PhysAgeNet Deliverable D3.2 Guidelines and framework for designing technology-assisted PA interventions including appropriate assessment standards

CA20104 – Network on evidence-based physical activity in old age (PhysAgeNet) Deliverable D3.2 Guidelines and framework for designing technology-assisted PA interventions including appropriate assessment standards RCO 8 Develop guidelines and framework for technology-assisted PA interventions in old age. Contributors - Working Group 3 Nadja Schott, University of Stuttgart, Germany David Beckwée, Vrije Universiteit Brussel & Universiteit Antwerpen, Belgium Niharika Bandaru, Otto-von-Guericke University Magdeburg, Germany Rafael A. Bernardes, Universidade Católica Portuguesa, Lisboa, Portugal Arben Boshnjaku, University "Fehmi Agani" Gjakova, Kosovo Rosemary Dubbeldam, Münster University, Germany Patrick Esser, Oxford Brookes University, United Kingdom Eleftheria Giannouli, ETH Zürich, Switzerland Antoine Langeard, University of Caen Normandy, France Christopher McCrum, Maastricht University, The Netherlands Jana Pelclova, Palacký University Olomouc, Czech Republic 1. INTRODUCTION Advancements in many technologies have opened new possibilities for promoting physical activity (PA) in older adults. PA is crucial for maintaining health and functional independence in older adults. However, participation rates in structured exercise programs remain low due to various barriers, such as mobility limitations, lack of motivation, and difficulty accessing fitness facilities. The advent of wearable technology, smart fitness equipment, live streaming classes, interactive gaming, and client apps provides new opportunities to engage older adults in regular physical activity. These innovations represent a powerful technology push, yet adoption remains uneven. “Technology push” refers to innovations driven primarily by technological advances rather than by user or clinical needs. In other words, developers create new devices, apps, or systems because the technology makes it possible, and only afterwards seek ways to apply them in practice, but they are not always aligned with older adults’ capabilities, clinicians’ requirements, or evidence-based guidelines. As a result, their adoption and sustained use can lag behind the pace of technological innovation. A central challenge lies in bridging two worlds: the technology sector and the clinical field. Developers often emphasize technical capabilities, while clinicians focus on evidence, guidelines, and patient outcomes. This difference in language and priorities can hinder the effective translation of promising tools into daily practice. Building a shared vocabulary and fostering mutual understanding are essential to ensure that new technologies are both clinically meaningful and practically usable. The PhysAgeNet Cost Action network was established to address specific challenges related to evidence-based PA interventions in old age, including those associated with using technology (PhysAgeNet, 2021; Brach et al., 2023). In this context, the current deliverable presents a position statement articulating a shared vision for the development, research, and clinical use of technology-assisted PA interventions for older adults. By integrating insights from technology, health sciences, and clinical practice, it aims to guide researchers, developers, and practitioners towards co-created solutions that are evidence-based, acceptable, and impactful. The following sections provide concise guidance for key stakeholders, covering an overview of technologies and key definitions, integrating evidence-based guidelines into digital solutions, and essential technological factors such as accuracy, connectivity, interface design, security, and system integration. They also address determinants of technology acceptance and sustained use, the role of behaviour change theory and user-centred design, and specific considerations for developers, researchers, and clinicians. Together, these sections form a roadmap to ensure that digital innovations translate into meaningful, equitable, and evidence-based improvements in the health and well-being of older adults. 2. THEORETICAL FRAMEWORKS FOR TECHNOLOGY-ASSISTED PA INTERVENTIONS In recent decades, scientists have used various models of health behavior to explain technology acceptance and use among older people (see Figure 1). Most of these models are based on the established Theory of Reasoned Action (TRA) developed by Fishbein and Ajzen (1975). This theory for predicting human behaviour assumes that actual behaviour is preceded by an intention to behave in a certain way. This intention is, in turn, influenced by two factors: the attitude toward the behaviour and the subjective norm. While the attitude toward the behaviour depends on positive or negative beliefs and assumptions about the action and its outcome (evaluative affect), the subjective norm is based on normative assumptions (social desirability) and the motivation to live up to these assumptions. A) B) Figure 1. A) Overview of behaviour change theories & technology acceptance models; B) Using the Technology Acceptance Model to identify factors that predict the likelihood of increased physical activity among older adults (revised after Chan et al. (2023), Davis (1989)) This chapter is divided into two main parts to capture the multifaceted processes influencing technology-assisted PA interventions. Section 2.1 presents key behaviour change theories, which help explain how interventions can effectively motivate and sustain physical activity. Section 2.2 introduces technology acceptance models, which clarify whether and why older adults are likely to adopt and maintain the use of digital tools. Together, these perspectives provide a complementary foundation: behaviour change theories explain the processes of sustained engagement, while acceptance models highlight the determinants of adoption. Integrating both is key to designing interventions that are effective and acceptable in practice. 