Clinical and patient-centered implementation outcomes of mHealth interventions for type 2 diabetes in low-and-middle income countries: a systematic review.

BACKGROUND: The prevalence of Type 2 Diabetes is rising in Low- and Middle-Income Countries (LMICs), affecting all age categories and resulting in huge socioeconomic implications. Mobile health (mHealth) is a potential high-impact approach to improve clinical and patient-centered outcomes despite the barriers of cost, language, literacy, and internet connectivity. Therefore, it is valuable to examine the clinical and implementation outcomes of mHealth interventions for Type 2 Diabetes in LMICs.
METHODS: The Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) guidelines were applied in framing and reporting the review criteria. A systematic search of Cochrane Library, Web of Science, PubMed, Scopus, and Ovid databases was performed through a combination of search terms. Randomized Controlled Trials (RCTs) and cohort studies published in English between January 2010 and August 2021 were included. Risk of bias for missing results in the included studies was assessed using the Cochrane risk-of-bias tool for randomized trials (RoB 2). Quantitative and qualitative methods were used to synthesize the results.
RESULTS: The search identified a total of 1161 articles. Thirty studies from 14 LMICs met the eligibility criteria. On clinical outcomes, 12 and 9 studies reported on glycated hemoglobin (HbA1c )and fasting blood glucose (FBG) respectively. Text messages was the most commonly applied mHealth approach, used in 19 out of the 30 studies. Ten out of the 12 studies (83.3%) that reported on HbA1c had a percentage difference of <0.3% between the mHealth intervention and the comparison group. Additionally, studies with longer intervention periods had higher effect size and percentage difference on HbA1c (1.52 to 2.92%). Patient-centred implementation outcomes were reported variedly, where feasibility was reported in all studies. Acceptability was reported in nine studies, appropriateness in six studies and cost in four studies. mHealth evidence reporting and assessment (mERA) guidelines were not applied in all the studies in this review.  
CONCLUSION: mHealth interventions in LMICs are associated with clinically significant effectiveness on HbA1 but have low effectiveness on FBG. The application of mERA guidelines may standardize reporting of patient-centered implementation outcomes in LMICs. TRIAL REGISTRATION: PROSPERO: Registration ID 154209.

Introduction

Type 2 Diabetes is now a leading public health problem in Low-and Middle Income Countries (LMICs) [1] affecting all age categories and resulting in huge economic implications to healthcare [2, 3]. LMICs are home to 80 % of all people with type 2 diabetes (336 million) [4] and more than 80% of all undiagnosed people with diabetes [2]. It is projected that between 2019 and 2030, the prevalence of type 2 diabetes is likely to increase from 13.5% to 15.0% in LMICs compared to 10.4% to 11.4% in high-income countries [2]. Further, out of the total number of deaths related to diabetes globally, 41.8% and 58.2% occur in Low- and Middle- Income Countries, respectively [2]. The rising prevalence of type 2 diabetes in LMICs is attributed to the nutrition transition, and the increasing prevalence of overweight and obesity. The other factors include, urbanization, cultural and social changes, sedentary lifestyles, changes in diagnostic criteria and screening practices [5-7].

Optimal diabetes management requires a systematic approach, and the involvement of a coordinated, multidisciplinary team that is committed to patient-centered outcomes [8]. It is recommended that clinicians apply a patient-centered approach and minimum clinically important difference (MCID) treatment models by considering the statistical significance and clinical significance of research findings [9]. Essential guidelines for the patient-centered approach include individualized therapy and shared decision-making [10]. Additionally, effective patient-centered diabetes self-management requires the support and promotion of essential self-care behaviors [11]. These behaviors include healthy eating, physical activity, medication usage, monitoring and usage of patient-generated data, prevention, detection and treatment of acute and chronic complications, healthy coping with psychosocial issues and problem solving [12]. These behaviors have been described as Diabetes Self-Management Education and Support (DSMES) domains. Self-management education is linked to clinically important benefits on glycated hemoglobin (HbA1c), and cost of treatment [13-18]. This notwithstanding, self-care in most LMICs is not optimally attained due to disadvantaged access to healthacre and low-quality healthcare, poverty, low literacy levels and incorrect perceptions about diabetes [19-21].

