Sage Journals: Discover world-class research

Abstract

Inhaled therapies remain the cornerstone in managing chronic airway diseases, offering direct treatment delivery to the lungs with minimal systemic adverse effects. With advancements in respiratory care, digital inhalers have emerged as a transformative innovation. Their functions extend beyond delivering inhaled medication, providing deeper insights into patients’ medication use behaviour and intervening through complementary platform features and integrated data analytics. However, despite being available for over two decades, the widespread adoption of digital inhaler platforms remains limited due to uncertainties regarding their cost-effectiveness, feasibility in real-world settings, and concerns regarding sustainability. Identifying patient groups that could benefit most from these technologies and designing strategies for effective deployment across diverse healthcare contexts is important. To achieve this, bridging the gap between innovation and accessibility is required so that digital inhaler platforms evolve into inclusive, patient-centred tools rather than niche technologies. This narrative review provides a summary of the evolution and current landscape of digital inhaler technology, its impact on clinical outcomes in chronic airway disease, and key challenges that stakeholders should address for the successful integration of these tools into respiratory care. We also propose key components of a patient-centred digital inhaler adherence support model that prioritises accessibility and efficacy.

Keywords

digital inhalers chronic airways disease adherence patient-centered accessibility

Introduction

Optimising adherence to inhaled therapy remains a significant challenge,^1,2 with poor adherence consistently associated with increased morbidity and mortality in chronic airways disease, asthma and chronic obstructive airway disease (COPD).^3–5 While newer therapies such as monoclonal antibodies^3,4 offer effective treatment options, they are costly (nearly $300,000 per quality-adjusted life-year⁶) and cannot replace inhaled therapies. Indeed, nearly 65% of patients with difficult-to-control severe asthma could potentially avoid biologics if their inhalers were used effectively.⁷ Among severe asthma patients who are receiving anti-interleukin (IL)-5 therapies, the subgroup with poor adherence to inhaled corticosteroids (ICS) experienced a higher exacerbation frequency and less tendency to stop maintenance oral corticosteroids compared to those who are adherent.⁸ In the recently published SHAMAL study, switching from regular to as-required anti-inflammatory reliever (AIR) ICS regimes led to a lung function decline among patients receiving anti-IL5R.⁹ Thus, National Institute for Health and Care Excellence (NICE) guidelines and the Global Initiative for Chronic Obstructive Lung Disease (GOLD) report recommend assessment of adherence to the prescribed inhaled regimes before considering advanced therapies.^10,11

Complexities in adherence assessment and digital solutions

Inhaler adherence is a multifactorial behavioural pattern that varies within and among patients over time across treatments.^12,13 It constitutes three important elements: (1) initiation (i.e., commencement of prescribed inhaled therapy), (2) implementation (i.e., taking treatment precisely as prescribed) and (3) persistence (i.e., adherence over time).¹² Intentional non-adherence often stems from individual beliefs regarding the treatment necessity and concerns,¹⁴ while unintentional non-adherence may arise from the technical challenges of inhaler use^13,15 forgetting to take medication regularly. The recent GOLD 2025 report highlighted influencing factors for medication adherence, including co-morbidities, mental and socio-economic status, and ageing.^11,16

Addressing adherence requires more than sporadic interactions between healthcare professionals (HCPs) and patients, especially when not all forms of non-adherence are easily reversible. Timely assessment is crucial to differentiate between treatable and non-treatable non-adherence forms. Otherwise, strict adherence thresholds for advanced therapies may compromise clinical outcomes (e.g., in the UK National Health Service (NHS), satisfactory adherence to routine therapies is essential to get access to biologics prescription in asthma).^17,18

Not every adherence monitoring method is ideal. Self-report measures have recall biases whereas canister weighing and dose counting can be confounded with dose dumping. The medicine prescription ratio (MPR) often oversimplifies adherence patterns, categorising them into static underuse or overuse groups. Besides, collecting prescriptions does not guarantee the implementation of treatment regimens. Overall, conventional assessment methods often fail to capture the daily adherence behaviour in real life. This is important, especially with the emerging role of patient-led ICS dosing regimens in chronic airway disease (e.g., maintenance-and-reliever therapy (MART) and AIR regimens), where ICS and long-acting beta agonists are taken as required.

On the other hand, the technique of inhaler device handling also plays a crucial role in determining the treatment efficacy. Currently, nearly 200 aerosolised medications are available for managing chronic airway diseases, delivered via three primary device types: pressurised metered-dose inhalers (pMDIs), dry powder inhalers (DPIs), and soft mist inhalers (SMIs). Each device requires specific handling techniques: slow and coordinated inhalation for pMDIs, rapid and forceful inhalation for DPIs, and slow but deep inhalation for SMIs.^19,20 Fewer than 20% of patients can use their inhalers correctly, with almost 90% demonstrating at least two critical technique errors if left unaddressed.^21–23 In vitro studies demonstrated that subtle errors, such as 0.5-second delays between actuation (release of medication from the inhaler) and inhalation (breathing in the medication), can substantially reduce the amount of drug deposited in the lungs.²⁴ These errors are often imperceptible to patients, yet they can compromise treatment efficacy. This highlights a significant gap in adherence intervention as nearly a third of patients have never received any inhaler training.²³ Alarmingly, as low as 15% of HCPs can adequately evaluate inhaler techniques.^23,25 These shortcomings are particularly concerning for socioeconomically disadvantaged individuals, who face higher risks of adverse outcomes.^26,27

Emerging needs for digital solutions in adherence support

The future of healthcare is poised for innovations driven by artificial intelligence (AI), virtual healthcare, wearable devices, and large language models. Against the backdrop of the COVID-19 pandemic, the adoption of digital innovations or telemedicine (i.e., provision of clinical care using information and communication technologies²⁸) in the healthcare sector became the ‘new normal’. Nearly 78% of WHO/European Member States are using telemedicine and 90% of global health services use social media platforms and electronic health records.^29,30 In the UK NHS service, 28 million more people had used NHS websites or digital apps between the year 2021 and 2023.³¹

At the forefront of digital health advancements in respiratory care are digital inhalers, with a global market expected to grow by 15% annually and reach nearly $3.42 billion by 2029.^32,33 These devices can inform more granular longitudinal adherence behavioural patterns, such as attempted inhaler usage versus the true adherence, considering sub-optimal inhalation techniques.³⁴ Additionally, digital inhaler platforms can enhance access to vulnerable and hard-to-reach populations, who may be excluded from traditional interventions.^26,27 On the other hand, there are apprehensions regarding the feasibility of digital inhaler platforms in the long term. Despite the increase in revenue of digital inhalers, occurrences of device withdrawal from markets were also not uncommon (see Table 1). More details on both perspectives will be discussed in this review.

Table 1.

Overview of evidence on digital inhaler devices that are available in the market and stratified according to the sensor type.

