How are digital health technologies used in glycogen storage disease care?

A map of evidence, not a test of effectiveness

This is a scoping review, a study design built to chart how much research exists, what it covers, and where it clusters — not to pool outcomes or estimate how well a technology performs. The authors followed PRISMA-ScR guidance, registered a protocol, and searched PubMed, Embase, Web of Science, and Google Scholar, with a final update in June 2026. They included primary studies of people with any GSD subtype that referred to digital health technologies, and excluded case reports and case studies.

That design choice sets the boundaries of every number below. The review reports counts of studies and participants, not effect sizes, and the authors deliberately did not perform critical appraisal of included studies. So a large share of studies using a technology means the field has looked there often, not that the technology has been shown to help.

Forty-four studies, but unevenly distributed

The final set comprised 44 studies published between 1997 and June 2026, enrolling 662 individuals with GSDs, with sample sizes from 3 to 59 participants. Seventeen GSD subtypes appear across the evidence. Observational designs made up 25 studies (57%), experimental studies 10 (23%), and descriptive studies 9 (20%). Most studies used a single digital technology: 40 of 44 (91%), while three used two and one used four.

Publication timing shows where momentum sits. Only five studies (11%) appeared in the 2000s, 11 (25%) in the 2010s, and 27 (61%) in the 2020s, with 2024 the single peak year at six studies (14%). The authors read this acceleration as growing recognition of digital health potential in rare disease management, though the review itself cannot say whether the growth reflects better tools, more funding, or simply more publishing.

Glucose monitoring dominates the digital toolkit

The review identified 23 distinct digital health technologies and grouped them using an adapted classification framework. Patient monitoring technologies formed the predominant category, and within it continuous glucose monitoring (CGM) — a sensor-based approach that tracks glucose levels over time rather than at single clinic visits — was used in 66% of studies. Other monitoring tools in the set included electrical impedance myography devices, portable lactate meters, and wearable sensors.

The remaining categories were care-supporting technologies such as mobile health applications and online emergency resources; digital diagnostic tools such as Holter electrocardiogram monitoring; health and wellness technologies including consumer wearables and trackers; and health system clinical software such as telemedicine platforms and electronic databases. The imbalance matters: the category most tied to a single metabolic signal is also the one with by far the most research attention.

Hepatic subtypes get the attention muscle subtypes lack

Studies of hepatic GSD subtypes accounted for 26 of 44 studies (59%), compared with 11 (25%) on muscle subtypes and 7 (16%) on mixed populations. The authors flag the underrepresentation of muscle GSDs as a critical gap, and it lines up with the technology mix: glucose monitoring maps naturally onto hepatic disease, where impaired glucose regulation is a defining management problem, while muscle subtypes would call for different measures.

The same pattern appears in what is measured. The review found minimal research on movement behaviour and on psychosocial evaluation. In other words, the digital tools being studied mostly answer a glucose question, and the evidence base has little to say about activity, sleep, or psychological outcomes in this population — even though the authors note that devices such as smartwatches could monitor physical activity and sleep.

Nearly half the research comes from Europe

Europe accounted for 21 studies (48%), followed by the Middle East with 7 (16%) and North America with 6 (14%); Asia, Oceania, and South America together contributed 7 (16%), and three studies (7%) were international collaborations. The authors connect this concentration to established rare disease infrastructure in Europe but also to equity concerns about who can access and implement these tools.

Their argument is specific to rare disease. Because GSD specialists are sparsely distributed, patients in resource-limited regions face two barriers at once: scarce rare-disease expertise and the concentration of digital health research and implementation in high-income settings. The review also notes that none of the included studies reported formal health economic evaluations such as cost-effectiveness or cost-utility analyses — a gap the authors say makes it hard to build the payer case for funding these tools in small patient populations.

Five themes describe what the technologies are for

The review grouped digital health use in GSD research into five themes: intervention evaluation, communication enhancement, clinical monitoring, population surveillance, and technology development. These themes describe the purpose a study assigned to a technology, and they are not interchangeable with the technology categories above — a single device type can appear under more than one theme depending on what the study was trying to do.

Read together with the counts, the themes explain why the field looks glucose-centric. Clinical monitoring and intervention evaluation around glucose control are well populated, while the themes that would capture broader daily-life management are thinner. The authors' conclusion follows from that structure: future work should prioritise comprehensive digital ecosystems that integrate metabolic, behavioural, and psychosocial monitoring.

Where the evidence supports a claim and where it stops

For anyone assessing digital health in GSDs, the review's most decision-relevant output is a distribution, not a verdict. CGM is the best-represented technology at 66% of studies, hepatic subtypes the best-represented population at 59%, and Europe the dominant setting at 48%. Those figures tell you where to look for existing evidence and where evidence is likely to be missing. They do not tell you whether CGM improves outcomes, because the review did not appraise study quality or pool results.

The authors' own priority is explicit: build digital ecosystems that combine metabolic, behavioural, and psychosocial monitoring rather than extending glucose-centric tools alone. The gap they emphasise most is the combination of underrepresented muscle subtypes, minimal movement-behaviour research, and minimal psychosocial evaluation — and, alongside it, the absence of health economic evidence that would let payers compare these tools on cost. The practical reading is that a proposal focused only on glucose monitoring in hepatic GSD is entering a crowded evidence space, while one addressing muscle subtypes, daily movement, or psychosocial outcomes is entering one the review found largely empty.

Sources used in this answer