The remarkable increase in ownership and use of mobile phones in LMICs provides a potential opportunity for the application of mobile health (mHealth) in self-care and behavior change interventions for type 2 diabetes [22-24]. mHealth is the medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices [25]. Evidence shows that mHealth has the potential to facilitate accessibility and coverage of healthcare services as well as positively influencing clinical outcomes, compliance, self-care practices and quality of life for people with type 2 diabetes [26-29]. Whereas there is close similarity between mHealth and e-health, the later refers to an emerging field that links medical informatics, public health and business, that delivers or enhances health services and information via web-based technologies [30]. However, eHealth heavily relies on internet technology, which limits its applicability in LMICs, due to unreliable access to internet [31].

A recent metanalysis on mHealth interventions for diabetes in LMICs revealed promising but limited evidence on the effectiveness of mHealth interventions on glycemic control [32]. Further, a pooled effect on HbA1c from three studies on mobile phone–based interventions showed a larger effect of 25.46 mmol/mol or 20.50%; (95% CI 20.7 to 20.3%; I2 = 0%) [33]. mHealth interventions have also been found to be cost-effective [34] despite being criticized for having meager user satisfaction ratings coupled with usability challenges [35]. In LMICs, a few studies on mHealth have shown changes in clinical outcomes, adherence and improved communication with providers, decreased travel time, ease to receive expert advice and cost-effective education [36].

Further, evidence from LMICs reveal unique patient circumstances that hinder optimal utilization of mHealth approaches. Inadequate resources, low digital literacy and low health literacy and limited inclusion of motivation techniques hinder optimal utilization of mHealth in LMICs [37]. As such, distinguishing treatment effectiveness or clinical outcomes from implementation effectiveness is important for transferring interventions from experimental settings to the community [38, 39]. This distinction, to the best of our knowledge, has not been examined on mHealth interventions for type 2 diabetes in LMICs.

The objective of this systematic review therefore was to examine the clinical outcomes and patient-centered implementation outcomes of mHealth interventions with a focus on type 2 diabetes in LMICs.

Methods

Data sources and registration

This review applied the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) guidelines [40] with the PICOS framing. The review has been registered and amended in PROSPERO https://www.crd.york.ac.uk/prospero/#recordDetails (Registration ID 154209) and funded by VLIR-UOS

(Grant-number: KE2017IUC037A101)

Search strategy

The search strategy was applied on Cochrane and Web of Science Cochrane Library, Web of Science, PubMed, Scopus, and Ovid databases. (Supplementary File 1). These databases were systematically searched with Boolean combinations of key words and MeSH headings. An electronic search was conducted using the following terms and Boolean Operators: ((mobile health OR mHealth) AND (type 2 diabetes) AND/OR (DSMES) AND/OR (acceptability) AND/OR (appropriateness) AND/OR (feasibility) AND/OR (cost) AND/OR (sustainability)). Acceptability, appropriateness, feasibility, cost and sustainability were based on the definitions in the conceptual framework for implementation outcomes by Proctor et al. [38]. We searched for articles published in English between January 2010 and August 2021. Additional records were searched through citations from relevant reviews given that online data bases can be incomplete [41].

