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Abstract

Objective:

Dietary self-management is one key component to achieve optimal glycemic control. Advances in mobile health (mHealth) technology have reduced the burden of diabetes self-management; however, limited evidence has been known regarding the status of the current body of research using mHealth technology for dietary management for adults with type 2 diabetes.

Methods:

Literature searches were conducted electronically using PubMed, CINAHL (EBSCO), Web of Science Core Collection, PsycINFO (Ovid), EMBASE (Ovid), and Scopus. Keywords and subject headings covered dietary management, type 2 diabetes, and mHealth. Inclusion criteria included studies that applied mHealth for dietary self-management for adults with type 2 diabetes and were published in English as full articles.

Results:

This review (N = 15 studies) revealed heterogeneity of the mHealth-based dietary self-management or interventions and reported results related to physiological, dietary behavioral, and psychosocial outcomes. Twelve studies applied smartphone apps with varied functions for dietary management or intervention, while three studies applied continuous glucose monitoring (CGM) to guide dietary changes. Among 15 reviewed studies, only three of them were two-arm randomized clinical trial (RCT) with larger sample and 12-month study duration and 12 of them were pilot testing. Nine of 12 pilot studies showed improved HbA1c; most of them resulted in varied dietary changes; and few of them showed improved diabetes distress and depression.

Conclusion:

Our review provided evidence that the application of mHealth technology for dietary intervention for adults with type 2 diabetes is still in pilot testing. The preliminary effects are inconclusive on physiological, dietary behavioral, and psychosocial outcomes.

Keywords

type 2 diabetes dietary self-management mHealth diabetes self-management

Approximately 37.1 million (~14.7%) of US adults have diabetes, with 90% to 95% having type 2 diabetes.¹ Globally, a total of 387 million people worldwide have diabetes, and the number is expected to rise to 592 million by 2035.² The estimated total cost (direct and indirect) attributed in the United States was 327 billion in 2017.³ Substantial evidence supports aiming for HbA1c <7% as the glycemic goal for decreasing the incidence of diabetic complications (eg, cardiovascular disease, retinopathy, nephropathy, and neuropathy).² However, the American Diabetes Association has suggested that less than 30% of individuals achieve the recommended glycemic goal (HbA1c < 7%).⁴ The strategies for achieving HbA1c < 7% involve performing dietary self-management, which requires data organization and arithmetic skills, including tracking food intake, estimating food portion size, and calculating carbohydrate intake.⁵ Mastering such skills for ideal dietary self-management are usually burdensome and challenging for most of individuals with diabetes.⁶

Advances in mHealth technology (eg, mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices)⁷ have reduced the burden of diabetes self-management.⁸ Reviews by Wu et al.,⁹ Fu et al.,¹⁰ Brzan et al.,¹¹ and Hou et al.¹² have synthesized evidence of effectiveness of using diabetes self-management smartphone technologies with patients with type 2 diabetes to reduce hyperglycemia and the self-monitoring burden for participants and staff. However, these previous reviews^9
-12 did not specifically focus on dietary intervention in self-management for type 2 diabetes. Previous studies also reviewed automatic dietary monitoring methods (eg, using computer vision or wearable sensor).^6,13 However, such reviews only focused on general-purpose diet monitoring or dietary management tool development, not specifically on implementing such tools for dietary self-management or intervention for type 2 diabetes.

Presently, there has been limited synthesis of the research evidence regarding the use of mHealth technology for dietary management for adults with type 2 diabetes. There are no current or underway systematic or scoping reviews that have explored the types and key components of the mHealth technologies used in the research on dietary interventions for type 2 diabetes. Moreover, there has not been a review of how these mHealth dietary interventions impact physiological (eg, HbA1c and weight), dietary behavioral (eg, dietary behavior changes and healthy eating), and psychosocial (eg, distress, quality of life, and depressive symptoms) outcomes. Thus, this review will seek to synthesize the current body of published research on the use of mHealth technology for dietary management for adults with type 2 diabetes to characterize these interventions and their outcomes.

Methods

Protocol

Because the purpose of this review is to systematically scope the current body of research literature on this topic to characterize the interventions and outcomes that are under investigation in the field, a scoping review was selected as the most appropriate methodology.¹⁴ This review was conducted in accordance with the protocol registered at https://osf.io/edwz6.

Search Strategies

The search strategy aimed to locate published studies, beginning with an initial limited search of PubMed, CINAHL, and Web of Science Core Collection. The text words contained in the titles and abstracts of relevant articles, and the index terms used to describe the articles were then used to develop a full search strategy for PubMed (Table 1). The PubMed search strategy, including all identified keywords and index terms, was then adapted for CINAHL (EBSCO), Web of Science Core Collection, PsycINFO (Ovid), EMBASE (Ovid), and Scopus (search queries for each database are available at https://osf.io/s5xqh). The reference lists of all included sources of evidence were screened for additional studies. Studies published in English between January 2011 and June 2022 were eligible for inclusion.

Table 1.

Search Strategy.

