https://orcid.org/0000-0001-5181-1311
Figures
Abstract
Background
Hospital-at-home (HaH) programs provide an effective and safe alternative to conventional hospitalization, particularly for patients with multimorbidity or complex chronic conditions. In this setting, nursing assessment plays a key role, given the intermittent nature of care and the limited presence of other professionals. However, nursing documentation in HaH remains challenging because of organizational heterogeneity, fragmented records, and the coexistence of multiple assessment tools that create redundancy and compromise information quality. The overall objective of this study is to develop and validate a meta-instrument (VALENF-HAD) that integrates the assessment of functional capacity, pressure injury risk, fall risk, frailty, nutritional status, and sleep quality in patients admitted to hospital-at-home units on the basis of the analysis of other validated measurement instruments used in nursing assessment.
Methods
A multicentre, cross-sectional study will be conducted in four public hospitals in Castellón Province, Spain. Participants will include adults (≥18 years) admitted to HaH units. Sociodemographic and care-related variables, along with risk-related variables, will be collected using the following instruments: functional capacity (Barthel Index and Lawton & Brody Scale), risk of pressure injuries (Braden Scale), risk of falls (STRATIFY Scale), frailty (FRAIL questionnaire), nutritional status (Nutritional Screening Initiative tool), and sleep quality (Athens Insomnia Scale). The development process of the meta-instrument consists of four phases: (I) analysis of instruments, constructs, and items; (II) meta-instrument design; (III) initial validation; and (IV) determination of clinimetric properties.
Discussion
This study is expected to produce a parsimonious and specific meta-instrument for hospital-at-home settings that is capable of integrating functional, nutritional, sleep, fall risk, frailty, and pressure injury assessments. Its implementation will enhance the quality and standardization of nursing documentation, reduce administrative workload, facilitate early risk detection, and free up time for direct patient care. Ultimately, this study will contribute to advancing evidence-based nursing practices in home hospitalization.
Citation: Barrué-García P, Llagostera-Reverter I, Cervera-Gasch Á, Esteve-Clavero A, Valero-Chillerón MJ, Ortiz-Mallasén V, et al. (2026) Design and validation of an integrated meta-instrument for nursing assessment in home hospitalization: A study protocol. PLoS One 21(6): e0351126. https://doi.org/10.1371/journal.pone.0351126
Editor: Majed Sulaiman Alamri, University of Hafr Al-Batin, SAUDI ARABIA
Received: December 10, 2025; Accepted: May 24, 2026; Published: June 23, 2026
Copyright: © 2026 Barrué-García et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: No datasets were generated or analysed during the current study. Upon completion of the study, the anonymized dataset at item level will be made publicly available in the institutional repository of Universitat Jaume I (UJI). All data will be processed in accordance with aplicable data protection regulations. Prior to data sharing, all directly and indirectly identifiable information will be removed to ensure full anonymization and protect participant confidentiality.
Funding: This project is funded by Jaume I University (UJI-2024-06; Principal Investigator: Irene Llagostera-Reverter). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Background
Hospital-at-home (HaH) care represents an efficient alternative to conventional inpatient care for numerous medical and surgical conditions [1]. It is defined as a hospital-level care modality delivered in the patient’s home providing active monitoring and treatment for the patients who would otherswise require acute hospital care. Fro conceptual perspective, HaH encompasses two main operational models: admission avoidance (preventing hospitalization) and early discharge (shortening hospital stay), when patients do not require hospital facilities but still need active monitoring [2]. Both are aimed at reducing costs and complications associated with the hospital environment [3,4]. In Spain, HaH was implemented in 1981, although its development has been uneven and limited by the lack of a regulatory framework and the heterogeneity of organisational models [1].
Nursing care at home differs substantially from hospital-based care, as it is intermittent and often delivered in the absence of other health care professionals [5]. These circumstances increase nurses’ responsibility in clinical decision-making and require high-quality assessments to ensure effective interprofessional communication [6]. Moreover, home care constitutes one of the main settings for managing patients with complex chronic multimorbidity, characterised by heterogeneous needs that range from low-intensity care to high-dependency, complex clinical support [7]. Central to this model is a biopsychosocial approach, where social and family dynamics are prioritized alongside clinical stability. This framework fosters shared responsibility, integrating patient autonomy with the essential role of the caregiver network to ensure the safety and continuity of the hospital-level care provided at home [1].