Study selection

This review included randomized controlled trials (RCTs), cluster randomized controlled trials, feasibility studies and prospective observational cohort studies from LMICs. The search also included cohort and follow-up studies of intervention studies that have been published in peer-reviewed journals. Our review was limited to studies that are designed for adults diagnosed with type 2 diabetes. We included studies in which the mHealth intervention was designed to be an enabler for delivery of DSMES for patients with type 2 diabetes [1]. mHealth or mobile health are medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices as defined by WHO [25]. DSMES domains include diabetes pathophysiology and treatment options; healthy eating; physical activity; medication usage; monitoring and usage of patient generated health data; prevention, detection, and treatment of acute and chronic complications; healthy coping with psychosocial issues; and problem solving [42]. The selection of studies was conducted by MM and independently reviewed by FK, CM and RV. We excluded studies on children and adolescents, pregnant women, or any other forms of diabetes besides type 2 diabetes such as pre-diabetes, type 1 diabetes or gestational diabetes [43]. We also excluded studies where the mHealth intervention was designed for to support healthcare workers and those studies that did not target the patient.

Data collection process

Data from all eligible articles was summarized by the first author (MM) and reviewed by the second and third authors (FK & CM) using structured evidence tables (Table 1 & 2). A standardized criterion for data collection was designed by the authors to extract and tabulate relevant study characteristics. These characteristics include study location, study type, duration of study, clinical outcomes (HbA1c and FBG), mHealth intervention and function, DSMES domains and patient-centered implementation outcomes.

