Prediction using a randomized evaluation of data collection integrated through connected technologies (PREDICT): Design and rationale of a randomized trial of patients discharged from the hospital to home

Using remotely monitored patient activity patterns after hospital discharge to predict 30 day hospital readmission: a randomized trial

Hospital readmission prediction models often perform poorly, but most only use information collected until the time of hospital discharge. In this clinical trial, we randomly assigned 500 patients discharged from hospital to home to use either a smartphone or wearable device to collect and transmit remote patient monitoring (RPM) data on activity patterns after hospital discharge. Analyses were conducted at the patient-day level using discrete-time survival analysis. Each arm was split into training and testing folds. The training set used fivefold cross-validation and then final model results are from predictions on the test set. A standard model comprised data collected up to the time of discharge including demographics, comorbidities, hospital length of stay, and vitals prior to discharge. An enhanced model consisted of the standard model plus RPM data. Traditional parametric regression models (logit and lasso) were compared to nonparametric machine learning approaches (random forest, gradient boosting, and ensemble). The main outcome was hospital readmission or death within 30 days of discharge. Prediction of 30-day hospital readmission significantly improved when including remotely-monitored patient data on activity patterns after hospital discharge and using nonparametric machine learning approaches. Wearables slightly outperformed smartphones but both had good prediction of 30-day hospital-readmission.

Association between behavioral phenotypes and sustained use of smartphones and wearable devices to remotely monitor physical activity

Smartphones and wearable devices can be used to remotely monitor health behaviors, but little is known about how individual characteristics influence sustained use of these devices. Leveraging data on baseline activity levels and demographic, behavioral, and psychosocial traits, we used latent class analysis to identify behavioral phenotypes among participants randomized to track physical activity using a smartphone or wearable device for 6 months following hospital discharge. Four phenotypes were identified: (1) more agreeable and conscientious; (2) more active, social, and motivated; (3) more risk-taking and less supported; and (4) less active, social, and risk-taking. We found that duration and consistency of device use differed by phenotype for wearables, but not smartphones. Additionally, "at-risk" phenotypes 3 and 4 were more likely to discontinue use of a wearable device than a smartphone, while activity monitoring in phenotypes 1 and 2 did not differ by device type. These findings could help to better target remote-monitoring interventions for hospitalized patients.

Interventions to improve adherence to pharmacological therapy for chronic obstructive pulmonary disease (COPD)

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a chronic lung condition characterised by persistent respiratory symptoms and limited lung airflow, dyspnoea and recurrent exacerbations. Suboptimal therapy or non-adherence may result in limited effectiveness of pharmacological treatments and subsequently poor health outcomes.

OBJECTIVES

To determine the efficacy and safety of interventions intended to improve adherence to single or combined pharmacological treatments compared with usual care or interventions that are not intended to improve adherence in people with COPD.

SEARCH METHODS

We identified randomised controlled trials (RCTs) from the Cochrane Airways Trials Register, CENTRAL, MEDLINE and Embase (search date 1 May 2020). We also searched web-based clinical trial registers.

SELECTION CRITERIA

RCTs included adults with COPD diagnosed by established criteria (e.g. Global Initiative for Obstructive Lung Disease). Interventions included change to pharmacological treatment regimens, adherence aids, education, behavioural or psychological interventions (e.g. cognitive behavioural therapy), communication or follow-up by a health professional (e.g. telephone, text message or face-to-face), multi-component interventions, and interventions to improve inhaler technique.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methodological procedures. Working in pairs, four review authors independently selected trials for inclusion, extracted data and assessed risk of bias. We assessed confidence in the evidence for each primary outcome using GRADE. Primary outcomes were adherence, quality of life and hospital service utilisation. Adherence measures included the Adherence among Patients with Chronic Disease questionnaire (APCD). Quality of life measures included the St George's Respiratory Questionnaire (SGRQ), COPD Assessment Test (CAT) and Clinical COPD Questionnaire (CCQ).

