IntroductionPhysical inactivity is a defining public‑health challenge1,2,3,4. Global evidence indicates that many adults do not achieve WHO‑recommended activity levels of Physical activity (PA)5,6,7. Physical activity levels decline with age, and older adults are consistently more inactive than younger groups5,6. Current trajectories suggest the world may miss the 2030 inactivity‑reduction target5,6. This context matters for Ghana, where healthy ageing is rapidly becoming a national priority.Population ageing amplifies the challenge. As the number of older adults grows, so too will the consequences of inactivity, higher NCD burden, functional decline, and reduced independence8.Ghana is already feeling the demographic shift. Older adults (60+) comprise a meaningful and growing share of the population, life expectancy has increased, and NCDs and mobility‑related disability are rising trends that will intensify without targeted action for older age groups9,10,11,12.Physical activity is a cornerstone of healthy ageing: it lowers chronic‑disease risk, supports cognitive and functional capacity, and enhances independence and quality of life1,7. Its societal benefits extend beyond health to social and economic participation5.Yet Ghana’s available national sources point to low PA and growing sedentary behaviour (SB) in older age groups13. These patterns elevate risks of disability, dependency, and poorer quality of life—precisely the outcomes Ghana aims to avert as the population ages.At the centre of the problem is surveillance. Ghana’s systems seldom capture how older adults move, sit, and sleep across a day. They rely heavily on self‑report and often exclude those aged 70+. The result is limited visibility of light‑intensity activity, sedentary accumulation, and daily variability, especially among older women making equitable design and evaluation of interventions difficult.This paper presents a narrative review of evidence on physical activity, sedentary behaviour, healthy ageing, and surveillance systems in Ghana and proposes a conceptual framework for integrating wearable technologies into national surveillance systems. We do not present new empirical data; rather, we offer an organising logic to guide surveillance, programme design, and future studies.We argue that wearable technologies used ethically and strategically should complement rather than replace Ghana’s established platforms (e.g., STEPs, Health and Demographic Surveillance System (HDSS)), adding objective data on PA, SB, sleep, and functional movement. When linked with sociodemographic indicators, these data make older adults, especially older women visible within routine surveillance and enable equity‑oriented action.Although this review considers evidence concerning older adults more broadly, adults aged 70 years and older are the primary target population for the proposed wearable-enhanced surveillance framework, reflecting their exclusion from the current Ghana STEPS age range and the limited availability of age-sensitive objective movement surveillance in this group.Review approachThis narrative review was developed as a literature-informed conceptual paper. Relevant evidence was identified iteratively between 4th and 27th February 2026 through searches of PubMed, Scopus, and Google Scholar. The searches were guided by the thematic domains, including physical activity and healthy ageing in Ghana, national surveillance systems, functional health, NCDs, mental well-being, sedentary behaviour, gender and health equity, and wearable technologies. Peer-reviewed literature, WHO publications and relevant institutional reports were considered. Because the paper was conceptual in design, no formal systematic-review protocol, predefined eligibility criteria or study-screening algorithm was applied. The synthesis therefore aims to integrate evidence relevant to the conceptual argument rather than provide an exhaustive systematic inventory of the literature.Selection scope: Sources were considered when they addressed one or more of the conceptual domains of the review and provided evidence relevant to Ghanaian ageing, physical activity/sedentary behaviour, surveillance, health equity, or wearable measurement. Ghanaian evidence was prioritised, while relevant international and sub-Saharan African evidence was included where it informed measurement, implementation or conceptual interpretation. No language restriction was specified a priori; the sources included in the review were English-language publications and reports.Claims regarding evidence gaps were assessed by cross-checking findings across the database searches, reference-list screening, identified Ghanaian studies and surveillance sources, and relevant review and policy documents; such gaps were interpreted as limitations of the available evidence rather than definitive evidence of absence.Physical activity patterns among older adults in GhanaIn Ghana, the 2023 WHO STEPwise Approach to Surveillance (STEPS) survey, which covers adults aged 18–69 years, provides the most recent nationally representative data on physical activity patterns. The survey indicates that 9.9% of adults do not meet recommended PA levels and that vigorous activity is uncommon, particularly among women13. Importantly, 37.9% of respondents