Table 1

Summary of general study Characteristics

Study and location	Study Design	Sample Size	Participant Age (Years)	Duration of Intervention	mHealth intervention delivery
Anzaldo et al. [44] Mexico	RCT	n=301 I1: (Project Dulce)102; I2: (Project Dulce with Technology enhancement) 99 C: 100	18-75	10 months	Arm 1: PD TE: Interactive surveys, text messages, short educational videos Arm 2: PD: combination of care management by a multidisciplinary team led by trained clinicians and nurses, as well as a peer-led group education Bidirectional
Chai et al. [45] China	Cohort	n=209	Mean age: 51.97 ± 12.76	4 Months	Upload insulin dosages three or more times for the FPG and postprandial plasma glucose (PPG) on a panel computer
Chao et al. [46] China	RCT	n=121; I: 62; C: 59	C: Not provided I: 63.71 (37 -88)	18 months	mobile app; Cloud-based IPMF Bidirectional
Dong et al [47] China	RCT	n=120 I: 60; C: 60	18-60 (23-60)	12 months	Web-based app: WeChat platform Bidirectional
Doocy et al. [48] Lebanon	Longitudinal Cohort	10 HC; 1020	< 40 a	20 months	mHealth app (PCHR),
Fottrell et al. [49] India	CRCT	n=13,728 I1: 4,093; I2: 4,079 C: 5,008;	Mean Age not provided Adults ≥30	18 months,	1. Voice messages 2-weekly (14 months 2. Monthly PLA group meetings (with a 4-phase PLA cycle)
Gunawardena et al. [50] Sri Lanka	RCT	n=67 I: 35 C: 32	I: 53 (SD 11), C: 52 (SD 12)	6 months	Android based Smart Glucose Manager (SGM) every 3 months
Goodarzi et al. [51] Iran	RCT	n=100 I: 50, C: 50	I:50.98 (SD = 10.32) C: 56.71 (SD = 9.77)	3 months	Text Messages: 4weekly messages Unidirectional
Haddad et al. [52] Iraq	Feasibility RCT	n=50	Mean: 51.4 (SD 10.3)	6 months	Text Messages 1 message per week 5 Bidirectional
Huo et al [53] China	RCT	n=502 I: 251 C:251	Mean: 59.5 (SD 9.4) I: 59.5 (SD 9.1) C: 59.5 (SD 9.3)	6 months	Text Messages 6 SMSs per week for 6 months; Weekly unidirectional messages Bidirectional
Islam et al. [54] Bangladesh	RCT	n=236 I:108; C:108	mean age: SD: 48.1 ± 6 9.7	6 Months	Text messages and Voice calls to the study team for any queries in response to the text message (with a 2-day response). Bidirectional
Kumar et al. [55] India	RCT	n=955 I: 479, C: 476	I: 57.5 (SD 10.8) C: 57.0 (SD 10.7)	12 months	Text Messages Patient specific frequency (average of 2 times per month for 12 months) Unidirectional
Li et al. [56] China	RCT	n=101 I: 55 C: 46	48.2 (SD 10.4)	3 months	R Plus Health app (Recovery Plus Inc), which connected wirelessly to a chest-worn heart rate band (Recovery Plus Inc) to
Liao et al [57]. China	CRCT	n=149 I:69, C: 80 13dancing groups	Mean age: 62	3 months	Wrist-worn activity trackers (Lifesense MAMBO2 wristbands) Activity uploaded to the cloud via a paired smartphone device) Physical activity reports on the paired smartphone via WeChat without support and peers
Limaye et al. [58] India	RCT	n=265; I: 132, C: 133	Mean: 36.2 (SD 9.3) I: 36.8 (7.2) C: 35.7 (8.1)	12 months	Text messages: 3 per wk Emails: 2 e-mails/ wk between 1000–1300 h; Website and Facebook Bidirectional 10% of messages required reply
Olmen et al. [59] Congo Cambodia Philippines	RCT	N=1471 Congo :506; Cambodia:484; Philippines:481;	Overall: NP I: 58 (SD 10) C: 60 (SD 10)	24 months	Text Messages Congo 5 times/wk; Cambodia: 6 times/wk Philippines: 2 times/week Voice messages Cambodia ¼ of all messages to specific groups, Unidirectional
Owolabi et al. [60] South Africa	RCT	n=216 I: 108, C: 108	Overall: 60.64 (SD 11.58)	6 Months	Standard of care plus Tailored Short message services (SMS) Unidirectional
Owolabi et al. [61] South Africa	RCT	n=216 I: 108; C: 108	Overall: 60.64 (SD 11.58)	6 Months	Text Messages Daily SMS: 2 times a week; Unidirectional
Patnaik et al. [62] India	RCT	n=66 I: 33 C: 33	42.29(SD 9.5)	24 Months	Mobile-based android application Bi-directional
Peimani et al. [63] Iran	RCT	n=150 I1: 50 I2: 50 I3: 50	I1:49.78(SD 9.76) I2:53.26 (SD 10.49) C: 54.56 (9.88)	3 months	Text Messages Voice Calls Arm 1: individually tailored SMS: each person received 75% of their messages tailored to 2 reported barriers to adherence Arm 2: non- tailored SMS: random messages sent irrespective of barriers with Voice Calls: Weekly
Pichayapinyo et al. [64] Thailand	Cohort	n=35	54.9 (SD:6.3)	4 Months	Interactive Voice Response (IVR) & email content was translated into Thai IVR calls lasting 5–10 minutes each for 12 weeks
Pfammatter et al. [65] India	Parallel Cohort	n= 1925 I: 982, C: 943	Overall: 32.2 (SD 10.6) I: 32.83 (SD 9.39) C: 31.66 (SD 11.64)	6 months (Dec 2012-June 2013)	Text Messages (Multilanguage texts) Daily text messages for the first 6 days followed by 2 SMSs per week Unidirectional
Rasoul et al. [66] Iran	RCT	n=98 I: 49 C: 49	32.1 (SD 4.9)	5 months	Weblogs Text, video, recorded voice, and nutrition pyramid for diabetic patients 3 days/week, 1:30 hr /session Total: 60 sessions
Rotheram-Borus et al. [67] South Africa	Cohort	n=22 (Women)	53 (SD 12.8)	3 months followed by a post-trial FU at 6 months	Text Messaging Diabetes Buddies program: 12 psycho-educational group sessions Daily mobile phone probes on health 3 text messaging to a buddy: Frequency: Daily Bidirectional
Shahid et al. [68] Pakistan	RCT	n=440 I: 220 C: 220	I: 48.93 SD(8.83), C 49.21 (SD 7.92)	6 months	Voice calls Calls every 15 days for a period of 4 months: Total of 8 calls
Steinman et al [69] Cambodia	CRCT	n=3948 C: 1,737, I: 1,099	NR	12 months	Tablet and mobile voice messages delivered via patient’s mobile phones
Sun et al. [70] China	RCT	n=91 I: 44, C: 47	Overall: NP I: 68.04 (66-72)b C: 67.9 (66-71) b	6 months	Mobile Application Bidirectional
Wang et al. [71] China	RCT	n=120 I: 60, C:60	Mean age: 45.4	6 months	Mobile application
Yasmin et al. [72] Bangladesh	RCT	n=320 I: 160 C: 160	I: 53 [30–85] C: 51 [30–75]	12 months	Personalized Voice calls every 10 days, except Fridays and other national holidays
Zhou et al. [73] China	RCT	n=100 I: 50 C: 50	Overall: NP I: 53.5 (SD 12.4) C: 55.0 (SD 13.1)	3 months	Mobile Application Welltang mobile App App for both pts and clinician: 1. Transfers data to servers. Once per week/ Every 2 wks. Feedback: 3-10mins

