Exploring usability characteristics in computer-based digital health technologies for family caregivers of people with chronic progressive conditions: A scoping review

Abstract

Objective

To examine usability characteristics/attributes and evaluation methods incorporated in the design and/or evaluation of computer-based digital technologies for caregivers of people with chronic progressive conditions.

Methods

We searched Medline (OVID), PsycINFO (Ovid), CINAHL (EBSCO), and Web of Science Core Collection to identify relevant studies published from 2012 to May 2024. Two reviewers screened studies for eligibility, and extracted and synthesized data.

Results

Across 71 included studies, sample sizes ranged between 5 and 127. Most participants were caregivers of people with dementia (n = 52, 73.2%). There was a mix of caregiving relationships across the 55 studies (77.5%) reporting this variable, with spouses most frequently included (n = 51, 92.7%). Samples were predominantly female (72.1%), with mean ages between 43.0 and 76.7 years old. Most technologies were Website/Internet-based (n = 43, 60.6%). Across the studies, we identified 31 distinct usability characteristics/attributes, with more than half of the studies (n = 45, 63.4%) including at least three characteristics/attributes. However, nearly half of the studies (n = 32, 45.1%) used a single usability evaluation method, predominantly inquiry-based interviews (n = 15, 21.1%).

Conclusion

Findings reflect a narrow focus on middle-aged, female, and spousal caregivers, limiting the utility of digital health technologies for more diverse groups of caregivers. In addition to commonly used inquiry-based usability evaluation methods, user-testing, heuristic evaluation, and analytic modelling may offer more adaptive and holistic approaches to address a wider range of digital technologies.

Keywords

Usability caregivers digital technologies review chronic conditions

Introduction

Chronic progressive conditions are incurable diseases with significant morbidity in the latter stages of illness.¹ These conditions may include degenerative neurological diseases (e.g. multiple sclerosis, Alzheimer's disease); irreversible organ failure (e.g. liver failure); widespread malignant disease (e.g. cancers); and acquired immune deficiency syndrome. Family caregivers are vital in providing ongoing and often complex care for individuals living with chronic progressive conditions.² These caregivers are the backbone of natural support in the community, bridging the gap between what formal health and social care systems can provide and the amount of care required by their care-recipients.^3,4 Family caregiving is a multidimensional experience wherein caregivers may derive value and positive benefits from providing care and simultaneously be vulnerable to the role's negative physical, psychological, financial, and social impacts.^4–6 Indeed, providing ongoing support for people with chronic progressive conditions is often associated with high levels of stress, poor physical and psychological outcomes, increased risk of morbidity and mortality, reduced opportunities for employment and participation, and poor quality of life.^7,8 Thus, supporting family caregiver well-being has been identified as a priority for sustaining this valuable care resource.⁹

A wide array of digital health technologies—including computer-based platforms, mobile apps, wearable devices, and telehealth tools—has emerged as a promising means of support for caregivers. With the rapid development of ubiquitous smart devices and advances in artificial intelligence over the past decade, digital health tools have become increasingly prevalent.¹⁰ These technologies offer flexible, accessible support that can overcome time and geographic barriers, supplementing traditional in-person services.¹¹ Indeed, leveraging cost-effective digital tools for caregiver support holds potential to reduce healthcare utilization (e.g. preventing hospitalizations) while empowering caregivers in their role.¹⁰

Several prior reviews have synthesized the growing landscape of digital health tools for family caregivers, offering an important foundation for the field.^10,12–15 Together, these prior reviews have demonstrated that digital health tools can improve caregiver health and well-being outcomes, and that caregivers often report satisfaction and comfort with these tools—especially when human-centered design principles are applied. However, existing reviews have important limitations. Many are narrowly focused by targeting a single specific condition (e.g. dementia or cancer), specific modalities (e.g. web-based programs), or specific outcomes (e.g. implementation cost effectiveness). More importantly, no reviews of caregiver digital health tools in chronic progressive conditions have systematically examined how user-oriented factors, such as usability, are evaluated and reported.

Usability, described as the extent to which specified users can use a system/tool/product/service in a specified context to achieve their specified goals with effectiveness, efficiency, and satisfaction,¹⁶ is increasingly recognized as a key determinant of digital health adoption and long-term success.^17–19 Incorporating strong usability principles into the design and evaluation of caregiver-focused technologies is fundamental to ensuring these tools truly meet caregivers’ needs and are embraced in routine care.²⁰ Good usability characteristics or attributes (e.g. comfort, ease of use, accessibility, flexibility, performance, satisfaction) are critical to driving adoption and improving user compliance with a system or product.^21–23 A variety of robust usability evaluation methods (i.e. inquiry-based, user testing, inspection, and analytical modelling) have been developed to test the usability of digital technologies/tools.²⁴ Inquiry-based methods involve gathering quantitative and qualitative data from users through questionnaires, interviews and focus groups. In contrast, user-testing methods may include think-aloud protocols, performance measurements (e.g. app performance in a given learning context), and log analysis (e.g. frequency of error message occurrence). Inspection methods include heuristic evaluation and cognitive walk-through, and analytical modelling methods can include cognitive task analysis and task environment analysis.^20,24

This review was necessary to address a timely and underexplored gap: while we know that digital health tools can work, we know much less about how usable and user-centered these tools are for diverse caregivers in practice. By mapping how usability and related factors have been assessed and operationalized across studies, our review aimed to offer new insights to inform the design, evaluation, and implementation of caregiver-focused digital health tools moving forward. Addressing this gap is timely: as digital health tools for caregivers proliferate, synthesizing knowledge on usability can guide developers and clinicians to create more user-friendly, acceptable, and impactful solutions. By focusing on how studies describe and evaluate usability, we can illuminate whether current caregiver digital technology tools truly align with end-user needs or where design improvements are needed for better adoption. Therefore, we sought to answer two major questions:

What usability characteristics/attributes (e.g. comfort, ease of use, accessibility, flexibility) are incorporated in the design and/or evaluation of computer-based digital technologies for caregivers in the context of chronic progressive conditions?

What evaluation methods (e.g. questionnaires, task completion, “Think-Aloud” protocol, interviews, heuristic testing, focus groups) are incorporated in the design and/or evaluation of computer-based digital technologies for caregivers in the context of chronic progressive conditions?

Methods

As described in the published study protocol,²⁵ the scoping review was conducted using the Joanna Briggs Institute (JBI) methodology.²⁶ The scoping review was registered on the Open Science Framework (https://doi.org/10.17605/OSF.IO/W4VK5). We report this review according to the PRISMA Extension for Scoping Reviews²⁷—see Supplemental File 2.

Literature search and study selection

A health sciences librarian (ARW) developed the preliminary search strategy. An initial search was undertaken in the MEDLINE (Ovid) platform to ensure relevant articles were captured using the preliminary strategy. Members of the research team provided feedback for further development of the preliminary strategy. Once refined, the strategy was reviewed by an independent team of medical librarians according to the criteria outlined in the Peer Review of Electronic Search Databases (PRESS) guideline.²⁸ Additional changes were made in collaboration with members of the research team. Using feedback from these two groups, a final strategy was developed for MEDLINE (Ovid), using text words in the titles, abstracts, and index terms used to describe relevant articles—see Supplemental File 1. The librarian translated the search strategy for the remaining databases—PsycINFO (Ovid), CINAHL (EBSCO), and Web of Science Core Collection. Database searches were conducted in November 2022 and re-ran in May 2024.

Study selection

All citations were exported into Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia) for deduplication and expunging known exclusions (e.g. conference abstracts). Then, two reviewers independently pilot-tested the screening process on a random sample of 10 citations. Following the piloting, two or more reviewers independently screened the remaining titles and abstracts according to the eligibility criteria outlined below, and then retrieved and screened full text articles. At both stages of the selection process, disagreements between pairs of reviewers were resolved through a consensus process.

Eligibility criteria

Using the Population Concept Context framework recommended by the JBI,²⁶ the following eligibility criteria were applied to identify studies for inclusion in this review.

Inclusion criteria:

Population: Studies had to focus on adult (≥18 years old) family caregivers of people with chronic progressive conditions. We used the definition of a family caregiver offered by the Canadian Center for Caregiving Excellence, as an unpaid family member or friend who provides help or assistance for an individual who needs care due to disabilities, medical conditions, or aging-related needs.²⁹ We broadly defined chronic progressive conditions as conditions for which there is no cure and have significant morbidity in the latter stages of illness. These conditions may include degenerative neurological diseases (e.g. multiple sclerosis, Alzheimer's disease), irreversible organ failure (e.g. liver failure), widespread malignant disease (e.g. cancers), and acquired immune deficiency syndrome.¹

Concept: Studies had to describe usability characteristics/attributes and/or usability evaluation methods of computer-based digital technologies aimed at educating family caregivers or enhancing their skills and/or experiences, including, but not limited to, web-based tools, mobile phone and smartphone-based tools, virtual reality, and wearable technologies.^20,24,30

Context: Studies conducted in any geographic location and setting (e.g. hospital/clinic, community, university research laboratory, or home) and published from 2012 to the present were included. This time frame was chosen to encompass studies that first conceptualized a new digital paradigm in the healthcare industry,^31,32 and that corresponded with the emergence of new knowledge about the deleterious and positive effects of caregiving in the context of chronic progressive conditions.³³ Given the pace of updates in this field, the time frame also allowed the capture of technology that is still accessible/available, ensuring that we excluded obsolete technology with limited relevance in today's world.

