This scoping review was conducted in accordance with the 5-stage framework established by Arksey and O’Malley [13], which involves (1) identifying research questions; (2) identifying relevant studies; (3) selecting studies; (4) charting data; and (5) collating, summarizing, and reporting results, further refined by the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis [14]. The protocol was prospectively registered on the Open Science Framework (DOI: 10.17605/OSF.IO/ZA6F2). Reporting followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram [15] and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist (Multimedia Appendix 1 [16]).

Studies were included based on the population, concept, and context (PCC) framework [14]: the population was defined as older adults (aged ≥60 years), and studies with mixed-age samples were included only if subgroup data for older adults were separately reported; the concept was defined as any DHT, defined as an electronic tool (eg, SMS, mobile app, sensor, smart pillbox, or telepharmacy platform) used to support medication adherence in chronic disease management, and adherence could be measured via validated self-report scales, objective monitoring, or clinical judgment; and the context was defined as any health care setting without geographic restrictions. We included original research (cross-sectional, longitudinal, interventional, qualitative, and mixed methods) and secondary studies (systematic reviews and meta-analyses) published in English or Chinese, with full-text availability. Exclusions comprised only abstracts, protocols, letters, unpublished articles, studies not involving chronic diseases, or studies not involving medication. Studies not specifically focusing on older adults or adherence were excluded unless subgroup results for these populations were reported separately.

The literature search was performed in 2 phases. The initial search covered January 1, 2013, to September 8, 2025, with a subsequent update conducted on September 2, 2026, covering September 9, 2025, to August 2026. Seven databases were searched: MEDLINE (via PubMed), Web of Science, CINAHL (via EBSCOhost), SinoMed, CNKI, Wanfang, and VIP. The selection was justified by their complementary coverage: MEDLINE for broad biomedical literature; Web of Science for multidisciplinary technology and aging research; CINAHL for nursing and allied health perspectives; and the 4 Chinese databases for capturing Chinese-language evidence given China’s large aging population and rapid DHT implementation. To minimize the risk of missing studies, we performed both backward citation chasing (reviewing reference lists of included studies) and forward citation chasing (using Google Scholar to identify citing articles).

The search strategy combined 3 concept groups using MeSH terms, field modifiers (eg, title [TI] and abstract [AB]), and a wide range of synonyms: (1) older adult terms, (2) DHT intervention terms, and (3) medication adherence and chronic disease terms. The complete strategies for all databases, copied exactly as run, are provided in Multimedia Appendix 2. The number of records retrieved per database and the duplication process are reported in Multimedia Appendix 2 and the PRISMA flow diagram.

All retrieved records were imported into EndNote 2021 (Clarivate) for the literature screening process. Two reviewers (LL and LZ) independently screened titles and abstracts against the eligibility criteria. Potentially relevant full-text articles were then independently assessed by the same 2 reviewers. Discrepancies at any stage were resolved through discussion, with arbitration by a senior reviewer (ZC) when consensus could not be reached. Reasons for exclusion during the full-text review were documented in the PRISMA-ScR flow diagram, which summarizes the screening process and ensures traceability of the decision-making process. Interrater reliability was not formally calculated, consistent with scoping review practice [17].

Prior to analyzing the final included studies, we developed a data extraction form through team discussion, updates, optimizations, and resolution of all differences, which included author, publication year, study purpose, population characteristics, tools for assessing medication adherence, and key findings. Given the large number of finally included studies, the full table is presented in Multimedia Appendix 3. In addition, to provide a theoretical lens for organizing and interpreting the heterogeneous extracted data, we drew on 2 complementary theories: social cognitive theory (SCT) [18] and person-environment fit (P-E fit) theory [19]. SCT posits that personal, behavioral, and environmental factors interact bidirectionally, making it suitable for understanding how older adults engage with digital health tools. P-E fit theory emphasizes that behavioral outcomes depend on the congruence between individual capabilities and environmental demands, which is particularly relevant for explaining why some older users adopt DHTs while others abandon them. Prior to data extraction, we operationalized these theoretical constructs into a structured coding manual to guide data charting. The manual specified 4 a priori categories: individual-level factors (derived from SCT’s personal factors), technical-level factors (derived from P-E fit theory’s environmental conditions), social-environmental factors (derived from SCT’s environmental factors), and interventional factors (which emerged inductively during pilot coding). The coding manual was pilot-tested on 5 randomly selected studies, refined through team discussion, and then applied to all included studies by 2 independent reviewers (LL and LZ). Discrepancies were resolved by consensus or senior reviewer arbitration. We did not contact study authors for missing information, as this is not mandatory for scoping reviews and is noted as a limitation.

