review and meta-analysis of randomized controlled trials. J Telemed Telecare. 2019;25(1):26-34.

28.

Wen

Wang

, et al. Effect of telemedicine intervention on hypoglycaemia in diabetes patients: a systematic review and meta-analysis of randomised controlled trials. J Telemed Telecare. 2019;25(7):402-413.

29.

European Commission. Tools and methodologies to assess the efficiency of health care services in Europe. An overview of current approaches and opportunities for improvement. Luxembourg; 2019. https://op.europa.eu/en/publication-detail/-/publication/27d2db38-76bc-11e9-9f05-01aa75ed71a1. Accessed November 26, 2021.

30.

Moher

Liberati

Tetzlaff

Altman

Group

. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6:e1000097. doi:10.1371/journal.pmed.1000097.

31.

Laursen

Hangaard

Udsen

Vestergaard

Hejlesen

. Effectiveness of telemedicine solutions for the management of patients with diabetes: protocol for a systematic review and meta-analysis. JMIR Res Protoc. 2020;9:e22062. doi:10.2196/22062.

32.

Sterne

JAC

Savović

Page

, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019;366:l4898. doi:10.1136/bmj.l4898.

33.

Wan

Wang

Liu

Tong

. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol. 2014;14:135. doi:10.1186/1471-2288-14-135.

34.

Hozo

Djulbegovic

Hozo

. Estimating the mean and variance from the median, range, and the size of a sample. BMC Med Res Methodol. 2005;5:13. doi:10.1186/1471-2288-5-13.

35.

Guyatt

Thorlund

Oxman

, et al. GRADE guidelines: 13. Preparing Summary of Findings tables and evidence profiles—continuous outcomes. J Clin Epidemiol. 2013;66:173-183. doi:10.1016/j.jclinepi.2012.08.001.

36.

Guyatt

Oxman

Sultan

, et al. GRADE guidelines: 11. Making an overall rating of confidence in effect estimates for a single outcome and for all outcomes. J Clin Epidemiol. 2013;66:151-157. doi:10.1016/j.jclinepi.2012.01.006.

37.

Guyatt

Oxman

Vist

, et al. GRADE guidelines: 4. Rating the quality of evidence—study limitations (risk of bias). J Clin Epidemiol. 2011;64:407-415. doi:10.1016/j.jclinepi.2010.07.017.

38.

Guyatt

Oxman

Kunz

, et al. GRADE guidelines: 8. Rating the quality of evidence—indirectness. J Clin Epidemiol. 2011;64:1303-1310. doi:10.1016/j.jclinepi.2011.04.014.

39.

Guyatt

Oxman

Kunz

, et al. GRADE guidelines 6. Rating the quality of evidence—imprecision. J Clin Epidemiol. 2011;64:1283-1293. doi:10.1016/j.jclinepi.2011.01.012.

40.

Guyatt

Oxman

Kunz

, et al. GRADE guidelines: 7. Rating the quality of evidence—inconsistency. J Clin Epidemiol. 2011;64:1294-1302. doi:10.1016/j.jclinepi.2011.03.017.

41.

Guyatt

Oxman

Montori

, et al. GRADE guidelines: 5. Rating the quality of evidence—publication bias. J Clin Epidemiol. 2011;64:1277-1282. doi:10.1016/j.jclinepi.2011.01.011.

42.

Abaza

Marschollek

. SMS education for the promotion of diabetes self-management in low & middle income countries: a pilot randomized controlled trial in Egypt. BMC Public Health. 2017;17:962. doi:10.1186/s12889-017-4973-5.

43.

Agarwal

Gaber

Richardson

, et al. Pilot randomized controlled trial of a complex intervention for diabetes self-management supported by volunteers, technology, and interprofessional primary health care teams. Pilot Feasibility Stud. 2019;5:118. doi:10.1186/s40814-019-0504-8.

44.

Agarwal

Mukerji

Desveaux

, et al. Mobile app for improved self-management of type 2 diabetes: multicenter pragmatic randomized controlled trial. JMIR Mhealth Uhealth. 2019;7:e10321. doi:10.2196/10321.

45.

Aguiar

da Silva

CHP

Chiann

Dórea

Lyra

Storpirtis

. Pharmacist-physician collaborative care model for patients with uncontrolled type 2 diabetes in Brazil: results from a randomized controlled trial. J Eval Clin Pract. 2018;24:22-30. doi:10.1111/jep.12606.

46.

Akinci

Yeldan

Satman

Dirican

Ozdincler

. The effects of Internet-based exercise compared with supervised group exercise in people with type 2 diabetes: a randomized controlled study. Clin Rehabil. 2018;32:799-810. doi:10.1177/0269215518757052.

47.

Al Omar

Hasan

Palaian

Mahameed

. The impact of a self-management educational program coordinated through WhatsApp on diabetes control. Pharm Pract (Granada). 2020;18:1841. doi:10.18549/PharmPract.2020.2.1841.

48.

Alanzi

Alanazi

Istepanian

Philip

. Evaluation of the effectiveness of mobile diabetes management system with social networking and cognitive behavioural therapy (CBT) for T2D. Mhealth. 2018;4:35-35. doi:10.21037/mhealth.2018.06.05.

49.

Albikawi

Petro-Nustas

Abuadas

. Self-care management intervention to improve psychological wellbeing for Jordanian patients with type two diabetes mellitus. Issues Ment Health Nurs. 2016;37:190-201. doi:10.3109/01612840.2015.1122118.

50.

Alghafri

Alharthi

Al-Farsi

, et al. “MOVEdiabetes”: a cluster randomized controlled trial to increase physical activity in adults with type 2 diabetes in primary health in Oman. BMJ Open Diabetes Res Care. 2018;6:e000605. doi:10.1136/bmjdrc-2018-000605.

51.

Ali

Singh

Kondal

, et al. Effectiveness of a multicomponent quality improvement strategy to improve achievement of diabetes care goals a randomized, controlled trial. Ann Intern Med. 2016;165:399-408. doi:10.7326/M15-2807.

52.

Ali

Chwastiak

Poongothai

, et al. Effect of a collaborative care model on depressive symptoms and glycated hemoglobin, blood pressure, and serum cholesterol among patients with depression and diabetes in India: the INDEPENDENT randomized clinical trial. JAMA. 2020;324:651-662. doi:10.1001/jama.2020.11747.

53.

Aliha

Asgari

Khayeri

Ramazani

Farajzadegan

Javaheri

. Group education and nurse-telephone follow-up effects on blood glucose control and adherence to treatment in type 2 diabetes patients. Int J Prev Med. 2013;4:797-802.

54.

Alonso-Domínguez

Patino-Alonso

Sánchez-Aguadero

García-Ortiz

Recio-Rodríguez

Gómez-Marcos

. Effect of a multifactorial intervention on the increase in physical activity in subjects with type 2 diabetes mellitus: a randomized clinical trial (EMID Study). Eur J Cardiovasc Nurs. 2019;18:399-409. doi:10.1177/1474515119835048.

55.

Alotaibi

Istepanian

Philip

. A mobile diabetes management and educational system for type-2 diabetics in Saudi Arabia (SAED). Mhealth. 2016;2:33-33. doi:10.21037/mhealth.2016.08.01.

56.

Anderson

Funnell

Aikens

, et al. Evaluating the efficacy of an empowerment-based self-management consultant intervention: results of a two-year randomized controlled trial. Ther Patient Educ. 2009;1:3-11. doi:10.1051/tpe/2009002.

57.

Anderson

Christison-Lagay

Villagra

Liu

Dziura

. Managing the space between visits: a randomized trial of disease management for diabetes in a community health center. J Gen Intern Med. 2010;25:1116-1122. doi:10.1007/s11606-010-1419-5.

58.

Anderson-Loftin

Barnett

Bunn

Sullivan

Hussey

Tavakoli

. Soul food light: culturally competent diabetes education. Diabetes Educ. 2005;31:555-563. doi:10.1177/0145721705278948.

59.

Andreae

Richman

Cherrington

Safford

. Peer-delivered cognitive behavioral training to improve functioning in patients with diabetes: a cluster-randomized trial. Ann Fam Med. 2020;18:15-23. doi:10.1370/afm.2469.

60.

Anzaldo-Campos

Contreras

Vargas-Ojeda

Menchaca-Díaz

Fortmann

Philis-Tsimikas

. Dulce wireless Tijuana: a randomized control trial evaluating the impact of project Dulce and short-term mobile technology on glycemic control in a family medicine clinic in Northern Mexico. Diabetes Technol Ther. 2016;18:240-251. doi:10.1089/dia.2015.0283.