Brand (developer)	Launch year	Sensor (device) types	Compatible devices	Smart functionalities and data captured
Brand (developer)	Launch year	Sensor (device) types	Compatible devices	Paired app	Time of use	AVR	Technique	Inhalation metrics	Direct technique feedback	PIFR	Clinician portal	Exacerbation alerts
Digital inhalers driven by sensors detecting inhalation efforts
Sensirion¹¹¹ (Sensirion, Switzerland)	1998	Pressure (add-on)	Most pMDIs	Yes	Yes	Yes	Yes	Measured airflow via the precise bypass system¹¹²	Yes	Yes	Yes	No
VeriHaler^113,114 (Sagentia Innovation, UK)	2010	Acoustic (add-on)	Most pMDIs & DPIs	Yes	Yes	Yes	Yes	Inhalation timing & delivery, use noise cancellation algorithm to improve accuracy	Yes	Yes	Yes	Yes
INCA^TM45 (Vitalograph, Ireland)	2014^a	Acoustic (add-on)	Diskus® DPIs	No	Yes	No	Yes	Device priming, exhalation into inhaler (memory stored in the device)	No	Yes	Yes	No
Intelligent control inhaler⁵⁰ (3M drug delivery systems, USA)	2016	Pressure (integral)	Dulera®, QVAR® pMDIs	Yes	Yes	Yes	Yes	Real-time adjustment of airflow resistance and release of drug to the lungs)	Yes	Yes	Yes	No
Inspair^TM115 (Biocorp, French)	2016	Acoustic (add-on)	Most pMDIs	Yes	Yes	Yes	Yes	Device priming & orientation, inhalation timing	Yes	Yes	Yes	Yes
PneumaHaler¹¹⁶ (Pneuma Respiratory, USA)	2017	Pressure (add-on)	Most SMIs	Yes	Yes	Yes	Yes	Inhalation timing & flow rate	Yes	Yes	Yes	No
Hailie®¹¹⁷ (Adherium, New Zealand) formerly SmartInhaler^TM	2018^b	Pressure (add-on)	Most pMDIs & DPIs (e.g., Diskus®, Turbohaler®, HandiHaler®, Ellipta®, Symbicort®, ProAir®)	Yes	Yes	Yes	Yes	Device priming & orientation	No	Yes	Yes	No
Digihaler^TM48,72 (Teva, Israel) discontinuation alert (06/01/2024)	2019^a	Flow (integral)	AirDuo, ProAir, armon Air®, Spiromax®, RespiClick DPIs	Yes	Yes	Yes	Yes	Device priming, exhalation into the inhaler	Yes	Yes	Yes	No
Respiro®^47,48 (Amiko, UK) (2018)	2020^a,b	Vibration (add-on)	pMDIs, DPIs (Ellipta®, Nexthaler®, Spiromax®)	Yes	Yes	Yes	Yes	Device priming & orientation, inhalation duration & volume	Yes	Yes	Yes	Yes
RS01X¹¹⁸ (Berry global healthcare & Amiko)	2020^a	Vibration (integral)	RS01® inhalers	Yes	Yes	Yes	Yes	Device priming & orientation, inhalation duration & volume	Yes	Yes	Yes	Yes
CapMedic®²⁴ (Cognita Labs, USA)	2020^a	Flow (add-on)	Most pMDIs	Yes	Yes	Yes	Yes	Device priming, orientation, inhalation duration & volume with FEV₁ & PEFR (subscription-only service)	Yes	Yes	Yes	No
Inspiromatic^TM119 (OPKO Health, Israeli)	2014	Flow (add-on)	Most DPIs	No	Yes	Yes	Yes	Inhalation time & flow rate	Yes	Yes	Yes	No
Smart aerochamber^@48,62 Trudell Medical UK Ltd	2019^a	Flow (add-on)	Most pMDIs & SMIs with AeroChamber Plus® Flow-Vu®	No	Yes	Yes	Yes	Inhalations within the 30s of actuation & inhalation volume	Yes	Yes	No	No
Digital inhalers using global positioning system (GPS) & environmental monitoring sensors
Propeller^@120 (Propeller Health, USA)	2014^a,b	Add-on	Most pMDIs	Yes	Yes	Yes	No	GPS tracking & environmental data (e.g., weather, air quality)	No	No	Yes	Yes
FindAir⁴⁴ (FindAir, Poland)	2019^a	Add-on	Most pMDIs	Yes	Yes	Yes	No	GPS tracking & environmental data (e.g., weather, air quality	No	No	Yes	Yes
Enerzair® Breezhaler®¹²¹ (Novartis & Propeller Health ltd)	2020^a	Add-on	Enerzair® DPIs (indacaterol/glycopyrronium/mometasone furoate)	Yes	Yes	Yes	No		No	No	Yes	No
USSC-03¹²² (Novartis & Propeller Health ltd)	2018	Add-on	USSC-03 DPIs (indacaterol/glycopyrronium/mometasone furoate)	Yes	Yes	Yes	No		No	No	Yes	No
Digital inhalers with integrated functionalities, combining several sensor types
BreatheSuite^@123 (BreatheSuite, Canada)	2021^b	Add-on	Most pMDIs	Yes	Yes	Yes	Yes	Device priming, orientation, coordination & inhalation duration	Yes	Yes	Yes	Yes
NuvoAir aos¹²⁴ NuvoAir ltd (Boston)	2021^a,b	Add-on	Most pMDIs	Yes	Yes	Yes	Yes	Device priming, inhalation duration & volume. Linked with NuvoAir spirometer for FEV₁ & PEFR, air quality, cough & symptom monitoring	Yes	Yes	Yes	Yes
Herotracter^@35 (Aptar Pharma, UK)	2016^a	Add-on	Most pMDIs and DPIs, (e.g., Diskus®, ProAir®, Ventolin® & Symbicort®)	Yes	Yes	Yes	Yes	Device priming, orientation, coordination & inhalation duration. Can be linked with BreatheSmart® spirometer to measure FEV1 & PEFR	Yes	Yes	Yes	Yes
Digital inhaler devices that were withdrawn from the market³⁵
• Chronolog^TM (Forefront Engineering ltd, USA) (1982 - launched)
• Smart mist^TM (Aradigm Corporation, Belgium) (1996 – launched)
• CareTRx (Teva ltd, Israel) (2014 – Launched)
• Diskus adherence logger^TM (GSK ltd, UK) (2004 – launched)
• MDILog^TM (Westmed Technologies ltd) (1997 -launched)
• SmartTrack^TM (Adherium ltd, New Zealand) (2009 – launched)

pMDIs: pressurised metered-dose inhalers; DPIs: dry powder inhalers; SMIs: soft mist inhalers; PIFR: peak inspiratory flow rate; PEFR: peak expiratory flow rate; FEV1: forced expiratory volume in 1 s.

Devices used solely for inhaler training (e.g., T-Haler, 2Tone Inhaler Trainer, AIM (Aerosol Inhalation Monitor), and Flo-Tone CR (controlled resistance)¹¹² are excluded from this table.

^aEMA approval securing CE marking.

^bFDA approved.

Evolution and current landscape of digital inhaler technology

Digital inhalers feature electronic sensing systems that constitute sensors, microcontroller units for processing data, and communication modules. Various types of sensors are employed to monitor inhaler use. Pressure sensors are most commonly used, detecting inhaler actuations and measuring inhalation effort, while acoustic sensors capture the sound of inhalation to assess technique and identify misuse. Environmental sensors monitor temperature, humidity, and air quality, further enhancing device functionality.

Digital inhalers fall into two main categories: integrated devices³⁵ with electronic sensing systems built within inhalers, and add-on devices that attach to existing inhalers for smart functionalities. These devices are often powered by non-rechargeable lithium coin cells or polymer batteries that support rechargeability via micro-USB or wireless charging. Add-on devices usually have a service life of around 12–24 months, whereas integrated devices face limitations as their battery functionality is tied to the lifespan of the inhaler, up to 13 months.³⁶ Many digital inhalers feature optional functionalities, such as LED indicators to track dose consumption, battery status and Bluetooth connectivity for data syncing with apps or healthcare systems.

Early development and milestones

The evolution of digital inhaler technology began with Chronolog^TM37,38 (1982), an electronic plastic holder for pMDI canisters, developed by Forefront Engineering Corporation. It was the first regulatory-approved device, capable of recording up to 4000 inhaler acutations.^37,38 Research at the time informed that conventional prescription refill records and canister weighing methods overestimated adherence by at least 30% when compared against EIM using Chronolog^TM.^37,38 Progress accelerated with add-on devices like Doser³⁹ in the 1990s, which stored 30 days of inhaler usage data but lacked data transfer capability to computers. Subsequent innovations followed: Smart Mist®)^35,40 (Aradigm Corporation) and MDILog™^35,40 (Westmed Technologies Ltd) devices that incorporated microprocessors to log inhalations, enabling clinicians to review the data retrospectively. Smart Mist®^35,40 introduced the breath-actuated mechanism, releasing medication only with an adequate inspiratory flow rate and or volume. Meanwhile, MDILog™^35,40 added wireless connectivity with users’ computers, enhancing research applications. By 2006, Smartinhaler^TM41 (now Hailie® by Adherium Ltd) integrated Bluetooth technology for real-time adherence tracking by transmitting data directly to healthcare providers. The device also marked a step forward via features like audio-visual reminders and LED indicators for inhaler usage.

Modern digital inhaler technology

In 2014, Propeller Health®^42,43 (Propeller Health Ltd) upscaled the landscape of EIM via its add-on sensors, featuring global positioning systems (GPS) and enhanced app functionalities. This allowed geospatial tracking of adherence and exacerbation triggers, providing actionable insights for patients and clinicians. Soon after, FindAir ONE⁴⁴ (FindAir Ltd) advanced these features by integrating information on pollen levels and air quality. In parallel, INCA^TM34,45 (Vitalograph Ltd) introduced acoustic sensing technology that utilises AI (Artificial Intelligence) algorithms to analyse inhalation profiles. This approach uncovered the significance of metrics like peak inspiratory flow rate (PIFR) to differentiate between attempted and correctly executed inhaler usage.^34,46 In asthma, the use of INCA^TM followed by fractional exhaled nitric oxide (FeNO) suppression helped identify patient groups who had non-intentional non-adherence due to poor technique errors and otherwise identified as having steroid-resistant airway disease.⁷ Although not commercially available, INCA^TM has significantly contributed to research on inhaler adherence.