reported no work-related PA, rather than SB; the corresponding figures were 45.4% for women and 30.7% for men13. Recreational PA was also uncommon, with 71.1% reporting no recreational activity, including 83.8% of women13. However, STEPS excludes adults aged 70 years and older and relies on self-report, limiting assessment of activity intensity, sedentary patterns and day-to-day movement variability.The WHO Physical Activity Country Profile for Ghana complements STEPS by providing additional national-level and modelled estimates, including evidence of higher physical inactivity among older women than men and important policy and environmental gaps14 Reviews of Ghanaian evidence similarly identify a relatively small body of predominantly urban or convenience-based studies, with limited age-disaggregated and objectively measured data15,16.Community-based studies provide greater detail on functional and contextual determinants but are less representative. Awlime-Ableh et al. reported low levels of guideline-concordant activity and an association between lower activity and poorer quality of life among adults aged 60 years and older, with gender, financial status, health, social support, age and neighbourhood safety contributing to activity patterns17. Awuviriy-Newton et al. found lower functional disability among more physically active older adults, although multimorbidity modified these associations18. Qualitative and mixed methods evidence further shows that walking, household activities and other everyday activities are common forms of movement, while limited knowledge of activity intensity, safety concerns and preferences for socially supported programmes influence participation and reporting19.PA, functional health, and mental well-beingGhanaian evidence links physical activity with several dimensions of healthy ageing, although the evidence varies in design and population. Studies report associations between greater activity and lower functional disability, better quality of life, glycaemic and blood-pressure control, and improved sleep-related outcomes20,21,22,23,24. These findings are broadly consistent with international evidence linking physical activity to functional independence and psychosocial well-being, but most Ghanaian studies rely on self-reported activity and use cross-sectional or observational designs, limiting causal interpretation.The evidence also highlights important contextual differences. Activity participation is influenced by health status, socioeconomic circumstances, education, social support, neighbourhood safety, cultural expectations and access to appropriate spaces or programmes17,19,24. These determinants are particularly relevant to older women and people living with multimorbidity or functional limitations. Overall, the Ghanaian evidence supports the relevance of physical activity to healthy ageing while indicating a need for more representative and objectively measured studies capable of examining movement patterns alongside functional and cognitive outcomes.Gaps in evidenceThe evidence identified in this review indicates that Ghana has an established but incomplete evidence base on physical activity and healthy ageing. Existing studies provide information on activity prevalence, functional health, quality of life, sleep and selected chronic conditions, but important limitations remain in representativeness, age coverage, measurement and outcome linkage. National surveillance provides population-level estimates but largely relies on self-report and excludes adults aged 70 years and older, whereas community-based studies provide richer contextual information but are generally smaller and less representative13,14,15,16,17,18,19,20,21,22,23,24. Objective evidence on sedentary behaviour remains particularly limited. Available studies provide little information on total sedentary time, sedentary-bout patterns, daily movement variability or the contexts in which sedentary time occurs among older Ghanaians. Evidence directly linking device-measured movement behaviour with cognitive outcomes is also limited, and age- and gender-specific analyses remain uncommon. These gaps restrict understanding of how movement behaviours vary across the later life course and how they relate to functional and cognitive ageing25,26,27,28,29,30.Taken together, the principal gap is therefore not the absence of ageing or movement research, but the limited availability of recent, nationally representative, age-sensitive and objectively measured surveillance, particularly among adults aged 70 years and older. This supports the potential role of wearable-generated movement data as a complementary surveillance layer, subject to validation, feasibility, cost and equity considerations.Key Ghanaian studies and surveillance sources, including their study designs, populations, measurement approaches, contributions and principal limitations, are summarised in Supplementary Table 1.Gender, ageing, and health equity in the Ghanaian contextGender, socioeconomic position and place shape opportunities for healthy ageing in Ghana and influence physical activity, sedentary behaviour, functional capacity and access to care. Older women, particularly those experiencing poverty, widowhood, limited education or insecure livelihoods, may