61.

Arora

Peters

Burner

Lam

Menchine

. Trial to examine text message-based mhealth in emergency department patients with diabetes (TExT-MED): a randomized controlled trial. Ann Emerg Med. 2014;63:745-754.e6. doi:10.1016/j.annemergmed.2013.10.012.

62.

Asante

Bam

Diji

AKA

, et al. Pilot mobile phone intervention in promoting type 2 diabetes management in an urban area in Ghana: a randomized controlled trial. Diabetes Educ. 2020;46:455-464. doi:10.1177/0145721720954070.

63.

Avdal

EÜ

Kizilci

Demirel

. The effects of web-based diabetes education on diabetes care results: a randomized control study. Comput Informatics Nurs. 2011;29:101-106. doi:10.1097/NCN.0b013e3181fcbdc6.

64.

Azizi

Aboutorabi

Mazloum-Khorasani

Afzal-Aghaea

Tabesh

Tara

. Evaluating the effect of web-based Iranian diabetic personal health record app on self-care status and clinical indicators: randomized controlled trial. JMIR Med Informatics. 2016;4:e32. doi:10.2196/medinform.6433.

65.

Benson

Sidebottom

Hayes

, et al. Impact of ENHANCED (diEtitiaNs Helping pAtieNts CarE for Diabetes) telemedicine randomized controlled trial on diabetes optimal care outcomes in patients with type 2 diabetes. J Acad Nutr Diet. 2019;119:585-598. doi:10.1016/j.jand.2018.11.013.

66.

Blackberry

Furler

Best

, et al. Effectiveness of general practice based, practice nurse led telephone coaching on glycaemic control of type 2 diabetes: the Patient Engagement and Coaching for Health (PEACH) pragmatic cluster randomised controlled trial. BMJ. 2013;347:f5272. doi:10.1136/bmj.f5272.

67.

Bluml

Kolb

Lipman

. Evaluating the impact of year-long, augmented diabetes self-management support. Popul Health Manag. 2019;22:522-528. doi:10.1089/pop.2018.0175.

68.

Bogner

Morales

de Vries

Cappola

. Integrated management of type 2 diabetes mellitus and depression treatment to improve medication adherence: a randomized controlled trial. Ann Fam Med. 2012;10:15-22. doi:10.1370/afm.1344.

69.

Bohingamu Mudiyanselage

Stevens

Watts

, et al. Personalised telehealth intervention for chronic disease management: a pilot randomised controlled trial. J Telemed Telecare. 2019;25:343-352. doi:10.1177/1357633X18775850.

70.

Lashkari

Borhani

Sabzevari

Abbaszadeh

. Effect of telenursing (telephone follow-up) on glycemic control and body mass index (BMI) of type 2 diabetes patients. Iran J Nurs Midwifery Res. 2013;18:451-456.

71.

Browning

Chapman

Yang

, et al. Management of type 2 diabetes in China: the Happy Life Club, a pragmatic cluster randomised controlled trial using health coaches. BMJ Open. 2016;6:e009319. doi:10.1136/bmjopen-2015-009319.

72.

Bujnowska-Fedak

Puchała

Steciwko

. The impact of telehome care on health status and quality of life among patients with diabetes in a primary care setting in Poland. Telemed J E Health. 2011;17:153-163. doi:10.1089/tmj.2010.0113.

73.

Buysse

Coremans

Pouwer

Ruige

. Sustainable improvement of HbA1c and satisfaction with diabetes care after adding telemedicine in patients on adaptable insulin regimens: results of the TeleDiabetes randomized controlled trial. Health Informatics J. 2020;26:628-641. doi:10.1177/1460458219844369.

74.

Capozza

Woolsey

Georgsson

, et al. Going mobile with diabetes support: a randomized study of a text message-based personalized behavioral intervention for type 2 diabetes self-care. Diabetes Spectr. 2015;28:83-91. doi:10.2337/diaspect.28.2.83.

75.

Carter

Nunlee-Bland

Callender

. A patient-centric, provider-assisted diabetes telehealth self-management intervention for urban minorities. Perspect Health Inf Manag. 2011;8:1b.

76.

Castelnuovo

Manzoni

Cuzziol

, et al. TECNOB study: ad interim results of a randomized controlled trial of a multidisciplinary telecare intervention for obese patients with type-2 diabetes. Clin Pract Epidemiol Ment Heal. 2011;7:44-50. doi:10.1186/1471-2458-10-204.

77.

Chao

Lin

W-Y

. Enhanced self-efficacy and behavioral changes among patients with diabetes: cloud-based mobile health platform and mobile app service. JMIR Diabetes. 2019;4:e11017. doi:10.2196/11017.

78.

Chen

Jap

Lin

Hsiao

Lin

. Improvement of glycaemia control in subjects with type 2 diabetes by self-monitoring of blood glucose: comparison of two management programs adjusting bedtime insulin dosage. Diabetes Obes Metab. 2008;10:34-40. doi:10.1111/j.1463-1326.2006.00676.x.

79.

Chen

Zhu

Tang

, et al. A mobile phone informational reminder to improve eye care adherence among diabetic patients in rural China: a randomized controlled trial. Am J Ophthalmol. 2018;194:54-62. doi:10.1016/j.ajo.2018.07.006.

80.

Chiu

Wray

, et al. Dissemination of evidence-base minimal psychological intervention for diabetes management in Taiwan adults with type 2 diabetes. Int J Clin Exp Med. 2016;9:14489-14498.

81.

Cho

Chang

Kwon

, et al. Long-term effect of the Internet-based glucose monitoring system on HbA1c reduction and glucose stability: a 30-month follow-up study for diabetes management with a ubiquitous medical care system. Diabetes Care. 2006;29:2625-2631. doi:10.2337/dc05-2371.

82.

Cho

Kim

Yoo

, et al. An Internet-based health gateway device for interactive communication and automatic data uploading: clinical efficacy for type 2 diabetes in a multi-centre trial. J Telemed Telecare. 2017;23:595-604. doi:10.1177/1357633X16657500.

83.

Choe

Mitrovich

Dubay

Hayward

Krein

Vijan

. Proactive case management of high-risk patients with type 2 diabetes mellitus by a clinical pharmacist: a randomized controlled trial. Am J Manag Care. 2005;11:253-260.

84.

Choudhry

Isaac

Lauffenburger

, et al. Effect of a remotely delivered tailored multicomponent approach to enhance medication taking for patients with hyperlipidemia, hypertension, and diabetes the STIC2IT cluster randomized clinical trial. JAMA Intern Med. 2018;178:1190-1198. doi:10.1001/jamainternmed.2018.3189.

85.

Clark

Hampson

Avery

Simpson

. Effects of a brief tailored intervention on the process and predictors of lifestyle behaviour change in patients with type 2 diabetes. Psychol Heal Med. 2004;9:440-449. doi:10.1080/13548500412331292250.

86.

Crowley

Powers

Olsen

, et al. The Cholesterol, Hypertension, and Glucose Education (CHANGE) study: results from a randomized controlled trial in African Americans with diabetes. Am Heart J. 2013;166:179-186. doi:10.1016/j.ahj.2013.04.004.

87.

Crowley

Edelman

McAndrew

, et al. Practical telemedicine for veterans with persistently poor diabetes control: a randomized pilot trial. Telemed J E Health. 2016;22:376-384. doi:10.1089/tmj.2015.0145.

88.

Dale

Caramlau

Sturt

Friede

Walker

. Telephone peer-delivered intervention for diabetes motivation and support: the telecare exploratory RCT. Patient Educ Couns. 2009;75:91-98. doi:10.1016/j.pec.2008.09.014.

89.

Dario

Toffanin

Calcaterra

, et al. Telemonitoring of type 2 diabetes mellitus in Italy. Telemed J E Health. 2017;23:143-152. doi:10.1089/tmj.2015.0224.

90.

Davis

Hitch

Salaam

Herman

Zimmer-Galler

Mayer-Davis

. TeleHealth improves diabetes self-management in an underserved community: diabetes TeleCare. Diabetes Care. 2010;33:1712-1717. doi:10.2337/dc09-1919.

91.

Del Prato

Nicolucci

Lovagnini-Scher

Turco

Leotta

Vespasiani

. Telecare provides comparable efficacy to conventional self-monitored blood glucose in patients with type 2 diabetes titrating one injection of insulin glulisine—the ELEONOR study. Diabetes Technol Ther. 2012;14:175-182. doi:10.1089/dia.2011.0163.

92.

