pharmaceuticals Systematic Review Clinical Impact of Electronic Monitoring Devices of Inhalers in Adults with Asthma or COPD: A Systematic Review and Meta-Analysis Noe Garin 1,2,3, *,† , Borja Zarate-Tamames 1,4,† , Laura Gras-Martin 1 , Raimon Milà 2 , Astrid Crespo-Lessmann 5 , Elena Curto 5 , Marta Hernandez 2 , Conxita Mestres 2 and Vicente Plaza 5 1 Department of Pharmacy, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, 08025 Barcelona, Spain 2 School of Health Science Blanquerna, Universitat Ramon Llull, 08025 Barcelona, Spain 3 Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, 28007 Madrid, Spain 4 Department of Medicine, Universitat Autònoma de Barcelona, 08025 Barcelona, Spain 5 Department of Respiratory Medicine, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, 08025 Barcelona, Spain * Correspondence: ngarin@santpau.cat † These authors contributed equally to this work. Abstract: We conducted a systematic review and meta-analysis to gain insight into the characteristics and clinical impact of electronic monitoring devices of inhalers (EMDs) and their clinical interventions in adult patients with asthma or COPD. The search included PubMed, Web of Science, Cochrane, Scopus and Embase databases, as well as official EMDs websites. We found eight observational studies and ten clinical trials, assessing a wide range of clinical outcomes. Results from the meta-analysis on adherence to inhalers in a period over three months were favourable in the EMD group (fixed effects Citation: Garin, N.; Zarate-Tamames, model: SMD: 0.36 [0.25–0.48]; random effects model SMD: 0.41 [0.22–0.60]). An exploratory meta- B.; Gras-Martin, L.; Milà, R.; analysis found an improvement in ACT score (fixed effect model SMD: 0.25 [0.11–0.39]; random effects Crespo-Lessmann, A.; Curto, E.; model: SMD: 0.47 [−0.14–1.08]). Other clinical outcomes showed mixed results in the descriptive Hernandez, M.; Mestres, C.; Plaza, V. analyses. The findings of this review highlight the benefits of EMDs in the optimization of adherence Clinical Impact of Electronic to inhaled therapy as well as the potential interest in other clinical outcomes. Monitoring Devices of Inhalers in Adults with Asthma or COPD: A Keywords: asthma; COPD; meta-analysis; electronic monitoring device; medication adherence; Systematic Review and Meta-Analysis. Pharmaceuticals 2023, 16, 414. clinical outcomes https://doi.org/10.3390/ph16030414 Academic Editors: Nathalie Wauthoz, Carine De Vriese and 1. Introduction Francesca Buttini Among chronic respiratory diseases, asthma and chronic obstructive pulmonary Received: 8 February 2023 disease (COPD) stand out for their high prevalence, impact on quality of life and clinical Revised: 1 March 2023 repercussion. Both pathologies share some characteristics, such as airflow obstruction Accepted: 2 March 2023 measured as forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC) < 0.7, and Published: 8 March 2023 the importance of inhaled treatment to achieve the best possible control, which will depend on the stage or severity of the patient, along with other strategies such as control of risk factors or smoking cessation. However, these pathologies are far from being controlled in multiple cases [1–3]. Copyright: © 2023 by the authors. Adherence to pharmacological treatment is poor in chronic pathologies, close to 50% Licensee MDPI, Basel, Switzerland. This article is an open access article according to estimates by the World Health Organization [4]. Moreover, administration of distributed under the terms and inhaled therapy is correct in a third of the patients [5]. Likewise, adequate adherence to in- conditions of the Creative Commons halers is associated with a reduction in asthma exacerbations, greater control of symptoms, Attribution (CC BY) license (https:// lower systemic cortico-steroid requirements and lower disease-related mortality [6–10]. creativecommons.org/licenses/by/ In COPD, several studies associate poorer adherence to inhalers with a higher number of 4.0/). Pharmaceuticals 2023, 16, 414. https://doi.org/10.3390/ph16030414 https://www.mdpi.com/journal/pharmaceuticals Pharmaceuticals 2023, 16, 414 2 of 25 exacerbations, admissions and mortality [11–14]. According to data from a Registry of Phar- maceutical provision in Spain, in 2017 only 63% of all prescribed prescriptions for inhalers (adrenergic receptor agonists, corticosteroids, anticholinergics and their combinations) were dispensed [15]. Despite technology advances in pulmonary delivery devices over the last years, ad- herence and their correct use remains a major issue in clinical practice [16]. Most of the adherence measures used in clinical practice rely on patients’ self-report data, including general questionnaires applied to respiratory diseases such as the Morisky Medication Adherence Scale [17,18] and specific questionnaires such as the Test of Adherence to In- halers [19]. However, over-reporting from patients may be compromising the accuracy of these tools in numerous cases [20,21]. Objective measures, such is the case of the assessment of dispensing records, may overcome this barrier but face the possibility of the dispensed medication not being administered or connection problems in the pharmacy systems being present [15,22]. A novel approach to cope with these difficulties is the use of electronic monitoring devices (EMDs) attached to inhalers as an objective measure of adherence. In this context, one of the most innovative proposals is the use of sensors attached to inhalers, in such a way that their use is electronically recorded and communicated to the patient, the clinical team or both [23]. With this information, interventions aimed to improve adherence can be conducted at any of those levels, allowing patient engagement and continuing education and the participation of the multidisciplinary team, with the pos- sibility of including activities and interventions covering other clinical problems of interest. Several promising experiences have been published indicating its potential in improving adherence and other clinical parameters of interest. For example, its employment has been related to a reduction in the use of rescue inhalers, improved results of the Asthma Control Test (ACT), increased rescue inhaler-free days and increased adherence to inhalers [23–28]. There have been some interesting efforts to summarize the characteristics of EMDs but some of them focus on functionalities, while others are literature or scoping reviews targeting technological interventions in a broad sense or including different types of patients (e.g., paediatrics and adults) that mostly include data from conference abstracts [29–35] rather than complete, peer-reviewed articles. Thus, we aimed to explore the characteristics and assess the impact of EMDs and their clinical interventions in adult patients with asthma or COPD by means of a systematic review and meta-analysis. 2. Methods 2.1. Study Registration This systematic review and meta-analysis were prospectively registered in the In- ternational Prospective Register of Systematic Reviews (PROSPERO), identification code CRD42022318919. The protocol adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [36]. 2.2. Eligibility Criteria A systematic review through computer searches was performed to identify studies on adult patients (≥18 years) with asthma and/or COPD that measured the clinical impact related to interventions based on EMDs. The outcomes assessed in the review were open to any clinical variable due to the potential broad range of interest in practice, such as: adherence, use of reliever medication, symptoms, exacerbations, Emergency Department (ED) visits, hospitalizations, pulmonary function tests and other relevant clinical outcomes. We included observational studies and clinical trials to gather all relevant information of the use of the EMDs and the nature of the interventions as this is a very novel approach, with the additional intention of conducting a meta-analysis with the results of randomized clinical trials. We included studies regardless of the nature of the comparator group in the case of randomized trials. Pharmaceuticals 2023, 16, x FOR PEER REVIEW 3 of 30 Pharmaceuticals 2023, 16, 414 randomized clinical trials. We included studies regardless of the nature of the comparator 3 of 25 group in the case of randomized trials. We included studies in the English and Spanish language, with no time restrictions. Unpublished articles We included and in studies conference abstracts the English were excluded. and Spanish language,In addition, with no timestudies were restrictions. considered Unpublishedineligible articles when the outcomes and conference of interest abstracts werewere not measured excluded. or reported. In addition, studies were considered ineligible when the outcomes of interest were not measured or reported. 