Parkinson disease (PD) is a progressive neurodegenerative condition that affects approximately 8.5 million people globally and significantly shapes how people move and participate in everyday life [1]. For many people with PD, maintaining walking ability, balance, and confidence in movement remains a central priority because mobility limitations can reduce independence, participation in daily activities, and quality of life [2-4]. PD is characterized by a range of cardinal motor symptoms, such as bradykinesia, rigidity, and postural instability, which are commonly associated with impaired mobility [5]. In this context, mobility refers to the physical ability to move freely and easily from one place to another [6] and includes other aspects of successful movement, such as balance. As the disease progresses, these difficulties often become more pronounced [7,8]. Mobility may also fluctuate throughout the day, partly relating to medication response and transitions between “on” and “off” states, making movement less predictable and further affecting confidence and participation [2]. Monitoring mobility is therefore valuable for informing condition management, optimizing treatment, and assessing disease progression over time [9-11].

In routine clinical practice, mobility assessment in PD often relies on episodic clinical evaluations and patient self-report [12]. Although standardized rating scales and brief functional assessments are widely used [13], they provide only a snapshot and may fail to capture a true reflection of symptom variability and context-specific challenges [14,15]. People with PD have highlighted this limitation, reporting frustration with assessments that do not reflect their typical functioning [16]. These assessment methods also rely on patients’ memory and are therefore vulnerable to recall bias [16,17]. Consequently, important longitudinal changes in mobility may go undetected, potentially delaying both timely intervention and the diagnosis of PD.

Wearable technologies use inertial sensors, such as accelerometers and gyroscopes, embedded in smartwatches and other body-worn devices to enable continuous, real-world monitoring of movement and gait, which can generate objective and ecologically valid measures of mobility in everyday settings [9,18]. These possibilities have generated considerable enthusiasm for wearable devices as a way to address the limitations of current episodic clinic-based assessment [19,20]. By capturing mobility beyond the clinic, such devices may provide a more accurate picture of everyday functional performance and detect subtle changes that may otherwise go unnoticed between appointments. In PD specifically, wearable devices can record clinically relevant motor information, such as gait variability, tremor, and movement patterns, supporting more precise monitoring of progression and treatment response [9,11,21]. Device configurations vary in sensor type and placement, worn at locations such as the wrist, chest, waist, thigh, shank, feet, head, and lower back; these design choices influence the signals captured and the accuracy of the algorithms used to derive clinically meaningful outcomes [22]. Wearables may also facilitate richer information exchange between clinicians and patients by complementing self-report with objective mobility data, enabling more informed clinical discussions and shared decision-making [23]. Wearable devices are therefore increasingly positioned as promising tools for supporting more responsive, data-informed approaches to PD management.

Successful implementation of wearable devices in PD care depends not only on their technical validity but also on their acceptability to the people who use them. Acceptability is a multifaceted concept encompassing perceptions of burden, comfort, usability, and perceived value and is a key determinant of the uptake and sustained use of health care innovations [24]. In PD, this is especially important because motor and cognitive symptoms may create condition-specific barriers that influence whether and how people are able or willing to engage with wearable technology [25]. For example, tremor or reduced dexterity may make it difficult to fasten straps or manage device setup [26], and concerns about comfort, stigma, privacy, and effort may further reduce willingness to engage, even when potential clinical benefits are recognized [26,27]. These challenges may be especially important for underserved groups, who are often underrepresented in digital health research and may be disproportionately affected by digital exclusion, resulting in disparities in access, motivation, and ability to use digital health technologies [28]. Broader concerns about the lack of diversity in PD research have also been raised more generally, raising questions about whose needs and preferences are reflected in the existing evidence base [29]. Carers must also be considered in this context; in PD, they often play a central role in daily support, symptom management, and clinical discussions [30] and may be directly involved in the use and management of wearable devices. Yet, carers remain much less frequently included as participants in PD digital health research specifically, even where the technologies depend on their involvement.

While a growing body of research has examined the feasibility and technical validity of wearable technology in PD [9,31], comparatively less attention has been paid to how the devices are experienced by people with PD and their carers in everyday life [26,32,33] or whether they are acceptable across groups with differing needs and priorities. Given the variation in device configurations and sensor placements used across PD research, it is also important to understand how such differences may shape perceptions of acceptability in real-world use. Without a clear understanding of acceptability, there is a risk of implementing tools that do not align with users’ needs, capabilities, or preferences, limiting their adoption and impact in practice [24]. More person-centered approaches to wearable technology development and evaluation have been proposed as a route to improving long-term engagement [33], reinforcing the value of exploring user perspectives alongside technical performance. This paper presents qualitative research exploring the perspectives of people with PD and carers on the use of wearable devices for remote continuous mobility monitoring, identifying the barriers and facilitators shaping their use and exploring expectations for how these technologies might be integrated into PD care.