Delahanty

Levy

Chang

, et al. Effectiveness of lifestyle intervention for type 2 diabetes in primary care: the REAL HEALTH-diabetes randomized clinical trial. J Gen Intern Med. 2020;35:2637-2646. doi:10.1007/s11606-019-05629-9.

93.

Dugas

Crowley

Gao

, et al. Individual differences in regulatory mode moderate the effectiveness of a pilot mHealth trial for diabetes management among older veterans. PLoS One. 2018;13:e0192807. doi:10.1371/journal.pone.0192807.

94.

Duruturk

Özköslü

. Effect of tele-rehabilitation on glucose control, exercise capacity, physical fitness, muscle strength and psychosocial status in patients with type 2 diabetes: a double blind randomized controlled trial. Prim Care Diabetes. 2019;13:542-548. doi:10.1016/j.pcd.2019.03.007.

95.

Döbler

Belnap

Pollmann

Farin

Raspe

Mittag

. Telephone-delivered lifestyle support with action planning and motivational interviewing techniques to improve rehabilitation outcomes. Rehabil Psychol. 2018;63:170-181. doi:10.1037/rep0000224.

96.

Eakin

Reeves

Winkler

, et al. Six-month outcomes from living well with diabetes: a randomized trial of a telephone-delivered weight loss and physical activity intervention to improve glycemic control. Ann Behav Med. 2013;46:193-203. doi:10.1007/s12160-013-9498-2.

97.

Eakin

Winkler

Dunstan

, et al. Living well with diabetes: 24-month outcomes from a randomized trial of telephone-delivered weight loss and physical activity intervention to improve glycemic control. Diabetes Care. 2014;37:2177-2185. doi:10.2337/dc13-2427.

98.

Egede

Williams

Voronca

Knapp

Fernandes

. Randomized controlled trial of technology-assisted case management in low income adults with type 2 diabetes. Diabetes Technol Ther. 2017;19:476-482. doi:10.1089/dia.2017.0006.

99.

Egede

Walker

Payne

Knapp

Acierno

Frueh

. Effect of psychotherapy for depression via home telehealth on glycemic control in adults with type 2 diabetes: subgroup analysis of a randomized clinical trial. J Telemed Telecare. 2018;24:596-602. doi:10.1177/1357633X17730419.

100.

Estey

Tan

Mann

. Follow-up intervention: its effect on compliance behavior to a diabetes regimen. Diabetes Educ. 1990;16:291-295. doi:10.1177/014572179001600408.

101.

Faridi

Liberti

Shuval

Northrup

Ali

Katz

. Evaluating the impact of mobile telephone technology on type 2 diabetic patients’ self-management: the NICHE pilot study. J Eval Clin Pract. 2008;14:465-469. doi:10.1111/j.1365-2753.2007.00881.x.

102.

Farsaei

Sabzghabaee

Zargarzadeh

. Effect of pharmacist-led patient education on glycemic control of type 2 diabetes: a randomized controlled trial. J Res Med Sci. 2011;16:43-49.

103.

Fernandes

BSM

Reis

Torres

HdC

. Evaluation of the telephone intervention in the promotion of diabetes self-care: a randomized clinical trial. Rev Lat Am Enfermagem. 2016;24:e2719. doi:10.1590/1518-8345.0632.2719

104.

Fortmann

Gallo

Garcia

, et al. Dulce digital: an mHealth SMS-based intervention improves glycemic control in Hispanics with type 2 diabetes. Diabetes Care. 2017;40:1349-1355. doi:10.2337/dc17-0230.

105.

Fottrell

Ahmed

Morrison

, et al. Community groups or mobile phone messaging to prevent and control type 2 diabetes and intermediate hyperglycaemia in Bangladesh (DMagic): a cluster-randomised controlled trial. Lancet Diabetes Endocrinol. 2019;7:200-212. doi:10.1016/S2213-8587(19)30001-4.

106.

Fountoulakis

Papanastasiou

Gryparis

Markou

Piaditis

. Impact and duration effect of telemonitoring on HbA1c, BMI and cost in insulin-treated diabetes mellitus patients with inadequate glycemic control: a randomized controlled study. Hormones. 2015;14:632-643. doi:10.14310/horm.2002.1603.

107.

Franc

Hanaire

Benhamou

, et al. DIABEO system combining a mobile app software with and without telemonitoring versus standard care: a randomized controlled trial in diabetes patients poorly controlled with a basal-bolus insulin regimen. Diabetes Technol Ther. 2020;22:904-911. doi:10.1089/dia.2020.0021.

108.

Franciosi

Lucisano

Pellegrini

, et al. ROSES: role of self-monitoring of blood glucose and intensive education in patients with type 2 diabetes not receiving insulin. A pilot randomized clinical trial. Diabet Med. 2011;28:789-796. doi:10.1111/j.1464-5491.2011.03268.x.

109.

Frosch

Ochoa

Mangione

. Evaluation of a behavior support intervention for patients with poorly controlled diabetes. Arch Intern Med. 2011;171:2011-2017. doi:10.1001/archinternmed.2011.497.

110.

García

Brown

Horner

Zuñiga

Arheart

. Home-based diabetes symptom self-management education for Mexican Americans with type 2 diabetes. Health Educ Res. 2014;30:484-496. doi:10.1093/her/cyv018.

111.

Garg

Hurwitz

Rein

Schuman

Underwood

Bhandari

. Effect of follow-up by a hospital diabetes care team on diabetes control at one year after discharge from the hospital. Diabetes Res Clin Pract. 2017;133:78-84. doi:10.1016/j.diabres.2017.08.014.

112.

Gagliardino

Arrechea

Assad

, et al. Type 2 diabetes patients educated by other patients perform at least as well as patients trained by professionals. Diabetes Metab Res Rev. 2013;29:152-160. doi:10.1002/dmrr.

113.

Wasif Gillani

. Determining effective diabetic care; a multicentre—longitudinal Interventional Study. Curr Pharm Des. 2016;22:6469-6476. doi:10.2174/1381612822666160813235704.

114.

Gimbel

Rennert

Crawford

, et al. Enhancing patient activation and self-management activities in patients with type 2 diabetes using the US Department of Defense mobile health care environment: feasibility study. J Med Internet Res. 2020;22:e17968. doi:10.2196/17968.

115.

Glasgow

Nutting

Toobert

, et al. Effects of a brief computer-assisted diabetes self-management intervention on dietary, biological and quality-of-life outcomes. Chronic Illn. 2006;2:27-38. doi:10.1179/174592006X93851.

116.

Glasgow

Strycker

King

Toobert

Rahm

Jex

. Robustness of a computer-assisted diabetes self-management intervention across patient characteristics, healthcare settings, and intervention staff. Am J Manag Care. 2006;12:137-145.

117.

Glasgow

Toobert

. Brief, computer-assisted diabetes dietary self-management counseling: effects on behavior, physiologic outcomes, and quality of life. Med Care. 2000;38:1062-1073.

118.

Glasgow

Toobert

Hampson

Strycker

. Implementation, generalization and long-term results of the “choosing well” diabetes self-management intervention. Patient Educ Couns. 2002;48:115-122. doi:10.1016/S0738-3991(02)00025-3.

119.

Glasgow

Nutting

King

, et al. Randomized effectiveness trial of a computer-assisted intervention to improve diabetes care. Diabetes Care. 2005;28:33-39.

120.

Goodarzi

Ebrahimzadeh

Rabi

Saedipoor

Jafarabadi

. Impact of distance education via mobile phone text messaging on knowledge, attitude, practice and self efficacy of patients with type 2 diabetes mellitus in Iran. J Diabetes Metab Disord. 2012;11:10. doi:10.1186/2251-6581-11-10.

121.

Goodarzi

Zahed

Esfahani

Nourian

Zamani-Alavijeh

. The effects of mobile text messages on raising knowledge of men with type 2 diabetes. Electron Physician. 2018;10:7243-7248.

122.

Graziano

Gross

. A randomized controlled trial of an automated telephone intervention to improve glycemic control in type 2 diabetes. Adv Nurs Sci. 2009;32:42-57. doi:10.1097/ANS.0b013e3181b117a9.

123.

Griffin

Simmons

Prevost

, et al. Multiple behaviour change intervention and outcomes in recently diagnosed type 2 diabetes: the ADDITION-Plus randomised controlled trial. Diabetologia. 2014;57:1308-1319. doi:10.1007/s00125-014-3236-6.

124.

Gupta

Jose

, et al. Effectiveness of a video-based lifestyle education program compared to usual care in improving HbA1c and other metabolic parameters in individuals with type 2 diabetes: an open-label parallel arm randomized control trial (RCT). Diabetes Ther. 2020;11:667-679. doi:10.1007/s13300-020-00769-2.