Search^a	Query	Filters	Results
4	#1 AND #2 AND #3	From December 12, 2011, to December 12, 3000	790
3	“Smartphone”[MeSH Terms] OR “Mobile Applications”[MeSH Terms] OR “Wearable Electronic Devices”[MeSH Terms] OR “Remote Sensing Technology”[MeSH Terms] OR “Telemedicine”[MeSH Terms] OR “Text Messaging”[MeSH Terms] OR “Wireless Technology”[MeSH Terms] OR “Digital Technology”[MeSH Terms] OR “Biomedical Technology”[MeSH Terms] OR “smartphone”[Text Word] OR “smart phone”[Text Word] OR “mobile app”[Text Word] OR “mobile apps”[Text Word] OR “mobile application”[Text Word] OR “mobile phone”[Text Word] OR “mobile telephone”[Text Word] OR “mobile device”[Text Word] OR “mobile telephone”[Text Word] OR “Cell phone”[Text Word] OR “Cell telephone”[Text Word] OR “continuous glucose”[Text Word] OR “continuous blood glucose”[Text Word] OR “continuous blood sugar”[Text Word] OR “flash glucose”[Text Word] OR “Flash Blood Glucose”[Text Word] OR “Dexcom”[Text Word] OR “freestyle libre”[Text Word] OR “CGM”[Text Word] OR “wearable”[Text Word] OR “mHealth”[Text Word] OR “mobile health”[Text Word] OR “mobile technolog”[Text Word] OR “telehealth”[Text Word] OR “telemonitor”[Text Word] OR “text messag”[Text Word]		157 288
2	“diabetes mellitus, type 2”[MeSH Terms] OR “type 2 diabet”[Text Word] OR “type ii diabet”[Text Word] OR “NIDDM”[Text Word] OR “T2DM”[Text Word] OR “T2D”[Text Word] OR “non insulin depend”[Text Word] OR “noninsulin depend”[Text Word]		223 491
1	“food”[MeSH Terms] OR “nutritional status”[MeSH Terms] OR “diet therapy”[MeSH Terms] OR “nutritional support”[MeSH Terms] OR “nutrition therapy”[MeSH Terms] OR “dietary carbohydrates”[MeSH Terms] OR “nutritional sciences”[MeSH Terms] OR “Meals”[MeSH Terms] OR “Eating”[MeSH Terms] OR “energy intake”[MeSH Terms] OR “Nutrients”[MeSH Terms] OR “diet”[Text Word] OR “utria”[Text Word] OR “carbohydrate”[Text Word] OR “macronutrient”[Text Word] OR “food”[Text Word] OR “meal”[Text Word] OR “Eating”[Text Word] OR “Eat”[Text Word] OR “calori”[Text Word] OR “breakfast”[Text Word] OR “lunch”[Text Word] OR “dinner”[Text Word] OR “snack”[Text Word] OR “supper”[Text Word]		2 314 377

, the complete search queries for each database can be viewed at https://osf.io/s5xqh.

Inclusion and Exclusion Criteria

To be eligible for inclusion in this scoping review, studies included (1) adult participants with type 2 diabetes; (2) the application of mHealth technology for diabetes dietary self-management; and (3) experimental and quasi-experimental study designs, including randomized controlled trials, nonrandomized controlled trials, pre-post studies, interrupted time-series studies, and feasibility studies.

Studies were excluded if: (1) the participants were exclusively patients with type 1 diabetes and/or gestational diabetes; (2) they focused on pediatric patients, patients with cancer or HIV, patients who had gone through gastric bypass, and inpatients, as the dietary intake for these conditions need special attention in nutrition; (3) the technological component was limited to the use of telephones or the Internet; and (4) they were qualitative studies, technology algorithm testing/safety studies, study protocols, review articles, dissertations, and commentaries/opinion pieces and conference abstracts/posters.

Study Selection

Following the database searches, all identified citations were collated and uploaded into EndNote 20, and duplicates were removed. The pooled set of records were uploaded into Covidence for eligibility screening. Following a pilot test of the screening criteria, titles and abstracts were screened by two independent reviewers for assessment against the inclusion criteria for the review. Potentially relevant sources were retrieved in full, with the exception of three records for which the full text could not be located. The full texts of relevant citations were assessed in detail against the inclusion criteria by two independent reviewers. Any disagreements that arose between the reviewers at each stage of the selection process were resolved through discussion, and with an additional reviewer.

The results related to the identification of eligible studies are summarized in Preferred Reporting Items for Systematic Reviews and Meta-Analyses (Figure 1). Database searches yielded 3874 citations, leaving 1559 titles and abstracts to be screened for eligibility after removing duplications. Following full-text retrieval of 134 potentially relevant articles, 120 were subsequently excluded, leaving 14 articles that applied mHealth for dietary management for individuals with type 2 diabetes that were eligible for inclusion in the review. The references of the included articles were searched manually, resulting in an additional 1263 references to review. This relevance screening of these results located five additional relevant records, from which one additional eligible article was found. As a result, a total of 15 articles were included in this review. Studies were summarized based on the following categories: authors/country, sample size/characteristics, study design, intervention components, and outcomes (Table 2).

Figure 1.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow chart.

Table 2.

Studies on Dietary Self-management Using mHealth Technology.

