The mHealth landscape for older adults extends beyond the relative ubiquity of smartphones and wearables to include tablets and emerging unobtrusive monitoring technologies. While personal mobile devices are effective for short-term PA promotion, their long-term utility is often limited by high abandonment rates once the novelty fades. There is also a persistent digital divide in active usage. Although hardware ownership is rising, particularly among highly educated older adults, sustained engagement remains a challenge across the demographic [17,18]. To mitigate the visual and dexterity deficits associated with these smaller screens, research demonstrates that tablet-based interventions can offer distinct usability advantages, allowing older adults to engage more effectively with complex health dashboards due to the larger screen real estate [19]. Furthermore, the landscape is shifting toward ambient computing and voice-first interfaces, which offer a lower barrier to entry for users with limited mobility or technical literacy [20].

Historically, low adoption of mHealth technologies among older adults was attributed to physical and cognitive barriers. Systematic reviews confirm that age-related declines—specifically in manual dexterity, visual acuity, and cognitive processing speed—create significant hurdles for standard smartphone interfaces [12]. Wildenbos et al [21] demonstrated that basic interface deficits, such as small touch targets, complex navigation hierarchies, and insufficient contrast, can render an app functionally unusable regardless of the app’s intent. However, addressing usability alone does not guarantee engagement. Foundational information systems theory, specifically the senior technology acceptance model (STAM), confirms that for older adults, attitude and utility ultimately mediate the impact of physical limitations [22].

To drive engagement beyond simple utility, gamification is frequently used to foster motivation. While substantial evidence supports its general efficacy in improving health outcomes [23], the evidence specifically for older adults is less robust [10]. Considering self-determination theory (SDT), mechanics designed to drive extrinsic motivation (eg, points, badges, and leaderboards) may fail to sustain engagement if they distract from the direct insight provided by self-monitoring or fail to support the user’s intrinsic health goals. Tabak et al [24] illustrated this risk, noting that specific game elements can sometimes weaken the correlation between the app and the actual health goal if they distract from the direct insight provided by self-monitoring. Consequently, it is argued that gamification for this demographic must be applied with “adaptive engagement,” allowing users to customize motivational affordances to their specific needs [11].

Social support is widely recognized as a determinant of PA, but digitizing it presents unique risks; purely cognitive behavior change techniques are often less suitable for older adults compared to meaningful social support [25]. This uncertainty is often driven by a virtual paradox of connectedness: while older adults desire connection, they frequently reject the specific digital mechanisms used to facilitate it. This tension can be understood through socioemotional selectivity theory (SST), which posits that as time horizons shrink, older adults prioritize emotionally meaningful relationships with close friends and family over knowledge building with peripheral acquaintances [26]. Consequently, mHealth features that broadcast data to a wide, impersonal network conflict with this preference for selective intimacy. Orji [27] found that while competition can foster commitment, it introduces significant risks for this demographic, including heightened anxiety regarding self-esteem. Privacy barriers further compound this issue; older adults are significantly more likely to raise concerns about data confidentiality [28]. As a result, forced digital interaction can alienate users, who often prefer offline socialization, such as discussing results privately with family [24], over built-in cooperative elements that appeal only to specific, highly social user types [29].

Conversational agents (CAs) are evolving rapidly, and those with unconstrained natural language input yield high user satisfaction and improved health outcomes, particularly in chronic disease management; they can foster a relationship similar to human coaching, where the natural language modality itself builds rapport [30]. This is supported by findings that the act of vocalizing health goals to an agent encourages psychological commitment [31,32]. Humans subconsciously apply social rules to computer interfaces, and this tendency toward anthropomorphism is particularly pronounced in older adults, who often use human social scripts to navigate digital interactions [33,34]. Embodiment appears to be helpful in gaining trust and acceptance among older adults [35,36]. An embodied virtual representation, commonly called an “avatar,” can be a tool for persons for mediating communication and to derive meaning from interactions with the environment [37]. This perception as social entities increases engagement but introduces design risks [38]. Hyperrealistic avatars can elicit “uncanny valley” discomfort, while cartoonish representations may be perceived as patronizing [39]. Concepts of homophily suggest a preference for peer-like avatars to facilitate trust [40], and recent research affirms that properly designed peer-like CAs are effective partners in increasing PA and dietary behaviors [41].

The existing literature is extensive but fragmented, often examining domains such as usability, gamification, and trust in isolation. Furthermore, the prevalence of interventionist studies creates a bias toward evaluation, where user insights are constrained by the specific features of a prototype rather than revealing the user’s broader context. Consequently, there is a lack of comprehensive research that synthesizes the relationship between an older adult’s foundational health goals, their existing technology habits, and their trust requirements for future AI interactions. To bridge this gap between current behaviors and future expectations, this study adopts a multitheoretical lens. By integrating the STAM for usability, SDT for motivational goals, and SST for social and privacy preferences, we conducted interviews with older adults about their attitudes toward PA, current PA practices and barriers, and their perceptions of an AI-based tool for supporting PA.