2.1. Behaviour change theories Technology-assisted physical ‑ activity (PA) and exercise interventions must be grounded in robust behaviour ‑ change theory to maximize adherence and effectiveness. This foundation spans (a) traditional models developed for face ‑ to ‑ face or community programmes, (b) more integrative frameworks tailored to technology ‑ mediated contexts, and (c) practical toolkits that translate theory into design features. In addition to these well-established models, emerging dual-process perspectives such as the Affective-Reflective Theory are increasingly recognized as important for understanding the emotional dimension of exercise engagement, particularly in contexts like exergaming. Reflecting this evolution, a recent scoping review calls for an overarching, modular meta ‑ framework that integrates shared constructs across theories and captures their dynamic influence over time (Simpson et al., 2025). 2.1.1. FOUNDATIONAL MOTIVATIONAL AND SOCIAL THEORIES Theory of Reasoned Action (TRA; (Fishbein and Ajzen, 1975)) presents the foundational framework to predict health behaviours, which asserts that actual behaviour is determined by behavioural intention. Behavioural intention, on the other hand, depends on the individual’s attitudes towards behaviour and perceived social norms. Within the context of PA interventions, TRA explains how older adults' willingness to adopt digital tools is shaped by perceived expectations from peers, family members, and healthcare providers, and not by personal beliefs alone. Theory of Planned Behaviour (TPB; (Ajzen, 1991)) is a theory that extended TRA by adding perceived behavioural control as a determinant of intention and behaviour. The foundation of TPB is especially relevant for older adults, with the ability to detect both confidence to manage PA and the ability to use digital technologies. This way, improvements in older adults’ PA adherence to PA were observed by TPB-informed interventions (Stolte et al., 2017). Health Belief Model (HBM; Rosenstock, 1990) is a model that aims to explain the health behaviours in the function of individually perceived susceptibility to illness, severity of consequences, benefits from action, and barriers to engagement, while moderated towards indications to actions and self-efficacy. In our context, HBM can be used to better understand why some older adults embrace technology in contrast to others who hesitate due to cost, usability, or privacy concerns. Self-Regulation Theory (SRT; (Carver and Scheier, 1982)), often known as Control Theory, embraces a rather distinct pathway, while noting the cycle of goal setting, self-monitoring, feedback, and individual adjustments as a crucial measurement to sustain behaviour. Through this theory, PA interventions can use wearable instruments, evaluate progress, and adjust based on the received feedback towards achieving the set goals. Self-Determination Theory (SDT; (Ryan and Deci, 2018)). At the motivational level, SDT holds that sustained engagement is related to satisfying three psychological needs: autonomy, competence, and relatedness. Technology ‑ assisted PA programs can meet these needs by allowing users to tailor goals and intensity (autonomy), offering adaptive challenges and real ‑ time feedback (competence), and embedding peer communities or virtual coaches (relatedness). A meta ‑ analysis of 166 SDT ‑ based interventions reported the strongest PA gains when all three needs were addressed by Ntoumanis et al. (2021). Recent Human ‑ Computer Interaction work translates these principles into actionable guidelines such as autonomy ‑ supportive personalization, competence ‑ enhancing feedback loops, and socially rich features (Alberts et al., 2024). Social Cognitive Theory (SCT; (Bandura, 2004)). SCT highlights the role of self-efficacy, observational learning, and social support in shaping and maintaining health behaviours. Technology-assisted PA interventions leveraging SCT can incorporate virtual peer modelling, online group sessions, and personalized goal-setting features to boost self-efficacy. Evidence supports this approach: a pedometer-based program explicitly grounded in SCT significantly improved mobility-related self-efficacy and functional outcomes in community-dwelling older adults (Richeson et al., 2006). Complementing this, a recent systematic review of behaviour change techniques in long-term care identified strategies such as goal setting, feedback, selfmonitoring, and social support as effective for increasing PA (Shi et al., 2025), which are widely recognized as mechanisms through which SCT strengthens self-efficacy, thereby supporting sustained engagement in physical activity. Transtheoretical Model (TTM; (Prochaska and Velicer, 1997). TTM views behaviour change as five successive stages (pre ‑ contemplation, contemplation, preparation, action, and