SynthesisJMIR mHealth and uHealth2026
Mobile Apps to Improve Health Parameters in Healthy Adults: Systematic Review.
Synthesis in JMIR mHealth and uHealth, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Recently, mobile health and mobile apps have been proposed as a potential tool to improve different outcomes (eg, daily steps, blood glucose) in both people with and without chronic conditions. In particular, healthy people could benefit from these tools by improving health variables and for prevention. Previous evidence investigated different types of health interventions adopting apps in various settings and populations, but evidence of their effectiveness is still unclear. Objective: The aim was to assess the effectiveness of mobile apps in improving health variables (eg, daily steps, maximal aerobic capacity) in healthy adults, involving an intervention regarding physical activity, diet, or their combination thereof. Evidence would suggest if apps could be effectively adopted in health interventions aiming toward prevention. Methods: A systematic review was performed using Medline via PubMed, Cochrane Library-CENTRAL, and Embase. Only randomized controlled trials comparing the same intervention provided with and without a mobile app or a treatment and a mobile app compared with the treatment only were included in this systematic review. The Risk of Bias tool 2.0 was used to assess the risk of bias, and the GRADE (Grading of Recommendations, Assessment, Development and Evaluation) was adopted for rating the certainty of evidence. Results: Considering studies up to June 2025, only 2 studies were included in the review of mobile apps for physical activity, and none were included for mobile apps for diet and none for mobile apps for physical activity and diet combined. The quality of evidence of the 2 studies included was low due to a high risk of bias, several missing data, and deviation from the original interventions, suggesting a scarce rigor in the methodology adopted. Therefore, mobile apps' effectiveness in improving diet, physical activity, or their combination cannot be assessed. Conclusions: Despite the widespread use of mobile apps for health and the large number of relative publications, the results of this systematic review did not allow us to ascertain the effectiveness of mobile apps for health, but they provided fundamental insights for future research. Hence, it is not possible to state if apps for health might be used as supporting tools for health interventions aiming toward prevention and health improvements in healthy people. There is an urgent need to develop stronger evidence of apps' effectiveness in addressing different populations and types of interventions for different health domains.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.