Author/country/study design	Sample characteristics	Intervention components	Physiological outcomes	Dietary outcomes	Psychological/other outcomes
Waki et al^15,16 JapanTwo-arm RCT, 3 mo	N = 54IG: n = 27Age (y), mean (SD): 57.1 (10.2)Female, n (%): 7 (25.9)Race: 100% AsianType of treatment; n (%): Antihyperglycemic drugs: 20 (74.1)Years of diabetes, mean (SD): 9.6 (7.0)Retention rate: 22/27 (81.5)CG: n = 27Age (y), mean (SD): 57.4 (9.4)Female, n (%): 6 (22.2)Race: 100% AsianType of treatment; n (%): Antidiabetic drugs: 21 (77.8)Years of diabetes, mean (SD): 9.5 (8.0)	IG: Enrolled in a smartphone app DialBetics. Patients received feedback from the team to help modify their diet based on the inputs of diet information by speech-recognition device and photographs of meal.CG: Did not perform Dialbetics dietary evaluation modules. Nutritional values in the first 2 wk of the study period were compared with examine how diet changed each group	HbA1c (%), mean (SD):Intervention:Baseline: 7.1 (1.0)3 mo: 6.7 (0.7)Control: Baseline: 7.0 (0.9)3 mo: 7.1 (1.1)P = .015BMI (kg/m²), mean (SD):Intervention:Baseline: 26.2 (6.1)3 mo: 25.9 (5.9)Control: Baseline: 27.1 (7.6)3 mo: 27.1 (7.5)P = .062	Diet self-management score, mean (SD):Intervention:Baseline: 15.7 (7.2)3 mo: 16.6 (7.1)Control: Baseline: 13.4 (6.5)3 mo: 14.5 (7.7)P = .48Compliance, total (%) Diet inputs: 40.1 (35.6)Photograph of a meal inputs: 68.8 ± 32.6	Interviews conducted evaluated satisfaction of participants with the application: four participants found uploading the photographs of their food onto their app to have improved their dietary habit; three participants found the advice they received from the app to have really improved their dietary habit
Rollo et al¹⁷ AustraliaSingle-arm intervention pilot study, 2 wk	N = 10Female, n (%): 4 (40.0)Age range: 48-69 yRace: NAType of treatment; n (%): NAYears of diabetes, mean (SD): NA	Intervention:Week 1: Dietary intake was assessed using the NuDAM that consisted of a prospective mobile phone Nutricam image-based dietary record and a brief follow-up phone call.Week 2: Participants weighed food prior to consumption as a record reference method called weighed food record (WFR) into a article-based diary. WFR was reviewed by a dietician in the presence of the subject to ensure info was complete.	Weight changes (kg), mean (SD):2 wk: −0.3 (1.2)P > .05	Intake overall for energy (MJ/day), mean (SD):NuDAM: 8.8 (2.0)WFR: 8.8 (1.8)r = .57P < .01Mean (SD) intake overall for protein (g/day):NuDAM: 95.5 (23.7)WFR: 90.8 (26.4)r = .78P < .001Mean (SD) intake overall for fat (g/day):NuDAM: 83.1 (20.3)WFR: 81.9 (21.8)r = .24P > .05Mean (SD) intake overall for carbohydrate:NuDAM: 207.4 (54.4)WFR: 208.5 (53.9)r = .63P < .05	All subjects preferred to use the NuDAM to record intake than WFR.Number of participants who would be willing to use the Nutricam mobile phone for up to 7 d or longer (n = 9), while number of participants who would be willing to use WFR for maximum 3 d (n = 5)
Ahn et al¹⁸ KoreaTwo-arm quasi-experimental study, 1 mo	N = 26Intervention: n = 14 Age (y), mean (SD): 50.5 (17.1)Female, n (%): 8 (57.1)Race, n (%): Asian 100%Type of treatment; n (%): NAYears of diabetes, mean (SD): NAControl: n = 12 Age (y), mean (SD): 49.7 (16.4)Female, n (%): 5 (41.7)Race, n (%): Asian 100%Type of treatment; n (%): NAYears of diabetes, mean (SD): NA	The u-Health mobile application was developed with three parts: (1) record diet intake and allows users to take pictures of their meals; (2) the alarm system that rings at each mealtime; (3) displays the diet record and the results of nutrient intake, which can also be viewed through the web-based program.	BMI (kg/m²), mean (SD):Intervention:Male (n = 6)Baseline: 25.6 (5.3)3 mo: 26.1 (6.0)Female (n = 8)Baseline: 25.0 (5.9)3 mo: 25.0 (5.8)Control:Male (n = 7)Baseline: 26.6 (3.9)3 mo: 26.0 (3.7)Female (n = 5)Baseline: 23.3 (3.3)3 mo: 23.2 (3.0)P > .05	Total dietary behavior score, mean (SD):Intervention:Baseline: 50.2 (5.8)3 mo: 54.9 (6.2)P < .001Control:Baseline: 53.0 (5.4)3 mo: 55.9 (5.8)P > .05Energy (kal), mean (SD):Intervention:Baseline: 1950.5 (917.1)3 mo: 1888.5 (461.9)P > .05Control:Baseline: 1779.0 (610.8)3 mo: 1786.3 (625.6)P > .05Carbohydrate (g), mean (SD):Intervention:Baseline: 290.5 (1120.7)3 mo: 279.5 (89.5)P > .05Control:Baseline: 273.4 (74.9)3 mo: 283.1 (92.0)P > .05	Total dietary attitude score, mean (SD):Intervention:Baseline: 48.4 (4.6)3 mo: 50.8 (5.4)P < .05Control:Baseline: 46.1 (5.8)3 mo: 49.4 (5.6)P < .05Total nutrition knowledge score, mean (SD):Intervention:Baseline: 11.5 (1.9)3 mo: 11.5 (2.2)P > .05Control:Baseline: 11.8 (1.9)3 mo: 10.7 (1.3)P > .05
Berman et al¹⁹ USAOne-group intervention, 12 wk	N = 118Age (y), mean (SD): 50.7 (9.4) yFemale, n (%): 96 (81.4)Race: NADiabetes medications (count), mean (SD): 1.4 (0.9)Years of diabetes, mean (SD): 2.6 (1.6) y	Intervention: Intervention app paired with specialized human support delivered digitally as a learning platform. Content incorporated evidence-based dietary and lifestyle recommendations such as a dietary pattern consisting mainly of whole food plant-based meals and regular exercise meeting or exceeding national guidelines. Additional content was included to enhance culinary skill acquisition with the aim of increasing meals prepared at home since there is an association with decreased disease burden.The app was designed primarily to facilitate the learning and adoption of plant-based meals, self-monitoring habits, and scheduling of coaching calls.	Changes in HbA1c (%), mean (SD): −0.8 (1.3)P < .001Decrease by 0.5% or more, n (%): 57 (58.8)Decrease by 1.0% or more, n (%): 38 (39.2)	Dietary change self-efficacy, mean (SD): 4.4 (0.8)	Self-efficacy: 92% (90/98) of those responding reported greater confidence in their ability to manage their diabetes compared to before the program, 91% (89/98) reported greater confidence in their ability to maintain a healthy dietary pattern.Of the individuals who consented to participate, 92.4% (109/118) were active in the study at the end of the 12-wk intervention period and 86.2% (94/109) were still using the app.Total distinct app engagements averaged 4.3 (SD: 2.5) per day, and average number of coaching calls completed was 4.1 (SD: 1.8) during the 12-wk period.Medication use:n = 97 participants who reported an end-study.4% (4/97) changed medications or dosages within 12-wk study.17% (16/97) reported decreasing or stopping one or more diabetic medications.8% (8/97) increased or added one or more diabetic medication.P < .001
