Impact of Gaming (Gamification) on Diabetes Self-Care Behaviors and Glycemic Outcomes Among Adults With Type 2 Diabetes

Abstract

Purpose:

The purpose of this systematic review was to determine if the use of gaming (gamification) among persons with type 2 diabetes improves diabetes self-management behaviors and diabetes outcomes.

Methods:

A systematic review was conducted using electronic databases including MEDLINE, Embase, Web of Science, and CINAHL. Studies reporting on the impact of gaming on at least 1 of the Association of Diabetes Care and Education Specialists self-care behaviors (ADCES7) were included.

Results:

The review included 9 studies, 8 of which were of strong/high quality. Five of the self-care behaviors were addressed in at least 1 of the included studies. However, taking medications and problem solving were not reported in any of the studies. Physical activity and self-efficacy or quality of life (healthy coping) were the most frequently reported ADCES7 behaviors. Six of the studies used A1C as an outcome measure, with a reduction reported in all the studies except 1.

Conclusion:

Type 2 diabetes affects a person holistically, necessitating a range of self-care behaviors to effectively manage the chronic condition. Novel gaming interventions may improve coping mechanisms, lifestyle behaviors, medication engagement, and monitoring of risks and problems, all of which are essential in facilitating optimal diabetes self-management.

Type 2 diabetes (T2DM) affects 422 million persons worldwide. ¹ In 2021, over 37 million people were diagnosed with diabetes, and 95% had T2DM. ² Diabetes is the seventh leading cause of death in the United States. ³ Following diagnosis of diabetes ongoing attainment of glycemic targets is necessary in order to prevent complications related to diabetes. Gregg et al ⁴ reported a resurgence in diabetes complications, indicating that a decrease in preventive care may have been associated with these finding. One of the primary resources utilized to assist in the attainment of glycemic targets is diabetes self-management education (DSME). S’wia˛toniowska et al ⁵ recognized DSME as a “preventive activity.” ⁵ DSME involves the transfer of knowledge and addressing and supporting behavioral change to improve clinical and health-related outcomes. The outcome of DSME is “behavior change,” which is evaluated based on the Association of Diabetes Care and Education Specialists (ADCES) framework (ADCES7) that includes these behaviors: healthy coping, healthy eating, being active, taking medication, monitoring, reducing risk, and problem solving. ⁶ Diabetes self-management behaviors are known to positively impact overall glycemic outcomes. The findings from an international online survey study conducted by Adu et al ⁷ revealed that an enabler of DSME was a desire to prevent complications and the use of technology. The barriers included frustrations with the chronic nature of the diabetes, financial constraints, environmental and work factors, and unrealistic expectations. ⁷

Recent studies have shown a benefit in using gaming/gamification in improving self-management behaviors.^8,9 Gaming/gamification involves the use of game design elements to engage patients in health promotion efforts. ¹⁰ Gamification may involve competition to further enhance engagement. ¹¹ Gaming involves the use of technology, such as smartphones, video games(ing), and virtual environments. The utilization of apps on smartphones to engage the patient in self-care behaviors is also a form of gaming that is used. However, a recent study by Gong et al ¹² reported that people with diabetes want apps that cover a variety of content, are interactive, and provide emotion and psychological support. Video gaming (active video gaming or exergaming) requires active physical movement to play. ¹³ Virtual environments provide a way in which patients can engage in realistic settings to receive health information, social support, and education while developing new skills. ¹⁴ Gamification uses technology as an educational tool and offers the patient a “fun” way to engage in the acquisition of knowledge while addressing and supporting behavioral change.

Several systematic reviews have been done evaluating the use of gaming to improve knowledge and self-management ¹⁵ among pediatric patients with asthma, cancer, and type 1 diabetes (T1DM); games and health education in persons with T1DM and T2DM ¹⁶ ; gaming applications in cardiovascular disease ¹⁷ ; and diabetes gamification among people with T1DM and T2DM ¹⁸ with reported improvement in knowledge and self-management. Recently, Kaihara et al ¹⁹ reported on their systematic review and meta-analysis of randomized control trials (RCTs) utilizing gamification to improve “glycemic control.” However, there have been no recent systematic reviews evaluating the impact of gaming/gamification on diabetes self- management behaviors and glycemic outcomes among adults with T2DM. The purpose of this systematic review was to determine if the use of gaming (gamification) among adults with T2DM improves diabetes self-management behaviors and diabetes outcomes.

Research Design and Methods

Research Design

A systematic review was conducted to identify, synthesize, and critically analyze the existing peer-reviewed literature on the use of virtual gaming to improve diabetes self-management behaviors and diabetes outcomes in patients with T2DM. The study protocol was registered with the National Institute for Health Research, International Prospective Register of Systematic Reviews (PROSPERO—Registration No. CRD42022299559).

Study Selection

Studies were included in this review if (1) they were original research studies (RCTs or quasi-experimental studies); (2) they were conducted among adults with T2DM; (3) they examined a gaming intervention via online environment, virtual system, video game, or smartphone technology; or (4) they evaluated at least 1 of the diabetes mellitus self-management behaviors (diet, exercise, medications, blood glucose monitoring, healthy coping, social support, doctors’ visits). The criteria for exclusion were as follows: (1) focused exclusively on children or adolescent populations (ie, mean age < 18 years), (2) were not available in English, (3) were literature reviews or systematic reviews, (4) centered on T1DM, or (e) was conducted using virtual reality.

Search Strategy

A medical librarian from the Texas Medical Center Library developed searches for the Ovid MEDLINE, Embase, Web of Science, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases to identify articles that were published from January 2013 to April 2022. The goal was to capture the most recent research articles regarding the effects of gamification on diabetes self-management behaviors to update the evidence on the topic. Search terms used to identify articles for “type 2 diabetes mellitus” were combined with search terms that encompassed “games” in each database (Table 1). Beginning in April 2022, the preliminary search was conducted in Ovid MEDLINE. Upon the research team’s approval, the main search was finalized in Ovid MEDLINE on May 12, 2022, and then translated to Ovid MEDLINE, Embase, Web of Science, and CINAHL on May 19, 2022. In addition to the robust database search strategies, references found in the retrieved articles were reviewed to conduct a comprehensive search of the literature. A combined total of 346 articles were identified. After removal of duplicates and screening of abstracts and full text, 9 studies were included in the review (see Figure 1).

Table 1.