Later, inspiratory capable devices using pressure sensors, such as CapMedic™²⁴ (Cognita labs Ltd), Respiro®⁴⁷ (Amiko Ltd), and Smart Aerochamber^®48 (Trudell Medical International Ltd) offered more precise inhalation parameters compared to traditional acoustic methods, which could be affected by external noises.²¹ CapMedic^TM technologies were reported to identify 40%–60% more of these errors compared to visual assessments by trained professionals.²⁴ Further advancing the field, integrated devices such as Teva Ltd’s Digihaler®⁴⁹ and Intelligent Control Inhaler (ICI)⁵⁰ (3M Drug Delivery Systems) assessed temporal adherence, inhalation efforts, and lung function, and provided real-time feedback on technique during the inhalation process. While Teva Ltd has announced the discontinuation of its Digihaler® products, ICI remains among the few commercially available integrated devices (See Table 1).

Evidence examining the impact of digital inhaler use on clinical outcomes

Clinical trials assessing digital inhalers for managing chronic airway disease have produced mixed results. Table 2 summarises the outcomes of key clinical studies reporting outcomes of adherence interventions using digital inhalers in chronic airway disease. The evidence is presented as a narrative synthesis. A significantly higher number of studies have been conducted in the asthma population compared with COPD, at approximately a 3:1 ratio. Published reviews generally agree that digital inhaler platforms can modestly improve adherence, but concerns about the quality and clinical significance of studies persist.^51–55 Ireti et al.⁵¹ reported that only 20% of studies examining digital inhalers were fully blinded and just 40% met the proposed power. Furthermore, recruitment is often biased toward tech-savvy participants, potentially skewing efficacy findings.

Table 2.

Summarised clinical outcomes from clinical studies using digital inhalers.

Study	Population	Sample size	Duration	Device used	Adherence	HRQoL	Exacerbations	HCU	Lung function
Study	Population	Sample size	Duration	Device used	Clinical outcome
Group 1. Intervention addressing practical factors (i.e., forming inhaler-taking habits)
Charles et al.⁵⁶	Asthma	110	6 month	Smartinhaler^TM	✓
Chan et al.⁵⁸	Asthma	220	6 month	SmartTrack^TM	✓	✓	✗		✗
Vasbinder et al.⁵⁷	Asthma	418	1 year	Adhaler^TM	✓	✗	✗	✗
Sykes et al.⁴²	Asthma	77	1 year	Propeller Health	✓	✓*
Yawn et al.⁵⁹	COPD	122	6 month	Propeller Health	✓	✗
Group 2. Personalised intervention addressing practical or perceptual factors
Onyirimba et al.⁶⁰	Asthma	19	10 weeks	MDI Chronolog^TM	✓	✗
Moore et al.¹²⁵	Asthma	437	6 month	Propeller Health	✓	✓*
Dierick et al.⁶²	Asthma	42	4 month	Smart spacer^@	✓	✗			✗
Sulaiman et al.⁶³	Asthma	218	3 month	INCA™	✓	✗
Broadbent et al.¹⁰²	COPD	60	4 month	Hailie®	✓	✗		✗
Criner et al.⁶¹	COPD	138	6 month	BreathMate®	✓	✗
Alshabani et al.⁴³	COPD	39	1 year	Propeller Health			✗	✓
Group 3. Personalised intervention addressing perceptual and practical factors
Weinstein et al.⁶⁷	Asthma	39	3 month	SmartTrack^TM		✓			✗
Mosnaim et al.⁶⁴	Asthma	100	6 month	Digihaler®	✓	✓	✗
Kuipers et al.⁴¹	Asthma	80	6 month	SmartInhaler^TM	✓	✗
Burgess et al.⁶⁸	Asthma	26	4 month	Smartinhalers	✓	✓			✓
Gregoriano et al.⁷⁰	Asthma/COPD	75	6 month	SmartTrack^TM	✓	✗	✓*
Morton et al.¹²⁶	Asthma	77	1 year	SmartTrack^TM	✓	✗		✓	✓*
O’Dwyer et al.⁶⁹	Asthma/COPD	152	6 month	INCA™	✓	✓	✓*
Lin et al.⁷¹	Asthma	21	6 month	Propeller Health	✓	✓	✓	✓
Foster et al.⁶⁵	Asthma	143	6 month	SmartTrack®	✓	✓	✓*		✗
Apter et al.⁶⁶	Asthma	333	6 month	Diskus logger	✗	✓*	✗	✗	✓*

*Positive outcome but did not reach a statistical significance or a selective sub-group.

A key framework to consider when assessing or intervening inhaler adherence is the Perceptions and Practicalities (PAPA) framework proposed by Rob Horne et al.¹³ It emphasises practical factors (e.g., inhaler technique, treatment-taking habit formation) and perceptual factors (e.g., patient beliefs, motivations) in tackling inhaler use behaviour. Through the lens of the PAPA framework, interventions using digital inhalers can be categorised into three distinct groups:

1. Interventions addressing practical and perceptual factors: These interventions employed automated app-based inhaler reminders or generic web-based content.

2. Personalised interventions addressing practical or perceptual factors: These involved tailored strategies, such as individual consultations regarding inhaler technique or adherence rate by healthcare practitioners (HCPs) using real-time data from digital inhalers.

3. Personalised interventions addressing both practical and perceptual factors: The most comprehensive approach, combining feedback and reminders from digital platforms with tailored consultations by HCPs.

Herein, we categorised adherence interventions using digital inhalers as follows;

Interventions addressing either practical and perceptual factors

Digital inhaler studies primarily targeted practical aspects of inhaler use, particularly establishing regular inhaler-taking habits. Non-personalised scheduled audio-visual (AV) reminders, SMS alerts, and app-based feedback on usage trends were commonly used.^42,56,57 In randomised controlled trials, control groups typically receive passive electronic inhaler adherence monitoring (EIM), with the inhaler reminder function disabled. These approaches consistently improved inhaler adherence across diverse age groups. Charles et al.⁵⁶ reported a mean (SD) adherence rate (AR) of 88% (16) in the intervention group compared to 66% (27) in the control group, while Chan et al.⁵⁸ found a median AR difference of 54%. Both studies included adolescents and adults with asthma, employing AV reminders in the intervention arm with passive EIM in the control group. Vasbinder et al.⁵⁷ demonstrated a mean improvement in AR by 12% (95% CI: 7%–18%) using SMS reminders in the paediatric asthma group. In Sykes et al.,⁴² nearly half of severe asthma patients who were eligible for biologic therapies with suboptimal adherence remained stable on conventional inhaled therapy after 12 months of digital inhaler-based interventions. Nonetheless, clinical improvements such as lung function, asthma control, and exacerbation rates were inconsistent across studies. A few studies addressed perceptual barriers by combining AV reminders with generic web-based contents^57,59 but benefits were limited to adherence boosts only.

Personalised interventions addressing practical or perceptual factors

Personalised adherence interventions combined digital inhaler features with healthcare practitioner (HCP)-led consultations. In most cases, app-generated alerts for inhaler overuse or underuse prompted HCPs to contact patients for adherence reinforcement. However, the resulting change in health-related quality of life (HRQoL), lung function, and healthcare utilisation remained statistically insignificant across most studies.^43,60,61 Besides, adherence often declined over time. Alshabani et al.⁴³ explicitly reported a decrease in mean AR from 56% to 30% over 280 days among patients with COPD using digital inhaler platforms, with a weekly AR drop of 0.46%. This highlights that while immediate improvement is feasible using digital solutions, sustaining long-term adherence may require continued engagement.

