Clinical Scorecard: Activity Monitoring With an Insertable Cardiac Monitor in Ambulatory Patients: Subanalysis of the LUX-Dx TRENDS Clinical Study of Heart Failure Sensors

At a Glance

Key Highlights

• The exercise substudy includes 104 participants at 26 US sites; 101 contribute to the 2 analysis datasets, and 3 do not contribute because device data are unavailable.

• Accelerometer activity signals increase by an estimated 33.7 mG per 1-mph increase in treadmill walking speed in dataset 1 and 32.3 mG in dataset 2; both associations have P < .0001.

• In dataset 2, activity-classification agreement with the prespecified reference standard is κ = 0.971 (95% CI, 0.947–0.985), exceeding the prespecified goal of κ > 0.8.

• Sensitivity is 0.974, specificity is 0.994, positive predictive value is 0.991, and negative predictive value is 0.982.

Guideline-Based Recommendations

Diagnosis

• The article does not provide guideline-based diagnostic recommendations. It evaluates activity sensing and classification using an investigational ICM.

Management

• The study does not establish treatment recommendations. The authors identify real-world applications for detection, risk stratification, and patient management as areas for future research.

Monitoring & Follow-up

• The findings support the potential for subcutaneous devices to provide objective activity measurements in ambulatory patients with ICMs. The article does not establish a clinical follow-up schedule.

Risks

• Treadmill testing continues until predefined safety stopping criteria are met or participants reach physical tolerance limits.

• External mechanical vibration may mimic patient motion and produce false-positive activity classifications.

Patient & Prescribing Data

Participants have a mean age of 69.6 years; 33% are women. Left ventricular ejection fraction is preserved in 63%, moderately reduced in 19%, and reduced in 18%. This subanalysis evaluates activity-monitoring performance rather than medication effects and does not provide prescribing recommendations.

Clinical Best Practices

• Interpret the findings within the controlled testing conditions and prespecified reference standard: treadmill walking at ≥1.2 mph is active, and stationary postures are inactive.

• The primary analysis excludes walking at 0.7 mph because intermittent walking may introduce classification ambiguity. The performance goal remains met when sensitivity analyses classify this stage as active or inactive.

• Consider the study’s limitations: other activities are not studied, selection may exclude frailer or gait-limited patients, and performance is not evaluated within patient subgroups.

Related Resources & Content

Kadhiresan VA, Pastore J, Auricchio A, et al. A novel method—the activity log index—for monitoring physical activity of patients with heart failure. Am J Cardiol. 2002;89:1435–1437.

Richards M, Olshansky B, Sharma AD, et al. Addition of minute ventilation to rate-response pacing improves heart rate score more than accelerometer alone. Heart Rhythm. 2018;15:1730–1735.

Stansfield B, Hajarnis M, Sudarshan R. Characteristics of very slow stepping in healthy adults and validity of the activPAL3 activity monitor in detecting these steps. Med Eng Phys. 2015;37:42–47.

Frodi DM, Kolk MZH, Langford J, et al. Long-term adherence to a wearable for continuous behavioural activity measuring in the SafeHeart implantable cardioverter defibrillator population. Eur Heart J Digit Health. 2024;5:622–632.

Kolk MZH, Frodi DM, Andersen TO, et al. Accelerometer-assessed physical behavior and the association with clinical outcomes in implantable cardioverter-defibrillator recipients: a systematic review. Cardiovasc Digit Health J. 2022;3:46–55.

Singh B, Chastin S, Miatke A, et al. Real-world accuracy of wearable activity trackers for detecting medical conditions: systematic review and meta-analysis. JMIR Mhealth Uhealth. 2024;12:e56972.