In this context, nursing records are an essential component of medical records, as they document the patient’s status and progress, enable risk identification, and ensure continuity of care despite less frequent visits compared with hospital settings [8,9]. Accurate and complete documentation of nursing assessments helps prevent errors and improves communication among professionals [10]. However, its use in home settings is less consistent than it is in hospitals [11], largely because of structural and organisational differences between both contexts. Home care usually involves longer and more fragmented care episodes, wich may hinder both the standardization of documentation and the systematic assessment of patients’ needs and risks [12]. Furthermore, the coexistence of multiple assessment tools leads to construct overlap and redundancy, compromising the quality and validity of records [13,14]. Evidence suggests that in both hospital [15] and home settings [7,16], simplifying documentation and implementing systematic screening could ensure the collection of essential information.
The available information on integrated and validated instruments for nursing assessment in HaH is limited. Several systematic reviews highlight the lack of consensus on indicators that capture the quality of nursing care [17,18] and the scarcity of tools adapted to complex care settings, wich have been predominantly developed and studied in older populations [19]. In this regard, the Fundamentals of Care Framework [20] provides a relevant reference, particularly in its physical care dimension. This framework identifies essential care needs such as hygiene, mobility, nutrition, prevention of pressure injuries, rest, and elimination, which are interrelated with the therapeutic relationship and the care context [21,22]. Its application in HaH enables a comprehensive, person-centred nursing assessment and facilitates the identification of outcomes that are sensitive to nursing practice, such as functional decline [23], sleep disturbances [24], or malnutrition [25]. Moreover, this framework offers a robust conceptual basis for incorporating dimensions that allow the synergistic identification of risks such as falls [26], pressure injuries [27], and frailty [28].
An innovative proposal to overcome these limitations is the development of meta-instruments, understood as integrated assessment instruments that combine items and constructs from multiple validated tools into a single parsimonious instrument while maintaining clinimetric properties and diagnostic capacity equivalent to those of the original tools [29]. For instance, the VALENF Instrument [30,31] is a predictive algorithm that estimates functional capacity, pressure injury risk, and fall risk by merging the Barthel, Braden, and Downton scales into a seven-item solution. This meta-instrument has shown high predictive accuracy and reliability for the original scales’ total scores, along with adequate clinimetric properties.
Adapting this approach to hospital-at-home care could optimize nurses’ time, enhance the early detection of risks, and promote the standardization of care [16]. Therefore, it seems pertinent to develop and validate a specific meta-instrument for hospital-at-home settings aimed at supporting more parsimonious, standardized, and person-centred nursing practices. This instrument, named VALENF-HAD (from the Spanish VALoración ENFermera–Hospitalización A Domicilio, meaning “Nursing Assessment – Hospital-at-Home”), is designed to integrate multiple dimensions of nursing assessment in hospital-at-home settings, including functional capacity, pressure injury risk, fall risk, frailty, nutritional status, and sleep quality.
The overall objective of this study is to develop and validate a meta-instrument (VALENF-HAD) that integrates the assessment of functional capacity, pressure injury risk, fall risk, frailty, nutritional status, and sleep quality in patients admitted to hospital-at-home units based on the analysis of validated nursing assessment instruments. Specifically, the study aims to identify patient profiles with similar care needs based on nursing assessment, analyse the influence of sociodemographic and care-related variables on assessment outcomes, examine the relationships among the included instruments to identify redundant items, and determine the clinimetric properties of the VALENF-HAD meta-instrument, including content validity, construct validity, and internal consistency.
Methods
Design
This multicentre, cross-sectional study involves the consecutive recruitment of patients admitted to the hospital-at-home (HaH) units of four public hospitals in the province of Castellón, Spain. These hospital-at-home units are organizationally integrated within the participating hospitals and provide hospital-level care delivered in patients’ home. At the time of manuscript submission, the study is in the recruitment and data collection phase. The planned study timeline is as follows: a) participant recruitment and data collection form 01/02/2025 to 31/05/2026; b) database download and data curation from 01/06/2026 to 31/08/2026; c) data analysis from 01/09/2026 to 31/08/2027; d) dissemination of study results from 01/09/2027 onward. The overall study timeline is shown in Fig 1.
Participants and sample
The study population will consist of nursing assessments performed on adult patients receiving Hospital-at-Home care in four public hospitals in the province of Castellón (Spain), which together provide health care coverage to 615.188 people: Hospital Universitario Comarcal de Vinaròs (HUCV), Hospital Universitario de La Plana (HULP), Consorcio Hospitalario Provincial de Castellón (CHPC), and Hospital General Universitario de Castellón (HGUCS).