Taylor et al^20,21 AustraliaTwo-arm RCT pilot study, 12 wk	N = 20RT-CGM: n = 10Blinded CGM: n = 10Age (y), mean (SD): 60.6 (8.4)Female, n (%): 10 (50%)Race: NAType of treatment; n (%): NAYears of diabetes, mean (SD): NA	IG: RT-CGM with access to visual displayCG: Blinded CGM Week 0: all participants received a low-carbohydrate, high protein, and unsaturated fat diet. The dietary prescription had a planned macronutrient profile of 14% of total energy as carbohydrate, 28% protein and 58% total fat (35% monounsaturated fat), individualized for energy level based on achieving a 30% energy restriction.Week 3: participants were provided with education on food exchanges.All wore the CGM for 13 wk. All participants were blinded CGM for 1 wk prior to commencement of the lifestyle intervention for baseline data collection	HbA1c (%), mean (SD):RT-CGM:Baseline: 6.60 (0.86)12 wk: 5.90 (0.57)Blinded CGM: Baseline: 7.11 (0.80)12 wk: 6.43 (0.92)P = .43Overall mean total glucose (mmol/L)RT-CGM Baseline: 7.00 (0.79)12 wk:6.75 (1.21)Change: −0.24 (0.87)Blinded CGM Baseline: 8.70 (0.86)12 wk:7.48 (1.450Change: −1.22 (1.37)P = .413	Diet behavior improved in both RT-CGM and Blind CGM groups.Mean ± (SD) diet behaviorRT-CGM:Baseline: 6 wk: 20.7 (2.98)12 wk: 21.5 (2.76)Blinded CGM:Baseline: -6 wk: 20.1 (4.12)12 wk: 19.7 (1.49)P = .08	Perceived Stress, mean (SD):RT-CGM:Baseline: 33.3 (5.1)12 wk: 33.7 (4.7)Blinded CGM:Baseline: 35.5 (3.5)12 wk: 35.2 (2.5)P > .05
Alonso-Dominguez et al²² SpainTwo-arm RCT, 12 mo	N = 204CG:n = 102Age (y), mean (SD): 60.4 (8.4)Female, n (%): 41 (40.2)Race: NAType of treatment; n (%): oral antihyperglycemic drugs: 89 (87.3)Insulins: 18 (17.6)Years of diabetes, mean (SD): NAIG:n = 102Age (y), mean (SD): 60.8 (7.8)Female, n (%): 52 (51.0%)Race: NAType of treatment; n (%): antidiabetic drugs: 93 (91.2)Insulins: 12 (11.8)Years of diabetes, mean (SD): NA	IG:Participated in a food workshop focused on Mediterranean diet, five walks (one per week for 5 wk) and received a smartphone application for 3 mo. The app is EVIDENTII (intellectual property registry number SA-81-14) (CGB, Salamanca, Spain).The application was designed by software engineers in collaboration with dieticians and physical activity experts with the aim of promoting adherence to the Mediterranean diet.CG:Both groups received brief advice on healthy eating. Follow-up conducted at baseline, 3 mo, and 12 mo to assess effect of the intervention	HbA1c (%), mean (SD):Intervention:Baseline: 6.9 (1.2)3 mo: 6.8 (1.0)12 mo: 6.9 (1.1)Control: Baseline: 6.8 (1.2)3 mo: 6.8 (1.1)12 mo: 7.0 (1.3)3 mo vs baseline: P = .145 12 mo vs baselineP = .241	Adherence to the Mediterranean diet Total score using Mediterranean Diet Adherence Screener, mean (SD):Intervention:Baseline: 7.2 (1.9)3 mo: 9.4 (1.7)12 mo: 8.5 (1.9)Control: Baseline: 6.9 (1.7)3 mo: 7.1 (1.7)12 mo: 7.0 (1.9)3 mo vs baseline: P < .001 12 mo vs baselineP < .001 Total score using Diet Quality Index, mean (SD):Intervention:Baseline: 39.8 (2.6)3 mo: 42.9 (2.5)12 mo: 41.8 (2.8)Control: Baseline: 40.2 (2.6)3 mo: 40.8 (2.7)12 mo: 40.4 (2.7)3 mo vs baseline: P < .001 12 mo vs baselineP < .001	NA
Cox et al²³ USATwo-arm RCT, 3 mo	N = 30IG:Age (y), mean (SD): 54.6 (12.2)Female, (%): (50)Race, (%)White: 85Black: 10Type of treatment; n (%): NAYears of diabetes, mean (SD): 5.4 (2.7)CG:Age (y), mean (SD): 50.8 (14.2)Female, n (%): (80)Race, n (%)White: 60Black: 30Type of treatment; n (%): NAYears of diabetes, mean (SD): 5.9 (2.5)	IG: Wore a blinded CGM and received GEM program. Program had four sessions, each lasting about a week and assisted with CGM feedback.Session 1: Education on how routine choices can affect their BG.Session 2: Reducing high carbohydrates and replacing with substitutions.Session 3: Increasing physical activity.Session 4: Continuing and enhancing optimal food and activity choices. Completed self-administered 24-h dietary recall on two work days and one weekend day via phone interview. 3-mo follow-up assessments were conducted.CG:RC. Completed self-administered 24-h dietary recall on two workdays and one weekend day via phone interview. 3-mo follow-up assessments were conducted.	Changes in HbA1c (%), mean (SD):IG: −1.30 (0.89)CG: −0.19 (1.81)P = .009	Change in net carbohydrate foods (%), mean (SD):IG: −72.6 (75.8)CG: 6.4 (112.5)P = .009	Diabetes distress, emotional, mean (SD):IG: −0.5 (1.1)CG: 0.2 (1.1)P = .02Diabetes distress, regimen, mean (SD):IG: −0.9 (1.1)CG: −0.1 (0.7)P = .01Depressive symptoms, mean (SD):IG: −2.3 (5.1)CG: −1.4 (6.7)P = .08
Alrige et al²⁴ USATwo-arm quasi-experimental study, 3 mo	N = 28Retention: n = 21 (75%)Intervention:n = 9Control:n = 12Age (y), mean (SD): NAFemales, n (%): 12 (57.1)Race: White, n (%): 12 (52.4%)Type of treatment; n (%): NAYears of diabetes, mean (SD): NA	Intervention Utilized EasyNutrition (app): a customized dietary tool. Users can create meal plans and find recipes with nutrient information. Participants in the IG used EasyNutrition every other week to learn about nutrition breakdown and nutritional value of the food recipes tailored to their food preferences. Every 2 wk, they would receive a text message/phone call asking about app usage.Control: Standard care provided by center	Change in HbA1c (%) (pre-post), mean (SD):Intervention: 1.41 (1.75)P = .04Control: 0.23 (0.62)P = .22Between-group difference: P = .04	Change in eating habits (pre-post), mean (SD):Intervention: 1.00 (2.74)P = .31Control: 0.25 (4.31)P = .84Between-group difference: P = .65	NA
