Impact of a novel smartphone application for remote monitoring of gestational diabetes on glycaemic control and birth outcomes: a pilot observational study

Abstract

The significance of increasing rates of gestational diabetes mellitus (GDM) is confronting healthcare systems. The advent of telemedicine provides an opportunity to alleviate this pressure. We sought to evaluate the impact of a tailored smartphone app-assisted care pathway (‘GDMapp’) designed for personalised self-management for patients with newly diagnosed GDM. Women with GDM, commencing a trial of medical nutrition and lifestyle therapy, were approached for consideration for enrolment. Glycaemic, maternal, perinatal, and neonatal outcomes were compared between the app-using group and matched historical controls. Data variables were summarised using univariable descriptive statistics. The groups were compared using statistical hypothesis testing. ‘GDMapp’ was used by 168 participants. Outcomes were measured against a historical control group of 162 prospectively recruited patients receiving standard GDM care. Patients using the app had lower overall glycaemic indices across both fasting (p = 0.022) and postprandial (p < 0.001) parameters. Overall, app-users had fewer instances of above threshold glycaemic control. Adjunctive use of app- based care demonstrated non-inferiority for adverse maternal, birth and neonatal outcomes. This app-assisted model of care for GDM safely facilitates self-management and remote monitoring of GDM. The improved postprandial glycaemic control among app-users reflects the benefit of the in-app educational, motivational, and self-management tools.

Introduction

Gestational diabetes (GDM) is defined as the diagnosis and first recognition of abnormal glucose metabolism, resulting in maternal hyperglycaemia, in pregnancy^1,2. There has been an unprecedented increase in the prevalence of this condition over the last two decades. This is largely attributable to the introduction and widespread use of the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria for GDM diagnosis, but also to a heightened prevalence of maternal obesity, advancing maternal age, sedentary lifestyles and a greater proportion of patients embarking on pregnancy with increasingly more complex medical backgrounds^3,4.

In order to optimise effective, efficient and safe service delivery for patients with GDM, innovators in healthcare service design have developed methods to reduce the financial and infrastructural impact this increasing prevalence⁵. The backbone of such design processes is embedded in capitalising on advancements in the information and communication technology sector, which has undergone tremendous growth from the early concepts of telemedicine to remote monitoring of health through the use of smartphone applications and wearable devices^6–8.

This growth and development have occurred in tandem with the increasing prevalence of GDM, resulting in a synergistic relationship between the two. The cornerstone of management of GDM encompasses medical nutrition therapy, lifestyle modifications and self-assessment of daily blood glucose levels, such that the patient with GDM is perfectly poised to benefit from an app-assisted, telemedical remote model of care.

In line with international experience, our institution experienced a notable rise in rates of GDM, most acutely evident since the adoption of the revised diagnostic criteria for the condition. Such an increase in prevalence called for an imaginative, innovative approach to healthcare adaptation for patients with GDM attending our unit. With this in mind, we collaborated with industry and technology experts to design, develop and implement a smartphone app-assisted care pathway to enable self-management and remote monitoring of GDM.

App development

A smartphone application (‘GDMapp’) was designed for a population diagnosed with diet- and exercise-controlled GDM in the Rotunda hospital, Dublin. At each stage of the design and development process, focus groups involving staff and patients were convened to ensure that each iteration of the device met the needs of all stakeholders with appropriate functionality for this specialised cohort. The primary function of the app is a repository of glycaemic indices, connected via Bluetooth to the patient’s glucometer. Linked to this ability is a hospital-based portal where glycaemic data collated from the app and glucometer pair is downloaded in real-time for review and assessment by the obstetric diabetes team. In addition, the app provides educational content aligned with national and international guidance on the management of GDM while also providing the app user with information to aid and support the development of good self-management. The app provides real-time feedback to the patient in an easily digestible format with colour coded iconography. Further, motivational, educational and instructional content is available to the app-user in the form of pop-up notifications or for review at their own convenience.