Type of sources: We included peer-reviewed quantitative (e.g. descriptive observational study designs, cross-sectional survey studies, randomized controlled trials), qualitative (e.g. interview-based, focus groups and other methods generating narrative data), and mixed methods study designs. We recognize that computer-based digital technology development may be conducted outside of academia and published in non-traditional or non-peer-reviewed outlets—but in the context of providing evidence-based information for health and social care providers and researchers, peer-reviewed evidence is considered the gold standard.³⁴

Exclusion criteria

Population: We excluded studies that focused on care-recipients, care providers (i.e. individuals who provide trained and paid care), or family caregivers of children, as the relationship of a paid care provider or a parent caring for a child with a chronic condition is considered qualitatively different from the adult family caregiver care-recipient relationship.^35,36 Studies that focused on dyads (i.e. any combination of family caregivers/formal care providers/researchers and care-recipients), where family caregiver data could not be separated from other data were also excluded.

Concept: We excluded studies that focused on electronic medical records, medical monitoring devices, biomedical apps, systems for intelligent processing of genetic data, assistive devices, etc. that are designed for care-recipients only. Multi-component studies in which data on the computer-based digital technology component could not be extracted and studies that only include general perspectives on mHealth/technology were also excluded.

Context: Studies published before 2012 were excluded.

Type of sources: We excluded conference abstracts, systematic and non-systematic literature reviews, opinion pieces, case studies, study protocols, letters to the editor, non-peer-reviewed articles, and gray literature.

Data extraction

We used a data extraction tool developed for this study. The data extraction tool was independently pilot-tested on a random sample of 10 full-text articles before the final data extraction. Based on the CONSORT E-HEALTH guidelines,³⁷ we extracted the following information from the included studies: (i) study details (author(s), publication title, source and year, country/continent of origin, purpose, design); (ii) participant characteristics (age, gender, education, caregiver relationship to care-recipient, care-recipient chronic progressive condition); (iii) characteristics of the computer-based digital technology (type of technology and delivery platform, development stage, theoretical framework underpinning development/evaluation); and (iv) usability characteristics/attributes, evaluation methods, and evaluator characteristics. We used descriptions of usability characteristics/attributes reported in previous literature on mobile apps²⁴ because no standardized descriptions exist. Two independent reviewers extracted relevant information and deductively characterized usability characteristics/attributes based on the description offered in the included study. Disagreements were resolved through a consensus-based discussion.

Data analysis and presentation

Data were synthesized using Microsoft Excel (Microsoft Office, 2019) and IBM SPSS Statistics for Windows, Version 28.0 (IBM Corp, Armonk, NY, USA). No formal measures of agreement were used during the data extraction because differences in capitalization and punctuation generated messages of inconsistency, even if the critical content was the same between reviewers. Our focus was on synthesizing descriptive features of the studies relative to usability, not on synthesizing study results/outcomes. We used descriptive statistics to generate summaries about the study characteristics (i.e. n% of publication year, country, study design); participant characteristics (i.e. n% of gender, relationship to care-recipient, and care-recipient chronic progressive condition; and mean or median age as reported in the included studies); computer-based digital technologies (i.e. n% of technology type, delivery setting, and stage of development, and theoretical framework); and usability evaluation (i.e. n% of usability characteristics, evaluation methods, questionnaires used, and technical knowledge of evaluators). The results are presented in narrative and tabular formats.

Results

The search of databases yielded 7652 citations (5836 from the initial search in November 2022, and 1816 from the second search in May 2024). After removing 1665 duplicates identified by Covidence and 47 identified manually, we screened the titles and abstracts of 5940 citations—see Figure 1 for the PRISMA flow chart. Following the title and abstract screening, 411 citations remained for full-text screening, where 340 citations were excluded. The most common reason for exclusion at the full-text screening stage was a lack of description of usability characteristics/attributions and/or evaluation methods (n = 86, 25.3%). It is important to note that although six technologies were reported in two (n = 5) or three (n = 1) studies, the focus, stage of technology development, and participants included in these studies differed. Hence, 65 unique digital technologies reported in 71 articles were included in the review.

Figure 1.

PRISMA flow diagram.

Characteristics of included studies and participants

Table 1 summarizes the key characteristics of the included studies and participants. Most studies (n = 55, 77.5%) were published between 2019 and 2024. Studies were conducted across 21 different countries, with six studies (8.5%) involving multi-country investigations. Findings were published in 48 different journals, 38 of which contained only a single article. The three most common journals in which articles were published included Journal of Medical Internet Research (n = 6), JMIR Aging (n = 6), and Dementia (n = 4). Most studies (n = 40, 56.3%) used mixed methods, followed by qualitative (n = 17, 23.9%), and quantitative (n = 14, 19.7%) designs.

Table 1.

Study and sample characteristics.