The study will include nursing assessments conducted within the first 48 hours after admission for patients aged 18 years or older who agree to participate and provide written informed consent, as the assessment instruments included in the study are primarily validated in adult populations. The first hours were selected because they correspond to the initial nursing assessment period in hospital-at-home units, allowing for a standardized baseline evaluation. Assessments from patients whose expected stay is less than 48 hours, such as those admitted under a day hospital regimen, will be excluded.
Regarding sample size, the literature recommends including between 5 and 10 participants per item in studies aimed at the development and validation of questionnaires [32]. The total number of items across all the instruments considered in this study is 59; therefore, a minimum of 590 assessments was initially estimated. However, due to the lack of specific recommendations for studies involving the integration of multiple assessment instruments and based on previous methodological references [29], the sample size was increased to 1.180 assessments to ensure adequate representativeness and to support more complex analytical strategies.
The sample will be stratified according to the number of admissions in each HaH unit, excluding 20% corresponding to day-hospital admissions. Consecutive sampling will be performed in each until the required sample size is reached, as shown in Table 1.
Variables and instruments
Sociodemographic variables are included (age, sex, educational level, marital status, type of cohabitation, presence of a caregiver, type of caregiver, and place where care is provided); variables related to the care process (hospital, source of admission, type of process, clinical profile, advanced or terminal palliative situation, Charlson Comorbidity Index [33], active infection on admission, pressure injury on admission, bedridden status, presence of invasive clinical devices, polypharmacy, pain, and presence of symptoms assessed using the Edmonton Symptom Assessment System [34]; and palliative sedation status, which is measured with the RAMSAY Sedation Scale [35].
Variables related to the nursing assessment will be collected, including functional capacity, measured with the Barthel Index [36] and the Lawton and Brody scale [37]; pressure injury risk, measured with the Braden Index [38]; and fall risk, measured with the STRATIFY scale [39]. These assessments will be recorded in the data collection form together with the VALENF Instrument [30], a meta-instrument that integrates the assessment of functional capacity, pressure injury risk, and fall risk. In addition, frailty will be measured with the FRAIL questionnaire [40], nutritional status with the NSI tool [41], and sleep quality with the Athens Insomnia Scale [42].
Data collection
Data collection will be conducted prospectively over an estimated 16-month period (01/02/2025 to 31/05/2026) through consecutive sampling in the home hospitalization units participating in the study. The time required for a complete evaluation in routine clinical practice is approximately 20 minutes. Data will be collected within the first 48 hours after admission, allowing for an initial assessment on the first day followed by a more comprehensive evaluation on the second. This phased approach has been designed to reduce the pressure of the first visit and minimize potential fatigue among both patients and clinical staff. Furthermore, the HaH model allows for longer and more flexible interaction with patients compared to conventional hospitalization. Data entry will be carried out using Research Electronic Data Capture (REDCap) software.
A common Standard Operating Procedure (SOP) was developed for all participating centers, detailing the instructions for instrument administration and data recording. Prior to the start of data collection, structured training sessions were conducted at each participating hospital to ensure consistency in the application of the instruments by nursing professionals. During the data collection period, continuous monitoring mechanisms were established to ensure measurement consistency, including regular communication between the research team and clinical staff, as well as periodic data quality checks to identify potential inconsistencies or deviations in data collection.
After informing patients and providing them with the study information sheet, written informed consent will be obtained. Decision-making capacity will be clinically assessed by the nursing team as part of routine care, based on the patients’s ability to understand the study information and provide informed consent. In cases where patients present cognitive impairment or are unable to provide valid consent, informed consent will be obtained from a legally authorized representative (LAR), in accordance with standard ethical procedures.
When necessary, the primary caregiver and/or family member will complete the questionnaires as proxy respondents. Proxy responses will be explicity identified in the dataset, allowing them to be distinguished from patient self-reported data.
Both the nursing professionals and the research team signed a confidentiality and data responsibility agreement, as well as an intellectual property commitment.
Statistical analysis
The statistical analysis will be conducted in four sequential phases. First, the sample and measurement instruments will be characterized through a descriptive analysis according to the nature of the variables. Significant differences in the assessment results of problems related to hospitalization will be examined, considering sociodemographic and care process variables, taking into account whether the data are patient self-reported or proxy-reported. The normality and homoscedasticity of the sample will be tested using the Kolmogorov–Smirnov and Levene tests, respectively. Depending on the results, Student’s t test or ANOVA will be applied, depending on the number of groups. Categorical variables will be analysed using the chi-square (χ²) test, and correlations will be assessed using Pearson’s or Spearman’s test, as appropriate. When the assumptions for these tests are not met, the corresponding nonparametric tests will be applied.
