Digital applications to support self-management of multimorbidity: A scoping review

Digital applications to support self-management of multimorbidity: A scoping review | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Digital applications to support self-management of multimorbidity: A scoping review View ORCID Profile Lucy Smith , View ORCID Profile Glenn Simpson , View ORCID Profile Sian Holt , View ORCID Profile Hajira Dambha-Miller doi: https://doi.org/10.1101/2025.04.14.25325811 Lucy Smith 1 Primary Care Research Centre, University of Southampton , Southampton, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lucy Smith Glenn Simpson 1 Primary Care Research Centre, University of Southampton , Southampton, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Glenn Simpson For correspondence: G.W.Simpson{at}soton.ac.uk Sian Holt 1 Primary Care Research Centre, University of Southampton , Southampton, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sian Holt Hajira Dambha-Miller 1 Primary Care Research Centre, University of Southampton , Southampton, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hajira Dambha-Miller Abstract Full Text Info/History Metrics Data/Code Preview PDF ABSTRACT Introduction Multimorbidity, defined as the co-occurrence of two or more long-term conditions, is increasing rapidly and poses challenges for healthcare systems. Advances in digital technologies offer solutions by facilitating personalised, scalable care interventions that empower individuals to manage their conditions more effectively. These applications have potential to improve access to care, enhance patient engagement, and support tailored approaches to self-management. Objectives This scoping review aims to synthesise current evidence on the use of digital applications for self-management in adults with multimorbidity. Methods A scoping review was conducted, systematically searching PubMed, Web of Science, OVID, CINAHL, EMBASE, and additional manual sources. Boolean operators and targeted key terms were employed to retrieve relevant studies from database inception to 16th January 2024. Results The search yielded 1,974 articles, of which 31 met the inclusion criteria. Digital applications for self-management in multimorbidity demonstrated high acceptability and varying efficacy. Key benefits included improved communication, symptom tracking, and autonomy. Barriers included privacy concerns, additional patient burden, and engagement challenges. Socio-demographics, self-efficacy, and digital literacy influenced both barriers and facilitators to tool usage. Theoretical models underpinning apps/conceptual models were limited. Older adults and the working-age population were rarely included. Conclusion The current evidence base does not fully address the needs of older adults with low digital literacy or working-age populations with multimorbidity. Our model highlights the importance of broader contextual mechanisms in digital tool adoption. Future research should prioritise theory-driven tool development tailored to disease clusters and aligned with sociodemographic profiles, health risks, and social care needs. Addressing these gaps could improve self-management and health outcomes for high-risk populations. Highlights Digital applications show promise for supporting self-management of multimorbidity. There is limited consideration of how digital applications operate across different patient populations and combinations of diseases. There is a paucity of theoretical modelling behind existing digital applications. Introduction Multimorbidity, the coexistence of two or more chronic conditions in an individual, is a growing challenge that places considerable strain on health and social care systems [ 1 ]. In England, over one-third of the population now lives with multimorbidity, driving increased demand for health and social care services, while posing complex care delivery challenges [ 2 ]. Multimorbidity is associated with higher mortality rates, more frequent hospitalisations, reduced quality of life, and places substantial physical, emotional, and financial burdens on patients and their caregivers [ 2 , 3 ]. Effective self-management, which involves the patient taking an active role in managing symptoms, medications, and lifestyle adjustments, is often promoted as a solution to addressing the challenges of multimorbidity [ 4 ]. However, adherence to self-management remains low, with many individuals requiring additional time and support to manage multiple conditions. A reported 436,000 hospital admissions could be avoided if the most vulnerable populations were supported with their self-management [ 5 ]. Digital applications, particularly mobile applications, have significant potential for supporting the self-management of multimorbidity. These applications can facilitate remote monitoring, improve communication with care providers, promote treatment plan adherence, and address health and social care needs, etc [ 7 ]. Despite their potential, digital interventions have primarily focused on managing single conditions [ 8 ], with few addressing the unique complexities of managing multiple, interacting conditions. Existing reviews have either concentrated on older populations or grouped diverse technologies (e.g., web-applications, wearables, telecare) under a single category, limiting insights into the specific role of digital applications for multimorbidity self-management [ 9 ]. There is also a need to consider the heterogeneity of people living with multimorbidity. For example, disease clusters associated with multimorbidity vary considerably across age groups, with younger populations experiencing different combinations of conditions compared to older adults, and there are differences arising between ethnic groups and socio-economic backgrounds [ 1 ]. Additionally, digital literacy varies by age and socioeconomic status, potentially affecting the usability and efficacy of digital applications in different demographic groups [ 10 ]. Addressing these gaps requires a more nuanced understanding of how digital applications can be designed to support the diverse clinical and non-clinical needs of people with multimorbidity. This scoping review aims to synthesise evidence on the use of digital applications for self-management in adults with multimorbidity, with a focus on identifying gaps in knowledge and informing future research and intervention development. Methods A scoping review methodology was used based on the framework developed by Arksey and O’Malley (2005) [ 11 ]. This method was chosen as it allows for rapid collation of key evidence in underexplored areas, enabling the synthesis of emerging evidence and trends to identify research gaps and inform future practice, policy and research [ 12 , 13 ]. The review followed the PRISMA-ScR guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews), [ 14 ] with the checklist provided in Supplementary Table 1 . The methodology involved six key stages as follows: 1. Defining the research question Based on our preliminary literature searches and the identification of gaps in this field of inquiry, the following research question was addressed: “What is the current evidence on the use of digital applications for self-management in multimorbidity, and what knowledge gaps remain to guide future research?” 