The aims of the study were to (1) explore the acceptability of wearable devices for remote continuous mobility monitoring from the perspectives of people with PD and informal carers, (2) identify barriers and facilitators influencing uptake and sustained use of wearable devices in PD care, and (3) examine user preferences and expectations for how wearable devices should be integrated into PD health care.

A qualitative approach was taken, using one-to-one semistructured interviews to explore the acceptability of wearable devices for monitoring mobility in PD from the perspectives of those with PD and from those who support or care for someone with PD.

This international qualitative study included participants from the United Kingdom, Ireland, Belgium, Germany, Spain, and the United States. Interviews were conducted primarily online via teleconferencing platforms, with additional interviews carried out via telephone and in person.

Participants were eligible if they self-reported a diagnosis of PD or supported or cared for someone with a diagnosis of PD, were aged ≥18 years, and had the capacity to provide informed consent. Previous experience of wearable devices used for digital health was not a requirement, as these devices are not yet part of routine PD care. Requiring experience would have limited eligibility to former research participants and risked excluding the underserved groups we aimed to reach. Including participants new to wearables helped us to capture the perspectives of those who might first encounter these devices if they were introduced into routine care.

Recruitment took place through multiple routes, including voluntary organizations and charities (eg, Parkinson’s UK, Cure Parkinson’s, and the Michael J Fox Foundation), the VOICE participation platform, social media, online newsletters and forums, and in-person engagement at support groups and exercise classes. Participants were also recruited through the East London Parkinson’s Disease project.

A purposive sampling strategy was used to capture a wide range of lived experiences, prioritizing groups known to be at risk of digital exclusion in health care, as guided by the culture, limiting conditions, education, age, residence, and socioeconomic status (CLEARS) framework [28]: people from rural or socioeconomically deprived areas, those with lower levels of formal education, ethnic minority communities, and those with limiting conditions. The research team also drew on wider evidence and discussion regarding underrepresentation in digital health and PD research to additionally prioritize people with young-onset PD (diagnosed aged ≤50 years), adults aged ≥80 years, and LGBTQIA+ individuals, all of whom are rarely reflected in existing research [34-36]. This approach was taken to ensure a comprehensive and inclusive understanding of acceptability, informed by those typically underrepresented in PD and digital health research.

Purposive sampling was executed through recruitment routes chosen for their reach into specific groups. The research team built relationships with community organizations that already worked with and were trusted by these groups. This approach was informed by 2 patient and public involvement and engagement (PPIE) workshops held with clinicians and researchers external to the project, which helped codevelop recruitment strategies. The composition of the sample was reviewed throughout so that efforts could be focused on groups that remained underrepresented. Participation was further supported by offering interviews online, via telephone, or in person according to participants’ preference and in their preferred languages. Our sample size was guided by the aim to capture a diverse sample, with representation across the targeted characteristics accruing gradually over a 32-month recruitment period.

Semistructured interviews, lasting on average 45 (SD 16.2; range 19‐109) minutes, explored the acceptability of digital mobility assessments, with a particular focus on wearable devices to monitor mobility in PD. Interviews were conducted in participants’ preferred language; most were in English, while interviews with participants whose preferred language was Bengali, German, or Dutch were conducted by bilingual members of the research team who had received training in the use of the interview guide. Each interview began with a brief demographic questionnaire covering age, gender, ethnicity, postcode, sexuality, employment status, and comorbidities to contextualize responses and support analysis of participant diversity. Postcode was used to derive socioeconomic status through local deprivation indicators (eg, Index of Multiple Deprivation [37], Pobal HP Deprivation Index [38], Area Deprivation Index [39], and Northern Ireland Multiple Deprivation Measure [40]) and to classify rurality (eg, Rural-Urban Classification [41], Am I Rural [42], and Urban Rural Life [43]). Previous experience of wearable devices was explored during the interviews and recorded from the interview data.