enter later life with accumulated disadvantages that increase vulnerability to disability and chronic disease31,32,33,34,35,36,37. These life-course inequalities also shape movement patterns: caregiving, domestic work and informal employment may involve substantial low-intensity activity while providing limited opportunities for structured or leisure-time physical activity. Economic insecurity, chronic disease, safety concerns, limited access to appropriate facilities, gendered responsibilities, widowhood and limited social protection may further restrict mobility and participation, particularly among women in rural or resource-constrained settings.These considerations support an equity-centred surveillance approach in which movement data are interpreted alongside functional status and social context. Relevant indicators should be disaggregated by sex and, where feasible, socioeconomic position, rurality, age and other determinants of vulnerability. Wearable-generated movement data could complement survey information, but their implementation should be designed to avoid excluding people because of digital literacy, connectivity, charging, device-management or mobility barriers. Priority for future research should therefore include adults aged 70 years and older, older women and other groups whose movement patterns are poorly represented in current surveillance.Evidence from comparable sub-Saharan African settings further illustrates that later-life health and access to appropriate services are shaped by intersecting social, economic, geographic and health-system conditions. Studies from Uganda highlight the roles of financial constraints, stigma, geographical access, infrastructure, staffing, community support and social relationships in shaping the experiences and service needs of older and other vulnerable populations38,39,40,41. These findings reinforce the importance of surveillance systems that account for social, economic, geographic and health-system inequalities.Existing surveillance infrastructureGhana has several established surveillance and ageing platforms, but they differ substantially in population coverage, purpose and capacity to measure movement. Navrongo HDSS provides longitudinal demographic and health information but does not routinely measure physical activity or sedentary behaviour. STEPS provides nationally representative NCD-risk surveillance and self-reported physical activity but excludes adults aged 70 years and older and does not capture detailed movement patterns. DHS provides important household and socioeconomic context but has limited direct coverage of older adults. WHO SAGE Ghana provides nationally representative ageing-related information, while Ghana-HCAP contributes cognitive and functional assessment experience; these initiatives should be distinguished from one another rather than treated as interchangeable surveillance systems.Collectively, these platforms provide important information on population structure, health, ageing and selected behavioural risk factors, but none currently provides comprehensive, nationally representative, objectively measured movement surveillance across the full older-age spectrum. Their limitations differ: some are constrained by age coverage, others by self-reported measurement, cross-sectional design, limited movement indicators or restricted geographic coverage. The resulting evidence gap is therefore one of integration and measurement, rather than an absence of ageing data. A complementary wearable layer could address some of these limitations by providing objective movement signals while retaining linkage to demographic, health and equity information from established platforms.Wearable technologies as an enabling tool for physical activity surveillanceWearable technologies may complement Ghana’s existing surveillance systems by providing objective, continuous measures of acceleration and derived indicators of movement. They should not be viewed as a replacement for established surveys or clinical and functional assessments. Their value lies in capturing aspects of daily movement that are difficult to obtain through self-report, including activity intensity, sedentary accumulation and movement variability. However, feasibility and validity depend on device type, placement, wear protocols, analytical methods and implementation context.Despite their potential, wearable technologies may introduce implementation barriers that are particularly relevant for older rural women. Limited digital literacy may affect participants’ ability to operate or troubleshoot devices, while unreliable access to electricity may complicate regular charging where this is required. Device maintenance, malfunction, loss and replacement may also present practical challenges in geographically dispersed communities. These considerations are important from a health-equity perspective, as a surveillance approach intended to improve the visibility of vulnerable populations should not inadvertently exclude those with the least access to digital and technical resources. Feasibility studies should therefore assess these barriers explicitly before larger-scale implementation.What wearable devices can add to surveillanceAccelerometer-based wearable devices directly record body