Participants by and large were motivated with the aim of living a quality long life and preventing functional immobility. They framed having a certain level of functional fitness as a prerequisite to valued activities, such as being able to engage with family, avoiding emergency health circumstances, preventing cognitive decline, and also looking or feeling good. P13 framed it as a “straightforward” choice:

PA was similarly linked to cognitive preservation; participants viewed exercise as essential for maintaining mental clarity alongside physical capabilities (especially P13 and P18). Rather than seeking continuous improvement, their long-term objectives focused on sustaining baseline health—such as retaining energy and speed (eg, P6) and avoiding hospitalization (eg, P5).

Participants expressed a strong desire to maintain their health to see their descendants grow (eg, P8). P20 vividly described how losing weight would allow her to safely engage with her family:

Most participants (12/20, 60%) cited social factors and motivations when discussing their PA. Participants liked working with other people such as their spouse, children, friends, or an exercise class.

The sense of community was crucial for some and helped with exercise adherence, providing motivation and even a sense of security. Participants noted that group settings, such as yoga or classes, instilled the discipline required to maintain routines (eg, P11), while also offering peer-monitoring that made them feel safe (eg, P20). P4 was very expressive in describing the value of community:

A healthy sense of competition helped some participants, but this was typically with close family members or even against oneself. P2 mentioned an example of competition between mother and daughter based on achieving the activity levels tracked by Apple Health, which are shown as rings.

While intimate competition within the family proved motivating, broad group-based competition was often rejected. Participants favored collaborative or self-referential tracking (eg, P9), actively disliking generalized leaderboards. As P11 noted regarding university-wide step challenges, comparing oneself against strangers “doesn’t motivate me, but against myself, yeah.”

One participant was motivated by optimizing physical health, focusing on proactively improving performance in competitive bodybuilding:

Having someone else to discuss one’s health objectives and progress with was listed as a key motivation by a number of participants, and this was often routine based. This accountability took various forms, ranging from formal weekly weigh-ins with nutritionists (eg, P3) to informal peer pressure from a spouse or “buddy” (eg, P14). Some participants even expressed a desire for “nagging reminders.”

For some, this accountability was maintained through rigid schedules rather than direct supervision, such as committing specific days of the week to racquetball or fencing (eg, P4). Others relied on self-styled, task-based routines as a proxy for tracking overall activity, measuring their physical output. For example, P7 measured it by daily gardening milestones:

Professional accountability from personal trainers or community instructors was highly valued for providing tailored motivation (eg, P17). More importantly, participants prized instructors who understood age-specific physiological issues and could proactively offer modifications for injuries or joint problems, ensuring the exercise remained both safe and effective (eg, P3).

All (20/20, 100%) our participants used technology regularly in their lives with many also possessing smartwatches (18/20, 90%) and voice-based personal assistants (8/20, 40%). Only 2 (10%) participants did not use smartphones, although they used computers. Nevertheless, many barriers and issues with technology were identified. These could be divided into accessibility issues and user preference mismatches. Although older adults aged ≥75 years reported more accessibility concerns, many aged 65 to 74 years also had similar concerns.

Accessibility issues proved to be a major barrier to usage. These ranged from physical constraints, such as arthritis affecting touchscreen use, to cognitive difficulties in learning new workflows. Small mobile interfaces actively hindered interaction, with participants struggling against illegible font sizes (eg, P18) and the physical pain of using tiny keyboards (eg, P9). Consequently, a significant number of participants explicitly preferred computers or tablets, seeking the physical affordances of larger screens and tactile keyboards (eg, P15). As P7 stated:

While some participants preferred voice interactions to bypass typing difficulties (eg, P4), they often encountered recognition errors, noting that systems struggled to understand their enunciation and frequently transcribed commands incorrectly (eg, P2).

Furthermore, modern devices were often viewed as unforgiving. Heightened touchscreen sensitivity and shifting interaction modes led to hesitation in adopting newer hardware. P13 expressed his exasperation:

Participants frequently indicated a strong preference for visual simplicity, frequently reporting feeling overwhelmed with complex menus (eg, P11) or excessive data visualizations such as multicolored line graphs (eg, P9).