maintenance) so interventions can tailor content to a person’s readiness. In older adults, the computer ‑ tailored programme Active Plus produced stage ‑ specific gains in daily steps (van Stralen et al., 2011), and a review of ≥ 60 ‑ year ‑ olds confirmed that TTM tailoring boosts moderate ‑ to ‑ vigorous PA and speeds stage progression (Jiménez et al., 2020). A meta ‑ analysis of 35 randomised trials found that interventions explicitly matched to TTM stages achieved larger PA effects than non ‑ matched controls (Romain et al., 2018). Nevertheless, only about one ‑ third of eHealth studies include stage ‑ based content (Muellmann et al., 2018, Jonkman et al., 2018), highlighting an implementation gap. Concise stage screeners, personalised prompts, and integrated self ‑ monitoring, therefore, remain promising features for technology ‑ assisted PA programmes aimed at older adults. Affective-Reflective Theory (ART; (Brand and Ekkekakis, 2018, Brand and Ekkekakis, 2021). Beyond these established theories, the Affective-Reflective Theory offers an emerging dual-process perspective. ART posits that physical-activity behaviour is shaped by the inter-play of immediate affective responses (impulsive system) and reflective evaluations (reflective system). While ART has not yet been widely applied in older adults, initial work in adults shows promise. The WalkToJoy proof-of-concept study, for example, used ART principles in a mobile intervention for adults aged 40+, enhancing affective associations with walking and intrinsic motivation (Choi et al., 2025). This illustrates how ART complements established models by highlighting the emotional dimension of exercise engagement. 2.1.2. INTEGRATIVE FRAMEWORKS COM-B Model (Capability, Opportunity, Motivation – Behaviour; (Michie et al., 2011, West and Michie, 2020)): COM ‑ B diagnoses whether behaviour is constrained primarily by capability (physical and psychological), opportunity (physical and social environment), and motivation (automatic and reflective processes) at any given moment. Technology ‑ assisted interventions can thus raise capability (instructional videos, graded plans), expand opportunity (flexible scheduling, context ‑ aware prompts), and strengthen motivation (just ‑ in ‑ time suggestions, digital badges, reflective journals). Health Action Process Approach (HAPA; (Schwarzer, 2008)) integrates motivational and volitional phases, emphasizing both the formation of intentions and their translation into action through detailed planning, action control, and coping strategies. In the context of technology, this approach entails the implementation of the HAPA model's principles to engineer applications, interventions, and digital instruments that facilitate the transition from the initial intention to act, for instance, a goal to engage in increased physical activity, to the actual performance and sustained maintenance of the desired behaviour. 2.1.3. DESIGN FRAMEWORKS Behaviour Change Wheel (BCW; (Michie et al., 2011)): provides a structured way to design behaviour change interventions. At its core is the COM-B model (Capability, Opportunity, Motivation), which helps identify the main barriers to behaviour. The BCW then links these determinants to broad intervention functions (e.g., education, training, modelling, persuasion) and specific behaviour change techniques (BCTs), such as goal setting, feedback, or social support. This stepwise structure makes it possible to move from understanding barriers to specifying concrete intervention components. The BCW is valuable for technology-assisted PA because it ensures that digital features like reminders, gamification, or peer support are explicitly grounded in behavioural theory. Recent reviews have confirmed that integrating BCTs in technology-assisted PA interventions improves outcomes (Ahmed et al., 2024, Bentlage et al., 2023, Dugas et al., 2020). While combining multiple BCTs remains limited in practice (Antezana et al., 2020), their broader adoption should be encouraged. Ideally, this should involve co-design with older adults or AI-supported personalization, given the heterogeneity of user preferences and the limited understanding of the specific effects of individual BCTs (Friel et al., 2025, Janols et al., 2022, Kuru, 2024). Theoretical Domains Framework (TDF; (Cane et al., 2012)): synthesises constructs from more than 30 behavioural theories into a set of 14 psychological domains, including knowledge, skills, beliefs about capabilities, beliefs about consequences, social influences, environmental context, and behavioural regulation. It is often used as a diagnostic tool to identify the main determinants of behaviour in a given context. In digital PA interventions, the TDF helps developers understand why older adults may or may not engage (e.g., lack of confidence, limited digital literacy, or absence of social encouragement) and guides the selection of relevant BCTs. Its strength lies in providing a broad but structured checklist that ensures no major determinant is overlooked. Fogg Behavior Model (FBM; (Fogg, 2009)): offers a simple but powerful principle: a behaviour will occur only when motivation, ability, and a prompt converge simultaneously. If one element is missing (e.g., too little ability, no timely prompt), the behaviour does not happen. For digital health design, this means that apps or exergames should provide just-in-time prompts when