Method

This observational pilot study was designed to compare app-assisted care, supplementing standard care for GDM, versus a historical cohort receiving standard care alone. Standard care was defined as review of glycaemic data through a virtual telemedicine clinic which was established in March 2020 as a response to the COVID-19 pandemic. Patients with a singleton pregnancy diagnosed with GDM were approached for inclusion in our study. An adapted OGTT was used for diagnosis with only above threshold fasting and 1-hour values required as a result of efforts to reduce length of patient facing service provision in the context of the pandemic which was ongoing during recruitment. A further adjustment to the Participants were required to have fluency in English to ensure informed consent was given following review of the patient information leaflet. Recruitment occurred throughout 2021 from January to December.

Consenting participants were given a unique code to permit use of the app following its successful download from the app store / play store. An instructional tutorial on app-use and pairing of app and glucometer was undertaken by a member of the study team at the point of recruitment with further follow-up scheduled to ensure continued ease of use. Recruitment to app-use occurred at the time point of collection of the glucometer to facilitate self-monitoring. As such app-use occurred in parallel with standard self-monitored care.

The historical cohort of patients attended the telemedicine virtual GDM clinic in a time-period directly antecedent to initiation of app-based care. The obstetrics diabetes service included a consultant obstetrician and maternal medicine physician, an endocrinologist, two midwives with specialist qualification in diabetes in pregnancy and a trainee obstetrician / MFM subspecialist. Patients in the historical group self-monitored their daily blood glucose levels logging their glycaemic indices in a paper diary and reporting these results to the midwife during a scheduled phone call every two weeks. If sub-optimal control was identified, equating to 30% of readings reaching an above threshold level over 14 days, the patient was referred for dietetics input and for review by the obstetric medicine physician who would recommend increased surveillance, commencement of metformin or onward referral to the team endocrinologist for consideration of insulin. Patients were seen face-to-face by the obstetric diabetes team only if sub-optimal control was identified with all other in-person visits in line with routine antenatal care. In addition to standard care delivered through the telemedicine clinic, patients in the app-assisted care group used ‘GDMapp’ to log and track their glycaemic control. The app results were reviewed daily by the sub-PI on the team who also was a member of the obstetric diabetes service. Decisions regarding treatment remained the same as for standard care however if a tendency towards suboptimal control was identified through app-readings alone then an in-person review was facilitated in line with standard of care. If there was a concern regarding the number of available measurements uploaded from the app to the hospital server the patient was contacted to identify if there was a technical problem which could be addressed. Patients in both groups were asked to self-monitor their BSL four times a day—one fasting reading in the morning and one further reading 1 h after each of breakfast, lunch and dinner. Patients in both groups self-monitored their BSL from the time of diagnosis and recruitment to delivery.

The primary outcome of this observational study was assessment of maternal glycaemic control, defined by maintenance of a fasting blood glucose threshold < 5.3 mmol/l and a 1-hour post-prandial glucose threshold < 7.8 mmol/l in line with international recommendations⁹. For the purposes of this study we ultimately defined poorly controlled GDM as glycaemia exceeding thresholds on > 30% of occasions. Another study that has used this cut off identified 35% increased occurrence of adverse outcomes for patients in the above-threshold group¹⁰. The investigators noted that a fasting glucose level of 6.8 mmol/l on 30% of occasions or more resulted in a two-fold increase in the incidence of adverse neonatal outcome, compared to subjects with a mean fasting level of 5.3 mmol/l.

Secondary outcomes included metrics of fetal and perinatal health and service utilisation. Data relating to mode of delivery, birth weight centile and any observed maternal complications such as hypertensive disorders of pregnancy, preterm birth or postpartum haemorrhage were collated. Requirement for admission to the neonatal intensive care unit, in addition to the development of neonatal hypoglycaemia or jaundice were noted. Total length of hospital stay in addition to postnatal re-admissions was calculated for the maternal-neonate dyad. Data were collected on both the number of telemedicine clinical encounters and the requirement for additional dietetics assessment and review as surrogate markers of service usage. A telemedicine clinic encounter qualified as any GDM-related contact between the patient and the obstetric diabetes clinic, either by telephone or in-person.