acceleration, while measures such as physical activity intensity, sedentary behaviour, posture transitions and gait characteristics are derived from these signals using device placement, wear protocols and analytical algorithms. Consequently, the validity of these measures varies according to device type, body location, sampling and processing methods. Research-grade accelerometers are generally preferred for surveillance research because they provide greater control over sampling, wear protocols and data processing, whereas consumer devices may offer greater usability but vary in sensor specifications, proprietary algorithms and access to raw data. Wearable devices may be worn at the wrist, hip/waist, thigh, or chest, and these placements are not interchangeable. Placement influences the signals available for classifying activity intensity, sedentary behaviour, posture and gait; therefore, device placement should be matched to the surveillance outcome and analytical method. For older adults, this matters profoundly. Unlike self-reported questionnaires, wearables can detect:Physical activity intensity, distinguishing between light, moderate, and vigorous movements with precision. This is crucial given that light-intensity activity dominates the daily routines of older adults, especially older women who engage in domestic, caregiving, and market-related tasks that rarely register on traditional PA instruments.Sedentary behaviour measured not only as total sitting time but also through patterns. Long uninterrupted bouts versus intermittent sitting, each with distinct implications for frailty, cardiometabolic risk, and functional decline.Sleep and circadian patterns, capturing rest-activity cycles that influence metabolic health, cognitive function, and fall risk. As sleep disturbance increases with age and is shaped by gendered caregiving demands, wearables provide an additional lens into health inequalities across the life course.Evidence of feasibility in sub-Saharan AfricaThough still emerging, evidence from sub-Saharan Africa42,43,44, demonstrates that the use of wearable devices is both feasible and informative. In a feasibility study among 65 rural-dwelling older adults in Tanzania, 59 datasets were successfully analysed; one dataset was lost because of a sensor battery problem, while variable electricity access occasionally hindered charging and slow internet made cloud data transfer time-consuming. Some participants also reported skin irritation, and adherence to recommended sensor reapplication was imperfect. These findings suggest that feasibility depends not only on device acceptability but also on charging, connectivity, technical support and participant follow-up45. Feasibility assessments should also quantify non-wear time and missing observations using predefined valid-wear criteria, because incomplete wear may affect estimates of daily physical activity and sedentary behaviour.Acceptability and complianceAcceptability and adherence depend on device comfort, participant understanding, interference with daily activities and the availability of follow-up support. Lightweight devices and clear instructions may facilitate adherence, while brief researcher or community-health-worker contacts can help address non-wear and technical problems. Ghanaian feasibility work should therefore report wear-time compliance, non-wear, missing observations, device loss or malfunction and reasons for incomplete data.Data quality and scalabilityData quality may also be affected by humidity, dust, charging constraints, connectivity and device management. These factors should be evaluated prospectively rather than assumed to be negligible. Larger studies should additionally assess data-processing capacity, technical staffing, device replacement and costs, and whether the procedures used in feasibility studies can be maintained at population scale.Ethical, cultural, and implementation considerationsImplementation should address informed consent, comfort, privacy, data ownership and trust. Participants should understand what the device records, how data will be stored and who will have access to them. Gender, digital literacy, electricity access and other socioeconomic constraints may influence participation and adherence. Community engagement and locally appropriate support will therefore be important to ensure that wearable-based surveillance does not systematically exclude older adults with fewer technical or material resources.Financial cost and logistical feasibilityFinancial and logistical feasibility will be important for translating the proposed framework into routine surveillance. Potential costs include device procurement and replacement, staff training and time, participant support, data management, charging and connectivity, and device distribution and retrieval. Within Ghana’s existing health infrastructure, implementation may require coordination with existing surveillance platforms and community- and primary-care structures, particularly where participants are geographically dispersed or transport is difficult. These requirements should be assessed through pilot and implementation studies before large-scale deployment. Scalability would also depend on access to technical and analytic capacity for data processing, quality