125.

Haider

Hyun

Cheung

Redfern

Thiagalingam

Chow

. Effect of lifestyle focused text messaging on risk factor modification in patients with diabetes and coronary heart disease: a sub-analysis of the TEXT ME study. Diabetes Res Clin Pract. 2019;153:184-190. doi:10.1016/j.diabres.2019.04.030.

126.

Hansen

Perrild

Koefoed

Zander

. Video consultations as add-on to standard care among patients with type 2 diabetes not responding to standard regimens: a randomized controlled trial. Eur J Endocrinol. 2017;176:727-736.

127.

Hare

Hordern

Leano

Stanton

Prins

Marwick

. Application of an exercise intervention on the evolution of diastolic dysfunction in patients with diabetes mellitus efficacy and effectiveness. Circ Hear Fail. 2011;4:441-449. doi:10.1161/CIRCHEARTFAILURE.110.959312.

128.

Hee-Sung

. Impact of web-based nurse’s education on glycosylated haemoglobin in type 2 diabetic patients. J Clin Nurs. 2007;16;1361-1366. doi:10.1111/j.1365-2702.2005.01506.x.

129.

Heisler

Choi

Mase

Long

Reeves

. Effectiveness of technologically enhanced peer support in improving glycemic management among predominantly African American, low-income adults with diabetes. Diabetes Educ. 2019;45:260-271. doi:10.1177/0145721719844547.

130.

Hidrus

Kueh

Norsaádah

, et al. Effects of brain breaks videos on the motives for the physical activity of Malaysians with type-2 diabetes mellitus. Int J Environ Res Public Health. 2020;17:2507. doi:10.3390/ijerph17072507.

131.

Hokanson

Anderson

Hennrikus

Lando

Kendall

. Integrated tobacco cessation counseling in a diabetes self-management training program: a randomized trial of diabetes and reduction of tobacco. Diabetes Educ. 2006;32:562-570. doi:10.1177/0145721706289914.

132.

Holbrook

Thabane

Keshavjee

, et al. Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial. CMAJ. 2009;181:37-44. doi:10.1503/cmaj.081272.

133.

Holmen

Torbjørnsen

Wahl

, et al. A mobile health intervention for self-management and lifestyle change for persons with type 2 diabetes, part 2: one-year results from the Norwegian randomized controlled trial RENEWING HEALTH. JMIR Mhealth Uhealth. 2014;2:e57. doi:10.2196/mhealth.3882.

134.

Hordern

Coombes

Cooney

Jeffriess

Prins

Marwick

. Effects of exercise intervention on myocardial function in type 2 diabetes. Heart. 2009;95:1343-1349. doi:10.1136/hrt.2009.165571.

135.

Huang

Tan

Lum

Sloot

Boehm

Car

. A smartphone app to improve medication adherence in patients with type 2 diabetes in Asia: feasibility randomized controlled trial. JMIR Mhealth Uhealth. 2019;7:e14914. doi:10.2196/14914.

136.

Huizinga

Gebretsadik

Garcia Ulen

, et al. Preventing glycaemic relapse in recently controlled type 2 diabetes patients: a randomised controlled trial. Diabetologia. 2010;53:832-839. doi:10.1007/s00125-010-1658-3.

137.

Hunt

Sanderson

Ellison

. Support for diabetes using technology: a pilot study to improve self-management. Medsurg Nurs. 2014;23:231-237.

138.

Iljaž

Brodnik

Zrimec

Cukjati

. E-healthcare for diabetes mellitus type 2 patients—a randomised controlled trial in Slovenia. Zdr Varst. 2017;56:150-157. doi:10.1515/sjph-2017-0020.

139.

Islam

SMS

Peiffer

Chow

, et al. Cost-effectiveness of a mobile-phone text messaging intervention on type 2 diabetes—a randomized-controlled trial. Heal Policy Technol. 2020;9:79-85. doi:10.1016/j.hlpt.2019.12.003.

140.

Jahangard-Rafsanjani

Sarayani

Nosrati

, et al. Effect of a community pharmacist–delivered diabetes support program for patients receiving specialty medical care: a randomized controlled trial. Diabetes Educ. 2015;41:127-135. doi:10.1177/0145721714559132.

141.

Jain

Joshi

Idiculla

Xavier

. Community health worker interventions in type 2 diabetes mellitus patients: assessing the feasibility and effectiveness in Rural Central India. J Cardiovasc Dis Res. 2018;9:127-133. doi:10.5530/jcdr.2018.3.29.

142.

Jarab

Alqudah

Mukattash

Shattat

Al-Qirim

. Randomized controlled trial of clinical pharmacy management of patients with type 2 diabetes in an outpatient diabetes clinic in Jordan. J Manag Care Pharm. 2012;18:516-526.

143.

Jennings

Vandelanotte

Caperchione

Mummery

. Effectiveness of a web-based physical activity intervention for adults with type 2 diabetes—a randomised controlled trial. Prev Med. 2014;60:33-40. doi:10.1016/j.ypmed.2013.12.011.

144.

Jeong

Jeon

Bae

, et al. Smart care based on telemonitoring and telemedicine for type 2 diabetes care: multi-center randomized controlled trial. Telemed J E Health. 2018;24:604-613. doi:10.1089/tmj.2017.0203.

145.

Jiwani

Wang

Berndt

, et al. Changes in patient-reported outcome measures with a technology-supported behavioral lifestyle intervention among patients with type 2 diabetes: pilot randomized controlled clinical trial. JMIR Diabetes. 2020;5:e19268. doi:10.2196/19268.

146.

Kardas

Lewandowski

Bromuri

. Type 2 diabetes patients benefit from the COMODITY12 mHealth system: results of a randomised trial. J Med Syst. 2016;40:259. doi:10.1007/s10916-016-0619-x.

147.

Kassavou

Mirzaei

Brimicombe

, et al. A highly tailored text and voice messaging intervention to improve medication adherence in patients with either or both hypertension and type 2 diabetes in a UK primary care setting: feasibility randomized controlled trial of clinical effectiveness. J Med Internet Res. 2020;22:e16629. doi:10.2196/16629.

148.

Kempf

Altpeter

Berger

, et al. Efficacy of the telemedical lifestyle intervention program TeLiPro in advanced stages of type 2 diabetes: a randomized controlled trial. Diabetes Care. 2017;40:863-871. doi:10.2337/dc17-0303.

149.

Keogh

Smith

White

, et al. Psychological family intervention for poorly controlled type 2 diabetes. Am J Manag Care. 2011;17:105-113.

150.

Kim

Utz

. Effectiveness of a social media-based, health literacy-sensitive diabetes self-management intervention: a randomized controlled trial. J Nurs Scholarsh. 2019;51:661-669. doi:10.1111/jnu.12521.

151.

Kim

. A randomized controlled trial of a nurse short-message service by cellular phone for people with diabetes. Int J Nurs Stud. 2007;44:687-692. doi:10.1016/j.ijnurstu.2006.01.011.

152.

Kim

Jeong

. A nurse short message service by cellular phone in type-2 diabetic patients for six months. J Clin Nurs. 2007;16:1082-1087. doi:10.1111/j.1365-2702.2007.01698.x.

153.

Kim

Song

. Technological intervention for obese patients with type 2 diabetes. Appl Nurs Res. 2008;21:84-89. doi:10.1016/j.apnr.2007.01.007.

154.

Kim

. Effectiveness of mobile and Internet intervention in patients with obese type 2 diabetes. Int J Med Inform. 2008;77:399-404. doi:10.1016/j.ijmedinf.2007.07.006.

155.

Kim

. Adherence to diabetes control recommendations: impact of nurse telephone calls. J Adv Nurs. 2003;44:256-261. doi:10.1046/j.1365-2648.2003.02800.x.

156.

Kim

Lee

. Effects of nurse-coordinated intervention on patients with type 2 diabetes in Korea. J Nurs Care Qual. 2005;20:154-160. doi:10.1097/00001786-200504000-00011.

157.

Kim

Kang

. Utility of a web-based intervention for individuals with type 2 diabetes: the impact on physical activity levels and glycemic control. Comput Informatics Nurs. 2006;24:337-345. doi:10.1097/00024665-200611000-00008.

158.

Kim

Han

Song

, et al. A community-based, culturally tailored behavioral intervention for Korean Americans with type 2 diabetes. Diabetes Educ. 2009;35:986-994. doi:10.1177/0145721709345774.

159.