Univariate descriptive statistics were used to describe data variables. Statistical comparisons were made between participants engaged in app-assisted care as an adjunctive measure to standard care and those from a historical cohort who availed of standard care alone. The two- samples t-test was used for continuous data and either the Chi-squared test or Fisher’s exact test was used for categorical data. A p-value < 0.05 was considered statistically significant.

Funding for this project was obtained from Horizon 2020—the European Union funding programme for research and innovation under the grant agreement number: H2020-ICT-2015-78049. Clinical trial number: not applicable. This study was granted ethical approval by the institutional research ethics committee of the Rotunda Hospital in accordance with the guidelines of the Declaration of Helsinki. Approval was provided under submission REC-2019-021. All participants provided written informed consent for the study participation after careful review and consideration of the provided Patient Information Leaflet. All methods in this study were carried out in accordance with relevant guidelines and regulations specifically with regard to ethical principles underpinning medical research and data protection of all consenting participants.

Results

During the study period (January–December 2021) 483 positive oral glucose tolerance tests were reported by the public obstetric diabetes service at the Rotunda hospital. This tertiary level university teaching hospital cared for 10,715 pregnant mothers and delivered 9147 babies in 2021 with an 8.4% incidence of new-GDM diagnosis among antenatal patients. Patients in the app-assisted pathway of care were representative of the GDM population at our hospital for ethnicity, BMI, parity, and age. Table 1 demonstrates the baseline characteristics and demographic information of women in the app-assisted care group and of a historical cohort of women with GDM. Patients were recruited and consented to both standard approach to GDM care in addition to use of ‘GDMapp’. Demographic and outcome data for this app-assisted care group were compared to a similar group of GDM patients attending the obstetric diabetes service at the Rotunda Hospital in the time period directly antecedent to commencement of recruitment to app-based care.

Table 1.

Baseline characteristics of the app-assisted care group and a historical patient group.

		App-assisted care (n = 168)	Historical cohort (n = 160)	P-value
Mean age (sd) at diagnosis (years)		33.0 (4.9)	31.9 (5.3)	0.059
Nullipara (n/%)		74/44.6	76/46.9	0.674
Mean BMI at diagnosis (kg/m2)		31.8 (6.5)	30.0 (5.9)	0.010
Ethnicity (n/%)	Irish	92/54.7	75/46.3	0.609
	Non-Irish European	34/20.2	39/24.1
	Asian/Australasian	28/16.7	33/20.4
	South American	7/4.2	6/3.7
	African	7/4.2	9/5.6
Number of risk factors to prompt OGTT	1 (n/%)	117/69.6	129/79.6	0.002
	2 (n/%)	36/21.4	32/19.8
	≥ 3 (n/%)	15/8.9	1/0.6
Indication for OGTT	Family history (n/%)	65/38.6	64/39.4	0.879
	Raised BMI (n/%)	100/59.5	85/52.5	0.197
	Ethnicity (n/%)	24/14.3	20/12.4	0.604
	Previous macrosomic infant (n/%)	4/2.4	3/1.9	0.999
	Fetal factors (n/%)	10/6.0	10/6.2	0.933
	Advanced maternal age (n/%)	11/6.6	3/1.9	0.053
	PCOS (n/%)	16/9.5	8/4.9	0.109
Mean gestational age at GDM diagnosis (weeks)		26.0 (1.2)	27.0 (2.3)	< 0.001
OGTT mean plasma glucose (mmol/l)	Fasting	5.1 (0.4)	5.1 (0.4)	0.364
	1-Hour postprandial	9.9 (1.8)	9.9 (1.7)	0.629

Advanced maternal age defined as age > 40 years at the time of the antenatal booking visit.