Database Search Strategy

Preliminary Medline OVID	Medline OVID	Embase	Web of Science	CINAHL
Diabetes terms used: exp Diabetes Mellitus, Type 2/ OR (diabetes* adj3 ("type 2" or "2" or "type ii" or "II" or "adult-onset" or adult* or "maturity-onset" or "slow-onset*" or "ketosis-resistant" or "non-insulin-dependent" or niddm*)).ti,ab,kf,kw. OR ("blood sugar*" or "blood glucose*" or A1C or HbA1c or hyperglycemi* or "hyper-glycemi*").ti,ab,kf,kw.	Diabetes terms used: exp Diabetes Mellitus, Type 2/ OR (diabetes* adj3 ("type 2" or "2" or "type ii" or "II" or "adult-onset" or adult* or "maturity-onset" or "slow-onset*" or "ketosis-resistant" or "non-insulin-dependent" or niddm*)).ti,ab,kf,kw. OR ("blood sugar*" or "blood glucose*" or A1C or HbA1c or hyperglycemi* or "hyper-glycemi*").ti,ab,kf,kw.	Diabetes terms used: non insulin dependent diabetes mellitus'/exp OR (diabetes* NEAR/3 ('type 2' OR '2' OR 'type ii' OR 'ii' OR 'adult-onset' OR adult* OR 'maturity-onset' OR 'slow-onset*' OR 'ketosis-resistant' OR 'non-insulin-dependent' OR niddm*)):ti,ab,kw OR blood sugar*':ti,ab,kw OR 'blood glucose*':ti,ab,kw OR a1c:ti,ab,kw OR hba1c:ti,ab,kw OR hyperglycemi*:ti,ab,kw OR 'hyper-glycemi*':ti,ab,kw	Diabetes terms used: TS=(diabetes* NEAR/3 ("type 2" or "2" or "type ii" or "II" or "adult-onset" or adult* or "maturity-onset" or "slow-onset*" or "ketosis-resistant" or "non-insulin- dependent" or niddm*)) OR TS=("blood sugar*" or "blood glucose*" or A1C or HbA1c or hyperglycemi* or "hyper-glycemi*")	Diabetes terms used: (MH "Diabetes Mellitus, Type 2") OR TI ( diabetes* N3 ("type 2" or "2" or "type ii" or "II" or "adult-onset" or adult* or "maturity-onset" or "slow-onset*" or "ketosis-resistant" or "non-insulin-dependent" or niddm*) ) OR AB ( diabetes* N3 ("type 2" or "2" or "type ii" or "II" or "adult-onset" or adult* or "maturity-onset" or "slow-onset*" or "ketosis-resistant" or "non-insulin-dependent" or niddm*) ) OR TI ( ("blood sugar*" or "blood glucose*" or A1C or HbA1c or hyperglycemi* or "hyper-glycemi*") ) OR AB ( ("blood sugar*" or "blood glucose*" or A1C or HbA1c or hyperglycemi* or "hyper-glycemi*") )
Games terms used: exp Video Games/ OR ((video* or computer* or PC or desktop* or "desk-top" or handheld* or "hand-held*" or digital* or serious* or virtual* or exercise* or "active-video*" or motion* or console* or physical* roleplay* or "role-play*" or multiplayer* or "multi-player*" or software* or mobile* or smartphone* or phone* or tablet* or Kindle* or "smart-watch*" or smartwatch* or internet* or online*) adj3 (game* or gaming* or gamification*)).ti,ab,kf,kw. OR (xbox* or nintendo* or playstation* or Wii* or console* or "Apple store" or "google play" or MMORPG* or "Massively Multiplayer Online Role-Playing Game*" or multiplatform* or "multi-platform*" or exergam* or "exer-gam*").ti,ab,kf,kw.	Games terms used: exp Video Games/ OR ((video* or computer* or PC or desktop* or "desk-top" or handheld* or "hand-held*" or digital* or serious* or virtual* or exercise* or "active-video*" or motion* or console* or physical* roleplay* or "role-play*" or multiplayer* or "multi-player*" or software* or mobile* or smartphone* or phone* or tablet* or Kindle* or "smart-watch*" or smartwatch* or internet* or online*) adj3 (game* or gaming* or gamification*)).ti,ab,kf,kw. OR (xbox* or nintendo* or playstation* or Wii* or console* or "Apple store" or "google play" or MMORPG* or "Massively Multiplayer Online Role-Playing Game*" or multiplatform* or "multi-platform*" or exergam* or "exer-gam*").ti,ab,kf,kw.	Games terms used: video game'/exp OR ((video* OR computer* OR pc OR desktop* OR 'desk-top' OR handheld* OR 'hand-held*' OR digital* OR serious* OR virtual* OR exercise* OR 'active-video*' OR motion* OR console* OR 'physical* roleplay*' OR 'role-play*' OR multiplayer* OR 'multi-player*' OR software* OR mobile* OR smartphone* OR phone* OR tablet* OR kindle* OR 'smart-watch*' OR smartwatch* OR internet* OR online*) NEAR/3 (game* OR gaming* OR gamification*)):ti,ab,kw OR xbox*:ti,ab,kw OR nintendo*:ti,ab,kw OR playstation*:ti,ab,kw OR wii*:ti,ab,kw OR console*:ti,ab,kw OR 'apple store':ti,ab,kw OR 'google play':ti,ab,kw OR mmorpg*:ti,ab,kw OR 'massively multiplayer online role-playing game*':ti,ab,kw OR multiplatform*:ti,ab,kw OR 'multi-platform*':ti,ab,kw OR exergam*:ti,ab,kw OR 'exer-gam*':ti,ab,kw	Games terms used: TS=((video* or computer* or PC or desktop* or "desk-top" or handheld* or "hand-held*" or digital*) NEAR/3 (game* or gaming* or gamification*)) OR TS=((serious* or virtual* or exercise* or "active-video*" or motion* or console* or physical*) NEAR/3 (game* or gaming* or gamification*)) OR TS=((roleplay* or "role-play*" or multiplayer* or "multi-player*" or software* or mobile* or smartphone*) NEAR/3 (game* or gaming* or gamification*)) OR TS=((phone* or tablet* or Kindle* or "smart-watch*" or smartwatch* or internet* or online*) NEAR/3 (game* or gaming* or gamification*)) OR TS=((xbox* or nintendo* or playstation* or Wii* or console* or "Apple store" or "google play" or MMORPG* or "Massively Multiplayer Online Role-Playing Game*" or multiplatform* or "multi-platform*" or exergam* or "exer-gam*"))	Games terms used: (MH "Video Games+") OR TI ( (video* or computer* or PC or desktop* or "desk-top" or handheld* or "hand-held*" or digital* or serious* or virtual* or exercise* or "active-video*" or motion* or console* or physical* roleplay* or "role-play*" or multiplayer* or "multi-player*" or software* or mobile* or smartphone* or phone* or tablet* or Kindle* or "smart-watch*" or smartwatch* or internet* or online*) N3 (game* or gaming* or gamification*) ) OR AB ( (video* or computer* or PC or desktop* or "desk-top" or handheld* or "hand-held*" or digital* or serious* or virtual* or exercise* or "active-video*" or motion* or console* or physical* roleplay* or "role-play*" or multiplayer* or "multi-player*" or software* or mobile* or smartphone* or phone* or tablet* or Kindle* or "smart-watch*" or smartwatch* or internet* or online*) N3 (game* or gaming* or gamification*) ) OR TI ( xbox* or nintendo* or playstation* or Wii* or console* or "Apple store" or "google play" or MMORPG* or "Massively Multiplayer Online Role-Playing Game*" or multiplatform* or "multi-platform*" or exergam* or "exer-gam*" ) OR AB ( xbox* or nintendo* or playstation* or Wii* or console* or "Apple store" or "google play" or MMORPG* or "Massively Multiplayer Online Role-Playing Game*" or multiplatform* or "multi-platform*" or exergam* or "exer-gam*" )

Figure 1.