App Application; Personally controlled health record; PD Project Dulce–only; PD-TE Project Dulce technology enhanced with mobile tools; PPG postprandial plasma glucose; PT Patient; QA Question and answer; SBP systolic Blood Glucose; T2DM type 2 Diabetes Mellitus a Mean age not provided; b Interquartile rage

Table 2

Clinical and patient-centred Implementation outcomes

Study	Clinical Outcomes	Patient-centred Implementation outcomes
Anzaldo et al. [44]	HbA1c, TC, LDL-c, HDL-c, BMI, SBP, DBP	Feasibility, Appropriateness, Acceptability
Chai et al. [45]	FPG ≤ 7 mmol/l, PPG ≤ 10 mmol/l, HbA1c level ≤ 7%.	Feasibility, Appropriateness
Chao et al. [46]	Hb, HbA1c, weight, BMI	Feasibility, Appropriateness
Dong et al. [47]	FPG, 2hPG, HbA1c	Feasibility
Doocy et al. [48]	HbA1c, BP, FBG	Feasibility
Fottrell et al. [49]	PA, BP, HR, Waist Circumference, weight, Height, QoL, Urine Cotinine	Feasibility, Acceptability, Cost
Goodarzi et al. [51]	BMI, L-FBG, HbA1c, TC, TG, HDL-C, LDL-C, BUN, Cra, SE, SBP, DBP,	Feasibility, Appropriateness
Gunawardena et al. [50]	HbA1c	Feasibility
Haddad et al. [52]	Knowledge, HbA1c, cost	Feasibility, Acceptability, Cost, Appropriateness
Huo et al. [53]	Primary: HbA1c, Secondary: FBG, LDL, LDL-C, SBP, BMI, PA	Feasibility, Appropriateness, Cost
Islam et al. [74]	HbA1c	Feasibility
Kumar et al. [55]	FBG, TC, BMI, BP	Feasibility
Li et al. [56]	BMI, hemoglobin HbA1c, HOMA-IR, Resting heart rate (bpmc), Step test (bpm) Muscle strength	Feasibility
Liao et al. [57]	Heart rate	Acceptability, Feasibility, Appropriateness
Limaye et al. [58]	BMI, weight, waist circumference, BP, FBG, LDL-C, HDL-C	Acceptability, Feasibility, Cost, Sustainability
Owolabi et al. [60]	Diet adherence, PA adherence	Acceptability, Feasibility, Appropriateness
Owolabi et al. [61]	RBS, BMI, SBP, DBP	Acceptability, Feasibility
Patnaik et al. [62]	HbA1c	Feasibility, Appropriateness
Peimani et al. [63]	HbA1c, FBG, LDL-C, HDL-C, SCI, BMI, DMSES	Feasibility
Pichayapinyo et al. [64]	HbA1c, FBG	Feasibility, Acceptability
Pfammatter et al. [65]	Fruit, vegetable and fat consumption; Exercises	Feasibility, Acceptability
Rasoul et al [66]	FBG, BMI, SBP, DBP	Feasibility
Rotheram-Borus et al [67]	HbA1c, BMI, BP	Feasibility
Shahid et al. [68]	HbA1c, LDL	Feasibility
Steinman et al. [69]	FBG, SBP, DBP	Feasibility, Sustainability
Sun et al [70]	HbA1c, PBG, FBG, BMI, TG, HDL-C, LDL-C Cr, AST	Feasibility
Wang et al. [71]	HbA1c, FPG	Appropriateness, Feasibility
Yasmin et al. [72]	FBS: < 7.0 mmol/L, and the PPG 2 h after breakfast < 11.1 mmol/L	Feasibility
Zhou et al. [73]	HbA1c, BP, LDL-C, weight, BG, Satisfaction, T2DM knowledge	Feasibility