2. Identifying relevant studies The search strategy was developed collaboratively, with a librarian and reviewing previous studies focused on digital applications, self-management, and multimorbidity to inform the initial search terms [ 9 , 15 ]. A reflexive and iterative approach was employed to refine the search strategy. Preliminary searches were conducted on the OVID platform using title and abstract screening to evaluate the relevance of initial terms, enabling adjustments to optimise search outcomes. Both free-text and Medical Subject Headings (MeSH) terms were utilised, and the search terms were reviewed and refined in collaboration with all co-authors. Examples of terms used include: multiple chronic conditions OR multimorbidit AND self-manage* OR self-care OR social care need* OR social support OR digital health OR digital app* OR mobile app* OR mhealth OR website*. The final search terms are presented in Supplementary Table 2 . Systematic electronic searches covered databases including PubMed, Ovid Medline, CINAHL, and Web of Science for articles published from 1946 to December 12, 2024, up to January 16, 2025, were carried out. Manual searching of bibliographies and grey literature searches using BASE, Google, and Google Scholar were also carried out. Inclusion and Exclusion Criteria Articles were included if they met the following criteria: Published in the English language. Focused on adult populations with multimorbidity. Examined digital applications designed for the self-management of multimorbidity (i.e., two or more long-term conditions). In line with the exploratory nature of scoping reviews, quality assessment was not a criterion for exclusion. Studies were excluded if they: Focused on digital applications developed for a single chronic condition. Did not involve self-management. Were study protocols, books, or book chapters. Lacked full-text availability. 3. Selecting relevant studies All articles identified through the database and grey literature searches were uploaded to the Rayyan citation manager. Duplicates were removed, and titles and abstracts were screened by LS and GS, both of whom have expertise in multimorbidity research. To ensure accuracy, a subset of articles was also screened by SL and SH. Each article was assessed for relevance based on the inclusion criteria. Full-text articles were retrieved for those meeting the initial criteria. Data extraction was conducted independently by LS, GS and SH, with the final selection carried out by LS and GS. Any disagreements were resolved through discussion. In cases of unresolved discrepancies, a third reviewer (SH) made a final decision. 4. Data charting To address the study’s objectives, three data charting tables were used to organise and identify key characteristics of the included studies. These included the: study design, population characteristics, types of long-term conditions, geographic location, the digital application type, self-management targets, theoretical frameworks, outcome measurements, key findings, limitations, and recommendations for future research. Data charting was distributed across the team (GS, LS, SH). This process was iterative, with ongoing communication regarding the relevance of data charting to our research question. Some studies focused solely on digital applications, allowing the full population of all three tables. Others, particularly qualitative studies, explored user experiences and attitudes. Despite this, we deemed it important to include qualitative studies, as they provided valuable insights that enriched our understanding of digital app usage, thereby enhancing the overall comprehensiveness of the review. 5. Methods for collating the data Initially, data were synthesised descriptively through content analysis and frequency counts to summarise study characteristics across all three tables. Extracted data was descriptively interpreted and was structured and categorised in the results. to interpret the data in relation to the research question, identifying key themes and trends. This analysis was led by LS with discussion and review by the wider team for validation. 6. Stakeholder feedback Stakeholder feedback was obtained from our patient representative FD, a South Asian female living with multimorbidity. Results Screening, inclusion and exclusion of studies The search of electronic databases and grey literature identified 1,960 articles. After removing 345 duplicates, 1,615 articles were screened. Fourteen citations were also found by hand, making the total number of papers screened 1,629. Taken together, a total of 1,567 articles were excluded, leaving 62 articles eligible for full-text screening and, of these, a final 31 were included (two hand search citations and 29 studies sourced through databases). The PRISMA flowchart summarising the screening process and reasons for exclusion is presented in Table 1 . View this table: View inline View popup Download powerpoint Table 1: A PRISMA table showing the screening workflow Summary of study characteristics Data on research methodology are in Supplementary Table 3 . All 31 included studies were conducted in high-income countries, with (n = 12) originating from the United States [ 19 , 21 , 23 , 25 , 29 , 31 , 33 , 35 , 41 , 42 , 46 ], (n = 3) from Europe [ 20 , 30 , 36 ], (n = 4) from Canada [ 22 , 28 , 34 , 40 ], (n = 2) from the United Kingdom [ 17 , 18 ], (n = 2) from South Korea [ 24 , 45 ], (n = 2) from China [ 43 , 44 ] (n = 1) from Taiwan [ 27 ] and (n = 2) from the Republic of Ireland [ 32 , 39 ]. The timeline of publications ( Figure 1 ) indicates a notable increase in research on multimorbidity and digital applications in recent years. Download figure Open in new tab Figure 1: The number of publications of multimorbidity and digital applications by year Disease representation varied across the studies. Figure 2 illustrates the distribution of six conditions explored in more than four studies. Across all studies, a total of 52 distinct conditions were mentioned (see Supplementary Table 4 ). Mental health and behavioural conditions were the most frequently referenced conditions (n = 23), followed by cardiovascular disease (n = 11), diabetes (n = 9), hypertension (n = 7), COPD (n = 6) and asthma (n = 4). Download figure Open in new tab Figure 2: Frequency of conditions within multimorbidity amongst included studies on digital applications Objective and categories from included studies The study objectives were grouped into