Unfamiliar iconography compounded this frustration, sometimes leading to outright abandonment, as P1 recalled:

Extraneous options and constant notifications further contributed to information overload, occasionally driving users to the brink of returning their devices (eg, P13). Participants viewed these interruptions as highly disruptive to their workflow:

Advancing age made learning more difficult for participants, leading to them expressing a marked preference for short and smooth learning curves (eg, P12). Multistep procedures were flagged as a specific barrier to adoption. P3 described this frustration:

Convoluted navigation flows were censured, with some participants finding deeply nested tasks across multiple screens virtually impossible to complete. As P4 stated passionately:

Apps were also criticized for not putting user needs first but instead prioritizing developer needs or lazily adapting study results:

Participants strongly desired automated, minimal-effort setup processes for new wearables (eg, P4). When initial configuration proved too complex, users often had to surrender their independence and rely on younger family members for assistance before they could comfortably use the device (eg, P20).

The majority (14/20, 70%) of participants showed an affinity for gamification, considering it useful for motivation. Many features of gamification came up, ranging from levels and points to leaderboards and competitive streaks.

In this study, we found most participants to be current users of smart devices, but despite their comfort, there was a discrepancy between their general familiarity with apps and their actual comfort in practice, primarily due to accessibility issues. STAM [22] explains this discrepancy. When the procedural demands and learning curve of an app exceed users’ cognitive or sensory thresholds, the perceived utility of the technology collapses. This resonates with previous work done by Vaportzis et al [48] on older adults working with tablet computers. Even older adults who are adept current users of technology would prefer simplicity: their usage alone does not mean that the apps meet accessibility needs.

Participants in our study appeared to be developing adaptive strategies to cope with a fast-changing technological landscape in conjunction with their own cognitive and sensory declines. STAM explains this retreat to familiar desktop computers for substantive tasks as a rational calculation (not a digital deficit) with users actively filtering out complex apps to manage age-related barriers [22]. Excess features contributed to cognitive overload, creating a steep learning curve [48] that acted as a hard filter between basic device usage and mastering new health apps. This burden is compounded by hardware seen as failing to accommodate aging users [12]. While previous literature suggests a potential benefit for voice interfaces [32], our participants expressed mixed preferences, suggesting that giving users more interface options might be best.

Our typology also revealed that for some participants oriented toward the tool archetype, the demand for interface simplicity did not equate to a desire for simplified content. These expert users expected granular, high-level data, demonstrating that accessibility in mHealth should streamline navigation without diluting the scientific complexity of the information provided. This aligns with previous literature cautioning against oversimplification [14].

Among our predominantly well-educated sample, socialization and accountability were significant drivers, although whether this generalizes to more diverse populations warrants further investigation. SDT’s tenet of relatedness [49] illustrates why, for many, the exercise itself was secondary to the community interaction it provided. Facilitating these meaningful social connections acts as a vital relatedness-supportive mechanism [50]. Solitary app mechanics of most individual-centric mHealth tools fail to replicate this sense of communal security.

While digitizing social connection offers potential, it often conflicted with preferences for privacy and meaningful interaction. SST [26] accounts for this resistance, positing that as older adults prioritize emotionally meaningful relationships, mechanisms that broadcast personal data to peripheral networks conflict with their preference for selective intimacy. This theoretically contextualizes findings on social comparison by Koivisto and Malik [10] and Orji [27]. Thus, successful interventions would do well to shift from individual-centric models to community-centric ones that facilitate this safety net without enforcing high-pressure, privacy-invading competition.

The older adults’ emphasis on maintaining a high degree of functional independence for their full lifespan also resulted in more pragmatic reasons for adoption of health apps or wearables. However, monitoring of step count and heart rate is known to have a contributing effect on increasing levels of PA [51]. It thus might be advisable to consider in mainstream health apps features for monitoring current chronic conditions to attract older users. Moreover, daily routines too warrant inclusion. Many participants got PA done as part of daily activities, such as walking their dog. An app would connect more with people if it discussed PA as part of their daily life rather than as workout routines alone.

Gamification contributed to encouragement for many participants and also added to accountability. This is encouraging given the proven effectiveness of gamification in improving health interventions for older adults [52]. However, extrinsic feedback loops in the form of tracking and gamification were also disliked by some participants despite diligence in performing PA. Here, SDT is explanatory because it predicts that those exercising higher autonomy will reject these as patronizing or irrelevant [49,53]. The taxonomy of motivational behaviors by Ahmadi et al [50] categorizes this exact friction as an autonomy-thwarting mechanism. Conversely, participants adopting the “coach” orientation actively requested strict accountability and persistent prompts. This is also consistent with SDT because actively choosing a directive system to enforce personal discipline remains an autonomous decision and is a delegation. The divergence of participant needs on the complexity of PA tracking measures—optimization vs simplicity—also has implications. PA apps aimed at the older demographic would thus do well to have a simple mode with limited tracking and reduced clutter while allowing for an expanded mode that affords detailed tracking, because complex apps have shown no additional benefit over simple pedometers for PA, as found in a systematic review and meta-analysis [51].

