gms | German Medical Science

Background: Diabetes is one major factor in the global rise of multi-morbid chronic diseases (Oostrom et al. 2016). Lifestyle changes, such as increased physical activity and an adequate diet, are an important part not only of diabetes prevention but also of disease self-management (Tuomilehto et al. 2001). The latter, however, implies an active role of the individual patient in coping with a disease. Furthermore, self-management is regarded as effective when maintained over a period of at least six months (Prochaska and Velicer 1997).

Digital health tools such as telemedicine and eHealth are seen as an important driver for long-term, active patient engagement (Greenwood et al. 2017). Gamification, i.e. the use of digital interventions incorporating game-based components (Deterding et al. 2011), can be especially useful as games promote an active role of the individual (Kamel Boulos et al. 2015) and can change his/her attitude towards a certain behavior. The number and variety of available digital game-based interventions is increasing. However, there is limited understanding of their mechanisms, which, in turn, limits the potential of guidance for their use in specific patient populations.

Research question: Which components of game-based interventions are effective in improving diabetes-related hard clinical outcomes such as HbA1c?

Which components of game-based interventions are effective in improving patient-reported outcomes such as quality of life and disease-related knowledge?

Method: A systematic review according to the Cochrane Guidelines (Higgins and Green 2011) was conducted in PubMed and PsychInfo. Clinical studies using a control group (S) which received usual care (C) and involving patients with type I, II or gestational diabetes (P) in order to study the effectiveness of digital applications with game-based components on clinical (HbA1c, FPG, BMI, BP, HDL; LDL, TGC, waist-hip ratio) and patient-reported (knowledge, QOL, self-efficacy) outcomes were eligible for analysis.

The Cochrane Risk of Bias Tool was used to assess the quality of all included studies.

Both title and abstract and full text screening as well as quality assessment was done independently by two researchers.

Preliminary results: PubMed yielded 4.671, PsychInfo 816 records. Preliminary results presented here are based on 250 records from Pubmed, of which 26 were included in the full text screening. Seven original studies (4 RCTs, 1 multicenter pilot study with control group, 1 randomized cross-over and on experimental cross-sectional study) fit the inclusion criteria.

Studies targeting self-management (including self-care behavior, diabetes knowledge and physical activity) were more common than those aiming at metabolic control.

Significant effects on clinical outcomes were reported in two studies. Competitive Wii Fit Plus exercises significantly improved HbA1c (IG: -0.3 ± 1.1%) / CG: HbA1c in CG: −0.1 ± 0.5%. p = 0.0002) (Kempf and Martin 2013), and so did an online quiz competition with monetary incentives (IG: -8 ± mmol/mol [95% CI -10 to -7], CG: -5 mmol/mol [95% CI -7 to -3], p = 0,048) (Kerfoot et al. 2017).

Using Wii Fit Plus exergames combined with dexterity and cognitive components raised physical activity according to lactate values (IG: 2.5 ± 1.2 mmol/l, CG: 3.7 ± 1.1 mmol/l, p = 0.043) (Brinkmann et al. 2017).

A digital quiz using a robot for feedback improved diabetes knowledge according to the Diabetes Knowledge Questionnaire (baseline=13; t1=16.2; t2=17.6; t3=18.2, p = 0.025) (Blanson Henkemans et al. 2013). Knowledge on carbohydrate quantification and insulin titration also improved when playing a serious online game with a coherent story on risks of carbohydrate (Joubert et al. 2016).

Significant reductions were also found for quality of life, depression (both by competitive exercise) (Kempf and Martin 2013) and self-care behavior (level-based Super Nintendo quiz) (Brown 2013).

Discussion: The results show positive effects of competitive game elements (Weber et al. 2018), coherent narratives (Green 2006) and para-social interaction with a game-character (robot) (Hinyard and Kreuter 2007).

The implications are limited due to short intervention times, equally short or non-existent follow ups and small sample sizes. Studies reporting significant effects on clinical outcomes tend to have larger samples, follow rigorous RCT methodology and include at least 6 months of follow up.

Implications: Game-based interventions can be effective in changing diabetes-related behavior and metabolic outcomes independent of age and diabetes type. The results are promising for long-term self-management support.

Future research should employ more thorough study designs with longer study durations and follow-ups. Even though randomization and blinding remain an important challenges in studies evaluating digital interventions (Timpel et al. 2018), principle investigators should use control groups or consider adaptive study designs.