The interviews followed semistructured topic guides, which included a dedicated section on wearable device acceptability (Multimedia Appendices 1 and 2). Participants were given a brief verbal explanation of wearable devices used in PD mobility research, drawing on examples from the Mobilise-D project [44,45], including wrist-worn and lower-back sensors. Both were offered as illustrative examples to give participants a sense of what wearable mobility monitoring might involve. Participants were informed that while both device types can monitor mobility, lower-back sensors are generally considered to provide greater measurement accuracy [46,47]. This information was provided so that participants could form an informed view on a device type most would not have encountered, as wrist-worn devices are widely familiar as consumer products, whereas lower-back sensors are used almost exclusively in research. Nonetheless, participants were encouraged to describe what they would hypothetically want from a wearable device, ensuring their responses reflected their own needs and preferences rather than being anchored to any specific device type.

People with PD were asked what they thought about wearing a device to monitor their mobility, with prompts exploring preferred body placement; what they felt would be good about these devices, including what they would want the device to capture; any concerns, with prompts covering comfort, ease of use, and data privacy; and whether they would be willing to wear the device and, if so, for how long. Carers were asked whether they currently used technology to support their care partner; what they thought about using a device to monitor the mobility of the person they supported; how these devices would be helpful, including any specific features that would be useful; any concerns, with prompts covering comfort, ease of use, and privacy; and how the devices might support existing caring responsibilities and routines.

PPIE was embedded throughout the project, and reporting aligns with minimum PPIE recommendations [48]. A public contributor living with PD was a coapplicant on the funding application and contributed to the development of the interview topic guide. During recruitment, we held 2 PPIE workshops with clinicians and researchers external to the project to co-develop recruitment strategies, with particular attention to supporting participation from underserved groups. Pilot interviews were conducted with 2 public contributors, one person with PD and one informal carer, to ensure the language, structure, and content of the guides were clear and relevant. Their feedback highlighted areas where wording could be simplified, suggested additional questions and prompts to elicit deeper responses, and identified opportunities to improve question ordering. These insights directly informed iterative refinements to the language and flow of the topic guides. To assess feasibility for participants whose preferred language was not English, we piloted 1 interview using interpreter support. However, this approach disrupted the natural flow of conversation, making it difficult to maintain dialogue and follow up on responses in the moment, which is important for data quality in qualitative research. We therefore trained a small number of researchers to conduct interviews in participants’ preferred languages using the same topic guide, thereby broadening accessibility while maintaining consistency across interviews. One public contributor was also invited to comment on a draft of the final paper. These activities reflected a collaborative rather than consultative approach, involving public contributors across multiple stages of the study.

All interviews were analyzed using reflexive thematic analysis (RTA) to develop an in-depth understanding of the acceptability of digital mobility assessments, specifically wearable devices, from the perspectives of people with PD and carers. This approach allows for an interpretative account of acceptability grounded in participants’ own priorities. Analysis followed Braun and Clarke’s 6-phase process [49], which involves familiarizing oneself with the data, generating initial codes, searching for themes, reviewing themes, defining and naming themes, and producing the report.

Transcripts were read repeatedly to support familiarization with the data, which was further supported by the lead analyst (MH) having conducted 43 of the 92 interviews. Analysis ran alongside a 32-month period of data collection (June 2023 to February 2026). Early coding was undertaken collaboratively by MH, JL, and KB, who jointly coded a small subset of transcripts in shared sessions to build familiarity with the data and to explore initial codes and interpretations. Codes were generated and discussed as the transcripts were worked through together. MH then took the analysis forward across the full dataset, developing and revising codes inductively using NVivo (version 15; Lumivero) and Microsoft Word. Initial impressions were documented after each interview, and accounts that aligned with or diverged from emerging patterns were noted; this was revisited alongside the transcripts during analysis. Theme development and interpretation were discussed in regular sessions with RMA, KB, and JL. Consistent with RTA, collaboration was used to enrich the lead analyst’s developing interpretations rather than to establish coding consensus. Rigor and trustworthiness were supported through sustained engagement with the data, documentation of developing interpretations, ongoing reflexivity, and team discussion. Interview data from people with PD and carers were analyzed separately to develop within-group themes before exploring perspectives across groups to identify similarities and differences. In developing themes, we distinguished accounts grounded in previous use of wearable technology from those anticipating use; both informed the analysis, and where an account drew on previous experience, this is indicated in the Results section. Participant quotations are also presented with a participant identifier, gender, age, and length of diagnosis or diagnosis of person cared for.