PRISMA flow sheet for systematic review.

From: Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi:10.1136/bmj.n71.

Data Extraction

Abstracts from the 4 databases were imported to EndNote20 citation manager and de-duplicated for review on May 19, 2022, and then uploaded to Covidence, ²⁰ which helped to expedite the initial screening of abstracts and titles independently by the research team members. Covidence is a web-based collaboration software platform that streamlines the production of systematic and other literature reviews. ²⁰

Screening

Several meetings were conducted among all the reviewers to discuss discrepancies and build consensus for final inclusion of the articles. Abstracts of all articles were screened, and full-text copies of potentially relevant articles were reviewed. Three authors independently screened all the articles for eligibility using the inclusion and exclusion criteria. Data from each research article were organized into data extraction tables to facilitate systematic comparisons among the studies. Data synthesis led to a comprehensive portrayal of the research question, “What is the effect of virtual environment gaming on diabetes self-management behaviors and diabetes outcomes among patients with T2DM?” The search strategy and retrieved articles and abstracts were reviewed by the 3 researchers independently on 2 occasions to ensure adequate sampling.

Quality Appraisal

The quality of each study was evaluated using the Joanna Briggs Institute (JBI) critical appraisal criteria for RCTs or quasi-experimental studies. ²¹ Each question was scored to determine whether the criterion was met with options of yes (score of 1), no (score of 0), unclear (unscored), or not applicable (unscored). Total quality scores were calculated by adding all individual scores and dividing them by the number of items scored. Total scores of below 60%, between 60% and less than 80%, and 80% or higher were evaluated as low, medium, and high quality, respectively.

The identified 9 studies were examined for publication year, design, sample, sample size, sample characteristics, type of gaming (app, board game, virtual environment, etc), key variables, and key findings as changes in self-care behaviors.

The effects of gaming interventions in adults with T2DM were examined utilizing the holistic ADCES behaviors framework of diabetes care (ADCES7). ⁶ The ADCES7 behaviors include (1) healthy coping, which entails maintaining a positive outlook on diabetes self-management; (2) healthy eating, which describes a routine of eating nutrient-dense meals; (3) being active, defined as engaging in any regular physical activity; (4) taking medication, which involves engaging in the prescribed treatment plan; (5) monitoring, which includes assessing glucose level, activity, and food intake to make decisions; (6) problem solving, such as finding solutions to assist in diabetes self-management; and (7) reducing risks, which involves adopting habits to reduce complications associated with diabetes. The primary glycemic outcome of interest was hemoglobin A1C (A1C). Data in this systematic review were evaluated using constant comparison analysis.

Results

Characteristics of Included Studies

A summary of the characteristics of the studies that met inclusion/exclusion criteria are outlined in Table 2. Initially, 6 RCTs^{10,11,22 -24} were included, but the study by Joshi et al ²⁵ was excluded due to low quality. Thus, our review included 5 RCTs^{10,11,22 -24} and 4 quasi-experimental studies.^{13,26 -28} The articles were published between 2013 and 2021. Among these 9 studies, 1370 participants were enrolled with sample sizes ranging from 10 to 456. Four of the studies were conducted in the United States, 2 each were conducted in Switzerland and Germany, and 1 was conducted in Australia. The gaming platforms/methods used in the included studies were virtual environment, ²⁷ smartphone games,^11,22,23 video games,^13,24,28 and online games.^11,26

Table 2.

