The instrument used to measure the HGS in the present study was a modified constant electronic hand dynamo-meter 14192-709E-EH101–90 kg capacity range, in which the entire electric circuit was modified and replaced by one specific, designed, and constructed at the Technical Research Centre for Dependency Care and Autonomous Living (CETpD) of the Universitat Politécnica de Catalunya (UPC). Because of this modification, the dynamometer continuously measured the HGS over time, and includes the ability to store information, Bluetooth connectivity, and a micro-SD card for data storage using the Inertial Measurement Unit (IMU) developed by CETpD–UPC (10) for long-term monitoring of human pathological movement. The calibration of the system is fully described in at previous study (9).

The proposed pilot protocol was approved by the Ethics Committee for Drug Research of the Community of Madrid (Comité de Ética de la Investigación con Medicamentos de la Comunidad de Madrid – Ref 47/916546.9/19).

Participants, who signed informed consents, were selected via convenience sampling (non-probabilistic) from patients who received medical services provided by collaborating entities (“Hospital Central de la Cruz Roja San José” and “Santa Adela de Madrid”, “and “Casa d’Ampara de Vilanova i la Geltrú”), from November 2019 to July 2021. Only researchers from the Health Consortium “Alt Penedès-Garraf” had access to data base during and after data collection.

In the first part of the data acquisition, geriatricians obtained physiological information from each patient. The patient’s labelling between robust and frail of the database was made by the medical staff participating in the clinical trial based on all the information proportioned by the different questionnaires and scales (Fried, Barthel, Lawton-Brody, CGA Index). The second part of the protocol was the HGS test, which consisted of three trials carried out in a sitting position on a chair with the forearm placed on top of the leg in a neutral position (holding the dynamometer perpendicular to the leg), feet firmly on the floor at a shoulder-width distance, shoulders adducted, body neutrally rotated, and dominant hand used (Figure 1). The tests had a duration of six seconds each, with a rest interval of one minute between the tests.

Protocol position example

The sample was stratified by the degree of frailty (robust and frail participants). Participants meeting the following criteria were included in the study: age above 70 years, sufficient reading and writing ability to answer questionnaires, and willingness to participate in the study, and acceptance of the standards of performance and procedures established by researchers. Participants meeting the following criteria were excluded: alcohol and/or drug abuse, any type of neurological or osteoarticular affectation (Parkinson’s disease, osteoarthritis, or stroke), inability to provide informed consent and to cooperate with study procedures.

The patient’s labelling between robust and frail of the database was made by the medical staff participating in the clinical trial based on all the information proportioned by the different questionnaires and scales (2–5). Using the original database, the CRF provides different possible labels. If we choose the Fried Criteria (involuntary loss of weight, low energy or exhaustion, slow mobility, muscle weakness, low physical activity), then the classification is binary: frail and not-frail. Using the Fragile CGA criteria, we can distribute the patients between robust (CGA index < 0.2), prefrail (between 0.2 and 0.5), and frail (CGA index > 0.5). As a first step to achieve some practical results for classification purposes, we decided to combine frail and prefrail into a single class, and to have only two classes for the final classification action. The aim of this simplification was to distinguish between the robustness of frailty tendencies.

The sociodemographic characteristics of the participants, as well as their levels of fragility, are shown in Tables 1 and 2. The present cohort shows an unbalanced data set, so there are more than double frail participants than robust. Balancing techniques are required for obtaining coherent results. An overview of the data shows that there are no direct relationships between the different variables and guides us to search for more complex information, such as temporal variation in exerting the force of the hand (HGS).

First, geriatricians performed obtained physiological information from each patient and evaluated the following scales in clinical trials: Fried criteria (2), CGA Index (3), Barthel scale (4), and Lawton–Brody scale (5). The second part of the protocol was the HGS test, which consisted of three trials carried out in a sitting position on a chair with the forearm placed on top of the leg in a neutral position. The tests had a duration of six seconds each, with a rest interval of one minute between the tests.

In total, 223 valid HGS temporal series or force-time curves, were recorded for 138 patients. The validity of the HGS signal is based on simple geometric criteria of the recorded shape. Each patient was assigned a set of 19 geriatrician variables obtained from different scales and included in the Case Report Form (CRF).

Two Butterworth filters were used in this study. To obtain the geometric features, a 1 Hz low-pass filter was applied to determine the HGS shape. Three phases can be distinguished in the HGS time series after the first filtering process: the force generation phase (segment 1 in Figure 2), force maintenance phase (segment 2 in Figure 2), and force decay phase (segment 3 in the same Figure). A second 5 Hz filter was applied to obtain the dynamic and frequency features.

Strength (Kg) vs. time (s) signal and the four characteristic points related to the step shape of the time-series signal

A simple algorithm based on the first and second derivatives provided the HGS time series. These phases were used to extract specific characteristics that could be used to determine whether a patient was prone to developing frailty.