control, algorithm selection, secure storage and interpretation, as well as mechanisms for replacing devices and supporting participants across geographically dispersed communities.Conceptual boundaries and strategic positioningWhile smartphone sensors, self-reported digital tools, and community-based functional tests provide important insights, this paper focuses on accelerometer-based wearable devices due to their ability to deliver objective, continuous, and intensity-sensitive measurement across diverse contexts. Smartphone sensors are limited by unequal ownership, inconsistent carrying patterns among older adults, and battery or data constraints. Community-based tests like gait speed or sit-to-stand capture performance at a single time point rather than daily movement. Accelerometer-based wearables can support estimation of light-intensity activity, sedentary accumulation, posture transitions and daily movement variability, although these outcomes require appropriate device placement and validated analytical methods. These are behaviours central to healthy ageing yet largely invisible in Ghanaian surveillance systems. These devices are not replacements for functional assessments but complement existing systems by adding real-world behavioural data to established demographic and clinical platforms.Grounding the conceptual framework in theoryLife-course and lifespan development theoriesLife course and lifespan development theories46,47 highlight that ageing outcomes are shaped by cumulative exposures, social roles, and structural conditions. Gendered work histories, lifelong caregiving, socioeconomic disadvantage, and chronic stress influence mobility, functional health, and daily movement in later life. In Ghana, where older women often experience cumulative disadvantage, these theories justify focusing on light-intensity activity, daily routines, and sedentary accumulation. These are behaviours more indicative of healthy ageing than recreational PA, which is rare among older adults.Biopsychosocial models and the WHO ICF frameworkThe WHO International Classification of Functioning, Disability, and Health (ICF) and related biopsychosocial models48,49,50 view health in later life as emerging from interactions among individual capacities, health conditions, and contextual factors. Functional mobility, movement patterns, sleep, and sedentary behaviour are central to participation and well-being. Ghana’s current surveillance captures demographic and clinical indicators but lacks behavioural components. Wearable-generated data operationalise this “activity” layer, providing objective measures of movement, rest, and posture that complement existing demographic and health-condition data.Disablement process modelsDisablement models51,52,53 describe how chronic conditions interact with functional limitations, activity restrictions, and environmental factors over time. Functional decline is influenced by behavioural pathways, including sedentary time, mobility patterns, and strength-related movements. Traditional surveillance tools miss these behavioural transitions, which are early indicators of frailty and mobility loss. Wearable data enable detection of subtle changes such as prolonged sedentary bouts or decreasing daily variability before overt disability emerges.Mapping relationships within the conceptual modelThe framework links Ghana’s surveillance infrastructure to enhanced insights on ageing and equity. Existing systems (Layer 1) provide demographic, socioeconomic, and health-condition data but lack movement-sensitive information. Wearable-generated indicators (Layer 2) supply activity intensity, sedentary accumulation, sleep, and daily mobility patterns. Integrated analyses (Layer 3) combine these with sociodemographic and clinical data to interpret movement across gender, age, socioeconomic position, and rural-urban settings. Outputs (Layer 4) inform policy, primary care, and community-level interventions. Collectively, the layers create a pathway in which gaps identified in Layer 1 are addressed by Layer 2, interpreted in Layer 3, and translated into actionable insights in Layer 4.Integrating wearable data with surveillance systems: a conceptual framework for GhanaGhana has a diverse set of population-level surveillance platforms: DHS, STEPs, SAGE, HDSS sites, census systems, and emerging ageing-focused initiatives. However, these systems provide limited information on movement patterns relevant to healthy ageing: light-intensity activity, daily mobility, sedentary accumulation, and variability in functional movement. Wearables can bridge this gap when integrated into existing systems.This section outlines a Ghana-specific conceptual framework that recognises the country’ demographic realities, gendered inequities, and current surveillance architecture. The framework positions wearable-generated movement data as an enabling layer, not a replacement, strengthening what Ghana already has while addressing what it currently lacks. Figure 1 illustrates the proposed conceptual framework for integrating wearable-generated movement data into Ghana’s existing surveillance systems to support healthy ageing. The framework integrates the theoretical perspectives by linking life-course exposures and accumulated social and gender