three main categories. Category 1: App Development and Study methodologies This category included 17 studies focused on application development or feasibility trials. Methods used included: randomised controlled trials (n = 6) [ 19 , 22 , 23 , 28 , 42 , 45 ], qualitative studies (n = 2) [ 26 , 34 ], surveys (n = 1) [ 17 ] and combined usability/feasibility trials (n= 8) [ 7 , 20 , 29 , 30 , 31 , 33 , 38 , 39 ]. Qualitative methods were widely employed to gather feedback on issues such as the usability and user experience of applications, including stakeholder workshops, interviews, and think-aloud approaches. Innovative qualitative techniques, such as storyboarding and photo elicitation, were also reported in two studies. All studies recruited participants based on having MLTC, documenting specific diseases in the population retrospectively, and for trial studies, having at least a basic digital proficiency. Category 2: Patient Experiences and Perceptions Twelve studies explored patient, caregiver, and healthcare professional experiences with digital applications [ 14 , 18 , 24 , 25 , 32 , 35 , 36 , 40 , 41 , 43 , 44 , 46 ]. Most employed qualitative methods such as interviews and focus groups, while two used surveys. Innovative techniques included storyboarding to depict daily life with MLTC and photo elicitation to document everyday activities. Category 3: Systematic or Scoping Reviews Two reviews published in 2023 and 2021 explored digital applications for self-management of MLTC. One review [ 9 ] included studies from 2009-2019, focusing on older adults (≥60 years) and excluding single-condition interventions, yielding 25 papers. The second review (2010–2020) [ 37 ] included 44 studies evaluating self-management support, care coordination, and algorithm-based applications for MLTC management. Participant Demographics Demographic data were available in the 31 included studies. Twenty-five studies (80%) focused on older adults (≥60 years), while six included mixed-age cohorts. None exclusively focused on the working-age population [ 16 – 65 ]. Participants in Category 1 studies were often digitally literate and had higher education levels. Females were overrepresented, with one study exclusively including women [ 35 ]. Detailed figures are shown in Supplementary Table 3 . Type of digital technology used Table 2 summarises the characteristics of the digital applications and websites in the Theme 1 studies, detailing the type of application, self-management functions, and any relevant theoretical frameworks. Nine studies focused solely on web-based applications, while seven combined web applications with wearable devices to collect biometric, sensory, or clinical data. Ten interventions included a dyadic component, involving healthcare professionals, certified peer mentors, online forums, or caregivers in user interactions or app-generated data. View this table: View inline View popup Table 2: The types of applications, content, theoretical underpinnings and self-management strategies for Theme 1 studies Social support was the most common feature, present in 10 interventions with human involvement, while six provided digitally generated support via scripted responses, reminders, or reinforcements. Disease education and health improvement strategies were addressed in 11 studies, and symptom tracking was featured in 10, using either self-reported (16 interventions) or biometric (seven interventions) data. Health routine management, including appointment reminders, medication alerts, and sharing information with secondary care professionals, was reported in seven studies. Stakeholder involvement was consistently integrated throughout the design process, though no study explicitly referenced a theory of change or logic model. Three studies mentioned theoretical frameworks, one was informed by a prior systematic review and another incorporated 23 health outcome variables relevant to various diseases. Efficacy of the applications The findings from the Category 1 studies (app trials) are summarised in Supplementary Table 3 and illustrated in Figure 3 . Six studies reported high levels of acceptability and usability. Four studies noted improvements in social well-being and health self-management skills, while two observed enhanced quality of life, better symptom understanding, and reductions in hospitalisations. However, outcome measures varied considerably across studies, with little consistency in the measurement tools used. Negative outcomes were also reported, for example, one study indicated a non-significant decline in quality of life in the intervention group, another found no significant effects between intervention and control groups, one highlighted poor usability satisfaction, and several noted low adherence to device usage. Download figure Open in new tab Figure 3: Main effects observed across studies which examined the efficacy of digital applications for the self-management of MLTC Analysis of findings from Category 2 (qualitative experiences and perceptions) and Category 3 (systematic/scoping reviews), synthesised three overarching themes: barriers, mediators, and facilitators, as shown in Figure 4 . Download figure Open in new tab Figure 4: Digital applications and self-management of multiple long-term conditions Barriers to Use The included studies discussed a range of key barriers to digital applications for self-management among people with MLTC. Barriers identified included: concerns related to privacy, security, and data use; the perceived additional burden on patients to manage their health independently; shifting responsibility from traditional healthcare settings; and fears about the potential loss of in-person care. Facilitators of Use A number of facilitators were identified. An important factor discussed in a range of studies were the perceived benefits of digital applications over existing care models and practices. Key benefits identified included improved communication with healthcare professionals, access to personalised recommendations (e.g., early intervention based on symptom feedback), and the ability to tailor applications to individual combinations of conditions and action plans, fostering autonomy and empowerment. Digital applications were also viewed as providing other benefits to patients including facilitating social connectivity with healthcare providers and online patient and advice forums and enabling access to reliable sources of health information and providing skills to support self-management. Mediating factors of Use A number of mediating factors in the use of digital applications were discussed in the studies. An important mediator influencing both barriers and facilitators was socio-demographics. For example, older patients, those with lower education levels or experiencing greater deprivation were more likely to encounter barriers to use, whilst patients with internet access, higher