Following this initial analysis, the development and validation process of the new meta-instrument will be carried out by adapting the methodological proposal of Llagostera-Reverter et al. [29]. First, an analysis will be performed to explore the conceptual and semantic relationships among the dimensions of the instruments. In addition, the items of the instruments will be analysed to identify similarities, duplications, and redundancies, with the goal of establishing direct or indirect conceptual relationships through a nominal group [43] composed of members of the research team and nursing professionals working in home hospitalization units and analysing nursing physical care within the conceptual framework of the Fundamentals of Care [44]. Subsequently, conceptual similarities and redundancies between the dimensions and items of the instruments will be examined, and correlations will be tested using Pearson’s or Spearman’s tests, as appropriate. Multiple correspondence analysis and cluster analysis will be used to explore potential groupings of items and to identify clinical profiles of patients with similar care needs [45–47].
Second, linear model analyses will be performed using each assessment instrument as a dependent variable to determine the influence of sociodemographic and care process variables. Additional linear model analyses will be conducted to predict the scores of the original instruments on the basis of combinations of their own items.
Third, the initial validation of the new meta-instrument will be conducted. Agreement will be assessed using the intraclass correlation coefficient (ICC > 0.7) [48]. Participants will also be grouped according to the categorical levels of each assessment instrument on the basis of their obtained scores, and overall and category-specific agreement will be examined using Kendall’s tau-b and Cohen’s kappa tests (τ-b and κ > 0.7) [49]. To ensure the robustness and generalizability of these predictive models, cross-validation will be applied [50].
Fourth, the clinimetric properties will be determined following the recommendations of the COSMIN initiative [51]. Content validity will be evaluated through a nominal group of clinical nurses with at least 10 years of experience and university nursing faculty with doctoral degrees. They will assess the relevance of the proposed items and dimensions using the Polit and Beck (2006) methodology to calculate the item-level content validity index (I-CVI ≥ 0.78) and the scale-level content validity index (S-CVI ≥ 0.9) [52]. Construct validity will be assessed according to the methodology proposed by Efron and Tibshirani [53]. The sample will be randomly divided into two subgroups. In the first subgroup, an exploratory factor analysis with oblimin rotation and maximum likelihood estimation will be performed after verifying adequacy through Kaiser–Meyer–Olkin and Bartlett’s sphericity tests. In the second subgroup, confirmatory factor analysis and structural equation modelling will be conducted to validate the theoretical structure of the instrument. Model fit will be evaluated using χ²/df (<5), RMSEA (<0.05), and CFI (≥0.97). Internal consistency will be estimated using McDonald’s Omega (Ω > 0.7) [54] and Cronbach’s alpha (α > 0.7), as appropriate, depending on the characteristics of the data and the underlying assumptions of each method [55]. Finally, interrater reliability will be assessed in a pilot sample using the intraclass correlation coefficient (ICC > 0.61). A p value < 0.05 will be considered to indicate statistical significance, and analyses will be conducted using software, selected according to their specific analytical capabilities for descriptive, multivariate, and advanced modelling procedures.
The Statistical Analysis Plan (SAP) was defined during the study design phase, prior to the start of participant recruitment. This plan remained stable throughout the study and was considered finalized before the initiation of data analysis. The study was retrospectively registered (ISRCTN 94528203, 09/05/2025), including the objectives, variables, and analytical strategies. No relevant changes were made after the start of recruitment.
Data management plans
All data will be collected using the Research Electronic Data Capture (REDCap) platform, hosted on secure institutional servers at the Universitat Jaume I. Access to the database will be password-protected and restricted to authorized members of the research team. Each participant will be assigned a unique identification code to ensure pseudonymization, and no personal identifiers will be stored in the analytical dataset.
Data entry will be validated through automated range and logic checks to minimize transcription errors. Weekly quality control procedures will be conducted by the principal investigator to detect and correct missing or inconsistent entries. The final dataset will be stored in encrypted format and backed up regularly in compliance with institutional and European data protection regulations (Regulation [EU] 2016/679 and Spanish Organic Law 3/2018).
No datasets were generated or analysed during the current study. Upon completion of the study, the anonymized dataset at item level will be made publicly available in the institutional repository of Universitat Jaume I (UJI). All data will be processed in accordance with applicable data protection regulations. Prior to data sharing, all directly and indirectly identifiable information will be removed to ensure full anonymization and protect participant confidentiality.