Ku et al²⁵ Republic of KoreaTwo-arm RCT, 12-wk pilot study	N = 40IG:n = 20Age (y), mean (SD): 46.6 (11)Female, n (%): 11 (55%)Race: NAType of treatment; n (%): Insulin only: 4 (23.5)Insulin with oral hypoglycemic agents (OHA): 4 (23.5)OHA only: 6 (35.3)Lifestyle modificationOnly: 3 (17.6)Years of diabetes, mean (SD): NACG:n = 20Age(y), mean (SD): 52.4 (6.6)Female, n (%): 10 (50%)Race: NAType of treatment; n (%): Insulin only: 1 (5.6)Insulin with OHA: 2 (11.1)OHA only: 14 (77.8)Lifestyle modificationOnly: 1 (5.6)Years of diabetes, mean (SD): NA	IG (Smartphone-based care group): Had access to the smartphone application Noom. Noom Coach (Noom Inc., New York, NY, USA), a mHealth application was used as a dietary diary. This is an application intended for weight control and dietary management. Subjects in the smartphone-based care (SC) group were asked to log their dietary data using this application. This tool enables the user to calculate their dietary intake easily using a well-established database of local foods. The Noom database contains more than 3.7 million food options and has up-to-date 50 000 Koreanfood items that are updated by registered nutritionists.Noom used an internal database to calculate automatically the calories of food items entered by users. When a meal is recorded in the application, a color code (red, yellow, and green) is presented to help patients choose healthier foods within the allotted calories per day (green, “It’s a good choice!”; yellow, “Please eat only moderate amounts”; and red, “Sometimes, just a little”).CG (Conventional care group): Received conventional diabetes care education from a nurse at baseline. Taught to test and record their blood glucose levels.	Outcomes were analyzed based on the retaining participants:IG:n = 17CG:n = 18HbA1c (%), mean (SD):Baseline: IG: 8.8 (1.8)CG: 9.1 (1.9)Changes in HbA1c (%) from baseline to 12 wk, mean (SD):IG: −1.9 (1.6)CG: −1.0 (1.0)P = .036Changes in fasting plasma glucose levels (mg/dL), mean (SD):IG: −42.2 (81.1)CG: −32.4 (76.9)P = .015	Scores for overall eating habits improved in both CG and IG after 12 wk of study.General diet, mean (SD):Baseline: IG: 1.4 (2.0)CG: 0.4 (1.1)Week 12:IG: 2.6 (2.3)CG: 1.8 (2.2)P = .867Specific diet, mean (SD):Baseline:IG: 4.2 (1.7)CG: 3.8 (1.6)Week 12:IG: 5.4 (1.2)CG: 5.1 (1.1)P = 1.00	Percentage of patients reduced the dose of oral antidiabetic agents or insulin, n (%):IG: 3 (15.0)CG: 4 (20.0)P = .410.Increases in dosage of oral antidiabetic agents or insulin, n (%):IG: 0 (0.0)CG: 6 (30.0)P = .007
Koohmareh et al²⁶ IranTwo-arm RCT, 6 wk	N = 60IG:n = 30Age (y), mean (SD): 43.9 (9.0)Female, n (%): 16 (53.3)Race: NAType of treatment; n (%): oral medication: 21 (70.0)Oral medication + insulin: 9 (30.0)Years of diabetes, mean (SD): NACG: n = 30Age (y), mean (SD): 44.13 (7.91)Female, n (%): 16 (53.3)Race: NAType of treatment; n (%): Medication: 25 (83.3)Medication + insulin: 5 (16.7)Years of diabetes, mean (SD): NA	Intervention: Participants played a mobile game (called Amoo) for 15 min daily that educated the participants on food. Took a preintervention and postintervention test to show a possible improvement on dietary information. They were monitored by a WhatsApp group to ensure that the participants played the gameControl: Participants did not play the game.	Fasting blood sugar, mean (SD)IG: n = 30Before intervention: 160.70 (33.66)After intervention: 158.86 (33.11)CG:n = 30Before intervention: 161.13 (31.85)After intervention: 161.36 (30.79)Before intervention: P = .779After intervention: P = .125	Test score on dietary information, mean (SD): Before intervention: IG: 7.27 (2.59)CG: 7.47 (2.36)P = .756Test score, mean (SD) After intervention IG: 12.63 (1.13)CG: 7.63 (1.99)P > .001Attention to food glucoseLevel foods; n (%):IG: Before intervention, Yes: 18 (60.0)After intervention: 30 (100)CG:Before intervention, Yes: 13 (43.3)After intervention, Yes: 13 (43.3)Before intervention: P = .301After intervention: P < .001Attention to food calorie counts; n (%):IG: Before intervention, Yes: 11 (36.7)After intervention: 30 (100)CG:Before intervention, Yes: 8 (26.7)After intervention, Yes: 12 (40.0)Before intervention: P = .580After intervention:P < .001	NA
Kumar et al²⁷ IndiaTwo-arm RCT, 12 mo	N = 800IG:n = 400Age (y), mean (SD): 56.2 (11.7) Female, n (%): 262 (65.5)Race: NAType of treatment; n (%): NAYears of diabetes, %:<6: 59.76-10: 21.2>10: 19.1CG:n = 400Age (y), mean (SD): 56.9 (11.2) Female, n (%): 246 (61.5)Race: NAType of treatment; n (%): NAYears of diabetes, %:<6: 55.56-10: 25.2>10: 19.3Years of diabetes, %CG:<6: 55.56-10: 25.2>10: 19.3IG	IG: Asked to provide info in “electronic diary” so daily diet information was collected and entered in web-based online portal. Based on the web-based online registered information, participants of received personalized tailored feedback in the form of SMS over their mobile phone as dietary alerts and reminders.CG: Given “paper diary” to record daily dietary consumption such as recording time, amount, and type of food items consumed	Mean FBS in mg/dLIG: Baseline: 195.012 mo: 183.0P = .06CG:Baseline: 199.012 mo: 195.0	Dietary practice was observed on a Likert scale (0: not at all), mean (SD)Positive practicesIG: Baseline: 1.4 (0.5)12 mo: 1.0 (0.2)CG:Baseline: 1.4 (0.4)12 mo: 1.0 (0.2)P < .001Negative practicesIG: Baseline: 1.0 (0.2)12 mo: 0.8 (0.5)CG::Baseline: 1.1 (0.3)12 mo: 1.2 (0.1)P < .001	Dietary knowledge score, mean (SD)IG: Baseline: 28.9 (12.5)12 mo: 35.4 (13.1)CG:Baseline: 28.3 (12.7)12 mo: 22.2 (14.9)P < .001Dietary attitude score, mean (SD)IG: Baseline: 54.7 (28.3)12 mo: 40.2 (21.6)CG:Baseline: 51.0 (30.9)12 mo: 36.9 (22.4)P = .03