Macrosomia is defined as birthweight > 4.0 kg

BMI body mass index, OGTT oral glucose tolerance test, PCOS polycystic ovarian syndrome, GDM gestational diabetes mellitus

There were 77 patients during a two-month period of the study when the hospital systems were impacted by the after-effects of a malware attack on its IT services, where pre-screening was not possible in advance of their hospital visit. These patients were considered to have been missed. Of the remaining eligible patients, 169 (55.7%) consented to participate in this study. Figure 1a–c represents a flow diagram of recruitment to the study.

Fig. 1.

a Pathway to enrolment in app-assisted care arm. b Pathway to enrolment in historical cohort arm. c Pathway to allocation and analysis.

Glycaemic control

Despite the standardised approach to management through the telemedicine clinic, participants engaged in app-assisted care for GDM had a greater number of recorded glycaemic indices across both fasting and postprandial assessments, available for review by the obstetric diabetes team.

Further interrogation of the glycaemic results demonstrated a 2-point difference in the mean fasting results from 5.0 mmol/l in the historical group to 4.8mmol/l in the app-assisted group (Table 2). This finding was further buttressed by a 5-point reduction in mean postprandial glycaemic indices (p < 0.001). The standard deviation for both the mean fasting and mean postprandial levels in the app-assisted care group indicate a high degree of concentration of the overall values around their respective means. Achievement of good glycaemic control was demonstrated in both groups, but the app-assisted group demonstrated superior optimisation of that control across both fasting and postprandial assessments.

Table 2.

Summary of glycaemic data between the app-assisted care cohort and a matched historical cohort.

	App-assisted care	Historical cohort	p-value
Mean (sd) fasting plasma glucose level (mmol/l)	4.8 (0.4)	5.0 (0.92)	0.022
Mean (sd) postprandial plasma glucose level (mmol/l)	6.1 (0.4)	6.6 (1.2)	< 0.001
Mean (sd) percentage of below threshold (< 3.5) fasting blood glucose values per patient (n/%)	0.5 (1.9)	0.5 (2.4)	0.9556
Mean (sd) percentage of above threshold (> 5.3) fasting blood glucose levels per patient (n/%)	17.8 (20.0)	18.0 (21.8)	0.8993
Mean (sd) percentage of below threshold (< 3.5) postprandial blood glucose values per patient (n/%)	0.1 (0.2)	0.1 (0.4)	0.8659
Mean (sd) percentage of above threshold (> 7.8) postprandial blood glucose values per patient	76.2 (14.4)	83.1 (17.6)	< 0.001
Fasting blood glucose 90th percentile (mean ± SD)	5.4 (0.5)	6.1 (5.4)	0.083
Postprandial blood glucose 90th percentile (mean ± SD)	7.3 (0.6)	8.2 (6.4)	0.080
Requirement for metformin (n/%)	4 / 2.4	8/4.9	0.174
Requirement for insulin (n/%)	10/6.0	12/7.4
Requirement for metformin and insulin (n/%)	1/0.6	5/3.1
Mean gestational age at commencement of metformin or insulin	33.3 (3.0)	31.6 (2.5)	0.071
Mean time from diagnosis to commencement of metformin or insulin (weeks/range)	7.4 (2.8) (n = 14)	5.0 (2.4) (n = 25)	0.008

This study demonstrates the achievability of maintaining fasting glycaemic indices below the set threshold of 5.3 mmol/l on more than 70% of occasions with the use of app-based care (Figs. 2 and 3). Similarly, the 90th percentile for postprandial indices was 7.3 mmol/L, aligning well with the commonly adopted postprandial target threshold of 7.8 mmol/L which was chosen as a cut-off in this analysis⁹. An achievement of optimised postprandial blood glucose levels by 98.2% (n = 165) was noted in the app-assisted care group. While the postprandial data were excellent in the historical control group (96.3%, n = 162), further analysis identified that fewer patients with above-threshold postprandial readings at any time point in the app-using group (33.3%) subsequently required pharmacological intervention compared with patients reporting above threshold postprandial indices in the historical control group (83.3%).

Fig. 2.