2.3. Information Sources 2.3. Information The searchSources was conducted on 1 June 2022 on the following bibliographic databases: PubMed, Web ofwas The search Science, Cochrane, conducted Scopus on 1 June and 2022 onEmbase, according the following to the eligibility bibliographic cri- databases: teria, with no time restrictions. PubMed, Web of Science, Cochrane, Scopus and Embase, according to the eligibility criteria, with An additional no time search was performed including cites in reviews found in the previous restrictions. search, Ancitation examination additional search wasofperformed the selected articlescites including andininreviews the websites found in forthe Digihaler previous® (www.digihalerhcp.com), search, citation examination HeroTracker ® of the selected (www.coherohealth.com), articles and in the websites Respiro Amiko® for Digihaler ® (www.amiko.io), (www.digihalerhcp.com),CapMedic ® HeroTracker ® (www.capmedicinhaler.com), (www.coherohealth.com), Turbuplus™ ® (www.turbu- Respiro Amiko (www. plusinfo.co.uk), amiko.io), CapMedic ® (www.capmedicinhaler.com), Propeller Health® (www.propellerhealth.com), Turbuplus™ INCA™ (www.incade- (www.turbuplusinfo.co. vice.com) uk), Propeller Hailie®®(www.hailie.com) and Health (35). Accessed (www.propellerhealth.com), on 1 August INCA™ 2022. (www.incadevice.com) and ® Hailie (www.hailie.com) (35). Accessed on 1 August 2022. 2.4. Search Strategy 2.4. Search Strategy Firstly, an exploratory phase was conducted to detect the terminology used for elec- Firstly, tronical an exploratory interventions phaseofwas by means conducted reviewing to detect original the terminology articles presented inusedsomeforofelec- the aforementioned websites. The following four domains were found and developedofwith tronical interventions by means of reviewing original articles presented in some the aforementioned websites. The following four domains were found their corresponding keywords: sensor, monitoring, innovative connected technology andand developed with their corresponding devices (Figure 1). Thekeywords: sensor, full search monitoring, strategy innovative is available connected in File S1. technology The search and review pro- devices cess was(Figure 1). with assessed The full thesearch strategyofisElectronic Peer Review available in File S1. Search The search Strategies reviewchecklist (PRESS) process was [37]. assessed with the Peer Review of Electronic Search Strategies (PRESS) checklist [37]. Figure Figure 1. 1. Search strategy summary. Search strategy summary. 2.5. Selection and Data Collection Process database were exported The data from searches in each database exported to RefWorks, and an initial phase for fordetection detectionofof duplicates waswas duplicates performed. A subsequent performed. duplication A subsequent detection duplication phase detection was conducted phase in the in was conducted resulting database the resulting by DOI database by identifier and manual DOI identifier assessment. and manual The assessment. results of the refined database were screened (title/abstract) by two independent inves- tigators, with disagreements resolved by a third researcher. This method was replicated for the following full-text assessment and final inclusion of articles. Additionally, identi- fication of studies via websites and cites in systematic reviews and included studies (see Pharmaceuticals 2023, 16, 414 4 of 25 above) was performed by an independent researcher, with a subsequent validation by another researcher. Once selected, data were collected and validated by two independent investigators from each report. 2.6. Data Items and Synthesis Methods The outcomes of the review were divided into domains and their corresponding items to be assessed: • Adherence to chronic inhaled treatment. • Control and symptoms of the disease. E.g., ACT, Asthma Control Questionnaire (ACQ), modified Medical Research Council (mMRC) scale, COPD assessment test (CAT), etc. • Quality of life. • Acute worsening and related issues (use of reliever inhaled treatment, exacerbations, visits to ED, hospital admissions, primary care and need of systemic corticosteroids). • Pulmonary function tests results. • Other relevant clinical outcomes. All results compatible with each outcome domain were sought, regardless of the time frame of measurement. The data collected also included the possibility of “other” clinical variables as we were exploring a novel approach, and it was plausible that other relevant data may be available. Due to the nature of EMDs, we anticipated that adherence to medication may be the most frequent variable found in the studies. The other variables collected were author, year of publication, sample size, age of participants, study design, number of centres, follow-up period, control group, intervention group, intervention details, recruitment setting, healthcare professional involved, circuit type (focused on the healthcare professional, the patient or both of them), type of interaction, type of inhaler assessed, type of EMD, data recorded and health outcomes. 2.7. Study and Report of Risk of Bias Assessment The risk of bias was assessed with the Revised Cochrane risk-of-bias tool for random- ized trials (RoB2) [38] and with the Methodological index for non-randomized studies (MINORS) [39]. All methodological components/items of the tools were applied. As- sessment was performed by a researcher and validated by a second member of the team. Reporting of risk of bias was summarized in the text and in graphical figures for indepen- dent items and global results of each study. 2.8. Statistical Analysis The descriptive data of the participants’ characteristics were reported as a mean (SD). All meta-analyses’ calculations were conducted with the R software (Vienna, Austria) with meta and metafor packages for meta-analysis (Version 3.5.1.). Descriptive analyses and figures of the risk of bias were performed using Microsoft Excel for MAC, version 16.29.1 (Microsoft, Redmond, WA, USA). The mean and standardized mean differences (Hedges’ g) and 95% CI for each group were calculated. The analysis of pooled data was conducted using a random-effect model to estimate the change for each group at the same measurement time on primary and secondary outcomes. Standardized mean differences were weighted by the inverse of the variance to calculate the size of the effect and 95% confidence interval. Cohen’s criteria were used to interpret the magnitude of the effect: <|0.50|: small; |0.50| to |0.80|: moderate; and >|0.80|: large. Heterogeneity was assessed using Cochran’s Q statistics and its corresponding p-value as well as the I2 statistic, which describes the percentage of variability in effect estimates attributable to heterogeneity rather than chance when I2 was >30% (30–60% representing moderate heterogeneity). Publication bias was assessed with funnel plots and Begg’s test. Significance was set at p < 0.05. Pharmaceuticals 2023, 16, 414 5 of 25 3. Results 3.1. Study Selection A total of 10483 articles were identified through computer searches in the selected databases, with 5264 duplicates removed through electronical or manual methods. A total of 5219 studies were screened by title and abstract and 146 were assessed for eligibility by full-text assessment. An additional search was performed using websites, and citation searching of articles and systematic reviews, with 177 articles assessed for eligibility. Some of the retrieved studies might appear to meet the inclusion criteria, but were eventually discarded due to several reasons, such as: not providing separate results from adults and paediatric patients [40,41], using EMDs to confirm the validity of the response to the Fractional Exhaled nitric oxide (FeNO) outpatient test, not as an intervention itself [42], or assessing patient satisfaction with EMDs rather than its clinical impact on disease as study outcomes [43]. Eighteen studies were finally included in the systematic review (Figure 2). Seven out of eight observational studies were conducted in the USA, while one study was conducted in the Netherlands. The distribution of clinical trials was the USA (n = 5), Ireland (n = 2), New Zealand (n = 1), Switzerland (n = 1) and one multi-country study (Canada, Germany, Italy, Netherlands, Spain, UK and the USA). 