A few studies have intervened in the inhalation technique using the biofeedback approach, where HCPs provided coaching on inhaler technique based on observed errors detected by digital inhalers. In Dierick et al.,⁶² biofeedback intervention using smart spacers reduced technique errors by 26% while errors increased by 15% in the control group. Sulaiman et al.⁶³ found that INCA^TM-derived biofeedback improved adherence over 90 days compared to face-to-face coaching, although technique errors were similar between groups. For patients with COPD, evidence remains limited. The meta-analysis by the Leicester COPD group including 1432 COPD patients found that HCP-led digital interventions improved mean AR by 18% (95% CI: 9%–27%) compared to passive EIM, with a possible reduction in healthcare utilisation but minimal impact on HRQoL or exacerbation rates.⁵⁵

Personalised digital interventions addressing both practical and perceptual factors

A subset of studies implemented personalised interventions addressing both practical and perceptual barriers. These strategies typically combined digital AV reminders with HCP-led structured education and motivational counselling, often guided by frameworks such as the Information-Motivation-Behavioural model.^41,64–67 Interventions were commonly delivered by pharmacists, primary care physicians or dedicated study staff. Unsurprisingly, a consistent pattern of increased inhaler adherence resulted following intervention. Across 10 studies in asthma or COPD, seven reported improvements in HRQoL, and six documented reductions in lung function decline and or exacerbation frequency (See Table 2 and the Supplemental table).

Burgess et al.⁶⁸ found a mean AR of 79% versus 58% in children receiving app-based reminders and personalised adherence support compared to the control group, along with greater FEV₁ improvement. In Weinstein et al.,⁶⁷ mean adherence in the intervention group was 81%, with a greater improvement in mean ACQ score difference than control (0.75 vs. 0.19). Mosanim et al.⁶⁴ observed a 38.2% reduction in reliever use and a higher (though non-significant) odds of improved asthma control compared to the control. O’Dwyer et al.⁶⁹ used the biofeedback approach alongside the behavioural counselling. As a result, a higher adherence compared to traditional in-person technique coaching or control groups, with a reduction in St George Respiratory Questionnaire scores by −6.1 (p = 0.04) over 6 months.

In the context of exacerbation outcomes, Gregoriano et al.⁷⁰ reported a 40% lower risk of exacerbations among patients receiving app-based reminders and HCP-led counselling compared to controls (RR 0.60, 95% CI: 0.4–1.0). In the prospective study by Lin et al.,⁷¹ EIM with video consultations by asthma specialists and psychologists led to fewer asthma-related emergency department visits, improved asthma control, and reduced school absences among children with severe asthma. Foster et al.,⁶⁵ in particular, compared different clusters of digital adherence interventions in asthma: inhaler reminders with feedback and/or personalised discussions against the control group receiving written action plans. While all groups demonstrated improved asthma control, those receiving scheduled reminders experienced greater reductions in exacerbations and adherence gains. Overall, digital inhaler-based interventions generally improve inhaler adherence by supporting habit formation. Regardless of personalisation, interventions targeting only the practical domain showed limited and inconsistent effects on clinical outcomes beyond adherence gains. In contrast, multi-faceted individualised interventions that combine habit-building and behavioural modification supports appear to achieve broader clinical benefits. Future studies should compare different digital intervention packages with varying intensities of technology support across diverse patient demographics.

Advanced data analytics and use of digital biomarkers

There is a growing use of predictive analytics and machine learning to analyse real-time data from digital platforms for detecting imminent exacerbations.^36,49,72,73 The FDA defines such data, gathered through digital health technologies that reflect biological processes, as digital biomarkers.⁷⁴

Studies have demonstrated that electronically recorded reliever usage could predict exacerbations and mortality.^75–77 Alshabani et al.⁴³ found that high reliever use (≥1.64 times above the average) had 28% sensitivity and 99% specificity for predicting COPD exacerbations within a week. By incorporating additional factors, such as daily inhaler usage, and inhalation profile (i.e., peak inspiratory flow rate (PIFR), inhalation volume and duration, and time to PIF), AI algorithms from inspiratory-capable albuterol Digihaler®⁷⁸ can forecast asthma exacerbations, with an area under the curve of receiver operator characteristics (AU-ROC) values between 0.75 and 0.83. While increased reliever consumption is the strongest predictor of exacerbation in asthma, change in PIFR seems to be the most critical for COPD.^36,49,72,73 On the other hand, a quick recovery of PIFR was observed in asthma while the COPD group took more than 14 days post-exacerbation to recover, likely due to more severe airflow limitation and comorbidities.⁷²

Beyond patient-specific inhaler use patterns, environmental factors have also been identified as important predictors of exacerbations. Data from the Propeller Health platform revealed that a 1 μg/m³ increase in weekly PM2.5 exposure correlated with a 0.82% rise in weekly reliever use.⁷⁹ This highlights how machine learning could facilitate personalised management by accounting for patient-specific disease severity, environmental triggers and inhalation characteristics.

Challenges for stakeholders to integrate digital inhalers into healthcare systems

Despite strong evidence supporting digital inhalers improving adherence, their widespread adoption hinges on several additional factors.

Regulatory approval process and barriers to market entry

Manufacturers of digital inhalers face stringent regulatory requirements before entering the market (e.g., Food and Drug Administration (FDA)⁸⁰ in the U.S., Medical Device Regulation (MDR) bearing CE marking in the European Union,⁸¹ the Medical Device Regulation (MDR) bearing CE marking in the European Union in the UK.⁸² Integrated devices, classified as digital-drug combinations, undergo more rigorous scrutiny, requiring New Drug Applications or Premarket Approvals. This involves extensive clinical trials, in vitro tests and user surveys to demonstrate the safety and efficacy of both medicinal products and technology.⁸³ In contrast, add-on devices generally follow a simpler FDA 510(k) notification or CE marking process, primarily needing to demonstrate the performance equivalence to existing products.⁸⁰ However, even minor engineering changes can impact cost and approval timelines. For instance, if a critical electronic component is discontinued by the industry, manufacturers may face significant redesign and recertification challenges. Furthermore, regulatory and data protection frameworks are still evolving in many regions, which further complicates the approval process.³⁵

Data interoperability and privacy concerns

The use of real-time data involves integrating vast amounts of sensor-based information with electronic health records (EHRs). It requires strong governance, including robust IT infrastructure and strict adherence to privacy regulations such as GDPR and Data Protection Impact Assessment. A key challenge lies in ensuring that digital platforms meet interoperability standards such as Fast Healthcare Interoperability Resources (FHIR) and Health Level Seven (HL7) for integration into clinical workflow. However, the med-tech industry often lacks standard interoperability due to the proprietary nature of healthcare systems, complicating data integration and creating variations in data formats. Privacy concerns regarding the collection, storage, and sharing of sensitive health information further demand vigilant management.

Acceptance and trust among patients and healthcare providers

Early studies by Simpson and colleagues⁸⁴ revealed a great enthusiasm among patients for mHealth, the use of technologies and electronic devices for healthcare,^85,86 but data security concerns were prominent. Only 12% felt comfortable accepting treatment decisions based on mHealth data alone, rising to 41% when clinicians endorsed the changes, reflecting the importance of HCP involvement.

A recent survey by the Asthma and Lung UK⁸⁷ involving over 3000 respondents, indicated the evolving attitudes toward digital health tools. The majority were comfortable sharing their health data for healthcare and data analytics. Younger individuals (aged 18–29 years) were more supportive compared to those aged 70 and above. The systematic review of patients’ feedback on digital inhaler platforms conducted by our study group also mirrored similar feedback.⁵⁵ Over 90% of patients and researchers considered digital inhaler platforms easy to use and helpful. However, 14% (95% CI: 5%–23%) of research participants experienced issues of lost/malfunctioning study devices or apps, underscoring the need for careful design to address data privacy and technical reliability.⁵⁵ Besides, the duration of digital inhaler-based interventions ranges from less than a month to 2 years in asthma and COPD.^55,88 While short-term use is generally well accepted by patients, evidence on the long-term engagement and impact remains limited. In studies using conventional care, the average frequency of intervention, especially targeting inhaler technique, is three times per year.⁸⁹ Similarly, digital intervention programmes with repeated engagements resulted in meaningful success.⁵⁵ This comes at the risk of fatigue from digital tasks among participants. Future research should explore the optimal and acceptable frequency of digital intervention among patients, the longevity of impact and identify strategies to sustain engagement.

Environmental impact and sustainability of digital inhalers

Before implementing digital inhalers in health services, their environmental impact must be considered. Research examining the sustainability of digital inhalers is emerging. Digital inhalers may attach to either pMDI or DPI devices, but if attached to pMDIs, their environmental footprint rises. Hydrofluorocarbon propellants from pMDI account for 3%-4% of NHS carbon emissions in the UK.^90,91 Digital inhalers contain a mixture of recyclable (plastics, circuitry, metal and batteries) and non-recyclable components (some metal and residual medicine). Murphy et al. highlighted poor inhaler waste disposal practices among patients with 90% using domestic streams.⁹⁰ While some pharmaceutical companies^90,92,93 support regional inhaler disposal initiatives, dedicated recycling schemes remain limited in scale and have not achieved a country-wide impact yet. Besides, electronic waste created by digital inhalers poses another challenge. While individual inhalers consume minimal power, the supporting cloud infrastructure significantly increases cumulative energy demands. Nonetheless, the potential of digital inhalers to improve patient adherence and health outcomes may offset some of these impacts.