Percentage of observations per participant across both groups greater than 5.3 mmol/l fasting threshold.

Fig. 3.

Percentage of observations per participant across both groups greater than 7.8 mmol/l postprandial threshold.

Requirement for supplemental hypoglycaemic therapy

Decisions regarding initiation of treatment were made by the same physician for patients in both groups, reducing the impact of bias. A greater proportion of patients in the historical cohort (15.4%, n = 25) were commenced on supplemental therapy of any description compared with the app-assisted group (8.92%, n = 15) however this did not quite reach statistical significance (p = 0.064). The mean number of weeks prior to requirement for and initiation of pharmacological treatment was greater in the app-assisted care group, and this did reach statistical significance (p = 0.008). Supplemental therapy (metformin or insulin or both) was initiated at a mean gestational age of 33 weeks 3 days and 31 weeks 6 days for the app-assisted and historical cohorts respectively. For both groups, treatment was initiated when > 30% of blood glucose values in a set time-period exceeded the agreed-upon thresholds (fasting > 5.3 mmol/L and postprandial > 7.8 mmol/L).

Delivery and birth outcomes

Outcome data relating to labour, delivery and birth are presented in Table 3. The mean gestational age at delivery in the intervention (app-using) group was 38 + 3 and that for the historical control group was 39 + 3. Similar rates of induction of labour in both groups were observed (47.3% versus 46.9%) (p-value 0.999). The overall rate of caesarean delivery was higher in the historical control group (41.8%) compared with 32.7% in the app-using group. While the incidence of delivery by emergency caesarean section was similar in both groups (55% vs. 64%, p = 0.142), there were fewer elective caesarean sections (n = 25, 15%) in the app-using group but a higher rate of operative vaginal deliveries (n = 34, 20.4%).

Table 3.

Perinatal and neonatal outcomes.

		App-assisted care (n = 168)	Historical cohort (n = 160)	p-value
Induction of labour (n/%)		79/47.3	76/46.9	0.999
Mode of delivery	SVD (n/%)	79/47.3	74/45.7	0.142
	OVD (n/%)	34/20.4	21/13.0
	Elective CS (n/%)	25/15.0	38/23.5
	Emergency CS (n/%)	30/18.0	29/17.9
Mean (sd) birthweight (g)		3350.8 (608.8)	3466.2 (582.7)	0.080
Male Infants n (%)		88/52.7	79/48.8	0.476
Birthweight centile		51.216	53.41	0.348
Composite maternal outcome*	0	156/92.9	144/88.9	0.429
	1	11/6.6	17/10.5
	2	1/0.6	1/0.6
Birth complications**(n/%)	0	133/79.2	135/83.3	0.461
	1	34/20.2	25/15.4
	2	1/0.6	2/1.2
Neonatal hypoglycaemia (n/%)		21/12.5%	15/9.25%	0.308
Neonatal jaundice (n/%)		53/32.1	41/25.3	0.174
Mean neonatal bilirubin (µmol)		131.7 (63.4)	128.1 (61.8)	0.609
NICU admission (n/%)		26/15.6	26/16.1	0.905
Length of maternal postnatal stay (days)		2.9 (2.6)	3.8 (2.8)	0.002
Compliance with PNOGTT (n/%)
Fasting		64/38.0	58/35.8	0.666
Postprandial		63/37.5	58/35.8	0.749

Birth weight centile were calculated based on standardised measurements from the WHO Multicentre Growth Reference Study.

Neonatal hypoglycemia was defined as a blood glucose level < 2.6 mmol/L.

Neonatal jaundice was defined by hyperbilirubinemia with a bilirubin level > 5 mg/dL.

SVD spontaneous vaginal delivery, OVD operative vaginal delivery, CS caesarean section, NICU neonatal intensive care unit, PNOGTT postnatal oral glucose tolerance test.

*Composite maternal outcome (PET/PIH/PPROM/Postnatal readmission).