MH specializes in digital health and PD research, and the wider team brings complementary expertise spanning public health, neurology, clinical medicine, rehabilitation, psychology, aging, and social science. This collective expertise supported informed data collection and probing during interviews, although it may also have shaped how wearable technology is viewed in PD care. To address this, we took reflexive steps such as documenting notes while coding and developing themes, discussing theme development and interpretations with the wider team to enhance reflexivity, and noting down and revisiting assumptions.

Ethical approval was granted by Newcastle University’s Faculty of Medical Sciences Research Ethics Committee (references 2562_2/31981/2024 and 48672/2025) and Southwest—Central Bristol Research Ethics Committee (references: 39/SW/0255; IRAS ID 242395). All participants provided informed consent before taking part, including consent for audio recording, which was further reconfirmed on recording at the beginning of the interview. Online interviews were recorded using Zoom (Zoom Communications, Inc) and Microsoft Teams; telephone and in-person interviews were recorded, and recordings were transferred to secure Newcastle University storage and then deleted. Audio files and transcripts were stored on a university shared drive with restricted access. Transcription was completed by either UK Transcription under a confidentiality agreement or the researchers who had conducted the interview. Transcripts were checked for accuracy and anonymized by removing names and any other potentially identifying information before analysis. Participants were reminded that their involvement was voluntary and that they could stop the interview at any time without giving a reason. No financial incentives were provided.

This qualitative study provides an in-depth exploration of how people with PD and informal carers perceive wearable devices for remote continuous mobility monitoring. While previous qualitative work has examined views of wearables in people with PD [23,27], to our knowledge, this is the first study to examine carer perspectives as a direct analytical focus alongside those of people with PD, and to do so with a diverse sample recruited specifically to include groups at heightened risk of digital exclusion and underrepresentation in PD research. Although digital health research has increasingly acknowledged the potential of wearable technology in PD, the views of informal carers remain much less frequently considered, despite their central role in everyday support [30,32,50]. Across both groups, participants saw clear value in wearables for generating objective real-world evidence of mobility beyond what brief clinical appointments could provide. People with PD described consultations as artificial “snapshots” in which they “performed” for the clinician, often making them appear better than usual, consistent with the Hawthorne effect, where people modify their behavior when aware of being observed [51]. Carers similarly felt that the full picture of day-to-day functioning was rarely conveyed to clinicians and that the person they supported was often seen at a relatively good moment rather than their worst. These accounts echo longstanding concerns that episodic clinical assessment methods fail to capture symptom variability and context-specific difficulties in PD [12,14,16,17,23] and reinforce growing interest in wearables as a way of supporting more precise monitoring of progression and treatment response [9,11,18,21,31].

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The present findings extend this literature by showing how people with PD positioned wearable data as a tool for reshaping the consultation itself. They described how it could support more productive appointments, better-informed treatment discussions, and self-advocacy. This was partly because wearables were perceived to reduce reliance on memory and subjective description, challenges previously highlighted as barriers in PD care [16,17]. Carers, in contrast, focused on the practical side, hoping wearables would give them confidence to leave the home, provide reassurance about safety and falls, and ease the constant vigilance of caregiving [50,52], while recognizing that managing the device could add to their workload [52,53]. People with PD foregrounded clinical utility and self-management, whereas carers were more concerned with risk management, anticipation of problems, and the practical realities of supporting someone whose mobility may change unpredictably [50]. This contrast reinforces the view that PD mobility is felt as a shared experience within the care relationship and suggests that the value of wearable monitoring may ultimately depend on how well it supports the wider care dyad rather than the person with PD alone [52,54].

Although both groups were broadly open to wearable monitoring, acceptability was conditional, consistent with wider conceptualizations of acceptability as a multidimensional construct shaped by perceived burden, usability, ethical concerns, and effectiveness [24]. People with PD were willing to tolerate some inconvenience, including visibility or loss of privacy, when they believed the device had a clear purpose and would meaningfully inform their care. Carers were more cautious, with acceptability depending less on clinical promise and more on whether the device could fit into daily life without adding to the demands of caregiving. Together, these priorities align with the wider literature on self-management in PD, which emphasizes that digital tools are most likely to be adopted when they fit within existing routines, demand minimal additional effort, and offer a clear and personally relevant benefit to those involved [55-57]. Acceptability was further shaped by emotional responses to monitoring; some people with PD anticipated that wearable data would be reassuring by reducing uncertainty or confirming condition stability, while others felt that continuous monitoring risked heightening symptom awareness and making deterioration feel more visible. These 2 perspectives echo previous work showing that wearables can be both beneficial and emotionally challenging for people with PD [23,27] and reinforce the conceptualization of acceptability by Sekhon et al as encompassing affective as well as practical dimensions [24].