Assessment of cost versus benefits

Adopting digital inhalers in healthcare settings entails several upfront costs for purchasing devices, platform set-up and staff training. A digital inhaler can cost between $50 and $300,^94–96 though commercial insurance or bulk purchasing can reduce costs to as little as $20 per prescription.⁹⁵ Non-rechargeable models generally last at least a year, while rechargeable ones need to be powered up every 4–6 weeks.^48,97 Additional charges may be incurred for HCP for software access and platform maintenance. Critics have questioned whether the investment is justified, given the limited number of economic modelling studies. Furthermore, the time and staffing required to initiate patients on digital inhaler platforms may present additional barriers, particularly within resource-constrained primary care settings.

A comprehensive evaluation of cost-effectiveness is complex, factoring in patient demographics, reductions in hospitalisations, savings from decreased rescue medication usage, primary care visits, and daily symptom control over the long term. Cost-effectiveness differs widely based on the outcomes measured and unique factors within each healthcare setting. For instance, studies based in the UK have shown annual savings of 96 to 845 GBPs per COPD patient.^96,98,99 In the US, reimbursement mechanisms exist for remote monitoring, but coverage is not universal across insurers.⁹⁶ Thus, direct comparison across systems is challenging. As the digital inhaler market grows, increased competition may further reduce prices and enhance cost efficiency.

A systematic review on m-Health interventions in chronic airway diseases also highlighted that studies involving real-time monitoring or teleconsultations predominantly showed favourable economic outcomes.¹⁰⁰ Van Boven et al.^34,99 conducted a cost-benefit analysis of digital inhaler interventions for COPD (n = 226) from an Irish healthcare payer perspective. Patient groups with good inhaler techniques but using their inhalers irregularly (36% of cohort) showed the highest savings with €845/annum/person while gaining 0.33 QALYs (Quality-Adjusted Life Years). Those with poor inhaler techniques, regardless of the regularity of inhaler use , still achieved QALY gains but at a higher cost over 5 years. Economic benefits were more pronounced for patients with difficult-to-treat severe asthma,¹⁰¹ with cost savings between €3207 ($3377) and €26,309 ($27,703) per person over 10 years, largely due to reduced biologic therapy costs. These findings suggest that targeting the right patient group is key to maximising both clinical benefits and cost-effectiveness of interventions using digital inhalers.

Targeted patient groups for digital inhaler adherence interventions

Building on the need to prioritise patients most likely to benefit from interventions, target groups have been identified based on previous evidence.¹⁰³ We propose the following prime candidates:

• Patients having severe disease and poor adherence: Patients with severe asthma (e.g., GINA steps 4-5) or COPD with high healthcare utilisation burden stand out to benefit significantly. For these individuals, digital inhaler monitoring enables early risk detection and timely treatment adjustment.

• Responder group: Objective adherence monitoring mapped with changes in biomarkers such as blood eosinophil counts or fractional exhaled nitric oxide (FeNO) could facilitate precision therapies. Given that only a subset of COPD patients with type 2 inflammation benefit from ICS, biomarker-driven insights could refine the treatment target group and thresholds. Currently, this approach is rarely utilised in COPD studies.

• New users of inhaler regimes: For patients starting inhaler therapy, digital tools help establish proper technique and consistent usage habits from the outset.

• Patients in rural settings: Employing digital platforms holds promise for residents in rural areas where adherence is often hindered by access or education gaps. Favouring feedback from participants and success were received in both asthma and COPD.^41,102,103 As smartphone access expands, digital platforms can improve clinical outcomes for hard-to-reach populations.

• Research participants: A systematic review of research trials examining the efficacy of add-on therapies in asthma revealed that background inhaler adherence was drastically overlooked.¹⁰⁴ Only 23% of studies reported adherence and rarely used objective methods.¹⁰⁴ Trials that did track adherence saw reduced variance in FEV₁ outcomes, achieving higher statistical power. Thus, digital inhalers should become the new standard for research examining treatment efficacy.

• Patients on AIR and MART regimes: Recent guidelines endorsed the shift towards AIR and MART regimes from traditional reliever use.^2,105 Longitudinal evidence is limited on both these regimens, especially on the prevalence of symptom-driven inhaler overuse behaviour among those with advanced disease.^77,106 Moreover, our current adherence benchmarks (i.e., 75%–80%)^34,107,108 largely stem from studies using prescription refill data, which may overestimate adherence. Studies using EIM rarely achieved that target,^34,43,102 suggesting that traditional targets might be overly optimistic for certain subgroups. Evidence on MART regimes in the severe asthma group receiving biologics further supports this, as patients were clinically well at lower but regular ICS exposure.^9,105,109 For COPD patient groups, studies, even with self-reports, reported high daily ICS exposure, 900 mcg budesonide equivalent dose.¹¹⁰ As the MART prescription gains traction, EIM can help characterise the true inhaler use pattern in this cohort.³⁴

Future direction and proposed adherence intervention model

Digital inhaler platforms demonstrated a great potential to enhance patient care. However, to avoid exacerbating existing disparities, digital solutions must prioritise accessibility and user-friendliness across diverse patient demographics. Features such as voice commands, large displays, and multilingual options are important. Another key detail that must be worked out is the technical and interoperability standards so that the purchased technology can be integrated efficiently. With increasing smartphone penetration globally including in developing countries, it is possible that patients can potentially install digitalised health apps and utilise features such as app-based reminders and monitoring of physiological parameters without extra cost. Additionally, the environmental impact also needs consideration in prescribing decisions, emphasising initiatives for eco-friendly disposal, remanufacturing, and incentives for returning used devices among patients.

Current evidence proves that the personalisation of adherence behaviour (e.g., the right digital model for the right patient) is critical. However, what is lacking is the mechanistic aspect of how the treatment impacted patients to prioritise the patient selection or identification of responder groups. Data from large pilot studies fitting these platforms to the actual healthcare system is in demand. To facilitate an effective digital inhaler adherence intervention, we propose key pillars that incorporate exploring behavioural insights and biomarker profiling while examining optimal levels of digital technology support for an individual (see Figure 1). This approach will add sophistication to digital inhaler studies, enhancing their impact and value in real-world applications.

Figure 1.

Key elements to be considered for future digital adherence intervention study designs. EIM: electronic inhaler adherence monitoring; FeNO: fractional exhaled nitric oxide; BEC: blood eosinophil count; HRQoL: health-related quality of life; HCP: healthcare practitioners. Figure adapted from Aung et al.’s work in the European Respiratory Review⁵⁵.

Conclusion

Digital inhalers are at the forefront of personalised respiratory care. Beyond their technological advancements, they serve as catalysts for a broader shift in care delivery—one that prioritises patient-centred approaches and integrates behavioural insights with clinical data. Although digital inhaler platforms have made remarkable strides in improving adherence, their full potential remains untapped. Moving forward, continued innovation and collaboration between stakeholders, healthcare providers, and patients will be essential to unlock the full potential of these digital solutions.

Supplemental Material

Supplemental Material - The evolving landscape of digital inhaler platforms and adherence support in chronic airways disease

Supplemental Material for The evolving landscape of digital inhaler platforms and adherence support in chronic airways disease by Hnin Aung, Anna Murphy, Neil J. Greening in Chronic Respiratory Disease

Footnotes

ORCID iD

Hnin Wint Wint Aung

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study/research is supported by the National Institute for Health and Care Research (NIHR) Leicester Biomedical Research Centre (BRC).

Declaration of conflicting interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: H. Aung has no conflict of interest. A. Murphy has received funding for research studies, consultancy agreements, and honoraria for presentations from AstraZeneca, Chiesi, Orion, and Sanofi. N.J. Greening has received honoraria for lectures, conference travel and advisory boards from AstraZeneca, Boehringer Ingelheim, Chiesi, GlaxoSmithKline, Pulmonx, Roche and Sanofi and received grants and consultation fees (paid to institution) from Genentech, Roche and GlaxoSmithKline.

Supplemental Material

Supplemental material for this article is available online.

References

Agusti

Vogelmeier

. Gold 2024: a brief overview of key changes. J Bras Pneumol 2023; 49: e20230369.