users are both able and motivated, and ensure that the behaviour is easy enough to perform. This might mean lowering the technical threshold (clear instructions, intuitive design) and delivering reminders at convenient times for older adults. The FBM is particularly attractive for technology developers because of its simplicity and direct applicability. Persuasive System Design (PSD; (Oinas-Kukkonen and Harjumaa, 2009)): focuses on how digital systems can be designed to influence behaviour positively. It identifies key persuasive design principles such as tailoring (customising content to the user), personalisation, feedback, self-monitoring, reminders, rewards, and social support. In the context of PA, PSD is especially relevant for apps, wearables, and exergames that aim to motivate users through engaging interfaces and interactive features. For older adults, PSD encourages developers to combine usability with persuasive strategies that foster enjoyment, trust, and sustained adherence. 2.1.4. GENERAL AND CONTEXTUAL FRAMEWORKS PICOTS ‑ ComTeC framework (Zrubka et al., 2024) offers a complementary general model for defining, designing, and reporting digital PA interventions. It ensures that all critical dimensions—Population, Intervention, Comparator, Outcomes, Timing, Setting, Communication, Technology, and Context—are systematically specified and aligned with behavioral theory. By incorporating PICOTS-ComTeC, developers can explicitly document the theoretical basis, mechanisms of action, technological features, and implementation context, enhancing both scientific validity and practical application. Ecological models (Sallis and Owen, 2015; Sallis et al., 2008) emphasise that physical activity is shaped not only by individual factors (skills, motivation, health) but also by social relationships, organisational settings, community environments, and public policy. They highlight the interaction between people and their surroundings rather than focusing solely on individual psychology. For technology-assisted PA, this perspective reminds us that even well-designed digital tools require supportive social and environmental contexts to be effective. 2.1.5. KEY TECHNIQUES ACROSS FRAMEWORKS Consistent with these frameworks, evidence syntheses show that certain behaviour ‑ change techniques (BCTs) repeatedly drive success in technology ‑ assisted PA programmes for older adults. As also illustrated in Table 1, umbrella and systematic reviews highlight a core cluster, self ‑ monitoring, goal ‑ setting, feedback, prompts or cues, and social support, as the most potent components (Alley et al., 2024; Stockwell et al., 2019). Table 1. Overview of behaviour change theories and frameworks relevant for technology-assisted PA interventions in older adults Section Framework / Theory Core principle Relevance for technology-assisted PA in older adults (examples) 2.1.1 Foundational motivational and social theories SDT (Ryan & Deci, 2018) Autonomy, competence, relatedness Apps can personalise goals, give adaptive feedback, provide peer communities SCT (Bandura, 2004) Self-efficacy, modelling, social support Digital features can enhance selfefficacy via feedback, group sessions, peer modelling TTM (Prochaska & Velicer, 1997) Stages of change Programmes can tailor content and feedback to readiness stage ART (Brand & Ekkekakis, 2018) Dual-process: impulsive affect + reflective evaluation Explains role of enjoyment in exergaming, highlights affective drivers 2.1.2 Integrative frameworks COM-B (Michie et al., 2011) Capability, Opportunity, Motivation → Behaviour Diagnostic model to identify barriers, guides design features HAPA (Schwarzer, 2008, 2022) Motivation–volition, planning, coping Supports action planning, coping strategies in digital interventions 2.1.3 Design frameworks BCW (Michie et al., 2011) COM-B linked to intervention functions and BCTs Provides stepwise structure to design digital features TDF (Cane et al., 2012) 14 psychological domains Helps identify barriers/enablers in tech adoption FBM (Fogg, 2009) Behaviour = Motivation × Ability × Prompt Highlights importance of simplicity and timely prompts PSD (Oinas-Kukkonen & Harjumaa, 2009) Persuasive design principles Guides design of apps, wearables, exergames 2.1.4 General and contextual frameworks PICOTS-ComTeC (Zrubka et al., 2024) Structured reporting across dimensions Ensures systematic specification of digital interventions Ecological models (Sallis et al., 2008; Sallis & Owen, 2015) Multi-level influences on behaviour Emphasise role of social and environmental context 2.2. Technology Acceptance Models Technology acceptance is typically characterized as the behavioural intention to utilize or acquire knowledge regarding the use of technology, and it can be regarded as a precursor to its practical implementation (technology adoption) (Rogers et al., 2020). Alternatively, it can be defined more broadly as approval, favourable reception, and ongoing use of newly introduced devices and systems (Arning and Ziefle, 2009). A thorough examination of the construct and its influencing factors enables the formulation of conclusions regarding the motivations behind an individual's utilization or non-utilization of a specific technology. Understanding technology acceptance determinants is critical for predicting both initial adoption and long-term engagement in technology-assisted physical activity (PA) interventions in older adults. Models