** Perineal trauma, shoulder dystocia, birth trauma, PPH.

Maternal and neonatal outcomes

The individual instances relating to complications and adverse maternal outcomes were low across both groups investigated for this study and, as such, a composite maternal outcome of pre-eclampsia, pregnancy induced hypertension, preterm prelabour rupture of membranes and postnatal readmission rather than individual variables were considered (Table 3).

The mean birthweight was 115.4 g less among the app-assisted care group than the historical cohort (p-value 0.080). Reflecting the difference in the mean birthweights, the mean birthweight centiles were 51.216 (2.5–97.5) and 53.41 (2.5–97.5) in the app-cohort and historical cohort respectively (p-value 0.348). Macrosomia, defined as birthweight > 4000 g, was identified in 13.6% (n = 23) of app-assisted care cases and in 17.9% (n = 29) of the historical comparator group (p = 0.127).

The incidence of shoulder dystocia was low in each group and as such was considered as part of a composite measure of ‘birth complications’.

Admission rates to the neonatal intensive care unit (NICU) were similar across both groups (15.6%, n = 26; 16.1%, n = 26). The most common indication for admission to the NICU in the app-using group was for transient respiratory support. Jaundice surveillance and phototherapy represented the most common reason for NICU admission in the historical group. Incidence of neonatal jaundice and mean neonatal bilirubin levels did not reach statistical significance when comparisons between the two groups were made.

Service utilisation

The mean number of encounters in the app-assisted care group, for the purposes of GDM management alone, was 4.14 (SD 1.46) while that for the historical control group was 4.61 (SD 4.13), a finding that reached statistical significance (p < 0.012). Within the app-assisted care group there were 19.2% fewer participants who required additional dietetics review beyond the usual input from the dietician at the time of initial diagnosis with this again reaching statistical significance (p < 0.001).

Equal percentages of participants requiring supplemental dietetics guidance in both groups had above threshold glucose readings > 30% of occasions (41.3%, n = 24; 41.3%, n = 36).

App-using participants had a statistically significant shorter length of postnatal hospital stay compared to those in the historical cohort (p < 0.0045).

Stakeholder satisfaction

Both patient app-users and staff in the obstetric diabetes service were asked to complete specifically formulated questionnaires assessing app reliability, functionality and acceptability. The app and hospital portal scored high satisfaction rates across all three of these domains for both groups.

Discussion

M-health or ‘mobile health’ falls under the umbrella term of telemedicine, a constantly evolving phenomenon with up to 104 definitions¹¹. M-health specifically refers to the integration and capitalization of mobile phones and smart mobile technology in health service provision. M-health has the potential to overcome challenges associated with provision of healthcare for both the patient, in terms of geographical barriers, and the service provider in terms of economical, infrastructural, and organizational barriers.

Mobile health app proliferation has led to a change in how healthcare is delivered and accessed. It enables remote monitoring of care for patients and promotes delivery of appropriate healthcare, health advice and education in a timely manner. App publishers have been focusing on the ability and feasibility of mobile phone apps in the self-management and remote monitoring of chronic conditions. Diabetes has remained the area of greatest interest to app developers followed by obesity, hypertension, depression and chronic heart disease¹². Given that diabetes is at the forefront of the telemedicine drive it follows that this approach would be suitable and sustainable for gestational diabetes, both demonstrating similar pathogenicity and treatment strategies, as we have demonstrated in this study. An updated, innovative framework for revising service provision for GDM care is increasingly desired by patients and staff in obstetric diabetes departments both nationally and internationally to assist with the increasing workload driven by the rising prevalence of GDM rates^8,13. The market is currently saturated with general diabetes health apps but this is not the case for gestational diabetes specific apps such as the one presented here.