3.2. Study Characteristics and Results We found eight observational studies [28,44–50] and 10 clinical trials meeting the criteria [23,27,51–62] (Figure 3, Tables 1 and 2). Regarding observational studies, six of them focused on patients with COPD, one study on asthma and one study on patients with asthma and/or COPD (Table 1). Their sample size ranged from n = 11 to 2292 patients, with an average age ranging from 36.8 to 68.6 years and being over 60 years in five out of the eight studies. Clinical outcomes assessed in observational studies included adherence to maintenance medication, use of rescue medication, disease control, inhalation technique, quality of life, self-management variables (e.g., behaviour, knowledge and adherence to the intervention), steroid use, health-care utilization variables and mental health scores. Variables were differently opera- tionalized across studies (e.g., mean daily short-acting beta-agonist [SABA] use, change in mean of SABA use, percentage of SABA-free days, etc.). Six of the studies performed statistical analyses of the clinical outcomes [28,44,46,47,49,50], while two studies presented descriptive analyses only [45,48]. Adherence to maintenance inhalers improved in two studies [49,50] whereas it was similar or decreased in the other two studies [44,46]. SABA use was found to be decreased in all variables assessed in two different studies [28,44]. One study focused on symptom control and found a clinical improvement in ACT score, days with asthma symptoms and nights with asthma symptoms. However, no differ- ence was found regarding activity limitations [47]. Finally, one study found a decrease in COPD-related healthcare utilization and high accuracy of rescue alerts in predicting moderate–severe exacerbations but found no differences in variables such as all-cause healthcare utilization, number of pulmonary or primary care visits and antibiotic or steroid use [46]. Pharmaceuticals 2023, 16, 414 6 of 25 Table 1. Summary of results. ACQ (Asthma Control Questionnaire); ACT (Asthma Control Test); AQLQ (Asthma Quality of Life Questionnaire); Asthma-related quality of life (AQOL); AE (asthma education); C (Control group); CAT (COPD assessment test); CI (confidence interval); CCQ (Clinical COPD questionnaire); COPD (chronic obstructive pulmonary disease); DPI (dry-powder inhaler); ED (Emergency Department); FeNO (Fractional Exhaled nitric oxide); FEV1 (forced expiratory volume in 1 s); HCP (healthcare professionals); I (intervention group); ICS (inhaled corticosteroid); NA (not available); ND (no difference); PEF (peak expiratory flow); pMDI (pressurised Metered Dose Inhaler); POEMS (Polymedication Electronic Monitoring System); SABA (short acting beta agonist); SGRQ (St. George’s Respiratory Questionnaire). Sample Primary Clinical Author (Year) Disease Age Study Design Number of Centers Secondary Clinical Outcome Follow-Up Control Group Intervention Group Device Outcomes Size Outcome - COPD-related healthcare - All-cause healthcare utilization: ND utilization (decrease in the subgroup with - Number of pulmonary audio-visual reminders) and/or primary care - COPD-related healthcare utilization: clinic visits baseline: 2.2 (2.3) vs. follow-up: 3.4 (3.2) p - Adherence < 0.01 - Adherence determinants Access to - Number of pulmonary and/or primary Alshabani Observational (pilot All-cause healthcare - Outpatient antibiotic information and care clinic visits: ND (2020) [46] COPD 39 68.6 (9.9) study) Unicentric utilization 12 months No related interventions Propeller Health® and/or steroid courses - Antibiotic and/or steroid courses: ND - Adherence and by HCP. - Adherence: −0.46%/week p < 0.0001 healthcare utilization - Adherence determinants: not found correlation - Adherence and healthcare utilization - Accuracy of rescue alerts correlation: not found in predicting - Accuracy of rescue alerts in predicting moderate-to-severe moderate-to-severe exacerbations: 27.9% exacerbations (sensitivity); 99% (specificity) - Adherence to ICS - AQOL - Adherence to ICS: ND - ACQ Depending on the - AQOL: ND Standard AE: Access to inhaler: Clinical trial - FEV1 sessions not related information and - ACQ: ND Apter (2011) - ED (asthma) - Diskus - FEV1: ND Asthma 333 49 (14) (randomized Multi-centric None 6 months to the topics in the related interventions Adherence [52] - ED (any cause) - ED for asthma: ND controlled trial) intervention group for HCP + Standard Logger - Hospitalizations sessions. AE sessions. - ED for any cause: ND (asthma) - MDILog® - Hospitalizations for asthma: ND - Hospitalizations - Hospitalizations for any cause: ND (any cause) Self-management (reminders, - Number of hospitalizations: ND education, trends on - Adherence (%). Clinical trial (pilot - Adherence to medication ◦ control: 29.5 Broadbent C: 69.10 (9.85) adherence) for Smartinhaler + iRobi COPD 60 randomized control Unicentric Hospitalizations - Quality of life 4 months Routine care ◦ Intervention: 48.5 (p = 0.03; p = 0.07 (2018) [60] I: 70.57 (10.34) patients. Access to robot trial) - Healthcare costs after controlling for other variables) information and related interventions - Quality of life (CCQ): ND for HCP - Healthcare costs: ND - Change in mean Self-management for - Change in mean SABA use: −1.9 (95% CI SABA use patients. Access to 1.7–2.1) Chen (2019) Observational (pilot - Percentage of - Percentage of SABA-free days: +36% (95% [28] COPD 190 68.0 (9.2) study) Multi-centric None 12 months No information and Propeller Health® SABA-free days related interventions CI 33–39) - Night-time SABA by HCP. - Night-time SABA use: −0.8 (95% CI use −0.9–0.7) Pharmaceuticals 2023, 16, 414 7 of 25 Table 1. Cont. Sample Primary Clinical Author (Year) Disease Age Study Design Number of Centers Secondary Clinical Outcome Follow-Up Control Group Intervention Group Device Outcomes Size Outcome - Time to next exacerbation: ND - Frequency of exacerbations: ND - QOL: ND - Taking adherence (mean days in range, %)  pMDI ◦ C: 60.1 ◦ I: 81.6 (p < 0.001)  DPI ◦ C: 80.2 ◦ I: 89.6 (p = 0.01) - Timing adherence (% of correct dosing - Exacerbation Access to intervals during 24 h) frequency-Severe information and  pMDI exacerbation leading to Depending on the ◦ C: 50.6 Clinical trial related interventions Gregoriano Asthma, C: 69.0 (8.8) Time to next hospitalization inhaler: ◦ I: 68.9 (p < 0.01) 169 (randomized Multi-centric 6 months Routine care sessions for HCP. (2019) [55] COPD I: 64.7 (12.4) exacerbation - Health-related quality of (a) Smartinhaler  DPI: ND controlled trial) Also, daily reminders life (b) POEMS - Gaps (% of days without inhalation) of maintenance - Adherence (several inhalers for patients.  pMDI variables) ◦ C: 11.7 ◦ I: 3.2 (p = 0.008)  DPI ◦ C: 9.8 ◦ I: 4.6 (p = 0.009) - Maximal gap length  pMDI ◦ C: 11.6 ◦ I: 1.6 (p = 0.025)  DPI ◦ C: 5.9 (5.2) ◦ I: 2.6 (p = 0.002) - CAT score: −0.9 (95% CI: −1.4–−0.4); p < - CAT score 0.001 Kaye (2021) - Mean daily SABA Self-management for - Mean daily SABA use: −0.6 (95% CI [44] COPD 611 62 (8) Observational Not specified use None 6 monhs No patients Propeller Health® −0.8–0.4) p< 0.001 - Daily maintenance - Daily maintenance adherence: −4 (95% adherence CI −6.9; −1.2) p = 0.001 Asthma: - Adherence (asthma): OR 2.08 (95% CI Kaye (2021) asthma, 1629; Asthma 39.4 (12.6) - Maintenance Self-management for 1.98–2.19) < 0.001 [49] COPD COPD: COPD 60.9 (8.3) Observational Multi-centric medication adherence None 8–97 Days No patients Propeller Health® - Adherence (COPD): 1.61 (95% CI 663 1.49–1.75) < 0.001 Initially uncontrolled patients: - SABA use ◦ C: −0.51 ◦ I: −0.62 (p < 0.001) - SABA free days: ND - ACT score (change): ◦ C: +4.6 Self-management for ◦ I: +6.2 (p = 0.009) Clinical trial patients. Access to Merchant - ACT - Asthma control (% ACT > 19) (2016) [23] Asthma 345 36.0 (NA) (randomized in Multi-centric SABA use 12 meses Routine care information and Propeller Health® ◦ C: +49% parallel arms) - Control of asthma related interventions ◦ I: +63% (p = 0.02) by HCP. Initially controlled patients: - SABA use ◦ C: −0.12 ◦ I: −0.16 p = 0.001 - SABA free days: ND - ACT score (change): ND - Asthma control (% ACT > 19): ND Pharmaceuticals 2023, 16, 414 8 of 25 Table 1. Cont. Sample Primary Clinical Author (Year) Disease Age Study Design Number of Centers Secondary Clinical Outcome Follow-Up Control Group Intervention Group Device Outcomes Size Outcome - Mean daily adherence in %: ◦ Arm 1: 80.9 (3.19) p <0.001 Arm 1 (maintenance ◦ Arm 2: 77.2 (3.04) p = 0.016 data to patients and ◦ Arm 3: 78.3 (3.11) p = 0.006 HCPs) ◦ Arm 4: 77.1 (3.25) p = 0.018 Arm 2 (maintenance ◦ Control: 69.0 (3.19) (REF) Clinical trial - Rescue Free Days - ACT data to patients only) (b) Mean monthly % of rescue-free days: Moore (2021) (Open-label, Adherence to [51] Asthma 437 47 (15) randomised, parallel Multi-centric maintenance therapy - FeNO 6 months Routine care Arm 3 (maintenance Propeller Health® ◦ Arm 1: 81.1 (2.82) p = 0.118 - PEF and rescue data to ◦ Arm 2: 81.2 (2.66) p = 0.105 group study) ◦ Arm 3: 85.6 (2.76) p = 0.002 - Safety patients and HCPs) Arm 4 (maintenance ◦ Arm 4: 83.7 (2.80) p = 0.015 and rescue data to ◦ Arm 5: 76.4 (2.82) (REF) patients only) - ACT: ND - FeNO: ND - PEF: ND - Safety: descriptive data - SABA-free days (%) change