Rajvanshi

Kumar

Goyal

. Global initiative for asthma guidelines 2024: an update. Indian Pediatr 2024; 61: 781–786.

Murphy

Proeschal

Brightling

, et al. The relationship between clinical outcomes and medication adherence in difficult-to-control asthma. Thorax 2012; 67: 751–753.

Vestbo

Anderson

Calverley

, et al. Adherence to inhaled therapy, mortality and hospital admission in COPD. Thorax 2009; 64: 939–943.

Murphy

Boddy

Bradding

. Pro: access to advanced therapies for severe asthma should be restricted to patients with satisfactory adherence to maintenance treatment. Breathe 2021; 17: 210024.

Tice

Campbell

Synnott

, et al. The effectiveness and value of biologic therapies for the treatment of uncontrolled asthma. J Manag Care Spec Pharm 2019; 25: 510–514.

Heaney

Busby

Bradding

, et al. Remotely monitored therapy and nitric oxide suppression identifies nonadherence in severe asthma. Am J Respir Crit Care Med 2019; 199: 454–464.

d'Ancona

Kavanagh

Roxas

, et al. Adherence to inhaled corticosteroids and clinical outcomes in mepolizumab therapy for severe asthma. Eur Respir J 2020; 55: 1902259.

Jackson

Heaney

Humbert

, et al. Reduction of daily maintenance inhaled corticosteroids in patients with severe eosinophilic asthma treated with benralizumab (SHAMAL): a randomised, multicentre, open-label, phase 4 study. Lancet 2024; 403: 271–281.

10.

Reddel

Bacharier

Bateman

, et al. Global initiative for asthma strategy 2021: executive summary and rationale for key changes. Eur Respir J 2022; 59: 2102730.

11.

Venkatesan

. GOLD COPD report: 2025 update. Lancet Respir Med 2025; 13: e7–e8.

12.

Vrijens

De Geest

Hughes

, et al. A new taxonomy for describing and defining adherence to medications. Br J Clin Pharmacol 2012; 73: 691–705.

13.

Horne

Cooper

Wileman

, et al. Supporting adherence to medicines for long-term conditions. Eur Psychol 2019; 24: 82–96.

14.

Horne

Chapman

Parham

, et al. Understanding patients’ adherence-related beliefs about medicines prescribed for long-term conditions: a meta-analytic review of the necessity-concerns framework. PLoS One 2013; 8: e80633.

15.

Pearce

Chan

AHY

Jackson

, et al. Features of successful interventions to improve adherence to inhaled corticosteroids in children with asthma: a narrative systematic review. Pediatr Pulmonol 2022; 57: 822–847.

16.

MacLaughlin

Raehl

Treadway

, et al.

Assessing medication adherence in the elderly: which tools to use in clinical practice?

Drugs Aging 2005; 22: 231–255.

17.

Adejumo

Shaw

. Con: access to advanced therapies for severe asthma should be restricted to patients with satisfactory adherence to maintenance treatment. Breathe 2021; 17: 210049.

18.

Green

Shaw

. Strict adherence rules to obtain monoclonal therapy might cost lives. Lancet Respir Med. 2024; 5: 678–679.

19.

UpToDate . Inhaler techniques in adults: beyond the basics. https://www.uptodate.com/contents/inhaler-techniques-in-adults-beyond-the-basics (n.d.), (Accessed 17 July 2025).

20.

Anderson

. Use of respimat soft mist inhaler in COPD patients. Int J Chronic Obstr Pulm Dis 2006; 1: 251–259.

21.

Chrystyn

van der Palen

Sharma

, et al. Device errors in asthma and COPD: systematic literature review and meta-analysis. NPJ Prim Care Respir Med 2017; 27: 22.

22.

CapMedic . Simplifying inhalers for correct and regular use. Accessed July 17, 2025.

23.

Usmani

Lavorini

Marshall

, et al. Critical inhaler errors in asthma and COPD: a systematic review of impact on health outcomes. Respir Res 2018; 19: 10.

24.

Biswas

. CapMedic™: simplifying inhalers for regular and correct use. ONdrugDelivery 2018; 2018: 30–33.

25.

Ganguly

Das

Roy

, et al. Study of proper use of inhalational devices by bronchial asthma or COPD patients attending a tertiary care hospital. J Clin Diagn Res 2014; 8: Hc04–HC7.

26.

Alsallakh

Rodgers

Lyons

, et al. Association of socioeconomic deprivation with asthma care, outcomes, and deaths in Wales: a 5-year national linked primary and secondary care cohort study. PLoS Med 2021; 18: e1003497.

27.

Steiner

Lowe

Beckford

, et al. Socioeconomic deprivation and the outcome of pulmonary rehabilitation in England and Wales. Thorax 2017; 72: 530–537.

28.

Field

. Telemedicine: a guide to assessing telecommunications for health care. Springer, 1996.

29.

Borges do Nascimento

Abdulazeem

Vasanthan

, et al. Barriers and facilitators to utilizing digital health technologies by healthcare professionals. npj Digit Med 2023; 6: 161.

30.

World Health Organization (WHO) . WHO develops guidance to improve telemedicine services, 2024.

31.

Digital NHS England . Inclusive digital healthcare: a framework for NHS action on digital inclusion. Digital NHS England, 2024.

32.

Insights

. Respiratory inhalers market. https://www.insightaceanalytic.com/report/global-respiratory-inhalers-market/1288 (2024), (Accessed 17 July 2025).

33.

Intelligence

. Smart inhalers market size & share analysis - growth trends and forecasts (2024–2029) source. https://www.mordorintelligence.com/industry-reports/smart-inhalers-market (2023), (Accessed 17 July 2025).

34.

Sulaiman

Cushen

Greene

, et al. Objective assessment of adherence to inhalers by patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2017; 195: 1333–1343.

35.

Xiroudaki

Schoubben

Giovagnoli

, et al. Dry powder inhalers in the digitalization era: current status and future perspectives. Pharmaceutics 2021; 13: 1455.

36.

Chan

AHY

Pleasants

Dhand

, et al. Digital inhalers for asthma or chronic obstructive pulmonary disease: a scientific perspective. Pulm Ther 2021; 7: 345–376.

37.

Spector

Kinsman

Mawhinney

, et al. Compliance of patients with asthma with an experimental aerosolized medication: implications for controlled clinical trials. J Allergy Clin Immunol 1986; 77: 65–70.

38.

Tashkin

Rand

Nides

, et al. A nebulizer chronolog to monitor compliance with inhaler use. Am J Med 1991; 91: 33s–36s.

39.

Doser . Doser: portable inhaler technology. Doser.com (Accessed 17 July 2025).

40.

Kikidis

Konstantinos

Tzovaras

, et al. The digital asthma patient: the history and future of inhaler based health monitoring devices. J Aerosol Med Pulm Drug Deliv 2016; 29: 219–232.

41.

Kuipers

Wensing

de Smet

, et al. Self-management research of asthma and good drug use (SMARAGD study): a pilot trial. Int J Clin Pharm 2017; 39: 888–896.

42.

Sykes

See

Chow

ECY

, et al.

Digitally monitored inhaled therapy: a ‘smart’ way to manage severe asthma?

J Asthma 2024; 61: 970–975.

43.

Alshabani

Attaway

Smith

, et al. Electronic inhaler monitoring and healthcare utilization in chronic obstructive pulmonary disease. J Telemed Telecare 2020; 26: 495–503.

44.

Findair . Findair- smart inhaler. https://findair.eu/. 2021. Accessed July 17, 2025.

45.

Moran

Doyle

Sulaiman

, et al. The INCA(TM) (inhaler compliance assessment(TM)): a comparison with established measures of adherence. Psychol Health 2017; 32: 1266–1287.

46.

Hesso

Nabhani Gebara

Greene

, et al. A quantitative evaluation of adherence and inhalation technique among respiratory patients: an observational study using an electronic inhaler assessment device. Int J Clin Pract 2020; 74: e13437.

47.

Braido

Paa

Ponti

Canonica

. A new tool for inhalers’ use and adherence monitoring: the Amiko® validation trial. Int J Eng Res Sci. 2016; 2(10): 159–66.

48.

Eikholt

Wiertz

MBR

Hew

, et al. Electronic monitoring devices to support inhalation technique in patients with asthma: a narrative review. Curr Treat Options Allergy 2023; 10: 28–52.

49.