Diabetes and Gaming Characteristics of Included Studies

Author and study location	Study design	Objective	Sample size (T2DM)	Type of gaming (app, board game, VE, etc)	Metabolic indicators	Measurements/instruments	Outcomes
Johnson et al 27 (2014), California USA	Convenience sample Single group Pre/mid/postmeasure design	Evaluate feasibility and efficacy of virtual environment for DSME and support	20 65% White 35% AA 95% female Duration of DM ranged from 3 mo to 25 y (average, 12 y) Age: 39-72 y (54)	VE Encourage sign-in 2×/wk for first 4 wk Up to 5 mo	A1C a BP BMI	Assessments at 0, 3, and 6 mo; DSME education and support—using Summary of Diabetes Self-Care Activities The DES-SF was used to assess self-efficacy. Surveys were used to evaluate support for DM management. Assessment of DM knowledge (subset) was used to measure diabetes knowledge.	A1C decreased from 7.51% at baseline to 6.92% at 6 mo (P = .85). Improvement in self-efficacy, social support, and foot care (P < .05)
Höchsmann et al22 (2019), Basel, Switzerland	Convenience sample via online and newspaper advertising Randomized control trial	Evaluate self-developed, behavior change, technique-based smartphone game designed by an interdisciplinary team to motivate inactive individuals with T2DM for regular use and thereby increase their intrinsic PA motivation	N = 36 Intervention group (female = 8, male = 10) and control group (female = 9, male = 9); age range = 45-70 y (mean age, 56 y for intervention group and 58 y for control group)	Smartphone game		Play the smartphone game (intervention group) or implement the recommendations from the lifestyle counseling (control group) autonomously during the 24-wk intervention period Intrinsic PA motivation was measured using an abridged 12-item version of the Intrinsic Motivation Inventory at baseline and after the 24-wk intervention. Primary outcome is daily PA measured as steps per day. Secondary outcome is exercise adherence measured via the usage data from the participants’ smartphones (experimental intervention) and as self-recorded exercise log entries (control intervention).	Primary outcome is daily PA measured as steps per day. Secondary outcome is exercise adherence. Proposed PA recommendations measured via the usage data from the participants’ smartphones included daily PA (steps per day), completed and canceled in-game workouts, and patterns and total duration of game use in experimental intervention and self-recorded exercise log entries in control intervention. Intrinsic PA motivation
Höchsmann et al23 (2019), Basel, Switzerland	Randomized control trial	Evaluated whether a newly developed smartphone game delivering individualized exercise and PA promotion through an elaborate storyline can generate sustained improvements in daily PA (steps/d)	N = 36 Intervention group: female = 8, male = 10; control group: female = 9, male = 9); age range =45-70 y (mean age = 56 y for intervention group and 58 y for control group)	Smartphone game	BMI, duration of DM, body fat content via bioelectrical impedance analysis, resting BP, resting electrocardiography, LDL, HDL, triglycerides	Changes in daily physical activity (steps/d); changes in aerobic capacity, measured as oxygen uptake at the first ventilatory threshold; and changes in glycemic control, measured as A1C	Improvement in daily PA, A1C, relative V.O2 peak, total body fat mass, skeletal muscle mass, resting HR, systolic or diastolic BP at rest, total cholesterol, LDL cholesterol, and HDL cholesterol or triglycerides
Kempf and Martin 24 (2013), Germany	Randomized controlled trial	Evaluate the effectiveness of autonomous use of the interactive exercise game Wii Fit Plus over a period of 12 wk to improve A1C (primary outcome), weight, cardiometabolic risk factors, PA, and quality of life (secondary outcomes) in T2DM patients	220 T2DM patients (intervention n = 120; control n = 100; 46% male, 54 % female in intervention; 46% male, 54% female in control). Mean age 62 for intervention and 60 for control.	Interactive exercise game Wii Fit Plus	A1C, FBS, BMI, BP, blood lipids (triglycerides and total, HDL, and LDL cholesterol)	PA at least 30 min/d for 12 wk At baseline and after 12 wk (and for the control group additionally after 12 wk of intervention) the participants’ health parameters, medication, PA and validated questionnaires for quality of life (PAID, SF12, b WHO-5, CES-D)	Improvement in A1C, BMI, FBS, (weight loss, reduction of cardiometabolic risk factors), PA, diabetes-dependent impairment, mental health, subjective well-being, and quality of life (secondary outcomes)
Huang et al 13 (2021), Texas, USA	Initial, mid, and final evaluations	Assess the effects of an AVG program in physiological parameters, fitness levels, motivation to PA, and quality of life on people with T2DM	8 subjects (2 males, 6 females; age, 36-75 y)	8-wk AVG program using Xbox Kinect and/or Nintendo Wii	A1C, FBS, BMI, BP, blood lipids (triglycerides and total, HDL, and LDL cholesterol)	HR and RPE were monitored during exercise. Physiological and fitness assessment included A1C, HR, BP, BMI, body composition, aerobic and muscular endurance, muscular strength, and flexibility.	Improvement in A1C, HR, BP, BMI, body composition, aerobic and muscular endurance, muscular strength, and flexibility. Secondary outcome: psychological outcome
						Psychological outcome measures included the Physical Activity Measure-Revised, Situational Motivation Scale, and World Health Organization Quality of Life-BREF.
Cuevas and Carter 26 (2020), Texas, USA	Convenience sample	Determine feasibility of using web-based video conferencing and online cognitive training to improve cognitive function	10 participants in 2 groups Age: 40-65 y T2DM × 2 y Score ≥ 10 on Perceived Deficits Questionnaire (concerns for cognitive health)	Webinar classes 20 min, 7 d/wk Online cognitive training with BrainHQ		SDSCA, DES-SF, MMQ, and BDEFS-SF	Improved adherence to diet, exercise, and foot care recommendations
Patel et al 10 (2021), Pennsylvania, USA	4-arm randomized clinical trial	Test effectiveness of behaviorally designed gamification intervention to increase support, collaboration, or competition to promote physical activity and weight loss among adults with uncontrolled T2DM	361 randomized Age: 18-70 y (52.5 y) 56% women 39.6% White 51.2% Black A1C ≥8% (9.6%) BMI ≥25 (37.1) Groups of 3 87 in control, 92 in gamification with support and intervention, 95 in gamification with collaboration and intervention, 87 in gamification with competition and intervention	Smartphone game Wrist-worn wearable device Smart scale	Weight BMI A1C LDL cholesterol mean	Daily steps Weight BMI A1C LDL cholesterol mean	Increased PA in all intervention groups compared to control All groups = significant reductions in weight and A1C
Kerfoot et al 11 (2017), USA	Randomized control trial	To investigate whether an online team-based game delivering DSME to patients via email or mobile app can generate longer term improvements in A1C	456 randomized Age, 59.5 y (9.9) Male, 94.7% 227 in DSME spaced education game and civics booklet 229 in civics spaced education game and DSME booklet Veteran patients Oral agents =/- insulin A1C (75) >64 mmol/mol	Spaced education games Mobile app 2 questions on Tuesday and Thursday and sent again 4 wk later	Lab baseline and 6 and 12 mo A1C Urine albumin Urine creatinine	DES-SF PAID PPR of oral DM meds Diabetes Complications Severity Scale	DSME game sustained A1C improvement > than civic game Increase in empowerment with DSME spaced education game Reduction in A1C not associated with DSME knowledge
Senior et al 28 (2015), Ipswich, Australia	Pre-post study design (4 wk; no control group)	To investigate the effectiveness of participation in a Nintendo Wii tennis group activity on PA levels of sedentary people with T2DM	11 participants, with a mean age of 63.6 y (41-80 y); 4 female, 10 European, and 1 New Zealand Maori Self-reported medical history of T2DM	Nintendo Wii	Significant increase in moderate (P = .04) and total PA time (P < .01) No significant change was observed in the balance test, BMI, or quality of life. BMI levels and quality of life results were not reported in the article.	International Physical Activity Questionnaire Physical performance was measured using the timed up and go test, 400-m walk test, the 30-s chair-stand test, and the FICSIT-4 Balance Test. Quality of life was assessed using the EuroQoL EQ-5D.	Moderate MET (min/wk) including Wii exercise intervention; pretest: 300; posttest: 2559; P = .04 Total PA (MET min/week): pretest: 1272; posttest: 2940; P < .01
Joshi et al 25 (2017), Harrisburg, Pennsylvania Excluded	Pilot randomized controlled trial Those who attended a teaching hospital endocrinology clinic were referred by their physician.	Test hypothesis: Strategic management simulation using a new iOS app, Patient Partner (derived from complexity theory), is able to improve the thinking and decision making of patients, thereby improve adherence and outcomes	Total participants: 97 (intervention n = 66; control n = 31 Physicians referred patients who were “severely non-adherent” with A1C levels > 8.	iOS app, Patient/Partner	A1C dropped from 10.71 at baseline to 9.62 (P < .05) in the intervention group.	At the end of each week for 3 wk, all participants were called and self-report on their medication adherence, diet, and exercise. This was done over the telephone, and questions were strictly scripted to focus only on these adherence questions. “In the past week, how many days did you take your medicines/follow your diet/exercise?” Baseline and 3-mo postintervention A1C levels (done per usual care in this clinic) were recorded for intervention group.	In the intervention group, participants reported taking their medications more regularly (from ≈4 d/wk to almost every day), eating a healthier diet (from 3.5 d/wk to over 5), and exercising regularly (clearly the most difficult, it went up from 2 d/wk to 3 d/wk). A1C dropped from 10.71 at baseline to 9.62 (P < .05) in the intervention group.