education or digital literacy were more inclined to engage with digital applications. Self-efficacy also played a key role in accessing and using these applications. Among those with lower self-efficacy digital application use was lower, although there was evidence that training and support from carers or family members could enhance engagement. Further, engagement was reduced if patients felt coerced or pressurised into using digital applications. Stakeholder Feedback Stakeholder input was solicited to assess the research questions and validate the findings of this study. The feedback from our PPIE representative (FD) regarding the research question was: “As a South Asian female living with MLTC, and coming from a low SES background, I appreciate being involved in this important research. I myself live with both mental and physical health conditions, so as a patient - having access to digital apps for my own conditions is very beneficial to me if it will improve my quality of life” FD fed back on the conceptual model ( Figure 4 ), emphasizing the need to consider digital poverty as a barrier to use. The conceptual model was amended to incorporate this. Discussion This scoping review aims to synthesise current evidence on the use of digital applications for self-management in adults with multimorbidity. The findings show that within the existing literature, there is limited evidence of how digital applications can be applied to addressing the complexities inherent in managing different combinations of chronic diseases, which is critical to efficacious care in multimorbidity [ 47 , 48 ]. Evidence on this topic focused on high-income states, whilst research on low- and middle-income (LMIC) countries was absent. This may partly reflect the limited rollout of digital applications in LMICs due to a lack of technical systems and delivery infrastructure, patient digital connectivity and implementation costs, etc, although this absence of research is surprising given that there has been significant work in some LMIC countries to leverage digitalise healthcare to increase access to care and improve health outcomes [ 49 – 52 ]. Further, no studies were found that included participants under 18 years old, indicating an important knowledge gap relating to the role of digital applications in supporting self-management of care in multimorbidity across the age spectrum. We found that theoretical frameworks rarely informed application design. While common features such as symptom tracking, social support, and routine management were present, these applications often assumed user motivation based on education about health risks. However, this overlooks key underlying psychosocial factors, such as self-efficacy, which is recognised as playing a crucial role in disease self-management [ 53 ]. Our findings are consistent with previous reviews that highlight the lack of attention to the working-age population with multimorbidity and how digital applications can support both the clinical and non-clinical needs of this cohort [ 1 , 9 ]. Although age is an important marker of multimorbidity, over half of the ‘absolute burden of multimorbidity’ is among the working-age population, adding a further layer of complexity for many individuals managing their conditions whilst remaining economically active in the labour market [ 54 ]. The absence of this group is concerning given the heterogeneous nature of multimorbidity in this demographic and the need to more effectively support working-age people with chronic conditions to remain economically active and in work for as long as possible [ 55 ]. This cohort may benefit from tailored digital interventions, which offer both clinical and non-clinical support (including employment assistance, etc) at an early stage to prevent further deterioration in health or accrual of additional long-term conditions [ 56 , 57 ]. This will not only have potential to improve care outcomes among multimorbidity patients but also have wider societal benefits in terms of addressing high levels of economic inactivity among working-age populations, which has been a feature of labour markets in many states over the last two decades [ 58 ]. The existing evidence base shows that high attrition rates were observed in many app trials, suggesting that results are primarily reflective of digitally engaged participants, who are often white, highly educated, and from more affluent backgrounds [ 59 ]. This pattern mirrors the findings of Stone et al. (2020) [ 60 ], who noted that the efficacy of digital applications for individuals from lower socioeconomic backgrounds remains under-researched, despite these populations bearing the greatest burden of multimorbidity. The issue of digital inclusion has been described as the “super social determinant of health” [ 59 ]. Like previous studies [ 9 , 61 ], our review found a lack of involvement in research of marginalised populations or those lacking digital literacy. For example, only individuals with internet access and the necessary digital skills were included in trials, which risks exacerbating the digital divide and producing biased and unrepresentative findings. This is particularly concerning as underserved populations, who are more likely to be digitally excluded, face a disproportionate burden of multimorbidity [ 60 , 62 ]. These findings echo the concerns raised by earlier studies on the need for greater efforts to ensure digital health interventions are accessible to all groups, particularly those most affected by chronic conditions [ 61 ]. The finding that digital applications were generally well-accepted by participants is also consistent with previous research [ 63 , 64 ]. However, the complexity of self-management across multiple chronic conditions was rarely addressed in the evidence, an issue highlighted in previous work [ 6 ]. This contrasts with the call for a more integrated approach to managing multimorbidity. Most digital applications in our review targeted common physical health conditions like diabetes, cardiovascular disease, and hypertension, with only one addressing mental health alongside physical conditions. This finding aligns with recent research by [ 65 , 66 ] a, which highlighted that mental health problems significantly increase the likelihood of physical multimorbidity, underscoring the gap in digital applications for individuals with both chronic physical and mental health conditions. Although psychosocial variables such as quality of life, and social well-being were commonly measured across Category 1 studies through self-reported psychometric questionnaires, only one digital tool directly targeted psychosocial needs [ 21 ]. We found that most interventions focused predominantly on biological needs (such as blood pressure and glucose levels, symptom tracking and medication routines) rather than offering practical support for the broader psychosocial needs of