Ref.	Country	Study design	No. of caregivers (M:F)	Age (mean or median with SD or range)a	CG's relationship with CR	CR Chronic progressive condition
Aamodt et al³⁸	Norway; Lithuania	Qualitative	NR	NR	Spouse; Parent/Grandparent; Offspring	Heart failure
Arcia³⁹	The USA	Mixed methods	3:16	56 (12.3, 35–75)	NR	Dementia
Arthanat et al.⁴⁰	The USA	Mixed methods	0:5	NR	NR	ADRD
Ashida et al.⁴¹	The USA	Mixed methods	2:14	51.75 (16.58)	Spouse; Parent/Grandparent	Dementia
Atefi et al.⁴²	The Netherlands	Mixed methods	6:18	62.5 (13.1)	Spouse; Offspring	Dementia
Austrom et al.⁴³	The USA	Mixed methods	0:5	56.2 (5)	Spouse; Parent/Grandparent; Friend	Dementia
Bakas et al.⁴⁴	The USA	Mixed methods	5:17	49.18 (15.02); 62.78 (11.82)	Spouse; Offspring; Other Relative	Stroke
Boessen et al.⁴⁵	The Netherlands	Mixed methods	2:5	59.2 (17.6)	NR	Dementia
Boots et al.⁴⁶	The Netherlands	Mixed methods	9:8	63.6 (NR)	Spouse; Offspring; Friend; Sibling	ADRD; PD
Boots et al.⁴⁷	The Netherlands	Mixed methods	14:35	69.6 (9.2)	Spouse; Other	Dementia
Boutilier et al.⁴⁸	The USA	Mixed methods	13:38	60.3 (9.8)	Spouse; Offspring; Other relative	ADRD
Brown et al.⁴⁹	The USA	Mixed methods	2:9	56.5 (13.5)	Spouse; Offspring	ADRD
Bylund et al.⁵⁰	The USA	Quantitative	8:22	45.6 (11.4)	Offspring	Myeloma; Leukemia
Carr et al.⁵¹	The USA	Mixed methods	7:19	53.3 (17.7)	Spouse	Genitourinary and Lung Cancer
Caunca et al.⁵²	South Korea	Mixed methods	2:18	60(9); 57(3); 46(17)	Spouse; Parent/Grandparent; Offspring	Stroke
Cheon et al.⁵³	France	Mixed methods	2:18	64 (14.83); 58.89 (12.80)	Spouse; Offspring	Dementia
Cristancho-Lacroix et al.⁵⁴	France	Mixed methods	6:25	73.81 (7.03); 72.12 (7.03)	Spouse; Offspring	ADRD
Cristancho-Lacroix et al.⁵⁵	The Netherlands	Mixed methods	17:32	64.2 (10.3)	Spouse; Offspring	ADRD
Daemen et al.⁵⁶	South Korea	Mixed methods	9:8	30–39 years: 3 40–49 years: 0 50–59 years:7 60–65 years: 3 >65 years: 4	Spouse; Parent/Grandparent; Sibling	Dementia
Dorell et al.⁵⁷	Sweden	Qualitative	5:7	NR	Spouse; Offspring	Dementia
Duggleby et al.⁵⁸	Canada	Mixed methods	19:73	63.5 (12)	Spouse; Other	ADRD
Fan et al.⁵⁹	The USA	Qualitative	7:23	35–49: 5 50–64: 12 >65: 13	NR	Dementia
Ferre-Grau et al.⁶⁰	Spain	Quantitative	9:104	60.65 (12.37)	Spouse; Parent/Grandparent; Friend; Other	MCC; ADRD; Fragility
Gately et al.⁶¹	The USA	Mixed methods	2:8	62.8 (NR)	Spouse; Offspring	Dementia
Goldsmith et al.⁶²	The USA	Qualitative	4:14	NR	Spouse; Offspring	Cancer
Gomes et al.⁶³	Portugal	Mixed methods	NR	NR	NR	ADRD
Heynsbergh et al.⁶⁴	Australia	Quantitative	7:19	57 (12)	Spouse; Parent, child, or sibling	Cancer
Hong et al.⁶⁵	The USA	Quantitative	7:17	59.58 (11.99)	Spouse; Offspring; Relative or friend	Dementia
Howe et al.⁶⁶	The UK	Quantitative	11:26	66.59 (10.6)	Spouse; Offspring; Friend	ADRD
Hughes et al.⁶⁷	The USA	Qualitative	1:9	59 (NR)	Offspring; Friend	Dementia
Huis In Het Veld et al.⁶⁸	The Netherlands	Mixed methods	NR	NR	NR	Dementia
Killin et al.⁶⁹	The UK	Qualitative	NR	NR	Spouse; Offspring	ADRD
Kishita et al.⁷⁰	The UK	Qualitative	3:20	62.91 (10.73)	Spouse; Offspring; Sibling	Dementia
Kozlov et al.⁷¹	The USA	Quantitative	1:57	61.78 (11.44)	Spouse; Other (NR)	MCI; Dementia
Kubo et al.⁷²	The USA	Mixed methods	2:7	58.8 (38–73)	Spouse; Offspring; Friend	Cancer
Kwok et al.⁷³	The USA	Mixed methods	4:11	62.8	Spouse; Other family member	Dementia
Laidsaar-Powell et al.⁷⁴	Australia	Mixed methods	1:2	65 (8.7)	Spouse; Parent/Grandparent	Cancer
Leng et al.⁷⁵	China	Mixed methods	12:41	48.4 (10.95)	Spouse; Offspring; Other (NR)	Dementia
Leung et al.⁷⁶	China	Mixed methods	8:20	57.71 (9.88)	NR	Dementia
Lindauer et al.⁷⁷	The USA	Mixed methods	7:10	63.3 (11.2)	NR	ADRD
Lindauer et al.⁷⁸	The USA	Quantitative	3:67	10 (7.4)	Spouse; Sibling	ADRD
Masoud et al.⁷⁹	The USA	Qualitative	6:11	NR	Spouse; Offspring	Dementia
Mayahara et al.⁸⁰	The USA	Quantitative	1:11	53.7(11.6)	Spouse; Parent/Grandparent; Offspring; Friend; Other family member	Cancer; Dementia; Heart Failure; Other Chronic Conditions
McCrae et al.⁸¹	The USA	Mixed methods	1:3	51.4 (15.9)	Spouse; Offspring; Other (NR)	Dementia
Metcalfe et al.⁸²	The UK; Germany; France	Mixed methods	24:37	57.4 (10.2)	NR	Young-onset Alzheimer's Disease or Behavioural Variant FTD
Meyer et al.⁸³	Australia	Mixed methods	5:7	NR	Parent/Grandparent; Offspring	Dementia
Mishra et al.⁸⁴	The USA	Qualitative	2:12	68.0 (10.2)	NR	Dementia
Moyle et al.⁸⁵	Australia	Qualitative	2:4	54.33 (6.31)	Offspring	Dementia
Nunez-Naveira et al.⁸⁶	Denmark; Poland; Spain	Quantitative	22:39	NR	NR	Dementia
Oakley-Girvan et al.⁸⁷	The USA	Mixed methods	NR	22–74	NR	Cancer
Oakley-Girvan et al.⁸⁸	The USA	Mixed methods	31:19	54.4 (16.6)	Spouse; Parent/Grandparent; Offspring; Friend; Other relative	Cancer
Oostra et al.⁸⁹	The Netherlands	Qualitative	2:11	62.2(12.6)	Spouse; Offspring	Dementia
Perales-Puchalt et al.⁹⁰	The USA	Quantitative	4:20	52.6 (13.2)	Spouse; Offspring	Dementia
Perin et al.⁹¹	Germany	Mixed methods	7:13	57 (NR)	Spouse; Offspring; Sibling	Dementia
Rodriguez et al.⁹²	The USA	Quantitative	20:33	76.7 (10.1)	Spouse; Offspring	ADRD
Ruggiano et al.⁹³	The USA	Qualitative	0:8	57 (36–76)	Spouse; Offspring	Dementia
Santin et al.⁹⁴	The UK	Mixed methods	NR	NR	NR	Cancer
Schaller et al.⁹⁵	Germany	Qualitative	15:27	43	Spouse; Offspring; Friend; Other relatives	Dementia
Schaller et al.⁹⁶	Germany	Mixed methods	13:12	61 (29–80)	Spouse; Offspring; Other relative	Dementia
Sourbeer et al.⁹⁷	The USA	Quantitative	8:38	NR	NR	Dementia
Teles et al.⁹⁸	Portugal	Mixed methods	6:24	59.3 (15.7); 50.8 (8.5)	Spouse; Offspring	Dementia
Tibell et al.⁹⁹	Sweden	Mixed methods	12:14	63 (27–80)	Spouse	Cancer
Torkamani et al.¹⁰⁰	The UK; Greece; Spain	Quantitative	33:27	60.7 (13.9)	NR	Dementia
Van Goethem et al.¹⁰¹	The UK; Italy; Belgium; Ireland; Denmark; The Netherlands	Qualitative	7:15	18–24: 1; 35–54: 5 55–74: 13; 75+: 0	Spouse; Parent/Grandparent; Bereaved carers	Cancer
Walsh et al.¹⁰²	The USA	Mixed methods	6:4	58.8 (11.47)	Spouse; Offspring	Cancer
Walsh et al.¹⁰³	The USA	Qualitative	3:8	63.4 (17.04)	Spouse; Parent/Grandparent; Offspring; Sibling; Cousin; Aunt or Uncle	Cancer
White et al.¹⁰⁴	The UK; Italy; France; Spain	Qualitative	0:8	64 (NR)	Spouse; Offspring; Friend; Volunteer	Dementia
Wijma et al.¹⁰⁵	The Netherlands	Quantitative	8:27	55.1 (11.2)	Spouse; Offspring	Dementia
Williams et al.¹⁰⁶	The USA	Mixed methods	24:60	64.2 (12.8)	Spouse; Offspring; Other (NR)	Dementia
Wolverson et al.¹⁰⁷	The UK	Qualitative	16:27	67.1 (10.9)	Spouse; Offspring; Friend	Dementia
Zheng et al.¹⁰⁸	Australia	Mixed methods	3:9	55–74	NR	Dementia

Note: ADRD: Alzheimer's disease and related dementias; F: female; FTD: frontotemporal degeneration; M: male; NR: not reported; UK: United Kingdom; US: United States.

Age data were extracted as reported in the primary studies. Due to variability in reporting formats (Mean [SD] vs. Median [IQR]), we retained the original presentation. No conversions were made to standardize across studies, to avoid introducing bias from estimation.

Study samples ranged between 5 and 127 participants, with a median sample size of 26 (IQR = 27). Six (11.8%) of the 71 studies provided no participant demographic data. In the remaining 65 studies, about 70% of the participants were female (n = 1460, 72.1%). Most studies (n = 50, 70.4%) reported participants’ mean age ranging between 43.0 and 76.7 years old. Across the 55 studies (77.5%) that reported caregiving relationships, there was a mix of relationships, with spousal caregivers being the most frequently included (n = 51, 92.7%).

Over 70% of the studies (n = 52, 73.2%) targeted caregivers of people with Alzheimer's disease and dementia. Other chronic conditions represented were cancer (n = 13, 18.3%), stroke (n = 2, 2.8%) and heart failure (n = 1, 1.4%). Three studies (4.2%) included caregivers of people with multiple chronic conditions.

Characteristics of digital technologies

Table 2 summarizes the characteristics of the computer-based digital technologies in the included studies. Almost all the studies (n = 63, 88.7%) included a single type of technology, with the most common (n = 43, 60.6%) being Website/Internet, followed by Application-based (Apps) technologies (n = 15, 21.1%). The most common technology delivery setting was in the participant's home (n = 46, 64.8%). Ten studies included two settings: university research laboratory and home (n = 5), clinic and home (n = 4), or university research laboratory and community center (n = 1). Seven studies (9.9%) did not report setting. In terms of the stage of development, most technologies were in the initial development and testing (n = 12, 16.9%) or testing only (n = 37, 52.1%) stage, with less than 5% (n = 3) being labelled as the final versions. Thirteen studies (18.3%) did not report the stage of technology development. Over 60% of the studies (n = 45) did not report using any theoretical framework to guide technology development and/or evaluation. Across the 26 studies that reported using theory, the theories varied widely, limiting the ability to summarize findings. For example, three of the 26 studies (4.2%) were based on the Technology Acceptance Model, while two studies (2.8%) used both Stress and Coping Theory and Bandura's Self-Efficacy Model. Other theories were mentioned only once across the remaining 21 studies.