Abbreviations: AA, African American; AVG, active video game; BDEFS-SF, Barkley Deficits in Executive Function Scale-Short Form; BMI, body mass index; BP, blood pressure; CES-D, Center for Epidemiologic Studies Depression Scale; DES-SF, Diabetes Empowerment Scale-Short Form; DM, diabetes mellitus; DSME, diabetes self-management education; FBS, fasting blood sugar; HDL, high-density lipoprotein; HR, heart rate; LDL, low-density lipoprotein; MET, metabolic equivalents of task; MMQ, Multifactorial Memory Questionnaire; PA, physical activity; PAID, Problem Areas in Diabetes; PPR, pill possession ratio; RPE, rating of perceived exertion; SDSCA, Summary of Diabetes Self-Care Activities; T2DM, type 2 diabetes; VE, virtual environment; VO₂ peak, maximal oxygen consumption, maximal oxygen uptake or maximal aerobic capacity; WHO-5, 5-item World Health Organization Well-Being Index.

^aA1C test—also known as the hemoglobin A1C or HbA1C test—is a simple blood test that measures average blood sugar levels over the past 3 months.

^bThe 12-Item Short Form Health Survey (SF-12) was developed for the Medical Outcomes Study.

Appraisal of Quality

The quality of the articles was assessed using the JBI critical appraisal tools. Of the 6 RCTs,^{10,11,22 -24} 4 were determined to be of high quality, 1 was of medium quality, and 1 was of low quality ²⁵ and therefore excluded. All 4 quasi-experimental studies were found to be of high quality (Table 3). Based on the JBI Grades of Recommendation and Levels of Evidence, ²⁹ the 6 RCTs^{10,11,22 -24} were judged as 1c and given a grade A recommendation. All 4 high-quality quasi-experimental studies were rated as 2d and given a grade A recommendation (Table 3).

Table 3.

Quality Assessment Using Critical Appraisal Tools Adapted From Joanna Briggs Institute ^a

Author/y	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11	Q12	Q13	Total score	LOE/recommendation
Randomized controlled trials
Höchsmann et al22,23 (2019)	1	1	1	0	1	1	1	1	1	1	1	1	1	0.92	1C/A
Huang et al 13 (2021)	1	UC	1	0	UC	UC	UC	UC	1	1	1	1	1	0.88	1C/B
Joshi et al 25 (2017)	0	0	UC	0	0	0	UC	0	0	1	1	1	1	0.36	1C/B
Kempf and Martin 24 (2013)	1	1	1	1	1	1	1	1	1	1	1	1	1	1.00	1C/A
Kerfoot et al 11 (2017)	1	0	1	UC	0	0	1	1	1	1	1	1	1	0.75	1C/A
Patel et al 10 (2021)	1	1	1	UC	1	1	0	1	1	1	1	1	1	0.92	1C/A
Quasi-experimental studies
Cuevas (2020) 26	1	1	1	0	1	NA	1	1	1					0.88	2D/A
Senior (2015) 28	1	1	1	0	1	NA	1	1	1					0.88	2D/A
Johnson et al 27 (2014)	1	1	1	0	1	NA	1	1	1					0.88	2D/A

Abbreviations: 1, yes; 0, no; LOE, level of evidence; NA, not applicable; UC, unclear.

^aFor calculation of total score, NA and UC were not included in numerator of denominator. Total scores ≥ 80% are indicative of high quality, 60% to 80% are medium quality, and ≤ 60% suggest low quality.

Self-Care Behaviors

Five of the self-care behaviors were addressed in at least 1 of the included studies. However, taking medications and problem solving were not reported in any of the studies (Table 4).

Table 4.