To ensure the richness and completeness of the final results, we extracted the following variables from each included study using Microsoft Excel 2021: author, publication year, study design, sample characteristics (including age, gender, living situation, and number of medications), DHT type, medication adherence assessment method, medication use stages addressed, usability metrics (if reported), key findings related to adherence, and reported barriers and facilitators. These data were used to generate visual maps of the studies rather than to serve as the final data presentation.

To synthesize the heterogeneous findings extracted from the included studies, we adopted a multistep analytical process that progressively moved from descriptive mapping to explanatory modeling and finally to translational framework development. This process was guided by the same 2 theories (SCT and P-E fit) described above. In the first stage, descriptive mapping, we aimed to objectively characterize the distribution of existing research and identify structural gaps. To operationalize this, we drew on the behavioral dimension of SCT and disaggregated the broad behavior of medication management into 5 sequential and observable stages (prescription, pickup, storage, daily routine, and refill). By mapping the included studies onto these stages, we assessed which phases of the medication behavior chain have received research attention and which remain understudied. This stage yielded a study distribution bubble chart and a summary of underrepresented dimensions, moving beyond a general assessment of adherence to a more granular, behavior-focused visualization of the research landscape.

In the second stage, explanatory modeling, we sought to move from describing the research landscape to explaining why intervention outcomes vary across studies. Using the coded data generated from the SCT- and P-E fit theory–informed manual, we aggregated all reported barriers and facilitators from each study under the 4 coding categories. Through an iterative process of comparison and abstraction, we consolidated them into a 4-level influencing factor model. This model elucidates how mismatches across these levels, such as high technical complexity with low digital literacy, and matches, such as when strong social support features in DHT design align with the needs of older adults living alone, explain the heterogeneous intervention outcomes observed in the literature. This stage thereby provided a mechanistic understanding of intervention performance beyond mere effect aggregation.

In the third stage, translational framework development, we aimed to convert the mechanistic understanding derived from the second stage into actionable guidance for DHT design and optimization. To operationalize this, we applied P-E fit theory’s match logic to the user-reported usability barriers extracted from the coded data. Through an inductive thematic synthesis, we grouped these empirical findings into 5 distinct age-friendly design dimensions: perceptibility, comprehensibility, operability, emotional warmth, and security-interoperability. This framework was not imposed from existing guidelines but was directly derived from the real-world experiences of older adults. To enhance its practical utility, we further established a mapping matrix that explicitly links each user-reported barrier to its corresponding influencing factor level and its derived design solution. This mapping demonstrates how the theoretical understanding of barriers directly translates into actionable design guidance, bridging the gap between explanatory theory and practical optimization. The complete theoretical-analytical workflow is visualized in Figure 1.

The study selection process is presented in Figure 2. The initial search was performed on September 8, 2025, covering the period from January 1, 2013, to September 8, 2025. A total of 10,393 records were identified through database searching. After removing 726 duplicates, 9667 records remained. Two reviewers independently screened titles and abstracts, resulting in the exclusion of 8924 records. The remaining 743 records underwent full-text assessment, of which 32 were unavailable and 661 were excluded for not meeting the eligibility criteria. Backward and forward citation chasing of the 50 included studies identified no additional eligible records. A total of 50 studies were included in the initial review.

An updated search was conducted on September 2, 2026, covering the period from September 9, 2025, to August 2026, using the same databases and search strategies. This update identified 678 records. After removing 108 duplicates, 570 records were screened. Of these, 485 were excluded at the title and abstract stage. The remaining 85 full-text articles were assessed for eligibility, of which 17 were unavailable and 58 were excluded. Backward and forward citation chasing of the newly identified studies yielded no additional eligible records. Ten new studies met the inclusion criteria. Combining both searches, a total of 60 studies were included in this scoping review [11,12,20-77].

