What is the clinical evidence from this study?

Study design: RCT. Population: Atrial fibrillation (n=150). Intervention: Bed sensor (EMFIT QS) and smartphone application (CardioSignal) vs. Usual care. Primary outcome: Occurrence of new atrial fibrillation within the three-month follow-up period (ARR 7.7%, 95% CI 1.8-13.6%, p=0.029).

What does this research mean for the field?

A bed sensor and smartphone application significantly increase atrial fibrillation detection compared to usual care in high-risk patients following invasive cardiac procedures, but high false alarm rates currently limit their routine clinical implementation. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

March 3, 2026Open Access

Randomized trial of smartphone application and bed sensor for atrial fibrillation detection in high-risk patients

Key Result

A bed sensor and smartphone application increased atrial fibrillation detection to 7.7% compared to 0.0% with usual care in high-risk patients following invasive cardiac procedures.

Key Points

Atrial fibrillation was detected in 7.7% of patients using the intervention, while none in the control group were diagnosed in three months.
Among 68 patients, 48.5% of alarms did not lead to confirmed AF diagnosis, indicating a need for improvement in alarm accuracy.
The study involved a two-arm randomized controlled trial with a bed sensor and smartphone app integrated for ongoing monitoring of patients' heart rhythms after procedure. The intervention seeks to enhance AF detection but may require refinement to be useful in clinical settings.

Study Design

Type

RCT (n=150)

Blinding

Open-label

Randomization

Stratified block randomization

Multicenter

Structured PICO

Does a bed sensor and twice-daily smartphone recordings improve atrial fibrillation detection in high-risk patients after an invasive cardiac procedure?

Population

150 high-risk patients after an invasive cardiac procedure

Intervention

Bed sensor (EMFIT QS) and twice-daily smartphone recordings (CardioSignal app), followed by a 12-lead ECG and continuous 3-to-7-day ECG monitoring if alerts occurred

Comparator

Usual care

Outcome

Atrial fibrillation (AF) detection during a three-month follow-up

While the combination of a bed sensor and smartphone app significantly increased AF detection compared to usual care, a high rate of false alarms currently limits its routine clinical implementation.

Main Result

Effect estimate: ARR 7.7% (95% CI 1.8-13.6%)

Absolute Event Rate: 7.7% vs 0%

p-value: p=0.029

Limitations

Single-center trial with a limited number of patients
Lack of continuous rhythm monitoring may have missed short AF paroxysms
No preoperative cardiac monitoring to rule out subclinical AF before the operation
Control group received only standard clinical monitoring without comparison to other available AF screening methods
Sample size was reduced due to the high number of alarms not leading to AF diagnosis, leading to an underpowered analysis
Enrichment criteria were broadened during the study, potentially affecting population homogeneity

Abstract

This two-arm single-center exploratory randomized controlled trial evaluated the efficacy of prolonged rhythm monitoring in atrial fibrillation (AF) detection after an invasive cardiac procedure. Altogether 150 patients were enrolled. In the intervention group (IG), a bed sensor (EMFIT QS) and twice-daily smartphone recordings (CardioSignal app) were used, followed by a 12-lead ECG and a continuous three-to-seven-day ECG monitoring if alerts occurred. The control group (CG) received usual care. Overall, 78 patients were assigned to the IG and 72 to CG. During the three-month follow-up, AF was detected in 6/78 (7.7%) patients in the IG and in 0/72 (0.0%) in the CG (absolute risk difference 7.7%, 95% CI 1.8–13.6%, p = 0.029). After exclusion of patients who withdrew before the 3-month follow-up, 33/68 (48.5%) patients had alarms not leading to ECG-verified AF diagnosis, indicating that the current approach, in its present form, is not suitable for routine clinical implementation. Future studies should concentrate on minimizing alarms not leading to AF diagnosis when developing these novel non-ECG-based technologies. ClinicalTrials.gov Identifier: NCT05351775, 2022/04/28.

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