like the Technology Acceptance Model (TAM) (Davis, 1989), the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003), and the Senior Technology Acceptance Model (STAM) (Chen and Chan, 2014) provide valuable frameworks for identifying key determinants that shape older adults’ intentions to adopt and use technology. All models emphasize Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) as critical for influencing Behavioural Intention (BI) to use technology, which is essential in promoting physical activity interventions (O'Dea, 2025). TAM, one of the oldest and most widely used models, emphasizes how system use is a response deriving from user motivation to use the system, which is directly influenced by an external stimulus from the system’s features and capabilities (Davis, 1989). Furthermore, TAM identifies Perceived Ease of Use, Perceived Usefulness, and Attitude Toward Using as primary predictors of users’ motivation to use technology (Davis, 1989), which is essential in promoting PA interventions (O'Dea, 2025). An updated version of TAM was provided by Venkatesh and Davis (Venkatesh and Davis, 2000), which extended the original TAM by integrating social influence processes (like subjective norms, voluntariness, and image) as well as cognitive instrumental factors (like job relevance, output quality, result demonstrability). This way, TAM2 explains how external validation and performance expectations highly influence the perceived functionality and the aims of adopting technology. Then Venkatesh and Bala (2008) went further by introducing another version (TAM3), which further integrated determinants of perceived simplicity of use (like computer self-efficacy, perceptions of external control, computer anxiety, and perceived enjoyment), this way making it especially relevant for older adults’ adaptability towards technology. UTAUT extends TAM by proposing performance expectancy, effort expectancy, and social influence to predict behavioural intention towards accepting information technology (Venkatesh et al., 2003). The importance of this model lies in the context of the influence that confidence in using technology and environmental support plays in sustained engagement. UTAUT is characterized by a wide validation in multiple populations and settings, thus providing a consistent framework for predicting technology acceptance amongst different population groups (e.g., Wu and Lim (2024)). A later extended version of UTAUT(2) was provided by Venkatesh et al. (2012), which incorporated three additional determinants: hedonic motivation, price value, and habit. The UTAUT2 provides another valuable theory that can help design PA interventions for older adults. Considering the unique needs of older populations, STAM further extended these theories by integrating factors like gerontechnology self-efficacy, technology anxiety, and perceived physical or cognitive barriers (Chen and Chan, 2014). Typical examples are seen when older adults decline trying or persist in not using new technological tools due to fears of errors or even data privacy concerns. This may happen even if they recognize the potential health-related benefits. A complex interplay of personal, cognitive, emotional, and environmental factors shapes technology use among older adults (Chan et al. (2023)). Key barriers include limited access, low selfefficacy, perceived risks, and negative emotional responses, while perceived benefits, social support, and positive affective experiences can facilitate use. Addressing these issues requires measure perceived usefulness, ease of use, and openness to new technologies. The pre-intervention discussion should also include questions regarding previous experiences with similar tools, privacy concerns, or perceived barriers. Finally, functional constraints related to sensory or cognitive impairments should be evaluated. Visual acuity, hearing ability, and basic cognitive function can significantly affect the ability to use technological tools and follow exercise instructions. Simple screening tools like the MiniCog (Abayomi et al., 2024) or MoCA-Blind, along with vision and hearing self-report questions or basic tests (e.g., Snellen chart (McGraw et al., 1995), whisper test (Pirozzo et al., 2003)), can guide necessary adaptations to the interface (e.g., larger fonts, audio prompts) or delivery method. Altogether, the pre-intervention assessment should provide a detailed profile of the older participant’s needs, capacities, and preferences, enabling the research or clinical team to design an intervention that is both safe and acceptable, maximizing potential benefits. 