over time: - Mean ICS adherence ◦ C: 6% control (p = 0.18). - Asthma control (ACT) ◦ I: 19% (p< 0.001) - −10% increase SABA-free Self-management for - Mean ICS adherence (%) change over days patients. Access to time. Clinical trial - ACT improvement ≥3 information, related ◦ C: −17% (p < 0.01) Mosnaim (randomized points Asthma 100 48.5 (12.3) Unicentric SABA-free days 3 months Routine care interventions and Propeller Health® ◦ I: +2% (p = 0.40) (2021) [27] Controlled - ACT score from <20 to periodical review - Asthma control: ND Trial) ≥20 with patients for - 10% increase SABA-free days: ND - Asthma exacerbations HCP. - ACT improvement ≥3 points: ND - >1 course of oral - ACT score from <20 to ≥20: ND corticosteroids - Asthma exacerbations: ND - >1 ED visit or - >1 course of oral corticosteroids: ND hospitalization - >1 ED visit or hospitalization: ND Self-management for Mean percent adherent days: feedback group: Observational patients. Access to Nides (1993) 205 (I:116; I: 49.0 (6.4) Adherence to inhaler 60.2 (25.9) vs. 40.4 (28.2) p< 0.0001 COPD (ancillary study of a Multi-centric None 4 months No information and Nebulizer Chronolog [50] C: 89) C: 50.3 (6.3) treatment NOTE: additional secondary variables of clinical trial) related interventions adherence were provided by HCP. - Adherence (%): ◦ Biofeedback group 60.8 vs. demonstration group 44.2 vs. control group 33.2. ◦ Biofeedback-demonstration: p = 0.025; Biofeedback-control: p = 0.004 (b) Proportion with actual adherence ≥80% (%): - Proportion with actual ◦ Biofeedback group 29.27 vs. adherence ≥80% (%) demonstration group 14.29 vs. control - Proportion with actual group 1.00. Between group difference: adherence <50% (%) 0.030. - Attempted adherence - Routine care ◦ Biofeedback-demonstration: no rate (%): Access to I: 54 (15) Clinical trial (cluster - Other difference; Biofeedback-control: p = O’Dwyer Asthma, - Critical technique error information and comparator: 0.015 (2020) [58] COPD 152 C: 55 (13) randomized clinical Multi-centric Adherence (%) rate 6 months related interventions INCATM C2: 53 (15) trial) inhaler - Proportion with actual adherence <50% - QOL (SGRQ score) for HCP. technique (%) - Self-reported symptoms education ◦ Biofeedback group 31.7 vs. ◦ cough demonstration group 60.7 vs. control ◦ breathlessness group 62.50. Between group ◦ nocturnal symptoms difference: 0.023. ◦ wheeze ◦ Biofeedback-demonstration: p = 0.017; - Exacerbation rate Biofeedback-control: p = 0.033 - Attempted adherence rate (%): ◦ Biofeedback group 77.7 vs. demonstration group 66.1 vs. control group 52.1. Between group difference: 0.027. ◦ Biofeedback-demonstration: p = 0.036 Biofeedback-control: p = 0.04 Pharmaceuticals 2023, 16, 414 9 of 25 Table 1. Cont. Sample Primary Clinical Author (Year) Disease Age Study Design Number of Centers Secondary Clinical Outcome Follow-Up Control Group Intervention Group Device Outcomes Size Outcome - Critical technique error rate ◦ Biofeedback group 17.5 vs. demonstration group 21.9 vs. control group 36.1. Between group difference: ND ◦ Biofeedback-demonstration: ND; Biofeedback-control: p = 0.032 - QOL (SGRQ score) ◦ Biofeedback group: −6.1 (p = 0.04) ◦ Other arms: ND - Self-reported symptoms (Magnitude of the results graphically described). Statistical value: ◦ cough: Biofeedback (p < 0.05); demonstration: ND ◦ breathlessness: biofeedback (p < 0.01); demonstration (p < 0.05) ◦ nocturnal symptoms: biofeedback (p < 0.05); demonstration: ND ◦ wheeze: Biofeedback (p < 0.05); demonstration (p < 0.05) - Exacerbation rate: ND Access to information and - Adherence (%): graphical data (p = 0.003) - SABA use Asthma education Onyirimba I: 45 (11) Clinical trial Adherence to related interventions - SABA use: ND Asthma 19 Unicentric - Quality of life 10 weeks and management Chronologs (2003) [61] C: 53 (14) (randomized trial) maintenance therapy for HCP + same - Quality of life (AQLQ): ND - FEV1 plan intervention of - FEV1: ND control group Adherence to the e-health self-management - Inhalation technique intervention: - Health-related quality of - completing life Sloots (2021) Observational (pilot digital daily - COPD self-management Self-management for Descriptive analyses only (no comparison with [45] COPD 11 66.8 (2.9) study) Multi-centric symptom 4 months No patients Respiro® baseline) behavior and knowledge diaries - COPD self-efficacy - following the - Anxiety/and depression advised actions score - using inhaled medication - Adherence: ◦ Intensive education group: 63% ◦ Biofeedback group: 73% (p < 0.01) - Average adherence from - Average adherence from dose counter: dose counter Access to Clinical trial Adherence (critical Intensive education: ND - Attempted rate information and (Randomised, errors with missed repeated training in - Attempted rate Sulaiman - Overdoses related-interventions Asthma 218 49.2 (16.5) controlled, Multi-centric doses were 3 months inhaler use, INCATM ◦ Intensive education group: 82% (2018) [53] - Missed doses: sessions for HCP + open-label clinical combined in this adherence and ◦ Biofeedback group: 73% (p = 0.01) - Technique error rate Standard Intensive trial) variable) disease management. - Overdoses - Composite variable education sessions. ◦ Intensive education group: 6 including PEF, ACT, ◦ Biofeedback group: 3 (p = 0.02) AQLQ and adherence - Missed doses: ND - Technique error rate: ND - Composite variable: ND - ACT score: baseline: 17.6 (3.35) vs. - ACT score Self-management for follow-up: 20.1 (3.66) - Activity limitations patients. Access to Van Sickle Observational (pilot - Activity limitations: ND Asthma 29 36.8 (19–74) Multi-centric No clinical outcome - Days with asthma 4 months No information and Not specified (2013) [47] study) - Days with asthma symptoms: baseline: symptoms related interventions 4.84 (4,13) vs. follow-up: 2.77 (3.56) - Nights with asthma by HCP. - Nights with asthma symptoms: baseline: symptoms 2.03 (3.35) vs. follow-up: 0.55 (0.98) Pharmaceuticals 2023, 16, 414 10 of 25 Table 1. Cont. Sample Primary Clinical Author (Year) Disease Age Study Design Number of Centers Secondary Clinical Outcome Follow-Up Control Group Intervention Group Device Outcomes Size Outcome Self-management for patients. Training - ACQ (change): Clinical trial (pilot Weinstein Asthma control Adherence to medication and access to ◦ C: +1.41 Asthma 39 40 (23-69) randomized control Unicentric 3 months Standard care SmartTrack device (2019) [62] (ACQ) (>60%) information and ◦ I: +1.11 (p = 0.046) trial) related interventions - Adherence: descriptive data only for HCP. - Adherence to maintenance medication Yawn (2021) Observational (pilot - Rescue inhaler use Self-management for Descriptive analyses only (no comparison with [48] COPD 122 65.2 (8.6) study) Multi-centric No clinical outcome 24 weeks No patients. Propeller Health® baseline) - CAT scores - Modified COPD treatment ratio (mCTR) Table 2. Summary of methodology and interventions by studies. ACT (Asthma Control Test); AE (asthma education); CAT (COPD assessment test); COPD (chronic obstructive pulmonary disease); DPI (dry-powder inhaler); HADS (Hospital Anxiety and Depression Scale); HCP (healthcare professionals); ICS (inhaled corticosteroid); pMDI (pressurised Metered Dose Inhaler); POEMS (Polymedication Electronic Monitoring System); SABA (short acting beta agonist); SGRQ (St. George’s Respiratory Questionnaire). Data Collected with the Author (Year) Disease Setting HCP Involved Device Type of Inhaler Monitoring Device and Related Follow-Up Circuit Type Type of Interaction Intervention E-Health Systems - HCP: platform-generated alerts were - HCP: e-mail emailed to the clinicians when maintenance (platform generated therapy was not used for four consecutive alerts) days or when rescue inhaler use increased - SABA use Alshabani (2020) - HCP + patient: for a day by 1.64 times the standard [46] COPD Medical Clinic Not specified Propeller Health® Not specified - Maintenance medication 12 months HCP open-ended use deviation above their average. conversations - Patient + HCP: patients were contacted by (unspecified means HCP and open-ended conversations were of communication) held aiming to foster adherence and detect exacerbations. - HCP: access to data from inhaler use to develop interventions. Primary care and asthma Depending on the inhaler: - Patient + HCP: Four 30-min sessions specialty practices in related to self-management, adherence or - HCP: computer low-income - Diskus Adherence ICS therapy involving discussion of Apter (2011) [52] Asthma Not specified pMDI, DPI - Maintenance ICS use 6 months HCP dashboard neighbourhoods with a Logger problem-solving, adherence or related - Face-to-face sessions high prevalence of - MDILog® issues. Data of inhalers use were showed to asthma patients in these sessions. Furthermore, the same Standard AE