DM and Gaming Findings: Changes in Diabetes Self-Care Behaviors

Author/y	Healthy coping	Healthy eating	Being active	Taking medications	Monitoring	Reducing Risks	Problem solving	Glycemic control
Johnson et al 27 (2014)	Self-efficacy improved from neutral 3.89 (SD, 0.81) to moderate 4.45 (SD, 0.67) at 3 mo and toward high at 6 mo 4.64 (SD, 0.39), P = .02.	Change in behavior (d/wk) 3 mo = 0.51 (P = .618) 6 mo = −0.69 (P = .50)	Change in behavior (d/wk) measured as time in the virtual gym (exercise videos = cardio, yoga, strength training) 3 mo = 0.00 (P = 1.00) 6 mos = −0.76 (P = .46)		Change in behavior (d/wk) 3 mo = 0.18 (P = .859) 6 mo = 0.70 (P = .50)	Average weight loss of 9.1 lb from baseline to 6 mo Performing foot care improved in No. of d/wk from mean 3.68 (SD, 2.08 d/wk) to mean 6.17 (SD, 1.54 d/wk at 6 mo) P = .03)		A1C a ↓ from 7.51% (SD, 1.15%) at baseline to 6.92% (SD, 1.37%) at 6 mo
Cuevas and Carter 26 (2020)		Adherence to diet ↑ from 3 to 5 d/wk	↑ from1 to 3 d/wk		↑ from 1 to 3 d/wk
Patel et al 10 (2021)			Change in mean steps from baseline to 3 mo: Control: 4410 → 4221 steps Gaming with competition: 4681 → 5257 steps Gaming with support: 4353 → 4909 steps Gaming with collaboration: 4122 → 4385 steps At 1-y, significant changes in steps compared to control group for: Gaming with support → 503 steps (P = .01) Gaming with competition → 606 steps (P = .003) Gaming with collaboration → 280 steps (P = .16) From first 6 mo to last 6 mo Control ↓ –43 steps (P = .60) Gaming with support ↓ –423 steps (P = .003) Gaming with competition ↓ −143 steps (P = .23) Gaming with collaboration ↓ −394 steps (P < .001)			From baseline to 6 mo, all arms had ↓ in mean weight. From 6 to 12 mo control: slight ↑ in weight and gaming arms slight ↓ At 12 mo, all groups had significant decrease in mean weight: Control: −2.0 kg (P = .04) Gaming with support: –3.6 kg (P < .001) Gaming with collaboration: −3.4 kg (P < .001) Gaming with competition: −2.9 kg (P < .001)		A1C decline in all arms Control: baseline, 9.5% 6 mo, 8.8% 12 mo, 8.8% Gaming with competition: Baseline, 9.7% 6 mo, 8.8% 12 mo, 8.7%
Kerfoot et al 11 (2017)	↑ Empowerment among DSME gamers (+1.7) ↓ Empowerment among civics (−0.1; P = .010) Greater reductions in diabetes distress among DSME gamers							Reduction in A1C: DSME game: −8 mmol/mol Civics game: −5 mmol/mol (P = .048)
Höchsmann et al22 (2019)			Intrinsic PA motivation (IMI total score) increased significantly by an average of 6.39 (SD, 4.19; P < .001) points in the intervention group and decreased by an average of 1.94 (SD, 16.46; P = .62) in control group. Adherence to PA: mean, 6559 (SD, 1182) steps/d during the 24-wk intervention period Mean, 4.9 (SD, 1.3) in-game walking trainings/wk (average duration of 26.8 min/training; SD, 8.2), leading to a total average duration of 131.1 (SD, 48.7) min of in-game walking/wk
			15.3 (SD, 24.6) min of strength training/wk Stride cadence: 83.7% (SD, 3.5%) of all in-game walking was done at a cadence of ≥100 steps/min, indicating an average weekly amount of 109.8 (SD 43.4) min of moderate to vigorous intensity in-game walking
Höchsmann et al23 (2019)	Diabetes-dependent impairment, mental health, subjective well-being and quality of life also improved significantly, and the number of patients with depression decreased.		PA increased by an average of 3998 (SD, 1,293) steps/d in the intervention group and by an average of 939 (SD, 1156) steps/d in the control group.			Total body fat mass decreased by 2.7 kg (SD, 2.5) in the intervention group and by 0.9 kg (SD, 3.4) in the control group. No change in skeletal muscle mass		A1C remained unchanged at 6.2% (SD, .7) in the intervention group and increased by 0.1 percentage points (SD, 1.3) in the control group during the intervention period.
Kemp and Martin 24 (2013)	Diabetes-dependent impairment was reduced significantly (P = .03). Subjective well-being improved significantly (P < .001). Well-being increased during the intervention by 8.6 + 19.4% (P < .001) in the intervention group and 8.1 + 21.3% in the control group (P = .004). Quality of life increased in the intervention group by 2.4 + 12.9% (P = .03) compared to the control group, which showed a slight decrease during waiting phase (P = .05).		Patients in the intervention group reported an increase in PA from 42 ± 19 to 59 ± 17; P < .0001. Daily PA increased significantly by 10 ± 10% (P < .001) when the control group started the exercise game intervention.			BMI (from 34.1 ± 6.5 kg/m2 to 33.5 ± 6.5 kg/m2; P < .001)		Improved A1C (from 7.1 ± 1.3% to 6.8 ± 0.9%; −0.3 ± 1.1%; P = .0002) in comparison to the control group (from 6.8 ± 0.9% to 6.7 ± 0.7%; −0.1 ± 0.5%)
Huang et al 13 (2021)	No significant differences on MPAM-R, SIMS, and WHOQOL-BREF. But participants demonstrated positive behavior and attitudes toward health and PA at 1-mo follow-up.		Moderate level of exercise intensity during AVG based on average HR and RPE. HR increased 32 ± 21% from their resting HR and peaked at 57 ± 26% of resting HR. Subjects’ exercise intensity ranged between 61% and 83% of their max HR, and the average was 71 ± 8% max HR.			The body weight, BMI, body fat %, and HR did not have any significant change. Diastolic blood pressure decreased from 80.8 mmHg to 70.9 mmHg (not statistically significant).		A1C decreased from 7.5% to 7.1% (not statistically significant).
Senior et al 28 (2015)	Quality of life was assessed using the EuroQoL EQ-5D. No significant change was observed in the balance test, BMI, or quality of life.		International Physical Activity Questionnaire Moderate (MET min/wk) pretest: 300; posttest: 2559; P = .04) Total PA (MET min/wk) pretest: 1272; posttest: 2940; P < .01

Abbreviations: AVG, active video game; BMI, body mass index; DSME, diabetes self-management education; HR, heart rate; MET, metabolic equivalent of task; MPAM-R, Physical Activity Measure-Revised; PA, physical activity; RPE, rating of perceived exertion; SIMS, Situational Motivation Scale; WHOQOL-BREF, World Health Organization Quality of Life.

^aA1C test—also known as the hemoglobin A1C or HbA1C test—is a simple blood test that measures average blood sugar levels over the past 3 mo.

Healthy Coping

Six studies examined changes in self-reported efficacy or quality of life.^{11,13,24,26 -28} Three studies assessed the use of exercise video games. One study used a virtual environment, which facilitated diabetes self-management and support. ²⁷ Two studies used online games, 1 of which used a point scoring system to promote competition and community.^11,26

Video games

In the 3 studies that evaluated healthy coping using exercise video games, 2 found no difference in participants’ self-reported quality of life after 4 weeks ²⁸ and 8 weeks of intervention, while a study by Kempf and Martin ²⁴ found a significant increase in reported quality of life by 2.4 + 12.9% (P = .03), increase in subjective well-being by 8.6 + 19.4% (P < .001), and a reduction in diabetes-related impairment of 5.2 + 13.2% (P < .001) after 12 weeks (about 3 months) of video game intervention.

Virtual environment

Likewise, in a virtual environment, Johnson et al ²⁷ found that participants reported an increase in self-efficacy from neutral to moderate at baseline (mean = 3.89, SD = 0.81) to moderate at 3 months (mean = 4.45, SD = 0.67) and then significantly (P = .02) toward high at 6 months (mean = 4.64, SD = 0.39).

Online gaming

Both studies that assessed online games found nonsignificant improvement in self-efficacy measures using the).^11,26 Cuevas et al ²⁶ reported that diabetes-related self-efficacy increased from a preintervention mean score of 23 (SD = 11.2) to a postintervention mean score of 32.2 (SD = 1.4) after 8 weeks (about 2 months) of game-like online intervention. Kerfoot et al ¹¹ found that a 6-month team-based diabetes self-management education online game improved empowerment measured by DES-SF (+ 1.7; 95% CI, 0.7-2.7; P = .010) compared with those who did not participate in the online game.

Healthy Eating

Two of the 9 studies (22%) discussed healthy eating.

Virtual environment

Johnson et al ²⁷ used a virtual environment and indicated that there was an improvement in eating habits at 3 and 6 months, but these changes were not significant.

Online gaming

Cuevas and Carter ²⁶ reported that using an online game-like intervention increased dietary adherence from 3 to 5 days a week.