Researchers for the HAPO study defined a linear relationship between increasing levels of glycaemia at the time of a diagnostic OGTT and subsequent adverse maternal and neonatal outcomes¹⁴. An increase in glycaemic indices of 1 SD increased the odds ratio for a range of outcomes assessed in the HAPO study including birthweight > 90th percentile, cord blood C-peptide > 90th percentile and rates of primary caesarean section. Management of GDM with medical nutrition therapy, lifestyle modification, self-monitoring of BSL and use of hypoglycaemic agents in the setting of persistent hyperglycaemia is known to improve pregnancy outcomes through improved glycaemic indices. The mean glucose achieved by participants, who ultimately required insulin, in the Maternal and Fetal Medicine Units (MFMU) RCT investigating treatment of mild GDM was 0.6mmol/l higher than in the non-insulin requiring group, with insulin use indicating a higher incidence of suboptimal glycaemic control¹⁵. Both the MFMU trial and the ACHOIS trial established that treatment improves outcomes in GDM the inference being that the benefit is as a result of improved glycaemic control^15,16. Where controversy persists however is defining thresholds above which glycaemic control is considered suboptimal. This is however an integral part of the GDM management strategy.

The requirement for supplemental hypoglycaemic agents represents an important indicator of glycaemic control in the context of gestational diabetes management. Successful achievement of glycaemic control in this study was reflected in the relatively low requirement for hypoglycaemic agents among women using the smartphone app. Fewer patients in this group had a requirement to commence supplemental glucose-lowering agents (8.92% versus 15.4%) and where additional pharmacological therapy was required this was for a shorter duration of therapy in app-users compared with patients availing of standard care alone (p-value 0.008).

Oral hypoglycaemic agents or insulin are warranted among women with GDM who have not optimised their glycaemic indices despite a trial of dietary and lifestyle amendments. While the benefit of such pharmacological intervention is not disputed, the requirement for its initiation can further alienate patients who already perceive a stigma attached to the diagnosis and management of the condition^17,18. Therefore, if avoidance of medical therapy is achievable based on set glycemic targets, this represents a key objective and metric of success for the majority of women with GDM. App-use can be considered, in this context, to engender empowerment with self-management and recruitment of appropriate health education and knowledge. Additionally, the provision by the app of personalised graphs and imagery related to daily, weekly and monthly glycemic control can serve as a motivational tool for continued adherence to recommended dietary and lifestyle amendments.

The app-using group and the historical cohort group were well-matched in terms of age, parity, BMI and ethnicity with no statistically significant difference identified in these areas. These are important parameters to assess given the associated increased risk of GDM noted with advancing maternal age, increasing BMI and South Asian ethnicity. Further importance can be applied to our equality in baseline characteristics when outcome data are considered and interpreted.

Overall glycaemic control was optimised by 80% of app-users who maintained their fasting and postprandial levels within set thresholds. This compares favourably to a historical cohort of GDM patients, who were self-monitoring and attending the telemedicine clinic but not availing of app-assisted care, where 75.4% of attendees achieved optimised glycaemic control. The mean fasting blood glucose level among app-users was 4.8 mmol/L, representing a 0.2 mmol/L improvement compared with the historical control group. While this was not statistically significant, reflecting on the results from the HAPO study each 0.1 mmol/L reduction in glycaemic results can be considered to confer clinical benefit. The achievement of optimised postprandial blood glucose levels by 98.2% (n = 165) in the app-assisted care group is remarkable and reflects the impact of the app on behavioural intentions of the user from a dietary and lifestyle point of view. A reduction of 0.5mmol/L among app-users was identified when mean postprandial glycaemic indices were compared between groups, meeting both statistical and clinical significance. While the postprandial data were excellent in the historical control group (96.3%, n = 162), further analysis demonstrates that only 1 in 3 (33.3%) participants with above-threshold readings in the app-using group required supplemental therapy compared with 5 of 6 (83.3%) in the historical control group. The inference is that re-alignment with recommended dietary and lifestyle practices was achieved at a greater rate for app-assisted care participants who may have gained benefit from the in-built features of the app beyond mere surveillance of blood glucose data. Educational and motivational information is also provided within the app allowing it to function as a reinforcement tool.