Table 2.

Technology characteristics.

Ref.	Name of technology	Purpose of technology	Technology type^a	Delivery setting	Stage of development	Theoretical framework
Aamodt et al.³⁸	CardioSet Edema Guard Monitor (Model 001: CardioSet Medical LTD)	Remote assessment of care-recipient lung impedance by caregiver	Wearables	Home	NR	NR
Arcia³⁹	Interactive Functional Assessment Staging Navigator (I-FASTN)	Understand dementia and connect with resources	App	Laboratory; Home	Testing	NR
Arthanat et al.⁴⁰	Smart home automation	Home automation technology	Wearables; Other: Home automation technologies	Home	Testing	HAAT; UTAUT
Ashida et al.⁴¹	Dental Aging Tips	Oral health literacy	App	Home	Initial development; Testing	NR
Atefi et al.⁴²	NR	Acceptance and Commitment Therapy	Website/Internet	NR	Testing	NR
Austrom et al.⁴³	Video support group using the Cisco MOVI program	Videoconference support forum	Website/Internet	Home	NR	NR
Bakas et al.⁴⁴	TASK III Resource guide	Resource guide	Telehealth	Home	Testing; Final iteration	NR
Boessen et al.⁴⁵	Platform cubes	Communication tool	Website/Internet	Home	Initial development	UTAUT
Boots et al.⁴⁶	Partners in Balance	Educational modules and a discussion forum	Website/Internet	NR	Initial development; Testing	Stress and Coping; Bandura's Concept of Self-Efficacy
Boots et al.⁴⁷	Partner in Balance	Educational modules and a discussion forum	Website/Internet	Home	Testing	MRC framework
Boutilier et al.⁴⁸	CareVirtue	Care planning, documentation, and communication, and resources	Website/Internet	NR	Testing	NR
Brown et al.⁴⁹	CareHeroes	Communication with healthcare providers and support	App; Website/Internet	NR	NR	Family-centered theory
Bylund et al.⁵⁰	Healthy Communication Practice	Skills training	Website/Internet	NR	Initial development; Testing	Communication and education theories
Carr et al.⁵¹	Pep-Pal	Psychoeducation and skills-based intervention	Website/Internet	NR	Testing	NR
Caunca et al.⁵²	Stroke Caregiver Support System	Caregiver support tool	Website/Internet	Home	Initial development; Testing	NR
Cheon et al.⁵³	VR-EduBPSD	Education	Virtual reality	Laboratory; Community	Initial development; Testing; Final iteration	VERA framework
Cristancho-Lacroix et al.⁵⁴	Diapason	Education; Skills training; and Peer support	Website/Internet	Clinic; Home	Initial development; Testing	NR
Cristancho-Lacroix et al.⁵⁵	Diapason	Education; Skills training; and Peer support	Website/Internet	Home	Testing	Stress and coping theory and self-efficacy model
Daemen et al.⁵⁶	RHAPSODY	Support program	Website/Internet	Home	Testing	NR
Dorell et al.⁵⁷	STAV	Communication with healthcare providers; Connection with resources; Health promotion	App	Home	Final iteration	NR
Duggleby et al.⁵⁸	My Tools 4 Care (MT4C)	Education	Website/Internet	Home	NR	NR
Fan et al.⁵⁹	Olera.care	Care planning	Website/Internet	Laboratory	Initial development; Testing	Build-measure-learn framework
Ferre-Grau et al.⁶⁰	TIVA	Health promotion; Peer support	App	Home	Final iteration	Positive mental health assessment model
Gately et al.⁶¹	NR	Home safety	Website/Internet	Home	Testing	PEO model
Goldsmith et al.⁶²	Plain Language Planner for Palliative Care	Communication support	App	Home	Testing	NR
Gomes et al.⁶³	Help2Care	Skills training	App; Website/Internet	Laboratory	Testing	NR
Heynsbergh et al.⁶⁴	Carer Guide App	Education; Connection with resources	App	Home	Testing	NR
Hong et al.⁶⁵	Wellness Enhancement for Caregivers (WECARE)	Education; Networking; Peer Support	App	Home	Testing	NR
Howe et al.⁶⁶	CAREGIVERSPRO-MMD	Education; Social Support; Health Promotion	Website/Internet	Home	Testing	NR
Hughes et al.⁶⁷	CAST 2.0	Assessment of task performance and stress	App	Conference	Testing	NR
Huis In Het Veld et al.⁶⁸	NR	Education; Skills training	Website/Internet	Home	NR	5A model of self-management support and Person-centered care theory
Killin et al.⁶⁹	Digital support platform (DSP)	Education; Connection with resources; Care coordination	Website/Internet	Home	Testing	NR
Kishita et al.⁷⁰	iACT4CARERS	Acceptance and Commitment Therapy	Website/Internet	Home	Testing	Theoretical Framework of Acceptability of Interventions
Kozlov et al.⁷¹	Mindfulness Coach	Education; Health promotion	App	Home	NR	NR
Kubo et al.⁷²	Headspace	Health promotion	App; Website/Internet	Home	Final iteration	NR
Kwok et al.⁷³	SAGE LEAF	Skills training	Website/Internet	Home	Testing	NR
Laidsaar-Powell et al.⁷⁴	e-Triadic Oncology (eTRIO) modules	Education	Website/Internet	Clinic; Home	Initial development; Testing	The person-based co-design approach by Yardley et al.
Leng et al.⁷⁵	A knowledge graph-based dementia care intelligent recommender system (DCIRS)	Care planning support	Website/Internet	Home	Testing	The IDEAS (Integrate, Design, Assess, and Share) framework
Leung et al.⁷⁶	NR	Health promotion; Health, and Social Support	App	Clinic; Home	Testing	NR
Lindauer et al.⁷⁷	STAR-C-Telemedicine	Education	Website/Internet	Home	NR	NR
Lindauer et al.⁷⁸	Tele-STAR	Skills training	Website/Internet	Home	NR	NR
Masoud et al.⁷⁹	Virtual Memory café	Peer social support	Website/Internet	Home	NR	NR
Mayahara et al.⁸⁰	e-Pain reporter	Health monitoring	App	Home	Testing	NR
McCrae et al.⁸¹	NiteCAPP CARES	Cognitive behavioural therapy	Website/Internet	Laboratory; Home	Initial development; Testing; Final iteration	NR
Metcalfe et al.⁸²	RHAPSODY	Education	Website/Internet	Home	Initial development; Testing	NR
Meyer et al.⁸³	Hear-Communicate-Remember	Health promotion; Communication	Website/Internet	Home	Testing	BCTT
Mishra et al.⁸⁴	Care4AD	Care coordination; Health behaviour monitoring	Wearables; App	Laboratory; Home	Testing	TAM
Moyle et al.⁸⁵	Giraff and VGo	Communication	Telepresence robot	Home	NR	NR
Nunez-Naveira et al.⁸⁶	UnderstAID	Education	App; Website/Internet	Home	NR	NR
Oakley-Girvan et al.⁸⁷	TOGETHERCare	Health monitoring; Connection with resources	App	Home	Initial development; Testing	TAM
Oakley-Girvan et al.⁸⁸	TOGETHERCare	Health monitoring; Connection with resources	App	Home	Testing	NR
Oostra et al.⁸⁹	REsilience Monitor for INformal caregivers in Dementia [REMIND]	Health monitoring	NR	Laboratory; Home	Initial development; Testing	Human-centered design approach
Perales-Puchalt et al.⁹⁰	CuidaTEXT	Education; Health promotion; Social support	Other: Text messaging	Home	Testing	Stress Process Framework and Social Cognitive Theory
Perin et al.⁹¹	RHAPSODY	Education	Website/Internet	Clinic; Home	Testing	NR
Rodriguez et al.⁹²	BrainCare Notes	Education; Connection with resources; Symptom management	App	Home	Testing	NR
Ruggiano et al.⁹³	Private Facebook groups	Support group	Website/Internet	Home	Testing	TAM
Santin et al.⁹⁴	Peer-led web-based resource	Education	Website/Internet	Home	Initial development; Testing; Final iteration	MRC Framework
Schaller et al.⁹⁵	eHealth Monitor Dementia Portal	Education; Connection with resources	Website/Internet	Home	Testing	NR
Schaller et al.⁹⁶	eHealth Monitor Dementia Portal	Education; Connection with resources	Website/Internet	Home	Testing	NR
Sourbeer et al.⁹⁷	BESI	Symptom monitoring and management	App	Laboratory	Testing	NR
Teles et al.⁹⁸	iSupport-Portugal	Education; Skills training; Social support	Website/Internet	Laboratory	Testing	NR
Tibell et al.⁹⁹	narstaende.se (in English, “family caregiver.com”)	Education	Website/Internet	Home	Not reported	Framework of Andershed and Ternestedt
Torkamani et al.¹⁰⁰	ALADDIN	Education; Connection with resources; Symptom monitoring	Website/Internet	Home	Testing	NR
Van Goethem et al.¹⁰¹	IFocus	Education	Website/Internet	NR	Initial development; Testing	Transactional Model of Stress & Coping
Walsh et al.¹⁰²	Peacefully Website	End of life planning; Connection with resources	Website/Internet	Laboratory	Initial development	Obesity-Related Behavioral Intervention Trials model; Goal-Setting Theory of Motivation
Walsh et al.¹⁰³	Planning Advanced Care Together (PACT)	Advance directives planning	Website/Internet	Laboratory Other: Remotely	Testing	Obesity-Related Behavioral Intervention Trials model
White et al.¹⁰⁴	Caregiverspro-MMD	Care coordination; Education; Peer support; Health promotion	Website/Internet	Laboratory	Testing	NR
Wijma et al.¹⁰⁵	Through the D'mentia Lens (TDL)	Education	Other: VR simulation movie and e-course	Home	Testing	NR
Williams et al.¹⁰⁶	FamTechCare	Skills training	Website/Internet	Home	NR	NR
Wolverson et al.¹⁰⁷	Caregiverspro-MMD	Care coordination; Education; Peer support; Health promotion	Website/Internet	Home	Testing	NR
Zheng et al.¹⁰⁸	Forward with Dementia	Education	Website/Internet	Other: physical setting of participant choice	Initial development; Testing	NR