Integrating SCT [18] and P-E fit theory [19], factors shaping older adults’ engagement with DHT-supported medication management were synthesized inductively into a 4-level framework. This framework offers a theory-grounded explanation of the observed variability in intervention effects. The hierarchical structure of this framework is visualized using an interactive sunburst diagram (Multimedia Appendix 4). The inner ring represents the 4 theoretical dimensions derived from the integrated framework, with successive outer rings detailing subfactors and their specific manifestations as reported in the included studies. The proportional size of each sector reflects the relative richness of factors within each category, thereby enabling rapid identification of dimensions and subfactors that are more extensively addressed in the current literature. Detailed descriptions and mechanisms are presented in Table 2.

At the individual level, cognitive and physical functioning [23], digital literacy and attitudes [64], and personal health motivation [45] consistently shaped older adults’ ability and willingness to engage with DHTs. Of note, higher educational attainment predicted greater digital engagement, whereas negative attitudes, including digital anxiety and privacy concerns, diminished adoption. At the technical level, usability [32], perceived usefulness [40], and age-friendly design [33] features shaped user experiences. Age-friendly design elements such as large-font display and voice-enabled interaction were associated with improved usability, whereas unresponsive interfaces and poor voice recognition contributed to user frustration and disengagement. At the social-environmental level, social support from family and providers boosted confidence and sustained engagement [42]. Older adults living alone and lacking such interpersonal support faced particular barriers when interacting with fully automated, impersonal digital tools [25]. Digital infrastructure, network access, and device affordability acted as necessary preconditions for implementation, especially within low-resource contexts [57]. At the intervention program level, alignment with patient preferences [52], personalization, interactivity [61], and multidisciplinary support [31] were associated with superior outcomes. However, high-intensity reminders improved short-term adherence but caused long-term resistance [67], and excessive reminder frequency led to alert fatigue and intentional non-adherence [42].

Based on thematic synthesis of usability-related barriers and corresponding optimization strategies extracted from included publications, 5 empirically grounded age-friendly design dimensions were generated: perceptibility, comprehensibility, operability, emotional warmth, and security-interoperability. Perceptibility addresses age-related visual, auditory, and memory-related limitations through multimodal reminder functions and high-contrast interface settings [30]. Comprehensibility targets barriers originating from insufficient digital literacy by simplifying information presentation and lowering learning burdens [33]. Operability focuses on physical interaction requirements, including enlarged touch targets and adaptive interface logic [11]. Emotional-warmth-oriented functions respond to motivational deficits and lack of caregiver support by incorporating positive reinforcement and family-linked communication channels [24], particularly critical for older adults living alone [25]. The security-interoperability dimension encompasses data protection requirements as well as technical demands for bidirectional connectivity between DHT tools and clinical EHR workflows [79]. Mapping between user-reported barriers and corresponding age-friendly design solutions is summarized in Table 3.

This scoping review synthesized applications of DHTs targeting medication adherence among older adults with chronic diseases, with 60 studies ultimately included. Research activity has grown rapidly since 2022, reflecting markedly accelerated global academic interest in digitally enabled medication management for older adults. Synthesizing all results, this review yielded 3 progressive tiers of analytical outputs. The descriptive tier mapped DHT typologies and research distribution across the full medication journey. The explanatory tier constructed a 4-level influencing factor framework to identify conditions under which DHTs succeed or fail among older adults. The translational tier derived 5 age-friendly design dimensions based on real-world usability barriers reported by older adults. This multitiered analysis addresses a long-standing deficiency in this field: numerous randomized controlled trials report mixed intervention outcomes, yet few systematically unpack the underlying drivers of intervention failure and provide optimization solutions.

DHTs create new opportunities for out-of-hospital health management, yet heterogeneous intervention outcomes depend on preconditions concerning capacity, willingness, and sustained usage among older adults [79]. The 4-level model developed in this review, built upon SCT and P-E fit theory, systematically explains interactive mechanisms operating across these 4 domains. As shown in the sunburst diagram (Multimedia Appendix 4), the individual level occupies the largest sector area, indicating that it has received the most extensive research attention, whereas the social-environmental level shows the smallest sector area, reflecting that existing literature on social support and infrastructure-related factors remains markedly insufficient. The following will elaborate factor definitions and derived practical implementation strategies for each level.