Being Active

Eight studies examined the use of games to promote physical activity.^{10,13,22,24,26 -28} Three of the 8 studies involved the use of exercise video games.^13,24,28 One study created a virtual environment, which facilitated diabetes self-management and support. ¹⁶ One study used an online game-like intervention. ²⁶ Two studies evaluated the use of a smartphone game.^22,23 One study involved a digital game, which used a point-scoring system to foster competition and collaboration. ¹⁰

Video games

In the 3 studies that evaluated exercise video games, 2 found significant changes in physical activity. According to Kempf and Martin, ²⁴ participants reported a significant increase in their physical activity by 10 + 10% (P < .001) during the 12-week study period of using the video game Wii Fit Plus. Participants also reported an increase in their overall physical activity that persisted beyond their involvement in the video game. ²⁴ Likewise, Senior et al ²⁸ found that playing the Nintendo Wii tennis video game twice a week for 4 weeks increased moderate physical activity time from 300 (0-2800) metabolic equivalent (MET) minutes in a week to 2559 (734-5560; P = .04) and increased total physical activity time from 1272 (300-3592) MET minutes in a week to 2940 (1176-7910; P < .01). In a study by Huang et al, ¹³ aerobic endurance measured by the YMCA 3-minute Step Test remained the same after participants played an exercise video game on Xbox Kinect and/or Nintendo Wii twice a week for 8 weeks (about 2 months).

Virtual environment

In the virtual environment study by Johnson et al, ²⁷ physical activity outcomes did not show statistically significant changes at 3 months (0.00; P = 1.00) or 6 months (−0.76; P = .46) compared with baseline activity levels.

Smartphone

A 24-week smartphone game intervention was associated with significant improvements in physical activity and intrinsic motivation for exercise.^22,23 In the intervention group, participants’ daily physical activity increased on average by 3998 steps (SD = 1293), while in the control group, it increased on average by 939 steps (SD = 1156). Between the 2 groups, there was an adjusted difference of 3128 more steps per day in favor of the intervention group in terms of the increase in daily physical activity (95% CI, 2313-3943; P < .001). ²² With an adjusted difference of changes in motivation for exercise of 8.15 points (95% CI, 0.90-15.39; P = .03) between the 2 groups, intrinsic physical activity motivation significantly increased by an average of 6.39 points (SD 4.19; P < .001) in the intervention group and decreased by an average of 1.94 points (SD 16.46; P = .62) in the control group. ²³

Patel et al ¹⁰ examined a yearlong digital gaming intervention with several social incentive components that used points and levels to foster competition, collaboration, or support. Compared with the control group, the gaming intervention with support increased steps per day from an average 4353 at baseline to 4909 at 3 months and at 1 year (503 steps; P = .01), and gaming with competition also increased physical activity from an average of 4681 steps per day at baseline to 5257 at 3 months and at 1 year (606 steps; P = .003). Participants who received gaming with collaboration experienced a modest increase in physical activity from a mean of 4122 steps/day at baseline to 4385 steps/day at 3 months but no significant change at 1 year (280 steps; P = .16) compared with the control group of an average of 4410 steps/day at baseline and 4221 at 3 months. In the gaming with support and collaboration groups, the mean number of daily steps significantly declined from the first 6 months to the last 6 months of the study (–423 steps, P = .003 and −394 steps, P < .001, respectively); however, in the gaming with competition group, physical activity did not decrease (–143 steps; P = .23). The mean daily steps for the control group also did not change significantly (–43 steps; P = .60). Although gaming with collaboration and support was effective, only gaming with competition produced sustained impacts. ¹⁰

Online gaming

Additionally, in an 8-week online game-like intervention, Cuevas and Carter ²⁶ reported an increase in physical activity from 1 day per week to 3 days per week.

Monitoring

Two (22%) of the studies^26,27 reported on monitoring.

Virtual environment

In their study using a virtual environment, Johnson et al ²⁷ noted that although blood glucose monitoring increased over the 6-month study period, the increase was not statistically significant.

Online gaming

Cuevas and Carter ²⁶ found that their online game-like intervention resulted in an increase in glucose monitoring among participants from 1 day per week to 3 days per week.

Reducing Risk

Five of the studies evaluated the impact of gaming on reducing risk.^{10,13,23,24,27}

Virtual environment

In their virtual environment study, Johnson et al ²⁷ found that on average, participants lost 9.1 pounds from baseline to 6 months. They also reported that performance of foot care improved from a mean of 3.68 days per week (SD, 2.08) to a mean of 6.17 days per week (SD, 1.54) by 6 months.

Smartphone

Two of the studies used smartphone-based gaming.^10,23 Participants in all arms of the study by Patel et al ¹⁰ experienced a decrease in mean weight from baseline to 6 months, and at 12 months, all groups had a significant decrease in mean weight (control: 2.0 kg; gaming with support: 3.6 kg; gaming with collaboration: 3.4 kg; and gaming with competition: 2.9 kg). Among those in the study by Höchsmann et al, ²³ total body mass decreased in both the intervention (−2.5 kg) and control groups (−0.9 kg), with no change in skeletal muscle mass.

Video games

Risk-reducing behaviors were evaluated in 2 of the video-game-based studies. Kempf and Martin ²⁴ reported significant reductions in weight and body mass index (BMI) among participants in the control and intervention groups. They also reported a reduction in systolic and diastolic blood pressure in both groups, a reduction in total cholesterol, and an increase in high-density lipoprotein in both groups. In their study, Huang et al ¹³ reported no significant change in weight, BMI, or body fat percentage. They did report a decrease in diastolic blood pressure; however, these changes were not statistically significant. Of note, they also reported an increase in muscle strength on lower extremity flexion and extension on the left and right extremity and increased muscle strength in the left upper extremity with a decrease on the right. Furthermore, flexibility increased as well, but these changes were not statistically significant.

Taking Medications

No studies reported on taking medications according to prescribed treatment plans as an outcome measure.

Problem Solving

No studies reported on problem solving as an outcome measure.

Glycemic Outcomes

The effects of gaming interventions on glycemic outcomes differed across the 6 research studies that examined changes in A1C.^{10,11,13,23,24,27} Three studies reported a significant improvement in A1C, 2 showed a nonsignificant reduction in A1C, and another found no changes in A1C. Two of the 6 studies targeted patients with diabetes who had A1C targets for inclusion (>8% ¹⁰ and > 58 mmol/mol ¹¹ at baseline, respectively), whereas the other studies did not have this restriction.

Video games

In the 2 studies that evaluated A1C using exercise video games, Kempf and Martin ²⁴ found that the A1C level decreased significantly from 7.1 + 1.3% to 6.8 + 0.9% (P = .0002) after participants in the intervention group received a Wii Fit Plus and were instructed to play it for at least 30 minutes a day for 12 weeks (about 3 months), while Huang et al ¹³ found that playing an active video game for 16 sessions over 8 weeks on an Xbox Kinect and/or Nintendo Wii nonsignificantly reduced participants’ A1C from 7.5% to 7.1%.

Virtual environment

Smilarly, in a virtual environment study that facilitated diabetes self-management, Johnson et al ²⁷ reported a decrease in A1C from 7.51% at baseline (SD, 1.15%) to 6.92% (SD, 1.37%) after 6 months. Although not statistically significant, it was considered clinically significant.