Ethical considerations
The project received positive evaluations from the Research and Ethics Committees of the participating hospitals (HGUC [version 2, code VALENF-HAD], HULP [code VALENF-HAD], and CHPC [minutes no. 65, code VALENF-HAD]). The Hospital Universitario Comarcal de Vinaròs (HUCV) did not have an independent ethics committee at the time of study initiation. However, its participation was conducted under the same study protocol previously approved by the aforementioned Research Ethics Committees, ensuring ethical consistency across all sites. Institutional authorization was additionally obtained from the HUCV management board in accordance with local procedures. The Hospital General Universitario de Castellón (HGUC) acted as the reference centre for ethical approval within the multicentre study framework. This study was designed in accordance with Spanish Organic Law 3/2018 of December 5 on Personal Data Protection and Guarantee of Digital Rights, as well as with Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons.
Participants will be clearly informed, at the appropriate time, about the purpose and procedures of the study as well as their rights to access information related to their participation and to withdraw from the study at any time if they wish. This information will be provided by the nursing team involved in the study. After receiving this information, participants will be given the informed consent form to voluntarily sign. The database will not contain any personal information that could be used to identify participants.
Discussion
The application of meta-instruments in nursing assessment offers significant advantages through the integration of common constructs from different instruments into a single tool, thereby reducing redundancies and simplifying documentation while promoting the collection of higher-quality information. Furthermore, streamlining the completion process allows nurses to devote more time to direct patient care and enhances the detection of risk situations, facilitating clinical decision-making.
However, the literature on how to develop and validate meta-instruments remains scarce [29], and few previous studies have been reported, all of which employed retrospective cross-sectional designs [30,56]. In fact, we did not identify any study with a similar aim that did not use registry data. The choice of a prospective design in this study is based on the variability in the assessment systems used across the four participating home hospitalization units. Indeed, a necessary preliminary step was reaching a consensus on the assessment dimensions and measurement instruments to be used.
In addition, previous studies on meta-instruments such as the VALENF instrument have demonstrated a strong capacity to detect clinically relevant events, including functional decline, risk of falls, and pressure injuries. These findings support the potential utility of integrated assessment tools not only to simplify documentation but also to enhance early risk identification and clinical decision-making [57].
This study design is intended to help minimize the information bias associated with the quality of data recorded in clinical histories [58]. Nonetheless, one of the main challenges of this study is maintaining the motivation of the nursing staff to collaborate over at least a 12-month data collection period and achieving the required sample size. To address these limitations, comprehensive training sessions on the REDCap platform were provided to professionals in each home hospitalization unit prior to data collection to ensure standardized measurements. In addition, during data collection, the research team maintains regular contact with the clinical nursing teams to resolve questions and issues related to recruitment and data collection. The researchers, together with the designated lead in each home hospitalization unit, perform weekly monitoring in the CDRe system to identify potential missing or erroneous data.
Acknowledgments
The authors gratefully acknowledge the contribution of all nursing professionals from the Home Hospitalization Units of the participating hospitals, VALENF-HAD Group: Olga Domingo-Tomás1, Olga Ventura-Arnal1, Iván Mora-Sánchez1, Eva M. Bou Rodríguez1, Verónica Cano-Sanz1, Raquel Centelles-Montañés1, Saray Villarroya-Gascón1, Irene C. Pino-Bonet2, Susana García-Barreda2, Esther Beltrán-García2, Sara Alhiane-Jabrone2, Marcos Vallejo-Torrejón2, Florencia V. Tirado-Molina2, Marc Buigues-Escuder2, David Remón-Cucó2, Rosa Bou-Escrig3, Elidia Tena-Martínez3, María Sanz-Villalba3, Adrián Fuster-Izquierdo3, Beatriz Martínez-Sabuco3, Eugenia Galindo-Rincón3, Lucía Calderín-Suárez3, Nuria Andreu-Silvestre3, Rebeca Salt-Garrigues3, Piedad Agulleiro-Cánovas3, Paloma Monreal-Soria3, Marta Castel-Burriel4, Gemma Conesa-Carbó4, Noelia Figuerola-Rambla4, Azahara Fuentes-Saura4, Alexia García-González4, Alba Gellida-Sebastià4, Montserrat López-González4, Enzo San Abdon-Lorente4, María Lores-Pedrosa4, and Lidia Guimerà-Foix4. Affiliations: 1Hospital General Universitario de Castellón (FISABIO), Castellón de la Plana, Castellón, Spain; 2Hospital Universitario de La Plana (FISABIO), Vila-Real, Castellón, Spain; 3Consorcio Hospitalario Provincial de Castellón, Castellón de la Plana, Castellón, Spain; 4Hospital Universitario Comarcal de Vinaròs (FISABIO), Vinaròs, Castellón, Spain. The VALENF-HAD Group is coordinated by Irene Llagostera-Reverter, who acts as the group’s guarantor and can be contacted at llagoste@uji.es.
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