Kim

Park

Kang

, et al. Insulin dose titration system in diabetes patients using a short messaging service automatically produced by a knowledge matrix. Diabetes Technol Ther. 2010;12:663-669. doi:10.1089/dia.2010.0031.

160.

Kim

Park

Lee

, et al. Efficacy of a new medical information system, ubiquitous healthcare service with voice inception technique in elderly diabetic patients. Sci Rep. 2015;5:18214. doi:10.1038/srep18214.

161.

Kim

Sun

Yang

, et al. Randomized, open-label, parallel group study to evaluate the effect of Internet-based glucose management system on subjects with diabetes in China. Telemed J E Health. 2016;22:666-674. doi:10.1089/tmj.2015.0170.

162.

King

Estabrooks

Strycker

Toobert

Bull

Glasgow

. Outcomes of a multifaceted physical activity regimen as part of a diabetes self-management intervention. Ann Behav Med. 2006;31:128-137. doi:10.1207/s15324796abm3102_4.

163.

Kirkman

Weinberger

Landsman

, et al. A telephone-delivered intervention for patients with NIDDM: effect on coronary risk factors. Diabetes Care. 1994;17:840-846. doi:10.2337/diacare.17.8.840.

164.

Kleinman

Shah

Phatak

Viswanathan

. Improved medication adherence and frequency of blood glucose self-testing using an m-health platform versus usual care in a multisite randomized clinical trial among people with type 2 diabetes in India. Telemed J E Health. 2017;23:733-740. doi:10.1089/tmj.2016.0265.

165.

Krein

Klamerus

Vijan

, et al. Case management for patients with poorly controlled diabetes: a randomized trial. Am J Med. 2004;116:732-739. doi:10.1016/j.amjmed.2003.11.028.

166.

Park

Jeon

Choi

. Clinical efficacy and plausibility of a smartphone-based integrated online real-time diabetes care system via glucose and diet data management: a pilot study. Intern Med J. 2020;50:1524-1532. doi:10.1111/imj.14738.

167.

Kusnanto

Widyanata KAJ

Suprajitno, Arifin

. DM-calendar app as a diabetes self-management education on adult type 2 diabetes mellitus: a randomized controlled trial. J Diabetes Metab Disord. 2019;18:557-563. doi:10.1007/s40200-019-00468-1.

168.

Kwon

Cho

Kim

, et al. Establishment of blood glucose monitoring system using the Internet. Diabetes Care. 2004;27:478-483. doi:10.2337/diacare.27.2.478.

169.

Lauffenburger

Ghazinouri

Jan

, et al. Impact of a novel pharmacist-delivered behavioral intervention for patients with poorly-controlled diabetes: the ENhancing outcomes through Goal Assessment and Generating Engagement in Diabetes Mellitus (ENGAGE-DM) pragmatic randomized trial. PLoS One. 2019;14:e0214754. doi:10.1371/journal.pone.0214754.

170.

Lazo-Porras

Bernabe-Ortiz

Taype-Rondan

, et al. Foot thermometry with mHeath-based supplementation to prevent diabetic foot ulcers: a randomized controlled trial. Wellcome Open Res. 2020;5:23. doi:10.12688/wellcomeopenres.15531.2.

171.

Lee

Wong

Tan

CSS

Nasir

Lee

SWH

. Telemonitoring in fasting individuals with type 2 diabetes mellitus during Ramadan: a prospective, randomised controlled study. Sci Rep. 2017;7:10119. doi:10.1038/s41598-017-10564-y.

172.

Lee

Yoo

Min

Park

. Effect of voluntary participation on mobile health care in diabetes management: randomized controlled open-label trial. JMIR Mhealth Uhealth. 2020;8:e19153. doi:10.2196/19153.

173.

Lee

Chan

CKY

Chua

, et al. Telemonitoring and team-based management of glycemic control on people with type 2 diabetes: a cluster-randomized controlled trial. J Gen Intern Med. 2019;35:87-94. doi:10.1007/s11606-019-05316-9.

174.

Yao

Hsue

Lou

. Impact of “Conversation Maps” on diabetes distress and self-efficacy of Chinese adult patients with type 2 diabetes: a pilot study. Patient Prefer Adherence. 2016;10:901-908. doi:10.2147/PPA.S95449.

175.

Liebreich

Plotnikoff

Courneya

Boulé

. Diabetes NetPLAY: a physical activity website and linked email counselling randomized intervention for individuals with type 2 diabetes. Int J Behav Nutr Phys Act. 2009;6:18. doi:10.1186/1479-5868-6-18.

176.

Lim

Kang

Shin

, et al. Improved glycemic control without hypoglycemia in elderly diabetic patients using the ubiquitous healthcare service, a new medical information system. Diabetes Care. 2011;34:308-313. doi:10.2337/dc10-1447.

177.

Lim

Kang

Kim

, et al. Multifactorial intervention in diabetes care using real-time monitoring and tailored feedback in type 2 diabetes. Acta Diabetol. 2016;53:189-198. doi:10.1007/s00592-015-0754-8.

178.

Lorig

Ritter

Laurent

, et al. Online diabetes self-management program: a randomized study. Diabetes Care. 2010;33:1275-1281. doi:10.2337/dc09-2153.

179.

Lujan

Ostwald

Ortiz

. Promotora diabetes intervention for Mexican Americans. Diabetes Educ. 2007;33:660-670. doi:10.1177/0145721707304080.

180.

Luley

Blaik

Reschke

Klose

Westphal

. Weight loss in obese patients with type 2 diabetes: effects of telemonitoring plus a diet combination—the Active Body Control (ABC) Program. Diabetes Res Clin Pract. 2011;91:286-292. doi:10.1016/j.diabres.2010.11.020.

181.

Lutes

Cummings

Littlewood

Dinatale

Hambidge

. A community health worker–delivered intervention in African American women with type 2 diabetes: a 12-month randomized trial. Obesity. 2017;25:1329-1335. doi:10.1002/oby.21883.

182.

Lynch

Liebman

Ventrelle

Avery

Richardson

. A self-management intervention for African Americans with comorbid diabetes and hypertension: a pilot randomized controlled trial. Prev Chronic Dis. 2014;11:E90. doi:10.5888/pcd11.130349.

183.

MacPhail

Mullan

Sharpe

MacCann

Todd

. Using the health action process approach to predict and improve health outcomes in individuals with type 2 diabetes mellitus. Diabetes Metab Syndr Obes Targets Ther. 2014;7:469-479. doi:10.2147/DMSO.S68428.

184.

Marios

Dalton

Smart

. The effect of tele-monitoring on exercise training adherence, functional capacity, quality of life and glycemic control in patients with type II diabetes. J Sport Sci Med. 2012;11:51-56.

185.

Maslakpak

Razmara

Niazkhani

. Effects of face-to-face and telephone-based family-oriented education on self-care behavior and patient outcomes in type 2 diabetes: a randomized controlled trial. J Diabetes Res. 2017;2017:8404328. doi:10.1155/2017/8404328.

186.

Mayberry

Berg

Greevy

, et al. Mixed-methods randomized evaluation of FAMS: a mobile phone-delivered intervention to improve family/friend involvement in adults’ type 2 diabetes self-care. Ann Behav Med. 2021;55:165-178. doi:10.1093/abm/kaaa041.

187.

McEwen

Pasvogel

Murdaugh

Hepworth

. Effects of a family-based diabetes intervention on behavioral and biological outcomes for Mexican American adults. Diabetes Educ. 2017;43:272-285. doi:10.1177/0145721717706031.

188.

McKay

King

Eakin

Seeley

Glasgow

. The Diabetes Network Internet-based physical activity intervention. Diabetes Care. 2001;24:1328-1334.

189.

McKay

Glasgow

Feil

Boles

Barrera

. Internet-based diabetes self-management and support: initial outcomes from the Diabetes Network project. Rehabil Psychol. 2002;47:31-48. doi:10.1037/0090-5550.47.1.31.

190.

McKee

Fletcher

Sigal

Giftos

Schechter

Walker

. A collaborative approach to control hypertension in diabetes: outcomes of a pilot intervention. J Prim Care Community Health. 2011;2:148-152. doi:10.1177/2150131911401028.

191.

McMahon

Fonda

Gomes

Alexis

Conlin

. A randomized comparison of online- and telephone-based care management with Internet training alone in adult patients with poorly controlled type 2 diabetes. Diabetes Technol Ther. 2012;14:1060-1067. doi:10.1089/dia.2012.0137.

192.

Mease

. Telemedicine improved diabetic management. Mil Med. 2000;165:579-584. doi:10.1093/milmed/165.8.579.

193.

