Effect of digital health applications with or without gamification on physical activity and cardiometabolic risk factors: a systematic review and meta-analysis of randomized controlled trials

Abstract

Background: Use of health applications (apps) to support healthy lifestyles has intensified. Different app features may support effectiveness, including gamification defined as the use of game elements in a non-game situation. Whether health apps with gamification can impact behaviour change and cardiometabolic risk factors remains unknown. We conducted a systematic review and meta-analysis to determine the effect of health apps with gamification compared to non-gamified apps (control) on physical activity and cardiometabolic risk factors.

Methods: MEDLINE, EMBASE, and Cochrane library databases were searched through May 21st, 2024. We included controlled trials in adults (≥1 years) of all health backgrounds, with intervention periods ≥8-weeks, assessing the effect of gamification strategies used in health behaviour apps on adherence, cardiometabolic risk factors, total energy, and dietary nutrients of concern. Independent reviewers extracted relevant data and assessed risk of bias. Outcomes included physical activity and cardiometabolic risk factors (adiposity, glycemia, lipids, blood pressure and dietary factors). Data were pooled using the inverse variance method and expressed as mean differences (MD) with 95% confidence intervals (CI). Certainty of evidence was assessed using Grading of Recommendations Assessment, Development, and Evaluation (GRADE). Protocol registration was on ClinicalTrials.gov (NCT04633070).

Findings: 36 trials (49 trial comparisons, n = 10,079) met eligibility criteria; most targeted physical activity or weight loss. Use of gamification in apps compared to non-gamified interventions resulted in trivial increases in steps (489 steps/day [64 to 914]; high), and reductions in body mass index (-0.28 kg/m² [-0.44 to -0.12]; moderate) and body weight (-0.70 kg [-1.18 to -0.22]; moderate), and small important reductions in body fat (-1.92% [-2.71 to -1.14]; high) and waist circumference (-1.16 cm [-1.93 to -0.39]; moderate). No differences were observed for other outcomes (very low-to-high).

Interpretation: Current evidence provides a good indication that gamification features in apps targeting physical activity or measures of adiposity results in slight improvements in these outcomes compared to non-gamified versions. Recommendations to use an app for increasing physical activity or targeting weight loss should consider those with gamification features.

Funding: None.

Keywords: Behavioural change; Cardiovascular health; Gamification; Mobile app; Systematic review; mHealth.

Fig. 1

Summary of evidence search and selection.

Fig. 2

Summary plot for the effect of using gamification in health applications on physical activity and cardiovascular disease risk factors. Data are weighted mean differences (MD) [95% confidence intervals (CI)] and standardized mean differences (SMD). Analyses were conducted by generic, inverse variance random effects models (when ≥ five trial comparisons available) or fixed effects models (when Q < 0.100 is considered statistically significant, and quantified by the I² statistic, where I² ≥ 50% is considered evidence of substantial heterogeneity. Any statistically significant beneficial effects are highlighted in green and significant harm in red. The GRADE of randomized controlled trials are rated as “high” certainty of evidence and can be downgraded by five domains and upgraded by one domain. The white squares represent no downgrades, the filled black squares indicate a single downgrade or upgrades for each outcome, and the black square with a white “2” indicates a double downgrade for each outcome. Criteria for downgrades included risk of bias (downgraded if the majority of trials were considered to be at high risk of bias); inconsistency (downgraded if there was substantial unexplained heterogeneity [I² ≥ 50%, P < 0.10]; indirectness (downgraded if there were factors absent or present relating to the participants, interventions, or outcomes that limited the generalizability of the results); imprecision (downgraded if the 95% confidence interval crossed the minimally important difference [MID] for harm or benefit set as ± 30 min/week for MVPA,^, ±2000 steps/day for Steps, ±0.1 mmol/L for LDL-C, non-HDL-C, HDL-C and triglycerides,, , ±0.3% for HbA1c, ±0.5 mmol/L for fasting glucose,^, ±2 mmHg for SBP and DBP, ±0.4 kg/m² for BMI, ±1 kg for body weight, ±2% for body fat percentage^38,39), ±1 cm for waist circumference, ±0.02 for waist-to-hip ratio,^, ±500 kcals/day for total energy, ±5 g/day for total sugars,^, ±115 mg/day for sodium intake,^, and ±1 g/day for saturated fat intake^43,44); and publication bias (downgraded if there is evidence of publication bias based on funnel plot asymmetry and/or significant Egger’s or Begg’s tests (P < 0.10) with confirmation by adjustment by Duval and Tweedie trim-and-fill analysis). Criteria for upgrades included a significant dose–response gradient. ^aFor the interpretation of the magnitude, we used the MIDs (see above) to assess the importance of magnitude of our pooled estimates using the effect size categories according to new GRADE guidance. We then used the MIDs to assess the importance of the magnitude of our point estimates using the effect size 82 categories according GRADE guidance, , as follows: large effect (≥5x MID); moderate effect (≥2x MID); small important effect (≥1x MID); and trivial/unimportant effect (<1 MID). Abbreviations: BMI = body mass index; CI = confidence interval; DBP = diastolic blood pressure; GRADE = Grading of Recommendations, Assessment, Development and Evaluation; HbA1c = haemoglobin A1c; HDL-C = high-density lipoprotein-cholesterol; LDL-C = low-density lipoprotein-cholesterol; MD = mean difference; MID = minimally important difference; N = number; non-HDL-C = non-high-density lipoprotein-cholesterol; SBP = systolic blood pressure; SMD = standardized mean difference; TG = triglycerides.