individuals with multimorbidity. The biology or clinical dominance in existing applications overlooks the broader challenges faced by patients with multimorbidity, especially when individuals lack the capacity to self-manage [ 67 ]. This further highlights the risk of increasing the burden on people with multimorbidity through the use of digital applications without the concomitant provision of appropriate user support. Our review also identified other important potential barriers to the use of digital applications, such as privacy concerns, the burden of managing health digitally, and fears about losing in-person care. These barriers have been identified in earlier research limiting access to digital health applications among particular groups [ 63 , 64 ]. However, few studies have explored these within the context of multimorbidity or outlined specific measures to mitigate potential barriers to digital application take-up among this cohort. This review has several limitations. While we employed a comprehensive search strategy, the exclusion of telehealth and wearable-only interventions may have restricted the breadth of our findings. Additionally, we limited our inclusion criteria to studies that explicitly referenced multimorbidity and multiple long-term conditions, potentially overlooking studies involving older adults with multimorbidity that did not specifically label their population as such. By focusing on studies involving both digital applications and wearable devices, we may have unintentionally narrowed the scope, potentially omitting insights from research that did not involve these technologies. Furthermore, while stakeholder feedback was integrated into the review, its value would have been enhanced if it had been embedded in the research design from the outset, allowing for a more iterative and informed approach to the review process. Despite these limitations, the review possesses several notable strengths. We included a range of patient populations, with a particular focus on working-age adults, a group often underrepresented in existing literature on multimorbidity. This focus helps address a significant gap in the current research, as working-age adults with multimorbidity have specific challenges that are less frequently explored. Additionally, we adhered to a rigorous scoping review methodology, ensuring an extensive and systematic search of both grey literature and peer-reviewed publications, which contributed to the comprehensiveness of the findings. This methodical approach strengthens the validity of the review by capturing a broad spectrum of evidence across diverse contexts. Conclusion Digital applications for self-management of multimorbidity continue to be developed, but research remains limited and fragmented, often neglecting the complexities of managing multiple chronic conditions clinically and in relation to non-clinical factors like psychological support and social care. Current evidence does not sufficiently assess the efficacy of these applications for underserved populations, including older adults with low digital literacy and working-age individuals with chronic conditions. Our conceptual model highlights the importance of considering broader contextual mechanisms influencing digital tool uptake. To move forward, digital self-management interventions must consider psychosocial factors, such as self-efficacy, involve standardised outcome measures and incorporate evidence based on long-term trials. Future research should focus on tailoring interventions to specific multimorbidity clusters and vulnerable populations to ensure greater accessibility and effectiveness, following theory-driven intervention development processes. By addressing these gaps, digital applications can become more accessible, inclusive, and effective in improving the self-management of multimorbidity across diverse groups. Funding HDM has received funding from the National Institute for Health and Care Research - the Artificial Intelligence for Multiple Long-Term Conditions, or “AIM”. ‘The development and validation of population clusters for integrating health and social care: A mixed-methods study on multiple long-term conditions’ (NIHR202637); receives funding from the National Institute for Health and Care Research ‘Multiple Long-Term Conditions (MLTC) Cross NIHR Collaboration (CNC)’ (NIHR207000); and receives funding from the National Institute for Health and Care Research ‘Developing and optimising an intervention prototype for addressing health and social care need in multimorbidity’ (NIHR206431). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care. Declaration of conflicting interests None to declare. Ethical Considerations Ethical approval was not required. Consent to Participate Not applicable. Consent for Publication Not applicable. Data Availability Data are available from authors at reasonable request. Supplementary materials View this table: View inline View popup Supplementary Table 1: Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist View this table: View inline View popup Supplementary Table 2: Summary of search terms View this table: View inline View popup Supplementary Table 3: Study characteristics and findings for all included studies focused on digital applications, MLTC and self-management View this table: View inline View popup Supplementary Table 4: 51 chronic conditions referred to in the studies and frequency Acknowledgements We would like to acknowledge Dr Sridhar Lankey and Dr Neil Singh for their contributions towards the screening process, and Firoza Davies for providing patient feedback on the manuscript. References 1. ↵ Valabhji , J. , Barron , E. , Pratt , A. , Hafezparast , N. , Dunbar-Rees , R. , Turner , E. B. , Roberts , K. , Mathews , J. , Deegan , R. , Cornelius , V. , Pickles , J. , Wainman , G. , Bakhai , C. , Johnston , D. G. , Gregg , E. W. , & Khunti , K . ( 2024 ). Prevalence of multiple long-term conditions (multimorbidity) in England: A whole population study of over 60 million people . Journal of the Royal Society of Medicine , 117 ( 3 ), 104 – 117 . doi: 10.1177/01410768231206033 OpenUrl CrossRef PubMed 2. ↵ Salisbury , C. , Man , M. S. , Bower , P. , Guthrie , B. , Chaplin , K. , Gaunt , D. M. , Brookes , S. , Fitzpatrick , B. , Gardner , C. , Hollinghurst , S. , Lee , V. , McLeod , J. , Mann , C. , Moffat , K. R. , & Mercer , S. W . ( 2018 ). Management of multimorbidity using a patient-centred care model: A pragmatic cluster-randomised trial of the 3D approach . The Lancet , 392 ( 10141 ), 41 – 50 . doi: 10.1016/S0140-6736(18)31308-4 OpenUrl CrossRef PubMed 3. ↵ Barnett , K. , Mercer , S. W. , Norbury , M. , Watt , G. , Wyke , S. , & Guthrie , B . ( 2012 ). Epidemiology of multimorbidity and implications for health care, research, and medical education: A cross-sectional study . The Lancet , 380 ( 9836 ), 37 – 43 . doi: 10.1016/S0140-6736(12)60240-2 