Mons

Raum

Krämer

, et al. Effectiveness of a supportive telephone counseling intervention in type 2 diabetes patients: randomized controlled study. PLoS One. 2013;8:e77954. doi:10.1371/journal.pone.0077954.

194.

Moriyama

Nakano

Kuroe

Nin

Niitani

Nakaya

. Efficacy of a self-management education program for people with type 2 diabetes: results of a 12 month trial. Japan J Nurs Sci. 2009;6:51-63. doi:10.1111/j.1742-7924.2009.00120.x.

195.

Mwangi

Bascaran

Ng’ang’a

, et al. Feasibility of a cluster randomized controlled trial on the effectiveness of peer–led health education interventions to increase uptake of retinal examination for diabetic retinopathy in Kirinyaga, Kenya: a pilot trial. Pilot Feasibility Stud. 2020;6:102. doi:10.1186/s40814-020-00644-8.

196.

Namjoo Nasab

Ghavam

Yazdanpanah

Jahangir

Shokrpour

. Effects of self-management education through telephone follow-up in diabetic patients. Health Care Manag (Frederick). 2017;36:273-281. doi:10.1097/HCM.0000000000000172.

197.

Nesari

Zakerimoghadam

Rajab

Bassampour

Faghihzadeh

. Effect of telephone follow-up on adherence to a diabetes therapeutic regimen. Japan J Nurs Sci. 2010;7:121-128. doi:10.1111/j.1742-7924.2010.00146.x.

198.

Nicolucci

Cercone

Chiriatti

, et al. A randomized trial on home telemonitoring for the management of metabolic and cardiovascular risk in patients with type 2 diabetes. Diabetes Technol Ther. 2015;17:563-570. doi:10.1089/dia.2014.0355.

199.

Niswender

Piletic

Andersen

Conradsen Hiort

Hollander

. Weight change upon once-daily initiation of insulin detemir with or without dietary intervention in overweight or obese insulin-naïve individuals with type 2 diabetes: results from the DIET trial. Diabetes Obes Metab. 2014;16:186-192. doi:10.1111/dom.12218.

200.

O’Neil

Miller-Kovach

Tuerk

, et al. Randomized controlled trial of a nationally available weight control program tailored for adults with type 2 diabetes. Obesity. 2016;24:2269-2277. doi:10.1002/oby.21616.

201.

Odegard

Christensen

. MAP study: RCT of a medication adherence program for patients with type 2 diabetes. J Am Pharm Assoc (2003). 2012;52:753-762.

202.

Odnoletkova

Goderis

Nobels

, et al. Optimizing diabetes control in people with Type 2 diabetes through nurse-led telecoaching. Diabet Med. 2016;33:777-785. doi:10.1111/dme.13092.

203.

Kim

Yoon

Choi

. A telephone-delivered intervention to improve glycemic control in type 2 diabetic patients. Yonsei Med J. 2003;44:1-8.

204.

Orsama

Lähteenmäki

Harno

, et al. Active assistance technology reduces glycosylated hemoglobin and weight in individuals with type 2 diabetes: results of a theory-based randomized trial. Diabetes Technol Ther. 2013;15:662-669. doi:10.1089/dia.2013.0056.

205.

Owolabi

Goon

Ter Ajayi

. Efficacy, acceptability and feasibility of daily text-messaging in promoting glycaemic control and other clinical outcomes in a low-resource setting of South Africa: a randomised controlled trial. PLoS One. 2019;14:e0224791. doi:10.1371/journal.pone.0224791.

206.

Parsons

Luzio

Harvey

, et al. Effect of structured self-monitoring of blood glucose, with and without additional TeleCare support, on overall glycaemic control in non-insulin treated Type 2 diabetes: the SMBG Study, a 12-month randomized controlled trial. Diabet Med. 2019;36:578-590. doi:10.1111/dme.13899.

207.

Patja

Absetz

Auvinen

, et al. Health coaching by telephony to support self-care in chronic diseases: clinical outcomes from The TERVA randomized controlled trial. BMC Health Serv Res. 2012;12:147. doi:10.1186/1472-6963-12-147.

208.

Peasah

Granitz

Jacob

. Effectiveness of a student pharmacist-led telephone follow-up intervention to improve hemoglobin A _1C in diabetic patients. J Pharm Pract. 2020;33:832-837. doi:10.1177/0897190019857409.

209.

Peimani

Rambod

Omidvar

, et al. Effectiveness of short message service-based intervention (SMS) on self-care in type 2 diabetes: a feasibility study. Prim Care Diabetes. 2016;10:251-258. doi:10.1016/j.pcd.2015.11.001.

210.

Piette

Richardson

Himle

, et al. A randomized trial of telephone counseling plus walking for depressed diabetes patients. Med Care. 2011;49:641-648. doi:10.1038/jid.2014.371.

211.

Plotnikoff

Karunamuni

Courneya

Sigal

Johnson

. The Alberta diabetes and physical activity trial (ADAPT): a randomized trial evaluating theory-based interventions to increase physical activity in adults with type 2 diabetes. Ann Behav Med. 2013;45:45-56. doi:10.1007/s12160-012-9405-2.

212.

Presley

Agne

Shelton

Oster

Cherrington

. Mobile-enhanced peer support for African Americans with type 2 diabetes: a randomized controlled trial. J Gen Intern Med. 2020;35:2889-2896. doi:10.1007/s11606-020-06011-w.

213.

Quinn

Swasey

Crabbe

JCF

, et al. The impact of a mobile diabetes health intervention on diabetes distress and depression among adults: secondary analysis of a cluster randomized controlled trial. JMIR Mhealth Uhealth. 2017;5:e183. doi:10.2196/mhealth.8910.

214.

Quinn

Shardell

Terrin

Barr

Ballew

Gruber-Baldini

. Cluster-randomized trial of a mobile phone personalized behavioral intervention for blood glucose control. Diabetes Care. 2011;34:1934-1942. doi:10.2337/dc11-0366.

215.

Raj

Mathews

. Effect of behavioral intervention on medication adherence among elderly with select non-communicable diseases (ENDORSE): pilot randomized controlled trial. Geriatr Gerontol Int. 2020;20:1079-1084. doi:10.1111/ggi.14032.

216.

Ralston

Hirsch

Hoath

Mullen

Cheadle

Goldberg

. Web-based collaborative care for type 2 diabetes a pilot randomized trial. Diabetes Care. 2009;32:234-239. doi:10.2337/dc08-1220.

217.

Ramadas

Chan

CKY

Oldenburg

Hussein

Quek

. Randomised-controlled trial of a web-based dietary intervention for patients with type 2 diabetes: changes in health cognitions and glycemic control. BMC Public Health. 2018;18:716. doi:10.1186/s12889-018-5640-1.

218.

Rasmussen

Lauszus

Loekke

. Telemedicine compared with standard care in type 2 diabetes mellitus: a randomized trial in an outpatient clinic. J Telemed Telecare. 2016;22:363-368. doi:10.1177/1357633X15608984.

219.

Rodríguez-Idígoras

Sepúlveda-Muñoz

Sánchez-Garrido-Escudero

, et al. Telemedicine influence on the follow-up of type 2 diabetes patients. Diabetes Technol Ther. 2009;11:431-437. doi:10.1089/dia.2008.0114.

220.

Ruggiero

Riley

Hernandez

, et al. Medical assistant coaching to support diabetes self-care among low-income racial/ethnic minority populations: randomized controlled trial. West J Nurs Res. 2014;36:1052-1073. doi:10.1177/0193945914522862.

221.

Sacco

Malone

Morrison

Friedman

Wells

. Effect of a brief, regular telephone intervention by paraprofessionals for type 2 diabetes. J Behav Med. 2009;32:349-359. doi:10.1007/s10865-009-9209-4.

222.

Sacco

Bykowski

Mayhew

White

. Educational attainment moderates the effect of a brief diabetes self-care intervention. Diabetes Res Clin Pract. 2012;95:62-67. doi:10.1016/j.diabres.2011.08.027.

223.

Samuel-Hodge

Keyserling

Park

Johnston

Gizlice

Bangdiwala

. A randomized trial of a church-based diabetes self-management program for African Americans with type 2 diabetes. Diabetes Educ. 2009;35:439-454. doi:10.1177/0145721709333270.

224.

Sarayani

Mashayekhi

Nosrati

, et al. Efficacy of a telephone-based intervention among patients with type-2 diabetes; a randomized controlled trial in pharmacy practice. Int J Clin Pharm. 2018;40:345-353. doi:10.1007/s11096-018-0593-0.

225.