sessions as in the control group were performed. - Patient: Robot programmed to deliver COPD management with several features: (1) measure of health variables; (2) oral and inhaled medication reminders and record of their adherence; (3) reminders of - Patient: robot device - HCP: web browser rehabilitation and related videos; (4) Broadbent (2018) Smartinhaler + iRobi education about COPD with videos and COPD Hospital Physiotherapist Not specified - Maintenance medication use 4 months Mixed - Patient + HCP: [60] robot pop-up messages; (5) Option of "I am phone call interventions feeling unwell" function on demand; and (6) show trends over time of health status and adherence to the patient. - HCP: access to data on "alert function" and parameters outside the normal range to develop interventions. Pharmaceuticals 2023, 16, 414 11 of 25 Table 2. Cont. Data Collected with the Author (Year) Disease Setting HCP Involved Device Type of Inhaler Monitoring Device and Related Follow-Up Circuit Type Type of Interaction Intervention E-Health Systems - HCP: access to data from inhaler use to develop. interventions (support calls). - HCP + patients: Collected data could be used by providers to perform medication adjustments or early intervention at the sign of increasing SABA use. Fruthermore, at-risk notifications were sent to providers - Patient: Smartphone if a patient was considered at increased risk application or web for an exacerbation (excessive SABA use). Chen (2019) [28] COPD Medical Clinic Not specified (providers) Propeller Health® pMDI - SABA use 12 months Mixed platform Appropriate follow-up via telephone or an - HCP: Web in-person visit could be determined by dashboard providers. A respiratory therapist assisted patients with technical questions by phone and in person. - Patients: access to a web or mobile platform promoting self-management with information about their SABA use trends, environmental triggers and guidelines-based education. - Patient: audio-reminder generated by an - Patient: Smartphone app (for Smartinhaler devices) or an alarm Depending on the inhaler: application with clock (for POEMS) that was directly audio-reminder or transferred to the participants’ Gregoriano (2019) Pharmacist - Smartinhaler pMDI - SABA use Asthma, COPD Hospital 6 months Mixed alarm-clock smartphones (reminder independent from [55] Nurse - POEMS DPI - Maintenance medication use - HCP: Online access passive monitoring). to data - HCP: access to data from inhaler use to - Phone call sessions develop interventions (support calls). - Patient + HCP: support calls when needed. Patient - Patient: self-management with - CAT score (potentially evidence-based education, feedback on - SABA use mixed if the Patient: Smartphone medication use and reminders for Kaye (2021) [44] COPD Not specified Not specified Propeller Health® pMDI - Maintenance therapy use (if 6 months patients allowed application scheduled medications. [Patients had the sensor compatible) access to their option to share their information with their data) providers but were not required to do so.] - Patient: The app engaged the patient through evidence-based asthma and/or COPD content, feedback on medication use and trends, and schedule-based medication reminders through the sensor and - Maintenance medication use Patient: Smartphone smartphone application. In case of Kaye (2021) [49] Asthma, COPD Social media campaigns Not specified Propeller Health® Not specified - ACT, CAT 8–97 Days Patient application continued poor medication adherence, additional gamified features and challenges were presented to improve daily medication adherence. The app also prompts users to complete ACT and CAT test monthly. - Patient: Self-management including - Patient: smartphone data-driven asthma assessment and Merchant (2016) - SABA use application guidance (education, reminders and alerts) [23] Asthma Hospital Physician Propeller Health® pMDI - - ACT 12 meses Mixed - HCP: computer with the smartphone application. dashboard - HCP: access to dashboards and possibility to intervene with patients. - Patient: data on inhaler use, identification - Patient: smartphone of potential triggers, etc. available in the application smartphone application to improve - HCP: computer self-management. pMDI - SABA use Moore (2021) [51] Asthma Hospital Not specified Propeller Health® DPI - Maintenance medication use 6 months Mixed dashboard - HCP: information of inhaler use in the - HCP + patient: computer dashboard. If necessary, the HCP email, phone, could e-mail or phone the patient, or see face-to-face meeting them in the clinic to have an open, non-judgmental discussion. Pharmaceuticals 2023, 16, 414 12 of 25 Table 2. Cont. Data Collected with the Author (Year) Disease Setting HCP Involved Device Type of Inhaler Monitoring Device and Related Follow-Up Circuit Type Type of Interaction Intervention E-Health Systems - Patient: self-management including access - Patient: smartphone to ICS and SABA usage in the smartphone application application and reminders for missed or - SABA use late doses. Mosnaim (2021) NorthShore University Nurse (supervised by an - HCP: computer [27] Asthma HealthSystem allergist or immunologist) Propeller Health® Not specified - Maintenance ICS use 3 months Mixed dashboard - HCP: Access to dashboards and the - HCP + patient: possibility to intervene with patients if poor monthly phone calls adherence was noted. In addition, monthly phone calls to provide feedback on ICS adherence and SABA use. - Patient + HCP: Participants in the feedback - HCP + patient: group had face-to-face reviews with the face-to-face review health educator and printed copies with the health regarding their inhalers use. Praise was Nides (1993) [50] COPD Universities Health educator Nebulizer Chronolog pMDI - Maintenance medication use 4 months Mixed educator and given for the areas in which usage was printed copies satisfactory, and individual behavioural regarding their strategies were collaboratively developed inhalers use. to address problem areas. - HCP: access to data on inhaler use to provide personalized visual feedback (time - HCP: Online access and technique of inhaler use) obtained by - Maintenance to data O’Dwyer (2020) analysis of the data recorded to the INCA [58] Asthma, COPD Community pharmacy Pharmacist INCATM DPI salmeterol/fluticasone 6 months HCP - HCP + patient: device and generation of visual graphs, therapy use face-to-face training which the pharmacist reviewed and sessions discussed with the patient. - Patient + HCP: discussion of visual graphs - HCP: Access to computer printout - HCP: access to data on inhaler use to Onyirimba (2003) Pulmonologist - SABA use Asthma Hospital Chronologs pMDI 10 weeks HCP data provide personalized intervention in [61] Nurse - Maintenance ICS use - Patient + HCP: follow-up sessions when needed. face-to-face sessions - Maintenance therapy use - Inhalation technique - Daily symptoms - Health-related quality of life - Patient: self-management intervention in (SGRQ) Patient: e-health which self-report of symptoms in diaries - COPD self-management application modules and data collected with an inhaler sensor Sloots (2021) [45] COPD Hospital Nurse (training) Respiro® DPI behaviour and knowledge 4 months Patient (tablet or PC) were linked to an automated decision (Partners in Health [Training: (a) Group support system with actions communicated questionnaire) session; (b) Phone calls] with an e-health application. - COPD self-efficacy (COPD SelfEfficacy Score) - Anxiety/and depression score (HADS). - HCP: access to data from inhaler use to - Maintenance develop interventions. - HCP: access to data Sulaiman (2018) salmeterol/fluticasone - Patient + HCP: interventional sessions [53] Asthma Hospital Nurse INCATM DPI therapy use 3 months HCP - Patient + HCP: face-to-face sessions guided by visual (bio)feedback on their - PEF adherence behaviours from information recorded with the sensor. - Patients received weekly e-mail reports and - Patient: email had access to an online interface displaying Van Sickle (2013) Medical clinics and - Time, location, and use of reports maps and charts of their inhaler use, Asthma Not specified Not specified pMDI 4 months Patient [47] community care SABA - Patient: online assessment of their asthma control and interface simple advice derived from the NAEPP guidelines. Pharmaceuticals 2023, 16, 414 13 of 25 Table 2. Cont. Data Collected with the Author (Year) Disease Setting HCP Involved Device Type of Inhaler Monitoring Device and Related Follow-Up Circuit Type Type of Interaction Intervention E-Health Systems - Patient: web - HCP: Specific training to implement application motivational interviewing adherence Allergist, clinical - HCP: web strategies + monitoring on data adherence + Weinstein (2019) - SABA use application