Patnaik et al²⁸ IndiaTwo-arm RCT, 12 mo	N = 66Intervention:n = 33Control:n = 33Age (y), mean (SD): 42.3 (9.5)Female, n (%): 16 (34.8)Race: Asian 100%Type of treatment; n (%): oral antihyperglycemic medication: 66 (100.0)Years of diabetes, mean (SD): NA	Printed education material and daily intake of 1600 calories diet, recommended by endocrinologist in both English and local Odia language was provided to both the groups.Intervention: Mobile application-based software. Received SMS containing educational tips about diet, drugs, etc, every weekControl: Web-based intervention	Blood glucose level FBS, mean (SD)Control: Baseline: 145.9 (38.4)12 mo: 128.7 (29.9)P = .057Intervention: Baseline: 156.5 (50.1)12 mo: 129.4 (26.0)P = .148post-prandial blood sugar (PPBS), mean (SD)Control: Baseline: 206.7 (67.5)12 mo: 155.5 (51.8)P < .001Intervention: Baseline: 203.7 (58.6)12 mo: 159.0 (32.2)P < .001	Food habits:Starch serving/day, n (%)Control: Baseline: serving/day: 19 (57.6)two or more serving/day: 14 (42.4)12 mo: serving/day: 20 (100.0)two or more serving/day: 0 (0.00)P < .001Intervention: Baseline: serving/day: 18 (54.5)two or more serving/day: 15 (45.5)12 mo: 0-1 serving/day: 23 (100.0)two or more serving/day: 0 (0.00)P < .001Sweet servings/day, usually none, n (%)Control: Baseline: 32 (97)12 mo: 19 (95)P = .809Intervention: Baseline: 29 (87.9)12 mo: 23 (100)P = .017	NA
Tsunemi et al²⁹ JapanOne-group intervention, 3 mo	N = 18Age (y), mean (SD): 53.4 (7.8)Females, n (%): 7 (38.9)Race: Asian 100%Type of treatment; n (%): oral antihyperglycemic medication: 10 (55.6)GLP-1 analogue: 4 (22.2)Insulin: 3 (16.7)GLP-1 analogue and insulin: 1 (5.6)Years of diabetes, mean (SD): 13.7 (8.2)	Intervention: Participants used the “Calomeal” app, commercially available Japanese mobileapp, which has two functions: specifically manual search and analysis of foods and BW logging. A graph of daily energy intake and the intake of different nutritional components was displayed on the smartphone screen, and if the target intake for any of these was exceeded, the line turned red. Participants did not receive any advice about nutrition therapy from staff members/registered dieticians.	Change in HbA1c (%), mean (95% CI)Baseline: 7.9 (7.58-8.23)3 mo: 7.6 (7.3-8.23)P = .041BMI (kg/m²), mean (95% CI)Baseline: 25.17 (21.63-28.59)3 mo: 24.54 (21.57-27.81)P = .006Glycated albumin (%), mean (95% CI)Baseline: 18.9 (17.9-20.1)3 mo: 18.75 (17.025-9.825)P = .0032	Energy by app (kcal/day), mean (95% CI)Baseline: 1998.5 (1754.25-2407.75)3 mo: 1991 (1752.75-2154)P = .107Carbohydrate by app (g/day), mean (95% CI)Baseline: 238.1 (213.35-301.75)3 mo: 237.6 (209.68-277.35)P = .194Protein by app (g/day), mean (95% CI)Baseline: 69.35 (63.25-82.3)3 mo: 70 (59.78-83.98)P = .975Lipid by app (g/day), mean (95% CI)Baseline: 75.8 (65.15-96.25)3 mo: 73.55 (62.55-86.38)P = .207	The mean percentage adherence of logging meal records (at least 3 d per week) was 80.3% ± 27.3%.
Oser et al³⁰ USAOne-group, pilot study, 3 mo	N = 17Age (y), mean (SD): 52 (11.6)Gender: Female, (%): (40%)Race: Non-white = 33.3%Type of treatment; n (%): NAYears of diabetes, mean (SD): NA	Intervention: Participants used GEM an alternative lifestyle treatment option in booklet form with CGM feedback. They would receive text messages to encourage GEM engagement. There are four units of the GEM: Unit 1: participants spend 5 d learning which of their routine food and physical activity choices have major and minor impacts on their glucose excursions.Unit 2: participants spend 14 days focusing on reducing, substituting, replacing, or eliminating high-impact carbohydrates to diminish glucose excursions.Unit 3: participants spend 14 days learning how to hasten recovery of glucose excursions by changing the type, intensity, duration, and timing of routine physical activity.Unit 4: participants learn to continue experimenting with new nutrients and activities, sustain support of significant others, and prevent, anticipate, and recover from relapses resulting from fatigue, life, stress, or change in routine.Participants used a Likert scale to rate how helpful the program was.	HbA1c (%), mean (SD)Pretreatment: 8.0 (1.6)3 mo: 6.2 (1.1)P < .001Percentage of time when CGM values were <140 mg/dL (%), mean (SD)Pretreatment: 23.9 (28.9)3 mo: 14.5 (22)P =.03BMI (kg/m²), mean (SD):Pretreatment: 36.5 (8.1)3 mo: 34.4 (8.2)P = .002	High carbohydrate foods (%), mean (SD)Pretreatment: 39.6 (21.9)3 mo: 10.3 (6.8)P < .001	Diabetes distress, emotional, mean (SD)Pretreatment: 2.2 (0.8)3 mo: 1.8 (1.0)P = .10Diabetes distress, regimen, mean (SD):Pretreatment: 2.8 (1.4)3 mo: 1.8 (0.9)P = .03Depressive symptoms, mean (SD)Pretreatment: 6.1 (4.5)3 mo: 2.3 (3.9)P = .001
Bretschneider et al³¹ Germany and Czech RepublicSingle-arm intervention, 3 mo	N = 37Age (y), mean (SD): 57 (7.4)Female, n (%): 55%Race: NAType of treatment; n (%): Medication: 37 (100)Medication + insulin: 14 (37.8)Years of diabetes, mean (SD): 7.6 (6.4)	Intervention: Vitadio is a digital care program designed to empower pts with effective self-management and lifestyle change. It is a mobile application with a system of daily tasks and automated messages.To track dietary habits, pts can use a feature designated to upload photographs of their meals. Program also has a personal advisor available by chat to answer patient questions. Patients can participate in a peer support group.	HbA1c (%), mean (SD): Baseline: 7.9 (1.0)3 mo: 6.9 (0.9)Change: −0.9 (1.1)P < .001BMI (kg/m²), mean (SD):Baseline: 35.1 (7.3)3 mo: 33.6 (7.1)P = .03	Average meal photographs per participants: 215 meal photographsAverage daily self-evaluation of dietary habits: 7/10*based on Summary of Diabetes Self-Care Activities measure (SDSCA)General diet: Baseline: 5.3 (1.2)3 mo: 5.5 (1.3)P = .30Specific diet: Baseline: 4.6 (1.5)3 mo: 4.5 (1.7)P = .77Meal evaluation (n = 24)Portion size: Baseline: 2.44 (0.40)3 mo: 2.18 (0.36)P = .01Protein: Baseline: 2.61 (0.56)3 mo: 2.28 (0.47)Carbohydrate: Baseline: 3.07 (0.51)3 mo: 2.68 (0.56)P = .01Fat: Baseline: 2.96 (0.69)3 mo: 2.59 (0.50)P = .02Fiber: Baseline: 3.33 (0.56)3 mo: 2.82 (0.54)P < .001 Vegetable: Baseline: 3.31 (0.92)3 mo: 2.67 (1.00)P = .003Processed food: Baseline: 2.10 (0.50)3 mo: 1.80 (0.43)P = .007Overall grade: Baseline: 3.08 (0.39)3 mo: 2.71 (0.36)P < .001	Self-efficacy based on in-app questionnaire (n = 29) on a scale of 1 to 5Ability to select proper food Baseline: 4.86 (1.67)3 mo: 6.85 (1.74)Change = 1.99 (1.75)P < .001