OpenUrl CrossRef PubMed Web of Science 4. ↵ Barlow , J. , Wright , C. , Sheasby , J. , Turner , A. , & Hainsworth , J . ( 2002 ). Self-management approaches for people with chronic conditions: A review . Patient Education and Counseling , 48 ( 2 ), 177 – 187 . doi: 10.1016/S0738-3991(02)00032-0 OpenUrl CrossRef PubMed Web of Science 5. ↵ Wood , S. ( 2018 ). Supporting self-management: Understanding its impact on the system . The Health Foundation . https://www.health.org.uk/news-and-comment/blogs/supportingself-management 6. ↵ Simpson , G. , Stokes , J. , Farmer , A. , & Dambha-Miller , H . ( 2023 ). Social care need in multimorbidity . Journal of the Royal Society of Medicine , 116 ( 4 ), 124 – 127 . doi: 10.1177/01410768231168382 OpenUrl CrossRef PubMed 7. ↵ Cocosila , M. , Coursaris , C. , & Yuan , Y . ( 2004 ). M-healthcare for patient self-management: A case for diabetics . International Journal of Electronic Healthcare , 1 ( 2 ), 221 – 241 . doi: 10.1504/IJEH.2004.005869 OpenUrl CrossRef PubMed 8. ↵ Winkler , R. ( 2021 ). Digital-health startups are booming. Their customers are overwhelmed . The Wall Street Journal . https://www.wsj.com/articles/digital-health-startups-are-booming-theircustomers-are-overwhelmed-11620039601 9. ↵ Cajamarca , G. , Proust , V. , Herskovic , V. , Cádiz , R. F. , Verdezoto , N. , & Fernández , F. J . ( 2023 ). Technologies for managing the health of older adults with multiple chronic conditions: A systematic literature review . Healthcare , 11 ( 21 ), 2897 . doi: 10.3390/healthcare11212897 OpenUrl CrossRef PubMed 10. ↵ MacRae , C. , Mercer , S. W. , Henderson , D. , et al. ( 2023 ). Age, sex, and socioeconomic differences in multimorbidity measured in four ways: UK primary care cross-sectional analysis . British Journal of General Practice , 73 ( 729 ), e249 – e256 . doi: 10.3399/BJGP.2022.0405 OpenUrl Abstract / FREE Full Text 11. ↵ Arksey , H. , & O’Malley , L . ( 2005 ). Scoping studies: Towards a methodological framework . International Journal of Social Research Methodology , 8 ( 1 ), 19 – 32 . doi: 10.1080/1364557032000119616 OpenUrl CrossRef 12. ↵ Daudt , H. M. , van Mossel , C. , & Scott , S. J. ( 2013 ). Enhancing the scoping study methodology: A large, inter-professional team’s experience with Arksey and O’Malley’s framework . BMC Medical Research Methodology , 13 , 48 . doi: 10.1186/1471-2288-13-48 OpenUrl CrossRef PubMed 13. ↵ Tricco , A. C. , Lillie , E. , & Zarin , W . ( 2016 ). A scoping review on the conduct and reporting of scoping reviews . BMC Medical Research Methodology , 16 , Article 15. doi: 10.1186/s12874-016-0116-4 OpenUrl CrossRef PubMed 14. ↵ McGowan , J. , Sampson , M. , Salzwedel , D. M. , Cogo , E. , Foerster , V. , & Lefebvre , C . ( 2016 ). PRESS peer review of electronic search strategies: 2015 guideline statement . Journal of Clinical Epidemiology , 75 , 40 – 46 . doi: 10.1016/j.jclinepi.2016.01.021 OpenUrl CrossRef PubMed 15. ↵ Honeyman , M. , Maguire , D. , Evans , H. , & Davies , A . ( 2020 ). Digital technology and health inequalities: A scoping review . Public Health Wales NHS Trust . 16. ↵ Levac , D. , Colquhoun , H. , & O’Brien , K. K . ( 2010 ). Scoping studies: Advancing the methodology . Implementation Science , 5 , 69 . doi: 10.1186/1748-5908-5-69 OpenUrl CrossRef PubMed 17. ↵ Balasubramanian , G. V. , Beaney , P. , & Chambers , R . ( 2021 ). Digital personal assistants are smart ways for assistive technology to aid the health and wellbeing of patients and carers . BMC Geriatrics , 21 , 643 . doi: 10.1186/s12877-021-02436-y OpenUrl CrossRef PubMed 18. ↵ Cummings , M. , Bradley , J. , & Teal , G . ( 2022 ). Patient co-design of digital health storytelling applications for multimorbidity: A phenomenological study . Health Expectations , 25 ( 6 ), 3073 – 3084 . doi: 10.1111/hex.13614 OpenUrl CrossRef PubMed 19. ↵ DeGroot , L. , Gillette , R. , Villalobos , J. P. , et al. ( 2024 ). Feasibility of a digital palliative care intervention (Convoy-Pal) for older adults with heart failure and multiple chronic conditions and their caregivers: A waitlist randomized control trial . BMC Palliative Care , 23 , 234 . doi: 10.1186/s12904-024-01561-w OpenUrl CrossRef PubMed 20. ↵ Dinsmore , J. , Hannigan , C. , Smith , S. , Murphy , E. , Kuiper , J. M. L. , O’Byrne , E. , Galvin , M. , Jacobs , A. , Sillevis Smitt , M. , van Leeuwen , C. , McAleer , P. , Tompkins , L. , Brady , A. M. , McCarron , M. , & Doyle , J. ( 2021 ). A digital health platform for integrated and proactive patient-centered multimorbidity self-management and care (ProACT): Protocol for an action research proof-of-concept trial . JMIR Research Protocols , 10 ( 12 ), e22125 . doi: 10.2196/22125 OpenUrl CrossRef 21. ↵ Fortuna , K. L. , DiMilia , P. R. , Lohman , M. C. , Bruce , M. L. , Zubritsky , C. D. , Halaby , M. R. , Walker , R. M. , Brooks , J. M. , & Bartels , S. J . ( 2018 ). Feasibility, acceptability, and preliminary effectiveness of a peer-delivered and technology-supported self-management intervention for older adults with serious mental illness . Psychiatric Quarterly , 89 ( 2 ), 293 – 305 . doi: 10.1007/s11126-017-9534-7 OpenUrl CrossRef PubMed 22. ↵ Gray , C. , Chau , E. , Tahsin , F. , Harvey , S. , Loganathan , M. , McKinstry , B. , Mercer , S. W. , Nie , J. X. , Palen , T. E. , Ramsay , T. , Thavorn , K. , Upshur , R. , & Wodchis , W. P . ( 2021 ). Assessing the implementation and effectiveness of the electronic patient-reported outcome tool for older adults with complex care needs: Mixed methods study . Journal of Medical Internet Research , 23 ( 12 ), e29071 . doi: 10.2196/29071 OpenUrl CrossRef PubMed 23. ↵ Gustafson , D. H. Sr. , Kornfield , R. , Mares , M. L. , Johnston , D. C. , Cody , O. J. , Yang , E. F. , Gustafson , D. H. Jr. , Hwang , J. , Mahoney , J. E. , Curtin , J. J. , Tahk , A. , & Shah , D. V. ( 2022 ). Effect of an eHealth intervention on older adults’ quality of life and health-related outcomes: A randomized clinical trial . Journal of General Internal Medicine , 37 ( 3 ), 521 – 530 . doi: 10.1007/s11606-021-06888-1 OpenUrl CrossRef PubMed 24. ↵ Ha , J. , & Park , H. K . ( 2020 ). Factors affecting the acceptability of technology in health care among older Korean adults with multiple chronic conditions: A cross-sectional study adopting the senior technology acceptance model . Clinical Interventions in Aging , 15 , 1873 – 1881 . doi: 10.2147/CIA.S268606 OpenUrl CrossRef PubMed 25. ↵ Haverhals , L. M. , Lee , C. A. , Siek , K. A. , Darr , C. A. , Linnebur , S. A. , Ruscin , J. M. , & Ross , S. E . ( 2011 ). Older adults with multi-morbidity: Medication management processes and design implications for personal health applications . Journal of Medical Internet Research , 13 ( 2 ), e44 . doi: 10.2196/jmir.1813 OpenUrl CrossRef PubMed 26. ↵ Kobekyaa , F. K. , Sien , S. W. , Puts , M. , Currie , L. M. , Tompson , M. , Hedges , P. , McGrenere , J. , Mariano , C. , & Haase , K. R . ( 2024 ). A tailored self-management app to support older adults with cancer and multi-morbidities: Development and usability testing . Studies in Health Technology and Informatics , 315 , 767 – 768 . doi: 10.3233/SHTI240320 OpenUrl CrossRef PubMed 27. ↵ Kuo , M. C. , Chen , C. M. , Wu , F. G. , Chen , C. H. , Yin , Z. X. , & Wang , C. Y . ( 2022 ). Use of photo diary and focus group to explore needs for digital disease management program among community older adults with chronic disease . Health & Social Care in the Community , 30 ( 3 ), 926 – 936 . doi: 10.1111/hsc.13241 OpenUrl CrossRef PubMed 28. ↵ Lear , S. A. , Norena , M. , Banner , D. , Whitehurst , D. G. T. , Gill , S. , Burns , J. , Kandola , D. K. , Johnston , S. , Horvat , D. , Vincent , K. , Levin , A. , Kaan , A. , Van Spall , H. G. C. , & Singer , J. ( 2021 ). Assessment of an interactive digital health-based self-management program to reduce hospitalizations among patients with multiple chronic diseases: A randomized clinical trial . JAMA Network Open , 4 ( 12 ), e2140591 . doi: 10.1001/jamanetworkopen.2021.40591 OpenUrl CrossRef 29. ↵ Massoudi , B. L. , Olmsted , M. G. , Zhang , Y. , Carpenter , R. A. , Barlow , C. E. , & Huber , R . ( 2010 ). A web-based intervention to support increased physical activity among at-risk adults . Journal of Biomedical Informatics , 43 ( 5 Suppl ), S41 – S45 . doi: 10.1016/j.jbi.2010.07.012 OpenUrl CrossRef PubMed 30. ↵ Medina-García , R. , López-Rodríguez , J. A. , Lozano-Hernández , C. M. , Ruiz Bejerano , V. , Criscio , P. , & Del Cura-González , I . ( 2024 ). A technological tool aimed at self-care in patients with multimorbidity: Cross-sectional usability study . JMIR Human Factors , 11 , e46811 . doi: 10.2196/46811 OpenUrl CrossRef 31. ↵ Monahan , P. O. , Kroenke , K. , Callahan , C. M. , Bakas , T. , Harrawood , A. , Lofton , P. , Frye , D. , Draucker , C. , Stump , T. , Saliba , D. , Galvin , J. E. , Keegan , A. , Austrom , M. G. , & Boustani , M . ( 2019 ). Development and feasibility of SymTrak, a multi-domain tool for monitoring symptoms of older adults in primary care . Journal of General Internal Medicine , 34 ( 6 ), 915 – 922 . doi: 10.1007/s11606-018-4772-4 OpenUrl CrossRef PubMed 32. ↵ Moran , O. , Doyle , J. , Smith , S. , Giggins , O. , & Dinsmore , J . ( 2022 ). Investigating the needs and concerns of older adults with multimorbidity and their healthcare professionals for conceivable digital psychotherapeutic interventions . Digital Health , 8 , 20552076221089097 . doi: 10.1177/20552076221089097 OpenUrl CrossRef PubMed 33. ↵ Nambisan , P. , Stange , K. C. , Lyytinen , K. , Kahana , E. , Duthie , E. , & Potnek , M . ( 2023 ). A comprehensive digital self-care support system for older adults with multiple chronic conditions: Development, feasibility, and usability testing of myHESTIA . Journal of Applied Gerontology , 42 ( 2 ), 170 – 184 . doi: 10.1177/07334648221129859 OpenUrl CrossRef PubMed 34. ↵ Northwood , M. , Saari , M. , Heckman , G. , et al. ( 2024 ). Use of an electronic wellness instrument in the integrated health and social care of older adults: A group concept mapping study . BMC Health Services Research , 24 , 864 . doi: 10.1186/s12913-024-11320-5 OpenUrl CrossRef PubMed 35. ↵ Pettus , A. J. , Mendez-Luck , C. A. , Bergeron , C. D. , Ahn , S. , Towne , S. D. Jr . ., Ory , M. G. , & Smith , M. L. ( 2017 ). Internet-based resources for disease self-care among middle-aged and older women with chronic conditions . Journal of Women’s Health , 26 ( 3 ), 222 – 233 . doi: 10.1089/jwh.2016.5843 OpenUrl CrossRef 36. ↵ Runz-Jørgensen , S. M. , Schiøtz , M. L. , & Christensen , U . ( 2017 ). Perceived value of eHealth among people living with multimorbidity: A qualitative study . Journal of Comorbidity , 7 ( 1 ), 96 – 111 . doi: 10.15256/joc.2017.7.98 OpenUrl CrossRef PubMed 37. ↵ Samal , L. , Fu , H. N. , Camara , D. S. , Wang , J. , Bierman , A. S. , & Dorr , D. A . ( 2021 ). Health information technology to improve care for people with multiple chronic conditions . Health Services Research , 56 ( Suppl 1 ), 1006 – 1036 . doi: 10.1111/1475-6773.13860 OpenUrl CrossRef PubMed 38. ↵ Shaw , R. J. , Steinberg , D. M. , Bonnet , J. , Modarai , F. , George , A. , Cunningham , T. , Mason , M. , Shahsahebi , M. , Grambow , S. C. , Bennett , G. G. , & Bosworth , H. B . ( 2016 ). Mobile health devices: Will patients actually use them? Journal of the American Medical Informatics Association , 23 ( 3 ), 462 – 466 . doi: 10.1093/jamia/ocv186 OpenUrl CrossRef PubMed 39. ↵ Sheng , Y. , Doyle , J. , Bond , R. , Jaiswal , R. , Gavin , S. , & Dinsmore , J . ( 2022 ). Home-based digital health technologies for older adults to self-manage multiple chronic conditions: A data-informed analysis of user engagement from a longitudinal trial . Digital Health , 8 , 20552076221125957 . doi: 10.1177/20552076221125957 OpenUrl CrossRef PubMed 40. ↵ Steele Gray , C. , Miller , D. , Kuluski , K. , & Cott , C. ( 2014 ). Tying eHealth applications to patient needs: Exploring the use of eHealth for community-dwelling patients with complex chronic disease and disability . JMIR Research Protocols , 3 ( 4 ), e67 . doi: 10.2196/resprot.3500 OpenUrl CrossRef PubMed 41. ↵ Trinh , M. , Hattakitjamroen , V. , & Rogers , W . ( 2024 ). Understanding older adults’ motivations to use digital health portals . Human Factors in Healthcare , 6 , 100079 . doi: 10.1016/j.hfh.2024.100079 OpenUrl CrossRef 42. ↵ Wicks , P. , Massagli , M. , Frost , J. , Brownstein , C. , Okun , S. , Vaughan , T. , Bradley , R. , & Heywood , J . ( 2010 ). Sharing health data for better outcomes on PatientsLikeMe . Journal of Medical Internet Research , 12 ( 2 ), e19 . doi: 10.2196/jmir.1549 OpenUrl CrossRef PubMed 43. ↵ Wu , Y. , Wen , J. , Wang , X. , et al. ( 2022 ). Associations between e-health literacy and chronic disease self-management in older Chinese patients with chronic non-communicable diseases: A mediation analysis . BMC Public Health , 22 , 2226 . doi: 10.1186/s12889-022-14695-4 OpenUrl CrossRef PubMed 44. ↵ Xu , Z. , Zhang , D. , Zheng , X. , et al. ( 2023 ). Use, satisfaction, and preference of online health services among older adults with multimorbidity in Hong Kong primary care during COVID-19 . BMC Geriatrics , 23 , 368 . doi: 10.1186/s12877-023-04061-3 OpenUrl CrossRef PubMed 45. ↵ Yoo , H. J. , Park , M. S. , Kim , T. N. , Yang , S. J. , Cho , G. J. , Hwang , T. G. , Baik , S. H. , Choi , D. S. , Park , G. H. , & Choi , K. M . ( 2009 ). A ubiquitous chronic disease care system using cellular phones and the internet . Diabetic Medicine , 26 ( 6 ), 628 – 635 . doi: 10.1111/j.1464-5491.2009.02732.x OpenUrl CrossRef PubMed 46. ↵ Zulman , D. M. , Jenchura , E. C. , Cohen , D. M. , Lewis , E. T. , Houston , T. K. , & Asch , S. M . ( 2015 ). How can eHealth technology address challenges related to multimorbidity? Perspectives from patients with multiple