The findings of this study are consistent with data presented in the literature regarding smartphone app-assisted healthcare for GDM in other settings⁷. App-use has demonstrated, at the very least, non-inferior blood glucose control compared with usual non-app use^19–22. Encouragingly, however, many studies of app use in other jurisdictions have demonstrated improved glycaemic control among app-users with GDM compared to usual care^23–26. Yew et al. designed the largest of the RCTs to date evaluating smartphone app-use for GDM and recruited 340 patients to their study of the ‘Habits-GDM’ app. While the odds of the primary outcome (gestational weight gain) were not reduced in app-users, similar to this ‘GDMapp’ study, they did report an improvement in glycaemic control among app-users. They found a 0.15 mmol/l lower mean glucose level with 5% and 30% above target for fasting and postprandial respectively.

Analysis of the data accrued from participants availing of app-assisted care through ‘GDMapp’ demonstrates better glycaemic control than that reported by Yew et al. This paper demonstrates that app-based care resulted in achievement of threshold glycaemic indices for 80% of participants and a reduction in both the mean fasting and postprandial glycaemic levels. Translation of the ‘GDMapp’ system into clinical practice can therefore be expected to have a beneficial effect on the number of patients requiring in-person review for consideration of treatment intensification. The implications of this observation include anticipated cost savings for the patient and the care provider, enabling the provision of additional support and education by skilled practitioners to those patients identified as most in need of support and reduced attendances at busy outpatient departments.

This improvement in glycaemic control is likely the result of enhanced engagement of women with GDM who are encouraged on a daily basis to continue adherence with the recommended dietary and lifestyle advice through glycaemic performance feedback delivered through their app. The delivery of this targeted information to a patient in a health literate accessible manner, in the confines of their own environment, assists with promotion of self-monitoring and continued app use. The pandemic itself did not interfere with recruitment. In fact, this study took on increasing merit in the context of the COVID-19 crisis when global health services immediately sought to implement solutions enabling remote patient review and surveillance.

Conclusions

The development of an app-assisted model of care for GDM has shown promise in alleviating the healthcare burden on an over-stretched, resource limited hospital department. Introduction of this smartphone application with remote monitoring capabilities through a hospital-based web portal represents a huge leap from the status quo of GDM care provision in Ireland at time of inception of this study. Data generated from smartphones through the use of apps such as GDMapp represent an invaluable source of information, offering assessment of patient health between hospital visits and facilitating the development of precision medicine²⁷. This pathway of care has attracted greater attention in the face of the COVID-19 pandemic and offers an economically viable, clinically safe, and effective care-provision structure for women diagnosed with GDM in Ireland.

Abbreviations

BMI

Body mass index

BSL

Blood sugar level

C

Peptide–connecting peptide

CS

Caesarean delivery

GDM

Gestational Diabetes Mellitus

HPRA

The Health Products Regulatory Authority

ISDPSG

International Association of the Diabetes and Pregnancy Study Groups

MFMU

Maternal and Fetal Medicine Units

mmol/l

Millimoles per litre

NICU

Neonatal intensive care unit

OGTT

Oral glucose tolerance test

OVD

Operative vaginal delivery

PCOS

Polycystic ovary syndrome

PNOGTT

Post natal oral glucose tolerance test

RCT

Randomised control trial

SVD

Spontaneous vaginal delivery

TTN

Transient tachypnoea of the new born

Author contributions

FB drafted the proposal and SS wrote the main manuscript text, FB critically reviewed and revised the manuscript. FBo performed the statistical analysis of the data, ZM and ET reviewed the evidence and contributed towards the figures and tables. All authors reviewed the manuscript.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Prior to commencing work on this research, the authors consulted with The Health Products Regulatory Authority (HPRA) of Ireland. The HPRA confirmed that GDMapp was not considered a medical device as no clinical advice was being provided to patients and local institutional research ethics should be sought. Therefore the principal investigator sought approval for the research via The Rotunda Hospital Research Ethics Committee (REC) – our institutional Ethics Approval Committee. Approval was provided under submission REC-2019-021.

Competing interests

The authors declare no competing interests.