interventions based on a decision-making Asthma Hospital immunologist, SmartTrack device pMDI 3 months Mixed [62] - Maintenance ICS/LABA use - Patient + HCP: tool. The tool identified barriers and pulmonologist motivational provided material for the interventions. interviewing - Written information and videos to reinforce sessions the written response. - The smartphone application allowed patients to check their medication adherence and inhaler use. Notifications were sent if their maintenance inhaler was not used for more than 4 days or in case of Inclusion based on an pMDI an increased rescue therapy use. - SABA or SABA/SAMA use Patient: smartphone Yawn (2021) [48] COPD online registry of COPD Not specified Propeller Health® DPI (Ellipta® inhaler; - Maintenance medication use 24 weeks Patient application Participants also received maintenance patients GlaxoSmithKline BV, UK. medication reminders according to their schedule. Participants could also receive feedback on rescue mediation use and had access to evidence-based COPD-related information on triggers and symptoms. Pharmaceuticals 2023, 16, x FOR PEER REVIEW 6 of 30 Pharmaceuticals 2023, 16, 414 14 of 25 Figure 2. Search results. (a) PRISMA Flow Diagram; (b) distribution of studies by country. * One Figure clinical2.trial Search was results. (a) PRISMA a multi-country studyFlow [51]: Diagram; (b) distribution Canada, Germany, of studies by Italy, Netherlands, country. Spain, * One UK and clinical the USA.trial was a multi-country study [51]: Canada, Germany, Italy, Netherlands, Spain, UK and the USA. Pharmaceuticals 2023, Pharmaceuticals 2023, 16, 16, 414 x FOR PEER REVIEW 715of of 30 25 Figure 3. Figure Visual summary 3. Visual summary of of results. results. Colour Colour guide guide in in the the clinical clinical outcomes outcomes section: green (positive section: green (positive confirmed in the meta−analysis); effects in some studies, confirmed meta–analysis); yellow yellow (positive (positive effects effects in in some some studies, studies, meta−analysis meta–analysis not performed); and grey (mixed or no effects with the data available). Acronyms: n (number (number ofof studies assessing aa specific studies assessing specific outcome); outcome); SABA SABA (short-acting (short-acting beta beta agonist). agonist). As for the clinical trials, seven studies focused on asthma, one study on COPD and two studies on asthma and/or COPD (Table 1). Their sample size ranged from 19 in a pilot clinical trial to 437 participants, with an average age ranging from 36 to 70 years. Clinical outcomes assessed in clinical trials comprised adherence to maintenance med- ication, use of rescue medication, disease control, inhalation technique, quality of life, self-management variables, self-reported symptoms, exacerbations, lung function tests, hospitalizations, steroids use, ED visits and composite clinical variables. Variables were differently operationalized across studies resulting in great variability across studies (e.g., Pharmaceuticals 2023, 16, 414 16 of 25 time to next exacerbation, exacerbation rate, severe exacerbations leading to hospitaliza- tion, etc.) (complete list of variables assessed in clinical trials: File S2). Adherence to maintenance inhalers was assessed in eight out of the ten clinical trials. The intervention group showed a statistically significant improvement in adherence compared with the control in six of the trials at the end of the study analysis [51,53–61]. Two studies found no differences in adherence results [27,52]. Regarding SABA use, mixed results were found in three studies, as improvement or no difference was found depending on the variable or subgroup assessed (e.g., SABA use vs. SABA-free days) [23,27,51]. No differences in SABA use were found in one study [61]. Studies assessing disease control with ACT or ACQ questionnaires showed mixed results: three studies found no differences [27,51,52], one study showed better results in the intervention group [62] and one study showed improvement for the previously uncontrolled subgroup only [23]. One study assessed independently additional symptoms (cough, breathlessness, nocturnal symptoms, wheeze, etc.). The intervention group improved all the symptoms over the study period, but no direct comparison across groups was performed [58]. In relation to quality of life, four trials found no differences between groups [52,55,60,62] and one study found better results over time in the intervention group [58]. As for other variables, none found statistical significance: FeNO, PEF, exacerbations, time to exacerbations, steroids use, ED visits, hos- pitalizations, FEV1, technique error, critical technique error rate and composite clinical variable criteria [27,51–61]. 3.3. Monitoring Sensor-Based Interventions Methodology of the interventions varied greatly across the studies. For example, recruitment and co-ordination settings included hospitals, medical clinics, community care, community pharmacies, universities, social media campaigns, online registries or was not specified. Healthcare professionals involved in the studies were not specified in eight studies, while they were varied across the studies providing this information: healthcare educator, nurse, pharmacist, physician (allergist, immunologist, pulmonolo- gist, etc.) and physiotherapist. The most frequently used EMD was Propeller health® (Madison, WI, USA), used in eight of the studies. Other studies based their monitoring in Chronologs (Medtrac Technologies, Model MC-311, Lakewood, CO, USA); Diskus Ad- herence Logger (DAL, developed by a study researcher); INCATM (Inhaler Compliance AssessmentTM); MDILog® (Life Link Monitoring, Inc, Kingston, NY, USA); Nebulizer Chronolog (Forefront Engineering Corp, Denver, Colo); POEMS (Pharmis GmbH, Beinwil am See, Switzerland); Smartinhaler (Adherium Ltd., Auckback, New Zealand); SmartTrack® device (Nexus6, Auckland, New Zealand); and Respiro® (Amiko Digital Health Limited, London, UK). Sensors were attached to pMDI in seven studies [23,28,44,47,50,61,62], to DPI in three studies [45,53,58], both to pMDI and DPI in four studies [48,51,52,55] and was not specified in four studies [27,46,49,60]. Data from EMDs lead to interactions with patients through smartphone applications in eight studies [23,27,28,44,48,49,51,55]. Other means of interconnectivity with patients or healthcare professionals were web dashboards, e-health application modules on tablets or PCs, online interfaces/web browsers, web applications, platform-generated emails, robot devices, phone calls or face-to-face ses- sions. Some smartphone applications and additional means of interconnectivity allowed patients to provide, autonomously, data on variables such as ACT, symptoms, self-efficacy or QOL in several studies. Feedback and related actions from the monitoring sensor sys- tem were patient-oriented in five studies [44,45,47–49], healthcare professional-oriented in five studies [46,52,53,58,61] and comprised both patients and healthcare professionals in eight studies [23,27,28,50,51,55,60,62]. Patients received self-management interventions in 13 studies, with a varied range of actions: feedback of inhaler use, reminders, auto- mated decision support system, gamified challenges, identification of potential triggers and guidelines-based education [23,27,28,44,45,47–49,51–53,55,60]. In the studies including healthcare professionals, interventions included medication adjustments, phone follow-up, problem-solving assistance, open-ended conversations, face-to-face reviews, e-mail advice, Pharmaceuticals 2023, 16, 414 17 of 25 support calls, provision of individual behavioural strategies, general education sessions, visual feedback and decision-making tool interventions [23,27,28,46,50–53,55,58,60–62]. 