At the individual level, age-related cognitive decline directly shapes older adults’ processing of reminder messages, retention of operational procedures, and speed of interpreting interface feedback. Cognitive and physical functional decline and digital literacy currently represent the primary research focuses (Multimedia Appendix 4). When cognitive load exceeds individual capacities, even simple SMS reminders may be ignored or misinterpreted. Digital literacy governs older adults’ adaptation speed and self-troubleshooting capacity when encountering novel technologies [95]. Health motivation reflects intrinsic drive to overcome initial learning curves [96]. Although it is critical for maintaining long-term medication behaviors, research on this factor remains relatively sparse (Multimedia Appendix 4). These 3 factors frequently exert cumulative effects that jointly shape older adults’ DHT adoption capacity and willingness to engage. Therefore, for clinical practice, nurses should complete 3 baseline assessments prior to DHT implementation. Cognitive screening can be performed using brief instruments such as the Mini-Cog [97]. Simplified digital-literacy scales can measure digital self-efficacy [98]. Open-ended questioning can be applied to gauge health motivation levels. Patients can then be stratified into 4 profiles with corresponding implementation pathways. Fully featured interactive applications may be directly recommended for participants with high digital literacy and high motivation. Individuals with low literacy but high motivation require hands-on training alongside concise graphic guidance. Motivational interviewing should be prioritized for patients with high literacy but low motivation. Those with both low literacy and low motivation may begin with basic 1-way SMS reminders before gradual functional escalation.

At the technical level, usability determines whether older adults can complete tasks under acceptable cognitive-load burdens. Intuitive interfaces, unambiguous feedback, and low learning-curve requirements constitute prerequisites for sustained engagement [99]. Perceived usefulness shapes user valuation of tool utility and is theoretically a key factor in promoting continued use, but empirical research in this area remains limited (Multimedia Appendix 4). Age-friendly design features account for a prominent proportion of entries (Multimedia Appendix 4), indicating that interface adaptation is a current research priority, primarily reflected in whether interface components and interaction modalities align with older adults’ sensory and motor capabilities. In clinical practice, usability should serve as a prerequisite for technical adoption [100]. Nursing teams should lead clinical pretrials for DHT usability. For example, representative older participants can be recruited for 1-week real-world field testing, with outcome metrics including task error rates, task-completion times, System Usability Scale scores, and subjective difficulty feedback.

At the social-environmental level, encouragement and practical assistance from family members and clinical providers substantially mitigate user frustration, while digital-infrastructure access forms fundamental usage prerequisites. Multimedia Appendix 4 reveals that both social support and infrastructure factors are under-researched relative to their real-world importance. The scarcity of studies on living alone status and infrastructure is particularly noteworthy, as these 2 factors have a decisive influence on a considerable proportion of older adults. For instance, older adults living alone exhibit significantly higher dropout risk owing to the absence of routine interpersonal support. In clinical practice, living-alone status should act as a core stratifying variable for DHT selection. Solutions embedding human-interaction modules ought to be prioritized for this subgroup [101]. Digital-infrastructure assessments should also be incorporated into patient profiles as constraining criteria for technology recommendations.

At the intervention program level, personalization determines alignment between intervention content and individual medication regimens and daily rhythms. Interactivity supports long-term participant engagement, and reminder intensity requires careful balancing [102]. Multimedia Appendix 4 shows a relatively balanced distribution of factors at this level, suggesting moderate research attention has been received, though this area has not yet become a research hotspot. Among these, patient preference-related content remains relatively sparse, yet this factor is likely critical for influencing initial acceptance and sustained willingness to use. In practical application, greater attention should be paid to the needs of patients and their usage preferences. Regarding the intensity of reminders, an adaptive-reminder strategy is recommended for real-world deployment. For instance, higher-frequency alerts are deployed initially to support habit formation. Reminder density is then gradually reduced according to observed medication-taking stability, finally transitioning toward on-demand alert triggers.

In summary, the 4-level model should move beyond theoretical description and be translated into standardized assessment checklists and stratified intervention algorithms embedded within clinical nursing pathways. Correspondingly, nurses’ roles evolve from basic technical instructors toward technology-matching decision-makers who empower older adults.

Each of the 5 design dimensions thematically synthesized within this review is directly grounded in empirically reported barriers articulated by older adult end users. Compared with existing generic age-accessibility guidelines, this framework possesses 2 distinct strengths.