AST aspartate transaminase; BG Blood Glucose; BMI Body Mass Index; BP Blood pressure; Cr Creatinine; DSME Diabetes Self-Management and Education; FBG Fasting Blood glucose; FBS Fasting Blood Glucose; FU follow-up; HbA1c Glycated Haemoglobin; HDL-c High Density Lipoprotein Cholesterol; I1 First Intervention Arm; I2 Second Intervention Arm; IPMF interactive personalized management framework; IVR Interactive Voice Response; LDL-c Low Density Lipoprotein Cholesterol; Med medication; Mos Months; NO Number of; NP Not Provided; OP outpatient; PA Physical Activity; PBG Post prandial Blood Glucose; PCHR Personally controlled health record; TC Total cholesterol; TG Triglycerides

Quality of studies and risk of bias assessment

To assess quality of the articles, we applied the 2010 CONSORT (Consolidated Standards of Reporting Trials) guidelines [75] and the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines [76]. This approach has been used elsewhere to assess the quality of studies [77-79]. An analysis of the quality of the studies included in this review is presented as heat maps (Supplementary File 2). A percentage quality score of >66.6% is rated as high, 50-66.6% as fair and <50% as low. Assessment of quality was conducted by two independent researchers, MM and ES. Additionally, the risk of bias in the included studies was assessed using the Cochrane risk of bias tool for randomized trials (RoB 2) (Supplementary File 3).

Summary measures

The primary outcome measures in this study are clinical outcomes and patient-centered implementation outcomes for type 2 diabetes mHealth interventions. Specifically, clinical outcomes were synthesized using quantitative methods based on effect sizes of HbA1c and FBG. HbA1c and FBG measure the effectiveness of interventions for the management of type 2 diabetes [80]. Additionally, the percentage difference between the mHealth intervention and the comparison group for HbA1c was analyzed. The patient centered implementation outcomes included acceptability, feasibility, appropriateness, cost and sustainability [38, 81–84]. Acceptability is the perception amongst implementation stakeholders that a particular treatment, service is agreeable, palatable, or satisfactory [33]. Appropriateness is the perceived fit, relevance or compatibility of the innovation for a given practice setting, provider or consumer; and/or perceived fit of the innovation to address a particular issue [33]. Cost is defined as the incremental, implementation or overall costs of delivery based on the settings [33]. Feasibility is the extent to which a new treatment, or an innovation, can be successfully applied or implemented in a specific setting [34]. Sustainability is the extent to which an implemented treatment is maintained within a service setting’s usual or stable operations, as defined by various authors [35-37].

Synthesis of results

To conduct a quantitative synthesis for clinical outcomes, standardized effect sizes were calculated using two-stage process [85]. In the first stage the effect size of HbA1c and FBG was calculated separately for each study from means and standard deviations. In the second stage, the combined effect size as a weighted average of the intervention effects was derived from the individual studies. The effect sizes were calculated using the formula d = (-