Note: NR: not reported; HAAT: human activity assistive technology; MRC: medical research council; PEO: person-environment-occupation; UTAUT: unified theory for acceptance and use of technology; TAM: technology acceptance model; VERA: validation, emotion, reassurance, activity.

Website/Internet (e.g. online portal providing educational content and self-management resources); App (e.g. smartphone application for symptom tracking and caregiver reminders); Telehealth system (e.g. video-based consultation platform for remote interaction with healthcare providers); Wearables (e.g. smartwatch used to monitor sleep or physical activity of the care-recipient); Home automation technologies (e.g. .in-home motion sensors that track daily routines and alert caregivers to irregularities).

Usability characteristics/attributes and evaluation methods

Table 3 summarizes the usability characteristics/attributes and evaluation methods applied across the studies. Across the studies, we identified 31 distinct usability characteristics/attributes, with more than half of the studies (n = 45, 63.4%) including at least three characteristics/attributes. The most commonly included characteristics/attributes were “ease of use” (n = 43, 60.1%), followed by “usefulness” (n = 20, 28.2%), “relevance” (n = 16, 22.5%), and “satisfaction” (n = 15, 21.1%). Each of the remaining 26 characteristics/attributes was reported across less than 20% of studies.

Table 3.

Usability characteristics/attributes, evaluation methods and evaluators.