Safioti

Granovsky

, et al. A predictive model for clinical asthma exacerbations using albuterol eMDPI (proAir digihaler): a twelve-week, open-label study. Iproc 2019; 5: e15173.

50.

Down

Beresford

Blatchford

Cooper

Griffiths

. Developing clinically relevant methodology for existing pMDI aerosol products when coupled to the Intelligent Control Inhaler. In Journal of Aerosol Medicine and Pulmonary Drug Delivery 2019 Apr 1 (Vol. 32, No. 2, pp. A25-A25). USA: MaryA Ann Liebert, INC.

51.

Adejumo

Shaw

. Electronic monitoring devices as an intervention in asthma: the story so far. Curr Respir Med Rev 2018; 14: 5–22.

52.

Mosnaim

Safioti

Brown

, et al. Digital health technology in asthma: a comprehensive scoping review. J Allergy Clin Immunol Pract 2021; 9: 2377–2398.

53.

Janjua

Pike

Carr

, et al. Interventions to improve adherence to pharmacological therapy for chronic obstructive pulmonary disease (COPD). Cochrane Database Syst Rev 2021; 9: Cd013381.

54.

Normansell

Kew

Stovold

. Interventions to improve adherence to inhaled steroids for asthma. Cochrane Database Syst Rev 2017; 18: CD012226.

55.

Aung

Tan

Flynn

, et al. Digital remote maintenance inhaler adherence interventions in COPD: a systematic review and meta-analysis. Eur Respir Rev 2024; 33: 240136.

56.

Charles

Quinn

Weatherall

, et al. An audiovisual reminder function improves adherence with inhaled corticosteroid therapy in asthma. J Allergy Clin Immunol 2007; 119: 811–816.

57.

Vasbinder

Goossens

LMA

Rutten-van Mölken

MPMH

, et al. e-monitoring of asthma therapy to improve compliance in children (e-MATIC): a randomised controlled trial. Eur Respir J 2016; 48: 758–767.

58.

Chan

Stewart

Harrison

, et al. The effect of an electronic monitoring device with audiovisual reminder function on adherence to inhaled corticosteroids and school attendance in children with asthma: a randomised controlled trial. Lancet Respir Med 2015; 3: 210–219.

59.

Yawn

McCreary

Linnell

, et al. Pilot study of a patient experience with an ELLIPTA inhaler electronic medication monitor and associated integrated system: a prospective observational study using the COPD patient-powered research network. Chronic Obstr Pulm Dis 2021; 8: 488–501.

60.

Onyirimba

Apter

Reisine

, et al. Direct clinician-to-patient feedback discussion of inhaled steroid use: its effect on adherence. Ann Allergy Asthma Immunol 2003; 90: 411–415.

61.

Criner

Cole

Hahn

, et al. The impact of budesonide/formoterol pMDI medication reminders on adherence in chronic obstructive pulmonary disease (COPD) patients: results of a randomized, phase 4, clinical study. Int J Chronic Obstr Pulm Dis 2021; 16: 563–577.

62.

Dierick

BJH

Achterbosch

Eikholt

, et al. Electronic monitoring with a digital smart spacer to support personalized inhaler use education in patients with asthma: the randomized controlled OUTERSPACE trial. Respir Med 2023; 218: 107376.

63.

Sulaiman

Greene

MacHale

, et al. A randomised clinical trial of feedback on inhaler adherence and technique in patients with severe uncontrolled asthma. Eur Respir J 2018; 51: 1701126.

64.

Mosnaim

Hoyte

FCL

Safioti

, et al. Effectiveness of a maintenance and reliever digihaler system in asthma: 24-week randomized study (CONNECT2). J Allergy Clin Immunol Pract 2024; 12: 385.e4–395.e4.

65.

Foster

Usherwood

Smith

, et al. Inhaler reminders improve adherence with controller treatment in primary care patients with asthma. J Allergy Clin Immunol 2014; 134: 1260.e3–1268.e3.

66.

Apter

Wang

Bogen

, et al. Problem solving to improve adherence and asthma outcomes in urban adults with moderate or severe asthma: a randomized controlled trial. J Allergy Clin Immunol 2011; 128: 516.e235–523.e235.

67.

Weinstein

Singh

Laurenceau

J-P

, et al. A pilot study of the effect of an educational web application on asthma control and medication adherence. J Allergy Clin Immunol Pract 2019; 7: 1497–1506.

68.

Burgess

Sly

Devadason

. Providing feedback on adherence increases use of preventive medication by asthmatic children. J Asthma 2010; 47: 198–201.

69.

O'Dwyer

Greene

MacHale

, et al. Personalized biofeedback on inhaler adherence and technique by community pharmacists: a cluster randomized clinical trial. J Allergy Clin Immunol Pract 2020; 8: 635–644.

70.

Gregoriano

Dieterle

Breitenstein

, et al. Does a tailored intervention to promote adherence in patients with chronic lung disease affect exacerbations? a randomized controlled trial. Respir Res 2019; 20: 273.

71.

Lin

Ramsey

Miller

, et al. Telehealth delivery of adherence and medication management system improves outcomes in inner-city children with asthma. Pediatr Pulmonol 2020; 55: 858–865.

72.

Snyder Gs

Reich

Neely

, et al. A predictive model for COPD exacerbations using ProAir digihaler: a 12-week, open-label study. Am J Respir Crit Care Med 2020; 201: A4485.

73.

Chrystyn

Soussi

Tarsin

, et al. Inhalation characteristics when patients use a dry powder inhaler (DPI) are related to their lung function when they are stable and when recovering from an acute exacerbations. In: B65 asthma: pathophysiology and clinical trials. American Thoracic Society, 2018, pp. A3939.

74.

Bousquet

Shamji

Anto

, et al. Patient-centered digital biomarkers for allergic respiratory diseases and asthma: the ARIA-EAACI approach – ARIA-EAACI task force report. Allergy 2023; 78: 1758–1776.

75.

Amin

Soliman

McIvor

, et al. Usage patterns of short-acting β2-agonists and inhaled corticosteroids in asthma: a targeted literature review. J Allergy Clin Immunol Pract 2020; 8: 2556.e8–2564.e8.

76.

Janson

Wiklund

Telg

, et al. High use of short-acting β₂-agonists in COPD is associated with an increased risk of exacerbations and mortality. ERJ Open Res. 2023; 9(3): 00722–02022.

77.

Patel

Pilcher

Reddel

, et al. Metrics of salbutamol use as predictors of future adverse outcomes in asthma. Clin Exp Allergy 2013; 43: 1144–1151.

78.

Lugogo

DePietro

Reich

, et al. A predictive machine learning tool for asthma exacerbations: results from a 12-week, open-label study using an electronic multi-dose dry powder inhaler with integrated sensors. J Asthma Allergy 2022; 15: 1623–1637.

79.

Knittel

Miller

Sanders

. Short-term impact of PM2.5 on contemporaneous asthma medication use: behavior and the value of pollution reductions. Proc Natl Acad Sci 2019; 116(12): 5246–5253.

80.

U.S. Food and Drug Administration (FDA) . Premarket notification [510(k)], 2019. https://www.fda.gov/medical-devices/premarket-submissions/premarket-notification-510k (Accessed 17 July 2025).

81.

European Medicines Agency (EMA) . Human regulatory overview: medical devices. https://www.ema.europa.eu/en/human-regulatory-overview/medical-devices (Accessed 17 July 2025).

82.

Agency MaHpR . About us. https://www.gov.uk/government/organisations/medicines-and-healthcare-products-regulatory-agency/about (2024), (Accessed 17 July 2025).

83.

Mishra

. FDA, CE mark or something else? thinking fast and slow. Indian Heart J 2017; 69: 1–5.

84.

Simpson

Honkoop

Kennington

, et al. Perspectives of patients and healthcare professionals on mHealth for asthma self-management. Eur Respir J 2017; 49: 1601966.

85.

Istepanian

RSH

. Mobile health (m-health) in retrospect: the known unknowns. Int J Environ Res Publ Health 2022; 19: 3747.

86.

Istepanian

Lacal

. Emerging mobile communication technologies for health: some imperative notes on m-health. In: Proceedings of the 25th annual international conference of the IEEE engineering in medicine and biology society (IEEE Cat No 03CH37439). IEEE, 2003, pp. 1414–1416.

87.

UK AL . Data sharing and technology: exploring the attitudes of people with asthma. https://www.asthmaandlung.org.uk/sites/default/files/2023-03/data-sharing-and-technology---exploring-the-attitudes-of-people-with-asthma (2018), (Accessed 17 July 2025).