Saslow

Moskowitz

Mason

, et al. Intervention enhancement strategies among adults with type 2 diabetes in a very low-carbohydrate web-based program: evaluating the impact with a randomized trial. JMIR Diabetes. 2020;5:e15835. doi:10.2196/15835.

226.

Sazlina

Browning

Yasin

. Effectiveness of personalized feedback alone or combined with peer support to improve physical activity in sedentary older Malays with type 2 diabetes: a randomized controlled trial. Front Public Heal. 2015;3:178. doi:10.3389/fpubh.2015.00178.

227.

Schillinger

Handley

Wang

Hammer

. Effects of self-management support on structure, process, and outcomes among vulnerable patients with diabetes: a three-arm practical clinical trial. Diabetes Care. 2009;32:559-566. doi:10.2337/dc08-0787.

228.

Shahid

Mahar

Shaikh

ZUD

. Mobile phone intervention to improve diabetes care in rural areas of Pakistan: a randomized controlled trial. J Coll Physicians Surg Pak. 2015;25:166-171. doi:03.2015/JCPSP.166171.

229.

Shahsavari

Bakhshandeh Bavarsad

. Is telenursing an effective method to control BMI and HbA1c in illiterate patients aged 50 years and older with type 2 diabetes? a randomized controlled clinical trial. J Caring Sci. 2020;9:73-79. doi:10.34172/jcs.2020.011.

230.

Shetty

Chamukuttan

Nanditha

Raj

RKC

Ramachandran

. Reinforcement of adherence to prescription recommendations in Asian Indian diabetes patients using short message service (SMS)—a pilot study. J Assoc Physicians India. 2011;59:711-714.

231.

Shreck

Gonzalez

Cohen

Walker

. Risk perception and self-management in urban, diverse adults with type 2 diabetes: the improving diabetes outcomes study. Int J Behav Med. 2014;21:88-98. doi:10.1007/s12529-013-9291-4.

232.

Sigurdardottir

Benediktsson

Jonsdottir

. Instruments to tailor care of people with type 2 diabetes. J Adv Nurs. 2009;65:2118-2130. doi:10.1111/j.1365-2648.2009.05040.x.

233.

Skelly

Carlson

Leeman

Soward

Burns

. Controlled trial of nursing interventions to improve health outcomes of older African American women with type 2 diabetes. Nurs Res. 2009;58:410-418.

234.

Sone

Katagiri

Ishibashi

, et al. Effects of lifestyle modifications on patients with type 2 diabetes: the Japan Diabetes Complications Study (JDCS) study design, baseline analysis and three year-interim report. Horm Metab Res. 2002;34:509-515. doi:10.1055/s-2002-34791.

235.

Sone

Tanaka

Iimuro

, et al. Long-term lifestyle intervention lowers the incidence of stroke in Japanese patients with type 2 diabetes: a nationwide multicentre randomised controlled trial (the Japan Diabetes Complications Study). Diabetologia. 2010;53:419-428. doi:10.1007/s00125-009-1622-2.

236.

Song

Kim

. Intensive management program to improve glycosylated hemoglobin levels and adherence to diet in patients with type 2 diabetes. Appl Nurs Res. 2009;22:42-47. doi:10.1016/j.apnr.2007.05.004.

237.

Spencer

Kieffer

Sinco

, et al. Outcomes at 18 months from a community health worker and peer leader diabetes self-management program for Latino adults. Diabetes Care. 2018;41:1414-1422. doi:10.2337/dc17-0978.

238.

Steventon

Bardsley

Doll

Tuckey

Newman

. Effect of telehealth on glycaemic control: analysis of patients with type 2 diabetes in the Whole Systems Demonstrator cluster randomised trial. BMC Health Serv Res. 2014;14:334. doi:10.1186/1472-6963-14-334.

239.

Sun

, et al. Mobile phone–based telemedicine practice in older Chinese patients with type 2 diabetes mellitus: randomized controlled trial. JMIR Mhealth Uhealth. 2019;7:e10664. doi:10.2196/10664.

240.

Sunil Kumar

Prakash

Subhash Chandra

, et al. Technological innovations to improve health outcome in type 2 diabetes mellitus: a randomized controlled study. Clin Epidemiol Glob Heal. 2021;9:53-56. doi:10.1016/j.cegh.2020.06.011.

241.

Takenga

Berndt

Musongya

, et al. An ICT-based diabetes management system tested for health care delivery in the African context. Int J Telemed Appl. 2014;2014:437307. doi:10.1155/2014/437307.

242.

Tamban

Isip-Tan

Jimeno

. Use of short message services (SMS) for the management of type 2 diabetes mellitus: a randomized controlled trial. J ASEAN Fed Endocr Soc. 2013;28:143-149. doi:10.15605/jafes.028.02.08.

243.

Tan

CCL

Cheng

KKF

Hwang

Zhang

Holydard

Wang

. Effect of a diabetes self-efficacy enhancing program on older adults with type 2 diabetes: a randomized controlled trial. Clin Nurs Res. 2020;29:293-303. doi:10.1177/1054773818792480.

244.

Tang

Overhage

Chan

, et al. Online disease management of diabetes: engaging and motivating patients online with enhanced resources-diabetes (EMPOWER-D), a randomized controlled trial. J Am Med Informatics Assoc. 2013;20:526-534. doi:10.1136/amiajnl-2012-001263.

245.

Teston

Arruda

de Sales

Serafim

Marcon

. Nursing appointment and cardiometabolic control of diabetics: a randomized clinical trial. Rev Bras Enferm. 2017;70:468-474. doi:10.1590/0034-7167-2016-0352.

246.

Thom

Ghorob

Hessler

DeVore

Chen

Bodenheimer

. Impact of peer health coaching on glycemic control in low-income patients with diabetes: a randomized controlled trial. Ann Fam Med. 2013;11:137-144. doi:10.1370/afm.1443.

247.

Torbjørnsen

Jenum

Småstuen

, et al. A low-intensity mobile health intervention with and without health counseling for persons with type 2 diabetes, part 1: baseline and short-term results from a randomized controlled trial in the Norwegian part of RENEWING HEALTH. JMIR Mhealth Uhealth. 2014;2:e52. doi:10.2196/mhealth.3535.

248.

McDaniel

Templeton Gay

. Diabetes self-care knowledge, behaviors, and metabolic control of older adults—the effect of a posteducational follow-up program. Diabetes Educ. 1993;19:25-30. doi:10.1177/014572179301900105.

249.

Van Dyck

De Greef

Deforche

, et al. The relationship between changes in steps/day and health outcomes after a pedometer-based physical activity intervention with telephone support in type 2 diabetes patients. Health Educ Res. 2013;28:539-545. doi:10.1093/her/cyt038.

250.

Van Dyck

De Greef

Deforche

, et al. Mediators of physical activity change in a behavioral modification program for type 2 diabetes patients. Int J Behav Nutr Phys Act. 2011;8:105. doi:10.1186/1479-5868-8-105.

251.

Van Vugt

De Wit

Sieverink

, et al. Uptake and effects of the e-vita personal health record with self-management support and coaching, for type 2 diabetes patients treated in primary care. J Diabetes Res. 2016;2016:5027356. doi:10.1155/2016/5027356.

252.

Varney

Weiland

Inder

Jelinek

. Effect of hospital-based telephone coaching on glycaemic control and adherence to management guidelines in type 2 diabetes, a randomised controlled trial. Intern Med J. 2014;44:890-897. doi:10.1111/imj.12515.

253.

Vaughan

Hyman

Naik

Samson

Razjouyan

Foreyt

. A Telehealth-supported, Integrated care with CHWs, and MEdication-access (TIME) program for diabetes improves HbA1c: a randomized clinical trial. J Gen Intern Med. 2021;36:455-463. doi:10.1007/s11606-020-06017-4.

254.

Vervloet

van Dijk

de Bakker

, et al. Short- and long-term effects of real-time medication monitoring with short message service (SMS) reminders for missed doses on the refill adherence of people with type 2 diabetes: evidence from a randomized controlled trial. Diabet Med. 2014;31:821-828. doi:10.1111/dme.12439.

255.

Vinitha

Nanditha

Snehalatha

, et al. Effectiveness of mobile phone text messaging in improving glycaemic control among persons with newly detected type 2 diabetes. Diabetes Res Clin Pract. 2019;158:107919. doi:10.1016/j.diabres.2019.107919.

256.

Von Storch

Graaf

Wunderlich

Rietz

Polidori

Woopen

. Telemedicine-assisted self-management program for type 2 diabetes patients. Diabetes Technol Ther. 2019;21:514-521. doi:10.1089/dia.2019.0056.