Smartphone

Also, in a 1-year gamification intervention to achieve step goals and weight loss targets, Patel et al ¹⁰ showed that A1C significantly decreased in the competition group (from 9.7% at baseline to 8.8% at 6 months and 8.7% at 12 months) and in the control group (from 9.5% at baseline to 8.8% at 6 and 12 months). Regular feedback and goal setting were provided to participants through digital devices in the control group. Last, Höchsmann et al ²³ showed that a 24-week individualized smartphone game intervention focused on physical activity did not change participants’ A1C (remained at 6.2%) over the study period.

Online games

Furthermore, both of the digital game studies using point-scoring systems found significant improvements in A1C levels. Kerfoot et al ¹¹ reported that those who participated in a team-based online game experienced significantly higher reductions in A1C over the 12-month study period compared with those who did not participate in the game (−8 mmol/mol vs −5 mmol/mol; P = .048).

Discussion

Self-Care Behaviors Impacted/Improved by Gaming

Healthy coping

Four studies found favorable effects in promoting healthy coping strategies by playing a video game ²⁴ or online games^11,26 or using a virtual environment. ²⁷ The positive benefits of gaming coupled with increased social support suggest that identifying effective gaming interventions in people with diabetes may present opportunities for tailored therapies. Our findings are consistent with a previous review supporting the use of gamification in increasing perceptions of positive emotions, self-efficacy, and motivation. ³⁰ Given that psychosocial factors influence coping mechanisms and well-being, studies should consider a holistic approach based on individual psychosocial risk factors to promote positive physical and psychosocial health outcomes in people with diabetes. Efforts should be made to assess and manage the psychosocial aspects of diabetes in clinical practice to promote healthy coping behaviors and improve quality of life in this population.

Healthy eating

Two studies found a positive impact on healthy eating using a virtual environment ²⁷ and online gaming. ¹⁴ Several studies showed that gamification was an effective method to improve knowledge about nutrition, weight management, and intention to follow a healthy diet.^9,31,32 However, studies should evaluate improvements in weight, BMI, waist circumference, and percentage of body fat maintained over time in persons with T2DM. When counseling individuals with T2DM, one must emphasize that meal planning and healthy eating plus physical activity, when combined with glucose-lowering medications, can lead to an optimal glucose-lowering effect in addition to other health benefits.

Being active

One of the self-care behaviors, being active by engaging in any regular physical activity, was improved by engaging in gaming via smartphone,^{10,13,22 -24,26 -28} a virtual environment, ²⁷ and online. ²⁶ These findings were consistent with those of other studies using diabetes self-management apps incorporating behavior change techniques and gamification to engage in and improve physical activity in adults with T2DM. ³³ This information can be used by clinicians to make safe and effective recommendations for integrating physical activity/exercise into self-management plans for individuals with diabetes or at risk for its development. Diabetes Care and Education Specialists (DCESs) play an important role in informing, motivating, and supporting all individuals, including those with T2DM, in their efforts to make positive lifestyle changes for improved health. Advice about physical activity is an essential lifestyle recommendation and should be offered at an appropriate level based on careful evaluation of patients’ ability to exercise. Patients should also be provided all the necessary exercise guidelines and resources established by recognized sources to ensure the appropriateness, accuracy, and safety of physical activity recommendations. Being competitive may serve as a motivator to continue to stay active in this population.

Monitoring

One ¹⁴ of the 2 studies evaluating blood glucose monitoring showed a positive impact on monitoring in that participants increased their monitoring from 1 day to 3 days per week.

Reducing risk

Contrary to the findings of the study conducted by Arnaez et al, ⁸ which showed that among adults, gaming was associated with obesity, our review revealed that gaming was associated with weight loss, lowering of blood pressure and cholesterol levels, and increased frequency of foot exams among participants in all 5 of the studies that reported on this self-care behavior.^{10,13,22,24,27} Although the changes in the study by Huang et al ¹³ were not significant, Kempf and Martin ²⁴ and Höchsmann et al ²³ reported a positive change in both the intervention and control groups.

Impact of Various Forms of Gaming on Glycemic Outcomes

Glycemic outcomes

Most studies showed improvements in glycemic outcomes, except for a 24-week smartphone gaming intervention focused on physical activity, where participants’ A1C stayed at 6.2%. ¹⁰ This lack of change may be due to the baseline A1C being at a desired level, making further improvements unlikely. Various exercise video games had varying effects on participants’ glycemic outcomes, with some people experiencing significant improvements and others experiencing only minor improvements. However, those who participated in score-based, competitive online games considerably improved their A1C levels.^10,11 In a recent systematic review, 2 studies that explored educational games for children with diabetes did not show improvements in A1C levels. ³⁴ Findings suggest that games that encourage competition by employing points or other scoring mechanisms may be more likely to have a greater impact. Although most of the gaming interventions were effective at improving participants’ A1C outcomes, the game’s design and level of competition may affect achieving glycemic improvements in this population. Future research must consider the factors that influence the effectiveness of games in improving glycemic outcomes to inform the design and implementation of gaming interventions.

Strengths and Limitations

A strength of this review is that it includes only studies conducted among adults with T2DM. Also, the ADCES7 framework was used as the lens through which to evaluate the study findings. However, many of the included studies lacked diversity in terms of age, gender, and ethnicity and had small samples and short durations, which limits the generalizability of the findings. For example, in lower-income populations, participants may not be able to afford smartphones or have access to high-speed Internet and may experience barriers to gaming interventions. Additionally, the gaming modalities and outcome measures used in the included studies were highly varied, further hampering the ability to make appropriate comparisons. Several studies examined the effect of gaming on “glycemic control.” However, there were many potential confounding variables due to minimal information regarding participants’ diabetes duration, metabolic factors, glycemic level, and diabetes medications. Finally, due to the constantly evolving gaming industry, new gaming technologies must be adapted to appeal to the demographics of people with diabetes to determine the sustainability and effectiveness of gaming interventions in improving diabetes self-management behaviors and outcomes. Future research studies are needed to determine the long-term effect of gamification on the health parameters of patients with T2DM.

Conclusions

This review demonstrates that gamification is a promising way to support patients with diabetes to improve their self-management behaviors. Although different gaming modalities have been found to be beneficial, those that include competition (teams, leaderboards, etc) have the most significant impact. With the emergence and subsequent reliance on mobile technology, novel game-based platforms have been integrated with developing personalized diabetes self-management plans through lifestyle behavior modification, medication engagement, and monitoring of risks and problems. Ideally, these game-based interventions can promote long-term engagement in diabetes self-care behaviors. Gamification offers the advantages of enhancing patient care without face-to-face contact and with flexible timing, thereby reducing transportation time and cost for patients with T2DM. Virtual gaming also provides a novel way for the DCES to engage with patients to provide education and assess the attainment of knowledge.