3.4. Risk of Bias in Studies A separate assessment of risk of bias was conducted for observational and clinical trials (Figure 4). Regarding observational studies, we obtained a range from 7 to 11 points with the MINORS tool (0 = lowest risk of bias; 16: higher risk of bias) [28,44–50]. One observational study scored 16 points in the additional assessment of comparative projects (0 = lowest risk of bias; 24: higher risk of bias) [50]. As for the clinical trials, the assessment was performed with the RoB2 tool. Five studies were assessed as having low risk-of-bias criteria [23,27,51,52,60], four studies as high risk-of-bias criteria [53,58,61,62] and one study was classified as having some concerns criteria [55]. For studies with high risk of bias, the main reason was potential bias arising from the randomization process criteria due to the Pharmaceuticals 2023, 16, x FOR PEERinherent REVIEW use of electronic devices and consequent interventions [53,58,61,62]. Due22toofthe 30 nature and small number of studies, no exclusions for this reason were pre-established in the meta-analysis. Figure 4. Risk-of-bias assessment. (a) Observational studies; (b) clinical trials. NA: not applicable. Figure 4. Risk-of-bias assessment. (a) Observational studies; (b) clinical trials. NA: not applicable. References: [23,27,28,44–53,55,58,60–62]. References: 23, 27, 28, 44-53, 55, 58, 60-62 3.5. Impact of Interventions on Adherence to Maintenance Inhaled Therapy 3.5. Impact A total of of Interventions on Adherence six trials were found toto measure Maintenance Inhaled Therapy adherence in percentage as a clinical A total of six trials were found to measure adherence in percentage as a clinical out- outcome. come.The impact of interventions in studies assessing adherence up to 3 months was as- sessedThe in impact of interventions three studies, in studies including 318 assessing participants adherence [53,58,61]. up5to Figure 3 months shows was as- the results of sessed the in three forest studies, including plot analysis. Although318 participants adherence [53,58,61]. results Figure 5 shows were favourable in the the results of intervention the forest group, no plot analysis. significant Although differences adherence were found inresults were both the favourable fixed in the effect model intervention analysis (SMD: group, no significant differences were found in both the fixed effect model analysis (SMD: 0.07 [−0.16 to 0.31]] or the random effects model analysis (SMD: 0.77 [−0.26 to 1.80]]. The estimate of the between-study variance (heterogeneity) was considered high (I2 = 91%; τ2 = 0.69; p = 0.01). 3.5. Impact of Interventions on Adherence to Maintenance Inhaled Therapy A total of six trials were found to measure adherence in percentage as a clinical out- come. The impact of interventions in studies assessing adherence up to 3 months was as- Pharmaceuticals 2023, 16, 414 sessed in three studies, including 318 participants [53,58,61]. Figure 5 shows the results 18 of of 25 the forest plot analysis. Although adherence results were favourable in the intervention group, no significant differences were found in both the fixed effect model analysis (SMD: 0.07 0.07 [[−0.16 −0.16to to0.31]] 0.31]] or or the the random random effects model analysis effects model analysis (SMD: (SMD: 0.77 0.77 [[−0.26 −0.26 to to 1.80]]. 1.80]]. The The estimate estimate of of the the between-study between-studyvariance variance(heterogeneity) (heterogeneity)was wasconsidered considered high high (I2(I=2 =91%; 91%;τ2τ=2 =0.69; 0.69;p =p 0.01). = 0.01). Pharmaceuticals 2023, 16, x FOR PEER REVIEW 23 of 30 Figure 5. Adherence outcomes outcomes up up to to 33 months. months.References: References:[53,58,61]. 53, 58, 61. interventions in studies assessing adherence in a period of 3 months The impact of interventions months or longer was assessed in five five studies, studies, including including 1223 1223 participants participants [27,51–53,58]. [27,51–53,58]. Figure 6 results of shows the results of the the forest forest plot plot analysis. analysis. Adherence results were favourable in the intervention group in all studies and resulted in significant differences for both the fixed effect model modelanalysis analysis(SMD: (SMD:0.36 [0.25 0.36 to 0.48]) [0.25 or the to 0.48]) orrandom effectseffects the random modelmodel analysis (SMD: analysis 0.41 [0.22 (SMD: 0.41to[0.22 0.60]). The estimate to 0.60]). of the of The estimate between-study variance the between-study (heterogeneity) variance was con- (heterogeneity) was sidered moderate considered moderate(I2 =(I259%; = 59%;τ2 =τ20.04; p = p0.02). = 0.04; = 0.02). 6. Adherence Figure 6. Adherence outcomes outcomesin instudies studies33months monthsororlonger. longer.References: References: 27, 51-53, 58. [27,51–53,58]. 3.6. 3.6. Impact Impact of of Interventions Interventions on on Other Other Clinical Clinical Outcomes Outcomes The The variability of other variables in terms of variability of other variables in terms of definition, definition, operationalization operationalization andand study study timeline timeline duration prevented us to perform additional meta-analysis. However, two of the duration prevented us to perform additional meta-analysis. However, two of the trials trials were were found found toto measure measure control control with with the the ACT ACT questionnaire questionnaire asas aa clinical outcome, clinical outcome, leading leading toto an an exploratory exploratoryassessment assessmentwith with799799individuals individuals[23,51]. [23,51]. Figure Figure 7 shows 7 shows thethe re- results of the forest plot analysis. ACT results were favourable in the intervention sults of the forest plot analysis. ACT results were favourable in the intervention group. group. Significant differences were found in the fixed effect model analysis (SMD: 0.25 [0.11 to Significant differences were found in the fixed effect model analysis (SMD: 0.25 [0.11 to 0.39]] but not in the random effects model analysis (SMD: 0.47 [−0.14 to 1.08]]. The estimate 0.39]] but not in the random effects model analysis (SMD: 0.47 [−0.14 to 1.08]]. The estimate of the between-study variance (heterogeneity) was considered high (I2 2 = 94%;2 τ2 = 0.46; of the between-study variance (heterogeneity) was considered high (I = 94%; τ = 0.46; p p < 0.01). < 0.01). trials were found to measure control with the ACT questionnaire as a clinical outcome, leading to an exploratory assessment with 799 individuals [23,51]. Figure 7 shows the re- sults of the forest plot analysis. ACT results were favourable in the intervention group. Significant differences were found in the fixed effect model analysis (SMD: 0.25 [0.11 to Pharmaceuticals 2023, 16, 414 0.39]] but not in the random effects model analysis (SMD: 0.47 [−0.14 to 1.08]]. The estimate 19 of 25 of the between-study variance (heterogeneity) was considered high (I2 = 94%; τ2 = 0.46; p < 0.01). Figure7.7.Asthma Figure Asthmacontrol controltest test(ACT) (ACT)outcomes. outcomes.References: References:[23,51]. 23, 51. 4.4. Discussion Discussion To Tothe thebest bestofofour ourknowledge, knowledge,this thisisisthe thefirst firstsystematic systematicreview reviewwith withmeta-analysis meta-analysis exploring the characteristics and assessing the impact of clinical interventions exploring the characteristics and assessing the impact of clinical interventions derived from derived data fromofdata EMDs of in adultinpatients EMDs with asthma adult patients withor COPD.orThe asthma most The COPD. remarkable result to emerge most remarkable result from our study is the positive impact of EMD-based interventional to emerge from our study is the positive impact of EMD-based interventional programmes on adher- pro- ence to inhaler treatment and the tendency to positive or mixed results in other outcomes grammes on adherence to inhaler treatment and the tendency to positive or mixed results such as symptom control, with potential benefits for daily practice in the optimization of in other outcomes such as symptom control, with potential benefits for daily practice in inhaled therapy management. the optimization of inhaled therapy management. Firstly, we would like to highlight the impact of these interventions on adherence. Adherence on maintenance therapy was the most frequent clinical outcome assessed in both the observational studies and clinical trials. Mixed-favourable results were found in the observational studies, which principally focused on COPD patients. However, the clinical trials showed an improvement in adherence for the intervention group in the meta-analysis. Although most trials focused on asthma patients, those studies focusing exclusively on COPD patients or including both participants with COPD or asthma also showed an improvement in adherence, which suggests that COPD patients may also benefit from this type of interventions. Poor adherence is an acknowledged risk factor by GINA and GOLD guidelines to symptom burdens, exacerbations, and poor quality of life to be assessed and tackled by the multidisciplinary team [1,63]. Inhaler treatment involves factors contributing to suboptimal adherence at three levels: medication, unintentional and intentional issues [64]. One of the main relevant results of the review is the ability of these interventional programmes to include a wide range of aspects related to these factors, such as patient self-awareness and efficacy, disease and management education, inhaler technique, triggers, age-related factors such as comorbidities, misunderstood direc- tions, forgetfulness, dissatisfaction or inappropriate expectations [64]. The methodology of interventions tackling adherence in the assessed studies was aligned with successful interventions to improve adherence in previous studies regardless of the use of monitor- ing devices, such as shared decision