Ref.	Usability characteristics/attributes	Evaluation methods	Specific questionnaire, if applicable	Evaluators^b	Assessment of evaluators’ technical knowledge^a
Aamodt et al.³⁸	Practical or technical issues, concerns with the technology	Interviews	NA	Caregivers	No
Arcia³⁹	Ease of navigation, search functionality, relevance, ease of navigation	Questionnaires; Task completion; Interviews; Think aloud protocol; Heuristic testing	Health Information Technology Usability Evaluation Scale (Health-ITUES, 20 items); Wellness Portal Survey (WPS) of user satisfaction	Caregivers; Heuristics experts	eHEALs, a self-report measure of eHealth literacy
Arthanat et al.⁴⁰	Performance expectancy, effort expectancy, relevance, ease of use	Interviews	NA	Caregivers; Care-recipients	No
Ashida et al.⁴¹	Relevance, ease of use, ease of navigation, user-friendliness	Task completion; Interviews	NA	Caregivers; Health/social care providers	No
Atefi et al.⁴²	Relevance, ease of use, stressfulness, acceptability	Questionnaires; Interviews	Program Participation Questionnaire	Caregivers	No
Austrom et al.⁴³	Accessibility, ease of use	Focus groups	NA	Caregivers	No
Bakas et al.⁴⁴	Useful, work well, easy to use, convenient, feasible, and acceptable	Interviews	NA	Caregivers	Asked about types of devices used
Boessen et al.⁴⁵	System efficiency, information quality, interface quality	Questionnaires; Interviews; Other: monitoring log data	Post-Study System Usability Questionnaire	Caregivers; Health/social care providers	Inclusion criteria—sufficient level of experience with technologies
Boots et al.⁴⁶	Accessibility, clarity, comfort, ease of use	Questionnaires; Think-aloud protocol	Rating scales developed by the research team	Caregivers; Health/social care providers	No
Boots et al.⁴⁷	General usability, value	Questionnaires; Interviews	Rating scales developed by the research team	Caregivers; Intervention coaches	No
Boutilier et al.⁴⁸	Perceived usefulness, usability	Questionnaires; Interviews; Other: log in tracking	System Usability Scale; Rating scales developed by the research team	Caregivers; Care-recipients	No
Brown et al.⁴⁹	Appearance, display of information	Questionnaires; Interviews; Focus groups	Agency for Healthcare Research and Quality's Health Information Technology Survey Compendium; Shared Care Plan Satisfaction Survey	Caregivers; Health/social care providers	Caregivers were queried about their computer skills and the average number of hours spent each week on the Internet
Bylund et al.⁵⁰	Acceptability, clarity, usefulness	Questionnaires; Other: program usage tracking	Acceptability of Intervention Measure	Caregivers	No
Carr et al.⁵¹	Accessibility, ease of navigation, acceptability	Questionnaires; Interviews	Rating scales developed by the research team	Caregivers	No
Caunca et al.⁵²	Ease of use, accessible, organized, usefulness	Questionnaires	Rating scales developed by the research team	Caregivers	Self-reported smartphone use
Cheon et al.⁵³	Usefulness, ease of use, satisfaction	Questionnaires	Rating scales developed by the research team	Caregivers	No
Cristancho-Lacroix et al.⁵⁴	Ease of use, coherence, satisfaction	Questionnaires; Interviews; Think aloud protocol	Rating scales developed by the research team	Caregivers; Health/social care providers; Healthy controls	Internet experience in years, frequency of internet use in days per month
Cristancho-Lacroix et al.⁵⁵	Applicability, utility, clarity and comprehensiveness	Questionnaires; Interviews	Rating scales developed by the research team	Caregivers	No
Daemen et al.⁵⁶	Acceptability, ease of use, satisfaction, quality	Questionnaires; Interviews	Technology Acceptance Model Questionnaire	Caregivers; Health/social care providers	No
Dorell et al.⁵⁷	Ease of use, convenience	Interviews	NA	Caregivers	No
Duggleby et al.⁵⁸	Ease of use, accessibility	Interviews; Other: checklist—MT4C	NA	Caregivers	No
Fan et al.⁵⁹	Engagement, functionality, aesthetics, and information quality, ease of use, ease of navigation	Questionnaires; Task completion	Modified Mobile Application Rating Scale	Caregivers	Researcher designed question (confidence level in using technology)
Ferre-Grau et al.⁶⁰	Ease of use, adaptable, fast, satisfaction	Questionnaires	Rating scales developed by the research team	Caregivers	No
Gately et al.⁶¹	Satisfaction, privacy, understanding, comfort, efficient	Questionnaires; Other: field notes	Rating scales developed by the research team	Caregivers	Inclusion criteria—self-reported basic computer skills
Goldsmith et al.⁶²	Accessibility, content understandability, readability, usefulness	Focus groups	NA	Caregivers	No
Gomes et al.⁶³	PSSUQ—ease of use, comfort, efficiency, understandability, satisfaction, clarity and organization of information	Questionnaires; Task completion	Post-Study System Usability Questionnaire	Caregivers; Health/social care providers	Inclusion criteria—some aptitude to use smartphones
Heynsbergh et al.⁶⁴	Ease of navigation and design and use of technical support or instructional videos; acceptability	Questionnaires	Rating scales developed by the research team	Caregivers	No
Hong et al.⁶⁵	Acceptability, user satisfaction, usefulness	Questionnaires	Rating scales developed by the research team	Caregivers	No
Howe et al.⁶⁶	Number of visits to the platform, time spent on the platform, number of actions performed on the platform, ease of use, usefulness	Questionnaires	Rating scales developed by the research team	Caregivers; Care-recipients	Assessed how often the carer used the internet prior to the study
Hughes et al.⁶⁷	Simplicity, ease of use, helpfulness, overall appearance	Interviews	NA	Caregivers	No
Huis In Het Veld et al.⁶⁸	Clarity, comfortability, ease of use, relevance	Questionnaires; Interviews	Rating scales developed by the research team	Caregivers; Health/social care providers	No
Killin et al.⁶⁹	Relevancy, accessibility	Interviews	NA	Caregivers; Care-recipients	No
Kishita et al.⁷⁰	Usefulness, relevance, acceptability	Interviews	NA	Caregivers	No
Kozlov et al.⁷¹	Ease of use, usefulness, satisfaction	Questionnaires	Modified Ease of Use Scale (Davis, 1989)	Caregivers	No
Kubo et al.⁷²	Ease of use, convenience	Interviews	NA	Caregivers; Care-recipients	No
Kwok et al.⁷³	Usability	Questionnaires; Interviews	System Usability Scale	Caregivers	No
Laidsaar-Powell et al.⁷⁴	Ease of navigation, relevance, ease of use	Questionnaires; Think aloud protocol; Heuristic testing	System Usability Scale	Caregivers; Care-recipients; Health/social care providers	No
Leng et al.⁷⁵	Ease of use, usefulness, information quality, interface quality	Questionnaires; Interviews	The Computer System Usability Questionnaire (CSUQ)	Caregivers	No
Leung et al.⁷⁶	Ease of use	Focus groups	NA	Caregivers	No
Lindauer et al.⁷⁷	Ease of use	Focus groups	NA	Caregivers	No
Lindauer et al.⁷⁸	Understandable	Questionnaires	Rating scales developed by the research team	Caregivers; Content experts	Single question on comfort with videoconferencing
Masoud et al.⁷⁹	Accessibility	Interviews	NA	Caregivers; Care-recipients	No
Mayahara et al.⁸⁰	Satisfaction, ease of use	Questionnaires; Other: app data	Rating scales developed by the research team	Caregivers; Care-recipients	No
McCrae et al.⁸¹	Ease of use, relevance and amount of information, easy to understand, user satisfaction	Questionnaires; Focus groups	Rating scales developed by the research team	Caregivers; Health/social care providers; Dementia experts	No
Metcalfe et al.⁸²	Functionality, accessibility, ease of use	Questionnaires; Interviews	Technology Acceptance Model Questionnaire	Caregivers	No
Meyer et al.⁸³	Ease of use, relatability, accessibility	Questionnaires; Interviews	Rating scales developed by the research team	Caregivers	No
Mishra et al.⁸⁴	Ease of use, satisfaction,	Questionnaires; Interviews	Technology Acceptance Model Questionnaire	Caregivers; Care-recipients; Dementia experts	No
Moyle et al.⁸⁵	Convenience, comfort, usefulness	Interviews	NA	Caregivers	No
Nunez-Naveira et al.⁸⁶	Ease of use, usefulness, accessibility	Questionnaires	Rating scales developed by the research team	Caregivers	No
Oakley-Girvan et al.⁸⁷	Ease of use, usefulness of app, functionality	Interviews	NA	Caregivers	No
Oakley-Girvan et al.⁸⁸	Usability, acceptability, and user satisfaction	Questionnaires; Interviews	System Usability Scale; Mobile App Rating Scale	Caregivers	No
Oostra et al.⁸⁹	Ease of use, frequency of use, training, functionality	Interviews	NA	Caregivers; Health/social care providers	No
Perales-Puchalt et al.⁹⁰	Acceptability; SUS	Questionnaires	System Usability Scale	Caregivers	Ever signed up for text message reminders (yes/no)
Perin et al.⁹¹	Usefulness, acceptability	Interviews	NA	Caregivers; Health/social care providers	An assessment of their ability to access RHAPSODY and use communication technology on their home computer
Rodriguez et al.⁹²	SUS	Questionnaires	System Usability Scale	Caregivers; Care-recipients	Single-item literacy screener
Ruggiano et al.⁹³	Usefulness, relevance, ease of use	Interviews	NA	Caregivers	Self-reported use of the Internet and social media for support and education
Santin et al.⁹⁴	Function and usefulness, ease of use	Questionnaires; Interviews	Rating scales developed by the research team	Caregivers	No
Schaller et al.⁹⁵	Functionality	Interviews	NA	Caregivers; Health/social care providers	No
Schaller et al.⁹⁶	Usefulness, perceived impact	Questionnaires; Interviews	CNA-D, PrepDm,	Caregivers; Health/social care providers	eHEALs, a self-report measure of eHealth literacy
Sourbeer et al.⁹⁷	Ease of use, efficiency	Questionnaires; Task completion	System Usability Scale	Caregivers	No
Teles et al.⁹⁸	Trustworthiness, organization, ease of use, clarity, accessibility, complexity	Questionnaires; Task completion	System Usability Scale	Caregivers; Health/social care providers	The Media and Technology Usage and Attitudes Scale
Tibell et al.⁹⁹	Ease of navigation, relevance	Questionnaires; Interviews	Rating scales developed by the research team	Caregivers	No
Torkamani et al.¹⁰⁰	Ease of use	Questionnaires	Rating scales developed by the research team	Caregivers	No
VanGoethem et al.¹⁰¹	User-friendliness, content, and layout, relevance	Task completion; Interviews	NA	Caregivers; Care-recipients; Other: Experts in palliative and supportive care	No
Walsh et al.¹⁰²	Ease of use, user-friendliness, relevance, satisfaction	Questionnaires; Task completion; Interviews; Think-aloud protocol	System Usability Scale	Caregivers; Care-recipients	No
Walsh et al.¹⁰³	Relevance, user-friendliness, functionality	Questionnaires; Task completion; Interviews	System Usability Scale	Caregivers; Care-recipients; Health/social care providers	No
White et al.¹⁰⁴	Ease of use, clarity and accessibility, user-friendly language	Interviews; Focus groups	NA	Caregivers; Care-recipients; Health/social care providers	No
Wijma et al.¹⁰⁵	Usefulness, ease of use, satisfaction, acceptability	Questionnaires	USE-questionnaire	Caregivers	No
Williams et al.¹⁰⁶	Acceptability, ease of use	Questionnaires; Other: number and duration of submitted videos, number and duration of phone calls	Rating scales developed by the research team	Caregivers	No
Wolverson et al.¹⁰⁷	Engagement, ease of use, balanced, security risk	Interviews; Focus groups	NA	Caregivers; Care-recipients	Access and frequency of Internet use
Zheng et al.¹⁰⁸	Ease of use and navigation, efficiency and effectiveness of use, relevance, satisfaction	Questionnaires; Task completion; Interviews; Think-aloud protocol	System Usability Scale	Caregivers; Care-recipients	Participants were required to be competent in the use of all basic computer functions

Note: NA: not applicable, a questionnaire was not used.

The assessment of evaluators’ technical knowledge reflects criteria defined by the original study authors. These requirements were not set by the authors of this review but are reported here to accurately represent the methodological approaches used in the primary studies.

Not all usability evaluations reported in the included studies involved direct input from caregiver end-users. In some cases, evaluations were conducted by heuristic experts, researchers, or interdisciplinary team members. These evaluator types reflect the methodological choices of the primary study authors and were retained in this review to accurately represent the range of approaches used.

When considering the methods used to evaluate usability across the studies, there was an almost equal split, wherein 32 studies (45.1%) used a single method of evaluation, while the remaining 39 studies utilized two (n = 29, 40.9%) or more (n = 10, 14.1%) methods. Usability was most commonly evaluated using inquiry-based or a combination of inquiry-based and user testing methods. These methods included questionnaires/surveys and interviews (n = 15, 21.1%), interviews alone (n = 15, 21.1%), questionnaires/surveys alone (n = 13, 18.3%), focus groups alone (n = 4, 5.6%), or questionnaires/surveys and task completion tests (n = 4, 5.6%). Within the 47 studies that used questionnaires/surveys alone or in combination, the measures used were varied, with most (n = 22, 31.0%) using scales developed by the research team. Of the studies that included a standardized usability questionnaire, the System Usability Scale¹⁰⁹ was the most commonly used. Nine studies used the System Usability Scale alone, while two studies combined it with other scales (i.e. scales developed by the research team and the Mobile App Rating Scale.¹¹⁰

Finally, about one quarter of the included studies (n = 19, 26.7%) assessed the technical knowledge of the individuals who evaluated the usability of the digital technology. It is important to note that this requirement was established by the authors of these studies and not the authors of this review. Across these studies, the methods and tools of assessment varied. For instance, two studies used the eHealth Literacy Scale (eHEALs),¹¹¹ while one used the Media and Technology Usage and Attitudes Scale.¹¹² The remaining 16 studies used varied self-reported mechanisms (e.g. asking about Internet experience and frequency of Internet usage) to assess technical knowledge.

Discussion

This scoping review sought to identify the usability characteristics/attributes and evaluation methods reported in studies of computer-based digital technologies for caregivers in the context of chronic progressive conditions. We also reported on the characteristics of the technology and the target population. Below, we discuss and situate our key findings within the extant usability literature and make recommendations for future work to advance the design, evaluation, and implementation of computer-based digital technologies for caregivers of persons with chronic progressive conditions.