The findings indicate that participants possess distinct and often contradictory requirements that necessitate personalization. The data reveal divergent design preferences that cannot be adequately addressed by a single, static app. A similar divergence appears in gamification preferences, where participants were quite polarized, although slightly more expressed motivation from game elements. Among those motivated by gamification, the underlying drivers varied significantly between those seeking competition and those interacting with features pragmatically to track habits. Those who actively rejected gamification viewed it as patronizing or irrelevant. A standardized strategy, such as a default leaderboard, thus risks actively demotivating the intrinsic and routine-driven groups. Avatar choices too are very divergent, and a wide range of choices are better if embodiment is used because individual preferences are strong.

These conflicting preferences suggest that adoption for this demographic is conditional on the ability to tailor the experience. Under SDT [49], the widespread desire to set one’s own goals reflects a fundamental need to preserve autonomy, driving participants to reject generic, prescriptive advice. Providing these meaningful choices operates as a core autonomy-supportive mechanism [50]. Consequently, implementing principles of autonomy support is critical, specifically the provision of choice and the minimization of external pressure [49]. Our findings also indicate that rigid system-defined metrics can further anxiety. Inflexible mandatory targets such as daily step counts can act as competency-thwarting features [50] that undermine user confidence. Therefore, streaks and targets that flexibly adjust to user circumstances may help avoid imposing undue pressure.

The study has a number of findings about the perception of AI that would be instrumental to future designers of apps using AI for aiding older adults in the context of PA. We found that participants were willing to try AI-based systems but nursed inherent skepticism. There are 2 major points of discussion concerning the use of AI by older adults. The first is on their hypothesized use cases, and the second is on the prerequisites that massively impact their adoption and perception of AI.

The inductive typology that emerged from the data clearly aligns with functional distinctions in existing human-computer interaction literature. Specifically, it identifies whether participants viewed the AI as a “tool” for data retrieval [54], or following the social actor paradigm [33], as a “coach” providing directive authority [55] or a “companion” acting as a relational confidante [56].

The study shows that older adults are open to increased AI functionality in their apps. A remarkable proportion of the participants wished to feel a sense of being accountable to someone for their PA; even motivation could be attributed to discussions with an intelligent CA. Many use cases were informational but their presumed use was different from search engine behavior because they expected the CA to remember detailed personal context and provide information; this use case, although informational, is closer to that of a personal assistant for health. Previous literature too has found social support to be very helpful in increasing PA among older adults [57]. Their liking for gamification within apps was part of such motivation. However, gamification alone lacks a social component. While the need for social accountability is ideally satisfied by friends and family or even through personal trainers, many older adults lack them; it can be hypothesized that social support CAs could assist in such contexts, which could also help in alleviating loneliness [58]. Research has found that quality goal-setting practices, self-efficacy, and social support predict PA among older adults [59]. These can be facilitated by CAs, and this study too finds older adults to be willing to use them for these purposes. The CA having relatable embodiment and personality might help further.

However, based on our findings, health apps, especially with AI, have to take care not to negatively affect feelings of self-efficacy among older adults. We resonate with Klasnja et al [60] in that the AI should have the provision of modularity features—allowing users to use it as coach, tool, or companion. Providing this modularity ensures the app can dynamically satisfy the differing theoretical drivers of our participants—whether seeking accountability (SDT), relatedness (SST), or pure usability (STAM)—thereby protecting self-efficacy and respecting each user’s distinct needs.

Trust emerged as a foundational prerequisite for adoption, linking privacy concerns, AI skepticism, and information preferences. Under STAM [22], a lack of trust acts as a hard stop for adoption; if an app feels unsafe, its usability and intended utility are lost. Our participants viewed AI as a tool requiring human oversight rather than as an autonomous authority. This heightened sensitivity aligns with research indicating that older adults with higher educational attainment are significantly more likely to raise data privacy concerns than their peers [28]. The findings on information accuracy and privacy are corroborated by other studies [61].

Trust is also closely related to accuracy and algorithmic transparency. The participants’ distrust of AI stems directly from a practical awareness of GenAI’s propensity for hallucinations [62]. Inaccurate AI poses severe hazards in health care, and older adults require systems that make their reasoning visible. This demand aligns closely with explainable AI frameworks tailored for older demographics [63,64]. Participants expected the technology to justify its outputs rather than act as an unquestionable authority [64]. Their rejection of dumbed-down summaries serves as a practical test of this requirement. They consistently preferred referenced explanations over simplifications because obscuring the source data is often perceived as a breach of honesty. Designing these explanations to be both transparent and accessible [63] forms the foundation necessary to earn their trust.

On the basis of participants’ stated preferences and hypothetical reactions, we propose the following considerations for future designers of PA apps aimed at older adults. As these are derived from interview data and visual probe responses rather than observed usage behavior, they serve as preliminary guideposts rather than definitive rules.