MAIN RESULTS

We included 14 trials (2191 participants) in the analysis with follow-up ranging from six to 52 weeks. Age ranged from 54 to 75 years, and COPD severity ranged from mild to very severe. Trials were conducted in the USA, Spain, Germany, Japan, Jordan, Northern Ireland, Iran, South Korea, China and Belgium. Risk of bias was high due to lack of blinding. Evidence certainty was downgraded due to imprecision and small participant numbers. Single component interventions Six studies (55 to 212 participants) reported single component interventions including changes to pharmacological treatment (different roflumilast doses or different inhaler types), adherence aids (Bluetooth inhaler reminder device), educational (comprehensive verbal instruction), behavioural or psychological (motivational interview). Change in dose of roflumilast may result in little to no difference in adherence (odds ratio (OR) 0.67, 95% confidence interval (CI) 0.22 to 1.99; studies = 1, participants = 55; low certainty). A Bluetooth inhaler reminder device did not improve adherence, but comprehensive verbal instruction from a health professional did improve mean adherence (prescription refills) (mean difference (MD) 1.00, 95% CI 0.46 to 1.54). Motivational interview improved mean adherence scores on the APCD scale (MD 22.22, 95% CI 8.42 to 36.02). Use of a single inhaler compared to two separate inhalers may have little to no impact on quality of life (SGRQ; MD 0.80, 95% CI -3.12 to 4.72; very low certainty). A Bluetooth inhaler monitoring device may provide a small improvement in quality of life on the CCQ (MD 0.40, 95% CI 0.07 to 0.73; very low certainty). Single inhaler use may have little to no impact on the number of people admitted to hospital compared to two separate inhalers (OR 1.47, 95% CI 0.75 to 2.90; very low certainty). Single component interventions may have little to no impact on the number of people expereincing adverse events (very low certainty evidence from studies of a change in pharmacotherapy or use of adherence aids). A change in pharmacotherapy may have little to no impact on exacerbations or deaths (very low certainty). Multi-component interventions Eight studies (30 to 734 participants) reported multi-component interventions including tailored care package that included adherence support as a key component or included inhaler technique as a component. A multi-component intervention may result in more people adhering to pharmacotherapy compared to control at 40.5 weeks (risk ratio (RR) 1.37, 95% CI 1.18 to 1.59; studies = 4, participants = 446; I² = 0%; low certainty). There may be little to no impact on quality of life (SGRQ, Chronic Respiratory Disease Questionnaire, CAT) (studies = 3; low to very low certainty). Multi-component interventions may help to reduce the number of people admitted to hospital for any cause (OR 0.37, 95% CI 0.22 to 0.63; studies = 2, participants = 877; low certainty), or COPD-related hospitalisations (OR 0.15, 95% CI 0.07 to 0.34; studies = 2, participants = 220; moderate certainty). There may be a small benefit on people experiencing severe exacerbations. There may be little to no effect on adverse events, serious adverse events or deaths, but events were infrequently reported and were rare (low to very certainty).

AUTHORS' CONCLUSIONS

Single component interventions (e.g. education or motivational interviewing provided by a health professional) can help to improve adherence to pharmacotherapy (low to very low certainty). There were slight improvements in quality of life with a Bluetooth inhaler device, but evidence is from one study and very low certainty. Change to pharmacotherapy (e.g. single inhaler instead of two, or different doses of roflumilast) has little impact on hospitalisations or exacerbations (very low certainty). There is no difference in people experiencing adverse events (all-cause or COPD-related), or deaths (very low certainty). Multi-component interventions may improve adherence with education, motivational or behavioural components delivered by health professionals (low certainty). There is little to no impact on quality of life (low to very low certainty). They may help reduce the number of people admitted to hospital overall (specifically pharmacist-led approaches) (low certainty), and fewer people may have COPD-related hospital admissions (moderately certainty). There may be a small reduction in people experiencing severe exacerbations, but evidence is from one study (low certainty). Limited evidence found no difference in people experiencing adverse events, serious adverse events or deaths (low to very low certainty). The evidence presented should be interpreted with caution. Larger studies with more intervention types, especially single interventions, are needed. It is unclear which specific COPD subgroups would benefit, therefore discussions between health professionals and patients may help to determine whether they will help to improve health outcomes.

Regulatory, Legal, and Market Aspects of Smart Wearables for Cardiac Monitoring

In the area of cardiac monitoring, the use of digitally driven technologies is on the rise. While the development of medical products is advancing rapidly, allowing for new use-cases in cardiac monitoring and other areas, regulatory and legal requirements that govern market access are often evolving slowly, sometimes creating market barriers. This article gives a brief overview of the existing clinical studies regarding the use of smart wearables in cardiac monitoring and provides insight into the main regulatory and legal aspects that need to be considered when such products are intended to be used in a health care setting. Based on this brief overview, the article elaborates on the specific requirements in the main areas of authorization/certification and reimbursement/compensation, as well as data protection and data security. Three case studies are presented as examples of specific market access procedures: the USA, Germany, and Belgium. This article concludes that, despite the differences in specific requirements, market access pathways in most countries are characterized by a number of similarities, which should be considered early on in product development. The article also elaborates on how regulatory and legal requirements are currently being adapted for digitally driven wearables and proposes an ongoing evolution of these requirements to facilitate market access for beneficial medical technology in the future.