Key findings related to characteristics of included studies and participants

We found a steady increase in studies on computer-based digital technologies for family caregivers of people with chronic progressive conditions from 2019 to date (n = 55), with studies being published in 21 different countries. This finding may reflect the current global health and social care climate following the impetus created by COVID-19 to invest in technology-supported mechanisms to promote population health and well-being.^113,114 Given that most of the technologies included in this review were in the initial development and/or testing stage, it can be expected that digital technology tools will continue to play a significant role in supporting population health and well-being into the foreseeable future.

A striking pattern in this review is the disproportionate representation of studies focused on family caregivers of people with Alzheimer's disease and other related dementias. Over 70% of the studies included in this review targeted this population. Alzheimer's disease and other related dementias are undoubtedly one of the most common chronic progressive conditions globally and place a considerable burden on family caregivers. However, the global prevalence of other chronic progressive conditions, such as multiple sclerosis and Parkinson's disease, is also rising, with increasing numbers of people living with these conditions requiring ongoing family caregiver support to manage complex care needs.^115,116 While reflective of the global burden of Alzheimer's disease, the concentration of studies focused on digital technologies for caregivers of persons with this condition raises important concerns about the generalizability of findings across other caregiving contexts. Specifically, this narrow focus risks reinforcing a one-size-fits-all approach to the design of digital technologies for caregivers. Caregivers supporting individuals with multiple sclerosis, for example, often contend with unpredictable symptom trajectories and variable disease progression,¹¹⁷ which may demand different types of technological support compared to those designed for dementia caregiving. Without a broader base of evidence that includes caregivers across diverse chronic progressive conditions, the development of digital health technologies would remain biased toward a relatively narrow subset of caregiving experiences. This not only limits innovation but also risks reproducing inequities in access, engagement, and usability of caregiver technologies. There is a clear need for future research to diversify its focus, including greater attention to caregivers supporting people with multiple chronic conditions and chronic progressive conditions, beyond dementia.

It is also important to consider how well the demographic profile of participant samples in the included studies reflect the broader caregiving population. While caregivers of people with chronic progressive conditions are still predominantly women, typically spouses or adult daughters^118,119—the demographic profile of caregivers is shifting to include men, grandchildren, sons, and other non-spousal family members.^120–122 As such, the predominance of female participants in the studies reviewed may not fully capture the changing landscape of caregiving relationships. With the increasing demands on formal healthcare systems/structures and shrinking nuclear family size, the involvement of diverse members of a care-recipient's social network, such as close friends and siblings, is further expected to grow.¹²³ However, the usability of digital technologies among underrepresented caregiver groups—such as male caregivers, young adult offspring and sibling caregivers—remains underexplored. Future research should prioritize the inclusion of these diverse caregiver identities to ensure that digital health innovations are equitably designed, rigorously tested, and responsive to the needs, preferences, and caregiving dynamics of the full spectrum of individuals supporting those living with chronic progressive conditions.

Key findings related to characteristics of digital technologies

A key finding from this review is that most of the technologies examined were in the initial development and/or testing phase. Fewer than 5% of technologies were classified as final versions ready for implementation. This pattern reflects a broader trend in digital health research where innovation often outpaces evaluation and implementation.¹²⁴ While early-stage testing is essential for refining usability, feasibility, and acceptability, the limited number of mature technologies raises important questions about real-world adoption and sustained use. Technologies in early development typically lack the robustness and user-informed design refinements necessary for successful implementation in real-world contexts. Moreover, early-phase technologies are often tested under controlled conditions or with small, homogenous samples, which may not reflect the complexity of caregiving environments and characteristics, particularly where caregivers face fluctuating demands, limited digital literacy, or multiple competing responsibilities. Long-term effectiveness is influenced not only by whether a technology works, but also by how it continues to meet evolving caregiver needs. In the context of caregiving in chronic progressive conditions, technologies may need to adapt to changes in care-recipient disease trajectory, caregiving intensity, and relationship dynamics. Yet, most early-stage technologies do not incorporate mechanisms for this kind of adaptability. Therefore, it remains unclear whether short-term usability metrics translate into long-term effectiveness of these technologies.

To bridge the gap between development and real-world effectiveness, studies should adopt a life-cycle evaluation approach that follows technologies from development through to implementation and sustainment. This approach should include ongoing partner engagement, particularly with caregivers themselves, to ensure technologies remain responsive and relevant. Co-design with end users can not only improve initial usability but also inform iterative updates that reflect changing needs and contexts. Furthermore, partnerships with health and community organizations can facilitate real-world testing in naturalistic settings and support integration into existing care pathways.¹²⁵ As technologies move beyond initial usability testing, they must also be evaluated in terms of accessibility, cost-effectiveness, data privacy, and equity, particularly for underserved or marginalized caregiving populations.

We found limited use of theoretical frameworks in the design and evaluation of the computer-based digital technologies included in this review. Few studies explicitly referenced conceptual or theoretical models to guide technology development, usability assessment, or interpretation of results. This gap presents a critical missed opportunity. The integration of theoretical frameworks is widely recognized as essential for informing the structure, implementation, and evaluation of digital health tools, ensuring that design decisions are grounded in evidence and aligned with user needs.¹²⁴ Theoretical frameworks can also help make explicit the assumptions underlying technology design and user interaction, offering structured ways to identify relevant constructs, guide the selection of usability attributes, and inform the tailoring of digital tools. In the context of caregiving—particularly among those supporting people with chronic progressive conditions—theoretical frameworks can be helpful for addressing the dynamic nature of caregiver roles, psychosocial demands, and relational factors that shape how technology is adopted and used. For example, User-Centered Design and Participatory Design approaches, while not formal theories per se, are grounded in principles of human-computer interaction and are increasingly paired with behaviour change theories to improve acceptability and usability.¹²⁶ The Technology Acceptance Model and its derivatives, such as the Unified Theory of Acceptance and Use of Technology, offer useful lenses to understand and predict caregiver technology uptake based on perceived ease of use, usefulness, and effort expectancy.¹²⁷ Yet, our findings suggest that these models are rarely used for computer-based digital technologies for caregivers of people with chronic progressive conditions.

Importantly, the lack of theoretical underpinning may partially explain the variability in usability characteristics/attributes evaluated. Tools developed without a theoretical foundation may prioritize surface-level usability (e.g. navigation) but fail to address deeper motivational, contextual, and relational drivers of engagement and sustained use. Moreover, theoretical grounding is essential when evaluating digital interventions across diverse caregiving contexts, helping researchers and developers interpret why certain design features may work well in one context but not in another. To enhance future work in this area, we recommend that researchers and developers adopt a theory-informed approach to usability evaluation and digital health design. Behaviour change and implementation frameworks such as the Theoretical Domains Framework¹²⁸ and the Behaviour Change Wheel¹²⁹ can be especially useful for mapping caregiver behaviours, identifying barriers and facilitators to engagement, and linking design features to evidence-based strategies for change. Integrating such frameworks from the outset can strengthen methodological rigor, increase intervention coherence, and ultimately improve technology adoption and caregiver outcomes.¹³⁰

Key findings related to usability characteristics/attributes and evaluation methods

Only 71 out of 411 full-text studies reviewed provided information about usability over a 10-year period, reflecting limited consideration of this important topic within the context of digital tools for family caregivers of persons with chronic progressive conditions. In terms of usability characteristics/attributes, about 20–60% of included studies evaluated “ease of use,” “usefulness,” and “relevance,” highlighting an opportunity to further explore under-utilized characteristics/attributes such as “efficiency,” “learning performance,” and “cognitive load,” which could provide a more comprehensive understanding of usability.²⁴ This is particularly relevant, for example, for digital technologies used in home environments where multiple distractions and daily tasks may influence cognitive load, learning performance, efficiency, and overall user experience.

Of note, several studies did not explicitly articulate the usability characteristics/attributes being evaluated. Instead, they used language such as “amount of time spent and duration to complete the task,” and “perceived impact” to reflect the usability characteristics/attributes of “efficiency” and “effectiveness,” respectively. The lack of consistent and standardized usability language across studies limits the ability to summarize existing literature effectively, offers limited direction to researchers, and muddles our understanding of what aspects of usability to focus on when developing, evaluating, and implementing computer-based digital technologies for caregivers of people with chronic progressive conditions. To advance the field, there is a clear need for greater harmonization of usability terminology, frameworks, and evaluation standards. Organizations with mandates to set digital health standards are well-positioned to lead or support these efforts. For instance, the International Organization for Standardization (ISO; https://www.iso.org/home.html) has published guidelines (e.g. ISO 9241-11:2018) that inform standardized usability language for researchers and developers. Interdisciplinary collaborations through national digital health strategy bodies, including the Office of the National Coordinator for Health Information Technology US (https://www.healthit.gov/) or Canada Health Infoway (https://www.infoway-inforoute.ca/en/), and research networks, such as the International Medical Informatics Association Human Factors Engineering for Healthcare Informatics Working Group (https://imia-medinfo.org/wp/human-factors-engineering-for-healthcare-informatics/) could be leveraged to co-develop consensus-driven taxonomies and field-specific guidance for usability evaluations in health contexts. In parallel, health policy organizations and major research funders can play a critical role by incentivizing the adoption of shared usability definitions and reporting practices through funding criteria and knowledge translation initiatives. Collectively, such efforts would support more coherent design, reporting, and evaluation of caregiver-focused technologies that align with broader digital health standards and guidelines.