Abbreviations: SD, standard deviation; BMI, body mass index; SMS, short message service; FBS, fasting blood sugar; GLP, glucagon-like peptide-1; CI, confidence interval; RCT, randomized clinical trial; GEM, glycemic excursion minimization; IG, intervention group; CG, control group; RT-CGM, real-time continuous glucose monitoring; RC, routine care; GLP-1, Glucagon-like peptide-1; NA, not available.

Results

Description of Included Studies

Among 15 reviewed studies, four were conducted in the United States,^19,23,24,30 seven were in Asia,^15,18,25
-29 two were in Australia,^17,20 and two were in Europe.^22,31 The sample sizes ranged from 10 to 800. The participants aged from 42.3 to 60.8 years and 25.9% to 81.4% were females. Of the three studies that reported on race, the percentage of non-white participants ranged from 15% to 47.6%.^23,24,30 A total of 35.5% to 100.0% participants took oral antihyperglycemic medication,^{19,15,22,25,26,28,29,31} 11.8% to 37.8% participants injected insulin,^22,25,29,31 and 23.5% to 30% had mixed oral medication and insulin.^25,26 A total of six studies reported the duration of diabetes, ranging from 2.6 to 13.7 y^{15,19,23,27,29,31} Among 15 reviewed studies, a total of eight studies were two-arm RCT intervention,^{15,20,22,23,25
-28} two studies were two-arm quasi-experimental studies,^18,24 and five studies were single-group intervention.^17,19,29
-31 A total of 12 studies lasted less than or equal to three months,^{15,17
-20,23
-26,29
-31} and three lasted 12 months.^22,27,28

Types of Mobile Health and the Key Components of Dietary Intervention

Among 15 reviewed studies, 12 studies applied smartphone apps with varied functions for dietary management or intervention. These apps included those with speech-recognition device and photographs of meals (ie, DialBetic),¹⁵ image-based dietary assessment,¹⁷ diet intake record, alarm systems, and displays of nutrient intake.¹⁸ Smartphone apps are also used for other purposes, such as to facilitate the learning and adoption of plant-based meals, self-monitoring habits, and scheduling of coaching calls,¹⁹ create meal plans and find recipes with nutrient information (ie, EasyNutrition),²⁴ act as a dietary diary (ie, Noom),²⁵ a mobile game player that educated the participants on healthy food intake (ie, Amoo),²⁶ log food and provide analysis with a graph of daily energy intake and the intake of different nutritional components being displayed (ie, Calomeal),²⁹ promote the Mediterranean diet (ie, EVIDENT II), and track dietary habits (ie, Vitadio).³¹ Two studies provided short message service (SMS) for tailored feedback as dietary alerts and reminders,²⁷ or for educational tips about dietary intake.²⁸ Three studies applied real-time continuous glucose monitoring (RT-CGM)^20,30 or blinded CGM²³ with sessions on reducing carbohydrate intake and providing substitutions.

Intervention Effects on Physiological, Dietary Behavioral, and Psychosocial Outcomes

Overall, the impact of dietary interventions using mHealth on outcomes was mixed. Among 15 reviewed studies, only three of them were two-arm RCT with larger sample and longer study duration,^22,27,28 which lasted 12 months. All these three full RCTs only reported physiological outcomes (one reported HbA1c²² and two reported glucose changes)^27,28 and dietary behavioral outcomes (eg, dietary behavior changes and healthy eating), but none of these three studies reported psychosocial outcomes (eg, distress, quality of life, and depressive symptoms).