chronic conditions . Journal of General Internal Medicine , 30 ( 8 ), 1063 – 1070 . doi: 10.1007/s11606-015-3222-9 OpenUrl CrossRef PubMed 47. ↵ Dambha-Miller , H. , Simpson , G. , Hobson , L. , Olaniyan , D. , Hodgson , S. , Roderick , P. , Fraser , S. D. , Little , P. , Everitt , H. , & Santer , M . ( 2021 ). Integrating primary care and social services for older adults with multimorbidity: A qualitative study . The British Journal of General Practice , 71 ( 711 ), e753 – e761 . doi: 10.3399/BJGP.2020.1100 OpenUrl Abstract / FREE Full Text 48. ↵ Hodgson , S. , Simpson , G. , Roderick , P. , Everitt , H. , Little , P. , Santer , M. , & Dambha-Miller , H . ( 2021 ). Integrating primary care and social services for older adults with multimorbidity: Policy implications . BJGP Open , 5 ( 4 ), BJGPO.2021.0035. doi: 10.3399/BJGPO.2021.0035 OpenUrl FREE Full Text 49. ↵ Labrique , A. B. , Wadhwani , C. , Williams , K. A. , et al. ( 2018 ). Best practices in scaling digital health in low and middle income countries . Global Health , 14 , 103 . doi: 10.1186/s12992-018-0424-z OpenUrl CrossRef PubMed 50. Chowdhury , A. , & Pick , A. ( 2019 ). Digital health in LLMICs: Current and future technological developments with the potential to improve health outcomes in low- and lower-middle-income countries (Pathways for Prosperity Commission Background Paper Series No. 28). University of Oxford . https://pathwayscommission.bsg.ox.ac.uk/sites/default/files/2019-10/digital_health_in_llmics.pdf 51. Nathan , J. J. , Agarwal , D. , Peres , I. T. , et al. ( 2024 ). Digital health interventions in primary care in low- and middle-income countries: A systematic scoping review protocol . Journal of Global Health Reports , 8 , e2024020 . doi: 10.29392/001c.120508 OpenUrl CrossRef 52. ↵ Yi , S. , Yam , E. L. Y. , Cheruvettolil , K. , Linos , E. , Gupta , A. , Palaniappan , L. , Rajeshuni , N. , Vaska , K. G. , Schulman , K. , & Eggleston , K. N . ( 2024 ). Perspectives of digital health innovations in low- and middle-income health care systems from South and Southeast Asia . Journal of Medical Internet Research , 26 , e57612 . doi: 10.2196/57612 OpenUrl CrossRef PubMed 53. ↵ Carrier , J . ( 2023 ). Managing long-term conditions and chronic illness in primary care ( 3rd ed .). Oxon : Taylor & Francis Group . 54. ↵ Head , A. , Fleming , K. , Kypridemos , C. , Schofield , P. , Pearson-Stuttard , J. , & O’Flaherty , M . ( 2021 ). Inequalities in incident and prevalent multimorbidity in England, 2004–19: A population-based, descriptive study . The Lancet Healthy Longevity , 2 ( 8 ), e489 – e497 . doi: 10.1016/S2666-7568(21)00146-X OpenUrl CrossRef 55. ↵ Henderson , M. , Adam , M. , McElvenny , D. , Relton , S. , & Stevelink , S . ( 2024 ). Economic inactivity and mental–physical multimorbidity . Occupational Medicine , 74 ( 7 ), 467 – 469 . doi: 10.1093/occmed/kqae010 OpenUrl CrossRef PubMed 56. ↵ Stagg , A. L. , Hatch , S. , Fear , N. T. , Dorrington , S. , Madan , I. , & Stevelink , S. A. M . ( 2022 ). Long-term health conditions in UK working-age adults: A cross-sectional analysis of associations with demographic, socioeconomic, psychosocial and health-related factors in an inner-city population . BMJ Open , 12 ( 11 ), e062115 . doi: 10.1136/bmjopen-2022-062115 OpenUrl CrossRef PubMed 57. ↵ Gurgel do Amaral , G. S. , Ots , P. , Brouwer , S. , & van Zon , S. K. R. ( 2022 ). Multimorbidity and exit from paid employment: The effect of specific combinations of chronic health conditions . European Journal of Public Health , 32 ( 3 ), [Page numbers missing]. 58. ↵ Steadman , K. , Sheldon , H. , & Donnaloja , V. ( 2016 ). Complexities and challenges: Working with multiple health conditions . The Work Foundation . https://www.lancaster.ac.uk/media/lancaster-university/content-assets/documents/lums/work-foundation/409_Complexitieschallenges1-1(1)accessible.pdf 59. ↵ Passison , C. ( 2022 ). Digital and remote primary care: The inverse care law with a 21st century twist? Nuffield Health . https://www.nuffieldtrust.org.uk/news-item/digital-andremote-primary-care-the-inverse-care-law-with-a-21st-century-twist 60. ↵ Stone , E. , Nuckley , P. , & Shapiro . ( 2020 ). Digital inclusion in health and care: Lessons learned from the NHS widening digital participation programme (2017– 2020) . https://www.goodthingsfoundation.org/insights/digital-participation-lessons-learned/ 61. ↵ Castek , C. , Sieck , A. , Perez , M. , & Sheon , A. ( 2021 ). Digital inclusion is a social determinant of health . Health Community . https://healthcommunity.nature.com/posts/digital-inclusion-is-a-social-determinant-ofhealth 62. ↵ Woodward , A. , Davies , N. , Walters , K. , Nimmons , D. , Stevenson , F. , Protheroe , J. , et al. ( 2023 ). Self-management of multiple long-term conditions: A systematic review of the barriers and facilitators amongst people experiencing socioeconomic deprivation . PLOS ONE , 18 ( 2 ), e0282036 . doi: 10.1371/journal.pone.0282036 OpenUrl CrossRef PubMed 63. ↵ Bondaronek , P. , Dicken , S. J. , Singh Jennings , S. , Mallion , V. , & Stefanidou , C . ( 2022 ). Barriers to and facilitators of the use of digital applications in primary care to deliver physical activity advice: Semistructured interviews and thematic analysis . JMIR Human Factors , 9 ( 3 ), e35070 . doi: 10.2196/35070 OpenUrl CrossRef 64. ↵ Berardi , C. , Antonini , M. , Jordan , Z. , et al. ( 2024 ). Barriers and facilitators to the implementation of digital technologies in mental health systems: A qualitative systematic review to inform a policy framework . BMC Health Services Research , 24 , 243 . doi: 10.1186/s12913-023-10536-1 OpenUrl CrossRef PubMed 65. ↵ Halstead , S. , Cao , C. , Høgnason Mohr , G. , Ebdrup , B. H. , Pillinger , T. , McCutcheon , R. A. , Firth , J. , Siskind , D. , & Warren , N . ( 2024 ). Prevalence of multimorbidity in people with and without severe mental illness: A systematic review and meta-analysis . The Lancet Psychiatry , 11 ( 6 ), 431 – 442 . doi: 10.1016/S2215-0366(24)00091-9 OpenUrl CrossRef PubMed 66. ↵ Fleetwood , K. J. , Guthrie , B. , Jackson , C. A. , Kelly , P. A. T. , Mercer , S. W. , Morales , D. R. , et al. ( 2025 ). Depression and physical multimorbidity: A cohort study of physical health condition accrual in UK Biobank . PLOS Medicine , 22 ( 2 ), e1004532 . doi: 10.1371/journal.pmed.1004532 OpenUrl CrossRef 67. ↵ Ng , J. H. , Halinski , C. , Nair , D. , & Diefenbach , M. A . ( 2023 ). Impact of COVID-19 on disease self-management among patients with advanced CKD: A qualitative study . Kidney Medicine , 5 ( 8 ), 100689 . doi: 10.1016/j.xkme.2023.100689 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted April 16, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Digital applications to support self-management of multimorbidity: A scoping review Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. 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