inequalities to physical activity, sedentary behaviour and functional health; applying the ICF to situate these behaviours within interactions among health conditions, individual capacities and environmental factors; and drawing on disablement models to represent pathways from health conditions and functional limitations to activity and participation. Within this theoretical structure, wearable-generated movement data are positioned as an additional surveillance layer that complements existing population and health data rather than as a direct determinant of health outcomes.The framework does not assume that surveillance directly improves functional ability, reduces NCD burden or decreases health inequalities. Rather, surveillance is proposed to generate more complete evidence that can inform policies, services and interventions, through which improvements in health and equity may potentially be achieved.Layer 1: surveillance foundationsExisting platforms: STEPS, DHS, HDSS, HCAP adaptations, and routine Primary Health Care (PHC) /NCD clinic data provide population denominators, demographic profiles, NCD prevalence, and equity variables such as rurality, income, and household characteristics. They lack movement-sensitive behavioural data.Layer 2: wearable-generated movement dataAccelerometer-based measures capture activity intensity (light, moderate, vigorous), sedentary accumulation and break patterns, daily movement variability, functional indicators such as posture transitions and sit-to-stand movements, sleep and circadian rhythms (but these should not be equated with comprehensive measures of sleep quality). These measures fill gaps in real-world patterns defining older adults’ daily functioning and health risks.Layer 3: integration and interpretationWearable data are linked with sociodemographic, health, and contextual indicators. Movement behaviours are stratified by gender, age (including 70+), socioeconomic status, rurality, and marital status, then associated with outcomes such as NCDs, disability, sleep quality, pain, and functional performance. This transforms raw accelerometer signals into policy-relevant insights that reflect Ghana’s gendered and socioeconomic realities.Layer 4: action and surveillance outputsOutputs include age-specific PA and SB profiles, gender-disaggregated risk maps, equity dashboards identifying high-risk groups (e.g., older rural women, adults with multimorbidity), and early-warning indicators for functional or mobility decline. These outputs can inform PHC counselling, district-level planning, infrastructure improvements, and national ageing policy.How Ghana benefits: the strategic valueLayering wearable data onto existing systems moves Ghana from self-reported proxies to objective, real-world monitoring of movement patterns that determine healthy ageing. This approach identifies high-risk subgroups, strengthens PHC services, enables gender-responsive and age-inclusive programmes, and advances national ageing and NCD policy. Older adults, particularly women, become visible in national data systems.Ethical and governance foundationsWearable surveillance must be embedded in strong ethical and governance structures. This includes informed-consent procedures, gender-sensitive communication, culturally appropriate device placement, community oversight, and transparent rules for data ownership, storage, access, and reuse. These safeguards build trust, protect autonomy, and prevent misuse.This governance layer operates across all four framework components, ensuring the system remains trustworthy, inclusive, and sustainable. Ethical practice supports older adults’ acceptance, particularly those wary of monitoring technologies.Ultimately, wearables are strategic enhancers within Ghana’s surveillance ecosystem. When ethically governed and integrated with existing platforms, they fill critical gaps in age-sensitive, equity-driven measurement, enabling whole-of-life-course movement surveillance and supporting healthy ageing for Ghana’s growing older population.Fig. 1Full size imageConceptual framework illustrating how wearable-generated movement data can be integrated with Ghana’s existing surveillance systems to strengthen age-inclusive, gender-responsive, and equity-centred monitoring of physical activity, sedentary behaviour, and healthy ageing.As illustrated in Fig. 1, the four layers are intended to operate as an interconnected surveillance pathway rather than as independent components. The first layer provides the demographic, socioeconomic, health and surveillance foundation; the second adds wearable-generated movement data; the third integrates and interprets these data alongside existing information; and the fourth translates the resulting indicators into surveillance outputs that can inform policy and programme development. The arrows in the figure indicate the direction of information flow between these layers, while feedback from implementation and evaluation can be used to refine subsequent surveillance activities.DiscussionGhana’s ageing landscape demands surveillance systems that capture behaviours shaping functional ability, cognitive health, and well-being in later life. Existing platforms: STEPS, HDSS, DHS, and HCAP offer essential demographic and health information but inadequately