Alonso-Dominguez et al²² compared mHealth (ie, EVIDENT II) intervention focusing on promoting the Mediterranean diet with control group who received brief advice about healthy eating, which found no significant difference in HbA1c changes at both three months (P = .145) and 12 months (P = .241) between groups, but found significant difference in adherence to the Mediterranean diet at both three months (P < .001) and 12 months (P < .001) between groups. The study by Kumar et al²⁷ that provided personalized tailored feedback SMS messages over the mobile phone as dietary alerts and reminders reported significantly reduced fasting blood glucose, while the study by Patnaik et al²⁸ that utilized SMS messages containing educational tips about diet did not show a significant reduction in fasting blood glucose but in postprandial blood glucose. Both studies improved dietary behaviors and food habits.^27,28

Of the two-arm RCT pilot studies that lasted less than or equal to three months,^{15,20,23,25,26} three studies resulted in a significant reduction in HbA1c,^15,23,25 one resulted in reduction in carbohydrate food consumption,²³ improvement in dietary information scores,²⁶ attention to high carbohydrate food, high calorie food,²⁶ and diet behavior,²⁰ and significant improvement in diabetes distress^20,23 and depression.²³ However, others did not result in HbA1c reduction,²⁰ fasting blood glucose changes,^20,26 did not resulted in improving diet self-management skills.^15,25 Of the two two-arm quasi-experimental studies,^18,24 one resulted in a significant reduction in HbA1c,²⁴ and the other achieved a BMI reduction and improved dietary behavior and dietary attitude.¹⁸ However, these two-arm studies did not show significant change in eating habits.²⁴ Of the five single-group interventions,^17,19,29
-31 four resulted in a significant reduction in HbA1c^19,29
-31 and BMI^29
-31 or weight,¹⁷ others resulted in improved in healthy dietary habits,³¹ reduced in consumption of food portion size,³¹ carbohydrate,^30,31 protein and fat,³¹ and improved dietary self-efficacy,^19,31 diabetes distress,³⁰ and depression.³⁰

Discussion

This scoping review aimed to synthesize the status of the current body of research using mHealth technology for dietary management for adults with type 2 diabetes. Fifteen studies reported using either mHealth apps or CGM for dietary self-management or intervention; however, the use of mHealth technology for dietary intervention for adults with type 2 diabetes was largely in the pilot testing stage. The pilot testing showed promise in improving glucose or weight control, improving dietary management behaviors, reducing carbohydrate intake, or reducing diabetes distress and depression. However, the preliminary effects were inconclusive on physiological outcomes (eg, HbA1c and weight), dietary behavioral outcomes (eg, dietary behavior changes and healthy eating), and psychosocial outcomes (eg, distress, quality of life, and depressive symptoms), because the dietary interventions using mHealth and reported outcomes were heterogeneous, which made it difficult to synthesize the outcomes.

Although dietary self-management is a key component of glycemic control and mHealth is advancing for diabetes management, the current body of research using mHealth technology for dietary management for adults with type 2 diabetes is limited. Wu et al.,⁹ Fu et al.,¹⁰ Brzan et al.,¹¹ and Hou et al.¹² synthesized the evidence of effectiveness of smartphone technologies on glycemic control and usability and clinical efficacy in patients with type 2 diabetes, indicating that smartphone technologies could reduce the self-monitoring burden for participants and staff. However, these reviews did not specifically synthesize the types of mHealth technology used in the current dietary intervention or dietary self-management research and how these interventions impact outcomes. Our review indicated that most of mHealth-based dietary intervention showed promise to improve glycemic control and dietary behaviors but few of them reported the impacts on the psychological outcomes. However, 12 out of 15 studies are pilot studies, which only tested the preliminary effects but not the effectiveness of mHealth-based dietary intervention on the outcomes, indicating full-scale studies are needed to further examine the impacts on physiological, dietary behavioral, and psychosocial outcomes for adults with type 2 diabetes.

Our review showed heterogenous types of mHealth and components of dietary interventions. Most of studies applied smartphone apps with features such as speech recognition and photographs of meal, image-based dietary assessment, diet intake record, alarm systems, and displays of nutrient intake. These features provide the opportunity to reduce the burden of dietary management for individuals with diabetes. Three studies applied CGM with sessions on reducing high carbohydrate choices and suggesting substitutions, indicating that CGM may be a useful and educational tool to allow individuals with diabetes to understand how their dietary intake impacts their postprandial glucose levels and glucose variability, particularly the consumption of high carbohydrate food.

One limitation of this review is that the mHealth-based interventions for dietary self-management were conducted in a less diverse population. Among 15 reviewed studies, only three studies reported race and seven studies were conducted in Asian populations, indicating that such types of interventions need to be tested in other race/ethnic populations, particularly in African Americans and Hispanics, due to the high prevalence of type 2 diabetes in these populations.¹ To the best of our knowledge, this is the first scoping review to synthesize the status of the current body of research using mHealth-based intervention for dietary self-management for adults with type 2 diabetes.

Conclusions

The present scoping review provided evidence that the application of mHealth technology for dietary intervention is still in the pilot testing stage. The preliminary effects are inconclusive on physiological, dietary behavioral, and psychosocial outcomes, largely due to the heterogeneous mHealth-based intervention for dietary self-management and the heterogeneous reported outcomes. Future full-scale studies are needed to further test the effectiveness of application of mHealth for dietary management in adults with type 2 diabetes. Future studies are also needed to test the effectiveness of application of mHealth for dietary management in diverse populations.

Footnotes

Acknowledgements

The authors acknowledge Marie Claire R Mortejo, BSN, and undergraduate student Jenelle Marius for their assistance with pilot article screening.

Abbreviations

CGM, continuous glucose monitoring; mHealth, mobile health; RCT, randomized clinical trial.

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 project is supported by NIH/NCATS UL1TR001445 CTSI Collaborative Translational Pilot Project Award (Yaguang Zheng, PhD), P30DK111022-08 New York Regional Center for Diabetes Translation Research (NY-CDTR) Pilot and Feasibility (P&F) Program Funding (Director: Judith Wylie-Rosett, EdD, RD; awardee: Yaguang Zheng, PhD), and R25 HL145323-01A1 Research and Education in Cardiovascular (RECV) training program (Contact PI: Victoria Vaughan Dickson, PhD, CRNP, FAHA, FHFSA, FAAN; trainee: Jenelle Marius and YiLan Zhang).

ORCID iDs

Yaguang Zheng

Brynne Campbell Rice

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