257.

Wakefield

Holman

Ray

, et al. Effectiveness of home telehealth in comorbid diabetes and hypertension: a randomized, controlled trial. Telemed J E Health. 2011;17:254-261. doi:10.1089/tmj.2010.0176.

258.

Wakefield

Holman

Ray

, et al. Outcomes of a home telehealth intervention for patients with diabetes and hypertension. Telemed J E Health. 2012;18:575-579. doi:10.1089/tmj.2011.0237.

259.

Wakefield

Koopman

Keplinger

, et al. Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes. Telemed J E Health. 2014;20:199-205. doi:10.1089/tmj.2013.0151.

260.

Waki

Fujita

Uchimura

, et al. DialBetics: a novel smartphone-based self-management support system for type 2 diabetes patients. J Diabetes Sci Technol. 2014;8:209-215. doi:10.1177/1932296814526495.

261.

Walker

Shmukler

Ullman

Blanco

Scollan-Koliopoulus

Cohen

. Results of a successful telephonic intervention to improve diabetes control in urban adults: a randomized trial. Diabetes Care. 2011;34:2-7. doi:10.2337/dc10-1005.

262.

Wang

Liu

Hao

Zheng

Yan

. The role of text messaging intervention in Inner Mongolia among patients with type 2 diabetes mellitus: a randomized controlled trial. BMC Med Inform Decis Mak. 2020;20:90. doi:10.1186/s12911-020-01129-7.

263.

Wang

Zhao

, et al. Effects of continuous care for patients with type 2 diabetes using mobile health application: a randomised controlled trial. Int J Health Plann Manage. 2019;34:1025-1035. doi:10.1002/hpm.2872.

264.

Wang

Zhang

Feng

, et al. Telemedicine in the management of type 2 diabetes mellitus. Am J Med Sci. 2017;353:1-5.

265.

Warren

Carlisle

Mihala

Scuffham

. Effects of telemonitoring on glycaemic control and healthcare costs in type 2 diabetes: a randomised controlled trial. J Telemed Telecare. 2018;24:586-595. doi:10.1177/1357633X17723943.

266.

Weinberger

Kirkman

Saa

, et al. A nurse-coordinated intervention for primary care patients with non-insulin-dependent diabetes mellitus: impact on glycemic control and health-related quality of life. J Gen Intern Med. 1995;10:59-66. doi:10.1007/BF02600227.

267.

Whittemore

Melkus

Sullivan

Grey

. A nurse-coaching intervention for women with type 2 diabetes. Diabetes Educ. 2004;30:795-804. doi:10.1177/014572170403000515.

268.

Wichit

Mnatzaganian

Courtney

Schulz

Johnson

. Randomized controlled trial of a family-oriented self-management program to improve self-efficacy, glycemic control and quality of life among Thai individuals with type 2 diabetes. Diabetes Res Clin Pract. 2017;123:37-48. doi:10.1016/j.diabres.2016.11.013.

269.

Wild

Hanley

Lewis

, et al. Supported telemonitoring and glycemic control in people with type 2 diabetes: the telescot diabetes pragmatic multicenter randomized controlled trial. PLoS Med. 2016;13:e1002098. doi:10.1371/journal.pmed.1002098.

270.

Williams

Bird

Forbes

, et al. Randomised controlled trial of an automated, interactive telephone intervention (TLC Diabetes) to improve type 2 diabetes management: baseline findings and six-month outcomes. BMC Public Health. 2012;12:602. doi:10.1186/1471-2458-12-602.

271.

Williams

Cairns

Simmons

Rush

. Face-to-face versus telephone delivery of the green prescription for Māori and New Zealand Europeans with type-2 diabetes mellitus: influence on participation and health outcomes. N Z Med J. 2017;130:71-79.

272.

Wolever

Dreusicke

Fikkan

, et al. Integrative health coaching for patients with type 2 diabetes: a randomized clinical trial. Diabetes Educ. 2010;36:629-639. doi:10.1177/0145721710371523.

273.

Wolf

Conaway

Crowther

, et al. Translating lifestyle intervention to practice in obese patients with type 2 diabetes. Diabetes Care. 2004;27:1570-1576.

274.

CJJ

Sung

Chang

Atherton

Kostner

McPhail

. Cardiac-diabetes self-management program for Australians and Taiwanese: a randomized blocked design study. Nurs Heal Sci. 2017;19:307-315. doi:10.1111/nhs.12346.

275.

Yang

Lee

Kim

Lee

Yoon

Cho

. Effect of a mobile phone-based glucose-monitoring and feedback system for type 2 diabetes management in multiple primary care clinic settings: cluster randomized controlled trial. JMIR Mhealth Uhealth. 2020;8:e16266. doi:10.2196/16266.

276.

Yasmin

Nahar

, et al. The influence of mobile phone-based health reminders on patient adherence to medications and healthy lifestyle recommendations for effective management of diabetes type 2: a randomized control trial in Dhaka, Bangladesh. BMC Health Serv Res. 2020;20:520. doi:10.1186/s12913-020-05387-z.

277.

Yoo

Park

Kim

, et al. A Ubiquitous Chronic Disease Care system using cellular phones and the Internet. Diabet Med. 2009;26:628-635. doi:10.1111/j.1464-5491.2009.02732.x.

278.

Yoon

Kim

. A short message service by cellular phone in type 2 diabetic patients for 12 months. Diabetes Res Clin Pract. 2008;79:256-261. doi:10.1016/j.diabres.2007.09.007.

279.

Young

Taylor

Friede

, et al. Pro-active call center treatment support (PACCTS) to improve glucose control in type 2 diabetes: a randomized controlled trial. Diabetes Care. 2005;28:278-282.

280.

Yan

, et al. Effects of mobile phone application combined with or without self-monitoring of blood glucose on glycemic control in patients with diabetes: a randomized controlled trial. J Diabetes Investig. 2019;10:1365-1371. doi:10.1111/jdi.13031.

281.

Zamanzadeh

Zirak

Hemmati Maslakpak

Parizad

. Distance education and diabetes empowerment: a single-blind randomized control trial. Diabetes Metab Syndr Clin Res Rev. 2017;11:S247-S251. doi:10.1016/j.dsx.2016.12.039.

282.

Zhou

Liu

Huang

Chen

. Web-based telemedicine for management of type 2 diabetes through glucose uploads: a randomized controlled trial. Int J Clin Exp Pathol. 2014;7:8848-8854.

283.

Yang

, et al. Evaluation of the clinical outcomes of telehealth for managing diabetes: a PRISMA-compliant meta-analysis. Medicine (Baltimore). 2018;97:e12962. doi:10.1097/MD.0000000000012962.

284.

Tchero

Kangambega

Briatte

Brunet-Houdard

Retali

Rusch

. Clinical effectiveness of telemedicine in diabetes mellitus: a meta-analysis of 42 randomized controlled trials. Telemed J E Health. 2019;25:569-583. doi:10.1089/tmj.2018.0128.

285.

Ekeland

Bowes

Flottorp

. Methodologies for assessing telemedicine: a systematic review of reviews. Int J Med Inform. 2012;81:1-11. doi:10.1016/j.ijmedinf.2011.10.009.

286.

Soriano

García-Río

Vázquez-Espinosa

, et al. A multicentre, randomized controlled trial of telehealth for the management of COPD. Respir Med. 2018;144:74-81. doi:10.1016/j.rmed.2018.10.008.

287.

Shojania

Ranji

McDonald

, et al. Effects of quality improvement strategies for type 2 diabetes on glycemic control: a meta-regression analysis. JAMA. 2006;296:427-440. doi:10.2337/diaclin.25.4.155.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.05 MB

The Effectiveness of Telemedicine Solutions for the Management of Type 2 Diabetes: A Systematic Review,Meta-Analysis,and Meta-Regression

Abstract

Background:

Methods:

Results:

Conclusions:

Keywords

Introduction

Methods

Study Design

Eligibility Criteria

Information Sources

Search Strategy

Selection Process

Data Extraction

Risk of Bias Assessment

Data Synthesis

Certainty Assessment

Results

Effect on HbA1c%

Certainty of the Evidence

Discussion

Conclusion

Supplemental Material

sj-docx-1-dst-10.1177_19322968211064633 – Supplemental material for The Effectiveness of Telemedicine Solutions for the Management of Type 2 Diabetes: A Systematic Review, Meta-Analysis, and Meta-Regression

Footnotes

Acknowledgements

Abbreviations

Declaration of Conflicting Interests

Funding

ORCID iDs

Supplemental Material

References

Supplementary Material