monitor movement behaviours. Reliance on self-report and limited age coverage leave critical dimensions of healthy ageing. Light-intensity movement, sedentary accumulation, daily variability, and functional mobility remain largely invisible. These gaps disproportionately affect older women, who face cumulative disadvantages across the life course: lower education, economic insecurity, caregiving burdens, and mobility constraints.The conceptual framework positions wearable-generated data as an enabling layer within Ghana’s surveillance ecosystem. Rather than replacing existing systems, wearables introduce objective indicators of PA, SB, sleep, and functional movement. Integrated with sociodemographic, contextual, and clinical information, these data enable risk mapping, identification of high-need groups, and generation of policy-relevant insights especially for older adults aged 70+, whose mobility patterns are minimally represented in national statistics.A key contribution of this framework is its explicit attention to equity. Movement behaviours in older age are shaped by gender, socioeconomic status, rurality, social protection, and life-course disadvantage. Gender-disaggregated and socioeconomic-stratified analyses make intersectional inequities visible particularly among older women, widowed adults, and rural residents. These support equitable policymaking and align with global calls for age- and gender-responsive surveillance.The framework has important health-system implications. Integration with PHC and NCD services can enable personalized movement counselling, early detection of functional decline, and identification of individuals at risk of frailty. At district and national levels, movement-sensitive data can inform age-friendly infrastructure, community PA programmes, and implementation of Ghana’s National Ageing Policy. Objective measurement of sedentary time and sleep also supports surveillance of metabolic, cognitive, and disability-related risk factors.This conceptual proposal is theoretical and requires empirical validation through pilot studies to test device acceptability, feasibility, and integration with Community-based Health Planning and Services (CHPS) and PHC systems. Ethical governance, community engagement, and robust data-management protocols are essential. Nonetheless, the framework offers a robust foundation for reimagining Ghana’s movement surveillance in ways that align with demographic change, health equity, and the lived realities of older adults.Implications for public health practice and policyIntegrating wearable-generated movement data into Ghana’s surveillance architecture offers opportunities for strengthening public health practice. First, objective PA and SB data support early identification of functional decline, particularly among adults aged 70+, who remain underrepresented in current systems. Wearables reveal prolonged sedentary bouts, disrupted sleep, and mobility declines that signal elevated frailty risk, which self-report tools fail to detect.Second, PHC and CHPS providers can use wearable data to deliver personalized movement counselling tailored to older adults’ daily routines and capabilities, addressing gendered and socioeconomic barriers. Third, wearable-enhanced surveillance can guide community-level interventions, identifying where activity is constrained by caregiving burdens, gender norms, environmental hazards, safety concerns, or limited age-friendly infrastructure.Finally, objective movement data support monitoring and evaluation of interventions designed for older adults, allowing Ghana to assess whether policies reduce sedentary time, improve mobility, and enhance quality of life.Wearable-enhanced surveillance has substantial implications for Ghana’s National Ageing Policy, NCD strategies, and healthy-ageing agenda. Objective, age- and gender-disaggregated data reveal which groups, such as rural older women, widowed adults, or individuals with multimorbidity. These groups face the greatest mobility limitations and sedentary burdens. This granularity is essential for equitable policy design.Wearable data can guide investment in age-friendly infrastructure: safe walkways, lighting, seating, and exercise spaces, especially in rural and peri-urban areas. Integration with PHC and district health systems strengthens monitoring of behavioural risk factors and aligns with global recommendations for PA promotion and SB reduction.Ethically governed wearable surveillance also aligns with digital-health priorities, supporting whole-of-life-course monitoring, social protection reforms, insurance coverage for older adults, and gender-responsive community programmes.Future research and surveillance innovationFuture studies should examine PA-cognition pathways among older Ghanaians, an area lacking local evidence despite global links to cognitive function, dementia risk, and psychosocial well-being. Research is also needed to generate objective sedentary behaviour data across socioeconomic, rural-urban, and gender lines, given SB’s association with frailty, metabolic dysregulation, and functional decline. Future studies should consider standardised reporting of core sedentary behaviour metrics, including total sedentary time, duration of prolonged sedentary bouts, and breaks