5.2. Peri-intervention Clinicians should address common accessibility challenges by providing clear guidance, userfriendly manuals, and multilingual support. This is essential not only to promote technology adoption but also to sustain user engagement over time, thereby enhancing adherence to the therapeutic program. Evidence suggests that interventions that include training sessions on technology usage, technical assistance during the program, and ongoing support from family members or healthcare professionals lead to better engagement and adherence (Coletta et al., 2025; Haase et al., 2021; Gell et al., 2021). Specifically, creating individualized orientation sessions can equip older adults with the necessary skills and confidence, thus enhancing their self-efficacy towards technology use (Haase et al., 2021; Gell et al., 2021). Also, providing ongoing technical support is essential for older adults navigating new technologies. Research indicates that many older adults face challenges with technology usage, leading to a desire for immediate and accessible support when issues arise. For instance, studies suggest that informal support mechanisms, such as family and friends, play a significant role in helping older adults troubleshoot technological issues (Geerts et al., 2023, Portz et al., 2019). It is common for grandchildren or adult children to assist older relatives in adopting new technologies, including devices and applications that promote physical activity (Elers et al., 2018, Luijkx et al., 2015). This familial support network is critical, as it helps bridge the gap between older adults and complex technologies, enhancing their confidence and reducing feelings of inadequacy related to technology use (Tsai et al., 2016). Moreover, structured support systems should be established that offer formal technical assistance, either through dedicated helpdesks or real-time support within the applications themselves. Encouraging organizations and service providers to develop dedicated resources, such as tutorials or FAQs tailored to older adults, will bolster user confidence and minimize frustration (Heinz et al., 2013). The design of user-friendly interfaces that include accessible help options can facilitate smoother interactions, helping older adults to manage any technological difficulties that may arise (Blocker et al., 2020; Ciemer et al., 2025). Mois et al. (2024) outlined best practices for implementing technology-based interventions, including adaptive training programs, ongoing technical support, and personalized feedback mechanisms (see Table 3). These elements should be incorporated into PA interventions to optimize long-term adherence. Moreover, psychological, physical, educational, and economic factors affecting the older adult should be considered when implementing technology-assisted physical activity interventions. Table 3: Guidelines and applications for application of technology-based interventions, adapted from Mois et al. (2024) Area Guidelines and Recommendations User Needs • Carefully consider the system requirements of the intervention to support successful participation. • Understand the target user in the design phase of the intervention to inform the development of training materials that account for differences in user preferences and needs. Training Design • Adapt and customize to meet the needs and preferences of the target user. • Leverage various methods to deliver training content (e.g., videos, PowerPoint presentation, handouts) to ensure participants have easy access to information shared via trainings. Personnel Responsibilities • Support user autonomy by informing and educating on the use and functionality of the technology. • Understand the user and their needs to ensure the proper resources are available to support participation. Structuring the Delivery/Content • Understand the benefits and challenges of the technology tools used to deliver the intervention. • Adapt and optimize support provided for participants throughout the duration of an intervention to support and meet the needs of the target population. Evaluating Success • During the intervention optimization phase, and to analyze the benefits and challenges for various types of delivery methodology and their implication on the target population. • Types of support provided for participants during the duration of an intervention should be adaptable and leveraged to support intervention delivery. 5.3. Post intervention The evaluation following the intervention serves several essential purposes: it documents changes in physical and psychological outcomes, identifies any adverse effects or challenges encountered, and provides insights into the older adult’s experience with the technology. To ensure consistency and allow the assessment of individual change, post-intervention evaluations should reflect the domains assessed at baseline while also addressing perceived usability and impact. 5.3.1. PHYSICAL OUTCOMES AND ADHERENCE Physical improvements should be evaluated using the same tools used at baseline that align with the goals of the intervention. These allow objective monitoring of functional gains and help determine whether the exercise modality was appropriate and effective. Adherence must be carefully documented, particularly in technology-based interventions where appor devicebased activity logs can offer precise data on frequency and duration of participation. Self-reported adherence and perceived barriers should also be collected to interpret variability in outcomes. Monitoring of adverse events, musculoskeletal pain, dizziness, fatigue, or any health incidents related to either the physical activity program or the technology used is essential to determine the safety of the intervention. 5.3.2. PSYCHOLOGICAL AND MOTIVATIONAL ASPECTS Post-intervention motivation and affective responses should be explored using instruments consistent with the pre-intervention phase, such as the PACES (Physical Activity Enjoyment Scale) and BREQ (Behavioural Regulation in Exercise Questionnaire). In addition to capturing shifts in emotional and motivational responses, these data help determine the potential for long-term behaviour change. Open-ended questions or short interviews may be used to explore participants' perceptions of personal benefit, satisfaction, and likelihood of continuing the activity independently. 