A further concern, raised particularly by carers, was that wearable technology risked adding to the work of those already providing substantial support, especially where the person with PD experienced cognitive difficulties or low confidence with technology. This resonates with wider discussions in digital health and long-term condition management about the hidden labor involved in sustaining home-based monitoring, and the risk that technologies framed as empowering may, in practice, redistribute responsibility onto patients and carers rather than relieve it [52,53]. Tools intended to support autonomy could simultaneously generate new forms of dependence on informal carers, particularly in progressive conditions where the capacity to engage with technology may decline over time. This explains why passive monitoring was preferred, as a way of ensuring that the device did not become another task within an already demanding role. Carers should therefore be recognized as key stakeholders in the design and implementation of wearable monitoring because its success may depend partly on whether it lessens or exacerbates the burden of caregiving [52-54].

Esthetics and functionality were also central to acceptability across both groups, with participants favoring designs that were unobtrusive, familiar, and easy to incorporate into daily life. Watch-like devices were generally preferred over more visible or medical-looking alternatives, reflecting concerns about stigma and self-consciousness in public. This finding is consistent with wider work showing that how a device is perceived socially, not just how well it performs, shapes whether people are willing to wear it long term [23,27]. However, these preferences may exist in tension with clinical utility, as wrist-worn devices may capture less detailed or accurate information about gait and mobility than lower-back sensors or multisite configurations [46,47]. Compounding these general preferences were condition-specific challenges; tremor, reduced dexterity, and symptom fluctuations could turn seemingly simple tasks, such as fastening straps, navigating menus, or managing charging, into significant obstacles, echoing previous concerns that PD symptoms create distinct demands for digital engagement [23,25]. These findings underline the importance of condition-sensitive, co-designed wearable design for PD.

A key implication of this study is that acceptability depended on remote continuous monitoring complementing rather than replacing clinical care. Although both groups valued the possibility of more informed and efficient consultations, some worried that wearable monitoring could be used to justify reducing in-person appointments altogether. This concern is particularly relevant in the context of England’s 10 Year Health Plan and its proposed shift from “analog to digital” [58], with participants’ accounts suggesting that they are open to digital tools when they enhance care, but not when they are used to substitute the human contact that remains central to managing a complex progressive condition. This was especially evident among several rural participants, who described limited access to specialist services and valued continuous monitoring as a means of maintaining clinical oversight across long intervals between appointments. These participants may benefit substantially from remote monitoring but could also feel the greatest loss if its introduction led to further reductions in specialist contact. Clinically, this points to wearable monitoring being most acceptable as an adjunct to, rather than a replacement for, routine assessment in PD, particularly where it can capture symptom fluctuations, inform medication management, and improve the quality of short and infrequent appointments [9-11,21,23]. Whether these benefits could be realized in practice remains uncertain; it would depend on appropriate clinical pathways, staffing, data infrastructure, and clinical engagement. The present findings capture participants’ expectations rather than evidence that these benefits would be achievable in practice. Implementation should therefore ensure real-world mobility data are only collected where a clear care pathway exists for reviewing and acting on them and with outputs designed to be interpretable not only to clinicians but also to people with PD and carers themselves.

The need for interpretable outputs highlights an important distinction: participants valued wearables for producing “objective” data, but objective measurements are not the same as clinically meaningful evidence. Wearables can measure aspects of mobility, such as walking speed, stride length, turn duration, and gait variability, which may help identify medication effects, fall risk, or changes over time [59,60]. However, before these measurements can guide care, they must be validated for their intended purpose and interpreted within the relevant clinical context [61]. Interpretability is therefore as important for clinicians as it is for people with PD and carers because clinicians need to know whether a change is important and what action it should lead to [62]. Clinicians have similarly identified a need for concise summaries of relevant measures, presented as trends with flags for important changes, and standardized actions to implement, rather than raw sensor data [62]. These conditions, alongside the limited availability of established care pathways and clinicians’ own concerns about clinical utility and workflow [63-65], help explain why wearable monitoring in PD remains largely confined to research settings. However, progress toward implementation is evident, with calls to integrate remote monitoring into PD care [66,67] and early examples of these approaches being introduced into routine care [68,69].