in sedentary time, together with device placement, valid wear time, and data-processing procedures to facilitate cross-national comparisons.Pilot studies should test wearable acceptability, feasibility, and data quality among adults aged 70+, those with mobility limitations, or rural residents. Mixed-methods research can illuminate cultural meanings of wearables, household dynamics affecting adoption, and older women’s specific concerns related to caregiving and domestic labour.Further methodological work is needed to integrate wearable data with existing platforms, develop data-linkage protocols, test interoperability with digital health systems, and refine analytic approaches for movement-sensitive ageing indicators. Such research will strengthen Ghana’s capacity to implement national movement-surveillance reforms and align with global standards for mobility and healthy-ageing metrics.Validation studies in older Ghanaian adults are needed before routine surveillance deployment, particularly among adults aged 70 years and older and those with slow gait, functional limitations or assistive-device use. Such studies should assess whether device placement and existing algorithms accurately classify movement, sedentary behaviour and functional transitions in these populations.ConclusionGhana’s ageing population and persistent gender and socioeconomic inequities require surveillance that complements self-report with real-world movement data. Appropriately validated and ethically governed wearable technologies could strengthen age-sensitive and equity-responsive measurement, particularly among adults aged 70 years and older, and improve the visibility of underrepresented groups. Further research is needed to establish validity, feasibility, acceptability, cost and scalability in Ghana.Data availabilityNo datasets were generated or analysed during the current study.ReferencesBlair, S. N. Physical inactivity: The biggest public health problem of the 21st century. Br. J. Sports Med. 43, 1–2 (2009).PubMed Google Scholar Kohl, H. W. et al. 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Med. 48, 1173–1187 (1999).Article CAS PubMed Google Scholar Download referencesAcknowledgementsNot applicable.FundingThis work was not funded.Author informationAuthors and AffiliationsDepartment of Physical Education and Sport Studies, University of Ghana, Legon, GhanaDoris Akosua Tay, Vida Korleki Nyawornota & Mavis AsareAuthorsDoris Akosua TayView author publicationsSearch author on:PubMed Google ScholarVida Korleki NyawornotaView author publicationsSearch author on:PubMed Google ScholarMavis AsareView author publicationsSearch author on:PubMed Google ScholarContributionsDoris Akosua Tay served as the Lead Author and provided the conceptual leadership for the manuscript. She conceived the idea for the review, developed the conceptual framework, conducted the literature synthesis, interpreted the evidence, wrote the manuscript, coordinated revisions, and prepared the final version for submission.Vida Korleki Nyawornota: Critically reviewed the manuscript, provided comments and suggestions that informed its revision, and approved the final version.Mavis Asare: Critically reviewed the manuscript and approved the final version.Author biographiesDoris Akosua Tay is a Lecturer in the Department of Physical Education and Sport Studies at the University of Ghana. Her research focuses on healthy ageing, physical activity and sedentary behaviour, population health, women’s health, wearable technologies, and surveillance systems for non-communicable disease prevention. She is the Lead Author of this perspective. ResearchGate: https://www.researchgate.net/profile/Doris-Akosua-Tay.Vida Korleki Nyawornota is a Senior Lecturer in the Department of Physical Education and Sport Studies at the University of Ghana. She specialises in physical education, physical activity, sports, and population health. Her research interests include physical activity among children and adolescents, the built environment, active transport, and youth development through sport. ResearchGate: https://www.researchgate.net/profile/Vida-Nyawornota-2.Mavis Asare is a Lecturer in the Department of Physical Education and Sport Studies at the University of Ghana. She holds a PhD in Sport, Exercise and Health Sciences from Loughborough University, United Kingdom, and has additional training in psychopharmacology. Her research interests include sport and exercise psychology, mental health, physical activity, and health promotion. ResearchGate: https://www.researchgate.net/profile/Mavis-Asare.Corresponding authorCorrespondence to Doris Akosua Tay.Ethics declarationsEthics approval and consent to participateNot applicable. This narrative review and conceptual framework paper does not involve human participants, primary data collection, or analysis of identifiable data.Consent for publicationNot applicable.Patient and public involvementPatients or members of the public were not involved in the design, conduct, or reporting of this conceptual review.Competing interestsThe authors declare no competing interests.Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationRights and permissionsOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 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