making, electronic reminders, visits and providing information to clinicians [65–71]. As for the rest of the variables, mixed results were found for SABA use and symptoms of asthma and COPD. Theoretically, positive results may be obtained if adherence is improved but these clinical outcomes could be shaped by additional factors such as weather, infections, environmental triggers, comorbidities, severity of illness and other factors. These aspects may have a higher impact on other clinical outcomes and be responsible for the lack of results in variables such as exacerbations, hospitalizations or spirometry results. However, some studies on children have found fewer exacerbations requiring oral corticosteroids at 12 months [67]. With this in mind, adherence management would be the primary goal of EMDs due to the nature of the data collected by the sensors, but there is a need for further research to assess their real clinical benefits in terms of other health outcomes or to what extent it would be useful to implement specific additional interventions linked to these programmes as a global strategy [35]. Pharmaceuticals 2023, 16, 414 20 of 25 A high degree of variability is inherent in the development of new technologies. In our review, we found 10 different EMDs, in line with previous literature [29–35]. Technological differences between their inhaler sensors should not pose a differential problem as they are validated systems. However, the associated digital-engagement tools, dashboards, data available, clinical setting, specific professionals involved, the number of associated interventions, usability and acceptability could make a difference between them [29–35,72]. In our review we have found a great variability in terms of clinical setting, professionals involved, follow-up, interventions, availability of smartphone applications and whether the clinical approach was focused on the patient, the healthcare professional or both. To what extent these factors may impact the clinical results remain uncertain. However, the results of our review show that all clinical trials included healthcare professionals as key roles in the intervention, highlighting the importance of interventions conducted by health- care professionals rather than focusing on self-management with apps only [23,27,51–62] (Table 2). Cost-effectiveness and feasibility are essential concerns to be considered when trans- lating interventional programmes based on data from monitoring devices into regular clinical practice. On one hand, there are direct costs associated with monitoring devices, e-health platforms, and related-products and interventions. Minimizing the cost of mon- itoring devices has been described as a relevant acceptability criterion to assess device characteristics [73]. On the other hand, suboptimal inhaler use results in poor clinical outcomes, with studies reporting increased direct and indirect costs that may be potentially reduced [74,75]. In this context, an economic analysis of monitoring devices and adherence- related interventions showed it may be cost-effective and cost-saving [76]. In terms of feasibility, some inexperienced patients face a considerable number of challenges, such as sensory impairment, intellectual ability, motivation, reduction of fine motor control, low self-efficacy of technology, fear or dislike of electronic devices, inexperience with e-health or computers, lack of awareness of e-health opportunities, previous unmet expectations, fear or losing traditional health services or lack of smart phones [77]. With regard to healthcare professionals, apart from e-health literacy skills, there is a need of training on EMD func- tioning, checking alerts, dashboards and typical errors [78]. Healthcare professionals have highlighted a high degree of administrative burden and complexity of these interventions, that would require additional employees to handle the corresponding workload [79]. Fur- thermore, a study showed that providing adherence information to healthcare professionals did not improve adherence unless the professional deliberately decided to check the details of a specific patient [69]. Moreover, the existence of an increasing number of EMDs can complicate their implementation and management in regular practice. Thus, a careful selection of patients who are most likely to benefit from interventions and the development of a common framework of the platforms seem to be convenient options to be prioritized in the near future. Our systematic review and meta-analysis have some limitations. Firstly, we found a great variability of clinical outcomes, operationalization of variables and follow-up length periods across studies, which could impact the results, their interpretation and comparability. In relation to the use of monitoring devices, patients in both the intervention and control groups can suffer from the Hawthorne Effect, which implies a change in behaviour since they know they are being monitored [80]. Another aspect to be considered is the decrease of user engagement to e-health interventions with time, its clinical impact and potential measures to minimize this effect [81,82]. In addition, some studies focused on severe cases while others focused on patients regardless of their severity, which could affect the results if specific subgroups benefited more from the intervention. Moreover, data on the use of biological agents in asthma were not provided and may represent a major cofounding factor. Furthermore, information on pharmacological-inhaled treatment and posology was not present in some studies. Moreover, some of the studies were pilot projects and may benefit from escalation and ulterior methodology improvements of this type of studies. With regard to the review process, some limitations may also arise. For Pharmaceuticals 2023, 16, 414 21 of 25 example, innovative technologies do not count with a definite terminology, so some articles might not have been found in the title/abstract search if alternative nomenclature was used by some authors. This limitation may be minimized if we performed preliminary searches to find synonyms in potential studies. In addition, as this is a novel technology, it is possible that additional features and interventions may improve clinical results in future trials. Finally, data from real-life studies would be of interest. The findings of our systematic review and meta-analysis confirm the positive impact of EMD-based interventions on adherence to inhalers in adults with asthma or COPD. Our data indicate that other clinical outcomes, such as symptom control, may also improve when using EMD interventional programmes, but further research is needed to confirm whether additional interventions would be necessary, as asthma and COPD clinical results also rely on additional aspects such as environmental issues, comorbidities, etc. The broad implication of the present research is that EMDs represent a valuable asset that should help healthcare providers to implement policies to address asthma and COPD management. Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/ph16030414/s1, File S1: Supplement 1. Search strategy; File S2: Supplement 2. Variables assessed in clinical trials. Author Contributions: Conceptualization, N.G., B.Z.-T., L.G.-M., R.M., A.C.-L., E.C., M.H., C.M. and V.P.; methodology, N.G., B.Z.-T., L.G.-M., R.M., A.C.-L., E.C., M.H., C.M. and V.P.; software, N.G., B.Z.-T., L.G.-M. and R.M.; formal analysis, N.G., B.Z.-T., L.G.-M. and R.M.; investigation, N.G., B.Z.-T. and L.G.-M.; data curation, N.G., B.Z.-T. and L.G.-M.; writing—original draft preparation, N.G. and B.Z.-T.; writing—review and editing, N.G., B.Z.-T., L.G.-M., R.M., A.C.-L., E.C., M.H., C.M. and V.P.; project administration, N.G. and B.Z.-T. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data sharing not applicable. Conflicts of Interest: The authors declare no conflict of interest in relation to the work submitted. The authors declare the following interests that might be perceived to influence the results and/or discussion reported in this paper. AC-L has received fees in the last 3 years for talks at meetings sponsored by Boehringer Ingelheim, Chiesi, Ferrer, MSD, Novartis, Orion Pharma, has received travel and attendance expenses for conferences from Novartis, and TEVA, and has received funds/grants for research projects from several state agencies, non-profit foundations, and AstraZeneca and GlaxoSmithKline. BZ reports nonfinancial support from Boehringer Ingelheim, Lilly, Galapagos Biopharma and personal fees from GlaxoSmithKline. EC reports nonfinancial support from ALK, AstraZeneca, Novartis, and Menarini, personal fees from Boehringer-Ingelheim and TEVA, and personal fees/nonfinancial support from Chiesi. 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