Across the studies, usability evaluations were mainly conducted using inquiry-based methods, consistent with a previous review of mobile apps.²⁴ While these methods may provide relevant and valuable information, relying solely on subjective measures can be problematic due to inherent biases. Further, we found that of the 47 studies that used a questionnaire alone or in combination, almost half (n = 22) used independent, non-validated questionnaires developed by the research team rather than psychometrically tested usability questionnaires, such as the System Usability Scale.¹⁰⁹ While the application of psychometrically sound usability questionnaires will support the advancement of the field,¹³¹ it is important to note that self-reported questionnaires, such as the System Usability Scale, do not provide a comprehensive approach to evaluating usability,¹³² and are not specific nor adapted to a particular computer-based digital technology. These issues limit the ability to evaluate specific usability characteristics/attributes beyond general usefulness and satisfaction.¹³¹ Applying other evaluation methods, including user-testing (e.g. Think-aloud protocols), inspection (e.g. heuristic evaluation), and analytical modelling (e.g. task environment analysis) together with inquiry-based methods, could be beneficial and provide a more holistic view of usability in future technology development and evaluation efforts.

Regarding the setting of usability evaluations, most technologies were evaluated in the participant's home, with a few studies (n = 10) including evaluations in both a research laboratory and field testing. While it is important to conduct field testing in real-life dynamic environments, such as in the participants’ homes where digital tools will most likely be used, testing in a variety of settings may be beneficial. Such an approach is particularly relevant in the early development phase, where initial testing in a laboratory may provide critical and rapid feedback that can be used to improve the technology within a feasible and efficient timeframe. Some researchers have suggested that it is beneficial to invest time and resources in conducting small-scale laboratory testing before conducting field testing and implementation trials,^17–19 and we echo this recommendation.

It is important to note that not all usability evaluations included direct input from caregivers, who were the intended end-users of the technologies. Several primary studies included in this review relied on evaluations conducted by researchers, heuristic experts, or healthcare providers. These expert-based methods (e.g. heuristic evaluations, cognitive walkthroughs) are commonly used in early-stage design to identify usability issues before engaging target users.¹³³ While such approaches can provide valuable technical and design insights, they do not replace the lived experience or contextual knowledge of caregiver end-users. This underscores the need for future evaluations to prioritize meaningful engagement with caregivers throughout the design and testing process.

Finally, the inconsistent assessment of evaluators’ technical knowledge across studies presents a potential source of bias in reported usability findings. When technical literacy is assessed using varied or non-standardized methods, it becomes difficult to determine whether usability feedback reflects the actual accessibility and design of the technology or the prior digital experience of the evaluator. For instance, users with higher technical literacy may navigate digital tools more easily, potentially overestimating usability, while those with lower technical confidence may report greater difficulty, regardless of the technology's actual design.¹³⁴ Moreover, studies that require participants to meet a certain threshold of technical proficiency may introduce selection bias, systematically excluding caregivers who are older, less digitally literate, or otherwise underserved—groups who often stand to benefit most from accessible digital health tools.¹³⁵

Strengths and limitations

In this scoping review, we present a novel contribution by zeroing in on the role of usability in caregiver-centered digital health tools in chronic progressive conditions. We charted the specific usability characteristics considered (e.g. comfort, accessibility, flexibility) and the methods used to evaluate usability (e.g. usability questionnaires, task completion metrics, heuristic testing, and user interviews) across a range of digital platforms. By comprehensively mapping these elements, our review clarifies how usability has been integrated into the design, development, and assessment of technologies supporting caregivers of chronic progressive conditions. This work builds upon prior systematic reviews of caregiver digital tools, which confirmed general benefits and advocated for user-centered design.^10,12–15 We further extend previous work by providing the first holistic overview of when, where, and how usability enters the picture in the creation and testing of these tools. Such insights are crucial for advancing the next generation of digital health innovations—ones that are not only effective, but also responsive to unique caregiver needs. Ultimately, our findings may inform researchers, developers, and healthcare providers on best practices for incorporating usability into the development pipeline, thereby improving the likelihood that digital health solutions for caregivers will be embraced and sustained in real-world care settings.

However, several limitations warrant consideration. First, scoping reviews usually include a wider spectrum of evidence gathered from various sources, for example, gray literature. We limited our search to peer-reviewed published literature. Therefore, we might have missed other relevant types of literature. Other types of sources should be examined in future work when this area is further developed. A second related limitation involves using search limiters to permit citations with full-text published in English. Although our intention is not to disregard publications in foreign languages, the decision was influenced by the global status of English language in modern science, specifically, peer-reviewed published literature, and the lack of resources for scientific translation. Second, data extraction of usability characteristics/attributes was challenging because “usability” has multiple meanings that do not always relate to usability testing. For instance, usability is sometimes used when describing general experiences or perspectives of tools but not in connection with the development and/or evaluation of the tool. There is potential for errors in categorizing characteristics/attributes described in the included studies and missing some sources that use different terminology. We attempted to mitigate this risk by involving two reviewers in the categorization process and having consensus-based discussions to ensure we captured relevant characteristics/attributes.

Conclusion

This review examined usability characteristics/attributes and evaluation methods reported in studies of computer-based digital technologies for caregivers in the context of chronic progressive conditions. We also examined the characteristics of the technology and the target population. The take-home message for developers and researchers interested in developing and evaluating computer-based digital technologies for caregivers of people with chronic progressive conditions is clear. There is a great need to expand the target audience for such technologies beyond caregivers of people with Alzheimer's disease and dementia and to consider usability characteristics/attributes at all stages from development to implementation. We also need to consider the potential advantages of expanding the types of usability characteristics/attributes evaluated and combining different usability evaluation methods, including both subjective and objective usability measures, to develop a more wholesome and comprehensive understanding of usability in this context. Furthermore, the use of psychometrically valid measures for evaluating usability should be prioritized in future studies to enhance the comparability of findings across studies and ensure that usability evaluations meaningfully capture user experiences in the context of caregiving for individuals with chronic progressive conditions. Investing time and effort in such work is vital for the planning and conduct of future usability studies to advance the use of computer-based digital technologies for supporting the health and well-being needs of caregivers of people with chronic progressive conditions.

Supplemental Material

sj-doc-1-dhj-10.1177_20552076251365141 - Supplemental material for Exploring usability characteristics in computer-based digital health technologies for family caregivers of people with chronic progressive conditions: A scoping review

Supplemental material, sj-doc-1-dhj-10.1177_20552076251365141 for Exploring usability characteristics in computer-based digital health technologies for family caregivers of people with chronic progressive conditions: A scoping review by Afolasade Fakolade, Katherine L. Cardwell, Emma Chow, Amanda Ross-White and Lara A. Pilutti in DIGITAL HEALTH

Supplemental Material

sj-docx-2-dhj-10.1177_20552076251365141 - Supplemental material for Exploring usability characteristics in computer-based digital health technologies for family caregivers of people with chronic progressive conditions: A scoping review

Supplemental material, sj-docx-2-dhj-10.1177_20552076251365141 for Exploring usability characteristics in computer-based digital health technologies for family caregivers of people with chronic progressive conditions: A scoping review by Afolasade Fakolade, Katherine L. Cardwell, Emma Chow, Amanda Ross-White and Lara A. Pilutti in DIGITAL HEALTH

Footnotes

Acknowledgments

The authors thank Emily Broitman, Taylor A. Hume, Mariah Keeling, Julia Ludgate, Isabelle Cardos, and Michelle Bromberg for contributing to the screening process.

ORCID iDs

Afolasade Fakolade

Katherine L. Cardwell

Amanda Ross-White

Lara A. Pilutti

Ethical approval

Ethical approval was deemed unnecessary as a scoping review without primary data collection.

Author contributions

Afolasade Fakolade contributed to conceptualization, methodology, data curation, supervision, and writing‒original draft. Katherine L. Cardwell contributed to conceptualization, methodology, data curation, and writing‒original draft. Emma Chow contributed to data curation and writing‒review and editing. Amanda Ross-White contributed to methodology and writing‒review and editing. Lara A. Pilutti contributed to conceptualization, methodology, and writing‒review and editing, supervision.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental material

Supplemental material for this article is available online.

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