At a policy level, these findings speak to the growing enthusiasm for digital technology in long-term condition management, which often anticipates that technology will improve monitoring, personalize care, and enhance service efficiency [9-11,18,32]. This study suggests that these benefits are not guaranteed and may come at a cost if implementation overlooks usability and the practical burdens of adopting new technologies [54]. Policy efforts to develop and implement wearable devices in PD should therefore be accompanied by clear standards for accessibility, co-design, transparent data governance, and clinical integration. Transparency is particularly important, given that participants valued clarity about what data would be collected, who would have access to it, and how it would be used, reinforcing the importance of trust in sustained engagement with digital technologies. Digital health policy should also move beyond a patient-only framing of technology adoption because informal carers are often essential to the successful use of digital health tools in PD yet remain underrecognized in both research and service planning [50,52,53]. A further gap concerns formal carers, who are likely to play a growing role in supporting wearable device use within residential, nursing, and home care settings, yet whose perspectives remain largely absent from the existing literature despite evidence that formal social care forms part of the wider PD support network [70]. Addressing this gap will be essential if wearable monitoring is to be integrated into PD care in ways that are feasible, clinically meaningful, and acceptable across the full support network.

Several limitations should be considered. This study explored anticipated acceptability of wearable devices rather than experiences of using these technologies in routine care. Although some participants drew on prior experience of PD specific (people with PD: 28/72, 39%; carers: 4/20, 20%) or consumer wearables (people with PD: 16/72, 22%; carers: 6/20, 30%), many had no prior experience (people with PD: 28/72, 39%; carers: 10/20, 50%), and a substantial proportion were responding to a proposed technology rather than lived experience. The findings therefore capture expectations, preferences, and concerns, but not how acceptability evolves through prolonged use. Relatedly, participants’ previous experience is likely to have shaped how they interpreted the device descriptions. Those with experience, especially with PD-specific research devices, could draw on first-hand examples when discussing usability, whereas others evaluated a more hypothetical concept. Differences in digital literacy and familiarity may also have influenced how readily they could envisage a device’s role in everyday care. A further limitation concerns the interview guide; before exploring preferences, participants were told that lower-back sensors generally offer greater measurement accuracy than wrist-worn devices. This information was intended to support informed responses regarding technologies with which participants often had limited experience; however, it may have shaped participants’ responses, including their preferences for device placement. Nonetheless, participants frequently preferred wrist-worn devices despite being informed of the greater accuracy of lower-back sensors and readily expressed concerns about the technology, suggesting responses were not solely directed by this framing.

The purposeful recruitment of participants often excluded from PD research was a key strength, particularly given concerns about lack of diversity in the field [29]; 65 (71%) of 92 participants had at least one characteristic associated with underrepresentation in PD or digital health research. Our aim was not to explore the experiences of separate underserved groups but to ensure that people typically absent from this research and at heightened risk of digital exclusion [28] contributed directly to a shared understanding of acceptability. Understanding the specific needs of individual groups remains important but would require studies with sufficient representation. Nonetheless, the sample remained predominantly White and relatively highly educated, and the most socioeconomically deprived and ethnic minority communities remained underrepresented. Reaching these groups fully is likely to require dedicated and adequately resourced recruitment through trusted community organizations, and we recommend this as a priority for future work, consistent with wider calls to strengthen diversity and inclusion in PD and care [29]. This community-based recruitment approach meant that PD diagnosis was self-reported, with no formal clinical assessment of disease stage or severity, and those who volunteered may have been more open to health technologies than those who chose not to take part, so the findings may somewhat overrepresent openness to wearable monitoring, although PPIE throughout the study supported the relevance and grounding of the findings.

Analyzing a large dataset using RTA, which understands rigor as sustained interpretative engagement [71], presents a recognized challenge. Interpretative depth was supported by the lead analyst conducting 43 of the 92 interviews and repeatedly reading all transcripts, alongside reflexive and postinterview notes that documented both similar and divergent accounts. A sole analyst led coding and theme development throughout, while regular team discussion provided opportunities to develop and critically reflect on emerging interpretations.

This study shows that wearable devices for remote continuous mobility monitoring were generally acceptable to people with PD and informal carers, particularly when they were perceived to capture mobility more accurately than clinic-based assessment and to support safer, more informed, and proactive care. However, acceptability was conditional, depending on devices being easy to use, interpretable, and integrated into care in ways that supported rather than replaced clinical relationships. The findings also highlight the central role of carers, whose needs, burden, and perspectives shape whether these technologies are workable in practice. Overall, the successful implementation of wearable mobility monitoring in PD will require more than technical validity. It will depend on person-centered and carer-aware design, and on thoughtful clinical integration capable of turning data into meaningful support.