5.3.3. TECHNOLOGY USABILITY AND ACCEPTANCE Evaluating the older adult’s experience with the technological components is crucial to understanding both individual engagement and the broader acceptability of the intervention. Following Lund’s framework (Lund, 2001), post-intervention assessment should specifically examine usefulness, satisfaction, and ease of use—three core dimensions of user experience. Validated instruments such as the System Usability Scale (SUS) or short Likert-based questions adapted from usability models (e.g., TechPH, STAM) can help quantify these perceptions. Qualitative feedback may further enrich this understanding, especially when participants report technical challenges, lack of clarity, or barriers related to sensory or cognitive limitations. Comparison of preand post-intervention usability and acceptance scores offers valuable information on whether exposure to the technology improved confidence, autonomy, or interest in continuing use. Attention should be paid to any evolution in attitudes, including reducing technophobia or increasing self-efficacy in using digital tools. 5.3.4. GLOBAL PERCEIVED IMPACT To complement standardized measures, participants should also be invited to reflect on the broader impact of the intervention on their daily lives. This may include perceived improvements in autonomy, self-confidence, mobility, or social participation. Brief structured questions or interviews can help capture these subjective effects, often central to real-world relevance and long-term adoption. 6. DISCUSSION AND CONCLUSIONS Integrating technology into physical activity (PA) programs for ageing populations is an emerging field, driven by the potential to overcome common barriers like limited mobility and low motivation. This strategy employs various "digital health" tools, such as wearables, smart fitness equipment, and remote coaching, to connect with a population that is increasingly comfortable with digital media. However, the effectiveness of these interventions depends not just on the technology but on a careful design process based on behavioural and psychological theories. As shown above, key principles use established behaviour-change models, such as Self-Determination Theory (SDT), focusing on fulfilling needs for autonomy, competence, and relatedness to promote ongoing engagement. Technology can support autonomy through personalized features, boost competence with feedback, and foster social connection. The Social Cognitive Theory (SCT) is also relevant, as tech features like virtual peer models and customized goal setting can increase self-efficacy. The Transtheoretical Model (TTM) helps tailor content based on a person's stage of readiness to change, though many digital health studies do not fully implement this approach, highlighting an "implementation gap". These theories provide essential guidance for designing digital interventions that do more than offer conveniencethey actively support habit formation. In addition to behavioural theories, understanding technology acceptance is vital. Models such as the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Senior Technology Acceptance Model (STAM) identify factors like perceived usefulness and ease of use as drivers of adoption. Older adults may face barriers like technological anxiety, low self-confidence, and privacy concerns, which can hinder use even if health benefits are recognized. The document notes that acceptance models often overlook emotional and cultural factors and recommends combining them with behavioural theories for a more complete approach. User-centred and participatory co-design are also emphasized, as involving older adults in the design process helps align solutions with their real experiences and expectations, reducing barriers and increasing relevance. Finally, the practical deployment of technology depends on factors such as internet access, physical design, and interaction usability. Technologies should be safe, reliable, and accessible, with features like real-time feedback that adapt to users' physical abilities to prevent injury. Ethical issues like ageism in AI are also consideredbiased algorithms and unequal care can occur if older adults are underrepresented in data sets. This underscores the need for age-inclusive data collection and diverse development teams. In conclusion, designing effective technology-assisted physical activity interventions for older adults requires a comprehensive approach that exceeds mere methodological convenience and incorporates a variety of theoretical and practical considerations. The discussion surrounding behaviour change theories and technology acceptance models within this deliverable provides an essential framework for this process. To optimize engagement and ensure sustained adherence, researchers must verify that interventions are user-friendly and perceived as beneficial and effectively address fundamental psychological needs, including autonomy and selfefficacy. One also has to recognize that there is an increasing need for a user-centred design philosophy. This means involving older adults and stakeholders in every stage of development, from initial concept to final implementation and dissemination of results. 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