88.

Chan

De Simoni

Wileman

, et al. Digital interventions to improve adherence to maintenance medication in asthma. Cochrane Database Syst Rev 2022; 6: CD013030.

89.

Takaku

Kurashima

Ohta

, et al.

How many instructions are required to correct inhalation errors in patients with asthma and chronic obstructive pulmonary disease?

Respir Med 2017; 123: 110–115.

90.

Murphy

Howlett

Gowson

, et al. Understanding the feasibility and environmental effectiveness of a pilot postal inhaler recovery and recycling scheme. NPJ Prim Care Respir Med 2023; 33: 5.

91.

National Health Service . The NHS long term plan. NHS England, 2019. https://www.longtermplan.nhs.uk/wp-content/uploads/2019/08/nhs-long-term-plan-version-1.2.pdf (Accessed 17 July 2025).

92.

Inhaler

. Disposal and recycling. https://www.tevadigihaler.co.uk/disposal-and-recycling/, (Accessed 17 July 2025).

93.

Contributor NT . GlaxoSmithKline ‘Complete the Cycle’ inhaler recycling and recovery scheme now accessible UK-wide. https://www.nursingtimes.net/opinion/glaxosmithkline-complete-the-cycle-inhaler-recycling-and-recovery-scheme-now-accessible-uk-wide-08-02-2013 (2013), (Accessed 17 July 2025).

94.

Jansen

van de Hei

Dierick

BJH

, et al. Global burden of medication non-adherence in chronic obstructive pulmonary disease (COPD) and asthma: a narrative review of the clinical and economic case for smart inhalers. J Thorac Dis 2021; 13: 3846–3864.

95.

Aungst

Smart inhalers: What they are and who can use them - GoodRx, What are Smart Inhalers? What's Available and How They Might Benefit You. https://www.goodrx.com/conditions/asthma/smart-inhalers-review (2025), (Accessed 17 July 2025).

96.

Pleasants

Chan

AHY

Mosnaim

, et al. Integrating digital inhalers into clinical care of patients with asthma and chronic obstructive pulmonary disease. Respir Med 2022; 205: 107038.

97.

Dhruve

Jackson

. Assessing adherence to inhaled therapies in asthma and the emergence of electronic monitoring devices. Eur Respir Rev 2022; 31: 210271.

98.

Orchard

Alderson

Buchanan

. PRS6 EARLY economic modelling of a SMART inhaler exploring cost-effectiveness in a severe asthma setting. Value Health 2020; 23: S717.

99.

van Boven

JFM

Cushen

Sulaiman

, et al. Personalising adherence-enhancing interventions using a smart inhaler in patients with COPD: an exploratory cost-effectiveness analysis. NPJ Prim Care Respir Med 2018; 28: 24.

100.

Ferreira

dos Santos

Sousa‐Pinto

, et al. Cost-effectiveness of digital health interventions for asthma or COPD: systematic review. Clin Exp Allergy 2024; 54: 651–668.

101.

van de Hei

Kim

Honkoop

, et al. Long-term cost-effectiveness of digital inhaler adherence technologies in difficult-to-treat asthma. J Allergy Clin Immunol Pract 2023; 11: 3064.e15–3073.e15.

102.

Broadbent

Garrett

Jepsen

, et al. Using robots at home to support patients with chronic obstructive pulmonary disease: pilot randomized controlled trial. J Med Internet Res 2018; 20: e45.

103.

Locke

Thomas

Woo

, et al. Using video telehealth to facilitate inhaler training in rural patients with obstructive lung disease. Telemed J e Health 2019; 25: 230–236.

104.

Mokoka

McDonnell

MacHale

, et al. Inadequate assessment of adherence to maintenance medication leads to loss of power and increased costs in trials of severe asthma therapy. Results from a systematic literature review and modelling study. Eur Respir J 2019; 53(53 53): 1802161.

105.

Beasley

Noble

Weatherall

. The evidence base for ICS/formoterol maintenance and reliever therapy in severe asthma. Eur Respir J 2024; 63: 2400523.

106.

Gondalia

Bender

Theye

, et al. Higher short-acting beta-agonist use is associated with greater COPD burden. Respir Med 2019; 158: 110–113.

107.

Bårnes

Ulrik

. Asthma and adherence to inhaled corticosteroids: current status and future perspectives. Respir Care 2015; 60: 455–468.

108.

Suissa

Ernst

Benayoun

, et al. Low-dose inhaled corticosteroids and the prevention of death from asthma. N Engl J Med 2000; 343: 332–336.

109.

Beasley

Holliday

Reddel

, et al. Controlled trial of budesonide-formoterol as needed for mild asthma. N Engl J Med 2019; 380: 2020–2030.

110.

Muiser

Imkamp

Seigers

, et al. Budesonide/formoterol maintenance and reliever therapy versus fluticasone/salmeterol fixed-dose treatment in patients with COPD. Thorax 2023; 78: 451–458.

111.

Alt

. Connecting drug delivery: enhancing asthma and COPD management with accurate flow measurement in inhalers. ONdrugDelivery 2018. https://www.ondrugdelivery.com/wp-content/uploads/2018/12/ONdrugDel-Connecting-Drug-Delivery-93-Dec-2018-Sensirion.pdf (Accessed 17 July 2025).

112.

Mysovskikh

Legg

Demidenko

. Flowrate sensing and measurement in portable smart inhalers. Sensors 2024; 24: 6848.

113.

Shakshuki

Agu

. Improving the efficiency of respiratory drug delivery: a review of current treatment trends and future strategies for asthma and chronic obstructive pulmonary disease. Pulm Ther 2017; 3: 267–281.

114.

Mehta

. Dry powder inhalers: a concise summary of the electronic monitoring devices. Ther Deliv 2021; 12: 1–6.

115.

Biocorp . Biocorp launches inspair to convert inhalers into connected devices. 2020. https://biocorpsys.com/en/biocorp-launches-inspair-to-convert-inhalers-into-connected-device/ (Accessed 17 July 2025).

116.

Kesten

Israel

, et al. Development of a novel digital breath-activated inhaler: initial particle size characterization and clinical testing. Pulm Pharmacol Ther 2018; 53: 27–32.

117.

Adherium . Hailie™ solution for asthma and COPD management: device features and specifications. Retrieved from. https://fcc.report/FCC-ID/pn2shd1/4054955.pdf (2021), (Accessed 17 July 2025).

118.

RS01X . RS01X - connected inhaler for better respiratory management. https://rs01x.com/, (Accessed 17 July 2025).

119.

Steuer

Prais

Mussaffi

, et al. Inspiromatic-safety and efficacy study of a new generation dry powder inhaler in asthmatic children. Pediatr Pulmonol 2018; 53: 1348–1355.

120.

Propeller Health . Propeller health receives new FDA clearance for next generation propeller platform share. https://propellerhealth.com/press/press-releases/propeller-health-receives-new-fda-clearance-next-generation-propeller-platform/ (2014), (Accessed 17 July 2025).

121.

Novartis . Novartis receives EC approval for enerzair breezhaler, including first digital companion sensor and app, which can be prescribed alongside treatment for uncontrolled asthma in the EU. https://www.novartis.com/news/media-releases/novartis-receives-ec-approval-enerzair-breezhaler-including-first-digital-companion-sensor-and-app-can-be-prescribed-alongside-treatment-uncontrolled-asthma-eu. 2020. Accessed July 17, 2025.

122.

Glusker

Axford

, et al. USSC-03: a modular breath-actuated multi-dose dry powder inhaler system with telehealth capability. Proc Respir Drug Deliv 2016; 3: 449–454.

123.

Alwashmi

Mugford

Vokey

, et al. Effectiveness of the BreatheSuite device in assessing the technique of metered-dose inhalers: validation study. JMIR Biomed Eng 2021; 6: e26556.

124.

NuvoAir . NuvoAir sensor expands possibilities for asthma and COPD control. https://nuvoair.com/blog/nuvoair_sensor_expands_possibilities_for_asthma_and_copd_control (2024), (Accessed 17 July 2025).

125.

Moore

Preece

Sharma

, et al. A randomised controlled trial of the effect of a connected inhaler system on medication adherence in uncontrolled asthmatic patients. Eur Respir J 2021; 57: 2003103.

126.

Morton

Elphick

Rigby

, et al. STAAR: a randomised controlled trial of electronic adherence monitoring with reminder alarms and feedback to improve clinical outcomes for children with asthma. Thorax 2017; 72: 347–354.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.60 MB