The first strength is situational specificity. The 5 dimensions were not deductively derived from universal design principles. Instead, they were inductively summarized from real-world difficulty narratives of older adults within the concrete context of medication management. Take the emotional warmth dimension as an illustration: this construct rarely appears within generic accessibility principles [103], yet it is repeatedly highlighted within qualitative feedback from older adults living alone and other end users. Medication-taking is inherently long-term and repetitive; purely functional tools lacking emotional support struggle to sustain durable behavior change. The second strength lies in its dynamic closed-loop property. The framework establishes explicit mapping pathways between identified usage barriers and concrete design solutions. Development teams can clearly define targeted problems and corresponding functional responses for every dimension, avoiding common design inertia that adds features without addressing actual user needs. Perceptibility maps to multimodal reminders and visual enhancement, comprehensibility maps to simplified information presentation and reduced learning burden, operability maps to enlarged touch targets and voice-command functionality, emotional warmth maps to positive reinforcement and interpersonal linkage, and security-interoperability maps to data-privacy safeguards and clinical-system integration.

It is important to highlight that interoperability depends on system-level infrastructure. Its realization demands institutional preconditions including development of health-information standards, construction of regional health-information platforms, and formal data-sharing agreements [104]. Even so, interoperability represents only one component of a broader evaluation framework. Other equally vital yet underemphasized metrics include privacy and data security, accessibility (device availability and network coverage), cognitive load, long-term sustainability, and user autonomy. Future evaluations should adopt this multidimensional framework rather than focusing on isolated single indicators.

For clinical appraisal purposes, this framework supplies 5 reference quantifiable evaluation dimensions. When reporting DHT intervention effectiveness, future studies should document achievement status across these 5 dimensions. Examples include verifying whether perceptibility is delivered through multimodal reminders, whether operability meets acceptable thresholds, and whether security-interoperability is achieved through connections to local health-information platforms. Such reporting enhances intervention reproducibility and cross-context comparability. Sustainable DHT deployment for older adults does not rely on feature proliferation or rapid technical iteration. Instead, success hinges on genuine embedding within older adults’ lived contexts, cognitive rhythms, and social-network environments [105]. The nursing discipline occupies a central position in advancing age-friendly digital health innovation. Real-world implementation nevertheless requires full-phase participatory co-design spanning concept through deployment, with involvement from older adults, caregivers, and interdisciplinary teams consisting of physicians, nurses, pharmacists, and health informaticians. This collaborative process guarantees contextual appropriateness and long-term sustainability [106].

This scoping review has several limitations. First, the study did not systematically search gray literature sources, although a few theses were retrieved through the main database searches; this may have introduced reporting bias. Second, cost factors were also important factors affecting digital interventions; however, as this drawback cannot be perfectly addressed from a nursing perspective, no in-depth discussion was conducted. Third, in accordance with PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Literature Search Extension), the following items were not applicable to the methodology of this scoping review and therefore were not performed: item 4 (online resource browsing), item 6 (contacting authors/experts), item 10 (published search filters), item 11 (adaptation from prior reviews), and item 14 (search peer review). These unexecuted items are explicitly acknowledged here to ensure full transparency in search reporting. Finally, the scoping review did not conduct a quality evaluation of the included articles, and some negative results in the study could not be clearly explained.

This scoping review maps the complete research landscape for DHT-supported medication adherence targeting older adults living with chronic diseases. Distinct from previous reviews centered on effect-size pooling, this work presents an empirically induced 4-level influencing-factor framework alongside 5 age-friendly design dimensions. It extends understanding beyond simple questions of intervention efficacy toward deeper rationales concerning when interventions work, for whom they work, and how systems can be better designed. For clinical practice, nurses and interdisciplinary teams should systematically assess older adults’ digital literacy, cognitive function, and social support prior to DHT roll-out, and stratify technical-solution selection according to living-alone status. For future research, priority should be given to investigating DHT requirements among older adults who live alone or experience cognitive impairment. Empirical testing of standardized usability assessment and interoperability design is needed, alongside expanded research coverage across the full medication journey. Sustainable digital health technology for older adults does not depend on feature accumulation or fast technical iteration. Real-world success depends instead on meaningful integration with older adults’ real-life contexts, cognitive patterns, and existing social-support networks.